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Open Access

Peer-reviewed

Research Article

Persistent anxiety among high school students: Survey results from the second year of the COVID pandemic

Roles Conceptualization, Data curation, Formal analysis, Writing – original draft

Affiliation Irvington High School, Irvington, New York, United States of America

Roles Conceptualization, Writing – review & editing

Roles Investigation

Roles Formal analysis, Methodology, Writing – review & editing

Affiliation HIV Center for Clinical and Behavioral Studies, NY State Psychiatric Institute and Columbia University, New York, New York, United States of America

Roles Conceptualization, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Olivia Yin, 
• Nadia Parikka, 
• Amy Ma, 
• Philip Kreniske, 
• Claude A. Mellins

• Published: September 30, 2022
• https://doi.org/10.1371/journal.pone.0275292
• Reader Comments

Introduction

National mental health surveys have demonstrated increased stress and depressive symptoms among high-school students during the first year of the COVID-19 pandemic, but objective measures of anxiety after the first year of the pandemic are lacking.

A 25-question survey including demographics, the Generalized Anxiety Disorder-7 scale (GAD-7) a validated self-administered tool to evaluate anxiety severity, and questions on achievement goals and future aspirations was designed by investigators. Over a 2-month period, all students from grade 9–12 in a single high-school (n = 546) were invited to complete an online survey after electronic parental consent and student assent. Bi-variate and chi-square analyses examined demographic differences in anxiety scores and the impact on outcomes; qualitative analyses examined related themes from open-ended questions.

In total, 155/546 (28%) completed the survey. Among students with binary gender classifications, 54/149 (36%) had GAD-7 scores in the moderate or severe anxiety range (scores≥10), with a greater proportion among females than males (47% vs 21%, P<0.001). Compared to students with GAD-7<10, those with ≥ 10 were more likely to strongly agree that the pandemic changed them significantly (51% vs 28%, p = 0.05), made them mature faster (44% vs 16%, p = 0.004), and affected their personal growth negatively (16% vs 6%, p = 0.004). Prominent themes that emerged from open-ended responses on regrets during the pandemic included missing out on school social or sports events, missing out being with friends, and attending family events or vacations.

In this survey of high school students conducted 2 years after the onset of COVID-19 in the United States, 47% of females and 21% of males reported moderate or severe anxiety symptoms as assessed by the GAD-7. Whether heightened anxiety results in functional deficits is still uncertain, but resources for assessment and treatment should be prioritized.

Citation: Yin O, Parikka N, Ma A, Kreniske P, Mellins CA (2022) Persistent anxiety among high school students: Survey results from the second year of the COVID pandemic. PLoS ONE 17(9): e0275292. https://doi.org/10.1371/journal.pone.0275292

Editor: Ravi Shankar Yerragonda Reddy, King Khalid University, SAUDI ARABIA

Received: June 27, 2022; Accepted: September 13, 2022; Published: September 30, 2022

Copyright: © 2022 Yin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information file.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

The long-term impact of the COVID-19 pandemic on the mental health of adolescents is still under investigation. A meta-analysis of 136 studies from various populations affected by COVID-19 found that at least 15–16% of the general population experienced symptoms of anxiety or depression [ 1 ]. The Adolescent Behaviors and Experiences Survey (ABES) an online survey of a probability-based nationally representative sample of students in grades 9–12 (N = 7,705) collected from January-June of 2021 in the United States, found that 37% of students experienced poor mental health during the pandemic [ 2 ]. During the 12 months before the survey, 44% experienced persistent feelings of sadness or hopelessness, 19.9% had seriously considered attempting suicide, and 9.0% had attempted suicide [ 2 ].

Adolescence is a development stage characterized by profound physiological, psychological and social change that could make them particularly vulnerable to stressful events [ 3 , 4 ]. Although fears of infection, sadness related to loss, and overwhelming uncertainty was experienced by people of all ages, the widespread disruption of education had profound effects on the mental health of children and adolescents [ 5 ]. Remote learning, restrictions placed on social gathering, cancellation or modification of sports or clubs, and in-school activities and events present major challenges for the education and social growth of young people. The disruption of school routines and isolation, loss of support from peers and teachers, not only makes learning difficult but can heighten the anxiety that adolescents already feel about their education and career [ 6 ]. Even before the pandemic, there were reports of increases in anxiety, depression, substance use among adolescents faced with excessive pressures to excel in affluent settings [ 7 ]. Social support from other students and teachers, especially during stressful times, is critical for the social-emotional well-being of adolescents and for sustaining academic engagement and motivation [ 8 – 10 ]. The COVID Experiences Survey, a nationwide survey of 567 adolescents in grades 7–12 performed in 2020, found that adolescents receiving virtual instruction reported more mentally unhealthy days, more persistent symptoms of depression, and a greater likelihood of considering suicide than students in other modes of instruction [ 11 ].

The Adolescent Behaviors and Experiences Survey and COVID Experiences Survey both assessed level of stress, symptoms of depression and consideration of suicide among high school students but did not specifically include an evaluation of anxiety [ 2 , 11 ]. Several smaller published surveys of mental health among adolescent high school students in the United States included assessments of anxiety, although not all of them included validated measures of anxiety or examined the consequences of heightened anxiety [ 12 , 13 ]. In addition, all were performed in 2020, during the first wave of the infection. To our knowledge, few if any studies have examined longer-term consequences of the COVID-19 pandemic on adolescent anxiety using validated tools. The goal of this study was to evaluate the longer-term impact of the COVID-19 pandemic on generalized anxiety in high school students using the General Anxiety Disorder-7 (GAD-7), a validated self-report measure, at the end of 2021. Variations by gender and the impact of anxiety on achievement goals, future aspirations and outlook of students were also explored.

Materials and methods

Study design.

This study was conducted at a single public high school in Westchester County of the State of New York. New York was one of the epicenters during the first wave of the COVID-19 epidemic in the United States with a peak daily infection rate of over 9,000 cases/day in April 2020. In response to the New York State Education Department Executive Order, the high school was closed to in-person learning in March 2020 and transitioned to online classes (remote learning). The school remained closed to in-person learning for the remainder of the academic year. After summer break, the school re-opened with remote learning and provided the option for students to return to hybrid learning on October 7, 2020. Hybrid learning consisted of in-person school for half the week and remote learning for the other half of the week with half the capacity of students in the school at any given time. The school also allowed students to continue with full-time remote learning. This decision was made to balance the benefits of in-person learning with safety guidelines by reducing the total number students in school at any given time. On April 7, 2021, the school transitioned from hybrid learning to 100% in-person learning for the remainder of the academic year but still allowed students the option of remote learning. On September 7, 2021, the school re-opened after summer break to 100% in-person learning for all students without the remote learning option. The decision to transition to in-person learning for all students in September 2021 was based upon the low case rates of COVID and the availability of COVID vaccination. The FDA announced the emergency use authorization of the Pfizer-BioNTech COVID-19 vaccine for individuals 16 years of age and older on December 11, 2020 and for individuals 12 years of age and older on April 9, 2021.

Participants

A total of 521 students were enrolled in the high school, with the following numbers of students in each grade: 142 in 9 th ,130 in 10 th , 120 in 11 th and 128 in 12 th grade. The student body composed of 242 females and 279 males, with the following racial/ethnic distribution: 79% White, 13% Asian, 7% Black/African American, 1% American Indian/Native American. This non-selective public high school is the only high school in town. For context, the racial distribution of Westchester County was 73% White, 7% Asian, 17% Black in the 2019 census, with a median household income (in 2019 dollars) of $96,610 and 49% of the population over 25 years having a bachelor’s degree or higher. In the same period, the median household income in the United States was $68,703 with 22.5% of population age 25 and older having bachelor’s degree or higher.

The Irvington School Board approved the survey instruments and the overall study. All students attending the high school in 9 th -12 th grade were eligible to participate. Participation was voluntary, each survey question was optional, and there were no incentives for completion of survey. All participants completed an electronic parental consent and student assent prior to performing the online survey. A survey link was posted by the science teachers on the science classroom pages for all eligible students to complete on November 24, 2021. Science teachers continued to promote the survey until its closure on January 13, 2022.

Study instruments

The survey was conducted online via Google Forms software (version 2018) in English, and contained 25 questions, 23 of which were multiple choice. Participants took approximately 10–15 minutes to complete the survey. The Generalized Anxiety Disorder-7 scale (GAD-7), a validated 7-item self-administered tool to evaluate anxiety severity, was utilized to measure anxiety [ 14 ]. GAD-7 has been utilized in adolescents and demonstrates an acceptable specificity and sensitivity for detecting clinically significant anxiety symptoms in comparison to the Pediatric Anxiety Rating Scale [ 15 ]. Participants are asked how often they were bothered by each of the following symptoms during the last 2 weeks with a 4-point scale ranging from “not at all” (0 points) to “nearly every day” (3 points): feeling nervous, anxious or on edge; not being able to stop or control worrying; worrying too much about different things; trouble relaxing; being so restless that it is hard to sit still; becoming easily annoyed or irritable; feeling afraid as if something awful might happen. The total score indicates the level of anxious symptoms ranging from minimal/no anxiety (0–4), mild (5–9), moderate (10–14) and severe (≥15).

Demographic data were collected, including current grade (9–12), gender (female, male, transgender man, transgender woman, non-binary, other), race (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or other Pacific Islander, other), whether attending school by hybrid or remote learning, and COVID-19 vaccination status (none, partial or full series).

Several questions were developed by the study team through an iterative process that included initial development of question by the student researcher, refinement of wording by all investigators including experts in adolescent development and cognition, and testing for comprehension and clarity through review by 2 additional students. Four questions on whether students had more anxiety upon return to in-person learning in April 2021 (after hybrid or remote learning) or September 2021 (after summer break), and factors associated with the anxiety associated with in-person learning were assessed. Thirteen questions were included to assess importance of relationships, safety, achievements and future aspirations (5-point Likert scale from very important to not important): having friends/socializing; perception by friends; making parents proud; maintaining family relationships; good health (not getting COVID); feeling safe; getting good grades; graduating high school; attending college; becoming famous; having adventure; having money/wealth; and having your own family. One additional question addressed outlook on future (5-point Likert scale from strongly agree to strongly disagree): “I think I will have more opportunities in life than my parents.” Three questions designed by the team assessed the impact of COVID-19 (5-point Likert scale from strongly agree to strongly disagree): “The COVID-19 pandemic has changed me significantly”; “The COVID-19 pandemic has made me mature faster”; Overall, the COVID-19 pandemic has affected my personal growth negatively.”

Two additional open-ended questions were included to allow students to reflect upon opportunities lost and gratitude experienced during COVID-19: “Share one moment that you regret missing out on during the COVID-19 pandemic,” and “Share one moment when you felt grateful during the COVID-19 pandemic”

Data analysis

Quantitative analysis..

Overall frequencies for demographics, GAD-7, and responses to questions on the importance of relationships, safety, achievements and opportunities were examined. Bivariate analyses by demographics characteristics (gender, grade, and learning type) were conducted with each response. Chi-square tests were conducted to determine whether responses differed by gender, grade, learning type, and severity of anxiety. All analyses were performed using SPSS Statistics for Mac, version 28.0 (SPSS Inc, Chicago, Ill, USA).

Qualitative analysis.

