research papers on mental health of students

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Springer Nature - PMC COVID-19 Collection

Stress, Anxiety, and Depression Among Undergraduate Students during the COVID-19 Pandemic and their Use of Mental Health Services

Jungmin lee.

1 Department of Educational Policy Studies and Evaluation, University of Kentucky, 597 S. Upper Street, 131 Taylor Education Building, Lexington, KY 40506-0001 USA

Hyun Ju Jeong

2 Department of Integrated Strategic Communication, University of Kentucky, Lexington, KY USA

3 Division of Biomedical Informatics, University of Kentucky, Lexington, KY USA

Associated Data

Not applicable.

The coronavirus 2019 (COVID-19) has brought significant changes to college students, but there is a lack of empirical studies regarding how the pandemic has affected student mental health among college students in the U.S. To fill the gap in the literature, this study describes stress, anxiety, and depression symptoms for students in a public research university in Kentucky during an early phase of COVID-19 and their usage of mental health services. Results show that about 88% of students experienced moderate to severe stress, with 44% of students showing moderate to severe anxiety and 36% of students having moderate to severe depression. In particular, female, rural, low-income, and academically underperforming students were more vulnerable to these mental health issues. However, a majority of students with moderate or severe mental health symptoms never used mental health services. Our results call for proactively reaching out to students, identifying students at risk of mental health issues, and providing accessible care.

The coronavirus 2019 (COVID-19) has brought significant and sudden changes to college students. To protect and prevent students, faculty, and staff members from the disease, higher education institutions closed their campus in the spring of 2020 and made a quick transition to online classes. Students were asked to evacuate on a short notice, adjust to new online learning environments, and lose their paid jobs in the middle of the semester. The pandemic has also raised concerns among college students about the health of their family and friends (Brown & Kafka, 2020 ). Because all these changes were unprecedented and intensive, they caused psychological distress among students, especially during the first few months of the pandemic. There is abundant anecdotal evidence describing students’ stress and emotional difficulties as impacted by COVID-19, but there are only a few empirical studies available that directly measure college student mental health since the outbreak (e.g., Huckins et al., 2020 ; Kecojevic et al., 2020 ; Son et al., 2020 ). Most existing studies focus on mental health for general populations (e.g., Gao et al., 2020 ) or health care workers (e.g., Chen et al., 2020 ), whose results may not be applicable to college students. Given that college students are particularly vulnerable to mental health issues (e.g., Kitzrow, 2003 ), it is important to explore their mental health during this unprecedented crisis.

In this study, we describe the prevalence of stress, anxiety, and depression for undergraduate students in a public research university during the six weeks after the COVID-19 outbreak alongside their usage of mental health services. Using a self-administered online survey, we measured stress, anxiety, and depression levels with well-established clinical tools and asked the extent to which college students used on-campus and off-campus mental health services for the academic year. Our results revealed that more than eight out of ten students surveyed experienced modest or severe stress, and approximately 36–44% of respondents showed moderate or severe anxiety and depression. However, more than 60% of students with moderate or severe stress, anxiety, or depression had never utilized mental health services on- or off-campus. Although focusing on a single institution, this paper is one of the few studies that empirically examine mental health of college students in the U.S. during the early phase of the pandemic. Findings from this paper reassure the seriousness of student mental health during the pandemic and call for a proactive mental health assessment and increased support for college students.

Literature Review

Covid-19 and student mental health.

Empirical studies reported a high prevalence of college mental health issues during the early phase of COVID-19 around the world (Cao et al., 2020 ; Chang et al., 2020 ; Liu et al., 2020 , Rajkumar, 2020 ; Saddik et al., 2020 ). In the U.S. a few, but a growing number of empirical surveys and studies were conducted to assess college students’ mental health during the pandemic. Three nationwide surveys conducted across the U.S. conclude that college student mental health became worse during the pandemic. According to an online survey administered by Active Minds in mid-April of 2020, 80% of college students across the country reported that COVID-19 negatively affected their mental health, with 20% reporting that their mental health had significantly worsened (Horn, 2020 ). It is also concerning that 56% of students did not know where to go if they had immediate needs for professional mental health services (Horn, 2020 ). Another nationwide survey conducted from late-May to early-June also revealed that 85% of college students felt increased anxiety and stress during the pandemic, but only 21% of respondents sought a licensed counselor or a professional (Timely MD, n.d. ) According to the Healthy Minds Network’s survey (2020), which collected data from 14 college campuses across the country between March and May of 2020, the percentage of students with depression increased by 5.2% compared to the year before. However, 58.2% of respondents never tried mental health care and about 60% of students felt that it became more difficult to access to mental health care since the pandemic. These survey results clearly illustrate that an overwhelming majority of college students in the U.S. have experienced mental health problems during the early phase of COVID-19, but far fewer students utilized professional help. Despite the timely and valuable information, only Healthy Minds Network ( 2020 ) used clinical tools to measure student mental health, and none of them explored whether student characteristics were associated with mental health symptoms.

To date, only a few scholarly research studies focus on college student mental health in the U.S. since the COVID-19 outbreak. Huckins et al. ( 2020 ) have longitudinally tracked 178 undergraduate students at Dartmouth University for the 2020 winter term (from early-January to late-March of 2020) and found elevated anxiety and depression scores during mid-March when students were asked to leave the campus due to the pandemic. The evacuation decision coincided with the final week, which could have intensified student anxiety and depression. The anxiety and depression scores gradually decreased once the academic term was over, but they were still significantly higher than those measured during academic breaks in previous years. Conducting semi-structured interviews with 195 students at a large public university in Texas, Son et al. ( 2020 ) found that 71% of students surveyed reported increased stress and anxiety due to the pandemic, but only 5% of them used counseling services. The rest of the students explained that they did not use counseling services because they assumed that others would have similar levels of stress and anxiety, they did not feel comfortable talking with unfamiliar people or over the phone, or they did not trust counseling services in general. Common stressors included concerns about their own health or their loved ones’, sleep disruption, reduced social interactions, and difficulty in concentration. Based on a survey from 162 undergraduate students in New Jersey, Kecojevic et al. ( 2020 ) found that female students had a significantly higher level of stress than male students and that upper-class undergraduate students showed a higher level of anxiety than first-year students. Having difficulties in focusing on academic work led to increased levels of stress, anxiety, and depression (Kecojevic et al., 2020 ).

College Student Mental Health and Usage of Mental Health Services Before COVID-19

College student mental health has long been studied in education, psychology, and medicine even before the pandemic. The general consensus of the literature is that college student mental health is in crisis, worsening in number and severity over time. Before the pandemic in the academic year of 2020, more than one-third of college students across the country were diagnosed by mental health professionals for having at least one mental health symptom (American College Health Association, 2020 ). Anxiety (27.7%) and depression (22.5%) were most frequently diagnosed. The proportion of students with mental health problems is on the rise as well. Between 2009 and 2015, the proportion of students with anxiety or depression increased by 5.9% and 3.2%, respectively (Oswalt et al., 2020 ). Similarly, between 2012 and 2020, scores for depression, general anxiety, and social anxiety have constantly increased among those who visited counseling centers on college campuses (Center for College Mental Health [CCMH], 2021 ).

Some groups are more vulnerable to mental health problems than others. For example, female and LGBTQ students tend to report a higher prevalence of mental health issues than male students (Eisenberg et al., 2007b ; Evans et al., 2018 ; Wyatt et al., 2017 ). However, there is less conclusive evidence on the difference across race or ethnicity. It is well-supported that Asian students and international students report fewer mental health problems than White students and domestic students, but there are mixed results regarding the difference between underrepresented racial minority students (i.e., African-American, Hispanic, and other races) and White students (Hyun et al., 2006 ; Hyun et al., 2007 ). Many researchers find either insignificant differences (e.g., Eisenberg et al., 2007b ) or fewer mental health issues reported for underrepresented minority students compared to White students (e.g., Wyatt et al., 2017 ). This may not necessarily mean that racial minority students tend to have fewer mental health problems, but it may reflect their cultural tendency against disclosing one’s mental health issues to others (Hyun et al., 2007 ; Wyatt & Oswalt, 2013 ). In terms of age, some studies (e.g., Eisenberg et al., 2007b ) reveal that students who are 25 years or older tend to have fewer mental health issues than younger students, while others find it getting worse throughout college (Wyatt et al., 2017 ). Lastly, financial stress significantly increases depression, anxiety, and suicidal thoughts among college students (Eisenberg et al., 2007b ).

Despite the high prevalence of mental health issues, college students tend to underutilize mental health services (Cage et al., 2018 ; Hunt & Eisenberg, 2010 ; Lipson et al., 2019 ; Oswalt et al., 2020 ). The Healthy Minds Study 2018–2019, which collected data from 62,171 college students across the country, reports that 57% of students with positive anxiety or depression screens have not used counseling or therapy, and 64% of them have not taken any psychotropic medications within the past 12 months (Healthy Minds, 2019 ). Even when students had visited a counseling center, about one-fourth of them did not return for a scheduled appointment, and another 14.1% of students declined further services (CCMH, 2021 ). When asked the barriers that prevented them from seeking mental health services, students reported a lack of perceived needs for help (41%), preference to deal with mental health issues on their own or with families and friends (27%), a lack of time (23%), financial difficulty (15%), and a lack of information about where to go (10%). Students who never used mental health services were not sure if their insurance covered mental health treatment or were more skeptical about the effectiveness of treatment (Eisenberg et al., 2007a ). Stigma, students’ view about getting psychological help for themselves, is another significant barrier in seeking help and utilizing mental health services (Cage et al., 2018 ).

Current Study

While previous studies have advanced our understanding of student mental health and their usage of mental health services, we find a lack of empirical studies on these matters, particularly in the context of COVID-19. The goal of this study is to fill the gap with specific investigations into the prevalence and pattern of U.S. college student mental health with regard to counseling service use during the early phase of COVID-19. First, very few studies focus on college students and their mental health during the pandemic, and most nationwide surveys conducted in the U.S. did not use clinically validated tools to measure student mental health. In this study, we have employed the three clinical measures to assess stress, anxiety, and depression, which are the most prevalent mental health problems among college student populations (Leviness et al., 2017 ). Secondly, it should be noted that while empirical research conducted in U.S. institutions clearly demonstrate that college students were under serious mental distress during the pandemic (Huckins et al., 2020 ; Son et al., 2020 ; Kecojevic et al., 2020 ), such studies have relatively small sample sizes and rarely examined whether particular groups were more vulnerable than others during the pandemic. To overcome such limitations, the present study has recruited a relatively large number of students from all degree-seeking students enrolled at the study institution. Further, given the high prevalence of mental health issues, we have identified vulnerable student groups and provided suggestions regarding necessary support for these students in an effort to reduce mental health disparity. Lastly, previous studies (e.g., Healthy Minds, 2019 ) show that college students, even those with mental health issues, tended to underutilize counseling services before the pandemic. Yet, there is limited evidence regarding whether this continued to be the case during COVID-19. Our study provides empirical evidence regarding the utilization of mental health services during the early phase of the pandemic and identifies its predictors. Based on the preceding discussions, we address the following research questions in this study:

First, how prevalent were stress, anxiety, and depression among college students during the early phase of the pandemic? Second, to what extent have students utilized mental health services on- and off-campus? Third, what are the predictors of mental health symptoms and the usage of mental health services?

We collected data via a self-administered online survey. This survey was designed to measure student mental health, the usage of mental health services, and demographics. The survey was sent to all degree-seeking students enrolled in a public research university in Kentucky for the spring of 2020. An invitation email was first sent on March 23, which was two days after the university announced campus closure, and two more reminder emails were sent in mid-April and late-April. The survey was available until May 8th, which was the last day of the semester.

A total of 2691 students (out of 24,146 qualified undergraduate and graduate degree-seeking students enrolled for the semester) responded to the survey. The response rate was 11.14%, but this is acceptable as it is within the range of Internet survey response rates, which is anywhere from 1 to 30% (Wimmer & Dominick, 2006 ). We deleted responses from 632 students who did not answer any mental health questions, which left 2059 valid students for the analysis. In this study, we focused on undergraduate students because they are significantly different from graduate students in terms of demographics (e.g., racial composition, age, and income) and major stressors (Wyatt & Oswalt, 2013 ). As a result, 1412 undergraduate students are included in our sample. 90% of these students had complete data. The rest of students skipped a couple of questions (usually related to their residency) but answered most of the question. Thus, we conducted multiple imputation, created ten imputed data sets, and ran regression models using these imputed data (Allison, 2002 ). Our regression results using imputed data are qualitatively similar to the estimates using original data; however, for comparison, we also provided the regression estimates using original data in Appendix Tables  6 and ​ and7. 7 . Please note that we still used original data for descriptive research questions (presented in Tables  1 , ​ ,2, 2 , and ​ and4) 4 ) to accurately describe the prevalence of mental health symptoms and use of counseling services.

Descriptive statistics of sample characteristics

Descriptive statistics for stress, anxiety, and depression prevalence

Usage of mental health services among students with moderate or severe symptoms

Ordinal logistic regression models for severity of mental health symptoms (original data)

Odds ratio are reported, and numbers in parentheses are standard error

+ p  < 0.1, * p  < 0.05, ** p  < 0.01, *** p  < 0.001

Logistic regression models predicting the usage of mental health services (original data)

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

Table  1 provides descriptive statistics for students in our data. Female (73%), White (86%), and students who are below 25 years old (95%) are the vast majority of our sample. About one in four students are rural students and/or students from Appalachian areas (27%) and first-generation students (23%). Wealthier students (whose family income was $100,000 or more) make up about 44% of the sample (44%). Compared to the undergraduate student population at the study site, female students (56.3% at the study site) are overrepresented in our study. The proportion of White students is slightly higher in our sample (86%) than the study population (84%), and that of first-generation students is slightly lower in our sample (23%) than that in the study population (26%).

There are five key outcome variables for this study. The first three outcome variables are stress, anxiety, and depression, and the other two variables are the extent to which students used on-campus and off-campus mental health services for the academic year, respectively. Our mental health measures are well-established and widely used in a clinical setting. For stress, we used the Perceived Stress Scale (PSS) that includes ten items asking students’ feelings and perceived stress measured on a 5-point Likert scale from 0 (strongly disagree) to 4 (strongly agree) (Cohen et al., 1983 ). Using the sum of scores from the ten items, the cut-off score for low, moderate, and high stress is 13, 26, and 40, respectively. PSS scale was used in hundreds of studies and validated in many languages (Samaha & Hawi, 2016 ). PSS also has a high internal consistency reliability. Of the recent studies that used the instrument to measure mental health of U.S. college students, Cronbach’s alpha was around 0.83 to 0.87, which exceeded the commonly used cut-off of 0.70 (Adams et al., 2016 ; Burke et al., 2016 ; Samaha & Hawi, 2016 ).

We used the General Anxiety Disorder 7-item (GAD-7) scale to measure anxiety. This is a brief self-report scale to identify probable cases of anxiety disorders (Spitzer et al., 2006 ). The GAD scores of 5, 10, and 15 are taken as the cut-off points for mild, moderate, and severe anxiety, respectively. In a clinical setting, anyone with a score of 10 or above are recommended for further evaluation. GAD is moderately good at screening three other common anxiety disorders - panic disorder (sensitivity 74%, specificity 81%), social anxiety disorder (sensitivity 72%, specificity 80%), and post-traumatic stress disorder (sensitivity 66%, specificity 81%) (Spitzer et al., 2006 ) In their recent study, Johnson, et al. ( 2019 ) validated that “the GAD-7 has excellent internal consistency, and the one-factor structure in a heterogeneous clinical population was supported” (p. 1).

Lastly, depression was assessed with the eight-item Patient-Reported Outcomes Measurement Information System (PROMIS) Depression Short Form (Pilkonis et al., 2014 ). A score less than 17 is considered as none to slight depression, a score between 17 and 21 is considered as mild depression, a score between 22 and 32 is considered as moderate depression, and a score of 33 or above is considered as severe depression. PROMIS depression scale is a universal, rather than a disease-specific, measure that was developed using item response theory to promote greater precision and reduce respondent burden (Shensa et al., 2018 ). The scale has been correlated and validated with other commonly used depression instruments, including the Center for Epidemiological Studies Depression Scale (CES-D), the Beck Depression Inventory (BDI-II), and the Patient Health Questionnaire (PHQ-9) (Lin et al., 2016 ).

When it comes to the usage of psychological and counseling services, we asked students to indicate the extent to which they used free on-campus resources (e.g., counseling center) and off-campus paid health professional services (e.g., psychiatrists) anytime during the academic year on a scale of 1 (never) to 5 (very often), respectively. These questions do not specifically ask if students utilized these services after the COVID-19 outbreak, but responses for these questions indicate whether and how often students had used any of these services for the academic year until they responded to our survey.

We also collected data about student demographics and characteristics including student gender, race or ethnicity, age, class levels (freshman, sophomore, junior, and senior), first generation student status (1 = neither parent has a bachelor’s degree, 0 = at least one parent with a bachelor’s degree), family income, residency (rural and/or Appalachian students, international students), GPAs, and perceived stigma about seeking counseling or therapy (i.e., “I am afraid of what my family and friends will say or think of me if I seek counseling/therapy”) measured on a 5-point Likert scale. We used these variables to see if they were associated with a high level of stress, anxiety, and depression and the usage of mental health services.

We used descriptive statistics, ordinal logistic regression, and logistic regression models in this study. To address the first and second research questions, we used descriptive statistics and presented the prevalence of stress, anxiety, and depression as well as the frequency of using mental health services. For the third research question, we adopted ordinal logistic regression and logistic regression models depending on outcome variables. We used ordinal logistic regression models to identify correlates of different levels of stress, anxiety, and depression, which were measured in ordinal variables (e.g., mild, moderate, and severe). For the usage of mental health service outcomes, we employed logistic regression models. Because more than two-thirds of students in the sample never utilized either type of mental health services, we re-coded the usage variables into binary variables (1 = used services, 0 = never used services) and ran logistic regression models.

Limitations

Our study is not without limitations. First, we do not claim a causal relationship in this study, but we describe the state of mental health for students soon after the COVID-19 outbreak. We acknowledge that many students may have suffered from mental health problems before the pandemic, with some experiencing escalation after the outbreak (e.g., Horn, 2020 ). Even if our study does not provide a causal relationship, we believe that it is important to measure and document student mental health during the pandemic so that practitioners can be aware of the seriousness of this issue and consider ways to better serve students. Secondly, our study results may not be applicable to students in other institutions or states. We collected data from a public research university in Kentucky where the number of confirmed cases and deaths were relatively lower than other states such as New York. The study site mainly serves traditional college students who attend college right after high school, who live on campus, and who do not have dependents. Therefore, mental health for students at other types of institutions or in other states could be different from what is presented in our study.

Prevalence of Stress, Anxiety, and Depression

Table  2 shows the prevalence of stress, anxiety, and depression. Overall, a majority of students experienced psychological distress during the early phase of the pandemic. When it comes to stress, about 63% of students had a moderate level of stress, and another 24.61% of students fell into a severe stress category. Only 12% of students had a low level of stress. In other words, more than eight in ten students in the survey experienced moderate to severe stress during the pandemic. This result is comparable to the Active Minds’ survey results that report 91% of college students reported experiencing feelings of stress and anxiety since the pandemic (Horn, 2020 ).

In terms of anxiety, approximately 24% and 21% of students in our study had moderate and severe anxiety disorders, respectively. Given that those who scored 10 or above on the GAD-7 scale (moderate to severe category) are recommended to meet with professionals (Spitzer et al., 2006 ), this finding implies that nearly half of students in this study needed to get professional help. This proportion of students with moderate to severe anxiety is almost double that for university students in China (e.g., Chang et al., 2020 ) or the United Arab Emirates soon after the COVID-19 outbreak (Saddik et al., 2020 ). Lastly, approximately 30% and 6% of students suffered from moderate and severe depression, respectively. These proportions are far higher than college students in China measured during the pandemic (Chang et al., 2020 ) but slightly higher than a nationwide sample of U.S. college students assessed before the pandemic (Healthy Minds, 2019 ). Given that our study measured these mental health symptoms for the first six weeks of the pandemic, we speculate that the proportion of students with moderate or severe depression would increase over time.

In order to explore predictors of a higher level of stress, anxiety, and depression, we ran ordinal logistic regression models as presented in Table  3 . Overall, it is clear and consistent that the odds of experiencing a higher level of stress, anxiety, and depression (e.g., severe than moderate, moderate than mild, etc.) were significantly greater for female students by a factor of 1.489, 1.723, and 1.246 than the odds for male students when other things were held constant. This gender difference in mental health symptoms is quite consistent with other studies before and during the pandemic (Eisenberg et al., 2007a ; Kecojevic et al., 2020 ). When it comes to race or ethnicity, the odds of experiencing a higher level of stress, anxiety, and depression for African-American students were almost as half as the odds for White students. However, there was no significant difference in the odds for Hispanic and Asian students compared to White students. Student class level was significantly related to stress and anxiety levels: The odds were greater for upper-class students than lower class students. This result is consistent with Kecojevic et al. ( 2020 ), which reported significantly higher levels of anxiety among upper-class students compared to freshman students. It may reflect that one of major stressors for college students during the pandemic is the uncertain future of their education and job prospects, which would be a bigger concern for upper-class students (Timely MD, n.d.).

Ordinal logistic regression models for severity of mental health symptoms (imputed data)

One’s rurality, family income, and GPA were significantly associated with the severity of mental health symptoms. The odds of experiencing a severe level of anxiety and depression were 1.325 and 1.270 times higher among rural students than urban and suburban students. With every one unit increase in family income or students’ GPAs, the odds of experiencing a more severe stress, anxiety, and depression significantly decreased. This result suggests that students from disadvantaged backgrounds were even more vulnerable to psychological distress during the early phase of the pandemic. The negative association between GPAs and mental distress levels was consistent with previous studies that showed that college students were very concerned about their academic performances and had difficulty in concentration during the early phase of the pandemic (Kecojevic et al., 2020 ; Son et al., 2020 ).

Usage of Mental Health Services

In Table  4 , we first describe the extent to which students with moderate to severe symptoms of stress, anxiety, or depression used mental health services on- and off-campus during the academic year. The university in this study has provided free counseling services for students, and the counseling services have continued to be available for students in the state via phone or Internet even after the university was closed after the outbreak. Table ​ Table4 4 presents the frequency of students using on-campus mental health services (Panel A) and off-campus paid mental health services (Panel B) on a five-point scale. For this table, we limited the sample to students with moderate to severe symptoms of stress, anxiety, or depression to focus on students who were in need of these services. Surprisingly, a majority of these students never used mental health services on- and off-campus even when their stress, anxiety, or depression scores indicated that they needed professional help. More than 60% of students with moderate to severe symptoms never used on-campus services, and more than two-thirds of students never used off-campus mental health services. This underutilization of mental health resources is concerning but not surprising given that college students tended not to use counseling services before and during the pandemic as presented in previous studies (e.g., CCMH, 2021 ; Healthy minds, 2019 ; Son et al., 2020 ).

In order to explore predictors of the usage of mental health services, we ran logistic regression models as shown in Table  5 . We included all students in these regression models to see whether a severity of mental health symptoms was related to the usage of mental health services. Table ​ Table5 5 presents the results for the usage of any mental health services, on-campus mental health services, and off-campus mental health services, respectively. Overall, stress, anxiety, and depression levels were positively associated with using mental health services on- and off-campus: With every one unit increase in each of these mental health symptoms, the odds of using on- and off-campus mental health services significantly increased. This result is relieving as it suggests that students who were in great need of these services actually used them. Other than mental health symptoms, there were different predictors for utilizing on-campus and off-campus services. African-American and Hispanic students were significantly more likely to use on-campus services than White students. The odds of using on-campus mental health services were 3.916 times higher for African-American students and 2.032 times higher for Hispanic students than White students. This result is interesting given that the odds of having severe mental distress were significantly lower for African-American students than White students, according to Table ​ Table3. 3 . It may suggest that African-American students reported relatively lower levels of mental health symptoms as they had been using on-campus mental health services at higher rates. The odds of using on-campus mental health services were 2.269 times higher for international students than domestic students, but there was no significant difference in the odds of using off-campus services between the two groups. Students’ age was significantly associated with the usage of on-campus and off-campus mental health services: The odds of using on-campus services were significantly lower for older students, while the odds of utilizing off-campus services were significantly higher for older students compared to younger students. When it comes to using off-campus mental health services, the odds were significantly higher for female students, older students, and upper-class students than male students, younger students, and lower classman students. Students who were concerned with stigma associated with getting counseling and therapy were less likely to utilize off-campus mental health services.

Logistic regression models predicting the usage of mental health services (imputed data)

Discussions

Our paper describes the prevalence of stress, anxiety, and depression among a sample of undergraduate students in a public research university during an early phase of the COVID-19 outbreak. Using well-established clinical tools, we find that stress, anxiety, and depression were the pervasive problems for college student population during the pandemic. In particular, female, rural, low-income, and academically low-performing students were more vulnerable to psychological distress. Despite its prevalence, about two-thirds of students with moderate to severe symptoms had not utilized mental health services on- and off-campus. These key findings are very concerning considering that mental health is strongly associated with student well-being, academic outcomes, and retention (Bruffaerts et al., 2018 ; Wyatt et al., 2017 ).

Above all, we reiterate that college student mental health is in crisis during the pandemic and call for increased attention and interventions on this issue. More than eight in ten students in our study had moderate to severe stress, and more than one thirds of students experienced moderate to severe anxiety and/or depression. This is much worse than American college students before the COVID-19 (e.g., American College Health Association, 2020 ) and postsecondary students in other countries during the pandemic (e.g., Chang et al., 2020 ; Saddik et al., 2020 ). In particular, rural students, low-income students, and students with low GPAs were more vulnerable to psychological distress. These students have already faced multiple barriers in pursuing higher education (e.g., Adelman, 2006 ; Byun et al., 2012 ), and additional mental health issues would put them at a high risk of dropping out of college. Lastly, although they were dropped from the main analysis due to the small sample size ( n  = 17), it is still noteworthy that a significantly higher proportion of LGBTQ students in our sample experienced severe stress, anxiety, and depression, which calls for significant attention and care for these students.

Despite the high prevalence of mental health problems, a majority of students with moderate to severe symptoms never used mental health services during the academic year, even though the university provided free counseling services. This result could be partially explained by the fact that the university’s counseling center switched to virtual counseling since the COVID-19 outbreak, which was available only for students who stayed within the state due to the license restriction across state boarders. This transition could limit access to necessary care for out-of-state students, international students, or students in remote areas where telecommunications or the internet connection is not very stable. Even worse, these students may also have limited access to off-campus health professionals due to the geographic restrictions (rural students), limited insurance coverage (international students), or a lack of financial means. Our results support that international students relied significantly more on on-campus resources than domestic students. We urge practitioners and policy makers to provide additional mental health resources that are accessible, affordable, and available for students regardless of their locations, insurance, and financial means, such as informal peer conversation groups or regular check-ins via phone calls or texts.

It is also important to point out that the overall usage of both on-campus and off-campus mental health services was generally low even before the COVID-19 outbreak. Previous studies consistently report that college students underutilize mental health services not only because of a lack of information, financial means, or available seats but also because of a paucity of perceived needs or stigma related to revealing one’s mental health issues to others (Cage et al., 2018 ; Eisenberg et al., 2007a ; Son et al., 2020 ). Our results support this finding by demonstrating that stigma one associated with getting counseling or therapy negatively influenced their utilization of off-campus mental health services. Considering these barriers, practitioners should deliver a clear message publicly that mental health problems are very common among college students and that it is natural and desirable to seek professional help if students feel stressed out, anxious, or depressed. In order to identify students with mental health needs and raise awareness among students, it can be also considered to administer a short and validated assessment in classes that enroll a large number of students (e.g., in a freshman seminar course), inform the entire class of how to interpret their scores on their own, and provide a list of available resources for those who may be interested. This would give students a chance to self-check their mental health without revealing their identities and seek help, if necessary.

We recommend that future researchers longitudinally track students and see whether the prevalence of mental health problems changes over time. Longitudinal studies are generally scarce in student mental health literature, but the timing of assessment can influence mental health symptoms reported (Huckins et al., 2020 ). The survey for our study was sent out right after the university of this study was closed due to the pandemic. It is possible that students may adjust to the outbreak over time and feel better, or that their stress may add up as the disease progresses. Tracking students over time can illustrate whether and how their mental health changes, especially depending on the way the pandemic unfolds combined with the cycle of an academic year. Secondly, there should be more studies that evaluate the effect of an intervention program on student mental health. Hunt and Eisenberg ( 2010 ) point out that little has been known about the efficacy of intervention programs while almost every higher education institution offers multiple mental health resources and counseling programs. During this pandemic, it can be a unique opportunity to implement virtual mental health interventions and evaluate their efficacy. Future research on virtual counseling and mental health interventions would guide practices to accommodate mental health needs for students who exclusively take online courses or part-time students who spend most of their time off campus. Lastly, we recommend future research investigate the extent of mental health service utilization among students with mental health needs. Existing surveys and studies on this topic usually rely on responses from those who visit a counseling center or students who respond to their surveys. Neither of these groups accurately represents those who are in need of professional help because there may be a number of students who are not aware of their mental health issues or do not want to reveal it. An effective treatment should first start with identifying those in need.

Our study highlights that college students are stressed, anxious, and depressed in the wake of COVID-19. Although college students have constantly reported mental health issues (e.g., American College Health Association, 2020 ), it is remarkable to note that the broad spectrum of COVID-19-related challenges may mitigate the overall quality of their psychological wellbeing. This is particularly the case for at-risk students (rural, international, low-income, and low-achieving students) who have already faced multiple challenges. We also present that a majority of students with mental health needs have never utilized on- and off-campus services possibly due to the limited access or potential stigma associated with mental health care. Systematic efforts with policy makers and practitioners are requested in this research to overcome the potential barriers. All these findings, based on the clinical assessment of student mental health during the early phase of the pandemic, will benefit scholars and practitioners alike. As many colleges and universities across the country have re-opened their campus for the 2020–2021 academic year, students, especially those who take in-person classes, would be concerned about the disease and continuing their study in this unprecedented time. On top of protecting students from the disease by promoting wearing masks and social distancing, it is imperative to pay attention to their mental health and make sure that they feel safe and healthy. To this end, higher education institutions should proactively reach out to all student populations, identify students at risk of mental health issues, and provide accessible and affordable care.

Biographies

is Assistant Professor of Higher Education at the University of Kentucky. She studies higher education policy, program, and practice and their effects on student success.

is an Assistant Professor of Integrated Strategic Communication at the University of Kentucky. She earned her Ph.D. in Media and Information Studies at Michigan State University. Her research interests include prosocial campaigns, consumer wellbeing, and civic engagement.

is an associate professor in the Division of Biomedical Informatics in the College of Medicine at the University of Kentucky. Dr. Kim’s current research includes: consumer health informatics, personal health information management, and health information seeking behaviors. She uses clinical natural language professing techniques and survey methodologies to better understand patients’ health knowledge and their health information uses and behaviors.

Author’s Contribution

The order of the authors in the title page reflects the share of each author’s contribution to the manuscript.

Data Availability

Code availability, declarations.

The authors declare that they have no conflicts of interest.

All authors agree to publish this paper.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Jungmin Lee, Email: [email protected] .

Hyun Ju Jeong, Email: [email protected] .

Sujin Kim, Email: ude.yku@miknijus .