The answers to each open-ended question were evaluated for themes. The iterative process took the form of a data analysis spiral such that following data collection, the data was organized, read and notated for emerging ideas, described and classified by thematic codes, assessed and interpreted, and presented in this research report [ 16 ]. Author 1 read all the responses and compiled the data and created preliminary thematic codes. Author 2 reviewed the thematic codes and believed that thematic saturation had been reached. Author 1 then discussed all preliminary codes with all authors who provided additional memos. Representative excerpts for each theme are presented in Table 4 . Data saturation was defined using the grounded theory standpoint by Urquhart, that defined saturation as “the point in coding when you find that no new codes occur in the data. There are mounting instances of the same codes, but no new ones”[ 17 , 18 ].

Among the 546 students enrolled in the high school, 155/546 (28%) completed the survey, including 90 females, 59 males, and 6 students who did not identify as gender binary. Since the number of gender non-binary students was too small to include as a separate group in analyses looking at gender differences, results were presented only for students who self-identified as either female or male (n = 149) ( Table 1 ). The proportion of respondents was greater among females (90/262, 34%) than males (59/284, 21%). The response rates were much lower in 12 th grade (25/137, 18%) than in 9 th grade (61/139, 44%). The students were mostly White (69%), Asian (16%) or multi-racial (9%), predominantly engaged in hybrid learning (86%), and almost all (97%) fully or partially vaccinated against SARS-CoV-2 at the time of survey completion ( Table 1 ).

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https://doi.org/10.1371/journal.pone.0275292.t001

Overall, 54/149 (36%) of the students had GAD-7 scores in the range for moderate or severe anxiety (scores≥10), with a greater proportion of the females than males experiencing moderate/severe anxiety (47% vs 21%, X 2 = 21.3984, P<0.001) ( Table 2 ). Among students who answered yes to any of the GAD-7 questions, 3% reported that anxiety made it extremely difficult and 12% reported that anxiety made it very difficult to do their work, take care of things at home, or get along with other people. More females than males (19% vs 7%, p<0.01) reported that anxiety made it very or extremely difficult to do their work, take care of things at home, or get along with other people ( Table 2 ). Severity of anxiety did not differ between students in the lower (9 th and 10 th ) versus the upper (11 th and 12 th ) grades. Severity of anxiety also did not differ between students engaged in hybrid versus remote learning ( Table 2 ).

https://doi.org/10.1371/journal.pone.0275292.t002

More females than males felt anxious returning to in-person school in April 2021 (52% vs 27%; X 2 = 9.3457, p = 0.002) ( Table 1 ). COVID-19 vaccinations were available for individuals 16 years of age or older by December 2020 with emergency use authorization for individuals 12 years of age and older only granted on April 9, 2021. All of the major factors contributing to anxiety measured were more frequently reported in females than males: fear of getting COVID-19 (26% vs 15%), anxiety toward social interactions (20% vs 8%), and schoolwork (10% vs 5%). By September 2021, 51% of females and 44% of males reported feeling less anxious for in-person school than in April 2021. The primary reasons reported for decreased anxiety were the receipt of COVID-19 vaccinations (38%) and normalization of social interactions with in-person school (16%) ( Table 1 ).

Overall, 34% of students strongly agreed that the COVID-19 pandemic “changed me significantly” and 24% strongly agreed that it “made me mature faster” ( Table 3A ). However, only 8% of students strongly agreed that the COVID-19 pandemic “has affected my personal growth negatively.” More females reported that COVID-19 affected their personal growth negatively, but it did not reach statistical significance (11% vs 5%, p = 0.15). In comparison to students with either mild anxiety or no anxiety (GAD-7<10), students with moderate to severe anxiety (GAD-7≥10) were more likely than students with either mild anxiety or no anxiety (GAD-7<10) to strongly agree that the COVID-19 pandemic changed them significantly (51% vs 28%, p = 0.05), made them mature faster (44% vs 16%, p = 0.004), and affected their personal growth negatively (16% vs 6%, p = 0.004) ( Table 3B ).

https://doi.org/10.1371/journal.pone.0275292.t003

We further explored whether moderate/severe anxiety affected students’ outlook on relationships, safety, achievements, aspirations and opportunities. Over half of students reported the following life factors as very important: having friends/socializing (53%), maintaining good health and not getting COVID-19 (53%), getting good grades (62%), graduating high school (82%), and attending college (74%) ( Table 4 ). Females were more likely than males to regard the following factors as very important: money/wealth (28% vs 12%, p<0.01) and having your own family (39% vs 29%, p = 0.02), but did not differ from boys in other reported factors. Students with moderate to severe anxiety (GAD-7≥10) were more likely than students with mild or no anxiety to regard the following as very important: attending college (81% vs 70%, p = 0.04), becoming famous (9% vs 1%, p = 0.04), and having your own family (44% vs 31%, p = 0.01). Only 23% of students reported that they strongly agree with the statement “I will have more opportunities in my life than my parents”, without apparent differences by anxiety status ( Table 4 ).

https://doi.org/10.1371/journal.pone.0275292.t004

In response to “share one moment that you regret missing out on during COVID-19 pandemic,” the following themes emerged, from most common to least common: missing out on school social events and sports; being with friends; family events and vacations, wasted new opportunities that were presented during COVID-19 pandemic, and celebrating milestones like bar mitzvahs, sweet-sixteens and birthdays. In response to “share one moment when you felt grateful during the COVID-19 pandemic,” the following themes emerged, from most common to least common: connecting with friends and family, health and safety, having time for personal development, moments during which there was a sense of return to normalcy, and the decreased stress of remote learning ( Table 5 ). Generally, the noted themes were similar in students with moderate-severe anxiety versus those with mild or no anxiety. However, in comparison to students with mild or no anxiety, more students with moderate-severe anxiety expressed that they regret missing out on being with friends, and less expressed regret for missing out on school-related social events such as the prom, school trips, or sports competitions. Notably, while all the students with moderate-severe anxiety reported missing out on something, 5% of students with either mild or no anxiety reported that they did not miss out on anything during the COVID-19 pandemic. Also, more students with moderate-severe anxiety expressed that they were grateful for health and safety and situations that provided a sense of normalcy.

https://doi.org/10.1371/journal.pone.0275292.t005

In one of the first reports on levels of anxiety in high school students during the second year of the COVID pandemic, this study found that 36% of the students reported moderate or severe anxiety, disproportionately affecting females. Although the GAD-7 is a screener for anxiety and meant to over detect, anxiety scores in this range are considered clinically meaningful and indications for further assessment and/or referral to a mental health professional for more definitive diagnoses. These surveys were completed in late 2021 at a point when over 95% of students had received partial or full vaccinations; therefore, our data suggest that the impact of COVID-19 on the generalized anxiety of high school students may be long-lasting.

Our findings are consistent with several large mental health surveys that included measures of anxiety were conducted on university students in 2020, earlier in the COVID-19 pandemic, and found that females were more likely to report moderate to severe general anxiety then males. The Healthy Minds Survey 2020, one of the largest studies of university students in the United States (N = 36,875), found that 32.2% of students reported moderate to severe anxiety, with a higher proportion in females than males (66.6% vs 28.6% of males) [ 19 ]. Similarly, a survey of over 69,000 university students in France found that females were more likely to report high levels of anxiety than males (30.8% vs 17.1%) [ 20 ].

As noted previously, the largest mental health surveys conducted among high school students in the United States did not specifically include an evaluation of anxiety [ 2 , 11 ], but anxiety was included in two smaller studies. Gazmararian et al. surveyed racial/ethnically and socioeconomically diverse students at 2 semi-rural public high schools in Georgia in 2020 and found that 25% of students were worried about the COVID-19 pandemic and a negative financial impact, with a similar gender difference in girls versus boys (29% vs 16%, p<0.0001) [ 13 ]. The Policy and Communication Evaluation (PACE) Vermont is an online cohort study of 212 adolescents (ages 12–17) and 662 young adults (ages 18–25) that completed questionnaires in the Fall of 2019 and 2020, before and after the onset of the COVID-19 pandemic [ 12 ]. The prevalence of anxiety symptoms measured by the GAD-2 increased from 24.3% to 28.4% among adolescents after COVID-19, similar to the increase from 35.3% to 42.3% observed among young adults [ 12 ].

In our study, 36% of high school students had moderate/severe anxiety by GAD-7, which is slightly higher than the prevalence in aforementioned high school studies, and similar to the prevalence among college students in the Healthy Minds Survey. Female high school students were more likely to report moderate or severe anxiety. Importantly, this study explored potential reasons for anxiety upon return to in-person learning in April 2021, informed by high school students (including lead author) and a greater proportion of females than males endorsed each category: COVID-19 (26% vs 15%), schoolwork (10% vs 5%) and social interactions (20% vs 8%). These data suggest that female high school students had higher anxiety levels not only because of fear of COVID, but also because of more normative stressors pre-COVID, such as school and social pressures. Furthermore, females reported more negative effects of their anxiety compared to boys, with 19% reporting that it is “extremely difficult to do their work, take care of things at home, or get along with other people” as compared to only 9% of males. Notably, severity of anxiety did not appear to differ between students in the lower (9 th and 10 th ) versus the upper (11 th and 12 th ) grades. This was unexpected given higher levels of stress associated with standardized testing and college applications in the upper grades. Severity of anxiety also did not differ between students engaged in hybrid versus remote learning ( Table 2 ). However, since most of the students were engaged in hybrid learning (87%), our power to detect differences was limited. Other investigators found no difference in risk for anxiety among students with remote versus in-person education [ 21 ]; however, the role of hybrid learning has never been adequately assessed.

Students who reported moderate/severe anxiety had very different responses than students with either mild or no anxiety regarding the impact of the COVID-19 pandemic. Students with moderate/severe anxiety were far more likely to strongly agree that the COVID pandemic changed them (51% vs 28%), made them mature faster (44% vs 16%), and affected their personal growth negatively (16% vs 6%). It is possible that COVID-19 had a greater negative impact on these students resulting in higher anxiety levels, or that students with higher anxiety levels before the pandemic were more susceptible to the negative effects of COVID-19. This question cannot be addressed without pre-pandemic data on these students. However, it is interesting that even though students with moderate/severe anxiety perceived a greater negative impact of COVID-19, they did not differ from other students in their hopes and aspirations for the future. In fact, more students with moderate to severe anxiety responded that attending college, becoming famous, and having their own family was very important ( Table 4 ). This may also reflect a greater underlying expectation for success and a desire for safety and security among students with greater anxiety. This is an important area for future study. While students reported being concerned about good health and “not getting COVID-19,” less than half of the students (45%) rated “feeling safe” as very important. While these data may reflect the higher risk tolerance of adolescents in general vs other age groups, the data also suggest that the heightened awareness of safety measures for COVID-19 did not translate into generalized fear affecting other aspects of their lives. Overall, these data suggest that despite the relatively high proportion of students reporting anxiety, the majority did not perceive negative effects and thus appeared to be coping with the stressors of COVID-19.

This study was not designed for formal qualitative research, but there were two open-ended questions on regrets and gratitude. Missing out on school social or sports events was the most common theme, followed by missing out being with friends or attending family events or vacations. Several students also articulated missed opportunities for growth presented by COVID-19 and shared regrets for not accomplishing more with the extra time. Students shared their gratitude mostly for connecting with friends and family and for health and safety. There were also appreciations written for having a time for personal growth, moments during COVID-19 that provided a sense of normalcy, and the decreased stress from school that remote learning offered ( Table 4 ). Based upon exploratory analyses, it appeared that students with moderate-severe anxiety were more likely to regret missing out on being with friends, less likely to regret missing out on school social or sports events, and more likely to be grateful for health and safety. Further work could examine how these constructs may be important for adolescents experiencing moderate-severe anxiety.

There are now several longitudinal studies of change in mental health measures among children and young adults before and during the COVID-19 pandemic [ 22 ]. Several comprehensive studies of college and university students in the United States include data on pre-pandemic mental health, analyses of predictors, and a focus on serious psychiatric and alcohol/drug use outcomes [ 23 , 24 ], but data are lacking for high school students. Stamatis et al found that the disruption due to the pandemic and limited confidence in the government response were the main predictors of depression among college students [ 24 ]. Bountress et al found that COVID-19 worry predicted post-traumatic stress disorder (PTSD), depression and anxiety even after adjusting for pre-pandemic symptom levels [ 23 ]. In addition, housing/food concerns predicted PTSD, anxiety and depression symptoms as well as suicidal ideation, after adjusting for pre-pandemic symptoms in college students [ 23 ]. Comprehensive longitudinal studies are necessary to assess the true impact of COVID on mental health in high school students. In particular, studies should assess whether symptoms are associated with serious clinical outcomes such as suicidal ideation, alcohol and substance misuse and missed milestones such as graduation from high school, admission to college, and employment.

Strengths and limitations

A strength of our study was the use of the well validated and extensively used GAD-7 to measure anxiety symptoms. There were no data on anxiety for the students prior to COVID-19 as a baseline for comparison nor measures of other indicator of mental health such as depression and suicidality. Other limitations of this study include the performance of the survey at a single high school—our sample size was limited and the analyses were performed on a convenience sample. While only 28% of the study body responded to the survey, this response rate was similar to the response rates of other high school surveys performed in the United States [ 12 , 13 , 25 ]. The lack of racial/ethnic diversity in the student population also limits generalizability to other populations of adolescents. We did not include potential risk factors elicited in other studies such as prior psychiatric history, financial hardship, or illness in family in our survey. We were also unable to evaluate the impact of hybrid versus remote learning on anxiety, since very few of our students chose remote learning. Lastly, the survey questions we created were done so because nothing specifically existed for this age group, the newness of COVID, and the need to implement questions quickly; therefore, we did not utilize a formal validation process.

In this survey of high school students performed almost 2 years after the onset of COVID-19 in the United States, a relatively high proportion reported moderate or severe anxiety symptoms as assessed by the GAD-7. Our data suggest that the negative impact of COVID-19 on the anxiety levels of high school students may be long-lasting. Whether the heightened anxiety results in functional deficits is still uncertain, but resources for assessment and treatment should be prioritized.

Supporting information

S1 dataset..

https://doi.org/10.1371/journal.pone.0275292.s001

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• 16. Cresswell J, Poth C. Qualitiative Inquiry and Research Design: Choosing Among Five Approaches. California: SAGE 2018.
• 17. Urquhart C. Grounded Theory for Qualitative Research: A Practical Guide. Thousand Oaks: Sage; 2013.

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• Published: 01 August 2023

Stressors in university life and anxiety symptoms among international students: a sequential mediation model

• Yue Wang 1 , 2 ,
• Xiaobin Wang 1 ,
• Xuehang Wang 3 ,
• Xiaoxi Guo 3 ,
• Lulu Yuan 4 ,
• Yuqin Gao 4 &
• Bochen Pan 1  

BMC Psychiatry volume  23 , Article number:  556 ( 2023 ) Cite this article

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Metrics details

Anxiety is a common mental health problem among university students, and identification of its risk or associated factors and revelation of the underlying mechanism will be useful for making proper intervention strategies. The aim of our study is to test the sequential mediation of self-efficacy and perceived stress in the association between stressors in university life and anxiety symptoms.

A cross-sectional study design was adopted and a sample of 512 international students from a medical university of China completed the survey with measurements of stressors in university life, self-efficacy, perceived stress and anxiety symptoms.

We found that 28.71% of the international students had anxiety symptoms, and stressors in university life were positively associated with anxiety symptoms ( β  = 0.23, t  = 5.83, p  < 0.01). Moreover, sequential mediating role of self-efficacy and perceived stress in the association between the stressors and anxiety symptoms was revealed.

Conclusions

Our study provided a new perspective on how to maintain the mental health, which suggested that self-efficacy improvement and stress reduction strategies should be incorporated in the training programs to support students.

Peer Review reports

There has been an increasing interest in mental health of university students, and one of the growing concerns is the high prevalence of stress and anxiety in this population [ 1 , 2 , 3 , 4 ]. According to the WHO survey project about mental health problems in university students, generalized anxiety was highly prevalent in students across countries [ 5 ]. As anxiety can seriously affect students’ social function, academic achievement or even physical health, efforts should be made to identify its risk factors and illuminate the mechanism of interactions so that proper intervention strategies may be made. International students are a special population in universities, because they may have to deal with the culture differences and encounter more difficulties. Thus, their mental health is of great concern. However, studies focusing on the mental health of international students in China have been limited.

Relationship between stressors and anxiety symptoms

Studies suggest that stress is one of the risk factors for anxiety [ 6 ]. Higher levels of stress among university students have been found associated with mental health problems [ 7 , 8 , 9 ]. There are many sources of stress that university students may experience, and the students must learn to balance the competing demands. Demands on an individual made by the external or internal environmental stimuli that affect the balance are defined as stressors [ 10 ]. In recent years, more and more researchers begin to focus on the stressors that university students are facing and the coping strategies students adopt. Stressors encountered by university students are categorized differently in different studies, but some major sources have been well recognized, such as the health issues, environmental problems, academic difficulties, financial pressure and interpersonal relation problems [ 11 , 12 ]. Among these sources, academic difficulties were viewed as the primary sources of stress and were shown contributing to a variety of mental health problems in many researches [ 13 , 14 ]. For international students, however, the stressors might be different, because they may face issues different from those of their domestic peers. Therefore, studies specific on the stressors perceived by international students are needed. In this study, based on the above observations, our first hypothesis (H1) is: Stressors in university life are positively and significantly associated with anxiety symptoms among international students.

However, researches have shown that the same stressors to one individual may not be stressful to another. Other factors may also contribute to the process. Lazarus and colleagues described this phenomenon in their transactional model of stress, and they pointed out that cognitive appraisals play an important role in the process to determine the presence or the severity of a stressor [ 15 ]. In this model, stress is defined as a transaction between an individual and the environment, and is generated by subjective cognitive judgement of the potential impact of a stressor on future functioning [ 16 ]. The process begins when a stressor represents a threat (primary appraisal) that activates a cognitive process for the individual to assess the degree of harm or loss, and then leads to a secondary appraisal in which the individual evaluates his or her resources to cope with the stressor. A stress response is elicited when the perceived demands outweigh the perceived resources. Therefore, it is the character of the individual rather than the environment that makes a difference in the meaning of a stressor. In addition, stress outcomes were known to involve physiological, emotional, behavioral and cognitive reactions [ 15 ].

Relationships among stressors, self-efficacy and anxiety symptoms

Self-efficacy is grounded in the social cognitive theory which emphasizes that the individual regulates his or her motivation and behavior through self-assessment [ 17 ]. General self-efficacy is defined as the degree to which individuals believe they are capable of dealing with challenging situations and is the mechanism through which individuals apply their existing knowledge and experience [ 18 ]. If individuals have a strong sense of self-efficacy, they will trust their ability to actively control stressors in the environment, which will motivate them to take action [ 19 ]. This is very similar to the appraisal concept in the transactional model of stress [ 19 ]. Studies revealed that the lower level of self-efficacy was related to mental disorders [ 20 , 21 ]. This may be true for the university students as students with more anxiety symptoms showed lower level of self-efficacy [ 22 , 23 ]. Individuals with higher levels of self-efficacy tend to experience more positive emotions, whereas those with lower levels of self-efficacy are more likely to experience more anxiety [ 24 ]. A possible explanation is that people who have lower perception of self-efficacy to control life and thoughts cannot help but be anxious at the thought of how to deal with the stressors [ 19 ]. Therefore, self-efficacy appears to be an effective protective factor against the negative psychological effects such as anxiety induced by stressors, and thus we propose the following hypotheses: (H2) Stressors in university life are negatively and significantly associated with self-efficacy among international students; (H3) Self-efficacy is negatively and significantly associated with anxiety symptoms among international students; (H4) Stressors in university life have a significant indirect effect on anxiety symptoms via self-efficacy among international students.

Relationships among stressors, perceived stress and anxiety symptoms

Appraisal or perception of stress is another factor that may mediate the association between the stressors and psychological responses. Based on the transactional model of stress, stress represents an imbalance between abilities of individuals and demands of environment, and the results of the transaction could lead to negative psychological outcomes [ 15 ]. Therefore, the effect of stressors depends on the perception of stress [ 16 ]. Some study results confirmed the presence of such a mechanism. For example, McCuaig Edge investigated the impact of combat exposure on psychological distress of military personnel and found the mediation effect of cognitive appraisal in the association [ 25 ]. Besharat et al. conducted a survey regarding anxiety among Iranian university students, and found that perceived stress played a mediating role in the association between facing existential issues and anxiety [ 26 ]. Zhang et al. examined the relationships of sleep quality and anxiety/depression among nursing students of a public university in the United States, and found that perceived stress not only mediated the association between sleep quality and anxiety symptoms, but also the association between sleep quality and depression symptoms [ 27 ]. These results strongly suggest that the appraisal or perception of stress can be the factor that determines whether the stressors will result in psychological responses or not. As a result, we posit the following hypotheses: (H5) Stressors in university life are positively and significantly associated with perceived stress among international students; (H6) Perceived stress is positively and significantly associated with anxiety symptoms among international students; (H7) Stressors in university life have a significant indirect effect on anxiety symptoms via perceived stress among international students.

Relationship between self-efficacy and perceived stress

A review of the literature related to self-efficacy and stress revealed a significant relationship between individuals’ self-efficacy and their effectiveness in coping with stress [ 24 ]. Self-efficacy is related to experiencing less negative emotions in risky situations and appraising the stressors as challenges rather than threats [ 20 ]. Individuals with higher level of self-efficacy believe they are capable of dealing with their demands, and this belief may result in adopting positive approaches and perceiving less stress in life. According to the transactional model of stress, self-efficacy may play a significant role in the primary and secondary appraisals which will lead to a decline in perceived stress, and then result in less negative psychological outcomes. Thus, we posit the following hypotheses: (H8) Self-efficacy is negatively and significantly associated with perceived stress among international students; (H9) Stressors in university life have a significant indirect effect on anxiety symptoms via self-efficacy and then perceived stress among international students.

Based on the above mentioned theoretical assumptions and research results, we have constructed the conceptual framework of this study (Fig.  1 ). We hope that this conceptual framework will become the theoretical basis for exploring intervention measures to prevent or manage mental health problem such as anxiety for the international students.