• Adams DR, Meyers SA, Beidas RS. The relationship between financial strain, perceived stress, psychological symptoms, and academic and social integration in undergraduate students. Journal of American College Health. 2016; 64 (5):362–370. doi: 10.1080/07448481.2016.1154559. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. U.S. Department of Education. https://files.eric.ed.gov/fulltext/ED490195.pdf . Accessed 15 Dec 2020.
• Allison, P. D. (2002). Missing data . Sage Publications .
• American College Health Association (2020). American college health association-National college health assessment III: Undergraduate student reference group executive summary spring 2020 . https://www.acha.org/documents/ncha/NCHA-III_Spring_2020_Undergraduate_Reference_Group_Executive_Summary.pdf . Accessed 9 Mar 2021.
• Brown, S., & Kafka, A. C. (2020). Covid-19 has worsened the student mental-health crisis. Can resilience training fix it? The Chronicle of Higher Education . https://www.chronicle.com/article/Covid-19-Has-Worsened-the/248753 . Accessed 3 Aug 2020.
• Bruffaerts R, Mortier P, Kiekens G, Auerbach RP, Cuijpers P, Demyttenaere K, Green JG, Nock MK, Kessler RC. Mental health problems in college freshmen: Prevalence and academic functioning. Journal of Affective Disorders. 2018; 225 (1):97–103. doi: 10.1016/j.jad.2017.07.044. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burke TJ, Ruppel EK, Dinsmore DR. Moving away and reaching out: Young adults’ relational maintenance and psychosocial well-being during the transition to college. Journal of Family Communication. 2016; 16 (2):180–187. doi: 10.1080/15267431.2016.1146724. [ CrossRef ] [ Google Scholar ]
• Byun S, Meece JL, Irvin MJ. Rural-nonrural disparities in postsecondary educational attainment revisited. American Educational Research Journal. 2012; 49 (3):412–437. doi: 10.3102/0002831211416344. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cage E, Stock M, Sharpington A, Pitman E, Batchelor R. Barriers to accessing support for mental health issues at university. Studies in Higher Education. 2018; 45 (8):1–13. doi: 10.1080/03075079.2018.1544237. [ CrossRef ] [ Google Scholar ]
• Cao W, Fang Z, Hou G, Han M, Xu X, Dong J, Zheng J. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Research. 2020; 287 :1–5. doi: 10.1016/j.psychres.2020.112934. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Center for Collegiate Mental Health. (2021). 2020 Annual Report. https://ccmh.memberclicks.net/assets/docs/2020%20CCMH%20Annual%20Report.pdf . Accessed 10 Mar 2021.
• Chang J, Yuan Y, Wang D. Mental health status and its influencing factors among college students during the epidemic of COVID-19. Nan Fang Yi Ke Da Xue Xue Bao. 2020; 40 (2):159–163. doi: 10.12122/j.issn.1673-4254.2020.02.06. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Chen Q, Liang M, Li Y, Guo J, Fei D, Wang L, He L, Sheng C, Cai Y, Li X, Wang J, Zhang Z. Mental health care for medical staff in China during the COVID-19 outbreak. The Lancet Psychiatry. 2020; 7 (4):e15–e16. doi: 10.1016/S2215-0366(20)30078-X. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24 (4), 385–396. 10.2307/2136404. [ PubMed ]
• Eisenberg, D., Golberstein, E., & Gollust, S. E. (2007a). Help-seeking and access to mental health care in a university student population. Medical Care, 45 (7), 594–601. www.jstor.org/stable/40221479 . Accessed 19 June 2020. [ PubMed ]
• Eisenberg D, Gollust SE, Golberstein E, Hefner JL. Prevalence and correlates of depression, anxiety, and suicidality among university students. American Journal of Orthopsychiatry. 2007; 77 (4):534–542. doi: 10.1037/0002-9432.77.4.534. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Evans TM, Bira L, Gastelum JB, Weiss LT, Vanderford NL. Evidence for a mental health crisis in graduate education. Nature Biotechnology. 2018; 36 (3):282–284. doi: 10.1038/nbt.4089. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S, Wang I, Fu H, Dai J. Mental health problems and social media exposure during COVID-19 outbreak. PLoS One. 2020; 15 (4):1–10. doi: 10.1371/journal.pone.0231924. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Healthy Minds. (2019). The healthy minds study: 2018–2019 data report . https://healthymindsnetwork.org/wp-content/uploads/2019/09/HMS_national-2018-19.pdf . Accessed 17 Aug 2020.
• Healthy Minds Network. (2020). The impact of COVID 19 on college students well-being . https://www.acha.org/documents/ncha/Healthy_Minds_NCHA_COVID_Survey_Report_FINAL.pdf . Accessed 1 Mar 2021.
• Horn, A. (2020) . Active minds and association of college and university educators release guide on practical approaches for supporting student wellbeing and mental health. Active Minds . https://www.activeminds.org/wp-content/uploads/2020/04/Active-Minds-ACUE-Release_Faculty-Guide_April-2020.pdf . Accessed 1 Mar 2021.
• Huckins JF, daSilva AW, Wang W, Hedlund E, Rogers C, Nepal SK, Wu J, Obuchi M, Murphy EI, Meyer ML, Wagner DD, Holtzheimer PE, Campbell AT. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: Longitudinal smartphone and ecological momentary assessment study. Journal of Medical Internet Research. 2020; 22 (6):1–13. doi: 10.2196/20185. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hunt J, Eisenberg D. Mental health problems and help-seeking behavior among college students. Journal of Adolescent Health. 2010; 46 (1):3–10. doi: 10.1016/j.jadohealth.2009.08.008. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Hyun JK, Quinn BC, Madon T, Lustig S. Graduate student mental health: Needs assessment and utilization of counseling services. Journal of College Student Development. 2006; 47 (3):247–266. doi: 10.1353/csd.2006.0030. [ CrossRef ] [ Google Scholar ]
• Hyun JK, Quinn BC, Madon T, Lustig S. Mental health need, awareness, and use of counseling services among international graduate students. Journal of American College Health. 2007; 56 (2):109–118. doi: 10.3200/JACH.56.2.109-118. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Johnson SU, Ulvenes PG, Øktedalen T, Hoffart A. Psychometric properties of the GAD-7 in a heterogeneous psychiatric sample. Frontiers in Psychology. 2019; 10 :1–8. doi: 10.3389/fpsyg.2019.01713. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kecojevic A, Basch CH, Sullivan M, Davi NK. The impact of the COVID-19 epidemic on mental health of undergraduate students in New Jersey, cross-sectional study. PLoS One. 2020; 15 (9):1–16. doi: 10.1371/journal.pone.0239696. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kitzrow, M. A. (2003). The mental health needs of today's college students: Challenges and recommendations. Journal of Student Affairs Research and Practice , 41 (1), 167–181. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.408.2605&rep=rep1&type=pdf . Accessed 19 June 2020.
• Leviness, P., Bershad, C., & Gorman, K. (2017). Annual survey 2017. The Association for University and College Counseling Center Directors. https://www.aucccd.org/assets/documents/Governance/2017%20aucccd%20survey-public-apr26.pdf . Accessed 19 June 2020.
• Lin LY, Sidani JE, Shensa A, Radovic A, Miller E, Colditz JB, Hoffman BL, Giles LM, Primack BA. Association between social media use and depression among U.S. young adults. Depression and Anxiety. 2016; 33 (4):323–331. doi: 10.1002/da.22466. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lipson SK, Lattie EG, Eisenberg D. Increased rates of mental health service utilization by U.S. college students: 10-year population-level trends (2007–2017) Psychiatric Services. 2019; 70 (1):60–63. doi: 10.1176/appi.ps.201800332. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Liu S, Liu Y, Liu Y. Somatic symptoms and concern regarding COVID-19 among Chinese college and primary school students: A cross-sectional survey. Psychiatry Research. 2020; 289 :1–5. doi: 10.1016/j.psychres.2020.113070. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Oswalt SB, Lederer AM, Chestnut-Steich K, Day C, Halbritter A, Ortiz D. Trends in college students’ mental health diagnoses and utilization of services, 2009–2015. Journal of American College Health. 2020; 68 (1):41–51. doi: 10.1080/07448481.2018.1515748. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Pilkonis PA, Yu L, Dodds NE, Johnston KL, Maihoefer CC, Lawrence SM. Validation of the depression item bank from the patient-reported outcomes measurement information system (PROMIS®) in a three-month observational study. Journal of Psychiatric Research. 2014; 56 :112–119. doi: 10.1016/j.jpsychires.2014.05.010. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Rajkumar RP. COVID-19 and mental health: A review of the existing literature. Asian Journal of Psychiatry. 2020; 52 :1–5. doi: 10.1016/j.ajp.2020.102066. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Saddik B, Hussein A, Sharif-Askari FS, Kheder W, Temsah MH, Koutaich RA, Haddad ES, Al-Roub NM, Marhoon FA, Hamid Q, Halwani R. Increased levels of anxiety among medical and non-medical university students during the COVID-19 pandemic in the United Arab Emirates. Risk Management and Healthcare Policy. 2020; 13 :2395–2406. doi: 10.2147/RMHP.S273333. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Samaha M, Hawi NS. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior. 2016; 57 :321–325. doi: 10.1016/j.chb.2015.12.045. [ CrossRef ] [ Google Scholar ]
• Shensa A, Sidani JE, Escobar-Viera CG, Chu KH, Bowman ND, Knight JM, Primack BA. Real-life closeness of social media contacts and depressive symptoms among university students. Journal of American College Health. 2018; 66 (8):747–753. doi: 10.1080/07448481.2018.1440575. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Son C, Hegde S, Smith A, Wang X, Sasangohar F. Effects of COVID-19 on college students’ mental health in the United States: Interview survey study. Journal of Medical Internet Research. 2020; 22 (9):1–14. doi: 10.2196/21279. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Archives of Internal Medicine. 2006; 166 (10):1092–1097. doi: 10.1001/archinte.166.10.1092. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Timely MD. (n.d.). What really has college students stressed during COVID-19 . Timely MD. https://www.timely.md/wp-content/uploads/2020/06/TimelyMD-Student-Survey-June-2020.pdf . Accessed 15 Feb 2021.
• Wimmer, R. D., & Dominick, J. R. (2006). Mass communication research: An introduction. Wadsworth.
• Wyatt TJ, Oswalt SB. Comparing mental health issues among undergraduate and graduate students. American Journal of Health Education. 2013; 44 (2):96–107. doi: 10.1080/19325037.2013.764248. [ CrossRef ] [ Google Scholar ]
• Wyatt TJ, Oswalt SB, Ochoa Y. Mental health and academic performance of first-year college students. International Journal of Higher Education. 2017; 6 (2):178–187. doi: 10.5430/ijhe.v6n3p178. [ CrossRef ] [ Google Scholar ]

• Open access
• Published: 20 September 2022

Factors that influence mental health of university and college students in the UK: a systematic review

• Fiona Campbell 1 ,
• Lindsay Blank 1 ,
• Anna Cantrell 1 ,
• Susan Baxter 1 ,
• Christopher Blackmore 1 ,
• Jan Dixon 1 &
• Elizabeth Goyder 1  

BMC Public Health volume  22 , Article number:  1778 ( 2022 ) Cite this article

104k Accesses

52 Citations

89 Altmetric

Metrics details

Worsening mental health of students in higher education is a public policy concern and the impact of measures to reduce transmission of COVID-19 has heightened awareness of this issue. Preventing poor mental health and supporting positive mental wellbeing needs to be based on an evidence informed understanding what factors influence the mental health of students.

To identify factors associated with mental health of students in higher education.

We undertook a systematic review of observational studies that measured factors associated with student mental wellbeing and poor mental health. Extensive searches were undertaken across five databases. We included studies undertaken in the UK and published within the last decade (2010–2020). Due to heterogeneity of factors, and diversity of outcomes used to measure wellbeing and poor mental health the findings were analysed and described narratively.

We included 31 studies, most of which were cross sectional in design. Those factors most strongly and consistently associated with increased risk of developing poor mental health included students with experiences of trauma in childhood, those that identify as LGBTQ and students with autism. Factors that promote wellbeing include developing strong and supportive social networks. Students who are prepared and able to adjust to the changes that moving into higher education presents also experience better mental health. Some behaviours that are associated with poor mental health include lack of engagement both with learning and leisure activities and poor mental health literacy.

Improved knowledge of factors associated with poor mental health and also those that increase mental wellbeing can provide a foundation for designing strategies and specific interventions that can prevent poor mental health and ensuring targeted support is available for students at increased risk.

Peer Review reports

Poor mental health of students in further and higher education is an increasing concern for public health and policy [ 1 , 2 , 3 , 4 ]. A 2020 Insight Network survey of students from 10 universities suggests that “1 in 5 students has a current mental health diagnosis” and that “almost half have experienced a serious psychological issue for which they felt they needed professional help”—an increase from 1 in 3 in the same survey conducted in 2018 [ 5 ]. A review of 105 Further Education (FE) colleges in England found that over a three-year period, 85% of colleges reported an increase in mental health difficulties [ 1 ]. Depression and anxiety were both prevalent and widespread in students; all colleges reported students experiencing depression and 99% reported students experiencing severe anxiety [ 5 , 6 ]. A UK cohort study found that levels of psychological distress increase on entering university [ 7 ], and recent evidence suggests that the prevalence of mental health problems among university students, including self-harm and suicide, is rising, [ 3 , 4 ] with increases in demand for services to support student mental health and reports of some universities finding a doubling of the number of students accessing support [ 8 ]. These common mental health difficulties clearly present considerable threat to the mental health and wellbeing of students but their impact also has educational, social and economic consequences such as academic underperformance and increased risk of dropping out of university [ 9 , 10 ].

Policy changes may have had an influence on the student experience, and on the levels of mental health problems seen in the student population; the biggest change has arguably been the move to widen higher education participation and to enable a more diverse demographic to access University education. The trend for widening participation has been continually rising since the late 1960s [ 11 ] but gained impetus in the 2000s through the work of the Higher Education Funding Council for England (HEFCE). Macaskill (2013) [ 12 ] suggests that the increased access to higher education will have resulted in more students attending university from minority groups and less affluent backgrounds, meaning that more students may be vulnerable to mental health problems, and these students may also experience greater challenges in making the transition to higher education.

Another significant change has been the introduction of tuition fees in 1998, which required students to self fund up to £1,000 per academic year. Since then, tuition fees have increased significantly for many students. With the abolition of maintenance grants, around 96% of government support for students now comes in the form of student loans [ 13 ]. It is estimated that in 2017, UK students were graduating with average debts of £50,000, and this figure was even higher for the poorest students [ 13 ]. There is a clear association between a student’s mental health and financial well-being [ 14 ], with “increased financial concern being consistently associated with worse health” [ 15 ].

The extent to which the increase in poor mental health is also being seen amongst non-students of a similar age is not well understood and warrants further study. However, the increase in poor mental health specifically within students in higher education highlights a need to understand what the risk factors are and what might be done within these settings to ensure young people are learning and developing and transitioning into adulthood in environments that promote mental wellbeing.

Commencing higher education represents a key transition point in a young person’s life. It is a stage often accompanied by significant change combined with high expectations of high expectations from students of what university life will be like, and also high expectations from themselves and others around their own academic performance. Relevant factors include moving away from home, learning to live independently, developing new social networks, adjusting to new ways of learning, and now also dealing with the additional greater financial burdens that students now face.

The recent global COVID-19 pandemic has had considerable impact on mental health across society, and there is concern that younger people (ages 18–25) have been particularly affected. Data from Canada [ 16 ] indicate that among survey respondents, “almost two-thirds (64%) of those aged 15 to 24 reported a negative impact on their mental health, while just over one-third (35%) of those aged 65 and older reported a negative impact on their mental health since physical distancing began” (ibid, p.4). This suggests that older adults are more prepared for the kind of social isolation which has been brought about through the response to COVID-19, whereas young adults have found this more difficult to cope with. UK data from the National Union of Students reports that for over half of UK students, their mental health is worse than before the pandemic [ 17 ]. Before COVID-19, students were already reporting increasing levels of mental health problems [ 2 ], but the COVID-19 pandemic has added a layer of “chronic and unpredictable” stress, creating the perfect conditions for a mental health crisis [ 18 ]. An example of this is the referrals (both urgent and routine) of young people with eating disorders for treatment in the NHS which almost doubled in number from 2019 to 2020 [ 19 ]. The travel restrictions enforced during the pandemic have also impacted on student mental health, particularly for international students who may have been unable to commence studies or go home to see friends and family during holidays [ 20 ].

With the increasing awareness and concern in the higher education sector and national bodies regarding student mental health has come increasing focus on how to respond. Various guidelines and best practice have been developed, e.g. ‘Degrees of Disturbance’ [ 21 ], ‘Good Practice Guide on Responding to Student Mental Health Issues: Duty of Care Responsibilities for Student Services in Higher Education’ [ 22 ] and the recent ‘The University Mental Health Charter’ [ 2 ]. Universities UK produced a Good Practice Guide in 2015 called “Student mental wellbeing in higher education” [ 23 ]. An increasing number of initiatives have emerged that are either student-led or jointly developed with students, and which reflect the increasing emphasis students and student bodies place on mental health and well-being and the increased demand for mental health support: Examples include: Nightline— www.nightline.ac.uk , Students Against Depression— www.studentsagainstdepression.org , Student Minds— www.studentminds.org.uk/student-minds-and-mental-wealth.html and The Alliance for Student-Led Wellbeing— www.alliancestudentwellbeing.weebly.com/ .

Although requests for professional support have increased substantially [ 24 ] only a third of students with mental health problems seek support from counselling services in the UK [ 12 ]. Many students encounter barriers to seeking help such as stigma or lack of awareness of services [ 25 ], and without formal support or intervention, there is a risk of deterioration. FE colleges and universities have identified the need to move beyond traditional forms of support and provide alternative, more accessible interventions aimed at improving mental health and well-being. Higher education institutions have a unique opportunity to identify, prevent, and treat mental health problems because they provide support in multiple aspects of students’ lives including academic studies, recreational activities, pastoral and counselling services, and residential accommodation.

In order to develop services that better meet the needs of students and design environments that are supportive of developing mental wellbeing it is necessary to explore and better understand the factors that lead to poor mental health in students.

Research objectives

The overall aim of this review was to identify, appraise and synthesise existing research evidence that explores the aetiology of poor mental health and mental wellbeing amongst students in tertiary level education. We aimed to gain a better understanding of the mechanisms that lead to poor mental health amongst tertiary level students and, in so doing, make evidence-based recommendations for policy, practice and future research priorities. Specific objectives in line with the project brief were to:

To co-produce with stakeholders a conceptual framework for exploring the factors associated with poorer mental health in students in tertiary settings. The factors may be both predictive, identifying students at risk, or causal, explaining why they are at risk. They may also be protective, promoting mental wellbeing.

To conduct a review drawing on qualitative studies, observational studies and surveys to explore the aetiology of poor mental health in students in university and college settings and identify factors which promote mental wellbeing amongst students.

To identify evidence-based recommendations for policy, service provision and future research that focus on prevention and early identification of poor mental health

Methodology

Identification of relevant evidence.

The following inclusion criteria were used to guide the development of the search strategy and the selection of studies.

We included students from a variety of further education settings (16 yrs + or 18 yrs + , including mature students, international students, distance learning students, students at specific transition points).

Universities and colleges in the UK. We were also interested in the context prior to the beginning of tertiary education, including factors during transition from home and secondary education or existing employment to tertiary education.

Any factor shown to be associated with mental health of students in tertiary level education. This included clinical indicators such as diagnosis and treatment and/or referral for depression and anxiety. Self-reported measures of wellbeing, happiness, stress, anxiety and depression were included. We did not include measures of academic achievement or engagement with learning as indicators of mental wellbeing.

Study design

We included cross-sectional and longitudinal studies that looked at factors associated with mental health outcomes in Table 5 .

Data extraction and quality appraisal

We extracted and tabulated key data from the included papers. Data extraction was undertaken by one reviewer, with a 10% sample checked for accuracy and consistency The quality of the included studies were evaluated using the Newcastle-Ottawa Scale [ 26 ] and the findings of the quality appraisal used in weighting the strength of associations and also identifying gaps for future high quality research.

Involvement of stakeholders

We recruited students, ex-students and parents of students to a public involvement group which met on-line three times during the process of the review and following the completion of the review. During a workshop meeting we asked for members of the group to draw on their personal experiences to suggest factors which were not mentioned in the literature.

Methods of synthesis

We undertook a narrative synthesis [ 27 ] due to the heterogeneity in the exposures and outcomes that were measured across the studies. Data showing the direction of effects and the strength of the association (correlation coefficients) were recorded and tabulated to aid comparison between studies.

Search strategy

Searches were conducted in the following electronic databases: Medline, Applied Social Sciences Index and Abstracts (ASSIA), International Bibliography of Social Sciences (IBSS), Science,PsycINFO and Science and Social Sciences Ciatation Indexes. Additional searches of grey literature, and reference lists of included studies were also undertaken.

The search strategy combined a number of terms relating to students and mental health and risk factors. The search terms included both subject (MeSH) and free-text searches. The searches were limited to papers about humans in English, published from 2010 to June 2020. The flow of studies through the review process is summarised in Fig.  1 .

Flow diagram

The full search strategy for Medline is provided in Appendix 1 .

Thirty-one quantitative, observational studies (39 papers) met the inclusion criteria. The total number of students that participated in the quantitative studies was 17,476, with studies ranging in size from 57 to 3706. Eighteen studies recruited student participants from only one university; five studies (10 publications) [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ] included seven or more universities. Six studies (7 publications) [ 35 , 36 , 37 , 38 , 39 , 40 , 41 ] only recruited first year students, while the majority of studies recruited students from a range of year groups. Five studies [ 39 , 42 , 43 , 44 , 45 ] recruited only, or mainly, psychology students which may impact on the generalisability of findings. A number of studies focused on students studying particular subjects including: nursing [ 46 ] medicine [ 47 ], business [ 48 ], sports science [ 49 ]. One study [ 50 ] recruited LGBTQ (lesbian, gay, bisexual, transgender, intersex, queer/questioning) students, and one [ 51 ] recruited students who had attended hospital having self-harmed. In 27 of the studies, there were more female than male participants. The mean age of the participants ranged from 19 to 28 years. Ethnicity was not reported in 19 of the studies. Where ethnicity was reported, the proportion that were ‘white British’ ranged from 71 – 90%. See Table 1 for a summary of the characteristics of the included studies and the participants.

Design and quality appraisal of the included studies

The majority of included studies ( n  = 22) were cross-sectional surveys. Nine studies (10 publications) [ 35 , 36 , 39 , 41 , 43 , 50 , 51 , 52 , 53 , 62 ] were longitudinal in design, recording survey data at different time points to explore changes in the variables being measured. The duration of time that these studies covered ranged from 19 weeks to 12 years. Most of the studies ( n  = 22) only recruited participants from a single university. The use of one university setting and the large number of studies that recruited only psychology students weakens the wider applicability of the included studies.

Quantitative variables

Included studies ( n  = 31) measured a wide range of variables and explored their association with poor mental health and wellbeing. These included individual level factors: age, gender, sexual orientation, ethnicity and a range of psychological variables. They also included factors that related to mental health variables (family history, personal history and mental health literacy), pre-university factors (childhood trauma and parenting behaviour. University level factors including social isolation, adjustment and engagement with learning. Their association was measured against different measures of positive mental health and poor mental health.

Measurement of association and the strength of that association has some limitations in addressing our research question. It cannot prove causality, and nor can it capture fully the complexity of the inter-relationship and compounding aspect of the variables. For example, the stress of adjustment may be manageable, until it is combined with feeling isolated and out of place. Measurement itself may also be misleading, only capturing what is measureable, and may miss variables that are important but not known. We included both qualitative and PPI input to identify missed but important variables.

The wide range of variables and different outcomes, with few studies measuring the same variable and outcomes, prevented meta-analyses of findings which are therefore described narratively.

The variables described were categorised during the analyses into the following categories:

Vulnerabilities – factors that are associated with poor mental health

Individual level factors including; age, ethnicity, gender and a range of psychological variables were all measured against different mental health outcomes including depression, anxiety, paranoia, and suicidal behaviour, self-harm, coping and emotional intelligence.

Six studies [ 40 , 42 , 47 , 50 , 60 , 63 ] examined a student’s ages and association with mental health. There was inconsistency in the study findings, with studies finding that age (21 or older) was associated with fewer depressive symptoms, lower likelihood of suicide ideation and attempt, self-harm, and positively associated with better coping skills and mental wellbeing. This finding was not however consistent across studies and the association was weak. Theoretical models that seek to explain this mechanism have suggested that older age groups may cope better due to emotion-regulation strategies improving with age [ 67 ]. However, those over 30 experienced greater financial stress than those aged 17-19 in another study [ 63 ].

Sexual orientation

Four studies [ 33 , 40 , 64 , 68 ] examined the association between poor mental health and sexual orientation status. In all of the studies LGBTQ students were at significantly greater risk of mental health problems including depression [ 40 ], anxiety [ 40 ], suicidal behaviour [ 33 , 40 , 64 ], self harm [ 33 , 40 , 64 ], use of mental health services [ 33 ] and low levels of wellbeing [ 68 ]. The risk of mental health problems in these students compared with heterosexual students, ranged from OR 1.4 to 4.5. This elevated risk may reflect the greater levels of isolation and discrimination commonly experienced by minority groups.

Nine studies [ 33 , 38 , 39 , 40 , 42 , 47 , 50 , 60 , 63 ] examined whether gender was associated mental health variables. Two studies [ 33 , 47 ] found that being female was statistically significantly associated with use of mental health services, having a current mental health problem, suicide risk, self harm [ 33 ] and depression [ 47 ]. The results were not consistent, with another study [ 60 ] finding the association was not significant. Three studies [ 39 , 40 , 42 ] that considered mediating variables such as adaptability and coping found no difference or very weak associations.

Two studies [ 47 , 60 ] examined the extent to which ethnicity was associated with mental health One study [ 47 ] reported that the risks of depression were significantly greater for those who categorised themselves as non-white (OR 8.36 p = 0.004). Non-white ethnicity was also associated with poorer mental health in another cross-sectional study [ 63 ]. There was no significant difference in the McIntyre et al. (2018) study [ 60 ]. The small number of participants from ethnic minority groups represented across the studies means that this data is very limited.

Family factors

Six studies [ 33 , 40 , 42 , 50 , 60 ] explored the association of a concept that related to a student’s experiences in childhood and before going to university. Three studies [ 40 , 50 , 60 ] explored the impact of ACEs (Adverse Childhood Experiences) assessed using the same scale by Feletti (2009) [ 69 ] and another explored the impact of abuse in childhood [ 46 ]. Two studies examined the impact of attachment anxiety and avoidance [ 42 ], and parental acceptance [ 46 , 59 ]. The studies measured different mental health outcomes including; positive and negative affect, coping, suicide risk, suicide attempt, current mental health problem, use of mental health services, psychological adjustment, depression and anxiety.

The three studies that explored the impact of ACE’s all found a significant and positive relationship with poor mental health amongst university students. O’Neill et al. (2018) [ 50 ] in a longitudinal study ( n  = 739) showed that there was in increased likelihood in self-harm and suicidal behaviours in those with either moderate or high levels of childhood adversities (OR:5.5 to 8.6) [ 50 ]. McIntyre et al. (2018) [ 60 ] ( n  = 1135) also explored other dimensions of adversity including childhood trauma through multiple regression analysis with other predictive variables. They found that childhood trauma was significantly positively correlated with anxiety, depression and paranoia (ß = 0.18, 0.09, 0.18) though the association was not as strong as the correlation seen for loneliness (ß = 0.40) [ 60 ]. McLafferty et al. (2019) [ 40 ] explored the compounding impact of childhood adversity and negative parenting practices (over-control, overprotection and overindulgence) on poor mental health (depression OR 1.8, anxiety OR 2.1 suicidal behaviour OR 2.3, self-harm OR 2.0).

Gaan et al.’s (2019) survey of LGBTQ students ( n  = 1567) found in a multivariate analyses that sexual abuse, other abuse from violence from someone close, and being female had the highest odds ratios for poor mental health and were significantly associated with all poor mental health outcomes [ 33 ].

While childhood trauma and past abuse poses a risk to mental health for all young people it may place additional stresses for students at university. Entry to university represents life stage where there is potential exposure to new and additional stressors, and the possibility that these students may become more isolated and find it more difficult to develop a sense of belonging. Students may be separated for the first time from protective friendships. However, the mechanisms that link childhood adversities and negative psychopathology, self-harm and suicidal behaviour are not clear [ 40 ]. McLafferty et al. (2019) also measured the ability to cope and these are not always impacted by childhood adversities [ 40 ]. They suggest that some children learn to cope and build resilience that may be beneficial.

McLafferty et al. (2019) [ 40 ] also studied parenting practices. Parental over-control and over-indulgence was also related to significantly poorer coping (OR -0.075 p  < 0.05) and this was related to developing poorer coping scores (OR -0.21 p  < 0.001) [ 40 ]. These parenting factors only became risk factors when stress levels were high for students at university. It should be noted that these studies used self-report, and responses regarding views of parenting may be subjective and open to interpretation. Lloyd et al.’s (2014) survey found significant positive correlations between perceived parental acceptance and students’ psychological adjustment, with paternal acceptance being the stronger predictor of adjustment.

Autistic students may display social communication and interaction deficits that can have negative emotional impacts. This may be particularly true during young adulthood, a period of increased social demands and expectations. Two studies [ 56 ] found that those with autism had a low but statistically significant association with poor social problem-solving skills and depression.

Mental health history

Three studies [ 47 , 51 , 68 ] investigated mental health variables and their impact on mental health of students in higher education. These included; a family history of mental illness and a personal history of mental illness.

Students with a family history or a personal history of mental illness appear to have a significantly greater risk of developing problems with mental health at university [ 47 ]. Mahadevan et al. (2010) [ 51 ] found that university students who self-harm have a significantly greater risk (OR 5.33) of having an eating disorder than a comparison group of young adults who self-harm but are not students.

Buffers – factors that are protective of mental wellbeing

Psychological factors.

Twelve studies [ 29 , 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 , 64 ] assessed the association of a range of psychological variables and different aspects of mental wellbeing and poor mental health. We categorised these into the following two categories: firstly, psychological variables measuring an individual’s response to change and stressors including adaptability, resilience, grit and emotional regulation [ 39 , 40 , 41 , 42 , 43 , 46 , 49 , 54 , 58 ] and secondly, those that measure self-esteem and body image [ 29 , 64 ].

The evidence from the eight included quantitative studies suggests that students with psychological strengths including; optimism, self-efficacy [ 70 ], resilience, grit [ 58 ], use of positive reappraisal [ 49 ], helpful coping strategies [ 42 ] and emotional intelligence [ 41 , 46 ] are more likely to experience greater mental wellbeing (see Table 2 for a description of the psychological variables measured). The positive association between these psychological strengths and mental well-being had a positive affect with associations ranging from r  = 0.2–0.5 and OR1.27 [ 41 , 43 , 46 , 49 , 54 ] (low to moderate strength of association). The negative associations with depressive symptoms are also statistically significant but with a weaker association ( r  = -0.2—0.3) [ 43 , 49 , 54 ].

Denovan (2017a) [ 43 ] in a longitudinal study found that the association between psychological strengths and positive mental wellbeing was not static and that not all the strengths remained statistically significant over time. The only factors that remained significant during the transition period were self-efficacy and optimism, remaining statistically significant as they started university and 6 months later.

Parental factors

Only one study [ 59 ] explored family factors associated with the development of psychological strengths that would equip young people as they managed the challenges and stressors encountered during the transition to higher education. Lloyd et al. (2014) [ 59 ] found that perceived maternal and paternal acceptance made significant and unique contributions to students’ psychological adjustment. Their research methods are limited by their reliance on retrospective measures and self-report measures of variables, and these results could be influenced by recall bias.

Two studies [ 29 , 64 ] considered the impact of how individuals view themselves on poor mental health. One study considered the impact of self-esteem and the association with non-accidental self-injury (NSSI) and suicide attempt amongst 734 university students. As rates of suicide and NSSI are higher amongst LGBT (lesbian, gay, bisexual, transgender) students, the prevalence of low self-esteem was compared. There was a low but statistically significant association between low self-esteem and NSSI, though not for suicide attempt. A large survey, including participants from seven universities [ 42 ] compared depressive symptoms in students with marked body image concerns, reporting that the risk of depressive symptoms was greater (OR 2.93) than for those with lower levels of body image concerns.

Mental health literacy and help seeking behaviour

Two studies [ 48 , 68 ] investigated attitudes to mental illness, mental health literacy and help seeking for mental health problems.

University students who lack sufficient mental health literacy skills to be able to recognise problems or where there are attitudes that foster shame at admitting to having mental health problems can result in students not recognising problems and/or failing to seek professional help [ 48 , 68 ]. Gorcyznski et al. (2017) [ 68 ] found that women and those who had a history of previous mental health problems exhibited significantly higher levels of mental health literacy. Greater mental health literacy was associated with an increased likelihood that individuals would seek help for mental health problems. They found that many students find it hard to identify symptoms of mental health problems and that 42% of students are unaware of where to access available resources. Of those who expressed an intention to seek help for mental health problems, most expressed a preference for online resources, and seeking help from family and friends, rather than medical professionals such as GPs.

Kotera et al. (2019) [ 48 ] identified self-compassion as an explanatory variable, reducing social comparison, promoting self-acceptance and recognition that discomfort is an inevitable human experience. The study found a strong, significant correlation between self-compassion and mental health symptoms ( r  = -0.6. p  < 0.01).

There again appears to be a cycle of reinforcement, where poor mental health symptoms are felt to be a source of shame and become hidden, help is not sought, and further isolation ensues, leading to further deterioration in mental health. Factors that can interrupt the cycle are self-compassion, leading to more readiness to seek help (see Fig.  2 ).

Poor mental health – cycles of reinforcement

Social networks

Nine studies [ 33 , 38 , 41 , 46 , 51 , 54 , 60 , 64 , 65 ] examined the concepts of loneliness and social support and its association with mental health in university students. One study also included students at other Higher Education Institutions [ 46 ]. Eight of the studies were surveys, and one was a retrospective case control study to examine the differences between university students and age-matched young people (non-university students) who attended hospital following deliberate self-harm [ 51 ].

Included studies demonstrated considerable variation in how they measured the concepts of social isolation, loneliness, social support and a sense of belonging. There were also differences in the types of outcomes measured to assess mental wellbeing and poor mental health. Grouping the studies within a broad category of ‘social factors’ therefore represents a limitation of this review given that different aspects of the phenomena may have been being measured. The tools used to measure these variables also differed. Only one scale (The UCLA loneliness scale) was used across multiple studies [ 41 , 60 , 65 ]. Diverse mental health outcomes were measured across the studies including positive affect, flourishing, self-harm, suicide risk, depression, anxiety and paranoia.

Three studies [ 41 , 60 , 62 ] measuring loneliness, two longitudinally [ 41 , 62 ], found a consistently positive association between loneliness and poor mental health in university students. Greater loneliness was linked to greater anxiety, stress, depression, poor general mental health, paranoia, alcohol abuse and eating disorder problems. The strength of the correlations ranged from 0–3-0.4 and were all statistically significant (see Tables 3 and 4 ). Loneliness was the strongest overall predictor of mental distress, of those measured. A strong identification with university friendship groups was most protective against distress relative to other social identities [ 60 ]. Whether poor mental health is the cause, or the result of loneliness was explored further in the studies. The results suggest that for general mental health, stress, depression and anxiety, loneliness induces or exacerbates symptoms of poor mental health over time [ 60 , 62 ]. The feedback cycle is evident, with loneliness leading to poor mental health which leads to withdrawal from social contacts and further exacerbation of loneliness.

Factors associated with protecting against loneliness by fostering supportive friendships and promoting mental wellbeing were also identified. Beliefs about the value of ‘leisure coping’, and attributes of resilience and emotional intelligence had a moderate, positive and significant association with developing mental wellbeing and were explored in three studies [ 46 , 54 , 66 ].

The transition to and first year at university represent critical times when friendships are developed. Thomas et al. (2020) [ 65 ] explored the factors that predict loneliness in the first year of university. A sense of community and higher levels of ‘social capital’ were significantly associated with lower levels of loneliness. ‘Social capital’ scales measure the development of emotionally supportive friendships and the ability to adjust to the disruption of old friendships as students transition to university. Students able to form close relationships within their first year at university are less likely to experience loneliness (r-0.09, r- 0.36, r- 0.34). One study [ 38 ] investigating the relationship between student experience and being the first in the family to attend university found that these students had lower ratings for peer group interactions.

Young adults at university and in higher education are facing multiple adjustments. Their ability to cope with these is influenced by many factors. Supportive friendships and a sense of belonging are factors that strengthen coping. Nightingale et al. (2012) undertook a longitudinal study to explore what factors were associated with university adjustment in a sample of first year students ( n  = 331) [ 41 ]. They found that higher skills of emotion management and emotional self-efficacy were predictive of stable adjustment. These students also reported the lowest levels of loneliness and depression. This group had the skills to recognise their emotions and cope with stressors and were confident to access support. Students with poor emotion management and low levels of emotional self-efficacy may benefit from intervention to support the development of adaptive coping strategies and seeking support.

The positive and negative feedback loops

The relationship between the variables described appeared to work in positive and negative feedback loops with high levels of social capital easing the formation of a social network which acts as a critical buffer to stressors (see Fig.  3 ). Social networks and support give further strengthening and reinforcement, stimulating positive affect, engagement and flourishing. These, in turn, widen and deepen social networks for support and enhance a sense of wellbeing. Conversely young people who enter the transition to university/higher education with less social capital are less likely to identify with and locate a social network; isolation may follow, along with loneliness, anxiety, further withdrawal from contact with social networks and learning, and depression.

Triggers – factors that may act in combination with other factors to lead to poor mental health

Stress is seen as playing a key role in the development of poor mental health for students in higher education. Theoretical models and empirical studies have suggested that increases in stress are associated with decreases in student mental health [ 12 , 43 ]. Students at university experience the well-recognised stressors associated with academic study such as exams and course work. However, perhaps less well recognised are the processes of transition, requiring adapting to a new social and academic environment (Fisher 1994 cited by Denovan 2017a) [ 43 ]. Por et al. (2011) [ 46 ] in a small ( n  = 130 prospective survey found a statistically significant correlation between higher levels of emotional intelligence and lower levels of perceived stress ( r  = 0.40). Higher perceived stress was also associated with negative affect in two studies [ 43 , 46 ], and strongly negatively associated with positive affect (correlation -0.62) [ 54 ].

University variables

Eleven studies [ 35 , 39 , 47 , 51 , 52 , 54 , 60 , 63 , 65 , 83 , 84 ] explored university variables, and their association with mental health outcomes. The range of factors and their impact on mental health variables is limited, and there is little overlap. Knowledge gaps are shown by factors highlighted by our PPI group as potentially important but not identified in the literature (see Table 5 ). It should be noted that these may reflect the focus of our review, and our exclusion of intervention studies which may evaluate university factors.

High levels of perceived stress caused by exam and course work pressure was positively associated with poor mental health and lack of wellbeing [ 51 , 52 , 54 ]. Other potential stressors including financial anxieties and accommodation factors appeared to be less consistently associated with mental health outcomes [ 35 , 38 , 47 , 51 , 60 , 62 ]. Important mediators and buffers to these stressors are coping strategies and supportive networks (see conceptual model Appendix 2 ). One impact of financial pressures was that students who worked longer hours had less interaction with their peers, limiting the opportunities for these students to benefit from the protective effects of social support.

Red flags – behaviours associated with poor mental health and/or wellbeing

Engagement with learning and leisure activities.

Engagement with learning activities was strongly and positively associated with characteristics of adaptability [ 39 ] and also happiness and wellbeing [ 52 ] (see Fig.  4 ). Boulton et al. (2019) [ 52 ] undertook a longitudinal survey of undergraduate students at a campus-based university. They found that engagement and wellbeing varied during the term but were strongly correlated.

Engagement and wellbeing

Engagement occurred in a wide range of activities and behaviours. The authors suggest that the strong correlation between all forms of engagement with learning has possible instrumental value for the design of systems to monitor student engagement. Monitoring engagement might be used to identify changes in the behaviour of individuals to assist tutors in providing support and pastoral care. Students also were found to benefit from good induction activities provided by the university. Greater induction satisfaction was positively and strongly associated with a sense of community at university and with lower levels of loneliness [ 65 ].

The inte r- related nature of these variables is depicted in Fig.  4 . Greater adaptability is strongly associated with more positive engagement in learning and university life. More engagement is associated with higher mental wellbeing.

Denovan et al. (2017b) [ 54 ] explored leisure coping, its psychosocial functions and its relationship with mental wellbeing. An individual’s beliefs about the benefits of leisure activities to manage stress, facilitate the development of companionship and enhance mood were positively associated with flourishing and were negatively associated with perceived stress. Resilience was also measured. Resilience was strongly and positively associated with leisure coping beliefs and with indicators of mental wellbeing. The authors conclude that resilient individuals are more likely to use constructive means of coping (such as leisure coping) to proactively cultivate positive emotions which counteract the experience of stress and promote wellbeing. Leisure coping is predictive of positive affect which provides a strategy to reduce stress and sustain coping. The belief that friendships acquired through leisure provide social support is an example of leisure coping belief. Strong emotionally attached friendships that develop through participation in shared leisure pursuits are predictive of higher levels of well-being. Friendship bonds formed with fellow students at university are particularly important for maintaining mental health, and opportunities need to be developed and supported to ensure that meaningful social connections are made.

The ‘broaden-and-build theory’ (Fredickson 2004 [ 85 ] cited by [ 54 ]) may offer an explanation for the association seen between resilience, leisure coping and psychological wellbeing. The theory is based upon the role that positive and negative emotions have in shaping human adaptation. Positive emotions broaden thinking, enabling the individual to consider a range of ways of dealing with and adapting to their environment. Conversely, negative emotions narrow thinking and limit options for adapting. The former facilitates flourishing, facilitating future wellbeing. Resilient individuals are more likely to use constructive means of coping which generate positive emotion (Tugade & Fredrickson 2004 [ 86 ], cited by [ 54 ]). Positive emotions therefore lead to growth in coping resources, leading to greater well-being.

Health behaviours at university

Seven studies [ 29 , 31 , 38 , 45 , 51 , 54 , 66 ] examined how lifestyle behaviours might be linked with mental health outcomes. The studies looked at leisure activities [ 63 , 80 ], diet [ 29 ], alcohol use [ 29 , 31 , 38 , 51 ] and sleep [ 45 ].

Depressive symptoms were independently associated with problem drinking and possible alcohol dependence for both genders but were not associated with frequency of drinking and heavy episodic drinking. Students with higher levels of depressive symptoms reported significantly more problem drinking and possible alcohol dependence [ 31 ]. Mahadevan et al. (2010) [ 51 ] compared students and non-students seen in hospital for self-harm and found no difference in harmful use of alcohol and illicit drugs.

Poor sleep quality and increased consumption of unhealthy foods were also positively associated with depressive symptoms and perceived stress [ 29 ]. The correlation with dietary behaviours and poor mental health outcomes was low, but also confirmed by the negative correlation between less perceived stress and depressive symptoms and consumption of a healthier diet.

Physical activity and participation in leisure pursuits were both strongly correlated with mental wellbeing ( r  = 0.4) [ 54 ], and negatively correlated with depressive symptoms and anxiety ( r  = -0.6, -0.7) [ 66 ].

Thirty studies measuring the association between a wide range of factors and poor mental health and mental wellbeing in university and college students were identified and included in this review. Our purpose was to identify the factors that contribute to the growing prevalence of poor mental health amongst students in tertiary level education within the UK. We also aimed to identify factors that promote mental wellbeing and protect against deteriorating poor mental health.

Loneliness and social isolation were strongly associated with poor mental health and a sense of belonging and a strong support network were strongly associated with mental wellbeing and happiness. These associations were strongly positive in the eight studies that explored them and are consistent with other meta-analyses exploring the link between social support and mental health [ 87 ].

Another factor that appeared to be protective was older age when starting university. A wide range of personal traits and characteristics were also explored. Those associated with resilience, ability to adjust and better coping led to improved mental wellbeing. Better engagement appeared as an important mediator to potentially explain the relationship between these two variables. Engagement led to students being able to then tap into those features that are protective and promoting of mental wellbeing.

Other important risk factors for poor mental wellbeing that emerged were those students with existing or previous mental illness. Students on the autism spectrum and those with poor social problem-solving also were more likely to suffer from poor mental health. Negative self-image was also associated with poor mental health at university. Eating disorders were strongly associated with poor mental wellbeing and were found to be far more of a risk in students at university than in a comparative group of young people not in higher education. Other studies of university students also found that pre-existing poor mental health was a strong predictor of poor mental health in university students [ 88 ].

At a family level, the experience of childhood trauma and adverse experiences including, for example, neglect, household dysfunction or abuse, were strongly associated with poor mental health in young people at university. Students with a greater number of ‘adverse childhood experiences’ were at significantly greater risk of poor mental health than those students without experience of childhood trauma. This was also identified in a review of factors associated with depression and suicide related outcomes amongst university undergraduate students [ 88 ].

Our findings, in contrast to findings from other studies of university students, did not find that female gender associated with poor mental health and wellbeing, and it also found that being a mature student was protective of mental wellbeing.

Exam and course work pressure was associated with perceived stress and poor mental health. A lack of engagement with learning activities was also associated with poor mental health. A number of variables were not consistently shown to be associated with poor mental health including financial concerns and accommodation factors. Very little evidence related to university organisation or support structures was assessed in the evidence. One study found that a good induction programme had benefits for student mental wellbeing and may be a factor that enables students to become a part of a social network positive reinforcement cycle. Involvement in leisure activities was also found to be associated with improved coping strategies and better mental wellbeing. Students with poorer mental health tended to also eat in a less healthy manner, consume more harmful levels of alcohol, and experience poorer sleep.

This evidence review of the factors that influence mental health and wellbeing indicate areas where universities and higher education settings could develop and evaluate innovations in practice. These include:

Interventions before university to improve preparation of young people and their families for the transition to university.

Exploratory work to identify the acceptability and feasibility of identifying students at risk or who many be exhibiting indications of deteriorating mental health

Interventions that set out to foster a sense of belonging and identify

Creating environments that are helpful for building social networks

Improving mental health literacy and access to high quality support services

This review has a number of limitations. Most of the included studies were cross-sectional in design, with a small number being longitudinal ( n  = 7), following students over a period of time to observe changes in the outcomes being measured. Two limitations of these sources of data is that they help to understand associations but do not reveal causality; secondly, we can only report the findings for those variables that were measured, and we therefore have to support causation in assuming these are the only factors that are related to mental health.

Furthermore, our approach has segregated and categorised variables in order to better understand the extent to which they impact mental health. This approach does not sufficiently explore or reveal the extent to which variables may compound one another, for example, feeling the stress of new ways of learning may not be a factor that influences mental health until it is combined with a sense of loneliness, anxiety about financial debt and a lack of parental support. We have used our PPI group and the development of vignettes of their experiences to seek to illustrate the compounding nature of the variables identified.

We limited our inclusion criteria to studies undertaken in the UK and published within the last decade (2009–2020), again meaning we may have limited our inclusion of relevant data. We also undertook single data extraction of data which may increase the risk of error in our data.

Understanding factors that influence students’ mental health and wellbeing offers the potential to find ways to identify strategies that enhance the students’ abilities to cope with the challenges of higher education. This review revealed a wide range of variables and the mechanisms that may explain how they impact upon mental wellbeing and increase the risk of poor mental health amongst students. It also identified a need for interventions that are implemented before young people make the transition to higher education. We both identified young people who are particularly vulnerable and the factors that arise that exacerbate poor mental health. We highlight that a sense of belonging and supportive networks are important buffers and that there are indicators including lack of engagement that may enable early intervention to provide targeted and appropriate support.

Availability of data and materials

Further details of the study and the findings can be provided on request to the lead author ([email protected]).

Association of Colleges. Association of Colleges’ survey on students with mental health conditions in further education. London: 2017.

Google Scholar  

Hughes G, Spanner L. The University Mental Health Charter. Leeds: Student Minds; 2019.

Sivertsen B, Hysing M, Knapstad M, Harvey AG, Reneflot A, Lønning KJ, et al. Suicide attempts and non-suicidal self-harm among university students: prevalence study. BJPsych Open. 2019;5(2):e26.

Article   PubMed   PubMed Central   Google Scholar  

Storrie K, Ahern K, Tuckett A. A systematic review: students with mental health problems—a growing problem. Int J Nurs Pract. 2010;16(1):1–6.

Article   PubMed   Google Scholar  

Pereira S, Reay K, Bottell J, Walker L, Dzikiti C, Platt C, Goodrham C. Student Mental Health Survey 2018: A large scale study into the prevalence of student mental illness within UK universities. 2019.

Bayram N, Bilgel N. The prevalence and socio-demographic correlations of depression, anxiety and stress among a group of university students. Soc Psychiatry Psychiatr Epidemiol. 2008;43(8):667–72.

Bewick B, Koutsopoulou G, Miles J, Slaa E, Barkham M. Changes in undergraduate students’ psychological well-being as they progress through university. Stud High Educ. 2010;35(6):633–45.

Article   Google Scholar  

Thorley C. Not By Degrees: Not by degrees: Improving student mental health in the UK’s universities. London: IPPR; 2017.

Eisenberg D, Golberstein E, Hunt JB. Mental health and academic success in college. BE J Econ Anal Pol. 2009;9(1):1–37.

Hysenbegasi A, Hass SL, Rowland CR. The impact of depression on the academic productivity of university students. J Ment Health Policy Econ. 2005;8(3):145.

PubMed   Google Scholar  

Chowdry H, Crawford C, Dearden L, Goodman A, Vignoles A. Widening participation in higher education: analysis using linked administrative data. J R Stat Soc A Stat Soc. 2013;176(2):431–57.

Macaskill A. The mental health of university students in the United Kingdom. Br J Guid Couns. 2013;41(4):426–41.

Belfield C, Britton J, van der Erve L. Higher Education finance reform: Raising the repayment threshold to£ 25,000 and freezing the fee cap at £ 9,250: Institute for Fiscal Studies Briefing note. London: Institute for Fiscal Studies; 2017. Available from https://ifs.org.uk/publications/9964 .

Benson-Egglenton J. The financial circumstances associated with high and low wellbeing in undergraduate students: a case study of an English Russell Group institution. J Furth High Educ. 2019;43(7):901–13.

Jessop DC, Herberts C, Solomon L. The impact of financial circumstances on student health. Br J Health Psychol. 2005;10(3):421–39.

(2020) SCSC. Canadians’ mental health during the COVID-19 pandemic. 2020.

(NUS) NUoS. Coronavirus Student Survey phase III November 2020. 2020.

Hellemans K, Abizaid A, Gabrys R, McQuaid R, Patterson Z. For university students, COVID-19 stress creates perfect conditions for mental health crises. The Conversation. 2020. Available from: https://theconversation.com/for-university-students-covid-19-stress-creates-perfect-conditions-for-mental-health-crises-149127 .

England N. Children and Young People with an Eating Disorder Waiting Times: NHS England; 2021 [Available from: https://www.england.nhs.uk/statistics/statistical-work-areas/cyped-waiting-times/

King JA, Cabarkapa S, Leow FH, Ng CH. Addressing international student mental health during COVID-19: an imperative overdue. Australas Psychiatry. 2020;28(4):469.

Rana R, Smith E, Walking J. Degrees of disturbance: the new agenda; the Impact of Increasing Levels of Psychological Disturbance Amongst Students in Higher Education. England: Association for University and College Counselling Rugby; 1999.

AMOSSHE. Responding to student mental health issues: 'Duty of Care' responsibilities for student services in higher education. https://www.amosshe.org.uk/resources/Documents/AMOSSHE_Duty_of_Care_2001.pdf [accessed 24.12.2020]. (2001).

Universities UK. Student mental wellbeing in higher education. Good practice guide. London: Universities UK; 2015.

Williams M, Coare P, Marvell R, Pollard E, Houghton A-M, Anderson J. 2015. Understanding provision for students with mental health problems and intensive support needs: Report to HEFCE by the Institute for Employment Studies (IES) and Researching Equity, Access and Partnership (REAP). Institute for Employment Studies.

Hunt J, Eisenberg D. Mental health problems and help-seeking behavior among college students. J Adolesc Health. 2010;46(1):3–10.

Wells GA, Shea B, O’Connell D, Peterson J, Welch V, Losos M, Tugwell P. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. In.: Oxford; 2000.

Campbell M, McKenzie JE, Sowden A, Katikireddi SV, Brennan SE, Ellis S, et al. Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline. BMJ. 2020;368:l6890.

El Ansari W, Adetunji H, Oskrochi R. Food and mental health: relationship between food and perceived stress and depressive symptoms among university students in the United Kingdom. Cent Eur J Public Health. 2014a;22(2):90–7.

El Ansari W, Dibba E, Stock C. Body image concerns: levels, correlates and gender differences among students in the United Kingdom. Cent Eur J Public Health. 2014b;22(2):106–17.

Ansari EL, W, Oskrochi R, Stock C. Symptoms and health complaints and their association with perceived stress: Students from seven universities in England, Wales and Northern Ireland. J Public Health. 2013;21(5):413–25.

El Ansari W, Sebena R, Stock C. Do importance of religious faith and healthy lifestyle modify the relationships between depressive symptoms and four indicators of alcohol consumption? A survey of students across seven universities in England, Wales, and Northern Ireland. Subst Use Misuse. 2014c;49(3):211–20.

El Ansari W, Stock C. Is the health and wellbeing of university students associated with their academic performance? Cross sectional findings from the United Kingdom. International Journal of Environmental Research & Public Health [Electronic Resource]. 2010;7(2):509–27.

Gnan GH, Rahman Q, Ussher G, Baker D, West E, Rimes KA. General and LGBTQ-specific factors associated with mental health and suicide risk among LGBTQ students. J Youth Stud. 2019;22(10):1393–408.

Jackson SL, Dritschel B. Modeling the impact of social problem-solving deficits on depressive vulnerability in the broader autism phenotype. Res Aut Spectr Disord. 2016;21:128–38.

Richardson T, Elliott P, Roberts R. The impact of tuition fees amount on mental health over time in British students. J Public Health. 2015;37(3):412–8.

Article   CAS   Google Scholar  

Richardson T, Mma Y, Jansen M, Elliott P, Roberts R. Financial difficulties and psychosis risk in British undergraduate students: a longitudinal analysis. J Public Ment Health. 2018;17(2):61–8.

Thomas L, Briggs P, Hart A, Kerrigan F. Understanding social media and identity work in young people transitioning to university. Comput Hum Behav. 2017;76:541–53.

Hixenbaugh P, Dewart H, Towell T. What enables students to succeed? An investigation of socio-demographic, health and student experience variables. Psychodyn Pract. 2012;18(3):285–301.

Holliman A, Martin A, Collie R. Adaptability, engagement, and degree completion: a longitudinal investigation of university students. Educ Psychol. 2018;38(6):785–99.

McLafferty M, Armour C, Bunting B, Ennis E, Lapsley C, Murray E, et al. Coping, stress, and negative childhood experiences: the link to psychopathology, self-harm, and suicidal behavior. Psychic J. 2019;8(3):293–306.

Nightingale S, Roberts S, Tariq V, Appleby Y, Barnes L, Harris R, et al. Trajectories of university adjustment in the United Kingdom: EMOTION management and emotional self-efficacy protect against initial poor adjustment. Learn Individ Differ. 2013;27:174–81.

Berry K, Kingswell S. An investigation of adult attachment and coping with exam-related stress. Br J Guid Couns. 2012;40(4):315.

Denovan A, Macaskill A. Stress and subjective well-being among first year UK undergraduate students. J Happiness Stud. 2017a;18(2):505–25.

Hassel S, Ridout N. An investigation of first-year students’ and lecturers’ expectations of university education. Front Psychol. 2018;8:2218.

Norbury R, Evans S. Time to think: subjective sleep quality, trait anxiety and university start time. Psychiatry Res. 2019;271:214–9.

Por J, Barriball L, Fitzpatrick J, Roberts J. Emotional intelligence: its relationship to stress, coping, well-being and professional performance in nursing students. Nurse Educ Today. 2011;31(8):855.

Honney K, Buszewicz M, Coppola W, Griffin M. Comparison of levels of depression in medical and non-medical students. Clin Teach. 2010;7(3):180–4.

Kotera Y, Conway E, Van Gordon W. Mental health of UK university business students: Relationship with shame, motivation and self-compassion. Journal of Education for Business. 2019;94(1):11–20.

Oliver EJ, Markland D, Hardy J. Interpretation of self-talk and post-lecture affective states of higher education students: a self-determination theory perspective. Br J Educ Psychol. 2010;80(Pt 2):307–23.

O’Neill S, McLafferty M, Ennis E, Lapsley C, Bjourson T, Armour C, et al. Socio-demographic, mental health and childhood adversity risk factors for self-harm and suicidal behaviour in College students in Northern Ireland. J Affect Disord. 2018;239:58–65.

Mahadevan S, Hawton K, Casey D. Deliberate self-harm in Oxford University students, 1993–2005: a descriptive and case-control study. Soc Psychiatry Psychiatr Epidemiol. 2010;45(2):211–9.

Boulton CA, Hughes E, Kent C, Smith JR, Williams HTP. Student engagement and wellbeing over time at a higher education institution. PLoS One [Electronic Resource]. 2019;14(11): e0225770.

Davies EL, Paltoglou AE. Public self-consciousness, pre-loading and drinking harms among university students. Subst Use Misuse. 2019;54(5):747–57.

Denovan A, Macaskill A. Stress, resilience and leisure coping among university students: Applying the broaden-and-build theory. Leisure Studies. 2017b;36(6):852–65.

El Ansari W, Vallentin-Holbech L, Stock C. Predictors of illicit drug/s use among university students in Northern Ireland, Wales and England. Glob J Health Sci. 2015;7(4):18–29.

Freeth M, Bullock T, Milne E. The distribution of and relationship between autistic traits and social anxiety in a UK student population. Autism. 2013;17(5):571–81.

Jessop DC, Reid M, Solomon L. Financial concern predicts deteriorations in mental and physical health among university students. Psychology Health. 2020;35(2):196–209.

Kannangara CS, Allen RE, Waugh G, Nahar N, Khan SZN, Rogerson S, Carson J. All that glitters is not grit: Three studies of grit in university students. Front Psychol. 2018;9:1539.

Lloyd J, Ward T, Young J. Do parental interpersonal power and prestige moderate the relationship between parental acceptance and psychological adjustment in U.K. Students? Cross-Cultural Research. The Journal of Comparative Social Science. 2014;48(3):326–35.

McIntyre JC, Worsley J, Corcoran R, Harrison Woods P, Bentall RP. Academic and non-academic predictors of student psychological distress: the role of social identity and loneliness. J Ment Health. 2018;27(3):230–9.

Ribchester C, Ross K, Rees EL. Examining the impact of pre-induction social networking on the student transition into higher education. Innov Educ Teach Int. 2014;51(4):355–65.

Richardson T, Elliott P, Roberts R. Relationship between loneliness and mental health in students. J Public Ment Health. 2017a;16(2):48–54.

Richardson T, Elliott P, Roberts R, Jansen M. A Longitudinal Study of Financial Difficulties and Mental Health in a National Sample of British Undergraduate Students. Community Ment Health J. 2017;53(3):344–52.

Taylor PJ, Dhingra K, Dickson JM, McDermott E. Psychological Correlates of Self-Harm within Gay, Lesbian and Bisexual UK University Students. Arch Suicide Res. 2020;24(sup1):41–56.

Thomas L, Orme E, Kerrigan F. Student loneliness: The role of social media through life transitions. Comput Educ. 2020;146:103754.

Tyson P, Wilson K, Crone D, Brailsford R, Laws K. Physical activity and mental health in a student population. J Ment Health. 2010;19(6):492–9.

Folkman S. The Oxford handbook of stress, health, and coping. Oxford: Oxford University Press; 2011.

Gorczynski P, Sims-schouten W, Hill D, Wilson JC. Examining mental health literacy, help seeking behaviours, and mental health outcomes in UK university students. J Ment Health Train Educ Pract. 2017;12(2):111–20.

Felitti VJ. Adverse childhood experiences and adult health. Acad Pediatr. 2009;9(3):131–2.

Denovan A, Macaskill A. An interpretative phenomenological analysis of stress and coping in first year undergraduates. Br Educ Res J. 2013;39(6):1002–24.

Bandura A. Self-efficacy: The foundation of agency. Control of human behavior, mental processes, and consciousness: Essays in honor of the 60th birthday of August Flammer. 2000;16.

Martin AJ, Nejad H, Colmar S, Liem GAD. Adaptability: Conceptual and empirical perspectives on responses to change, novelty and uncertainty. J Psychol Couns Sch. 2012;22(1):58–81.

Lazarus RS, Folkman S. Stress, appraisal, and coping: Springer publishing company; 1984.

Gross JJ. Emotion regulation: Past, present, future. Cogn Emot. 1999;13(5):551–73.

Mayer JD, Salovey P, Caruso DR. TARGET ARTICLES:" Emotional Intelligence: Theory, Findings, and Implications". Psychol Inq. 2004;15(3):197–215.

Duckworth AL, Peterson C, Matthews MD, Kelly DR. Grit: perseverance and passion for long-term goals. J Pers Soc Psychol. 2007;92(6):1087.

Snyder CR, Ilardi SS, Cheavens J, Michael ST, Yamhure L, Sympson S. The role of hope in cognitive-behavior therapies. Cognit Ther Res. 2000;24(6):747–62.

Scheier MF, Carver CS, Bridges MW. Optimism, pessimism, and psychological well-being. 2001.

Seligman ME. Positive psychology in practice: Wiley; 2012.

Masten AS. Ordinary magic: Lessons from research on resilience in human development. Education Canada. 2009;49(3):28–32.

Rosenberg M, Schooler C, Schoenbach C, Rosenberg F. Global self-esteem and specific self-esteem: Different concepts, different outcomes. Am Sociol Rev. 1995:141–56.

Oliver EJ, Markland D, Hardy J. Interpretation of self-talk and post-lecture affective states of higher education students: A self-determination theory perspective. Br J Educ Psychol. 2010;80(2):307–23.

Hofmann W, Friese M, Strack F. Impulse and self-control from a dual-systems perspective. Perspect Psychol Sci. 2009;4(2):162–76.

Aceijas C, Waldhausl S, Lambert N, Cassar S, Bello-Corassa R. Determinants of health-related lifestyles among university students. Perspect Public Health. 2017;137(4):227–36.

Fredrickson BL. The broaden–and–build theory of positive emotions. Philos Trans R Soc Lond B Biol Sci. 2004;359(1449):1367–77.

Tugade MM, Fredrickson BL, Feldman Barrett L. Psychological resilience and positive emotional granularity: Examining the benefits of positive emotions on coping and health. J Pers. 2004;72(6):1161–90.

Harandi TF, Taghinasab MM, Nayeri TD. The correlation of social support with mental health: A meta-analysis. Electron physician. 2017;9(9):5212.

Sheldon E, Simmonds-Buckley M, Bone C, Mascarenhas T, Chan N, Wincott M, Gleeson H, Sow K, Hind D, Barkham M. Prevalence and risk factors for mental health problems in university undergraduate students: A systematic review with meta-analysis. J Affect Disord. 2021;287:282–92.

Download references

Acknowledgements

We acknowledge the input from our public advisory group which included current and former students, and family members of students who have struggled with their mental health. The group gave us their extremely valuable insights to assist our understanding of the evidence.

This project was supported by funding from the National Institute for Health Research as part of the NIHR Public Health Research  Programme (fuding reference 127659 Public Health Review Team). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.

Author information

Authors and affiliations.

University of Sheffield, Sheffield, UK

Fiona Campbell, Lindsay Blank, Anna Cantrell, Susan Baxter, Christopher Blackmore, Jan Dixon & Elizabeth Goyder

You can also search for this author in PubMed   Google Scholar

Contributions

All of the included authors designed the project methods and prepared a protocol. A.C. designed the search strategy. F.C, L.B and C.B screened the identified citations and undertook data extraction. S.B. led the PPI involvement. JD participated as a member of the PPI group. F.C and L.B undertook the analysis. F.C. and L.B wrote the main manuscript text. All authors reviewed the manuscript. F.C designed Figs. 2 , 3 and 4 . The author(s) read and approved the final manuscript.

Corresponding author

Correspondence to Fiona Campbell .

Ethics declarations

Ethics approval and consent to participate.

Not applicable as this was secondary research.

Consent for publication

Not applicable.

Competing interests

None of the authors have competing interests or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1., additional file 2., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Campbell, F., Blank, L., Cantrell, A. et al. Factors that influence mental health of university and college students in the UK: a systematic review. BMC Public Health 22 , 1778 (2022). https://doi.org/10.1186/s12889-022-13943-x

Download citation

Received : 03 February 2022

Accepted : 25 July 2022

Published : 20 September 2022

DOI : https://doi.org/10.1186/s12889-022-13943-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Student mental health
• Mental wellbeing
• Risk factors
• Rapid review

BMC Public Health

ISSN: 1471-2458

• Submission enquiries: [email protected]
• General enquiries: [email protected]

This paper is in the following e-collection/theme issue:

Published on 3.9.2020 in Vol 22 , No 9 (2020) : September

Effects of COVID-19 on College Students’ Mental Health in the United States: Interview Survey Study

Authors of this article:

Original Paper

• Changwon Son 1 , BS, MS   ; 
• Sudeep Hegde 1 , BEng, MS, PhD   ; 
• Alec Smith 1 , BS   ; 
• Xiaomei Wang 1 , BS, PhD   ; 
• Farzan Sasangohar 1, 2 , BA, BCS, MASc, SM, PhD  

1 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, United States

2 Center for Outcomes Research, Houston Methodist Hospital, Houston, TX, United States

Corresponding Author:

Farzan Sasangohar, BA, BCS, MASc, SM, PhD

Department of Industrial and Systems Engineering

Texas A&M University

College Station, TX, 77843

United States

Phone: 1 979 458 2337

Email: [email protected]

Background: Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus.

Objective: Our study aims to conduct a timely assessment of the effects of the COVID-19 pandemic on the mental health of college students.

Methods: We conducted interview surveys with 195 students at a large public university in the United States to understand the effects of the pandemic on their mental health and well-being. The data were analyzed through quantitative and qualitative methods.

Results: Of the 195 students, 138 (71%) indicated increased stress and anxiety due to the COVID-19 outbreak. Multiple stressors were identified that contributed to the increased levels of stress, anxiety, and depressive thoughts among students. These included fear and worry about their own health and of their loved ones (177/195, 91% reported negative impacts of the pandemic), difficulty in concentrating (173/195, 89%), disruptions to sleeping patterns (168/195, 86%), decreased social interactions due to physical distancing (167/195, 86%), and increased concerns on academic performance (159/195, 82%). To cope with stress and anxiety, participants have sought support from others and helped themselves by adopting either negative or positive coping mechanisms.

Conclusions: Due to the long-lasting pandemic situation and onerous measures such as lockdown and stay-at-home orders, the COVID-19 pandemic brings negative impacts on higher education. The findings of our study highlight the urgent need to develop interventions and preventive strategies to address the mental health of college students.

Introduction

Mental health issues are the leading impediment to academic success. Mental illness can affect students’ motivation, concentration, and social interactions—crucial factors for students to succeed in higher education [ 1 ]. The 2019 Annual Report of the Center for Collegiate Mental Health [ 2 ] reported that anxiety continues to be the most common problem (62.7% of 82,685 respondents) among students who completed the Counseling Center Assessment of Psychological Symptoms, with clinicians also reporting that anxiety continues to be the most common diagnosis of the students that seek services at university counseling centers. Consistent with the national trend, Texas A&M University has seen a rise in the number of students seeking services for anxiety disorders over the past 8 years. In 2018, slightly over 50% of students reported anxiety as the main reason for seeking services. Despite the increasing need for mental health care services at postsecondary institutions, alarmingly, only a small portion of students committing suicide contact their institution counseling centers [ 3 ], perhaps due to the stigma associated with mental health. Such negative stigma surrounding mental health diagnosis and care has been found to correlate with a reduction in adherence to treatment and even early termination of treatment [ 4 ].

The COVID-19 pandemic has brought into focus the mental health of various affected populations. It is known that the prevalence of epidemics accentuates or creates new stressors including fear and worry for oneself or loved ones, constraints on physical movement and social activities due to quarantine, and sudden and radical lifestyle changes. A recent review of virus outbreaks and pandemics documented stressors such as infection fears, frustration, boredom, inadequate supplies, inadequate information, financial loss, and stigma [ 5 ]. Much of the current literature on psychological impacts of COVID-19 has emerged from the earliest hot spots in China. Although several studies have assessed mental health issues during epidemics, most have focused on health workers, patients, children, and the general population [ 6 , 7 ]. For example, a recent poll by The Kaiser Family Foundation showed that 47% of those sheltering in place reported negative mental health effects resulting from worry or stress related to COVID-19 [ 8 ]. Nelson et al [ 9 ] have found elevated levels of anxiety and depressive symptoms among general population samples in North America and Europe. However, with the exception of a few studies, notably from China [ 10 - 12 ], there is sparse evidence of the psychological or mental health effects of the current pandemic on college students, who are known to be a vulnerable population [ 13 ]. Although the findings from these studies thus far converge on the uptick of mental health issues among college students, the contributing factors may not necessarily be generalizable to populations in other countries. As highlighted in multiple recent correspondences, there is an urgent need to assess effects of the current pandemic on the mental health and well-being of college students [ 14 - 17 ].

The aim of this study is to identify major stressors associated with the COVID-19 pandemic and to understand their effects on college students’ mental health. This paper documents the findings from online interview surveys conducted in a large university system in Texas.

Study Design

A semistructured interview survey guide was designed with the purpose of assessing the mental health status of college students both quantitatively and qualitatively. In addition, the interview aimed to capture the ways that students have been coping with the stress associated with the pandemic situation. First, our study assesses participants’ general stress levels using the Perceived Stress Scale-10 (PSS) [ 18 ]. PSS is a widely used instrument to measure overall stress in the past month [ 19 ]. Second, participants were asked if their own and peers’ (two separate questions) stress and anxiety increased, decreased, or remained the same because of the COVID-19 pandemic. For those who indicated increased stress and anxiety during the pandemic, we questioned their stress coping strategies and use of available mental health counseling services. We then elicited pandemic-specific stressors and their manifestations across 12 academic-, health-, and lifestyle-related categories of outcomes such as effects on own or loved ones’ health, sleeping habits, eating habits, financial situation, changes to their living environment, academic workload, and social relations. Students were also asked about the impact of COVID-19 on depressive and suicidal thoughts. These constructs were derived from existing literature identifying prominent factors affecting college students’ mental health [ 20 , 21 ]. Feedback on the severity of COVID-19’s impact on these aspects were elicited using a 4-point scale: 0 (none), 1 (mild), 2 (moderate), and 3 (severe). Participants were asked to elaborate on each response. Third, participants were guided to describe stressors, coping strategies, and barriers to mental health treatment during a typical semester without associating with the COVID-19 pandemic. Although multiple analyses of the collected data are currently under progress, PSS results and the COVID-19–related findings are presented in this paper.

Participants

Participants were recruited from the student population of a large university system in Texas, United States. This particular university closed all their campuses on March 23, 2020, and held all its classes virtually in response to the COVID-19 pandemic. In addition, the state of Texas issued a stay-at-home order on April 2, 2020. Most interviews were conducted about 1 month after the stay-at-home order in April 2020. Figure 1 illustrates the trend of cumulative confirmed cases and a timeline of major events that took place in the university and the state of Texas. Participants were recruited by undergraduate student researchers through email, text messaging, and snowball sampling. The only inclusion criteria for participation was that participants should have been enrolled as undergraduate students in the university at the time of the interviews.

The interviews were conducted by 20 undergraduate researchers trained in qualitative methods and the use of the interview survey guide described above. None of the authors conducted the interviews. All interviews were conducted via Zoom [ 22 ] and were audio recorded. The recordings were later transcribed using Otter.ai [ 23 ], an artificial intelligence–based transcription service, and verified for accuracy manually. Prior to the interview, participants were provided an information document about the study approved by the university’s Institutional Review Board (No 2019-1341D). Upon verbal consent, participants were asked to respond to a questionnaire about their demographic information such as age, gender, year of college, and program of study before completing the interview. Participation was voluntary and participants were not compensated.

Data Analysis

First, descriptive statistics were compiled to describe participants’ demographics (eg, age, gender, academic year, and major) and the distribution of the ratings on PSS-10 survey items. A total PSS score per participant was calculated by first reversing the scores of the positive items (4-7, 9, and 10) and then adding all the ten scores. A mean (SD) PSS score was computed to evaluate the overall level of stress and anxiety among the participants during the COVID-19 pandemic. Second, participants’ answers to 12 academic-, health-, and lifestyle-related questions were analyzed to understand relative impacts of the pandemic on various aspects of college students’ mental health. Percentages of participants who indicated negative ratings (ie, mild, moderate, or severe influence) on these questions were calculated and ranked in a descending order. Qualitative answers to the 12 stressors and coping strategies were analyzed using thematic analysis [ 24 , 25 ] similar to the deductive coding step in the grounded theory method [ 26 ]. A single coder (CS), trained in qualitative analysis methods, analyzed the transcripts and identified themes using an open coding process, which does not use a priori codes or codes created prior to the analysis and places an emphasis on information that can be extracted directly from the data. Following the identification of themes, the coder discussed the codes with two other coders (XW and AS) trained in qualitative analysis and mental health research to resolve discrepancies among related themes and discuss saturation. The coders consisted of two Ph.D. students and one postdoctoral fellow at the same university. MAXQDA (VERBI GmbH) [ 27 ] was used as a computer software program to carry out the qualitative analysis.