Conceptual framework of this study

Study design and subjects

The present study was a cross-sectional design and a cluster sampling was adopted. Data were collected from the international students of China Medical University in November 2020. The inclusion criteria of the potential participants were (1) able to get access to internet, (2) a current student of the University, (3) able to read, fully understand and answer the survey questions. One thousand and fifteen students who met the inclusion criteria were initially contacted via electronic email. Then, in the online survey, there was a brief explanation about the study, and the participants were asked to complete an informed consent agreement, in which they were made aware that participation was completely voluntary. Research Ethics Committee of China Medical University approved our study (2020–25), and the study was performed in accordance with the Declaration of Helsinki. Finally, a total of 543 international students participated, and 512 of them were able to complete the questionnaires. The overall response rate was 50.44%.

Measurements

Measurement of anxiety symptoms.

The anxiety symptoms were measured with the Generalized Anxiety Disorder 7-item (GAD-7) questionnaire. The questionnaire consists of seven items for observing the frequency of anxiety symptoms with a four-point Likert scale from 0 “not at all” to 3 “almost every day” [ 28 ]. The anxiety level is reflected by the total score, where higher scores indicate more symptoms of anxiety. Scores of 5, 10 and 15 represent the cutoffs for mild, moderate and severe anxiety symptoms, respectively [ 29 ]. Previous studies have demonstrated the GAD-7 has high reliability as well as good criterion and construct validity [ 30 , 31 , 32 , 33 ]. The Cronbach’s alpha for this sample was 0.92.

Measurement of stressors in university life

Stressors in university life of international students were measured by 7 questions regarding (1) health problems, (2) financial pressure, (3) academic difficulties, (4) interpersonal relation difficulties, (5) daily life difficulties, (6) adverse life events and (7) language barrier [ 11 , 12 ]. Participants answered 1 (not at all) to 4 (very serious) to the questions. The total score represents the severity of the stressors perceived by the participant. A Cronbach’s alpha of 0.80 was found for the scale in this study.

Measurement of self-efficacy

Self-efficacy was assessed with the General Self-efficacy Scale (GSES), which is a 10-item measure of an individual’s confidence in his or her ability to deal with stressful situations [ 34 ]. Items are scored on a four-point Likert-type scale ranges from 1 (not at all true) to 4 (exactly true), and responses are calculated to yield a total score of all item scores where higher scores indicate higher levels of self-efficacy. The GSES has good psychometric properties, and many studies have confirmed its internal consistency reliability, convergent and discriminant validity [ 35 , 36 ], demonstrating that GSES is a reliable and valid measurement. The Cronbach’s alpha for GSES in the present study was 0.95.

Measurement of perceived stress

Perceived stress was evaluated by the 10-item version of Perceived Stress Scale (PSS-10), which is a self-report measure designed to assess the extent to which participants appraise their lives to be stressful [ 37 ]. Each item is rated on a 0 (never) to 4 (very often) Likert scale by the respondent to indicate how often the participant experienced specific feelings or thoughts. The total scores of the measure are obtained by adding the score of each item (4 items are reverse-scored) to provide a continuous measure of perceived stress, and higher scores indicate greater perceived stress. PSS-10 has demonstrated strong psychometrics. Its coefficient alpha reliability ranged between 0.84 and 0.91 in previous studies [ 6 , 38 ], and in this study it was 0.87.

Demographic characteristics

Age, gender, current place of residence (Asia/Africa/North America/Europe/Oceania) and educational background were investigated for demographic characteristics.

Statistical analysis

SPSS 17.0 was used for the statistical analysis. Descriptive statistics including frequency distributions for the nominally scaled demographic variables provided a profile of the sample. We found the scores of GAD-7 were not normal distribution after testing the normality for continuous variables. Therefore, Mann–whitney U test was conducted to determine if the groups were statistically equivalent on anxiety symptoms. Spearman’s rank correlation coefficients were used to examine relationships between continuous variables.

The sequential mediation was tested using PROCESS macro program for SPSS [ 39 ], which facilitated path analysis-based mediation analyses. We verified the hypothesis model by the bias-corrected percentile bootstrap method, with 5000 resampled samples. 95% confidence intervals for the mediation effects were estimated and the results were considered significant when the 95% confidence interval did not include zero. We generated direct effect of stressors in university life on anxiety symptoms and indirect effects of stressors in university life on anxiety symptoms through the mediators (self-efficacy and perceived stress) in the mediation using the model 6 of PROCESS. There were three routes of indirect effects in the sequential mediation model. When the direct effect became non-significant but the indirect effect was significant, full mediation was established. Partial mediation was confirmed if both effects are significant [ 40 ]. Continuous variables were all centralized before the model was validated to avoid multicollinearity. Two-tailed alpha 0.05 was used for significance testing purposes.

Descriptive statistics

The descriptive statistics of the sample are shown in Table 1 . Overall, 147 (28.71%) students had anxiety symptoms, including 93 (18.16%) mild, 33 (6.45%) moderate and 21 (4.10%) severe cases.

Severity of stressors

Stressors and their severity perceived by the international students are presented in Table 2 . Financial pressure and language barrier were the most prominent stressors affecting 72.07% and 69.34% of the students, respectively.

Differences of anxiety symptoms in categorical variables

The differences of GAD-7 scores in categorical variables are shown in Table 3 . There was no difference between the groups.

Correlations among continuous variables

The correlations among continuous variables are shown in Table 4 . Age, stressors in university life, perceived stress and self-efficacy were all significantly correlated with GAD-7 score. In addition, stressors in university life and PSS-10 negatively correlated with GSES. Finally, stressors in university life positively correlated with PSS-10, while age negatively correlated with PSS-10.

Results of the sequential mediation model testing

The results of regression analyses are listed in Table 5 . After controlling for age, stressors in university life were negatively associated with self-efficacy ( β  = -0.24, t  = -5.51, p  < 0.01) and positively associated with perceived stress ( β  = 0.38, t  = 9.68, p  < 0.01) and anxiety symptoms ( β  = 0.23, t  = 5.83, p  < 0.01). The association between self-efficacy and perceived stress was also significant ( β  = -0.24, t  = -6.13, p  < 0.01), but not in the association between self-efficacy and anxiety symptoms. Perceived stress had a positive and the strongest association with anxiety symptoms ( β  = 0.51, t  = 12.85, p  < 0.01), because the standardized regression coefficient was the largest in the model. Figure  2 represents the model plot after the testing.

Sequential mediation model result. Notes: * p  < 0.05, ** p  < 0.01

The direct, indirect and total effects in the sequential mediation model are shown in Table 6 . In the proposed model, stressors in university life impacted anxiety symptoms through four possible routes. The direct effect of stressors in university life on anxiety symptoms (route 1) was 0.32 with 95% bias-corrected CIs [0.21, 0.43] above 0, which was an indication of significance. The mediating effect of self-efficacy (route 2) was not significant, because the 95% bias-corrected CIs [-0.05, 0.01] included 0. Thus, self-efficacy did not play a mediating role in the association between stressors in university life and anxiety symptoms. The mediating effect of perceived stress (route 3) was 0.28 with 95% bias-corrected CIs [0.20, 0.36] excluding 0, supporting the positive mediating effect of perceived stress in the relationship between stressors in university life and anxiety symptoms. Similarly, the sequential mediating effect of self-efficacy and perceived stress (route 4) was 0.04 with 95% bias-corrected CIs [0.02, 0.07] excluding 0, representing the sequential mediating effect of self-efficacy and perceived stress in the relationship between stressors in university life and anxiety symptoms.

Previous studies mainly focused on the cross-cultural adaptation of international students, but less on the stressors and related stress responses. In our study sample, the mean scores of PSS-10 was 16.53, which is lower than the local students in Turkey (18.03) [ 41 ], Saudi Arabia (20.10) [ 42 ] and China (21.13) [ 43 ]. In addition, 28.71% of the international students had anxiety symptoms in the present study, which is also lower than the domestic Chinese students (46.85%) [ 43 ] and Libyan students (64.50%) [ 44 ]. A possible reason may be that the participants in our study all come from a medical university, and they may already have certain amount of knowledge on mental health. It is also possible that the measures taken by their university to manage the stress have been effective.

Our study showed that financial pressure and language barrier were the most serious stressors in university life among international students, which were different from the findings demonstrating that academic difficulties were the primary sources of stress in university students. As pointed out by Grable and Joo, the students who face financial crisis tend to be more likely to drop out of the university or achieve lower grades than others [ 45 ], which may cause serious stress to the students. The importance of financial pressure to international students in our study was in line with the previous studies on international students which indicated that the financial pressure was a particular concern and at higher risk for problem of mental health [ 46 , 47 ]. Language insufficiency has also been found to be a critical stressor that international students encounter in other studies, because language proficiency was essential in international students’ sociocultural adjustment [ 48 , 49 ]. In this situation, the students may face concomitant problems such as lack of confidence and low self-efficacy, again causing higher level of stress. This finding consisted with the results in previous studies which proved language deficit was a significant source of stress among international students [ 50 , 51 ]. Furthermore, in our study, stressors in university life were found positively associated with anxiety symptoms of international students ( β  = 0.23, t  = 5.83, p  < 0.01), which supported H1 and was consistent with other studies [ 52 ]. Since the students are exposed to various stressors in university life to different extent and it may not be possible to remove the stressors from their roots, understanding the internal mechanism becomes very important in order to reduce the adverse effect of stressors in university life and maintain the mental health of students.

In the present study, stressors in university life were negatively associated with self-efficacy ( β  = -0.24, t  = -5.51, p  < 0.01) and positively associated with perceived stress ( β  = 0.38, t  = 9.68, p  < 0.01), which supported H2 and H5. Self-efficacy was negatively associated with perceived stress ( β  = -0.24, t  = -6.13, p  < 0.01), and perceived stress was positively associated with anxiety symptoms ( β  = 0.51, t  = 12.85, p  < 0.01), which supported H8 and H6. Unexpectedly, sequential mediation model testing didn’t show a direct effect of self-efficacy on anxiety symptoms nor an indirect effect of self-efficacy in the association between stressors in university life and anxiety symptoms. Therefore, H3 and H4 were not supported, which indicated that the association of self-efficacy with anxiety symptoms was not direct, similar with the findings from a study on medical college students in Philippines [ 53 ]. Instead, self-efficacy played a sequential mediating role with perceived stress in the association between stressors in university life and anxiety symptoms, which supported H9 and indicated self-efficacy’s direct relationship with perceived stress rather than anxiety symptoms. Although the sequential mediation effect accounted only for 12.5% of the total mediation effect, it still implied that the impact of self-efficacy on anxiety symptoms was generated through perceived stress. This result supported the transactional model of stress. It also indicated that self-efficacy was an effective protective factor against stress. Individuals who have lower levers of self-efficacy do not have enough confidence and the ability to cope with the external and internal environment. They will perceive more severe stressors and stress, and are more prone to show anxiety symptoms. Self-efficacy improvement interventions in previous researches have shown that the methods were effective in empowering participants to cope with stress [ 22 , 24 , 54 , 55 , 56 ].

Another finding of our study was the partial mediation effect of perceived stress in the association of stressors in university life and anxiety symptoms among international students, which supported H7. Perceived stress alone accounted for 87.50% of the total mediation effect and 43.75% of the total effect. Its strong effect indicated its important role in facilitating translation of stressors in university life into anxiety symptoms, and this is in line with other studies that assumed appraisals were important determinants of adjustment to stressful encounters [ 57 ]. Previous empirical researches have shown similar findings of the mediation effect of perceived stress [ 25 , 26 , 27 ]. Combined with our findings of self-efficacy as a protective factor against stress, interventions can be considered using self-efficacy training to alleviate perceived stress and promote the positive appraisal on stressors in university life to reduce anxiety symptoms. There already have been some researches of stress management among university students using cognitive behavioral therapy which have achieved a significant reduction in perceived stress and anxiety symptoms after the intervention, with the enhancement of self-efficacy as well [ 17 ].