Of the 266 university students initially recruited by the undergraduate researchers, 17 retreated and 249 participated in this study. There were 3 graduate students and 51 participants who had missing data points and were excluded, and data from 195 participants were used in the analysis. The average age was 20.7 (SD 1.7) years, and there were more female students (111/195, 57%) than male students (84/195, 43%). Approximately 70% of the participants were junior and senior students. About 60% of the participants were majoring in the college of engineering, which was the largest college in the university population ( Table 1 ). The mean PSS score for the 195 participants was 18.8 (SD 4.9), indicating moderate perceived stress in the month prior to the interview ( Table 2 ).

a PSS: Perceived Stress Scale-10.

Challenges to College Students’ Mental Health During COVID-19

Out of 195 participants, 138 (71%) indicated that their stress and anxiety had increased due to the COVID-19 pandemic, whereas 39 (20%) indicated it remained the same and 18 (9%) mentioned that the stress and anxiety had actually decreased. Among those who perceived increased stress and anxiety, only 10 (5%) used mental health counseling services. A vast majority of the participants (n=189, 97%) presumed that other students were experiencing similar stress and anxiety because of COVID-19. As shown in Figure 2 , at least 54% (up to 91% for some categories) of participants indicated negative impacts (either mild, moderate, or severe) of COVID-19 on academic-, health-, and lifestyle-related outcomes. The qualitative analysis yielded two to five themes for each category of outcomes. The chronic health conditions category was excluded from the qualitative analysis due to insufficient qualitative response. Table 3 presents the description and frequency of the themes and select participant quotes.

a Not every participant provided sufficient elaboration to allow for identification of themes, so the frequency of individual themes does not add up to the total number of participants who indicated negative impacts of the COVID-19 outbreak.

b The five-digit alphanumeric value indicates the participant ID.

c TA: teaching assistant.

Concerns for One’s Own Health and the Health of Loved Ones

A vast majority of the participants (177/195, 91%) indicated that COVID-19 increased the level of fear and worry about their own health and the health of their loved ones. Over one-third of those who showed concern (76/177, 43%) were worried about their families and relatives who were more vulnerable, such as older adults, those with existing health problems, and those who are pregnant or gave birth to a child recently. Some of the participants (26/177, 15%) expressed their worry about their family members whose occupation increased their risk of exposure to COVID-19 such as essential and health care workers. Some participants (19/177, 11%) specifically mentioned that they were worried about contracting the virus.

Difficulty With Concentration

A vast majority of participants (173/195, 89%) indicated difficulty in concentrating on academic work due to various sources of distraction. Nearly half of them (79/173, 46%) mentioned that their home is a distractive environment and a more suitable place to relax rather than to study. Participants mentioned that they were more prone to be interrupted by their family members and household chores at home. Other factors affecting students’ concentration were lack of accountability (21/173, 12%) and social media, internet, and video games (19/173, 11%). Some (18/173, 10%) stated that online classes were subject to distraction due to lack of interactions and prolonged attention to a computer screen. Additionally, monotonous life patterns were mentioned by some to negatively affect concentration on academic work (5/173, 3%).

Disruption to Sleep Patterns

A majority of participants (168/195, 86%) reported disruptions to their sleep patterns caused by the COVID-19 pandemic, with over one-third (38%) reporting such disruptions as severe. Half of students who reported some disruption (84/168, 50%) stated that they tended to stay up later or wake up later than they did before the COVID-19 outbreak. Another disruptive impact brought by the pandemic was irregular sleep patterns such as inconsistent time to go to bed and to wake up from day to day (28/168, 17%). Some (12/168, 7%) reported increased hours of sleep, while others (10/168, 6%) had poor sleep quality.

Increased Social Isolation

A majority of participants answered that the pandemic has increased the level of social isolation (167/195, 86%). Over half of these students (91/167, 54%) indicated that their overall interactions with other people such as friends had decreased significantly. In particular, about one-third (52/167, 31%) shared their worries about a lack of in-person interactions such as face-to-face meetings. Others (9/167, 5%) stated that disruptions to their outdoor activities (eg, jogging, hiking) have affected their mental health.

Concerns About Academic Performance

A majority of participants (159/195, 82%) showed concerns about their academic performance being impacted by the pandemic. The biggest perceived challenge was the transition to online classes (61/159, 38%). In particular, participants stated their concerns about sudden changes in the syllabus, the quality of the classes, technical issues with online applications, and the difficulty of learning online. Many participants (36/159, 23%) were worried about progress in research and class projects because of restrictions put in place to keep social distancing and the lack of physical interactions with other students. Some participants (23/159, 14%) mentioned the uncertainty about their grades under the online learning environment to be a major stressor. Others (12/159, 8%) indicated their reduced motivation to learn and tendency to procrastinate.

Disruptions to Eating Patterns

COVID-19 has also negatively impacted a large portion of participants’ dietary patterns (137/195, 70%). Many (35/137, 26%) stated that the amount of eating has increased, including having more snacks since healthy dietary options were reduced, and others (27/137, 20%) addressed that their eating patterns have become inconsistent because of COVID-19, for example, irregular times of eating and skipping meals. Some students (16/137, 12%) reported decreased appetite, whereas others (7/137, 5%) were experiencing emotional eating or a tendency to eat when bored. On the other hand, some students (28/195, 14%) reported that they were having healthier diets, as they were cooking at home and not eating out as much as they used to.

Changes in the Living Environment

A large portion of the participants (130/195, 67%) described that the pandemic has resulted in significant changes in their living conditions. A majority of these students (89/130, 68%) referred to living with family members as being less independent and the environment to be more distractive. For those who stayed in their residence either on- or off-campus (18/130, 14%), a main change in their living environment was reduced personal interactions with roommates. Some (9/130, 7%) mentioned that staying inside longer due to self-quarantine or shelter-in-place orders was a primary change in their living circumstances.

Financial Difficulties

More than half of the participants (115/195, 59%) expressed their concerns about their financial situations being impacted by COVID-19. Many (44/115, 38%) noted that COVID-19 has impacted or is likely to impact their own current and future employment opportunities such as part-time jobs and internships. Some (21/115, 18%) revealed the financial difficulties of their family members, mostly parents, getting laid off or receiving pay cuts in the wake of COVID-19.

Increased Class Workload

The effect of COVID-19 on class workload among the college students was not conclusive. Although slightly over half of participants (106/195, 54%) indicated their academic workload has increased due to COVID-19, the rest stated the workload has remained the same (70/195, 36%) or rather decreased (19/195, 10%). For those who were experiencing increased workloads, nearly half (51/106, 48%) thought they needed to increase their own efforts to catch up with online classes and class projects given the lack of in-person support from instructors or teaching assistants. About one-third of the participants (33/106, 31%) perceived that assignments had increased or became harder to do. Some (6/106, 6%) found that covering the remainder of coursework as the classes resumed after the 2-week break to be challenging.

Depressive Thoughts

When asked about the impact of the COVID-19 pandemic on depressive thoughts, 44% (86/195) mentioned that they were experiencing some depressive thoughts during the COVID-19 pandemic. Major contributors to such depressive thoughts were loneliness (28/86, 33%), insecurity or uncertainty (10/86, 12%), powerlessness or hopelessness (9/86, 10%), concerns about academic performance (7/86, 8%), and overthinking (4/86, 5%).

Suicidal Thoughts

Out of 195 participants, 16 (8%) stated that the pandemic has led to some suicidal thoughts with 5% (10/16) reporting these thoughts as mild and 3% (6/16) as moderate. There were 6 participants (38%) that attributed their suicidal thoughts to the presence of depressive thoughts. Other reasons were related to academic performance (1/16, 6%), problems with family as they returned home (1/16, 6%), and fear from insecurity and uncertainty (1/16, 6%).

Coping Mechanism During COVID-19

To cope with stress and anxiety imposed by COVID-19, college students reported seeking support from others but were mainly using various self-management methods.

Self-Management

The majority of the participants (105/138, 76%) with increased stress due to the outbreak of COVID-19 explained that they were using various means to help themselves cope with stress and anxiety during the pandemic. Some (24/105, 23%) relied on negative coping methods such as ignoring the news about COVID-19 (10/105), sleeping longer (7/105), distracting themselves by doing other tasks (5/105), and drinking or smoking (2/105). Approximately one-third (30/105, 29%) used positive coping methods such as meditation and breathing exercises (18/105), spiritual measures (7/105), keeping routines (4/105), and positive reframing (2/105). A majority of the participants (73/105, 70%) who used self-management mentioned doing relaxing hobbies including physical exercise (31/105), enjoying streaming services and social media (22/105), playing with pets (7/105), journaling (5/105), listening to music (4/105), reading (2/105), and drawing (2/105). Finally, some participants (15/105, 14%) stated that they were planning activities (eg, drafting to-do lists) for academic work and personal matters as a self-distraction method.

Seeking Support From Others

Approximately one-third of the participants (47/138, 34%) mentioned that communicating with their families and friends was a primary way to deal with stress and anxiety during COVID-19. Some explicitly stated that they were using a virtual meeting application such as Zoom frequently to connect to friends and family. Only 1 participant claimed to be receiving support from a professional therapist, and another participant was using Sanvello, a mobile mental health service app provided by the university.

Barriers to Seeking Professional Support During COVID-19

Despite the availability of tele-counseling and widespread promotion of such services by the university, a vast majority of participants who indicated an increase in stress and anxiety (128/138, 93%) claimed that they had not used school counseling services during the pandemic. Reasons for such low use included the condition not being perceived as severe enough to seek the services (4/128, 3%), not comfortable interacting with unfamiliar people (1/128, 0.8%), not comfortable talking about mental health issues over the phone (1/128, 0.8%), and lack of trust in the counseling services (1/128, 0.8%).

Principal Findings

College students comprise a population that is considered particularly vulnerable to mental health concerns. The findings of this study bring into focus the effects of pandemic-related transitions on the mental health and well-being of this specific population. Our findings suggest a considerable negative impact of the COVID-19 pandemic on a variety of academic-, health-, and lifestyle-related outcomes. By conducting online survey interviews in the midst of the pandemic, we found that a majority of the participants were experiencing increased stress and anxiety due to COVID-19. In addition, results of the PSS showed moderate levels of stress among our participants. This is in line with a recent pre–COVID-19 survey conducted in the United Kingdom (mean PSS score 19.79, SD 6.37) [ 28 ]; however, the administration of PSS as interview questions (compared to allowing participants to read and respond to the 10 questions) might have introduced bias and resulted in underreporting.

Among the effects of the pandemic identified, the most prominent was worries about one’s own health and the health of loved ones, followed by difficulty concentrating. These findings are in line with recent studies in China that also found concerns relating to health of oneself and of family members being highly prevalent among the general population during the pandemic. Difficulty in concentrating, frequently expressed by our participants, has previously been shown to adversely affect students’ confidence in themselves [ 29 ], which has known correlations to increased stress and mental health [ 30 ]. In comparison with stress and anxiety in college students’ general life, it appears that countermeasures put in place against COVID-19, such as shelter-in-place orders and social distancing practices, may have underpinned significant changes in students’ lives. For example, a vast majority of the participants noted changes in social relationships, largely due to limited physical interactions with their families and friends. This is similar to recent findings of deteriorated mental health status among Chinese students [ 10 ] and increased internet search queries on negative thoughts in the United States [ 31 ]. The findings on the impact of the pandemic on sleeping and eating habits are also a cause for concern, as these variables have known correlations with depressive symptoms and anxiety [ 20 ].

Although a majority of participants expressed concerns regarding academic performance, interestingly, almost half of the participants reported lower stress levels related to academic pressure and class workload since the pandemic began. This may be due, in part, to decisions taken by professors and the university to ease the students’ sudden transition to distance learning. For instance, this university allowed students to choose a pass/fail option for each course instead of a regular letter grade. Additionally, actions taken by professors, such as reduced course loads, open book examinations, and other allowances on grading requirements, could also have contributed to alleviating or reducing stress. Although participants who returned to their parental home reported concerns about distractions and independence, students might have benefited from family support and reduced social responsibilities. Therefore, the increased stress due to the pandemic may have been offset, at least to some extent.

Alarmingly, 44% (86/195) of the participants reported experiencing an increased level of depressive thoughts, and 8% (16/195) reported having suicidal thoughts associated with the COVID-19 pandemic. Previous research [ 32 ] reported about 3%-7% of the college student population to have suicidal thoughts outside of the pandemic situation. Furthermore, with the exception of high-burnout categories, depression levels among students, reported in several recent studies [ 33 - 35 ], have varied between 29% and 38%, which may suggest an uptick in pandemic-related depressive symptoms among college students similar to recent studies in China [ 10 , 11 ]. Although our participants specifically mentioned several factors such as feelings of loneliness, powerlessness, as well as financial and academic uncertainties, other outcomes that were perceived to be impacted by the COVID-19 pandemic may also act as contributors to depressive thoughts and suicidal ideation. In particular, both difficulty concentrating and changes in sleeping habits are associated with depression [ 20 , 29 , 36 ].

Our study also identifies several coping mechanisms varying between adaptive and maladaptive behaviors. The maladaptive coping behaviors such as denial and disengagement have been shown to be significant predictors of depression among young adults [ 37 ]. In contrast, adaptive coping such as acceptance and proactive behaviors are known to positively impact mental health. Our findings suggest that the majority of our participants exhibited maladaptive coping behaviors. Identifying students’ coping behavior is important to inform the planning and design of support systems. In this regard, participatory models of intervention development can be used, in which researchers’ and psychologists’ engagement with the target population to adapt interventional programs to their specific context has shown promise [ 37 , 38 ]. For instance, Nastasi et al [ 37 ] used a participatory model to develop culture-specific mental health services for high school students in Sri Lanka. Similar approaches can be adopted to engage college students as well to develop a mental health program that leverages their natural positive coping behaviors and addresses their specific challenges.

Participants described several barriers to seeking help, such as lack of trust in counseling services and low comfort levels in sharing mental health issues with others, which may be indicative of stigma. Perceiving social stigma as a barrier to seeking help and availing counseling services and other support is common among students [ 29 ]. One study showed that only a minor fraction of students who screened positive for a mental health problem actually sought help [ 39 ]. Although overcoming the stigma associated with mental health has been discussed at length, practical ways of mitigating this societal challenge remains a gap [ 40 , 41 ]. Our findings suggest that self-management is preferred by students and should be supported in future work. Digital technologies and telehealth applications have shown some promise to enable self-management of mental health issues [ 42 ]. For instance, Youn et al [ 43 ] successfully used social media networks as a means to reach out to college students and screen for depression by administering a standardized scale, the Patient Health Questionnaire-9. Digital web-based platforms have also been proposed to enhance awareness and communication with care providers to reduce stigma related to mental health among children in underserved communities [ 44 ]. For instance, one of the online modules suggested by the authors involves providing information on community-identified barriers to communicating with care providers. Technologies such as mobile apps and smart wearable sensors can also be leveraged to enable self-management and communication with caregivers.

In light of the aforementioned projections of continued COVID-19 cases at the time of this writing [ 45 ] and our findings, there is a need for immediate attention to and support for students and other vulnerable groups who have mental health issues [ 17 ]. As suggested by a recent study [ 46 ] based on the Italian experience of this pandemic, it is essential to assess the population’s stress levels and psychosocial adjustment to plan for necessary support mechanisms, especially during the recovery phase, as well as for similar events in the future. Although the COVID-19 pandemic seems to have resulted in a widespread forced adoption of telehealth services to deliver psychiatric and mental health support, more research is needed to investigate use beyond COVID-19 as well as to improve preparedness for rapid virtualization of psychiatric counseling or tele-psychiatry [ 47 - 49 ].

Limitations and Future Work

To our knowledge, this is the first effort in documenting the psychological impacts of the COVID-19 pandemic on a representative sample of college students in the United States via a virtual interview survey method in the middle of the pandemic. However, several limitations should be noted. First, the sample size for our interview survey was relatively small compared to typical survey-only studies; however, the survey interview approach affords the capture of elaboration and additional clarifying details, and therefore complements the survey-based approaches of prior studies focusing on student mental health during this pandemic [ 10 , 11 , 50 ]. Second, the sample used is from one large university, and findings may not generalize to all college students. However, given the nationwide similarities in universities transitioning to virtual classes and similar stay-at-home orders, we expect reasonable generalizability of these findings. Additionally, a majority of our participants were from engineering majors. Therefore, future work is needed to use a stratified nationwide sample across wider disciplines to verify and amend these findings. Third, although a vast majority of participants answered that they have not used the university counseling service during the pandemic, only a few of them provided reasons. Since finding specific reasons behind the low use is a key to increasing college students’ uptake of available counseling support, future research is warranted to unveil underlying factors that hinder college students’ access to mental health support. Finally, we did not analyze how student mental health problems differ by demographic characteristics (eg, age, gender, academic year, major) or other personal and social contexts (eg, income, religion, use of substances).

Future work could focus on more deeply probing the relationships between various coping mechanisms and stressors. Additionally, further study is needed to determine the effects of the pandemic on students’ mental health and well-being in its later phases beyond the peak period. As seen in the case of health care workers in the aftermath of the severe acute respiratory syndrome outbreak, there is a possibility that the effects of the pandemic on students may linger for a period beyond the peak of the COVID-19 pandemic itself [ 51 ].

Acknowledgments

This research was partly funded by a Texas A&M University President’s Excellence (X-Grant) award.

Conflicts of Interest

None declared.

• Unger K. Handbook on Supported Education: Providing Services for Students With Psychiatric Disabilities. Charleston, SC: BookSurge Publishing; 2007.
• 2019 annual report. Center for Collegiate Mental Health. University Park, PA: Penn State University; 2020.   URL: https://ccmh.memberclicks.net/assets/docs/2019-CCMH-Annual-Report_3.17.20.pdf [accessed 2020-05-11]
• Shuchman M. Falling through the cracks — Virginia Tech and the restructuring of college mental health services. N Engl J Med 2007 Jul 12;357(2):105-110. [ CrossRef ]
• Eisenberg D, Downs MF, Golberstein E, Zivin K. Stigma and help seeking for mental health among college students. Med Care Res Rev 2009 Oct;66(5):522-541. [ CrossRef ] [ Medline ]
• Brooks SK, Webster RK, Smith LE, Woodland L, Wessely S, Greenberg N, et al. The psychological impact of quarantine and how to reduce it: rapid review of the evidence. Lancet 2020 Mar 14;395(10227):912-920 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Lai J, Ma S, Wang Y, Cai Z, Hu J, Wei N, et al. Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open 2020 Mar 02;3(3):e203976 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Xie X, Xue Q, Zhou Y, Zhu K, Liu Q, Zhang J, et al. Mental health status among children in home confinement during the coronavirus disease 2019 outbreak in Hubei Province, China. JAMA Pediatr 2020 Apr 24:e201619 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Kirzinger A, Kearney A, Hamel L, Brodie M. KFF health tracking poll - early April 2020: the impact Of coronavirus on life In America. Kaiser Family Foundation. 2020 Apr 02.   URL: https://www.kff.org/health-reform/report/kff-health-tracking-poll-early-april-2020/ [accessed 2020-05-10]
• Nelson B, Pettitt A, Flannery J, Allen N. Rapid assessment of psychological and epidemiological correlates of COVID-19 concern, financial strain, and health-related behavior change in a large online sample. Int J Methods in Psychiatr Res 2020 Apr 13;21(3):169-184. [ CrossRef ]
• Cao W, Fang Z, Hou G, Han M, Xu X, Dong J, et al. The psychological impact of the COVID-19 epidemic on college students in China. Psychiatry Res 2020 May;287:112934 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Liu X, Liu J, Zhong X. Psychological state of college students during COVID-19 epidemic. SSRN J 2020 Mar 24:A. [ CrossRef ]
• Wang C, Zhao H. The impact of COVID-19 on anxiety in Chinese university students. Front Psychol 2020;11:1168. [ CrossRef ] [ Medline ]
• Bruffaerts R, Mortier P, Kiekens G, Auerbach RP, Cuijpers P, Demyttenaere K, et al. Mental health problems in college freshmen: prevalence and academic functioning. J Affect Disord 2018 Jan 01;225:97-103 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhai Y, Du X. Addressing collegiate mental health amid COVID-19 pandemic. Psychiatry Res 2020 Jun;288:113003 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zhai Y, Du X. Mental health care for international Chinese students affected by the COVID-19 outbreak. Lancet Psychiatry 2020 Apr;7(4):e22 [ FREE Full text ] [ CrossRef ] [ Medline ]
• de Oliveira Araújo FJ, de Lima LSA, Cidade PIM, Nobre CB, Neto MLR. Impact of Sars-Cov-2 and its reverberation in global higher education and mental health. Psychiatry Res 2020 Jun;288:112977 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Holmes EA, O'Connor RC, Perry VH, Tracey I, Wessely S, Arseneault L, et al. Multidisciplinary research priorities for the COVID-19 pandemic: a call for action for mental health science. Lancet Psychiatry 2020 Jun;7(6):547-560 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983 Dec;24(4):385-396. [ Medline ]
• Cohen S, Kessler R, Gordon L. Measuring stress: A Guide for Health and Social Scientists. New York, NY: Oxford University Press; 1997.
• Acharya L, Jin L, Collins W. College life is stressful today - emerging stressors and depressive symptoms in college students. J Am Coll Health 2018 Oct;66(7):655-664. [ CrossRef ] [ Medline ]
• Baghurst T, Kelley BC. An examination of stress in college students over the course of a semester. Health Promot Pract 2014 May;15(3):438-447. [ CrossRef ] [ Medline ]
• Zoom. Zoom Video Communications.   URL: https://zoom.us/
• Otter.ai.   URL: https://otter.ai/login
• Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Res Psychol 2006 Jan;3(2):77-101. [ CrossRef ]
• Guest G, MacQueen K, Namey E. Applied Thematic Analysis. Thousand Oaks, CA: Sage Publications; 2011.
• Corbin J, Strauss A. Basics of Qualitative Research (3rd ed.): Techniques and Procedures for Developing Grounded Theory. Thousand Oaks, CA: Sage Publications; 2014.
• MAXQDA. VERBI Software.   URL: https://www.maxqda.com/
• Denovan A, Dagnall N, Dhingra K, Grogan S. Evaluating the perceived stress scale among UK university students: implications for stress measurement and management. Stud Higher Education 2017 Jun 22;44(1):120-133. [ CrossRef ]
• Martin JM. Stigma and student mental health in higher education. Higher Education Res Dev 2010 Jun;29(3):259-274. [ CrossRef ]
• Zuckerman DM. Stress, self-esteem, and mental health: how does gender make a difference? Sex Roles 1989 Apr;20(7-8):429-444. [ CrossRef ]
• Jacobson NC, Lekkas D, Price G, Heinz MV, Song M, O'Malley AJ, et al. Flattening the mental health curve: COVID-19 stay-at-home orders are associated with alterations in mental health search behavior in the United States. JMIR Ment Health 2020 Jun 01;7(6):e19347 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zivin K, Eisenberg D, Gollust SE, Golberstein E. Persistence of mental health problems and needs in a college student population. J Affect Disord 2009 Oct;117(3):180-185. [ CrossRef ] [ Medline ]
• Zeng Y, Wang G, Xie C, Hu X, Reinhardt JD. Prevalence and correlates of depression, anxiety and symptoms of stress in vocational college nursing students from Sichuan, China: a cross-sectional study. Psychol Health Med 2019 Aug;24(7):798-811. [ CrossRef ] [ Medline ]
• Nahas ARMF, Elkalmi R, Al-Shami A, Elsayed T. Prevalence of depression among health sciences students: findings from a public university in Malaysia. J Pharm Bioallied Sci 2019;11(2):170-175 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Fitzpatrick O, Biesma R, Conroy RM, McGarvey A. Prevalence and relationship between burnout and depression in our future doctors: a cross-sectional study in a cohort of preclinical and clinical medical students in Ireland. BMJ Open 2019 May 01;9(4):e023297 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Heiligenstein E, Guenther G, Hsu K, Herman K. Depression and academic impairment in college students. J Am Coll Health 1996 Sep;45(2):59-64. [ CrossRef ] [ Medline ]
• Nastasi BK, Sarkar S, Varjas K, Jayasena A. Participatory model of mental health programming: lessons learned from work in a developing country. Sch Psychol Rev 1998;27(2):260-276.
• Cappella E, Jackson DR, Bilal C, Hamre BK, Soulé C. Bridging mental health and education in urban elementary schools: participatory research to inform intervention development. Sch Psychol Rev 2011;40(4):486-508.
• Eisenberg D, Hunt J, Speer N, Zivin K. Mental health service utilization among college students in the United States. J Nerv Ment Dis 2011 May;199(5):301-308. [ CrossRef ] [ Medline ]
• Husain W. Barriers in seeking psychological help: public perception in Pakistan. Community Ment Health J 2020 Jan;56(1):75-78. [ CrossRef ] [ Medline ]
• Fischer EP, McSweeney JC, Wright P, Cheney A, Curran GM, Henderson K, et al. Overcoming barriers to sustained engagement in mental health care: perspectives of rural veterans and providers. J Rural Health 2016 Sep;32(4):429-438. [ CrossRef ] [ Medline ]
• Torous J, Jän Myrick K, Rauseo-Ricupero N, Firth J. Digital mental health and COVID-19: using technology today to accelerate the curve on access and quality tomorrow. JMIR Ment Health 2020 Mar 26;7(3):e18848 [ FREE Full text ] [ CrossRef ] [ Medline ]
• Youn SJ, Trinh N, Shyu I, Chang T, Fava M, Kvedar J, et al. Using online social media, Facebook, in screening for major depressive disorder among college students. Int J Clin Health Psychol 2013 Jan;13(1):74-80. [ CrossRef ]
• Ginossar T, Nelson S. Reducing the health and digital divides: a model for using community-based participatory research approach to e-health interventions in low-income Hispanic communities. J Computer-Mediated Commun 2010;15(4):530-551. [ CrossRef ]
• COVID-19 projections. Institute for Health Metrics and Evaluation. Seattle, WA: University of Washington   URL: https://covid19.healthdata.org/united-states-of-america [accessed 2020-05-20]
• de Girolamo G, Cerveri G, Clerici M, Monzani E, Spinogatti F, Starace F, et al. Mental health in the coronavirus disease 2019 emergency-the Italian response. JAMA Psychiatry 2020 Apr 30:A. [ CrossRef ] [ Medline ]
• Zhou J, Liu L, Xue P, Yang X, Tang X. Mental health response to the COVID-19 outbreak in China. Am J Psychiatry 2020 Jul 01;177(7):574-575. [ CrossRef ] [ Medline ]
• Zhou X, Snoswell CL, Harding LE, Bambling M, Edirippulige S, Bai X, et al. The role of telehealth in reducing the mental health burden from COVID-19. Telemed J E Health 2020 Apr;26(4):377-379. [ CrossRef ] [ Medline ]
• Shore JH, Waugh M, Calderone J, Donahue A, Rodriguez J, Peters D, et al. Evaluation of telepsychiatry-enabled perinatal integrated care. Psychiatr Serv 2020 May 01;71(5):427-432. [ CrossRef ] [ Medline ]
• Huckins JF, daSilva AW, Wang W, Hedlund E, Rogers C, Nepal SK, et al. Mental health and behavior of college students during the early phases of the COVID-19 pandemic: longitudinal smartphone and ecological momentary assessment study. J Med Internet Res 2020 Jun 17;22(6):e20185 [ FREE Full text ] [ CrossRef ] [ Medline ]
• McAlonan GM, Lee AM, Cheung V, Cheung C, Tsang KW, Sham PC, et al. Immediate and sustained psychological impact of an emerging infectious disease outbreak on health care workers. Can J Psychiatry 2007 Apr;52(4):241-247. [ CrossRef ] [ Medline ]

Abbreviations

Edited by G Eysenbach, G Fagherazzi, J Torous; submitted 10.06.20; peer-reviewed by T Liu, V Hagger; comments to author 28.07.20; revised version received 01.08.20; accepted 15.08.20; published 03.09.20

©Changwon Son, Sudeep Hegde, Alec Smith, Xiaomei Wang, Farzan Sasangohar. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 03.09.2020.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Review Article
• Published: 07 May 2024

Mechanisms linking social media use to adolescent mental health vulnerability

• Amy Orben   ORCID: orcid.org/0000-0002-2937-4183 1 ,
• Adrian Meier   ORCID: orcid.org/0000-0002-8191-2962 2 ,
• Tim Dalgleish   ORCID: orcid.org/0000-0002-7304-2231 1 &
• Sarah-Jayne Blakemore 3 , 4  

Nature Reviews Psychology ( 2024 ) Cite this article

5028 Accesses

168 Altmetric

Metrics details

• Psychiatric disorders
• Science, technology and society

Research linking social media use and adolescent mental health has produced mixed and inconsistent findings and little translational evidence, despite pressure to deliver concrete recommendations for families, schools and policymakers. At the same time, it is widely recognized that developmental changes in behaviour, cognition and neurobiology predispose adolescents to developing socio-emotional disorders. In this Review, we argue that such developmental changes would be a fruitful focus for social media research. Specifically, we review mechanisms by which social media could amplify the developmental changes that increase adolescents’ mental health vulnerability. These mechanisms include changes to behaviour, such as sharing risky content and self-presentation, and changes to cognition, such as modifications in self-concept, social comparison, responsiveness to social feedback and experiences of social exclusion. We also consider neurobiological mechanisms that heighten stress sensitivity and modify reward processing. By focusing on mechanisms by which social media might interact with developmental changes to increase mental health risks, our Review equips researchers with a toolkit of key digital affordances that enables theorizing and studying technology effects despite an ever-changing social media landscape.

You have full access to this article via your institution.

Similar content being viewed by others

The effect of social media on well-being differs from adolescent to adolescent

Probing the digital exposome: associations of social media use patterns with youth mental health

Social contextual risk taking in adolescence

Introduction.

Adolescence is a period marked by profound neurobiological, behavioural and environmental changes that facilitate the transition from familial dependence to independent membership in society 1 , 2 . This critical developmental stage is also characterized by diminished well-being and increased vulnerability to the onset of mental health conditions 3 , 4 , 5 , particularly socio-emotional disorders such as depression, and eating disorders 4 , 6 (Fig. 1 ). Notable symptoms of socio-emotional disorders include heightened negative affect, mood dysregulation and an increased focus on distress or challenges concerning interpersonal relationships, including heightened sensitivity to peers or perceptions of others 6 . Although some risk factors for socio-emotional disorders do not necessarily occur in adolescence (including genetic predispositions, adverse childhood experiences and poverty 7 , 8 , 9 ), the unique developmental characteristics of this period of life can interact with pre-existing vulnerabilities, increasing the risk of disorder onset 10 .

Meta-analytic proportion of age of onset of anxiety (red), obsessive-compulsive disorder (purple), eating disorders (orange), personality disorders (green), schizophrenia (grey) and mood disorders (blue). The peak age of onset (dotted lines) is 5.5 and 15.5 years for anxiety, 14.5 years for obsessive-compulsive disorder, 15.5 years for eating disorders and 20.5 years for personality disorders, schizophrenia and mood disorders. Adapted from ref. 258 , CC BY 4.0 ( https://creativecommons.org/licenses/by/4.0/ ).

Over the past decade, declines in adolescent mental health have become a great concern 11 , 12 . The prevalence of socio-emotional disorders has increased in the adolescent age range (10–24 years 2 ) 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , leading to mounting pressures on child and adolescent mental health services 16 , 21 , 22 . This increase has not been as pronounced among other age groups when compared with adolescents 20 , 22 , 23 (measured in ref.  20 , ref.  22 and ref.  23 as age 12–25 years, 12–20 years and 18–25 years, respectively), even if some studies have found increases across the entire lifespan 24 , 25 . Although these trends might not be generalizable across the world 26 or to subclinical indicators of distress 15 , similar trends have been found in a range of countries 27 . Declines in adolescent mental health, especially socio-emotional problems, are consistent across datasets and researchers have argued that they are not solely driven by changes in social attitudes, stigma or reporting of distress 28 , 29 .

Concurrently, adolescents’ lives have become increasingly digital, with most young people using social media platforms throughout the day 30 . Ninety-five per cent of UK adolescents aged 15 years use social media 31 , and 50% of US adolescents aged 13–17 years report being almost constantly online 32 . The social media environment impacts adolescent and adult life across many domains (for example, by enabling social communication or changing the way news is accessed) and influences individuals, dyads and larger social systems 33 , 34 , 35 , 36 . Because social media is inherently social and relational 37 , it potentially overlaps and interacts with the developmental changes that make adolescents vulnerable to the onset of mental health problems 38 , 39 (Fig. 2 ). Thus, it has been intensely debated whether the increase in social media use during the past decade has a causal role in the decline of adolescent mental health 40 . Indeed, rapid changes to the environment experienced before and during adolescence might be a fruitful area to explore when examining current mental health trends 41 .

During adolescence, the interaction between genetic programming (yellow), social determinants (red) and environmental factors (blue), as well as the developmental changes discussed in this Review, increases the risk for onset of mental health conditions. Digital environments, mediated behaviours and experiences, and the impact that this technology has on society and economy more generally, are one aspect of the complex forces that might lead to the declines in adolescent mental health observed in the last decade. Adapted from ref. 259 , Springer Nature Limited.

Although there are many environmental changes that could be relevant, a substantial body of research has emerged to investigate the potential link between social media use and declines in adolescent mental health 42 , 43 using various research approaches, including cross-sectional studies 44 , longitudinal observational data analyses 45 , 46 , 47 and experimental studies 48 , 49 . However, the scientific results have been mixed and inconclusive (for reviews, see refs. 43 , 50 , 51 , 52 , 53 ), which has made it difficult to establish evidence-based recommendations, regulations and interventions aimed at ensuring that social media use is not harmful to adolescents 54 , 55 , 56 , 57 .

Many researchers attribute the mixed results to insufficient study specificity. For instance, the relationship between social media use and mental health varies notably across individuals 45 , 58 and developmental time windows 59 . Yet studies often examine adolescents without differentiating them based on age or developmental stage 60 , which prevents systematic accounts of individual and subgroup differences. Additionally, most studies only rely on self-reported measures of time spent on social media 61 , 62 , and overlook more nuanced aspects of social media use such as the nature of the activities 63 and the content or features that users engage with 52 . These factors need to be considered to unpack any broader relationships 35 , 64 , 65 , 66 . Furthermore, the measurement of mental health often conflates positive and negative mental health outcomes as well as various mental health conditions, which could all be differentially related to social media use 52 , 67 .