Limitations

Our study has several limitations. Given the cross-sectional design, it’s unable to make any assertions regarding causation. A further experimental design of study in the future should be employed to determine causal relationships. Another limitation is that there may have been response biases in the self-report of the individuals completing the measures. Finally, as most of the participants were from Asia, the results in this study may not apply equally well to the students in other part of the world. Future research could expand the diversity of the university types to better capture the students from other part of the world.

Despite of the limitations, this study has discovered the sequential mediating role of self-efficacy and perceived stress in the association between stressors in university life and anxiety symptoms, and provided a new perspective on how to maintain mental health for international students. The sequential mediators provides a deeper insight into the underlying mechanism of stressors in university life towards anxiety symptoms among international students. At the same time, this study has broadened the application scope of self-efficacy in the field of stress research, and is also an empirical contribution to the theory of transactional model of stress using in the population of international students. In addition, our study shows that identification and evaluation of stressors in university life are important, and financial pressure and language barrier should be given more attention for international students. This would be valuable in ensuring implementation of stress reduction programs to effectively support students. Given that few studies on university life stressors exist in the literature of international students, our study is very important because it has filled in the gap. As for practical implications, our study findings may apply to all the international students who have poor self-efficacy or perceive higher levels of stress or are struggling with anxiety. Therefore, counselling focusing on financial pressure and language barrier, as well as introduction of specific interventions into university campus for international students should be encouraged, and the university educators should utilize self-efficacy improvement and stress reduction measures in the training programs to support students.

Our study has identified the financial pressure and language barrier as the most important university life stressors for international students. The findings have also confirmed the direct positive association between stressors in university life and anxiety symptoms, as well as the positive association between perceived stress and anxiety symptoms, and revealed the sequential mediating role of self-efficacy and perceived stress in the association between stressors in university life and anxiety symptoms. Results of our study indicate that in order to maintain the mental health of international students, counselling concerning finance and language and interventions with self-efficacy improvement and stress reduction should be involved in the training programs within the university campus.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to the protection of participants' privacy but are available from the corresponding author on reasonable request.

Abbreviations

Generalized Anxiety Disorder 7-item

General Self-efficacy Scale

10-Item version of Perceived Stress Scale

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Acknowledgements

We sincerely thank all the participants in the investigation.

This research was supported by the Clinical Tree-Planting Project (M1590) and 345 Talent Project (M1463) of Shengjing Hospital to Bochen Pan.

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Yue Wang and Xiaobin Wang drafted the manuscript and analyzed the statistics. Xuehang Wang and Xiaoxi Guo collected the data. Lulu Yuan and Yuqin Gao reviewed the statistical approach. Bochen Pan designed the study.

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Wang, Y., Wang, X., Wang, X. et al. Stressors in university life and anxiety symptoms among international students: a sequential mediation model. BMC Psychiatry 23 , 556 (2023). https://doi.org/10.1186/s12888-023-05046-7

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Procrastination, depression and anxiety symptoms in university students: a three-wave longitudinal study on the mediating role of perceived stress

• Anna Jochmann 1 ,
• Burkhard Gusy 1 ,
• Tino Lesener 1 &
• Christine Wolter 1  

BMC Psychology volume  12 , Article number:  276 ( 2024 ) Cite this article

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It is generally assumed that procrastination leads to negative consequences. However, evidence for negative consequences of procrastination is still limited and it is also unclear by which mechanisms they are mediated. Therefore, the aim of our study was to examine the harmful consequences of procrastination on students’ stress and mental health. We selected the procrastination-health model as our theoretical foundation and tried to evaluate the model’s assumption that trait procrastination leads to (chronic) disease via (chronic) stress in a temporal perspective. We chose depression and anxiety symptoms as indicators for (chronic) disease and hypothesized that procrastination leads to perceived stress over time, that perceived stress leads to depression and anxiety symptoms over time, and that procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress.

To examine these relationships properly, we collected longitudinal data from 392 university students at three occasions over a one-year period and analyzed the data using autoregressive time-lagged panel models.

Procrastination did lead to depression and anxiety symptoms over time. However, perceived stress was not a mediator of this effect. Procrastination did not lead to perceived stress over time, nor did perceived stress lead to depression and anxiety symptoms over time.

Conclusions

We could not confirm that trait procrastination leads to (chronic) disease via (chronic) stress, as assumed in the procrastination-health model. Nonetheless, our study demonstrated that procrastination can have a detrimental effect on mental health. Further health outcomes and possible mediators should be explored in future studies.

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Introduction

“Due tomorrow? Do tomorrow.”, might be said by someone who has a tendency to postpone tasks until the last minute. But can we enjoy today knowing about the unfinished task and tomorrow’s deadline? Or do we feel guilty for postponing a task yet again? Do we get stressed out because we have little time left to complete it? Almost everyone has procrastinated at some point when it came to completing unpleasant tasks, such as mowing the lawn, doing the taxes, or preparing for exams. Some tend to procrastinate more frequently and in all areas of life, while others are less inclined to do so. Procrastination is common across a wide range of nationalities, as well as socioeconomic and educational backgrounds [ 1 ]. Over the last fifteen years, there has been a massive increase in research on procrastination [ 2 ]. Oftentimes, research focuses on better understanding the phenomenon of procrastination and finding out why someone procrastinates in order to be able to intervene. Similarly, the internet is filled with self-help guides that promise a way to overcome procrastination. But why do people seek help for their procrastination? Until now, not much research has been conducted on the negative consequences procrastination could have on health and well-being. Therefore, in the following article we examine the effect of procrastination on mental health over time and stress as a possible facilitator of this relationship on the basis of the procrastination-health model by Sirois et al. [ 3 ].

Procrastination and its negative consequences

Procrastination can be defined as the tendency to voluntarily and irrationally delay intended activities despite expecting negative consequences as a result of the delay [ 4 , 5 ]. It has been observed in a variety of groups across the lifespan, such as students, teachers, and workers [ 1 ]. For example, some students tend to regularly delay preparing for exams and writing essays until the last minute, even if this results in time pressure or lower grades. Procrastination must be distinguished from strategic delay [ 4 , 6 ]. Delaying a task is considered strategic when other tasks are more important or when more resources are needed before the task can be completed. While strategic delay is viewed as functional and adaptive, procrastination is classified as dysfunctional. Procrastination is predominantly viewed as the result of a self-regulatory failure [ 7 ]. It can be understood as a trait, that is, as a cross-situational and time-stable behavioral disposition [ 8 ]. Thus, it is assumed that procrastinators chronically delay tasks that they experience as unpleasant or difficult [ 9 ]. Approximately 20 to 30% of adults have been found to procrastinate chronically [ 10 , 11 , 12 ]. Prevalence estimates for students are similar [ 13 ]. It is believed that students do not procrastinate more often than other groups. However, it is easy to examine procrastination in students because working on study tasks requires a high degree of self-organization and time management [ 14 ].

It is generally assumed that procrastination leads to negative consequences [ 4 ]. Negative consequences are even part of the definition of procrastination. Research indicates that procrastination is linked to lower academic performance [ 15 ], health impairment (e.g., stress [ 16 ], physical symptoms [ 17 ], depression and anxiety symptoms [ 18 ]), and poor health-related behavior (e.g., heavier alcohol consumption [ 19 ]). However, most studies targeting consequences of procrastination are cross-sectional [ 4 ]. For that reason, it often remains unclear whether an examined outcome is a consequence or an antecedent of procrastination, or whether a reciprocal relationship between procrastination and the examined outcome can be assumed. Additionally, regarding negative consequences of procrastination on health, it is still largely unknown by which mechanisms they are mediated. Uncovering such mediators would be helpful in developing interventions that can prevent negative health consequences of procrastination.

The procrastination-health model

The first and only model that exclusively focuses on the effect of procrastination on health and the mediators of this effect is the procrastination-health model [ 3 , 9 , 17 ]. Sirois [ 9 ] postulates three pathways: An immediate effect of trait procrastination on (chronic) disease and two mediated pathways (see Fig.  1 ).

Adopted from the procrastination-health model by Sirois [ 9 ]

The immediate effect is not further explained. Research suggests that procrastination creates negative feelings, such as shame, guilt, regret, and anger [ 20 , 21 , 22 ]. The described feelings could have a detrimental effect on mental health [ 23 , 24 , 25 ].

The first mediated pathway leads from trait procrastination to (chronic) disease via (chronic) stress. Sirois [ 9 ] assumes that procrastination creates stress because procrastinators are constantly aware of the fact that they still have many tasks to complete. Stress activates the hypothalamic-pituitary-adrenocortical (HPA) system, increases autonomic nervous system arousal, and weakens the immune system, which in turn contributes to the development of diseases. Sirois [ 9 ] distinguishes between short-term and long-term effects of procrastination on health mediated by stress. She believes that, in the short term, single incidents of procrastination cause acute stress, which leads to acute health problems, such as infections or headaches. In the long term, chronic procrastination, as you would expect with trait procrastination, causes chronic stress, which leads to chronic diseases over time. There is some evidence in support of the stress-related pathway, particularly regarding short-term effects [ 3 , 17 , 26 , 27 , 28 ]. However, as we mentioned above, most of these studies are cross-sectional. Therefore, the causal direction of these effects remains unclear. To our knowledge, long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress have not yet been investigated.

The second mediated pathway leads from trait procrastination to (chronic) disease via poor health-related behavior. According to Sirois [ 9 ], procrastinators form lower intentions to carry out health-promoting behavior or to refrain from health-damaging behavior because they have a low self-efficacy of being able to care for their own health. In addition, they lack the far-sighted view that the effects of health-related behavior only become apparent in the long term. For the same reason, Sirois [ 9 ] believes that there are no short-term, but only long-term effects of procrastination on health mediated by poor health-related behavior. For example, an unhealthy diet leads to diabetes over time. The findings of studies examining the behavioral pathway are inconclusive [ 3 , 17 , 26 , 28 ]. Furthermore, since most of these studies are cross-sectional, they are not suitable for uncovering long-term effects of trait procrastination on (chronic) disease mediated by poor health-related behavior.

In summary, previous research on the two mediated pathways of the procrastination-health model mainly found support for the role of (chronic) stress in the relationship between trait procrastination and (chronic) disease. However, only short-term effects have been investigated so far. Moreover, longitudinal studies are needed to be able to assess the causal direction of the relationship between trait procrastination, (chronic) stress, and (chronic) disease. Consequently, our study is the first to examine long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, using a longitudinal design. (Chronic) disease could be measured by a variety of different indicators (e.g., physical symptoms, diabetes, or coronary heart disease). We choose depression and anxiety symptoms as indicators for (chronic) disease because they signal mental health complaints before they manifest as (chronic) diseases. Additionally, depression and anxiety symptoms are two of the most common mental health complaints among students [ 29 , 30 ] and procrastination has been shown to be a significant predictor of depression and anxiety symptoms [ 18 , 31 , 32 , 33 , 34 ]. Until now, the stress-related pathway of the procrastination-health model with depression and anxiety symptoms as the health outcome has only been analyzed in one cross-sectional study that confirmed the predictions of the model [ 35 ].

The aim of our study is to evaluate some of the key assumptions of the procrastination-health model, particularly the relationships between trait procrastination, (chronic) stress, and (chronic) disease over time, surveyed in the following analysis using depression and anxiety symptoms.

In line with the key assumptions of the procrastination-health model, we postulate (see Fig.  2 ):

Procrastination leads to perceived stress over time.

Perceived stress leads to depression and anxiety symptoms over time.

Procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress.

The section of the procrastination-health model we examined

Materials and methods

Our study was part of a health monitoring at a large German university Footnote 1 . Ethical approval for our study was granted by the Ethics Committee of the university’s Department of Education and Psychology. We collected the initial data in 2019. Two occasions followed, each at an interval of six months. In January 2019, we sent out 33,267 invitations to student e-mail addresses. Before beginning the survey, students provided their written informed consent to participate in our study. 3,420 students took part at the first occasion (T1; 10% response rate). Of these, 862 participated at the second (T2) and 392 at the third occasion (T3). In order to test whether dropout was selective, we compared sociodemographic and study specific characteristics (age, gender, academic semester, number of assessments/exams) as well as behavior and health-related variables (procrastination, perceived stress, depression and anxiety symptoms) between the participants of the first wave ( n  = 3,420) and those who participated three times ( n  = 392). Results from independent-samples t-tests and chi-square analysis showed no significant differences regarding sociodemographic and study specific characteristics (see Additional file 1: Table S1 and S2 ). Regarding behavior and health-related variables, independent-samples t-tests revealed a significant difference in procrastination between the two groups ( t (3,409) = 2.08, p  < .05). The mean score of procrastination was lower in the group that participated in all three waves.

The mean age of the longitudinal respondents was 24.1 years ( SD  = 5.5 years), the youngest participants were 17 years old, the oldest one was 59 years old. The majority of participants was female (74.0%), 7 participants identified neither as male nor as female (1.8%). The respondents were on average enrolled in the third year of studying ( M  = 3.9; SD  = 2.3). On average, the students worked about 31.2 h ( SD  = 14.1) per week for their studies, and an additional 8.5 h ( SD  = 8.5) for their (part-time) jobs. The average income was €851 ( SD  = 406), and 4.9% of the students had at least one child. The students were mostly enrolled in philosophy and humanities (16.5%), education and psychology (15.8%), biology, chemistry, and pharmacy (12.5%), political and social sciences (10.6%), veterinary medicine (8.9%), and mathematics and computer science (7.7%).

We only used established and well evaluated instruments for our analyses.

• Procrastination

We adopted the short form of the Procrastination Questionnaire for Students (PFS-4) [ 36 ] to measure procrastination. The PFS-4 assesses procrastination at university as a largely stable behavioral disposition across situations, that is, as a trait. The questionnaire consists of four items (e.g., I put off starting tasks until the last moment.). Each item was rated on a 5-point scale ((almost) never = 1 to (almost) always = 5) for the last two weeks. All items were averaged, with higher scores indicating a greater tendency to procrastinate. The PFS-4 has been proven to be reliable and valid, showing very high correlations with other established trait procrastination scales, for example, with the German short form of the General Procrastination Scale [ 37 , 38 ]. We also proved the scale to be one-dimensional in a factor analysis, with a Cronbach’s alpha of 0.90.

Perceived stress

The Heidelberger Stress Index (HEI-STRESS) [ 39 ] is a three-item measure of current perceived stress due to studying as well as in life in general. For the first item, respondents enter a number between 0 (not stressed at all) and 100 (completely stressed) to indicate how stressed their studies have made them feel over the last four weeks. For the second and third item, respondents rate on a 5-point scale how often they feel “stressed and tense” and as how stressful they would describe their life at the moment. We transformed the second and third item to match the range of the first item before we averaged all items into a single score with higher values indicating greater perceived stress. We proved the scale to be one-dimensional and Cronbach’s alpha for our study was 0.86.

Depression and anxiety symptoms

We used the Patient Health Questionnaire-4 (PHQ-4) [ 40 ], a short form of the Patient Health Questionnaire [ 41 ] with four items, to measure depression and anxiety symptoms. The PHQ-4 contains two items from the Patient Health Questionnaire-2 (PHQ-2) [ 42 ] and the Generalized Anxiety Disorder Scale-2 (GAD-2) [ 43 ], respectively. It is a well-established screening scale designed to assess the core criteria of major depressive disorder (PHQ-2) and generalized anxiety disorder (GAD-2) according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). However, it was shown that the GAD-2 is also appropriate for screening other anxiety disorders. According to Kroenke et al. [ 40 ], the PHQ-4 can be used to assess a person’s symptom burden and impairment. We asked the participants to rate how often they have been bothered over the last two weeks by problems, such as “Little interest or pleasure in doing things”. Response options were 0 = not at all, 1 = several days, 2 = more than half the days, and 3 = nearly every day. Calculated as the sum of the four items, the total scores range from 0 to 12 with higher scores indicating more frequent depression and anxiety symptoms. The total scores can be categorized as none-to-minimal (0–2), mild (3–5), moderate (6–8), and severe (9–12) depression and anxiety symptoms. The PHQ-4 was shown to be reliable and valid [ 40 , 44 , 45 ]. We also proved the scale to be one-dimensional in a factor analysis, with a Cronbach’s alpha of 0.86.

Data analysis

To test our hypotheses, we performed structural equation modelling (SEM) using R (Version 4.1.1) with the package lavaan. All items were standardized ( M  = 0, SD  = 1). Due to the non-normality of some study variables and a sufficiently large sample size of N near to 400 [ 46 ], we used robust maximum likelihood estimation (MLR) for all model estimations. As recommended by Hu and Bentler [ 47 ], we assessed the models’ goodness of fit by chi-square test statistic, root mean square error of approximation (RMSEA), standardized root mean square residual (SRMR), Tucker-Lewis index (TLI), and comparative fit index (CFI). A non-significant chi-square indicates good model fit. Since chi-square is sensitive to sample size, we also evaluated fit indices less sensitive to the number of observations. RMSEA and SRMR values of 0.05 or lower as well as TLI and CFI values of 0.97 or higher indicate good model fit. RMSEA values of 0.08 or lower, SRMR values of 0.10 or lower, as well as TLI and CFI values of 0.95 or higher indicate acceptable model fit [ 48 , 49 ]. First, we conducted confirmatory factor analysis for the first occasion, defining three factors that correspond to the measures of procrastination, perceived stress, and depression and anxiety symptoms. Next, we tested for measurements invariance over time and specified the measurement model, before testing our hypotheses.

Measurement invariance over time

To test for measurement invariance over time, we defined one latent variable for each of the three occasions, corresponding to the measures of procrastination, perceived stress, and depression and anxiety symptoms, respectively. As recommended by Geiser and colleagues [ 50 ], the links between indicators and factors (i.e., factor loadings and intercepts) should be equal over measurement occasions; therefore, we added indicator specific factors. A first and least stringent step of testing measurement invariance is configural invariance (M CI ). It was examined whether the included constructs (procrastination, perceived stress, depression and anxiety symptoms) have the same pattern of free and fixed loadings over time. This means that the assignment of the indicators to the three latent factors over time is supported by the underlying data. If configural invariance was supported, restrictions for the next step of testing measurement invariance (metric or weak invariance; M MI ) were added. This means that each item contributes to the latent construct to a similar degree over time. Metric invariance was tested by constraining the factor loadings of the constructs over time. The next step of testing measurement invariance (scalar or strong invariance; M SI ) consisted of checking whether mean differences in the latent construct capture all mean differences in the shared variance of the items. Scalar invariance was tested by constraining the item intercepts over time. The constraints applied in the metric invariance model were retained [ 51 ]. For the last step of testing measurement invariance (residual or strict invariance; M RI ), the residual variables were also set equal over time. If residual invariance is supported, differences in the observed variables can exclusively be attributed to differences in the variances of the latent variables.

We used the Satorra-Bentler chi-square difference test to evaluate the superiority of a more stringent model [ 52 ]. We assumed the model with the largest number of invariance restrictions – which still has an acceptable fit and no substantial deterioration of the chi-square value – to be the final model [ 53 ]. Following previous recommendations, we considered a decrease in CFI of 0.01 and an increase in RMSEA of 0.015 as unacceptable to establish measurement invariance [ 54 ]. If a more stringent model had a significant worse chi-square value, but the model fit was still acceptable and the deterioration in model fit fell within the change criteria recommended for CFI and RMSEA values, we still considered the more stringent model to be superior.

Hypotheses testing

As recommended by Dormann et al. [ 55 ], we applied autoregressive time-lagged panel models to test our hypotheses. In the first step, we specified a model (M 0 ) that only included the stabilities of the three variables (procrastination, perceived stress, depression and anxiety symptoms) over time. In the next step (M 1 ), we added the time-lagged effects from procrastination (T1) to perceived stress (T2) and from procrastination (T2) to perceived stress (T3) as well as from perceived stress (T1) to depression and anxiety symptoms (T2) and from perceived stress (T2) to depression and anxiety symptoms (T3). Additionally, we included a direct path from procrastination (T1) to depression and anxiety symptoms (T3). If this path becomes significant, we can assume a partial mediation [ 55 ]. Otherwise, we can assume a full mediation. We compared these nested models using the Satorra-Bentler chi-square difference test and the Akaike information criterion (AIC). The chi-square difference value should either be non-significant, indicating that the proposed model including our hypotheses (M 1 ) does not have a significant worse model fit than the model including only stabilities (M 0 ), or, if significant, it should be in the direction that M 1 fits the data better than M 0 . Regarding the AIC, M 1 should have a lower value than M 0 .

Table  1 displays the means, standard deviations, internal consistencies (Cronbach’s alpha), and stabilities (correlations) of all study variables. The alpha values of procrastination, perceived stress, and depression and anxiety symptoms are classified as good (> 0.80) [ 56 ]. The correlation matrix of the manifest variables used for the analyses can be found in the Additional file 1: Table  S3 .

We observed the highest test-retest reliabilities for procrastination ( r  ≥ .74). The test-retest reliabilities for depression and anxiety symptoms ( r  ≥ .64) and for perceived stress ( r  ≥ .54) were a bit lower (see Table  1 ). The pattern of correlations shows a medium to large but positive relationship between procrastination and depression and anxiety symptoms [ 57 , 58 ]. The association between procrastination and perceived stress was small, the one between perceived stress and depression and anxiety symptoms very large (see Table  1 ).

Confirmatory factor analysis showed an acceptable to good fit (x 2 (41) = 118.618, p  < .001; SRMR = 0.042; RMSEA = 0.071; TLI = 0.95; CFI = 0.97). When testing for measurement invariance over time for each construct, the residual invariance models with indicator specific factors provided good fit to the data (M RI ; see Table  2 ), suggesting that differences in the observed variables can exclusively be attributed to differences of the latent variables. We then specified and tested the measurement model of the latent constructs prior to model testing based on the items of procrastination, perceived stress, and depression and anxiety symptoms. The measurement model fitted the data well (M M ; see Table  3 ). All items loaded solidly on their respective factors (0.791 ≤ β ≤ 0.987; p  < .001).

To test our hypotheses, we analyzed the two models described in the methods section.