This research space presents substantial complexity 68 . There is an ever-increasing range of potential combinations of social media predictors, well-being and mental health outcomes and participant groups of varying backgrounds and demographics that can become the target of scientific investigation. However, the pressure to deliver policy and public-facing recommendations and interventions leaves little time to investigate comprehensively each of these combinations. Researchers need to be able to pinpoint quickly the research programmes with the maximum potential to create translational and real-world impact for adolescent mental health.

In this Review, we aim to delineate potential avenues for future research that could lead to concrete interventions to improve adolescent mental health by considering mechanisms at the nexus between pre-existing processes known to increase adolescent mental health vulnerability and digital affordances introduced by social media. First, we describe the affordance approach to understanding the effects of social media. We then draw upon research on adolescent development, mental health and social media to describe behavioural, cognitive and neurobiological mechanisms by which social media use might amplify changes during adolescent development to increase mental health vulnerability during this period of life. The specific mechanisms within each category were chosen because they have a strong evidence base showing that they undergo substantive changes during adolescent development, are implicated in mental health risk and can be modulated by social media affordances. Although the ways in which social media can also improve mental health resilience are not the focus of our Review and therefore are not reviewed fully here, they are briefly discussed in relation to each mechanism. Finally, we discuss future research focused on how to systematically test the intersection between social media and adolescent mental health.

Social media affordances

To study the impact of social media on adolescent mental health, its diverse design elements and highly individualized uses must be conceptualized. Initial research predominately related access to or time spent on social media to mental health outcomes 46 , 69 , 70 . However, social media is not similar to a toxin or nutrient for which each exposure dose has a defined link to a health-related outcome (dose–response relationship) 56 . Social media is a diverse environment that cannot be summarized by the amount of time one spends interacting with it 71 , 72 , and individual experiences are highly varied 45 .

Previous psychological reviews often focused on social media ‘features’ 73 and ‘affordances’ 74 interchangeably. However, these terms have distinct definitions in communication science and information systems research. Social media features are components of the technology intentionally designed to enable users to perform specific actions, such as liking, reposting or uploading a story 75 , 76 . By contrast, affordances describe the perceptions of action possibilities users have when engaging with social media and its features, such as anonymity (the difficulty with which social media users can identify the source of a message) and quantifiability (how countable information is).

The term ‘affordance’ came from ecological psychology and visuomotor research, and was described as mainly determined by human perception 77 . ‘Affordance’ was later adopted for design and human–computer interaction contexts to refer to the action possibilities that are suggested to the user by the technology design 78 . Communication research synthesizes both views. Affordances are now typically understood as the perceived — and therefore flexible — action possibilities of digital environments, which are jointly shaped by the technology’s features and users’ idiosyncratic perceptions of those features 79 .

Latent action possibilities can vary across different users, uses and technologies 79 . For example, ‘stories’ are a feature of Instagram designed to share content between users. Stories can also be described in terms of affordances when users perceive them as a way to determine how long their content remains available on the platform (persistence) or who can see that content (visibility) 80 , 81 , 82 , 83 , 84 . Low persistence (also termed ephemerality) and comparatively low visibility can be achieved through a technology feature (Instagram stories), but are not an outcome of technology use itself; they are instead perceived action possibilities that can vary across different technologies, users and designs 79 .

The affordances approach is particularly valuable for theorizing at a level above individual social media apps or specific features, which makes this approach more resilient to technological changes or shifts in platform popularity 79 , 85 . However, the affordances approach can also be related back to specific types of social media by assessing the extent to which certain affordances are ‘built into’ a particular platform through feature design 35 . Furthermore, because affordances depend on individuals’ perceptions and actions, they are more aligned than features with a neurocognitive and behavioural perspective to social media use. Affordances, similar to neurocognitive and behavioural research, emphasize the role of the user (how the technology is perceived, interpreted and used) rather than technology design per se. In this sense, the affordances approach is essential to overcome technological determinism of mental health outcomes, which overly emphasizes the role of technology as the driver of outcomes but overlooks the agency and impact of the people in question 86 . This flexibility and alignment with psychological theory has contributed to the increasing popularity of the affordance approach 35 , 73 , 74 , 85 , 87 and previous reviews have explored relevant social media affordances in the context of interpersonal communication among adults and adolescents 35 , 88 , 89 , adolescent body image concerns 73 and work contexts 33 . Here, we focus on the affordances of social media that are relevant for adolescent development and its intersection with mental health (Table  1 ).

Behavioural mechanisms

Adolescents often use social media differently to adults, engaging with different platforms and features and, potentially, perceiving or making use of affordances in distinctive ways 35 . These usage differences might interact with developmental characteristics and changes to amplify mental health vulnerability (Fig.  3 ). We examine two behavioural mechanisms that might govern the impact of social media use on mental health: risky posting behaviours and self-presentation.

Social media affordances can amplify the impact that common adolescent developmental mechanisms (behavioural, cognitive and neurobiological) have on mental health. At the behavioural level (top), affordances such as permanence and publicness lead to an increased impact of risk-taking behaviour on mental health compared with similar behaviours in non-mediated environments. At the cognitive level (middle), high quantifiability influences the effects of social comparison. At the neurobiological level (bottom), low synchronicity can amplify the effects of stress on the developing brain.

Risky posting behaviour

Sensation-seeking peaks in adolescence and self-regulation abilities are still not fully developed in this period of life 90 . Thus, adolescents often engage in more risky behaviours than other age groups 91 . Adolescents are more likely to take risks in situations involving peers 92 , 93 , perhaps because they are motivated to avoid social exclusion 94 , 95 . Whether adolescent risk-taking behaviour is inherently adaptive or maladaptive is debated. Although some risk-taking behaviours can be adaptive and part of typical development, others can increase mental health vulnerability. For example, data from a prospective UK panel study of more than 5,500 young people showed that engaging in more risky behaviours (including social and health risks) at age 16 years increases the odds of a range of adverse outcomes at age 18 years, such as depression, anxiety and substance abuse 96 .

Social media can increase adolescents’ engagement in risky behaviours both in non-mediated and mediated environments (environments in which the behaviour is executed in or through a technology, such as a mobile phone and social media). First, affordances such as quantifiability in conjunction with visibility and association (the degree with which links between people, between people and content or between a presenter and their audience can be articulated) can promote more risky behaviours in non-mediated environments and in-person social interactions. For example, posts from university students containing references to alcohol gain more likes than posts not referencing alcohol and liking such posts predicts an individual’s subsequent drinking habits 97 . Users expecting likes from their audience are incentivized to engage in riskier posting behaviour (such as more frequent or more extreme posts containing references to alcohol). The relationship between risky online behaviour and offline behaviour is supported by meta-analyses that found a positive correlation between adolescents’ social media use and their engagement in behaviours that might expose them to harm or risk of injury (for example, substance use or risky sexual behaviours) 98 . Further, affordances such as persistence and visibility can mean that risky behaviours in mediated and non-mediated environments remain public for long periods of time, potentially influencing how an adolescent is perceived by peers over the longer term 39 , 99 .

Adolescence can also be a time of more risky social media use. For most forms of semi-public and public social media use, users typically do not know who exactly will be able to see their posts. Thus, adolescents need to self-present to an ‘imagined audience’ 100 and avoid posting the wrong kind of content as the boundaries between different social spheres collapse (context collapse 101 ). However, young people can underestimate the risks of disclosing revealing information in a social media environment 102 . Affordances such as visibility, replicability (social media posts remain in the system and can be screenshotted and shared even if they are later deleted 39 ), association and persistence could heighten the risk of experiencing cyberbullying, victimization and online harassment 103 . For example, adolescents can forward privately received sexual images to larger friendship groups, increasing the risk of online harassment over the subject of the sexual images 104 . Further, low bandwidth (a relative lack of socio-emotional cues) and high anonymity have the potential to disinhibit interactions between users and make behaviours and reactions more extreme 105 , 106 . For example, anonymity was associated with more trolling behaviours during an online group discussion in an experiment with 242 undergraduate students 107 .

Thus, social media might drive more risky behaviours in both mediated and non-mediated contexts, increasing mental health vulnerability. However, the evidence is still not clear cut and often discounts adolescent agency and understanding. For example, mixed-methods research has shown that young people often understand the risks of posting private or sexual content and use social media apps that ensure that posts are deleted and inaccessible after short periods of time to counteract them 39 (even though posts can still be captured in the meantime). Future work will therefore need to investigate how adolescents understand and balance such risks and how such processes relate to social media’s impact on mental health.

Self-presentation and identity

The adolescent period is characterized by an abundance of self-presentation activities on social media 74 , where the drive to present oneself becomes a fundamental motivation for engagement 108 . These activities include disclosing, concealing and modifying one’s true self, and might involve deception, to convey a desired impression to an audience 109 . Compared with adults, adolescents more frequently take part in self-presentation 102 , which can encompass both realistic and idealized portrayals of themselves 110 . In adults, authentic self-presentation has been associated with increased well-being, and inauthentic presentation (such as when a person describes themselves in ways not aligned with their true self) has been associated with decreased well-being 111 , 112 , 113 .

Several social media affordances shape the self-presentation behaviours of adolescents. For example, the editability of social media profiles enables users to curate their online identity 84 , 114 . Editability is further enhanced by highly visible (public) self-presentations. Additionally, the constant availability of social media platforms enables adolescents to access and engage with their profiles at any time, and provides them with rapid quantitative feedback about their popularity among peers 89 , 115 . People receive more direct and public feedback on their self-presentation on social media than in other types of environment 116 , 117 . The affordances associated with self-presentation can have a particular impact during adolescence, a period characterized by identity development and exploration.

Social media environments might provide more opportunities than offline environments for shaping one’s identity. Indeed, public self-presentation has been found to invoke more prominent identity shifts (substantial changes in identity) compared with private self-presentation 118 , 119 . Concerns have been raised that higher Internet use is associated with decreased self-concept clarity. Only one study of 101 adolescents as well as adults reviewed in a 2021 meta-analysis 120 showed that the intensity of Facebook use (measured by the Facebook Intensity Scale) predicted a longitudinal decline in self-concept clarity 3 months later, but the converse was not the case and changes in self-concept clarity did not predict Facebook use 121 . This result is still not enough to show a causal relationship 121 . Further, the affordances of persistence and replicability could also curtail adolescents’ ability to explore their identity freely 122 .

By contrast, qualitative research has highlighted that social media enables adolescents to broaden their horizons, explore their identity and identify and reaffirm their values 123 . Social media can help self-presentation by enabling adolescents to elaborate on various aspects of their identity, such as ethnicity and race 124 or sexuality 125 . Social media affordances such as editability and visibility can also facilitate this process. Adolescents can modify and curate self-presentations online, try out new identities or express previously undisclosed aspects of their identity 126 , 127 . They can leverage social media affordances to present different facets of themselves to various social groups by using different profiles, platforms and self-censorship and curation of posts 128 , 129 . Presenting and exploring different aspects of one’s identity can have mental health implications for minority teens. Emerging research shows a positive correlation between well-being and problematic Internet use in transgender, non-binary and gender-diverse adolescents (age 13–18 years), and positive sentiment has been associated with online identity disclosures in transgender individuals with supportive networks (both adolescent and adult) 130 , 131 .

Cognitive mechanisms

Adolescents and adults might experience different socio-cognitive impacts from the same social media activity. In this section, we review four cognitive mechanisms via which social media and its affordances might influence the link between adolescent development and mental health vulnerabilities (Fig.  3 ). These mechanisms (self-concept development, social comparison, social feedback and exclusion) roughly align with a previous review that examined self-esteem and social media use 115 .

Self-concept development

Self-concept refers to a person’s beliefs and evaluations about their own qualities and traits 132 , which first develops and becomes more complex throughout childhood and then accelerates its development during adolescence 133 , 134 , 135 . Self-concept is shaped by socio-emotional processes such as self-appraisal and social feedback 134 . A negative and unstable self-concept has been associated with negative mental health outcomes 136 , 137 .

Perspective-taking abilities also develop during adolescence 133 , 138 , 139 , as does the processing of self-relevant stimuli (measured by self-referential memory tasks, which assess memory for self-referential trait adjectives 140 , 141 ). During adolescence, direct self-evaluations and reflected self-evaluations (how someone thinks others evaluate them) become more similar. Further, self-evaluations have a distinct positive bias during childhood, but this positivity bias decreases in adolescence as evaluations of the self are integrated with judgements of other people’s perspectives 142 . Indeed, negative self-evaluations peak in late adolescence (around age 19 years) 140 .

The impact of social media on the development of self-concept could be heightened during adolescence because of affordances such as personalization of content 143 (the degree to which content can be tailored to fit the identity, preferences or expectations of the receiver), which adapts the information young people are exposed to. Other affordances with similar impacts are quantifiability, availability (the accessibility of the technology as well as the user’s accessibility through the technology) and public visibility of interactions 89 , which render the evaluations of others more prominent and omnipresent. The prominence of social evaluation can pose long-term risks to mental health under certain conditions and for some users 144 , 145 . For example, receiving negative evaluations from others or being exposed to cyberbullying behaviours 146 , 147 can, potentially, have heightened impact at times of self-concept development.

A pioneering cross-sectional study of 150 adolescents showed that direct self-evaluations are more similar to reflected self-evaluations, and self-evaluations are more negative, in adolescents aged 11–21 years who estimate spending more time on social media 148 . Further, longitudinal data have shown bidirectional negative links between social media use and satisfaction with domains of the self (such as satisfaction with family, friends or schoolwork) 47 .

Although large-scale evidence is still unavailable, these findings raise the interesting prospect that social media might have a negative influence on perspective-taking and self-concept. There is less evidence for the potential positive influence of social media on these aspects of adolescent development, demonstrating an important research gap. Some researchers hypothesize that social media enables self-concept unification because it provides ample opportunity to find validation 89 . Research has also discussed how algorithmic curation of personalized social media feeds (for example, TikTok algorithms tailoring videos viewed to the user’s interests) enables users to reflect on their self-concept by being exposed to others’ experiences and perspectives 143 , an area where future research can provide important insights.

Social comparison

Social comparison (thinking about information about other people in relation to the self 149 ) also influences self-concept development and becomes particularly important during adolescence 133 , 150 . There are a range of social media affordances that can amplify the impact of social comparison on mental health. For example, quantifiability enables like or follower counts to be easily compared with others as a sign of status, which facilitates social ranking 151 , 152 , 153 , 154 . Studies of older adolescents and adults aged, on average, 20 years have also found that the number of likes or reactions received predict, in part, how successful users judge their self-presentation posts on Facebook 155 . Furthermore, personalization enables the content that users see on social media to be curated so as to be highly relevant and interesting for them, which should intensify comparisons. For example, an adolescent interested in sports and fitness content will receive personalized recommendations fitting those interests, which should increase the likelihood of comparisons with people portrayed in this content. In turn, the affordance of association can help adolescents surround themselves with similar peers and public personae online, enhancing social comparison effects 63 , 156 . Being able to edit posts (via the affordance of editability) has been argued to contribute to the positivity bias on social media: what is portrayed online is often more positive than the offline experience. Thus, upward comparisons are more likely to happen in online spaces than downward or lateral comparisons 157 . Lastly, the verifiability of others’ idealized self-presentations is often low, meaning that users have insufficient cues to gauge their authenticity 158 .

Engaging in comparisons on social media has been associated with depression in correlational studies 159 . Furthermore, qualitative research has shown that not receiving as many positive evaluations as expected (or if positive evaluations are not provided quickly enough) increases negative emotions in children and adolescents aged between age 9 and 19 years 39 . This result aligns with a reinforcement learning modelling study of Instagram data, which found that the likes a user receives on their own posts become less valuable and less predictive of future posting behaviour if others in their network receive more likes on their posts 160 . Although this study did not measure mood or mental health, it shows that the value of the likes are not static but inherently social; their impact depends on how many are typically received by other people in the same network.

Among the different types of social comparison that adolescents engage in (comparing one’s achievements, social status or lifestyle), the most substantial concerns have been raised about body-related comparisons. One review suggested that social media affordances create a ‘perfect storm’ for body image concerns that can contribute to both socio-emotional and eating disorders 73 . Social media affordances might increase young people’s focus on other people’s appearances as well as on their own appearance by showing idealized, highly edited images, providing quantified feedback and making the ability to associate and compare oneself with peers constantly available 161 , 162 . The latter puts adolescents who are less popular or receive less social support at particular risk of low self-image and social distress 35 .

Affordances enable more prominent and explicit social comparisons in social media environments relative to offline environments 158 , 159 , 163 , 164 , 165 . However, this association could have a positive impact on mental health 164 , 166 . Initial evidence suggests beneficial outcomes of upward comparisons on social media, which can motivate behaviour change and yield positive downstream effects on mental health 164 , 166 . Positive motivational effects (inspiration) have been observed among young adults for topics such as travelling and exploring nature, as well as fitness and other health behaviours, which can all improve mental health 167 . Importantly, inspiration experiences are not a niche phenomenon on social media: an experience sampling study of 353 Dutch adolescents (mean age 13–15 years) found that participants reported some level of social media-induced inspiration in 33% of the times they were asked to report on this over the course of 3 weeks 168 . Several experimental and longitudinal studies show that inspiration is linked to upward comparison on social media 157 , 164 , 166 . However, the positive, motivating side of social comparison on social media has only been examined in a few studies and requires additional investigation.

Social feedback

Adolescence is also a period of social reorientation, when peers tend to become more important than family 169 , peer acceptance becomes increasingly relevant 170 , 171 , 172 and young people spend increasing amounts of time with peers 173 . In parallel, there is a heightened sensitivity to negative socio-emotional or self-referential cues 140 , 174 , higher expectation of being rejected by others 175 and internalization of such rejection 142 , 176 compared with other phases in life development. A meta-analysis of both adolescents and adults found that oversensitivity to social rejection is moderately associated with both depression and anxiety 177 .

Social media affordances might amplify the potential impact of social feedback on mental health. Wanting to be accepted by peers and increased susceptibility to social rewards could be a motivator for using social media in the first place 178 . Indeed, receiving likes as social reward activated areas of the brain (such as the nucleus accumbens) that are also activated by monetary reward 179 . Quantifiability amplifies peer acceptance and rejection (via like counts), and social rejection has been linked to adverse mental health outcomes 170 , 180 , 181 , 182 . Social media can also increase feelings of being evaluated, the risk of social rejection and rumination about potential rejection due to affordances such as quantifiability, synchronicity (the degree to which an interaction happens in real time) and variability of social rewards (the degree to which social interaction and feedback occur on variable time schedules). For example, one study of undergraduate students found that active communication such as messaging was associated with feeling better after Facebook use; however, this was not the case if the communication led to negative feelings such as rumination (for example, after no responses to the messages) 183 .

In a study assessing threatened social evaluation online 184 , participants were asked to record a statement about themselves and were told their statements would be rated by others. To increase the authenticity of the threat, participants were asked to rate other people’s recordings. Threatened social evaluation online in this study decreased mood, most prominently in people with high sensitivity to social rejection. Adolescents who are more sensitive to social rejection report more severe depressive symptoms and maladaptive ruminative brooding in both mediated and non-mediated social environments, and this association is most prominent in early adolescence 185 . Not receiving as much online social approval as peers led to more severe depressive symptoms in a study of American ninth-grade adolescents (between age 14 and 15 years), especially those who were already experiencing peer victimization 153 . Furthermore, individuals with lower self-esteem post more negative and less positive content than individuals with higher self-esteem. Posted negative content receives less social reward and recognition from others than positive content, possibly creating a vicious cycle 186 . Negative experiences pertaining to social exclusion and status are also risk factors for socio-emotional disorders 180 .

The impact of social media experiences on self-esteem can be very heterogeneous, varying substantially across individuals. As a benefit, positive social feedback obtained via social media can increase users’ self-esteem 115 , an association also found among adolescents 187 . For instance, receiving likes on one’s profile or posted photographs can bolster self-esteem in the short term 144 , 188 . A study linking behavioural data and self-reports from Facebook users found that receiving quick responses on public posts increased a sense of social support and decreased loneliness 189 . Furthermore, a review of reviews consistently documented that users who report more social media use also perceive themselves to have more social resources and support online 52 , although this association has mostly been studied among young adults using social network sites such as Facebook. Whether such social feedback benefits extend to adolescents’ use of platforms centred on content consumption (such as TikTok or Instagram) is an open question.

Social inclusion and exclusion

Adolescents are more sensitive to the negative emotional impacts of being excluded than are adults 170 , 190 . It has been proposed that, as the importance of social affiliation increases during this period of life 134 , 191 , 192 , adolescents are more sensitive to a range of social stimuli, regardless of valence 193 . These include social feedback (such as compliments or likes) 95 , 194 , negative socio-emotional cues (such as negative facial expressions or social exclusion) 174 and social rejection 172 , 185 . By contrast, social inclusion (via friendships in adolescence) is protective against emotional disorders 195 and more social support is related to higher adolescent well-being 196 .

Experiencing ostracism and exclusion online decreases self-esteem and positive emotion 197 . This association has been found in vignette experiments where participants received no, only a few or a lot of likes 198 , or experiments that used mock-ups of social media sites where others received more likes than participants 153 . Being ostracized (not receiving attention or feedback) or rejected through social media features (receiving dislikes and no likes) is also associated with a reduced sense of belonging, meaningfulness, self-esteem and control 199 . Similar results were found when ostracism was experienced over messaging apps, such as not receiving a reply via WhatsApp 200 .

Evidence on whether social media also enables adolescents to experience positive social inclusion is mostly indirect and mixed. Some longitudinal surveys have found that prosocial feedback received on social media during major life events (such as university admissions) helps to buffer against stress 201 . Adult participants of a longitudinal study reported that social media offered more informational support than offline contexts, but offline contexts more often offered emotional or instrumental support 202 . Higher social network site use is, on average, associated with a perception of having more social resources and support in adults (for an overview of meta-analyses, see ref. 52 ). However, most of these studies have not investigated social support among adolescents, and it is unclear whether early findings (for example, on Facebook or Twitter) generalize to a social media landscape more strongly characterized by content consumption than social interaction (such as Instagram or TikTok).

Still, a review of social media use and offline interpersonal outcomes among adolescents documents both positive (sense of belonging and social capital) and negative (alienation from peers and perceived isolation) correlates 203 . Experience sampling research on emotional support among young adults has further shown that online social support is received and perceived as effective, and its perceived effectiveness is similar to in-person social support 204 . Social media use also has complex associations with friendship closeness among adolescents. For example, one experience sampling study found that greater use of WhatsApp or Instagram is associated with higher friendship closeness among adolescents; however, within-person examinations over time showed small negative associations 205 .

Neurobiological mechanisms

The long-term impact of environmental changes such as social media use on mental health might be amplified because adolescence is a period of considerable neurobiological development 95 (Fig.  3 ). During adolescence, overall cortical grey matter declines and white matter increases 206 , 207 . Development is particularly protracted in brain regions associated with social cognition and executive functions such as planning, decision-making and inhibiting prepotent responses. The changes in grey and white matter are thought to reflect axonal growth, myelination and synaptic reorganization, which are mechanisms of neuroplasticity influenced by the environment 208 . For example, research in rodents has demonstrated that adolescence is a sensitive period for social input, and that social isolation in adolescence has unique and more deleterious consequences for neural, behavioural and mental health development than social isolation before puberty or in adulthood 206 , 209 . There is evidence that brain regions involved in motivation and reward show greater activation to rewarding and motivational stimuli (such as appetitive stimuli and the presence of peers) in early and/or mid adolescence compared with other age groups 210 , 211 , 212 , 213 , 214 .

Little is known about the potential links between social media and neurodevelopment due to the paucity of research investigating these associations. Furthermore, causal chains (for example, social media increasing stress, which in turn influences the brain) have not yet been accurately delineated. However, it would be amiss not to recognize that brain development during adolescence forms part of the biological basis of mental health vulnerability and should therefore be considered. Indeed, the brain is proposed to be particularly plastic in adolescence and susceptible to environmental stimuli, both positive and negative 208 . Thus, even if adults and adolescents experienced the same affective consequences from social media use (such as increases in peer comparison or stress), these consequences might have a greater impact in adolescence.

A cross-sectional study (with some longitudinal elements) suggested that habitual checking of social media (for example, checking for rewards such as likes) might exacerbate reward sensitivity processes, leading to long-term hypersensitization of the reward system 215 . Specifically, frequently checking social media was associated with reduced activation in brain regions such as the dorsolateral prefrontal cortex and the amygdala in response to anticipated social feedback in young people. Brain activation during the same social feedback task was measured over subsequent years. Upon follow-up, anticipating feedback was associated with increased activation of the same brain regions among the individuals who checked social media frequently initially 215 . Although longitudinal brain imaging measurements enabled trajectories of brain development to be specified, the measures of social media use were only acquired once in the first wave of data collection. The study therefore cannot account for confounds such as personality traits, which might influence both social media checking behaviours and brain development. Other studies of digital screen use and brain development have found no impact on adolescent functional brain organization 216 .

Brain development and heightened neuroplasticity 208 render adolescence a particularly sensitive period with potentially long-term impacts into adulthood. It is possible that social media affordances that underpin increased checking and reward-seeking behaviours (such as quantifiability, variability of social rewards and permanent availability of peers) might have long-term consequences on reward processing when experienced during adolescence. However, this suggestion is still speculative and not backed up by evidence 217 .

Stress is another example of the potential amplifying effect of social media on adolescent mental health vulnerability due to neural development. Adolescents show higher stress reactivity because of maturational changes to, and increased reactivity in, the hypothalamic–pituitary–adrenal axis 218 , 219 . Compared with children and adults, adolescents experience an increase in self-consciousness and associated emotional states such as self-reported embarrassment and related physiological measures of arousal (such as skin conductance), and heightened neural response patterns compared with adults, when being evaluated or observed by peers 220 . Similarly, adolescents (age 13–17 years) show higher stress responses (higher levels of cortisol or blood pressure) compared with children (age 7–12 years) when they perform in front of others or experience social rejection 221 .

Such changes in adolescence might confer heightened risk for the onset of mental health conditions, especially socio-emotional disorders 6 . Both adolescent rodents and humans show prolonged hypothalamic–pituitary–adrenal activation after experiencing stress compared with conspecifics of different ages 218 , 219 . In animal models, stress during adolescence has been shown to result in increased anxiety levels in adulthood 222 and alterations in emotional and cognitive development 223 . Furthermore, human studies have linked stress in adolescence to a higher risk of mental health disorder onset 218 and reviews of cross-species work have illustrated a range of brain changes due to adolescent stress 224 , 225 .

There is still little conclusive neurobiological evidence about social media use and stress, and a lack of understanding about which affordances might be involved (although there has been a range of work studying digital stress; Box  1 ). Studies of changes in cortisol levels or hypothalamic–pituitary–adrenal functioning and their relation to social media use have been mixed and inconclusive 226 , 227 . These results could be due to the challenge of studying stress responses in adolescents, particularly as cortisol fluctuates across the day and one-point readings can be unreliable. However, the increased stress sensitivity during the adolescent developmental period might mean that social media use can have a long-term influence on mental health due to neurobiological mechanisms. These processes are therefore important to understand in future research.

Box 1 Digital stress

Digital stress is not a unified construct. Thematic content analyses have categorized digital stress into type I stressors (for example, mean attacks, cyberbullying or shaming) and type II stressors (for example, interpersonal stress due to pressure to stay available) 260 . Other reviews have noted its complexity, and categorized digital stress into availability stress (stress that results from having to be constantly available), approval anxiety (anxiety regarding others’ reaction to their own profile, posts or activities online), fear of missing out (stress about being absent from or not experiencing others’ rewarding experiences) and communication overload (stress due to the scale, intensity and frequency of online communication) 261 .

Digital stress has been systematically linked to negative mental health outcomes. Higher digital stress was longitudinally associated with higher depressive symptoms in a questionnaire study 262 . Higher social media stress was also longitudinally related to poorer sleep outcomes in girls (but not boys) 263 . Studies and reviews have linked cyberbullying victimization (a highly stressful experience) to decreased mental health outcomes such as depression, and psychosocial outcomes such as self-esteem 103 , 146 , 147 , 264 , 265 . A systematic review of both adolescents and adults found a medium association ( r  = 0.26–0.34) between different components of digital stress and psychological distress outcomes such as anxiety, depression or loneliness, which was not moderated by age or sex (except for connection overload) 266 . However, the causal structure giving rise to such results is still far from clear. For example, surveys have linked higher stress levels to more problematic social media use and fear of missing out 267 , 268 .

Thus, the impact of digital stress on mental health is probably complex and influenced by the type of digital stressor and various affordances. For example, visibility and availability increase fear of negative public evaluation 269 and high availability and a social norm of responding quickly to messages drive constant monitoring in adolescents due to a persistent fear of upsetting friends 270 .

A range of relevant evidence from qualitative and quantitative studies documents that adolescents often ruminate about online interactions and messages. For example, online salience (constantly thinking about communication, content or events happening online) was positively associated with stress on both between-person and within-person levels in a cross-sectional quota sample of adults and three diary studies of young adults 271 , 272 . Online salience has also been associated with lower well-being in a pre-registered study of momentary self-reports from young adults with logged online behaviours. However, this study also noted that positive thoughts were related to higher well-being 273 . Furthermore, although some studies found no associations between the amount of communication and digital stress 272 , a cross-sectional study found that younger users’ (age 14–34 years and 35–49 years) perception of social pressure to be constantly available was related to communication load (measured by questions about the amount of use, as well as the urge to check email and social media) and Internet multitasking, whereas this was not the case for older users aged 50–85 years 274 . By contrast, communication load and perceived stress were associated only among older users.

Summary and future directions

To help to understand the potential role of social media in the decline of adolescent mental health over the past decade, researchers should study the mechanisms linking social media, adolescent development and mental health. Specifically, social media environments might amplify the socio-cognitive processes that render adolescents more vulnerable to mental health conditions in the first place. We outline various mechanisms at three levels of adolescent development — behavioural, cognitive and neurobiological — that could be involved in the decline of adolescent mental health as a function of social media engagement. To do so, we delineate specific social media affordances, such as quantification of social feedback or anonymity, which can also have positive impacts on mental health.

Our Review sets out clear recommendations for future research on the intersection of social media and adolescent mental health. The foundation of this research lies in the existing literature investigating the underlying processes that heighten adolescents’ risk of developing socio-emotional disorders. Zooming in on the potential mechanistic targets impacted by social media uses and affordances will produce specific research questions to facilitate controlled and systematic scientific inquiry relevant for intervention and translation. This approach encourages researchers to pinpoint the mechanisms and levels of explanation they want to include and will enable them to identify what factors to additionally consider, such as participants’ age 60 , the specific mental health outcomes being measured, the types of social media being examined and the populations under study 52 , 228 . Targeted and effective research should prioritize the most promising areas of study and acknowledge that all research approaches have inherent limitations 229 . Researchers must embrace methodological diversity, which in turn will facilitate triangulation. Surveys, experience sampling designs in conjunction with digital trace data, as well as experimental or neuroimaging paradigms and computational modelling (such as reinforcement learning) can all be used to address research questions comprehensively 230 . Employing such a multi-method approach enables the convergence of evidence and strengthens the reliability of findings 231 .

Mental health and developmental research can also become more applicable to the study of social media by considering how studies might already be exploring features of the digital environment, such as its design features and perceived affordances. Many cognitive neuroscience studies that investigate social processes and mental health during adolescence necessarily design tasks that can be completed in controlled experimental or brain scanning environments. Consequently, they tend to focus on digitally mediated interactions. However, researchers conceptualize and generalize their results to face-to-face interactions. For example, it is common across the discipline to not explicitly describe the interactions under study as being about social processes in digital environments (such as studies that assess social feedback based on the number of ‘thumbs up’ or ‘thumbs down’ received in social media 232 ). Considering whether cognitive neuroscience studies include key affordances of mediated (or non-mediated) environments, and discussing these in published papers, will make studies searchable within the field of social media research, enabling researchers to broaden the impact of their work and systematically specify generalizations to offline environments 233 .

To bridge the gap between knowledge about mediated and non-mediated social environments, it is essential to directly compare the two 233 . It is often assumed that negative experiences online have a detrimental impact on mental health. However, it remains unclear whether this mechanism is present in both mediated and non-mediated spaces or whether it is specific to the mediated context. For instance, our Review highlights that the quantification of social feedback through likes is an important affordance of social media 160 . Feedback on social media platforms might therefore elicit a greater sense of certainty because it is quantified compared with the more subjective and open-to-interpretation feedback received face to face 151 . Conducting experiments in which participants receive feedback that is more or less quantified and uncertain, specifically designed to compare mediated and non-mediated environments, would provide valuable insights. Such research efforts could also establish connections with computational neuroscience studies demonstrating that people tend to learn faster from stimuli that are less uncertain 234 .

We have chosen not to make recommendations concerning interventions targeting social media use to improve adolescent mental health for several reasons. First, we did not fully consider the bidirectional interactions between environment and development 35 , 235 , or the factors modulating adolescents’ differential susceptibility to the effects of social media 45 , 58 . For example, mental health status also influences how social media is used 47 , 58 , 59 , 236 , 237 (Box  2 ). These bidirectional interactions could be addressed using network or complexity science approaches 238 . Second, we do not yet know how the potential mechanisms by which social media might increase mental health vulnerability compare in magnitude, importance, scale and ease and/or cost of intervention with other factors and mechanisms that are already well known to influence mental health, such as poverty or loneliness. Last, social media use will probably interact with these predictors in ways that have not been delineated and can also support mental health resilience (for example, through social support or online self-help programmes). These complexities should be considered in future research, which will need to pinpoint not just the existence of mechanisms but their relative importance, to identify policy and intervention priorities.

Our Review has used a broad definition of mental health. Focusing on specific diagnostic or transdiagnostic symptomatology might reveal different mechanisms of interest. Furthermore, our Review is limited to mechanisms related to behaviour and neurocognitive development, disregarding other levels of explanation (such as genetics and culture) 34 , and also studying predominately Western-centric samples 239 . Mechanisms do not operate solely in linear pathways but exist within networks of interacting risk and resilience factors, characterized by non-linear and complex dynamics across diverse timescales 9 . Mechanisms and predisposing factors can interact and combine, amplifying mental health vulnerability. Mental health can be considered a dynamic system in which gradual changes to external conditions can have substantial downstream consequences due to system properties such as feedback loops 240 , 241 , 242 . These consequences are especially prominent in times of change and pre-existing vulnerability, such as adolescence 10 .

Indeed, if social media is a contributing factor to the current decline in adolescent mental health, as is commonly assumed, then it is important to identify and investigate mechanisms that are specifically tailored to the adolescent age range and make the case for why they matter. Without a thorough examination of these mechanisms and policy analysis to indicate whether they should be a priority to address, there is insufficient evidence to support the hypothesis that social media is the primary — or even just an influential and important — driver of mental health declines. Researchers need to stop studying social media as monolithic and uniform, and instead study its features, affordances and outcomes by leveraging a range of methods including experiments, questionnaires, qualitative research and industry data. Ultimately, this comprehensive approach will enhance researchers’ ability to address the potential challenges that the digital era poses on adolescent mental health.