The fit of the stability model (M 0 ) was acceptable (see Table  3 ). Procrastination was stable over time, with stabilities above 0.82. The stabilities of perceived stress as well as depression and anxiety symptoms were somewhat lower, ranging from 0.559 (T1 -> T2) to 0.696 (T2 -> T3) for perceived stress and from 0.713 (T2 -> T3) to 0.770 (T1 -> T2) for depression and anxiety symptoms, respectively.

The autoregressive mediation model (M 1 ) fitted the data significantly better than M 0 . The direct path from procrastination (T1) to depression and anxiety symptoms (T3) was significant (β = 0.16; p  < .001), however, none of the mediated paths (from procrastination (T1) to perceived stress (T2) and from perceived stress (T2) to depression and anxiety symptoms (T3)) proved to be substantial. Also, the time-lagged paths from perceived stress (T1) to depression and anxiety symptoms (T2) and from procrastination (T2) to perceived stress (T3) were not substantial either (see Fig.  3 ).

To examine whether the hypothesized effects would occur over a one-year period rather than a six-months period, we specified an additional model with paths from procrastination (T1) to perceived stress (T3) and from perceived stress (T1) to depression and anxiety symptoms (T3), also including the stabilities of the three constructs as in the stability model M 0 . The model showed an acceptable fit (χ 2 (486) = 831.281, p  < .001; RMSEA = 0.048; SRMR = 0.091; TLI = 0.95; CFI = 0.95), but neither of the two paths were significant.

Therefore, our hypotheses, that procrastination leads to perceived stress over time (H1) and that perceived stress leads to depression and anxiety symptoms over time (H2) must be rejected. We could only partially confirm our third hypothesis, that procrastination leads to depression and anxiety over time, mediated by perceived stress (H3), since procrastination did lead to depression and anxiety symptoms over time. However, this effect was not mediated by perceived stress.

Results of the estimated model including all hypotheses (M 1 ). Note Non-significant paths are dotted. T1 = time 1; T2 = time 2; T3 = time 3. *** p  < .001

To sum up, we tried to examine the harmful consequences of procrastination on students’ stress and mental health. Hence, we selected the procrastination-health model by Sirois [ 9 ] as a theoretical foundation and tried to evaluate some of its key assumptions in a temporal perspective. The author assumes that trait procrastination leads to (chronic) disease via (chronic) stress. We chose depression and anxiety symptoms as indicators for (chronic) disease and postulated, in line with the key assumptions of the procrastination-health model, that procrastination leads to perceived stress over time (H1), that perceived stress leads to depression and anxiety symptoms over time (H2), and that procrastination leads to depression and anxiety symptoms over time, mediated by perceived stress (H3). To examine these relationships properly, we collected longitudinal data from students at three occasions over a one-year period and analyzed the data using autoregressive time-lagged panel models. Our first and second hypotheses had to be rejected: Procrastination did not lead to perceived stress over time, and perceived stress did not lead to depression and anxiety symptoms over time. However, procrastination did lead to depression and anxiety symptoms over time – which is in line with our third hypothesis – but perceived stress was not a mediator of this effect. Therefore, we could only partially confirm our third hypothesis.

Our results contradict previous studies on the stress-related pathway of the procrastination-health model, which consistently found support for the role of (chronic) stress in the relationship between trait procrastination and (chronic) disease. Since most of these studies were cross-sectional, though, the causal direction of these effects remained uncertain. There are two longitudinal studies that confirm the stress-related pathway of the procrastination-health model [ 27 , 28 ], but both studies examined short-term effects (≤ 3 months), whereas we focused on more long-term effects. Therefore, the divergent findings may indicate that there are short-term, but no long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress.

Our results especially raise the question whether trait procrastination leads to (chronic) stress in the long term. Looking at previous longitudinal studies on the effect of procrastination on stress, the following stands out: At shorter study periods of two weeks [ 27 ] and four weeks [ 28 ], the effect of procrastination on stress appears to be present. At longer study periods of seven weeks [ 59 ], three months [ 28 ], six months, and twelve months, as in our study, the effect of procrastination on stress does not appear to be present. There is one longitudinal study in which procrastination was a significant predictor of stress symptoms nine months later [ 34 ]. The results of this study should be interpreted with caution, though, because the outbreak of the COVID-19 pandemic fell within the study period, which could have contributed to increased stress symptoms [ 60 ]. Unfortunately, Johansson et al. [ 34 ] did not report whether average stress symptoms increased during their study. In one of the two studies conducted by Fincham and May [ 59 ], the COVID-19 pandemic outbreak also fell within their seven-week study period. However, they reported that in their study, average stress symptoms did not increase from baseline to follow-up. Taken together, the findings suggest that procrastination can cause acute stress in the short term, for example during times when many tasks need to be completed, such as at the end of a semester, but that procrastination does not lead to chronic stress over time. It seems possible that students are able to recover during the semester from the stress their procrastination caused at the end of the previous semester. Because of their procrastination, they may also have more time to engage in relaxing activities, which could further mitigate the effect of procrastination on stress. Our conclusions are supported by an early and well-known longitudinal study by Tice and Baumeister [ 61 ], which compared procrastinating and non-procrastinating students with regard to their health. They found that procrastinators experienced less stress than their non-procrastinating peers at the beginning of the semester, but more at the end of the semester. Additionally, our conclusions are in line with an interview study in which university students were asked about the consequences of their procrastination [ 62 ]. The students reported that, due to their procrastination, they experience high levels of stress during periods with heavy workloads (e.g., before deadlines or exams). However, the stress does not last, instead, it is relieved immediately after these periods.

Even though research indicates, in line with the assumptions of the procrastination-health model, that stress is a risk factor for physical and mental disorders [ 63 , 64 , 65 , 66 ], perceived stress did not have a significant effect on depression and anxiety symptoms in our study. The relationship between stress and mental health is complex, as people respond to stress in many different ways. While some develop stress-related mental disorders, others experience mild psychological symptoms or no symptoms at all [ 67 ]. This can be explained with the help of vulnerability-stress models. According to vulnerability-stress models, mental illnesses emerge from an interaction of vulnerabilities (e.g., genetic factors, difficult family backgrounds, or weak coping abilities) and stress (e.g., minor or major life events or daily hassles) [ 68 , 69 ]. The stress perceived by the students in our sample may not be sufficient enough on its own, without the presence of other risk factors, to cause depression and anxiety symptoms. However, since we did not assess individual vulnerability and stress factors in our study, these considerations are mere speculation.

In our study, procrastination led to depression and anxiety symptoms over time, which is consistent with the procrastination-health model as well as previous cross-sectional and longitudinal evidence [ 18 , 21 , 31 , 32 , 33 , 34 ]. However, it is still unclear by which mechanisms this effect is mediated, as perceived stress did not prove to be a substantial mediator in our study. One possible mechanism would be that procrastination impairs affective well-being [ 70 ] and creates negative feelings, such as shame, guilt, regret, and anger [ 20 , 21 , 22 , 62 , 71 ], which in turn could lead to depression and anxiety symptoms [ 23 , 24 , 25 ]. Other potential mediators of the relationship between procrastination and depression and anxiety symptoms emerge from the behavioral pathway of the procrastination-health model, suggesting that poor health-related behaviors mediate the effect of trait procrastination on (chronic) disease. Although evidence for this is still scarce, the results of one cross-sectional study, for example, indicate that poor sleep quality might mediate the effect of procrastination on depression and anxiety symptoms [ 35 ].

In summary, we found that procrastination leads to depression and anxiety symptoms over time and that perceived stress is not a mediator of this effect. We could not show that procrastination leads to perceived stress over time, nor that perceived stress leads to depression and anxiety symptoms over time. For the most part, the relationships between procrastination, perceived stress, and depression and anxiety symptoms did not match the relationships between trait procrastination, (chronic) stress, and (chronic) disease as assumed in the procrastination-health model. Explanations for this could be that procrastination might only lead to perceived stress in the short term, for example, during preparations for end-of-semester exams, and that perceived stress may not be sufficient enough on its own, without the presence of other risk factors, to cause depression and anxiety symptoms. In conclusion, we could not confirm long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, as assumed for the stress-related pathway of the procrastination-health model.

Limitations and suggestions for future research

In our study, we tried to draw causal conclusions about the harmful consequences of procrastination on students’ stress and mental health. However, since procrastination is a trait that cannot be manipulated experimentally, we have conducted an observational rather than an experimental study, which makes causal inferences more difficult. Nonetheless, a major strength of our study is that we used a longitudinal design with three waves. This made it possible to draw conclusions about the causal direction of the effects, as in hardly any other study targeting consequences of procrastination on health before [ 4 , 28 , 55 ]. Therefore, we strongly recommend using a similar longitudinal design in future studies on the procrastination-health model or on consequences of procrastination on health in general.

We chose a time lag of six months between each of the three measurement occasions to examine long-term effects of procrastination on depression and anxiety symptoms mediated by perceived stress. However, more than six months may be necessary for the hypothesized effects to occur [ 72 ]. The fact that the temporal stabilities of the examined constructs were moderate or high (0.559 ≤ β ≤ 0.854) [ 73 , 74 ] also suggests that the time lags may have been too short. The larger the time lag, the lower the temporal stabilities, as shown for depression and anxiety symptoms, for example [ 75 ]. High temporal stabilities make it more difficult to detect an effect that actually exists [ 76 ]. Nonetheless, Dormann and Griffin [ 77 ] recommend using shorter time lags of less than one year, even with high stabilities, because of other influential factors, such as unmeasured third variables. Therefore, our time lags of six months seem appropriate.

It should be discussed, though, whether it is possible to detect long-term effects of the stress-related pathway of the procrastination-health model within a total study period of one year. Sirois [ 9 ] distinguishes between short-term and long-term effects of procrastination on health mediated by stress, but does not address how long it might take for long-term effects to occur or when effects can be considered long-term instead of short-term. The fact that an effect of procrastination on stress is evident at shorter study periods of four weeks or less but in most cases not at longer study periods of seven weeks or more, as we mentioned earlier, could indicate that short-term effects occur within the time frame of one to three months, considering the entire stress-related pathway. Hence, it seems appropriate to assume that we have examined rather long-term effects, given our study period of six and twelve months. Nevertheless, it would be beneficial to use varying study periods in future studies, in order to be able to determine when effects can be considered long-term.

Concerning long-term effects of the stress-related pathway, Sirois [ 9 ] assumes that chronic procrastination causes chronic stress, which leads to chronic diseases over time. The term “chronic stress” refers to prolonged stress episodes associated with permanent tension. The instrument we used captures perceived stress over the last four weeks. Even though the perceived stress of the students in our sample was relatively stable (0.559 ≤ β ≤ 0.696), we do not know how much fluctuation occurred between each of the three occasions. However, there is some evidence suggesting that perceived stress is strongly associated with chronic stress [ 78 ]. Thus, it seems acceptable that we used perceived stress as an indicator for chronic stress in our study. For future studies, we still suggest the use of an instrument that can more accurately reflect chronic stress, for example, the Trier Inventory for Chronic Stress (TICS) [ 79 ].

It is also possible that the occasions were inconveniently chosen, as they all took place in a critical academic period near the end of the semester, just before the examination period began. We chose a similar period in the semester for each occasion for the sake of comparability. However, it is possible that, during this preparation periods, stress levels peaked and procrastinators procrastinated less because they had to catch up after delaying their work. This could have introduced bias to the data. Therefore, in future studies, investigation periods should be chosen that are closer to the beginning or in the middle of a semester.