Box 2 Effects of mental health on social media use

Although a lot of scientific discussion has focused on the impact of social media use on mental health, cross-sectional studies cannot differentiate between whether social media use is influencing mental health or mental health is influencing social media use, or a third factor is influencing both 51 . It is likely that mental health status influences social media use creating reinforcing cycles of behaviour, something that has been considered in the communication sciences literature under the term ‘transactional media effects’ 58 , 236 , 237 . According to communication science models, media use and its consequences are components of reciprocal processes 275 .

There are similar models in mental health research. For example, people’s moods influence their judgements of events, which can lead to self-perpetuating cycles of negativity (or positivity); a mechanism called ‘mood congruency’ 276 . Behavioural studies have also shown that people experiencing poor mental health behave in ways that decrease their opportunity to experience environmental reward such as social activities, maintaining poor mental health 277 , 278 . Although for many people these behaviours are a form of coping (for example, by avoiding stressful circumstances), they often worsen symptoms of mental health conditions 279 .

Some longitudinal studies found that a decrease in adolescent well-being predicted an increase in social media use 1 year later 47 , 59 . However, other studies have found no relationships between well-being and social media use over long-term or daily time windows 45 , 46 . One reason behind the heterogeneity of the results could be that how mental health impacts social media use is highly individual 45 , 280 .

Knowledge on the impact of mental health on social media use is still in its infancy and studies struggle to reach coherent conclusions. However, findings from the mental health literature can be used to generate hypotheses about how aspects of mental health might impact social media use. For example, it has been repeatedly found that young people with anxiety or eating disorders engage in more social comparisons than individuals without these disorders 281 , 282 , and adolescents with depression report more unfavourable social comparisons on social media than adolescents without depression 283 . Similar results have been found for social feedback seeking (for example, reassurance), including in social media environments 159 . Specifically, depressive symptoms were more associated with social comparison and feedback seeking, and these associations were stronger in women and in adolescents who were less popular. Individuals from the general population with lower self-esteem post more negative and less positive content than individuals with higher self-esteem, which in turn is associated with receiving less positive feedback from others 185 . There are therefore a wide range of possible ways in which diverse aspects of mental health might influence specific facets of how social media is used — and, in turn, how it ends up impacting the user.

Savin-Williams, R. Adolescence: An Ethological Perspective (Springer, 1987).

Sawyer, S. M., Azzopardi, P. S., Wickremarathne, D. & Patton, G. C. The age of adolescence. Lancet Child. Adolesc. Health 2 , 223–228 (2018).

Article   PubMed   Google Scholar  

Paus, T., Keshavan, M. & Giedd, J. N. Why do many psychiatric disorders emerge during adolescence? Nat. Rev. Neurosci. 9 , 947–957 (2008).

Article   PubMed   PubMed Central   Google Scholar  

Solmi, M. et al. Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol. Psychiatry 27 , 281–295 (2022).

Orben, A., Lucas, R. E., Fuhrmann, D. & Kievit, R. A. Trajectories of adolescent life satisfaction. R. Soc. Open. Sci. 9 , 211808 (2022).

Rapee, R. M. et al. Adolescent development and risk for the onset of social-emotional disorders: a review and conceptual model. Behav. Res. Ther. 123 , 103501 (2019). This review describes why adolescence is a sensitive period for mental health vulnerability.

Arango, C. et al. Risk and protective factors for mental disorders beyond genetics: an evidence‐based atlas. World Psychiatry 20 , 417–436 (2021).

Ioannidis, K., Askelund, A. D., Kievit, R. A. & van Harmelen, A.-L. The complex neurobiology of resilient functioning after childhood maltreatment. BMC Med. 18 , 32 (2020).

Kraemer, H. C., Stice, E., Kazdin, A., Offord, D. & Kupfer, D. How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. AJP 158 , 848–856 (2001).

Article   Google Scholar  

Hankin, B. L. & Abramson, L. Y. Development of gender differences in depression: an elaborated cognitive vulnerability–transactional stress theory. Psychol. Bull. 127 , 773–796 (2001).

Collishaw, S., Maughan, B., Natarajan, L. & Pickles, A. Trends in adolescent emotional problems in England: a comparison of two national cohorts twenty years apart: twenty-year trends in emotional problems. J. Child. Psychol. Psychiatry 51 , 885–894 (2010).

Pitchforth, J. M., Viner, R. M. & Hargreaves, D. S. Trends in mental health and wellbeing among children and young people in the UK: a repeated cross-sectional study, 2000–14. Lancet 388 , S93 (2016).

Coley, R. L., O’Brien, M. & Spielvogel, B. Secular trends in adolescent depressive symptoms: growing disparities between advantaged and disadvantaged schools. J. Youth Adolescence 48 , 2087–2098 (2019).

Patalay, P. & Gage, S. H. Changes in millennial adolescent mental health and health-related behaviours over 10 years: a population cohort comparison study. Int. J. Epidemiol. 48 , 1650–1664 (2019).

Pitchforth, J. M. et al. Mental health and well-being trends among children and young people in the UK, 1995–2014: analysis of repeated cross-sectional national health surveys. Psychol. Med. 49 , 1275–1285 (2019).

Plana‐Ripoll, O. et al. Temporal changes in sex‐ and age‐specific incidence profiles of mental disorders—a nationwide study from 1970 to 2016. Acta Psychiatr. Scand. 145 , 604–614 (2022).

Twenge, J. M., Cooper, A. B., Joiner, T. E., Duffy, M. E. & Binau, S. G. Age, period, and cohort trends in mood disorder indicators and suicide-related outcomes in a nationally representative dataset, 2005–2017. J. Abnorm. Psychol. 128 , 185–199 (2019).

van Vuuren, C. L., Uitenbroek, D. G., van der Wal, M. F. & Chinapaw, M. J. M. Sociodemographic differences in 10-year time trends of emotional and behavioural problems among adolescents attending secondary schools in Amsterdam, The Netherlands. Eur. Child. Adolesc. Psychiatry 27 , 1621–1631 (2018).

Collishaw, S. Annual research review: secular trends in child and adolescent mental health. J. Child. Psychol. Psychiatry 56 , 370–393 (2015).

Goodwin, R. D. et al. Trends in U.S. depression prevalence from 2015 to 2020: the widening treatment gap. Am. J. Prev. Med. 63 , 726–733 (2022).

Mojtabai, R. & Olfson, M. National trends in mental health care for US adolescents. JAMA Psychiatry 77 , 703 (2020).

Mojtabai, R., Olfson, M. & Han, B. National trends in the prevalence and treatment of depression in adolescents and young adults. Pediatrics 138 , e20161878 (2016).

Goodwin, R. D., Weinberger, A. H., Kim, J. H., Wu, M. & Galea, S. Trends in anxiety among adults in the United States, 2008–2018: rapid increases among young adults. J. Psychiatr. Res. 130 , 441–446 (2020).

Beerten, S. G. et al. Trends in the registration of anxiety in Belgian primary care from 2000 to 2021: a registry-based study. Br. J. Gen. Pract. 73 , e460–e467 (2022).

Walrave, R. et al. Trends in the epidemiology of depression and comorbidities from 2000 to 2019 in Belgium. BMC Prim. Care 23 , 163 (2022).

Vuorre, M. & Przybylski, A. K. Global well-being and mental health in the internet age. Clin. Psychol. Sci . https://doi.org/10.1177/21677026231207791 (2023).

Steffen, A., Thom, J., Jacobi, F., Holstiege, J. & Bätzing, J. Trends in prevalence of depression in Germany between 2009 and 2017 based on nationwide ambulatory claims data. J. Affect. Disord. 271 , 239–247 (2020).

Ford, T. Editorial Perspective: why I am now convinced that emotional disorders are increasingly common among young people in many countries. J. Child. Psychol. Psychiatr. 61 , 1275–1277 (2020).

McElroy, E., Tibber, M., Fearon, P., Patalay, P. & Ploubidis, G. B. Socioeconomic and sex inequalities in parent‐reported adolescent mental ill‐health: time trends in four British birth cohorts. J. Child Psychol. Psychiatry 64 , 758–767 (2022).

OECD. Society at a Glance 2019: OECD Social Indicators (Organisation for Economic Co-operation and Development, 2019).

Ofcom. Online Nation (2021). Ofcom.org.uk https://www.ofcom.org.uk/research-and-data/online-research/online-nation (2022).

Anderson, M. & Jiang, J. Teens’ Social Media Habits and Experiences (Pew Research Center, 2018).

McFarland, L. A. & Ployhart, R. E. Social media: a contextual framework to guide research and practice. J. Appl. Psychol. 100 , 1653–1677 (2015).

Büchi, M. Digital well-being theory and research. N. Media Soc. 26 , 172–189 (2024).

Nesi, J., Choukas-Bradley, S. & Prinstein, M. J. Transformation of adolescent peer relations in the social media context: part 1—a theoretical framework and application to dyadic peer relationships. Clin. Child. Fam. Psychol. Rev. 21 , 267–294 (2018). This landmark paper applies the idea of affordances to understanding the impact of social media on adolescent social relationships.

Taffel, S. Perspectives on the postdigital: beyond rhetorics of progress and novelty. Convergence 22 , 324–338 (2016).

Papacharissi, Z. We have always been social. Soc. Media + Society 1 , 205630511558118 (2015).

Google Scholar  

Crone, E. A. & Konijn, E. A. Media use and brain development during adolescence. Nat. Commun. 9 , 1–10 (2018). This article describes adolescent cognitive and neural development and its intersection with new types of technology.

Weinstein, E. & James, C. Behind Their Screens: What Teens Are Facing (and Adults Are Missing) (MIT Press, 2022).

Twenge, J. M., Joiner, T. E., Rogers, M. L. & Martin, G. N. Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clin. Psychol. Sci. 6 , 3–17 (2017).

Gunnell, D., Kidger, J. & Elvidge, H. Adolescent mental health in crisis. BMJ 361 , k2608 (2018).

Odgers, C. L., Schueller, S. M. & Ito, M. Screen time, social media use, and adolescent development. Annu. Rev. Dev. Psychol. 2 , 485–502 (2020).

Valkenburg, P. M., Meier, A. & Beyens, I. Social media use and its impact on adolescent mental health: an umbrella review of the evidence. Curr. Opin. Psychol. 44 , 58–68 (2022).

Kreski, N. et al. Social media use and depressive symptoms among United States adolescents. J. Adolesc. Health 68 , 572–579 (2020).

Beyens, I., Pouwels, J. L., van Driel, I. I., Keijsers, L. & Valkenburg, P. M. The effect of social media on well-being differs from adolescent to adolescent. Sci. Rep. 10 , 10763 (2020). This landmark paper highlights that the impacts of social media on well-being are highly individual.

Jensen, M., George, M. J., Russell, M. R. & Odgers, C. L. Young adolescents’ digital technology use and mental health symptoms: little evidence of longitudinal or daily linkages. Clin. Psychol. Sci. 7 , 1416–1433 (2019).

Orben, A., Dienlin, T. & Przybylski, A. K. Social media’s enduring effect on adolescent life satisfaction. Proc. Natl Acad. Sci. USA 116 , 10226–10228 (2019).

Allcott, H., Braghieri, L., Eichmeyer, S. & Gentzkow, M. The welfare effects of social media. Am. Economic Rev. 110 , 629–676 (2020).

Nassen, L.-M., Vandebosch, H., Poels, K. & Karsay, K. Opt-out, abstain, unplug. A systematic review of the voluntary digital disconnection literature. Telemat. Inform. 81 , 101980 (2023).

Dienlin, T. & Johannes, N. The impact of digital technology use on adolescent well-being. Dialogues Clin. Neurosci. 22 , 135–142 (2020).

Odgers, C. L. & Jensen, M. R. Annual research review: adolescent mental health in the digital age: facts, fears, and future directions. J. Child. Psychol. Psychiatry 61 , 336–348 (2020).

Meier, A. & Reinecke, L. Computer-mediated communication, social media, and mental health: a conceptual and empirical meta-review. Commun. Res. 48 , 1182–1209 (2021). This review provides a hierarchical taxonomy of the levels of analysis at which social media can be conceptualized and measured.

Orben, A. Teenagers, screens and social media: a narrative review of reviews and key studies. Soc. Psychiatry Psychiatr. Epidemiol. 55 , 407–414 (2020).

Bell, V., Bishop, D. V. M. & Przybylski, A. K. The debate over digital technology and young people. BMJ 351 , h3064 (2015).

Online Safety Act 2023. legislation.gov.uk , https://www.legislation.gov.uk/ukpga/2023/50/enacted (2023).

Hawkes, N. CMO report is unable to shed light on impact of screen time and social media on children’s health. BMJ 364 , l643 (2019).

US Department of Health and Human Services. Social Media and Youth Mental Health: The U.S. Surgeon General’s Advisory (2023).

Valkenburg, P. M. & Peter, J. The differential susceptibility to media effects model: differential susceptibility to media effects model. J. Commun. 63 , 221–243 (2013). This landmark paper examines how the impact of media is influenced by individual differences.

Orben, A., Przybylski, A. K., Blakemore, S.-J. & Kievit, R. A. Windows of developmental sensitivity to social media. Nat. Commun. 13 , 1649 (2022). This large-scale data analysis shows that adolescent development potentially influences how social media impacts well-being.

Orben, A. & Blakemore, S.-J. How social media affects teen mental health: a missing link. Nature 614 , 410–412 (2023).

Shaw, H. et al. Quantifying smartphone “use”: choice of measurement impacts relationships between “usage” and health. Technol. Mind Behav . 1 , https://doi.org/10.1037/tmb0000022 (2020).

Parry, D. A. et al. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nat. Hum. Behav. 5 , 1535–1547 (2021).

Verduyn, P., Gugushvili, N. & Kross, E. Do social networking sites influence well-being? The extended active-passive model. Curr. Dir. Psychol. Sci. 31 , 62–68 (2022).

Davidson, B. I., Shaw, H. & Ellis, D. A. Fuzzy constructs in technology usage scales. Comput. Hum. Behav. 133 , 107206 (2022).

Shaw, D. J., Kaye, L. K., Ngombe, N., Kessler, K. & Pennington, C. R. It’s not what you do, it’s the way that you do it: an experimental task delineates among passive, reactive and interactive styles of behaviour on social networking sites. PLoS ONE 17 , e0276765 (2022).

Griffioen, N., Van Rooij, M., Lichtwarck-Aschoff, A. & Granic, I. Toward improved methods in social media research. Technol. Mind Behav . 1 , https://doi.org/10.1037/tmb0000005 (2020).

Valkenburg, P. M. Social media use and well-being: what we know and what we need to know. Curr. Opin. Psychol. 45 , 101294 (2022).

Yang, C., Holden, S. M. & Ariati, J. Social media and psychological well-being among youth: the multidimensional model of social media use. Clin. Child. Fam. Psychol. Rev. 24 , 631–650 (2021).

Kelly, Y., Zilanawala, A., Booker, C. & Sacker, A. Social media use and adolescent mental health: findings from the UK Millennium Cohort Study. EClinicalMedicine 6 , 59–68 (2019).

Orben, A. & Przybylski, A. K. The association between adolescent well-being and digital technology use. Nat. Hum. Behav. 3 , 173–182 (2019).

Sultan, M., Scholz, C. & van den Bos, W. Leaving traces behind: using social media digital trace data to study adolescent wellbeing. Comput. Hum. Behav. Rep. 10 , 100281 (2023).

Kaye, L., Orben, A., Ellis, D., Hunter, S. & Houghton, S. The conceptual and methodological mayhem of “screen time”. IJERPH 17 , 3661 (2020).

Choukas-Bradley, S., Roberts, S. R., Maheux, A. J. & Nesi, J. The perfect storm: a developmental–sociocultural framework for the role of social media in adolescent girls’ body image concerns and mental health. Clin. Child. Fam. Psychol. Rev. 25 , 681–701 (2022). This review focuses on how social media can influence adolescent development of body image.

Moreno, M. A. & Uhls, Y. T. Applying an affordances approach and a developmental lens to approach adolescent social media use. Digital Health 5 , 205520761982667 (2019).

Smock, A. D., Ellison, N. B., Lampe, C. & Wohn, D. Y. Facebook as a toolkit: a uses and gratification approach to unbundling feature use. Comput. Hum. Behav. 27 , 2322–2329 (2011).

Bayer, J. B., Triêu, P. & Ellison, N. B. Social media elements, ecologies, and effects. Annu. Rev. Psychol. 71 , 471–497 (2020).

Gibson, J. J. The Scological Approach to Visual Perception (Houghton Mifflin, 1979).

Norman, D. A. The Psychology of Everyday Things (Basic Books, 1988).

Evans, S. K., Pearce, K. E., Vitak, J. & Treem, J. W. Explicating affordances: a conceptual framework for understanding affordances in communication research. J. Comput. Mediat. Commun. 22 , 35–52 (2017).

Bayer, J. B., Ellison, N. B., Schoenebeck, S. Y. & Falk, E. B. Sharing the small moments: ephemeral social interaction on Snapchat. Information . Commun. Soc. 19 , 956–977 (2016).

Fox, J. & McEwan, B. Distinguishing technologies for social interaction: the perceived social affordances of communication channels scale. Commun. Monogr. 84 , 298–318 (2017).

Kreling, R., Meier, A. & Reinecke, L. Feeling authentic on social media: subjective authenticity across instagram stories and posts. Soc. Media + Society 8 , 205630512210862 (2022).

Leonardi, P. M. Social media, knowledge sharing, and innovation: toward a theory of communication visibility. Inf. Syst. Res. 25 , 796–816 (2014).

Treem, J. W. & Leonardi, P. M. Social media use in organizations: exploring the affordances of visibility, editability, persistence, and association. Ann. Int. Commun. Assoc. 36 , 143–189 (2013).

Ellison, N. B., Pyle, C. & Vitak, J. Scholarship on well-being and social media: a sociotechnical perspective. Curr. Opin. Psychol. 46 , 101340 (2022).

Orben, A. The Sisyphean cycle of technology panics. Perspect. Psychol. Sci. 15 , 1143–1157 (2020).

Granic, I., Morita, H. & Scholten, H. Beyond screen time: identity development in the digital age. Psychol. Inq. 31 , 195–223 (2020). This perspective discusses how adolescent identity development might be impacted by digital platforms including social media and video games.

Lieberman, A. & Schroeder, J. Two social lives: how differences between online and offline interaction influence social outcomes. Curr. Opin. Psychol. 31 , 16–21 (2020).

Valkenburg, P. M. & Peter, J. Online communication among adolescents: an integrated model of its attraction, opportunities, and risks. J. Adolesc. Health 48 , 121–127 (2011).

Steinberg, L. et al. Around the world, adolescence is a time of heightened sensation seeking and immature self-regulation. Dev. Sci. 21 , e12532 (2018).

Blakemore, S.-J. & Robbins, T. W. Decision-making in the adolescent brain. Nat. Neurosci. 15 , 1184–1191 (2012).

Steinberg, L. A social neuroscience perspective on adolescent risk-taking. Dev. Rev. 28 , 78–106 (2008).

Chein, J., Albert, D., O’Brien, L., Uckert, K. & Steinberg, L. Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry: peer influence on risk taking. Dev. Sci. 14 , F1–F10 (2011).

Blakemore, S.-J. Avoiding social risk in adolescence. Curr. Dir. Psychol. Sci. 27 , 116–122 (2018).

Blakemore, S.-J. & Mills, K. L. Is adolescence a sensitive period for sociocultural processing? Annu. Rev. Psychol. 65 , 187–207 (2014). This review presents adolescence as an important stage of development characterized by changes to social cognition.

Campbell, R. et al. Multiple risk behaviour in adolescence is associated with substantial adverse health and social outcomes in early adulthood: findings from a prospective birth cohort study. Prev. Med. 138 , 106157 (2020).

Kurten, S. et al. Like to drink: dynamics of liking alcohol posts and effects on alcohol use. Comput. Hum. Behav. 129 , 107145 (2022).

Vannucci, A., Simpson, E. G., Gagnon, S. & Ohannessian, C. M. Social media use and risky behaviors in adolescents: a meta‐analysis. J. Adolesc. 79 , 258–274 (2020).

Eichhorn, K. The End of Forgetting: Growing up with Social Media (Harvard Univ. Press, 2019).

Litt, E. & Hargittai, E. The imagined audience on social network sites. Soc. Media + Society 2 , 205630511663348 (2016).

Vitak, J. The impact of context collapse and privacy on social network site disclosures. J. Broadcast. Electron. Media 56 , 451–470 (2012).

Livingstone, S. Taking risky opportunities in youthful content creation: teenagers’ use of social networking sites for intimacy, privacy and self-expression. N. Media Soc. 10 , 393–411 (2008).

Marciano, L., Schulz, P. J. & Camerini, A.-L. Cyberbullying perpetration and victimization in youth: a meta-analysis of longitudinal studies. J. Comput.-Mediat. Commun. 25 , 163–181 (2020).

Mori, C., Temple, J. R., Browne, D. & Madigan, S. Association of sexting with sexual behaviors and mental health among adolescents: a systematic review and meta-analysis. JAMA Pediatr. 173 , 770 (2019).

Suler, J. The online disinhibition effect. Cyberpsychol. Behav. 7 , 321–326 (2004).

Wright, M. F., Harper, B. D. & Wachs, S. The associations between cyberbullying and callous-unemotional traits among adolescents: the moderating effect of online disinhibition. Pers. Individ. Differ. 140 , 41–45 (2019).

Nitschinsk, L., Tobin, S. J. & Vanman, E. J. The disinhibiting effects of anonymity increase online trolling. Cyberpsychol. Behav. Soc. Netw. 25 , 377–383 (2022).

Nadkarni, A. & Hofmann, S. G. Why do people use Facebook? Pers. Individ. Differ. 52 , 243–249 (2012).

Leary, M. R. & Kowalski, R. M. Impression management: a literature review and two-component model. Psychol. Bull. 107 , 34–47 (1990).

Zhao, S., Grasmuck, S. & Martin, J. Identity construction on Facebook: digital empowerment in anchored relationships. Comput. Hum. Behav. 24 , 1816–1836 (2008).

Bij de Vaate, N. A. J. D., Veldhuis, J. & Konijn, E. A. How online self-presentation affects well-being and body image: a systematic review. Telemat. Inform. 47 , 101316 (2020).

Reinecke, L. & Trepte, S. Authenticity and well-being on social network sites: a two-wave longitudinal study on the effects of online authenticity and the positivity bias in SNS communication. Comput. Hum. Behav. 30 , 95–102 (2014).

Twomey, C. & O’Reilly, G. Associations of self-presentation on Facebook with mental health and personality variables: a systematic review. Cyberpsychol. Behav. Soc. Netw. 20 , 587–595 (2017).

Vanden Abeele, M., Schouten, A. P. & Antheunis, M. L. Personal, editable, and always accessible: an affordance approach to the relationship between adolescents’ mobile messaging behavior and their friendship quality. J. Soc. Personal. Relatsh. 34 , 875–893 (2017).

Krause, H.-V., Baum, K., Baumann, A. & Krasnova, H. Unifying the detrimental and beneficial effects of social network site use on self-esteem: a systematic literature review. Media Psychol. 24 , 10–47 (2021).

Carr, C. T. & Foreman, A. C. Identity shift III: effects of publicness of feedback and relational closeness in computer-mediated communication. Media Psychol. 19 , 334–358 (2016).

Walther, J. B. et al. The effect of feedback on identity shift in computer-mediated communication. Media Psychol. 14 , 1–26 (2011).

Gonzales, A. L. & Hancock, J. T. Identity shift in computer-mediated environments. Media Psychol. 11 , 167–185 (2008).

Kelly, A. E. & Rodriguez, R. R. Publicly committing oneself to an identity. Basic. Appl. Soc. Psychol. 28 , 185–191 (2006).

Petre, C. E. The relationship between Internet use and self-concept clarity: a systematic review and meta-analysis. Cyberpsychology 15 , https://doi.org/10.5817/CP2021-2-4 (2021).

Appel, M., Schreiner, C., Weber, S., Mara, M. & Gnambs, T. Intensity of Facebook use is associated with lower self-concept clarity: cross-sectional and longitudinal evidence. J. Media Psychol. 30 , 160–172 (2018).

Talaifar, S. & Lowery, B. S. Freedom and constraint in digital environments: implications for the self. Perspect. Psychol. Sci. 18 , 544–575 (2022).

West, M., Rice, S. & Vella-Brodrick, D. Mid-adolescents’ social media use: supporting and suppressing autonomy. J. Adolesc. Res . https://doi.org/10.1177/07435584231168402 (2023).

Grasmuck, S., Martin, J. & Zhao, S. Ethno-racial identity displays on Facebook. J. Comput.-Mediat. Commun. 15 , 158–188 (2009).

DeVito, M. A., Walker, A. M. & Birnholtz, J. ‘Too Gay for Facebook’: presenting LGBTQ+ identity throughout the personal social media ecosystem. Proc. ACM Hum.–Comput. Interact. 2 , 1–23 (2018).

Ellison, N., Heino, R. & Gibbs, E. Managing impressions online: self-presentation processes in the online dating environment. J. Comput.-Mediat. Commun . 11 , https://doi.org/10.1111/j.1083-6101.2006.00020.x (2006).

Hancock, J. T. in Oxford Handbook of Internet Psychology (eds Joinson, A. et al.) 287–301 (Oxford Univ. Press, 2009).

Davidson, B. I. & Joinson, A. N. Shape shifting across social media. Soc. Media + Society 7 , 205630512199063 (2021).

Davis, J. L. Triangulating the self: identity processes in a connected era: triangulating the self. Symbolic Interaction 37 , 500–523 (2014).

Allen, B. J., Stratman, Z. E., Kerr, B. R., Zhao, Q. & Moreno, M. A. Associations between psychosocial measures and digital media use among transgender youth: cross-sectional study. JMIR Pediatr. Parent. 4 , e25801 (2021).

Haimson, O. L. Mapping gender transition sentiment patterns via social media data: toward decreasing transgender mental health disparities. J. Am. Med. Inform. Assoc. 26 , 749–758 (2019).

Harter, S. The Construction of the Self: Developmental and Sociocultural Foundations (Guilford Press, 2012).

Crone, E. A., Green, K. H., van de Groep, I. H. & van der Cruijsen, R. A neurocognitive model of self-concept development in adolescence. Annu. Rev. Dev. Psychol. 4 , 273–295 (2022). This extensive review discusses how adolescence is an important time for self-concept development.

Pfeifer, J. H. & Peake, S. J. Self-development: integrating cognitive, socioemotional, and neuroimaging perspectives. Deve. Cognit. Neurosci. 2 , 55–69 (2012).

Sebastian, C., Burnett, S. & Blakemore, S.-J. Development of the self-concept during adolescence. Trends Cognit. Sci. 12 , 441–446 (2008).

Crocetti, E., Rubini, M., Luyckx, K. & Meeus, W. Identity formation in early and middle adolescents from various ethnic groups: from three dimensions to five statuses. J. Youth Adolesc. 37 , 983–996 (2008).

Morita, H., Griffioen, N. & Granic, I. in Handbook of Adolescent Digital Media Use and Mental Health (eds Nesi, J., Telzer, E. H. & Prinstein, M. J.) 63–84 (Cambridge Univ. Press, 2022).

Dumontheil, I., Apperly, I. A. & Blakemore, S.-J. Online usage of theory of mind continues to develop in late adolescence. Dev. Sci. 13 , 331–338 (2010).

Weil, L. G. et al. The development of metacognitive ability in adolescence. Conscious. Cogn. 22 , 264–271 (2013).

Moses-Payne, M. E., Chierchia, G. & Blakemore, S.-J. Age-related changes in the impact of valence on self-referential processing in female adolescents and young adults. Cognit. Dev. 61 , 101128 (2022).

Scheuplein, M. et al. Perspective taking and memory for self- and town-related information in male adolescents and young adults. Cognit. Dev. 67 , 101356 (2023).

Rodman, A. M., Powers, K. E. & Somerville, L. H. Development of self-protective biases in response to social evaluative feedback. Proc. Natl Acad. Sci. USA 114 , 13158–13163 (2017).

Lee, A. Y., Mieczkowski, H., Ellison, N. B. & Hancock, J. T. The algorithmic crystal: conceptualizing the self through algorithmic personalization on TikTok. Proc. ACM Hum.–Comput. Interact. 6 , 1–22 (2022).

Thomaes, S. et al. I like me if you like me: on the interpersonal modulation and regulation of preadolescents’ state self-esteem. Child. Dev. 81 , 811–825 (2010).

Valkenburg, P. M., Peter, J. & Schouten, A. P. Friend networking sites and their relationship to adolescents’ well-being and social self-esteem. CyberPsychol. Behav. 9 , 584–590 (2006).

Kwan, I. et al. Cyberbullying and children and young people’s mental health: a systematic map of systematic reviews. Cyberpsychol. Behav. Soc. Netw. 23 , 72–82 (2020).

Przybylski, A. K. & Bowes, L. Cyberbullying and adolescent well-being in England: a population-based cross-sectional study. Lancet Child. Adolesc. Health 1 , 19–26 (2017).

Peters, S. et al. Social media use and the not-so-imaginary audience: behavioral and neural mechanisms underlying the influence on self-concept. Dev. Cognit. Neurosci. 48 , 100921 (2021).

Wood, J. V. What is social comparison and how should we study it? Pers. Soc. Psychol. Bull. 22 , 520–537 (1996).

Dahl, R. E., Allen, N. B., Wilbrecht, L. & Suleiman, A. B. Importance of investing in adolescence from a developmental science perspective. Nature 554 , 441–450 (2018).

Ferguson, A. M., Turner, G. & Orben, A. Social uncertainty in the digital world. Trends Cognit. Sci. 28 , 286–289 (2024).

Blease, C. R. Too many ‘friends,’ too few ‘likes’? Evolutionary psychology and ‘Facebook depression’. Rev. Gen. Psychol. 19 , 1–13 (2015).

Lee, H. Y. et al. Getting fewer “likes” than others on social media elicits emotional distress among victimized adolescents. Child. Dev. 91 , 2141–2159 (2020).

Nesi, J. & Prinstein, M. J. In search of likes: longitudinal associations between adolescents’ digital status seeking and health-risk behaviors. J. Clin. Child. Adolesc. Psychol. 48 , 740–748 (2019).

Carr, C. T., Hayes, R. A. & Sumner, E. M. Predicting a threshold of perceived Facebook post success via likes and reactions: a test of explanatory mechanisms. Commun. Res. Rep. 35 , 141–151 (2018).

Noon, E. J. & Meier, A. Inspired by friends: adolescents’ network homophily moderates the relationship between social comparison, envy, and inspiration on instagram. Cyberpsychol. Behav. Soc. Netw. 22 , 787–793 (2019).

Schreurs, L., Meier, A. & Vandenbosch, L. Exposure to the positivity bias and adolescents’ differential longitudinal links with social comparison, inspiration and envy depending on social media literacy. Curr. Psychol . https://doi.org/10.1007/s12144-022-03893-3 (2022).

Meier, A. & Krause, H.-V. Does passive social media use harm well-being? An adversarial review. J. Media Psychol. 35 , 169–180 (2023).

Nesi, J. & Prinstein, M. J. Using social media for social comparison and feedback-seeking: gender and popularity moderate associations with depressive symptoms. J. Abnorm. Child. Psychol. 43 , 1427–1438 (2015).

Lindström, B. et al. A computational reward learning account of social media engagement. Nat. Commun. 12 , 1311 (2021).

Fardouly, J., Diedrichs, P. C., Vartanian, L. R. & Halliwell, E. Social comparisons on social media: the impact of Facebook on young women’s body image concerns and mood. Body Image 13 , 38–45 (2015).

Scully, M., Swords, L. & Nixon, E. Social comparisons on social media: online appearance-related activity and body dissatisfaction in adolescent girls. Ir. J. Psychol. Med. 40 , 31–42 (2023).

Appel, H., Gerlach, A. L. & Crusius, J. The interplay between Facebook use, social comparison, envy, and depression. Curr. Opin. Psychol. 9 , 44–49 (2016).

Meier, A. & Johnson, B. K. Social comparison and envy on social media: a critical review. Curr. Opin. Psychol. 45 , 101302 (2022).

Verduyn, P., Gugushvili, N., Massar, K., Täht, K. & Kross, E. Social comparison on social networking sites. Curr. Opin. Psychol. 36 , 32–37 (2020).

Meier, A., Gilbert, A., Börner, S. & Possler, D. Instagram inspiration: how upward comparison on social network sites can contribute to well-being. J. Commun. 70 , 721–743 (2020).

Vaterlaus, J. M., Patten, E. V., Roche, C. & Young, J. A. #Gettinghealthy: the perceived influence of social media on young adult health behaviors. Comput. Hum. Behav. 45 , 151–157 (2015).

Valkenburg, P. M., Beyens, I., Pouwels, J. L., Van Driel, I. I. & Keijsers, L. Social media browsing and adolescent well-being: challenging the “passive social media use hypothesis”. J. Comput.-Mediat. Commun. https://doi.org/10.1093/jcmc/zmab015 (2022).

Larson, R. W., Richards, M. H., Moneta, G., Holmbeck, G. & Duckett, E. Changes in adolescents’ daily interactions with their families from ages 10 to 18: disengagement and transformation. Dev. Psychol. 32 , 744–754 (1996).

Sebastian, C., Viding, E., Williams, K. D. & Blakemore, S.-J. Social brain development and the affective consequences of ostracism in adolescence. Brain Cogn. 72 , 134–145 (2010).

Sebastian, C. et al. Developmental influences on the neural bases of responses to social rejection: implications of social neuroscience for education. NeuroImage 57 , 686–694 (2011).

Somerville, L. H. The teenage brain: sensitivity to social evaluation. Curr. Dir. Psychol. Sci. 22 , 121–127 (2013).

Larson, R. W. & How, U. S. Children and adolescents spend time: what it does (and doesn’t) tell us about their development. Curr. Dir. Psychol. Sci. 10 , 160–164 (2001).

Thomas, L. A., De Bellis, M. D., Graham, R. & LaBar, K. S. Development of emotional facial recognition in late childhood and adolescence. Dev. Sci. 10 , 547–558 (2007).

Gunther Moor, B., van Leijenhorst, L., Rombouts, S. A. R. B., Crone, E. A. & Van der Molen, M. W. Do you like me? Neural correlates of social evaluation and developmental trajectories. Soc. Neurosci. 5 , 461–482 (2010).

Silk, J. S. et al. Peer acceptance and rejection through the eyes of youth: pupillary, eyetracking and ecological data from the Chatroom Interact task. Soc. Cognit. Affect. Neurosci. 7 , 93–105 (2012).

Gao, S., Assink, M., Cipriani, A. & Lin, K. Associations between rejection sensitivity and mental health outcomes: a meta-analytic review. Clin. Psychol. Rev. 57 , 59–74 (2017).

Prinstein, M. J., Nesi, J. & Telzer, E. H. Commentary: an updated agenda for the study of digital media use and adolescent development—future directions following Odgers & Jensen (2020). J. Child. Psychol. Psychiatr. 61 , 349–352 (2020).

Meshi, D., Morawetz, C. & Heekeren, H. R. Nucleus accumbens response to gains in reputation for the self relative to gains for others predicts social media use. Front. Hum. Neurosci. 7 , 1–11 (2013).

Crone, E. A. & Dahl, R. E. Understanding adolescence as a period of social–affective engagement and goal flexibility. Nat. Rev. Neurosci. 13 , 636–650 (2012).

Platt, B., Kadosh, K. C. & Lau, J. Y. F. The role of peer rejection in adolescent depression. Depress. Anxiety 30 , 809–821 (2013).

Will, G.-J., Rutledge, R. B., Moutoussis, M. & Dolan, R. J. Neural and computational processes underlying dynamic changes in self-esteem. eLife 6 , e28098 (2017).

Macrynikola, N. & Miranda, R. Active Facebook use and mood: when digital interaction turns maladaptive. Comput. Hum. Behav. 97 , 271–279 (2019).

Grunewald, K., Deng, J., Wertz, J. & Schweizer, S. The effect of online social evaluation on mood and cognition in young people. Sci. Rep. 12 , 20999 (2022).

Andrews, J. L., Khin, A. C., Crayn, T., Humphreys, K. & Schweizer, S. Measuring online and offline social rejection sensitivity in the digital age. Psychol. Assess. 34 , 742–751 (2022).

Forest, A. L. & Wood, J. V. When social networking is not working: individuals with low self-esteem recognize but do not reap the benefits of self-disclosure on Facebook. Psychol. Sci. 23 , 295–302 (2012).

Valkenburg, P. M., Koutamanis, M. & Vossen, H. G. M. The concurrent and longitudinal relationships between adolescents’ use of social network sites and their social self-esteem. Comput. Hum. Behav. 76 , 35–41 (2017).

Burrow, A. L. & Rainone, N. How many likes did I get? purpose moderates links between positive social media feedback and self-esteem. J. Exp. Soc. Psychol. 69 , 232–236 (2017).

Seo, M., Kim, J. & Yang, H. Frequent interaction and fast feedback predict perceived social support: using crawled and self-reported data of Facebook users. J. Comput.-Mediat. Comm. 21 , 282–297 (2016).

Fuhrmann, D., Casey, C. S., Speekenbrink, M. & Blakemore, S.-J. Social exclusion affects working memory performance in young adolescent girls. Dev. Cognit. Neurosci. 40 , 100718 (2019).

Blakemore, S.-J. & Choudhury, S. Development of the adolescent brain: implications for executive function and social cognition. J. Child. Psychol. Psychiat 47 , 296–312 (2006).

Dreyfuss, M. et al. Teens impulsively react rather than retreat from threat. Dev. Neurosci. 36 , 220–227 (2014).

Guyer, A. E., Choate, V. R., Pine, D. S. & Nelson, E. E. Neural circuitry underlying affective response to peer feedback in adolescence. Soc. Cognit. Affect. Neurosci. 7 , 81–92 (2012).

Sherman, L. E., Payton, A. A., Hernandez, L. M., Greenfield, P. M. & Dapretto, M. The power of the like in adolescence: effects of peer influence on neural and behavioral responses to social media. Psychol. Sci. 27 , 1027–1035 (2016).

van Harmelen, A.-L. et al. Adolescent friendships predict later resilient functioning across psychosocial domains in a healthy community cohort. Psychol. Med. 47 , 2312–2322 (2017).

Chu, P. S., Saucier, D. A. & Hafner, E. Meta-analysis of the relationships between social support and well-being in children and adolescents. J. Soc. Clin. Psychol. 29 , 624–645 (2010).

Schneider, F. M. et al. Social media ostracism: the effects of being excluded online. Comput. Hum. Behav. 73 , 385–393 (2017).

Reich, S., Schneider, F. M. & Heling, L. Zero likes—symbolic interactions and need satisfaction online. Comput. Hum. Behav. 80 , 97–102 (2018).

Lutz, S. & Schneider, F. M. Is receiving dislikes in social media still better than being ignored? The effects of ostracism and rejection on need threat and coping responses online. Media Psychol. 24 , 741–765 (2021).

Lutz, S. Why don’t you answer me? Exploring the effects of (repeated exposure to) ostracism via messengers on users’ fundamental needs, well-being, and coping motivation. Media Psychol. 26 , 113–140 (2023).

Rodríguez-Hidalgo, C. T., Tan, E. S. H., Verlegh, P. W. J., Beyens, I. & Kühne, R. Don’t stress me now: assessing the regulatory impact of face-to-face and online feedback prosociality on stress during an important life event. J. Comput.-Mediat. Commun. 25 , 307–327 (2020).

Trepte, S., Dienlin, T. & Reinecke, L. Influence of social support received in online and offline contexts on satisfaction with social support and satisfaction with life: a longitudinal study. Media Psychol. 18 , 74–105 (2015).

Dredge, R. & Schreurs, L. Social media use and offline interpersonal outcomes during youth: a systematic literature review. Mass. Commun. Soc. 23 , 885–911 (2020).

Colasante, T., Lin, L., De France, K. & Hollenstein, T. Any time and place? Digital emotional support for digital natives. Am. Psychol. 77 , 186–195 (2022).

Pouwels, J. L., Valkenburg, P. M., Beyens, I., Van Driel, I. I. & Keijsers, L. Social media use and friendship closeness in adolescents’ daily lives: an experience sampling study. Dev. Psychol. 57 , 309–323 (2021).

Mills, K. L. et al. Structural brain development between childhood and adulthood: convergence across four longitudinal samples. NeuroImage 141 , 273–281 (2016).

Tamnes, C. K. et al. Development of the cerebral cortex across adolescence: a multisample study of inter-related longitudinal changes in cortical volume, surface area, and thickness. J. Neurosci. 37 , 3402–3412 (2017).

Larsen, B. & Luna, B. Adolescence as a neurobiological critical period for the development of higher-order cognition. Neurosci. Biobehav. Rev. 94 , 179–195 (2018).

Petanjek, Z. et al. Extraordinary neoteny of synaptic spines in the human prefrontal cortex. Proc. Natl Acad. Sci. USA 108 , 13281–13286 (2011).

Cohen, J. R. et al. A unique adolescent response to reward prediction errors. Nat. Neurosci. 13 , 669–671 (2010).

Ernst, M. et al. Amygdala and nucleus accumbens in responses to receipt and omission of gains in adults and adolescents. NeuroImage 25 , 1279–1291 (2005).

Galván, A. & McGlennen, K. M. Enhanced striatal sensitivity to aversive reinforcement in adolescents versus adults. J. Cognit. Neurosci. 25 , 284–296 (2013).

Braams, B. R., Van Duijvenvoorde, A. C. K., Peper, J. S. & Crone, E. A. Longitudinal changes in adolescent risk-taking: a comprehensive study of neural responses to rewards, pubertal development, and risk-taking behavior. J. Neurosci. 35 , 7226–7238 (2015).

Schreuders, E. et al. Contributions of reward sensitivity to ventral striatum activity across adolescence and early adulthood. Child. Dev. 89 , 797–810 (2018).

Maza, M. T. et al. Association of habitual checking behaviors on social media with longitudinal functional brain development. JAMA Pediatr. 177 , 160–167 (2023).

Miller, J., Mills, K. L., Vuorre, M., Orben, A. & Przybylski, A. K. Impact of digital screen media activity on functional brain organization in late childhood: evidence from the ABCD study. Cortex 169 , 290–308 (2023).

Flayelle, M. et al. A taxonomy of technology design features that promote potentially addictive online behaviours. Nat. Rev. Psychol. 2 , 136–150 (2023).

Lupien, S. J., McEwen, B. S., Gunnar, M. R. & Heim, C. Effects of stress throughout the lifespan on the brain, behaviour and cognition. Nat. Rev. Neurosci. 10 , 434–445 (2009).

Gunnar, M. R., Wewerka, S., Frenn, K., Long, J. D. & Griggs, C. Developmental changes in hypothalamus–pituitary–adrenal activity over the transition to adolescence: normative changes and associations with puberty. Dev. Psychopathol. 21 , 69–85 (2009).

Somerville, L. H. et al. The medial prefrontal cortex and the emergence of self-conscious emotion in adolescence. Psychol. Sci. 24 , 1554–1562 (2013).

Stroud, L. R. et al. Stress response and the adolescent transition: performance versus peer rejection stressors. Dev. Psychopathol. 21 , 47–68 (2009).

Avital, A. & Richter-Levin, G. Exposure to juvenile stress exacerbates the behavioural consequences of exposure to stress in the adult rat. Int. J. Neuropsychopharm. 8 , 163–173 (2005).

McCormick, C. M., Mathews, I. Z., Thomas, C. & Waters, P. Investigations of HPA function and the enduring consequences of stressors in adolescence in animal models. Brain Cogn. 72 , 73–85 (2010).

Eiland, L. & Romeo, R. D. Stress and the developing adolescent brain. Neuroscience 249 , 162–171 (2013).

Romeo, R. D. The teenage brain. Curr. Direc. Psychol. Sci. 22 , 140–145 (2013).

Afifi, T. D., Zamanzadeh, N., Harrison, K. & Acevedo Callejas, M. WIRED: the impact of media and technology use on stress (cortisol) and inflammation (interleukin IL-6) in fast paced families. Comput. Hum. Behav. 81 , 265–273 (2018).

Morin-Major, J. K. et al. Facebook behaviors associated with diurnal cortisol in adolescents: is befriending stressful? Psychoneuroendocrinology 63 , 238–46 (2016).

Ghai, S. It’s time to reimagine sample diversity and retire the WEIRD dichotomy. Nat. Hum. Behav. 5 , 971–972 (2021).

Munafò, M. R. & Davey Smith, G. Robust research needs many lines of evidence. Nature 553 , 399–401 (2018).

Dale, R., Warlaumont, A. S. & Johnson, K. L. The fundamental importance of method to theory. Nat. Rev. Psychol. 2 , 55–66 (2022).

Parry, D. A., Fisher, J. T., Mieczkowski, H., Sewall, C. J. R. & Davidson, B. I. Social media and well-being: a methodological perspective. Curr. Opin. Psychol. 45 , 101285 (2022).

Will, G.-J. et al. Neurocomputational mechanisms underpinning aberrant social learning in young adults with low self-esteem. Transl. Psychiatry 10 , 96 (2020).

Walther, J. B. Affordances, effects, and technology errors. Ann. Int. Commun. Assoc. 36 , 190–193 (2013).

Piray, P. & Daw, N. D. A model for learning based on the joint estimation of stochasticity and volatility. Nat. Commun. 12 , 6587 (2021).

Bronfenbrenner, U. The Ecology of Human Development: Experiments by Nature and Design (Harvard Univ. Press, 1979).

Slater, M. D. Reinforcing spirals: the mutual influence of media selectivity and media effects and their impact on individual behavior and social identity. Commun. Theory 17 , 281–303 (2007).

Valkenburg, P. M., Peter, J. & Walther, J. B. Media effects: theory and research. Annu. Rev. Psychol. 67 , 315–338 (2016).

Aalbers, G., McNally, R. J., Heeren, A., De Wit, S. & Fried, E. I. Social media and depression symptoms: a network perspective. J. Exp. Psychol. Gen. 148 , 1454–1462 (2019).

Ghai, S., Fassi, L., Awadh, F. & Orben, A. Lack of sample diversity in research on adolescent depression and social media use: a scoping review and meta-analysis. Clin. Psychol. Sci. 11 , 759–772 (2023).

Cramer, A. O. J. et al. Major depression as a complex dynamic system. PLoS ONE 11 , e0167490 (2016).

Kendler, K. S., Zachar, P. & Craver, C. What kinds of things are psychiatric disorders? Psychol. Med. 41 , 1143–1150 (2011).

van de Leemput, I. A. et al. Critical slowing down as early warning for the onset and termination of depression. Proc. Natl Acad. Sci. USA. 111 , 87–92 (2014).

Trepte, S. The social media privacy model: privacy and communication in the light of social media affordances. Commun. Theory 31 , 549–570 (2021).

Reinecke, L. et al. Permanently online and permanently connected: development and validation of the Online Vigilance Scale. PLoS ONE 13 , e0205384 (2018).

Trieu, P., Bayer, J. B., Ellison, N. B., Schoenebeck, S. & Falk, E. Who likes to be reachable? Availability preferences, weak ties, and bridging social capital. Inform. Commun. Soc. 22 , 1096–1111 (2019).

Daft, R. L. & Lengel, R. H. Organizational information requirements, media richness and structural design. Manag. Sci. 32 , 554–571 (1986).

Rhee, L., Bayer, J. B., Lee, D. S. & Kuru, O. Social by definition: how users define social platforms and why it matters. Telemat. Inform. 59 , 101538 (2021).

Valkenburg, P. M. Understanding self-effects in social media: self-effects in social media. Hum. Commun. Res. 43 , 477–490 (2017).

Thorson, K. & Wells, C. Curated flows: a framework for mapping media exposure in the digital age: curated flows. Commun. Theor. 26 , 309–328 (2016).

Zhao, H. & Wagner, C. How TikTok leads users to flow experience: investigating the effects of technology affordances with user experience level and video length as moderators. INTR 33 , 820–849 (2023).

Carr, C. T., Wohn, D. Y. & Hayes, R. A. As social support: relational closeness, automaticity, and interpreting social support from paralinguistic digital affordances in social media. Comput. Hum. Behav. 62 , 385–393 (2016).

Rice, R. E. et al. Organizational media affordances: operationalization and associations with media use: organizational media affordances. J. Commun. 67 , 106–130 (2017).

Scissors, L., Burke, M. & Wengrovitz, S. in Proc. 19th ACM Conf. Computer-Supported Cooperative Work & Social Computing—CSCW ’16 1499–1508 (ACM Press, 2016).

Boyd, D. M. in A Networked Self: Identity, Community and Culture in Social Networking Sites (ed. Papacharissi, Z.) 35–58 (Routledge, 2011).

Valkenburg, P. M. in Handbook of Adolescent Digital Media Use and Mental Health (eds Nesi, J., Telzer, E. H. & Prinstein, M. J.) 39–60 (Cambridge Univ. Press, 2022).

Dennis, Fuller & Valacich, Media Tasks, and communication processes: a theory of media synchronicity. MIS Q. 32 , 575 (2008).

DeAndrea, D. C. Advancing warranting theory: advancing warranting theory. Commun. Theor. 24 , 186–204 (2014).

Uhlhaas, P. J. et al. Towards a youth mental health paradigm: a perspective and roadmap. Mol. Psychiatry 28 , 3171–3181 (2023).

Kachuri, L. et al. Principles and methods for transferring polygenic risk scores across global populations. Nat. Rev. Genet. 25 , 8–25 (2024).

Weinstein, E. C. & Selman, R. L. Digital stress: adolescents’ personal accounts. N. Media Soc. 18 , 391–409 (2016).

Steele, R. G., Hall, J. A. & Christofferson, J. L. Conceptualizing digital stress in adolescents and young adults: toward the development of an empirically based model. Clin. Child. Fam. Psychol. Rev. 23 , 15–26 (2020).

Nick, E. A. et al. Adolescent digital stress: frequencies, correlates, and longitudinal association with depressive symptoms. J. Adolesc. Health 70 , 336–339 (2022).

Van Der Schuur, W. A., Baumgartner, S. E. & Sumter, S. R. Social media use, social media stress, and sleep: examining cross-sectional and longitudinal relationships in adolescents. Health Commun. 34 , 552–559 (2019).

Fabio, S. & Sonja, P. Is cyberbullying worse than traditional bullying? Examining the differential roles of medium, publicity, and anonymity for the perceived severity of bullying. J. Youth Adolesc. 42 , 739–750 (2013).

Tokunaga, R. S. Following you home from school: a critical review and synthesis of research on cyberbullying victimization. Comput. Hum. Behav. 26 , 277–287 (2010).

Khetawat, D. & Steele, R. G. Examining the association between digital stress components and psychological wellbeing: a meta-analysis. Clin. Child. Fam. Psychol. Rev. 26 , 957–974 (2023).

Beyens, I., Frison, E. & Eggermont, S. “I don’t want to miss a thing”: adolescents’ fear of missing out and its relationship to adolescents’ social needs, Facebook use, and Facebook related stress. Comput. Hum. Behav. 64 , 1–8 (2016).

Wartberg, L., Thomasius, R. & Paschke, K. The relevance of emotion regulation, procrastination, and perceived stress for problematic social media use in a representative sample of children and adolescents. Comput. Hum. Behav. 121 , 106788 (2021).

Winstone, L., Mars, B., Haworth, C. M. A. & Kidger, J. Types of social media use and digital stress in early adolescence. J. Early Adolescence 43 , 294–319 (2023).

West, M., Rice, S. & Vella-Brodrick, D. Exploring the “social” in social media: adolescent relatedness—thwarted and supported. J. Adolesc. Res . https://doi.org/10.1177/07435584211062158 (2021).

Gilbert, A., Baumgartner, S. E. & Reinecke, L. Situational boundary conditions of digital stress: goal conflict and autonomy frustration make smartphone use more stressful. Mob. Media Commun . https://doi.org/10.1177/20501579221138017 (2022).

Freytag, A. et al. Permanently online—always stressed out? The effects of permanent connectedness on stress experiences. Hum. Commun. Res. 47 , 132–165 (2021).

Johannes, N. et al. The relationship between online vigilance and affective well-being in everyday life: combining smartphone logging with experience sampling. Media Psychol. 24 , 581–605 (2021).

Reinecke, L. et al. Digital stress over the life span: the effects of communication load and internet multitasking on perceived stress and psychological health impairments in a german probability sample. Media Psychol. 20 , 90–115 (2017).

Schönbach, K. in The International Encyclopedia of Media Effects (eds Rössler, P., Hoffner, C. A. & Zoonen, L.) 1–11 (Wiley, 2017).

Mayer, J. D., Gaschke, Y. N., Braverman, D. L. & Evans, T. W. Mood-congruent judgment is a general effect. J. Pers. Soc. Psychol. 63 , 119–132 (1992).

Ferster, C. B. A functional analysis of depression. Am. Psychol. 28 , 857–870 (1973).

Carvalho, J. P. & Hopko, D. R. Behavioral theory of depression: reinforcement as a mediating variable between avoidance and depression. J. Behav. Ther. Exp. Psychiatry 42 , 154–162 (2011).

Helbig-Lang, S. & Petermann, F. Tolerate or eliminate? A systematic review on the effects of safety behavior across anxiety disorders. Clin. Psychol. Sci. Pract. 17 , 218–233 (2010).

Marciano, L., Driver, C. C., Schulz, P. J. & Camerini, A.-L. Dynamics of adolescents’ smartphone use and well-being are positive but ephemeral. Sci. Rep. 12 , 1316 (2022).

Rao, P. A. et al. Social anxiety disorder in childhood and adolescence: descriptive psychopathology. Behav. Res. Ther. 45 , 1181–1191 (2007).

Corning, A. F., Krumm, A. J. & Smitham, L. A. Differential social comparison processes in women with and without eating disorder symptoms. J. Couns. Psychol. 53 , 338–349 (2006).

Radovic, A., Gmelin, T., Stein, B. D. & Miller, E. Depressed adolescents’ positive and negative use of social media. J. Adolesc. 55 , 5–15 (2017).

Download references

Acknowledgements

A.O. and T.D. were funded by the Medical Research Council (MC_UU_00030/13). A.O. was funded by the Jacobs Foundation and a UKRI Future Leaders Fellowship (MR/X034925/1). S.-J.B. is funded by Wellcome (grant numbers WT107496/Z/15/Z and WT227882/Z/23/Z), the MRC, the Jacobs Foundation, the Wellspring Foundation and the University of Cambridge.

Author information

Authors and affiliations.

Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK

Amy Orben & Tim Dalgleish

School of Business, Economics and Society, Friedrich-Alexander-Universität Erlangen–Nürnberg, Nürnberg, Germany

Adrian Meier

Department of Psychology, University of Cambridge, Cambridge, UK

Sarah-Jayne Blakemore

Institute for Cognitive Neuroscience, University College London, London, UK

You can also search for this author in PubMed   Google Scholar

Contributions

A.O. conceptualized the manuscript; A.O and A.M wrote the original draft; A.O., A.M., T.D. and S.-J.B. reviewed and edited the manuscript. All authors contributed substantially to discussion of the content, and reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Amy Orben .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Reviews Psychology thanks Emily Weinstein, who co-reviewed with Beck Tench; Nastasia Griffioen; and Margarita Panayiotou for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Orben, A., Meier, A., Dalgleish, T. et al. Mechanisms linking social media use to adolescent mental health vulnerability. Nat Rev Psychol (2024). https://doi.org/10.1038/s44159-024-00307-y

Download citation

Accepted : 02 April 2024

Published : 07 May 2024

DOI : https://doi.org/10.1038/s44159-024-00307-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Open access
• Published: 22 June 2023

Social media and mental health in students: a cross-sectional study during the Covid-19 pandemic

• Abouzar Nazari   ORCID: orcid.org/0000-0003-2155-5438 1 ,
• Maede Hosseinnia   ORCID: orcid.org/0000-0002-2248-7011 2 ,
• Samaneh Torkian 3 &
• Gholamreza Garmaroudi   ORCID: orcid.org/0000-0001-7449-227X 4  

BMC Psychiatry volume  23 , Article number:  458 ( 2023 ) Cite this article

63k Accesses

2 Citations

17 Altmetric

Metrics details

Social media causes increased use and problems due to their attractions. Hence, it can affect mental health, especially in students. The present study was conducted with the aim of determining the relationship between the use of social media and the mental health of students.

Materials and methods

The current cross-sectional study was conducted in 2021 on 781 university students in Lorestan province, who were selected by the Convenience Sampling method. The data was collected using a questionnaire on demographic characteristics, social media, problematic use of social media, and mental health (DASS-21). Data were analyzed in SPSS-26 software.

Shows that marital status, major, and household income are significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Also, problematic use of social media (β = 3.54, 95% CI: (3.23, 3.85)) was significantly associated with higher mental health scores (a higher DASS21 score means worse mental health status). Income and social media use (β = 1.02, 95% CI: 0.78, 1.25) were significantly associated with higher DASS21 scores (a higher DASS21 score means worse mental health status). Major was significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status).

This study indicated that social media had a direct relationship with mental health. Despite the large amount of evidence suggesting that social media harms mental health, more research is still necessary to determine the cause and how social media can be used without harmful effects.

Peer Review reports

• Social media

Social media is one of the newest and most popular internet services, which has caused significant progress in the social systems of different countries in recent years [ 1 , 2 ]. The use of the Internet has become popular among people in such a way that its use has become inevitable and has made life difficult for those who use it excessively [ 3 ]. Social media has attracted the attention of millions of users around the world owing to the possibility of fast communication, access to a large amount of information, and its widespread dissemination [ 4 ]. Facebook, WhatsApp, Instagram, and Twitter are the most popular media that have attractive and diverse spaces for online communication among users, especially the young generation [ 5 , 6 ].

According to studies, at least 55% of the world’s population used social media in 2022 [ 7 ]. Iranian statistics also indicate that 78.5% of people use at least one social media. WhatsApp, with 71.1% of users, Instagram, with 49.4%, and Telegram, with 31.6% are the most popular social media among Iranians [ 8 , 9 ].

The use of social media has increased significantly in all age groups due to the origin of the COVID-19 pandemic [ 10 ] .It affected younger people, especially students, due to educational and other purposes [ 11 , 12 ]. Because of the sudden onset of the COVID-19 pandemic, educational institutions and learners had to accept e-learning as the only sustainable education option [ 13 ]. The rapid migration to E-learning has brought several challenges that can have both positive and negative consequences [ 14 ].

Unlike traditional media, where users are passive, social media enables people to create and share content; hence, they have become popular tools for social interaction [ 15 ].The freedom to choose to participate in the company of friends, anonymity, moderation, encouragement, the free exchange of feelings, and network interactions without physical presence and the constraints of the real world are some of the most significant factors that influence users’ continued activity in social media [ 16 ]. In social media, people can interact, maintain relationships, make new friends, and find out more about the people they know offline [ 17 ]. However, this popularity has resulted in significant lifestyle changes, as well as intentional or unintentional changes in various aspects of human social life [ 18 ]. Despite many advantages, the high use of social media brings negative physical, psychological, and social problems and consequences [ 19 ], but despite the use and access of more people to the Internet, its consequences and crises have been ignored [ 20 ].

Use of social media and mental health

Spending too much time on social media can easily become problematic [ 21 ]. Excessive use of social media, called problematic use, has symptoms similar to addiction [ 22 , 23 ]. Problematic use of social media represents a non-drug-related disorder in which harmful effects emerge due to preoccupation and compulsion to over-participate in social media platforms despite its highly negative consequences [ 24 , 25 , 26 ], which leads to adverse consequences of mental health, including anxiety, depression, lower well-being, and lower self-esteem [ 27 , 28 , 29 ].

Mental health & use of social media

Mental health is the main pillar of healthy human societies, which plays a vital role in ensuring the dynamism and efficiency of any society in such a way that other parts of health cannot be achieved without mental health [ 30 ]. According to World Health Organization’s (WHO) definition, mental health refers to a person’s ability to communicate with others [ 31 ]. Some researchers believe that social relationships can significantly affect mental health and improve quality of life by creating a sense of belonging and social identity [ 32 ]. It is also reported that people with higher social interactions have higher physical and mental health [ 33 ].

Scientific evidence also shows that social media affect people’s mental health [ 34 ]. Social studies and critiques often emphasize the investigation of the negative effects of Internet use [ 35 ]. For example, Kim et al. studied 1573 participants aged 18–64 years and reported that Internet addiction and social media use were associated with higher levels of depression and suicidal thoughts [ 36 ]. Zadar also studied adults and reported that excessive use of social media and the Internet was correlated with stress, sleep disturbances, and personality disorders [ 37 ]. Richards et al. reported the negative effects of the Internet and social media on the health and quality of life of adolescents [ 38 ]. There have been numerous studies that examine Internet addiction and its associated problems in young people [ 39 , 40 ], as well as reports of the effects of social media use on young people’s mental health [ 41 , 42 ].

A study on Iranian students showed that social media leads to depression, anxiety, and mental health decline [ 25 ]. A study on Iranian students showed that social media leads to depression, anxiety, and mental health decline [ 25 ]. But no study has investigated the effects of social media on the mental health of students from a more traditional province with lower individualism and higher levels of social support (where they were thought to have lower social media use and better mental health) during the COVID-19 pandemic. As social media became more and more vital to university students’ social lives during the lockdowns, students were likely at increased risk of social media addiction, which could harm their mental health. University students depended more on social media due to the limitations of face-to-face interactions. In addition, previous studies were conducted exclusively on students in specific fields. However, in our study, all fields, including medical and non-medical science fields were investigated.

The present study was conducted to determine the relationship between the use of social media and mental health in students in Lorestan Province during the COVID-19 pandemic.

Study design and participants

The current study was descriptive-analytical, cross-sectional, and conducted from February to March 2022 with a statistical population made up of students in all academic grades at universities in Lorestan Province (19 scientific and academic centers, including centers under the supervision of the Ministry of Health and the Ministry of Science).

Sample size

According to the convenience sampling method, 781 people were chosen as participants in the present study. During the sampling, a questionnaire was created and uploaded virtually on Porsline’s website, and then the questionnaire link was shared in educational and academic groups on social media for students to complete the questionnaire under inclusion criteria (being a student at the University of Lorestan and consenting to participate in the study).

The research tools included the demographic information questionnaire, the standard social media use questionnaire, and the mental health questionnaire.

Demographic information

The demographic information age, gender, ethnicity, province of residence, urban or rural, place of residence, semester, and the field of study, marital status, household income, education level, and employment status were recorded.

Psychological assessment

The students were subjected to the Persian version of the Depression Anxiety Stress Scale (DASS21). It consists of three self-report scales designed to measure different emotional states. DASS21 questions were adjusted according to their importance and the culture of Iranian students. The DASS21 scale was scored on a four-point scale to assess the extent to which participants experienced each condition over the past few weeks. The scoring method was such that each question was scored from 0 (never) to 3 (very high). Samani (2008) found that the questionnaire has a validity of 0.77 and a Cronbach’s alpha of 0.82 [ 43 ].

Use of social media questionnaire

Among the 13 questions on social media use in the questionnaire, seven were asked on a Likert scale (never, sometimes, often, almost, and always) that examined the problematic use of social media, and six were asked about how much time users spend on social media. Because some items were related to the type of social media platform, which is not available today, and users now use newer social media platforms such as WhatsApp and Instagram, the questionnaires were modified by experts and fundamentally changed, and a 22-item questionnaire was obtained that covered the frequency of using social media. Cronbach’s alpha was equal to 0.705 for the first part, 0.794 for the second part, and 0.830 for all questions [ 44 ]. Considering the importance of the problematic use of the social media, six questions about the problematic use were measured separately.

To confirm the validity of the questionnaire, a panel of experts with CVR 0.49 and CVI 0.70 was used. Its reliability was also obtained (0.784) using Cronbach’s alpha coefficient. Finally, the questionnaire was tested in a class with 30 students to check the level of difficulty and comprehension of the questionnaire. Finally, a 22-item questionnaire was obtained, of which six items were about the problematic use of social media and the remaining 16 questions were about the rate and frequency of using social media. Cronbach’s alpha was 0.705 for the first part, including questions about the problematic use of the social media, and 0.794 for the second part, including questions about the rate and frequency of using the social media. The total Cronbach’s alpha for all questions was 0.830. Six questions about the problematic use of social media were measured separately due to the importance of the problematic use of social media. Also, a separate score was considered for each question. The scores of these six questions on the problematic use of the social media were summed, and a single score was obtained for analysis.

Statistical analysis

Data were analyzed using the Statistical Package for Social Sciences (SPSS) version 26.0 (SPSS Inc., Chicago, IL, USA). The normal distribution of continuous variables was analyzed using the Kolmogorov-Smirnov test, histogram, and P-P diagram, which showed that they are not normally distributed. Descriptive statistics were calculated for all variables. Comparison between groups was done using Mann-Whitney and Kruskal-Wallis non-parametric tests. Multiple linear regression analysis was used to investigate the relationship between mental health, problematic use of social media, and social media use (The result of merging the Frequency of using social media and Time to use social media). Generalized Linear Models (GLM) were used to assess the association between mental health with the use of social media and problematic use of social media. Due to the high correlation (r = 0.585, p = < 0.001) between the use of social media and problematic use of social media, collinearity, we run two separate GLM models. Regression coefficients (β) and adjusted β (β*) with 95% CI and P-value were reported.

A total of 781 participants completed the questionnaires, of which 64.4% were women and 71.3% were single. The minimum age of the participants was 17 years, the maximum age was 45 years, and about half of them (48.9%) were between 21 and 25 years old. A total of 53.4% of the participants had bachelor’s degrees. The income level of 23.2% of participants was less than five million Tomans (the currency of Iran), and 69.7% of the participants were unemployed. 88.1% were living with their families and 70.8% were studying in non-medical fields. 86% of the participants lived in the city, and 58.9% were in their fourth semester or higher. Considering that the research was conducted in a Lorish Province, 43.8% of participants were from the Lorish ethnicity.

The mean total score of mental health was 12.30 with a standard deviation of 30.38, and the mean total score of social media was 14.5557 with a standard deviation of 7.74140.

Table  1 presents a comparison of the mean problematic use of social media and mental health with demographic variables. Considering the non-normality of the hypothesis H0, to compare the means of the independent variables, Mann-Whitney non-parametric tests (for the variables of gender, the field of study, academic semester, employment status, province of residence, and whether it is rural or urban) and Kruskal Wallis (for the variables age, ethnicity, level of education, household income and marital status). According to the obtained results, it was found that the score of problematic use of social media is significantly higher in women, the age group less than 20 years, unemployed, non-native students, dormitory students, and students living with friends or alone, Fars students, students with a household income level of fewer than 7 million Tomans(Iranian currency), and single, divorced, and widowed students were higher than the other groups(P < 0.05).

By comparing the mean score of mental health with demographic variables using non-parametric Mann-Whitney and Kruskal Wallis tests, it was found that there is a significant difference between the variable of poor mental health and all demographic variables (except for the semester variable), residence status (rural or urban) and education level. (There was a significant relationship (P < 0.05). In such a way that the mental health condition was worse in women, age group less than 20 years old, non-medical science, unemployed, non-native, and dormitory students. Also, Fars students, divorced, widowed, and students with a household income of fewer than 5 million Tomans (Iranian currency) showed poorer mental health status. (Table  1 ).

The final model shows that marital status, field, and household income were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Being single (β* = -23.03, 95% CI: (-33.10, -12.96), being married (β* = -38.78, 95% CI: -51.23, -26.33), was in Medical sciences fields (β* = -8.15, 95% CI: -11.37, -4.94), and have income 7–10 million (β* = -5.66, 95% CI: -9.62, -1.71) were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Problematic use of social media (β* = 3.54, 95% CI: (3.23, 3.85) was significantly associated with higher mental health scores (a higher DASS21 score means worse mental health status). (Table  2 )

Age, income, and use of social media (β* = 1.02, 95% CI: 0.78, 1.25) were significantly associated with higher DASS21 scores (a higher DASS21 score means worse mental health status). Marital status and field were significantly associated with lower DASS21 scores (a lower DASS21 score means better mental health status). Age groups < 20 years (β* = 6.36, 95% CI: 0.78, 11.95) and income group < 5 million (β* = 6.58, 95% CI: 1.47, 11.70) increased mental health scores. Being single (β* = -34.72, 95% CI: -47.06, -38.78), being married (β* = -38.78, 95% CI: -51.23, -26.33) and in medical sciences fields (β* = -8.17, 95% CI: -12.09, -4.24) decreased DASS21 scores. (Table  3 )

The main purpose of this study was to determine the relationship between social media use and mental health among students during the COVID-19 pandemic.

University students are more reliant on social media because of the limitations of in-person interactions [ 45 ]. Since social media has become more and more vital to the social lives of university students during the pandemic, students may be at increased risk of social media addiction, which may be harmful to their mental health [ 14 ].

During non-adulthood, peer relations and approval are critical and social media seems to meet these needs. For example, connection and communication with friends make them feel better and happier, especially during the COVID-19 pandemic and national lockdowns where face-to-face communication was restricted [ 46 ]. Kele’s study showed that the COVID-19 pandemic has increased the time spent on social media, and the frequency of online activities [ 47 ].

Because of the COVID-19 pandemic, e-learning became the only sustainable option for students [ 13 ]. This abrupt transition can lead to depression, stress, or anxiety for some students due to insufficient time to adjust to the new learning environment. The role of social media is also important to some university students [ 48 ].

Staying at home, having nothing else to do, and being unable to go out and meet with friends due to the lockdown measures increased the time spent on social media and the frequency of online activities, which influenced their mental health negatively [ 49 ]. These reasons may explain the findings of previous studies that found an increase in depression and anxiety among adolescents who were healthy before the COVID-19 pandemic [ 50 ].

According to the results, there was a statistically significant relationship between social media use and mental health in students, in such a way that one Unit increase in the score of social media use enhanced the score of mental health. These two variables were directly correlated. Consistent with the current study, many studies have shown a significant relationship between higher use of social media and lower mental health in students [ 45 , 51 , 52 , 53 , 54 ].

Inconsistent with the findings of the present study, some previous studies reported the positive effect of social media use on mental health [ 55 , 56 , 57 ]. The differences in findings could be attributed to the time and location of the studies. Anderson’s study in France in 2018 found no significant relationship between social media use and mental health. This may be because of the differences between the tools for measuring the ability to detect fake news and health literacy and the scales of the research [ 4 ].

The present study showed that the impact of using social media on the mental health of students was higher than Lebni’s study, which was conducted in 2020 [ 25 ]. Also, in Dost Mohammad’s study in 2018, the effect of using social media on the mental health of students was reported to be lower than in the present study [ 58 ]. Entezari’s study in 2021, was also lower than the present study [ 59 ]. It seems that the excessive use of social media during the COVID-19 pandemic was the reason for the greater effects of social media on students’ mental health.

The use of social media has positive and negative characteristics. Social media is most useful for rapidly disseminating timely information via widely accessible platforms [ 4 ]. Among the types of studies, at least one shows an inverse relationship between the use of social media and mental health [ 53 ]. While social media can serve as a tool for fostering connection during periods of physical isolation, the mental health implications of social media being used as a news source are tenuous [ 45 ].

The results of the GLM analysis indicated that there was a statistically significant relationship between the problematic use of social media and mental health in students in such a way that one-unit increase in the score of problematic use of social media enhanced the mental health score, and it was found that the two variables had a direct relationship. Consistent with our study, Boer’s study showed that problematic use of social media may highlight the potential risk to adolescent mental health [ 60 ]. Malaeb also reported that the problematic use of social media had a positive relationship with mental health [ 61 ], but that study was conducted on adults and had a smaller sample size before the COVID-19 pandemic.

Saputri’s study found that excessive social media use likely harms the mental health of university students since students with higher social media addiction scores had a greater risk of experiencing mild depression [ 62 ]. A systematic literature review before the COVID-19 pandemic (2019) found that the time spent by adolescents on social media was associated with depression, anxiety, and psychological distress [ 63 ]. Marino’s study (2018) reported a significant correlation between the problematic use of social media by students and psychological distress [ 64 ].

Social media has become more vital for students’ social lives owing to online education during the COVID-19 pandemic. Therefore, this group is more at risk of addiction to social media and may experience more mental health problems than other groups. Lebni also indicated that students’ higher use of the Internet led to anxiety, depression, and adverse mental health, but the main purpose of the study was to investigate the effects of such factors on student’s academic performance [ 25 ]. Previous studies indicated that individuals who spent more time on social media had lower self-esteem and higher levels of anxiety and depression [ 65 , 66 ]. In the present study, students with higher social media addiction scores were at higher mental health risk. Such a finding was consistent with research by Gao et al., who found that the excessive use of social media during the pandemic had adverse effects on social health [ 14 ]. Cheng et al. indicated that using the Internet, especially for communication with people, can harm mental health by changing the quality of social relationships, face-to-face communication, and changes in social support [ 24 ].

A reason for the significant relationship between social media use and mental health in students during the COVID-19 pandemic in the present study was probably the students’ intentional or unintentional use of online communication. Unfortunately, social media published information, which might be incorrect, in this pandemic that caused public fear and threatened mental health.

During the pandemic, social media played essential roles in learning and leisure activities. Due to electronic education, staying at home, and long leisure time, students had more time, frequency, and opportunities to use social media in this pandemic. Such a high reliance on social media may threaten student’s mental health. Lee et al. conducted a study during the COVID-19 pandemic and confirmed that young people who used social media had higher symptoms of depression and loneliness than before the COVID-19 pandemic [ 67 ].

The present study showed that there was a significant positive relationship between problematic use of social media and gender, so that women were more willing to use social media, probably because they had more opportunities to use social media as they stayed at home more than men; hence, they were more exposed to problematic use of social media. Consistent with our study, Andreassen reported that being a woman was an important factor in social media addiction [ 68 ]. In contrast to our study, Azizi’s study in Iran showed that male students use social media significantly more than female students, possibly due to differences in demographic variables in each population [ 69 ].

Moreover, there was a significant relationship between age and problematic use of social media in that people younger than 20 were more willing to use social media in a problematic way. Consistent with the present study, Perrin also indicated that younger people further used social media [ 70 ].

According to the findings, unemployed students used social media more than employed ones, probably because they had more time to spend in virtual space, leading to higher use and the possibility of problematic use of social media [ 71 ].

Moreover, non-native students were more willing to use the social media probably because students who lived far away from their families used social media problematically due to the lack of family control over hours of use and higher opportunities [ 72 ] .

The results showed that rural students have a greater tendency to use social Medias than urban students. Inconsistent with this finding, Perrin reported that urban people were more willing to use the social media. The difference was probably due to different research times and places or different target groups [ 70 ].

According to the current study, people with low household income were more likely to use social media, most likely because low-income people seek free information and services due to a lack of access to facilities and equipment in the real world or because they seek assimilation with people around them. Inconsistent with our findings, Hruska et al. reported that people with high household income levels made much use of social media [ 73 ], probably because of cultural, economic, and social differences or different information measurement tools.

Furthermore, single, divorced, and widowed students used social media more than married students. This is because they spend more time on social media due to the need for more emotional attention, the search for a life partner, or a feeling of loneliness. This also led to the problematic use of social media [ 74 ].

According to the results, Fars people used social media more than other ethnic groups, but this difference was insignificant. This finding was consistent with Perrin’s study, but the population consisted of people aged 18 to 65 [ 70 ].

In the current study, there was a significant relationship between gender and mental health, so that women had lower mental health than men. The difference was in health sociology. Consistent with the present study, Ghasemi et al. indicated that it appeared necessary to pay more attention to women’s health and create an opportunity for them to use health services [ 75 ].

The findings revealed that unemployed students had lower mental health than employed students, most likely because unemployed individuals have lower mental health due to not having a job and being economically dependent on others, as well as feeling incompetent at times. Consistent with the present study, Bialowolski reported that unemployment and low income caused mental disorders and threatened mental health [ 76 ].

According to this study, non-native students have lower mental health than native students because they live far from their families. The family plays an imperative role in improving the mental health of their children, and mental health requires their support. Also, the economic, social, and support problems caused by being away from the family have endangered their mental health [ 77 ].

Another important factor of the current study was that married people had higher mental health than single people. In addition, divorced and widowed students had lower mental health [ 78 ]. Possibly due to the social pressure they suffer in Iranian society. Furthermore, they received lower emotional support than married people. Therefore, their lower mental health seemed logical [ 79 , 80 , 81 ]. A large study in a European population also reported differences in the likelihood of mood, anxiety, and personality disorders between separated/divorced and married mothers [ 82 ].

A key point confirmed in other studies is the relationship between low incomes with mental health. A meta-analysis by Lorant indicated that economic and social inequalities caused mental disorders [ 83 ]. Safran also reported that the probability of developing mental disorders in people with low socioeconomic status is up to three times higher than that of people with the highest socioeconomic status [ 84 ]. Bialowolski’s study was consistent with the current study but Bialowolski’s study examined employees [ 76 ].

The present study was conducted during the COVID-19 pandemic and therefore had limitations in accessing students. Another limitation was the use of self-reporting tools. Participants may show positive self-presentation by over- or under-reporting their social media-related behaviors and some mental health-related items, which may directly or indirectly lead to social desirability bias, information bias, and reporting bias. Small sample sizes and convenience sampling limit student population representativeness and generalizability. This study was based on cross-sectional data. Therefore, the estimation results should be seen as associative rather than causative. Future studies would need to investigate causal effects using a longitudinal or cohort design, or another causal effect research design.

The findings of this study indicated that the high use of social media affected students’ mental health. Furthermore, the problematic use of the social media had a direct relationship with mental health. Variables such as age, gender, income level, marital status, and unemployment of non-native students had significant relationships with social media use and mental health. Despite the large amount of evidence suggesting that social media harms mental health, more research is still necessary to determine the cause and how social media can be used without harmful effects. It is imperative to better understand the relationship between social media use and mental health symptoms among young people to prevent such a negative outcome.

Data Availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Austin LL, Jin Y. Social media and crisis communication. Routledge New York; 2018.

Carr CT, Hayes RAJAjoc. Social media: defining, developing, and divining. 2015;23(1):46–65.

Fathi A, Sadeghi S, Maleki Rad AA, Sharifi Rahnmo S, Rostami H, Abdolmohammadi KJIJoP et al. The role of Cyberspace Use on Lifestyle promoting health and coronary anxiety in Young Peopl. 2020;26(3):332–47.

Anderson M, Jiang JJPRC. Teens, social media & technology 2018. 2018;31(2018):1673-89.

Nakaya ACJW. Internet and social media addiction. 2015;12(2):1–3.

Hemayatkhah M. The Impact Of Virtual Social Networks On Women’s Social Deviations. 2021.

Chaffey DJSISMM. Global social media research summary 2016. 2016.

Naderi N, Pourjamshidi HJJoBAR. The role of social networks on entrepreneurial orientation of students: a case study of Razi University. 2022;13(26):291–309.

Narmenji SM, Nowkarizi M, Riahinia N, Zerehsaz MJS. Investigating the behavior of sharing information of students in the virtual Social Networks: Instagram, Telegram and WhatsApp. Manage ToI. 2020;6(4):49–78.

Google Scholar  

Zarocostas JJTl. How to fight an infodemic. 2020;395(10225):676.

de Ávila GB, Dos Santos ÉN, Jansen K. Barros FCJTip, psychotherapy. Internet addiction in students from an educational institution in Southern Brazil: prevalence and associated factors. 2020;42:302–10.

Cain JJAjope. It’s time to confront student mental health issues associated with smartphones and social media. 2018;82(7).

Cavus N, Sani AS, Haruna Y, Lawan AAJS. Efficacy of Social networking Sites for Sustainable Education in the era of COVID-19: a systematic review. 2021;13(2):808.

Gao J, Zheng P, Jia Y, Chen H, Mao Y, Chen S et al. Mental health problems and social media exposure during COVID-19 outbreak. 2020;15(4):e0231924.

Moreno MA, Kolb JJPC. Social networking sites and adolescent health. 2012;59(3):601–12.

Claval PJLEg. Marxism and space. 1993;1(1):73–96.

Thomas L, Orme E, Kerrigan FJC. Student loneliness: the role of social media through life transitions. Education. 2020;146:103754.

Dixon SJCSOhwscsg-s-n-r-b-n-o-u. Global social networks ranked by number of users 2022. 2022.

Sindermann C, Elhai JD, Montag CJPR. Predicting tendencies towards the disordered use of Facebook’s social media platforms: On the role of personality, impulsivity, and social anxiety. 2020;285:112793.

Zheng B, Bi G, Liu H, Lowry PBJH. Corporate crisis management on social media: A morality violations perspective. 2020;6(7):e04435.

Schou Andreassen C, Pallesen SJCpd. Social network site addiction-an overview. 2014;20(25):4053–61.

Hussain Z, Griffiths MDJIJoMH. Addiction. The associations between problematic social networking site use and sleep quality, attention-deficit hyperactivity disorder, depression. anxiety and stress. 2021;19:686–700.

Hussain Z, Starcevic, VJCoip. Problematic social networking site use: a brief review of recent research methods and the way forward. 2020;36:89–95.

Cheng C, Lau Y-c, Chan L, Luk JWJAB. Prevalence of social media addiction across 32 nations: Meta-analysis with subgroup analysis of classification schemes and cultural values. 2021;117:106845.

Lebni JY, Toghroli R, Abbas J, NeJhaddadgar N, Salahshoor MR, Mansourian M, et al. A study of internet addiction and its effects on mental health. A study based on Iranian University Students; 2020. p. 9.

Griffiths MD, Kuss DJ, Demetrovics ZJBa. Social networking addiction: An overview of preliminary findings. 2014:119 – 41.

O’reilly M, Dogra N, Whiteman N, Hughes J, Eruyar S, Reilly PJCcp et al. Is social media bad for mental health and wellbeing? Exploring the perspectives of adolescents. 2018;23(4):601–13.

Huang CJC, Behavior, Networking S. Time spent on social network sites and psychological well-being: a meta-analysis. 2017;20(6):346–54.

MPh MMJJMAT. Facebook addiction and its relationship with mental health among thai high school students. 2015;98(3):S81–S90.

Galderisi S, Heinz A, Kastrup M, Beezhold J. Sartorius NJWp. Toward a new definition of mental health. 2015;14(2):231.

Health WHODoM, Abuse S, Organization WH, Health WHODoM, Health SAM, Evidence WHOMH, et al. Mental health atlas 2005. World Health Organization; 2005.

Massari LJISF. Analysis of MySpace user profiles. 2010;12:361-7.

Chang P-J, Wray L, Lin YJHP. Social relationships, leisure activity, and health in older adults. 2014;33(6):516.

Cunningham S, Hudson C, Harkness K. Social media and depression symptoms: a meta-analysis. Res Child Adolesc Psychopathol. 2021;49(2):241–53.

Article   PubMed   Google Scholar  

Haand R, Shuwang ZJIJoA. Youth. The relationship between social media addiction and depression: a quantitative study among university students in Khost. Afghanistan. 2020;25(1):780–6.

Kim B-S, Chang SM, Park JE, Seong SJ, Won SH. Cho MJJPr. Prevalence, correlates, psychiatric comorbidities, and suicidality in a community population with problematic Internet use. 2016;244:249 – 56.

Zadra S, Bischof G, Besser B, Bischof A, Meyer C, John U et al. The association between internet addiction and personality disorders in a general population-based sample. 2016;5(4):691–9.

Richards D, Caldwell PH. Go HJJop, health c. Impact of social media on the health of children and young people. 2015;51(12):1152–7.

Al-Menayes JJJP, Sciences B. Dimensions of social media addiction among university students in Kuwait. 2015;4(1):23–8.

Hou Y, Xiong D, Jiang T, Song L, Wang QJCJoproc. Social media addiction: Its impact, mediation, and intervention. 2019;13(1).

Chen I-H, Pakpour AH, Leung H, Potenza MN, Su J-A, Lin C-Y et al. Comparing generalized and specific problematic smartphone/internet use: longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress. 2020;9(2):410–9.

Tang CS-k. Koh YYWJAjop. Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder. 2017;25:175–8.

Samani S, Jokar B. Validity and reliability short-form version of the Depression, Anxiety and Stress. 2001.

Taghavifard MT, Ghafurian Shagerdi A, Behboodi OJJoBAR. The Effect of Market Orientation on Business Performance. 2015;7(13):205–27.

Haddad JM, Macenski C, Mosier-Mills A, Hibara A, Kester K, Schneider M et al. The impact of social media on college mental health during the COVID-19 pandemic: a multinational review of the existing literature. 2021;23:1–12.

Fiacco A. Adolescent perspectives on media use: a qualitative study. Antioch University; 2020.

Keles B, Grealish A, Leamy MJCP. The beauty and the beast of social media: an interpretative phenomenological analysis of the impact of adolescents’ social media experiences on their mental health during the Covid-19 pandemic. 2023:1–17.

Lim LTS, Regencia ZJG, Dela Cruz JRC, Ho FDV, Rodolfo MS, Ly-Uson J et al. Assessing the effect of the COVID-19 pandemic, shift to online learning, and social media use on the mental health of college students in the Philippines: a mixed-method study protocol. 2022;17(5):e0267555.

Cohen ZP, Cosgrove KT, DeVille DC, Akeman E, Singh MK, White E et al. The impact of COVID-19 on adolescent mental health: preliminary findings from a longitudinal sample of healthy and at-risk adolescents. 2021:440.

Levita L, Miller JG, Hartman TK, Murphy J, Shevlin M, McBride O et al. Report1: Impact of Covid-19 on young people aged 13–24 in the UK-preliminary findings. 2020.

Conrad RC, Koire A, Pinder-Amaker S, Liu CHJJoPR. College student mental health risks during the COVID-19 pandemic: Implications of campus relocation. 2021;136:117 – 26.

Li Y, Zhao J, Ma Z, McReynolds LS, Lin D, Chen Z et al. Mental health among college students during the COVID-19 pandemic in China: a 2-wave longitudinal survey. 2021;281:597–604.

Zhao N, Zhou GJAPH, Well-Being. Social media use and mental health during the COVID‐19 pandemic: moderator role of disaster stressor and mediator role of negative affect. 2020;12(4):1019–38.

Saputri RAM, Yumarni, TJIJoMH. Addiction. Social media addiction and mental health among university students during the COVID-19 pandemic in Indonesia. 2021:1–15.

Glaser P, Liu JH, Hakim MA, Vilar R, Zhang RJNZJoP. Is social media use for networking positive or negative? Offline social capital and internet addiction as mediators for the relationship between social media use and mental health. 2018;47(3):12–8.

Nabi RL, Prestin A, So JJC. Behavior, networking s. Facebook friends with (health) benefits? Exploring social network site use and perceptions of social support, stress, and well-being. 2013;16(10):721–7.

Campisi J, Folan D, Diehl G, Kable T, Rademeyer CJPR. Social media users have different experiences, motivations, and quality of life. 2015;228(3):774–80.

Dost Mohammadi M, Khojaste S, Doukaneifard FJJoQRiHS. Investigating the relationship between the use of social networks with self-confidence and mental health of faculty members and students of Payam Noor University. Kerman. 2020;8(4):16–27.

Franco JA. Carrier LMJHb, technologies e. Social media use and depression, anxiety, and stress in Latinos: A correlational study. 2020;2(3):227 – 41.

Boer M, Stevens GW, Finkenauer C, de Looze ME, Eijnden RJJCiHB. Social media use intensity, social media use problems, and mental health among adolescents: Investigating directionality and mediating processes. 2021;116:106645.

Malaeb D, Salameh P, Barbar S, Awad E, Haddad C, Hallit R et al. Problematic social media use and mental health (depression, anxiety, and insomnia) among Lebanese adults: Any mediating effect of stress? 2021;57(2):539 – 49.

Saputri RAM, Yumarni TJIJoMH. Addiction. Social media addiction and mental health among university students during the COVID-19 pandemic in Indonesia. 2023;21(1):96–110.

Keles B, McCrae N, Grealish AJIJoA. Youth. A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. 2020;25(1):79–93.

Marino C, Gini G, Vieno A, Spada MMJJoAD. The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: a systematic review and meta-analysis. 2018;226:274–81.

Vannucci A, Flannery KM. Ohannessian CMJJoad. Social media use and anxiety in emerging adults. 2017;207:163-6.

Huang CJC. behavior, networking s. Internet use and psychological well-being: A meta-analysis. 2010;13(3):241-9.

Lee Y, Yang BX, Liu Q, Luo D, Kang L, Yang F et al. Synergistic effect of social media use and psychological distress on depression in China during the COVID-19 epidemic. 2020;74(10):552.

Andreassen CS, Pallesen S, Griffiths MDJAb. The relationship between addictive use of social media, narcissism, and self-esteem: findings from a large national survey. 2017;64:287–93.

Azizi SM, Soroush A, Khatony AJBp. The relationship between social networking addiction and academic performance in iranian students of medical sciences: a cross-sectional study. 2019;7(1):1–8.

Perrin AJPrc. Social media usage. 2015;125:52–68.

Kemp. SJD-RAhdcrm-t-h-t-w-u-s-m. More than half of the people on earth now use social media. 2020.

Vejmelka L, Matković R, Borković DKJIJoC, Youth, Studies F. Online at risk! Online activities of children in dormitories: experiences in a croatian county. 2020;11(4):54–79.

Hruska J, Maresova PJS. Use of social media platforms among adults in the United States—behavior on social media. 2020;10(1):27.

Huo J, Desai R, Hong Y-R, Turner K, Mainous AG III, Bian JJCC. Use of social media in health communication: findings from the health information national trends survey 2013, 2014, and 2017. 2019;26(1):1073274819841442.

Ghasemi S, Delavaran A, Karimi ZJQEM. A comparative study of the total index of mental health in males and females through meta-analysis method. 2012;3(10):159–75.

Bialowolski P, Weziak-Bialowolska D, Lee MT, Chen Y, VanderWeele TJ, McNeely EJSS et al. The role of financial conditions for physical and mental health. Evidence from a longitudinal survey and insurance claims data. 2021;281:114041.

Moradi GJSWQ. Correlation between social capital and mental health among hostel students of tehran and shiraz universities. 2008;8(30):143–70.

Mohebbi Z, Setoodeh G, Torabizadeh C, Rambod MJIyeee. State of mental health and associated factors in nursing students from Southeastern Iran. 2019;37(3).

Grundström J, Konttinen H, Berg N, Kiviruusu OJS-ph. Associations between relationship status and mental well-being in different life phases from young to middle adulthood. 2021;14:100774.

Vaingankar JA, Abdin E, Chong SA, Shafie S, Sambasivam R, Zhang YJ et al. The association of mental disorders with perceived social support, and the role of marital status: results from a national cross-sectional survey. 2020;78(1):1–11.

McLuckie A, Matheson KM, Landers AL, Landine J, Novick J, Barrett T et al. The relationship between psychological distress and perception of emotional support in medical students and residents and implications for educational institutions. 2018;42:41–7.

Afifi TO, Cox BJ. Enns MWJSp, epidemiology p. Mental health profiles among married, never-married, and separated/divorced mothers in a nationally representative sample. 2006;41:122–9.

Lorant V, Deliège D, Eaton W, Robert A, Philippot P. Ansseau MJAjoe. Socioeconomic inequalities in depression: a meta-analysis. 2003;157(2):98–112.

Safran MA, Mays RA Jr, Huang LN, McCuan R, Pham PK, Fisher SK, et al. Mental health disparities. 2009;99(11):1962–6.

Download references

Acknowledgements

The authors would like to express their gratitude to all academic officials of Lorestan universities and Mr. Mohsen Amani for their cooperation in data collection.

Not applicable.

Author information

Authors and affiliations.

Department of Health Education and Promotion, Faculty of Health, Tehran University of Medical Sciences, Tehran, 1417613151, Iran

Abouzar Nazari

Department of Health Education and Promotion, Faculty of Health, Isfahan University of Medical Sciences, Isfahan, Iran

Maede Hosseinnia

Department of Epidemiology and Biostatistics, Faculty of Health, Iran University of Medical Sciences, Tehran, 1417613151, Iran

Samaneh Torkian

Department of Health Education and Promotion, School of Health, Tehran University of Medical Sciences, Tehran, 1417613151, Iran

Gholamreza Garmaroudi

You can also search for this author in PubMed   Google Scholar

Contributions

Abouzar Nazari and Maedeh Hossennia designed the study, collected the data and drafted the manuscript. Samaneh Torkian performed the statistical analysis and prepared the tables. Gholamreza Garmaroudi, as the responsible author, supervised the entire study. All authors reviewed and edited the draft manuscript and approved the final version.

Corresponding author

Correspondence to Gholamreza Garmaroudi .

Ethics declarations

Ethics approval and consent to participate.

Permission was obtained from the Ethics Committee of the Tehran University of Medical Sciences (IR.TUMS.SPH.REC.1400.258) before starting the study and follows the principles outlined in the 1964 Helsinki Declaration and its subsequent amendments. Participants were informed about the purpose and benefits of the study. Sending the completed questionnaire was considered as informed consent to participate in the research. The respondents’ participation was completely consensual, anonymous, and voluntary. (The present data were collected before social media filtering in Iran).

Consent for publication

Competing interests.

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Nazari, A., Hosseinnia, M., Torkian, S. et al. Social media and mental health in students: a cross-sectional study during the Covid-19 pandemic. BMC Psychiatry 23 , 458 (2023). https://doi.org/10.1186/s12888-023-04859-w

Download citation

Received : 31 January 2023

Accepted : 10 May 2023

Published : 22 June 2023

DOI : https://doi.org/10.1186/s12888-023-04859-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Mental Health

BMC Psychiatry

ISSN: 1471-244X

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Higher Education Today
• American Council On Education
• Carnegie Classifications
• Race and Ethnicity In Higher Education

Mental Health Supports Are Key in in Community College Student Success

May 13, 2024

Title: Challenges and Opportunity: An Examination of Barriers to Postsecondary Academic Success

Authors: Sade Bonilla and Veronica Minaya

Source: Annenberg Institute at Brown University

Community colleges are a critical component of the U.S. higher education system, providing access to students from traditionally underserved communities. While they are a vital entry point to postsecondary education, community colleges face lower levels of completion compared to four-year institutions, stemming from educational, economic, and social inequities that persist among low-income and first-generation students.

The Annenberg Institute launched a descriptive study to examine the relationship between academic success and students’ time utilization, campus engagement, financial and mental well-being. The study focuses on community college students and finds that a nationwide increase in adverse mental health among college students intersects with high rates of food and housing insecurity.

The report emphasizes the significance of mental health factors as a barrier to academic attainment and suggests that facilitating access to mental health supports is a prominent avenue for supporting student engagement and success.

The study investigates how academic support can tackle adverse student mental health and increase engagement. It argues that students and institutions are equally responsible for students’ persistence despite hardships. This theory rests on the idea that the level of learning and effort linked to academic success is directly tied to a sense of community and social integration within a campus. Accordingly, community colleges with more student activities often find higher levels of student success.

Additionally, the study examines how financial resources influence student success. Food, housing, and financial insecurity remain pervasive issues that affect first-generation, low-income students’ ability to complete college. Nearly 40 percent of community college students were low-income in 2020, and 31 percent of these students were living at or below the federal poverty line.

Not only does students’ inability to access basic needs harm their physical health and well-being, but it also has negative implications for their academic performance. The need to prioritize basic needs leads to a limited bandwidth for students to focus on academics. The study argues that integrating basic needs resources for students is part of combating student mental health.

To read the full report and access more information, please click here .

—Eliza Gonzalez

If you have any questions or comments about this blog post, please contact us .

Keep Reading

Discovering Hidden Barriers to Community College Enrollment and Success

When students apply to a community college, they expect enrollment to be a clear, orderly process. Unfortunately, for far too many students, it feels more like a maze, full of unknown steps, unfamiliar terminology and unexpected delays. David Bevevino of the Education Advisory Board looks at how community college student onboarding is like a game of Chutes and Ladders—and what institutions can do to change it.

It Begins with Difficult Conversations: How Community College Leaders Can Support Faculty-led Student Success Efforts

If community colleges want to make a difference in student persistence and attainment—and to make the improvement sustainable over time—these efforts must be led by those on campus who have the most frequent contact with students: the faculty. Carrie B. Kisker, a director of the Center for the Study of Community Colleges, looks at where to begin.

The Rise of Mental Health on College Campuses: Protecting the Emotional Health of Our Nation’s College Students

Nance Roy, chief clinical officer of the Jed Foundation, provides action steps to support emotional well-being and reduce suicide and substance abuse for college students.

Along with Stanford news and stories, show me:

• Student information
• Faculty/Staff information

We want to provide announcements, events, leadership messages and resources that are relevant to you. Your selection is stored in a browser cookie which you can remove at any time using “Clear all personalization” below.

Image credit: Getty Images

One of the most alarming developments across the United States in recent years has been the growing mental health crisis among children and adolescents.

The already dire situation is evolving 2024 already presenting a new set of challenges that Vicki Harrison, the program director at the Stanford Center for Youth Mental Health & Wellbeing , is closely monitoring and responding to.

Stanford Report sat down with Harrison to find out what concerns her the most about the upcoming year. Harrison also talked about some of the promising ways she and her colleagues are responding to the national crisis and the importance of bringing the youth perspective into that response.

Challenging current events

From the 2024 general election to evolving, international conflicts, today’s dialed-in youth have a lot to process. As teens turn to digital and social media sources to learn about current events and figure out where they stand on particular issues, the sheer volume of news online can feel overwhelming, stressful, and confusing.

One way Harrison is helping teens navigate the information they consume online is through Good for Media , a youth-led initiative that grew out of the Stanford Center for Youth Mental Health & Wellbeing to bring teens and young adults together to discuss using social media in a safe and healthy way. In addition to numerous youth-developed tools and videos, the team has a guide with tips to deal with the volume of news online and how to process the emotions that come with it.

Harrison points out that the tone of political discourse today – particularly discussions about reining in the rights a person has based on aspects of their identity, such as their religion, race, national origin, or gender – affects adolescents at a crucial time in their development, a period when they are exploring who they are and what they believe in.

“If their identity is being othered, criticized, or punished in some way, what messages is that sending to young people and how do they feel good about themselves?” Harrison said. “We can’t divorce these political and cultural debates from the mental health of young people.”

Harrison believes that any calls for solving the mental health crisis must acknowledge the critical importance of inclusion, dignity, and respect in supporting the mental health of young people.

Talking about mental health

Adolescence is a crucial time to develop coping skills to respond to stressful situations that arise – a skill not all teens and youth learn.

“It hasn’t always been normalized to talk about mental health and how to address feeling sad or worried about things,” Harrison said. “It’s not something that all of us have been taught to really understand and how to cope with. A lot of young people aren’t comfortable seeking professional services.”

The Stanford Center for Youth Mental Health & Wellbeing is helping young people get that extra bit of support to deal with problems before they get worse.

This year, they are rolling out stand-alone “one-stop-shop” health centers that offer youth 12-25 years old access to a range of clinical and counseling services with both trained professionals and peers. Called allcove , there are three locations open so far – Palo Alto, Redondo Beach, and San Mateo. More are set to open across the state in 2024.

“If we can normalize young people having an access point – and feeling comfortable accessing it – we can put them on a healthier track and get them any help they may need,” Harrison said.

Another emerging issue Harrison is monitoring is the growing role of social media influencers who talk openly about their struggles with mental health and well-being.

While this is helping bring awareness to mental health – which Harrison wants to see more of – she is also concerned about how it could lead some teens to mistake a normal, stressful life experience for a mental disorder and incorrectly self-diagnose themselves or to overgeneralize or misunderstand symptoms of mental health conditions. Says Harrison, “We want to see mental health destigmatized, but not oversimplified or minimized.”

“We can’t divorce these political and cultural debates from the mental health of young people.” —Vicki Harrison Program Director at the Stanford Center for Youth Mental Health & Wellbeing

Eyes on new technologies

Advances in technology – particularly generative AI – offer new approaches to improving teen well-being, such as therapeutic chatbots or detecting symptoms through keywords or patterns in speech.

“Digital solutions are a promising part of the continuum of care, but there’s the risk of rolling out things without the research backing them,” Harrison said.

Social media companies have come under scrutiny in recent years for inadequately safeguarding young adult mental health. Harrison hopes those mishaps serve as a cautionary tale for those applying AI tools more broadly.

There’s an opportunity, she says, to involve adolescents directly in making AI applications safe and effective. She and her team hope to engage young people with policy and industry and involve them in the design process, rather than as an afterthought.

“Can we listen to their ideas for how to make it better and how to make it work for them?” Harrison asks. “Giving them that agency is going to give us great ideas and make a better experience for them and for everyone using it.”

Harrison said she and her team are hoping to engage young people with policy and industry to elevate their ideas into the design process, rather than have it be an afterthought.

“There’s a lot of really motivated young people who see potential to do things differently and want to improve the world they inhabit,” Harrison said. “That’s why I always want to find opportunities to pass them the microphone and listen.”

New Survey Finds College Students Nearly 50% More Likely Than High Schoolers to Self-Report High-Risk Mental or Behavioral Health Concerns – and Parents May Not Know

(MAY 16, 2024) – UnitedHealthcare today announced new survey findings from its second annual Student Behavioral Health Report , revealing college students are more likely to self-report they or a roommate/friend encountered high-risk mental or behavioral health concerns in the past year compared to high school students­.

Findings further show a significant gap remains between what college students self-report and what parents of college students perceive. In contrast, parents of high schoolers report perceptions closely aligned with the experiences high school students self-report.

Data reveal among college students, 77% self-reported they or a classmate or friend encountered at least one mental or behavioral health concern in the past year, including anxiety/stress (55%), depression (41%) and suicidal ideation and intent* (13%). These figures represent a near-50% jump compared to self-reported high school student experiences with the same concerns (35%, 20% and 9%, respectively). Further, data show parent perceptions of these concerns do not change from high school to college – highlighting a possible “perception gap” that may impact parent-child conversations.

The Student Behavioral Health Report also reveals additional insights, including:

• An association between more frequent discussions of students’ mental or behavioral health and significant increases in students self-reporting feeling “supported.”
• Trends in the influencing factors that may surround students’ mental and behavioral health across high school and college years.
• Key differences in the way parents understand challenges facing high school vs. college students.

“Youth mental and behavioral health remains an urgent priority. The challenges students experience increase dramatically during college and parents may not realize it,” said Dr. Donald Tavakoli, national medical director for behavioral health, UnitedHealthcare. “Recognizing the potential gaps between parent perceptions and student experiences, especially in the transition from high school to college, can help parents stay connected and supportive.”

"Conversations about emotional health, whether peer-to-peer or intergenerational, can be uncomfortable or difficult to navigate. These new data from UnitedHealthcare underscore the importance of frequent discussions between parents and their children, which can make a substantial difference in a young person's life, empowering them to seek support," said John MacPhee, CEO of The Jed Foundation (JED) .

To help parents talk to their kids about mental well-being and spark conversations that move past one-word answers, UnitedHealthcare offers parent and youth conversation starter cards .

For those seeking support, UnitedHealthcare helps guide people to the right care at the right time across a broad range of behavioral health solutions. This includes digital self-care tools, telephonic emotional support, coaching, in-person and virtual visits, family support, employee assistance programs and more, allowing individuals to access support in a variety of ways.

For more information about behavioral health resources, visit UHC.com and UnitedHealthcare Student Resources . To explore additional data, insights and trends, access the full report here .

About the Student Behavioral Health Survey and Report

Commissioned by UnitedHealthcare and conducted by YouGov, “The Student Behavioral Health Report” surveyed a total sample of 2,058 Americans, of whom 526 are U.S. college students, 529 are parents of college students, 501 are high school students and 502 are parents of high school students. Fieldwork was undertaken between September 1 and October 12, 2023. The survey was carried out online.

UnitedHealthcare Media Contact:

Will Holman

952-931-5926

[email protected]

About UnitedHealthcare

UnitedHealthcare is dedicated to helping people live healthier lives and making the health system work better for everyone by simplifying the health care experience, meeting consumer health and wellness needs, and sustaining trusted relationships with care providers. In the United States, UnitedHealthcare offers the full spectrum of health benefit programs for individuals, employers, and Medicare and Medicaid beneficiaries, and contracts directly with more than 1.5 million physicians and care professionals, and 7,000 hospitals and other care facilities nationwide. The company also provides health benefits and delivers care to people through owned and operated health care facilities in South America. UnitedHealthcare is one of the businesses of UnitedHealth Group (NYSE: UNH), a diversified health care company. For more information, visit UnitedHealthcare at www.uhc.com or follow UnitedHealthcare on LinkedIn .

More articles

Sign up to get the latest news from the unitedhealthcare newsroom.