Furthermore, Sirois [ 9 ] did not really explain her understanding of “chronic disease”. However, it seems clear that physical illnesses, such as diabetes or cardiovascular diseases, are meant. Depression and anxiety symptoms, which we chose as indicators for chronic disease, represent mental health complaints that do not have to be at the level of a major depressive disorder or an anxiety disorder, in terms of their quantity, intensity, or duration [ 40 ]. But they can be viewed as precursors to a major depressive disorder or an anxiety disorder. Therefore, given our study period of one year, it seems appropriate to use depression and anxiety symptoms as indicators for chronic disease. At longer study periods, we would expect these mental health complaints to manifest as mental disorders. Moreover, the procrastination-health model was originally designed to be applied to physical diseases [ 3 ]. Perhaps, the model assumptions are more applicable to physical diseases than to mental disorders. By applying parts of the model to mental health complaints, we have taken an important step towards finding out whether the model is applicable to mental disorders as well. Future studies should examine additional long-term health outcomes, both physical and psychological. This would help to determine whether trait procrastination has varying effects on different diseases over time. Furthermore, we suggest including individual vulnerability and stress factors in future studies in order to be able to analyze the effect of (chronic) stress on (chronic) diseases in a more differentiated way.

Regarding our sample, 3,420 students took part at the first occasion, but only 392 participated three times, which results in a dropout rate of 88.5%. At the second and third occasion, invitation e-mails were only sent to participants who had indicated at the previous occasion that they would be willing to participate in a repeat survey and provided their e-mail address. This is probably one of the main reasons for our high dropout rate. Other reasons could be that the students did not receive any incentives for participating in our study and that some may have graduated between the occasions. Selective dropout analysis revealed that the mean score of procrastination was lower in the group that participated in all three waves ( n  = 392) compared to the group that participated in the first wave ( n  = 3,420). One reason for this could be that those who have a higher tendency to procrastinate were more likely to procrastinate on filling out our survey at the second and third occasion. The findings of our dropout analysis should be kept in mind when interpreting our results, as lower levels of procrastination may have eliminated an effect on perceived stress or on depression and anxiety symptoms. Additionally, across all age groups in population-representative samples, the student age group reports having the best subjective health [ 80 ]. Therefore, it is possible that they are more resilient to stress and experience less impairment of well-being than other age groups. Hence, we recommend that future studies focus on other age groups as well.

It is generally assumed that procrastination leads to lower academic performance, health impairment, and poor health-related behavior. However, evidence for negative consequences of procrastination is still limited and it is also unclear by which mechanisms they are mediated. In consequence, the aim of our study was to examine the effect of procrastination on mental health over time and stress as a possible facilitator of this relationship. We selected the procrastination-health model as a theoretical foundation and used the stress-related pathway of the model, assuming that trait procrastination leads to (chronic) disease via (chronic) stress. We chose depression and anxiety symptoms as indicators for (chronic) disease and collected longitudinal data from students at three occasions over a one-year period. This allowed us to draw conclusions about the causal direction of the effects, as in hardly any other study examining consequences of procrastination on (mental) health before. Our results indicate that procrastination leads to depression and anxiety symptoms over time and that perceived stress is not a mediator of this effect. We could not show that procrastination leads to perceived stress over time, nor that perceived stress leads to depression and anxiety symptoms over time. Explanations for this could be that procrastination might only lead to perceived stress in the short term, for example, during preparations for end-of-semester exams, and that perceived stress may not be sufficient on its own, that is, without the presence of other risk factors, to cause depression and anxiety symptoms. Overall, we could not confirm long-term effects of trait procrastination on (chronic) disease mediated by (chronic) stress, as assumed for the stress-related pathway of the procrastination-health model. Our study emphasizes the importance of identifying the consequences procrastination can have on health and well-being and determining by which mechanisms they are mediated. Only then will it be possible to develop interventions that can prevent negative health consequences of procrastination. Further health outcomes and possible mediators should be explored in future studies, using a similar longitudinal design.

Data availability

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

University Health Report at Freie Universität Berlin.

Abbreviations

Comparative fit index

Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

Generalized Anxiety Disorder Scale-2

Heidelberger Stress Index

Hypothalamic-pituitary-adrenocortical

Robust maximum likelihood estimation

Short form of the Procrastination Questionnaire for Students

Patient Health Questionnaire-2

Patient Health Questionnaire-4

Root mean square error of approximation

Structural equation modeling

Standardized root mean square residual

Tucker-Lewis index

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Conceptualization: A.J., B.G., T.L.; methodology: B.G., A.J.; validation: B.G.; formal analysis: A.J., B.G.; investigation: C.W., T.L., B.G.; data curation: C.W., T.L., B.G.; writing–original draft preparation: A.J., B.G.; writing–review and editing: A.J., T.L., B.G., C.W.; visualization: A.J., B.G.; supervision: B.G., T.L.; project administration: C.W., T.L., B.G.; All authors contributed to the article and approved the submitted version.

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Jochmann, A., Gusy, B., Lesener, T. et al. Procrastination, depression and anxiety symptoms in university students: a three-wave longitudinal study on the mediating role of perceived stress. BMC Psychol 12 , 276 (2024). https://doi.org/10.1186/s40359-024-01761-2

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Is academic anxiety good or bad for students? Investigating the moderating effects of anxiety on the reciprocal relations between self-efficacy and achievement in mathematics

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• Shu-Ling Peng 1 , 2 ,
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This longitudinal research, grounded in Bandura’s social cognitive theory, examined the cross-lagged relations between mathematics self-efficacy (MSE) and mathematics achievement (MACH), and tested how mathematics anxiety (MA) moderated these relations. Data from 777 Taiwanese seventh-graders on MSE, MA, and MACH were collected at multiple points throughout a school year. Structural equational modeling showed that (a) MSE and MACH were bidirectionally related over time, and (b) MA moderated the reciprocal relations between MSE and MACH. Specifically, lower MA amplified the positive association of MSE on MACH (i.e., students with lower MA and higher MSE scored higher in MACH), while higher MA attenuated the positive association. Conversely, higher MA boosted the positive association of MACH on MSE (i.e., students with higher MA and higher MACH reported higher MSE), whereas there was no such pattern among students with lower MA. These findings corroborate the reciprocal MSE-MACH interplay and underscore MA’s intriguing role in shaping learning trajectories depending on pathways between MSE and MACH.

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Data availability.

The data that support the findings of this study are available from the corresponding author upon request.

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Students Anxiety Experiences in Higher Education Institutions

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Students studying at higher education institutions face many challenges. Students who attempt to overcome these challenges may alter their behaviors. This may negatively affect their psychological state and cause them to feel anxiety. Anxiety is most prominent among college students. Many students face anxiety when they think they cannot achieve their academic or non-academic purposes; however, sometimes anxiety can encourage students to think more critically about how to achieve their goals. Students cope with anxiety in different ways, but some may struggle. This probably causes many symptoms that affect their mental health. Therefore, they should alleviate the anxiety to keep their mental health and persist in the institution.

• faculty members
• colleges’ students

Author Information

Nabila y. alkandari *.

• Kuwait University, Kuwait

*Address all correspondence to: [email protected]

1. Introduction

College students’ mental health plays a very important role in their success and persistence at their institution [ 1 ]. When the students can manage their anxiety, they feel less pressure. If the students remain anxious, they may not follow through with their academic studies. Also, anxiety could complicate their physical and psychological states and persist after graduation, and it may negatively affect their capacity to work in the future [ 2 ]. Therefore, it is important to understand the factors that cause students’ anxiety at higher education institutions.

To achieve the purposes of the study which focused to present the factors that cause students’ anxiety in higher education institutions and find the solutions to this issue, a literature review was used to describe students’ perception of anxiety feelings while they study in higher education institutions. Many studies that described students’ perceptions from different countries were used to present this issue.

2.1 Data analysis

The researcher collected the researches that focus on students’ anxiety and grouped them in different subcategories and explained each category based on the availability of recent researches that belong to the topic and the experience of the researcher. Although there were many factors that may cause students’ anxiety, the researcher focused on some critical factors that presented in most higher education institutions in recent years.

3. The factors that cause students’ anxiety at higher education institutions

3.1 studying a new language.

Studying a new language is a major factor that causes anxiety among many students, especially those who follow their studies in a non-native language. Most students in higher education institutions study English as a foreign language or as a second language. Some of them may learn English before college. However, they may not able to meet the high level of English proficiency required at the college level. They face anxiety because if they cannot pass successfully, they may not be able to graduate from the institution.

Tian [ 3 ] indicates Korean students face language anxiety when they must make presentations in class, and they lack language proficiency. Moreover, they feel anxiety because of their peers’ negative feedback. Charoensukmongkol [ 4 ] also indicates Thai students face language anxiety because they lack proficiency in the English language. Tsai and Lee [ 5 ] found Taiwanese students have anxiety when reading English because they do not understand the English vocabulary, the topics, or the long and complicated text structures, and they fear they will make mistakes when answering questions.

Jawas [ 6 ] found Indonesian students also have English language anxiety because they lack proficiency in writing, and the instructors present them with class essay assignments that probably increase their anxiety because of the limited time in class to finish the assignment with the appropriate answer. Also, some Indonesian students have anxiety when giving a presentation. Some of them may not prepare well and do not like when someone asks them difficult questions about the presentation [ 7 ].

Abdala and Elnadeef [ 8 ] also indicate that Saudi Arabian students feel anxious about learning the English language specifically when they make mistakes in the class and lack writing proficiency. They are scared when they hear unwanted comments and jokes from their classmates while they are speaking. Some other students face anxiety in learning the Chinese language [ 9 ]. The Chinese language is not easy to learn, specifically in speaking and writing. Also, some Hispanic students have difficulties when studying Spanish, specifically in writing, spelling words, and using conjunctions [ 10 ].

3.2 The curriculum difficulty

Curriculum difficulties are also considered a factor that causes students’ anxiety in most higher education institutions. Some students cannot understand the curriculum and struggle to answer questions in the classroom, do assignments, and write research papers or reports. Some students may hesitate to ask the instructors for help; therefore, they become anxious because they are unable to meet the course requirements. Some difficult curriculums include mathematics [ 11 , 12 , 13 ], biology [ 14 , 15 ], statistics [ 16 ], chemistry, physics, history [ 17 ], and law [ 18 ]. In addition, there are several medical specialization classes that cause students’ anxiety, such as dental and medical [ 19 ], medical and engineering [ 20 ], dentistry and veterinary [ 21 ], and pharmacy [ 22 ].

3.3 The difficulty in exams

Some students face anxiety with midterm and final exams because they are not well prepared. Some students may not understand the exam questions and write the wrong answers as a result [ 17 ]. More importantly, sometimes there is not enough time to answer the exam questions. Some faculty members do not know the differences in the students’ abilities, and they make exams that do not fit with some students’ intelligence. As a result, students may fail the exams. Exam anxiety is widespread among college students [ 23 , 24 ] in different majors like math, history, geography, chemistry, physics, engineering, arts, and music. For example, some students specializing in music have anxiety in exams where they must use a musical instrument [ 25 ]. Also, some pharmacy students have anxiety about their clinical exams [ 22 ].

Some students also face anxiety about online exams because some faculty members make the exams with a time limit, and some students may need more time. Also, the computer may not work well or Internet services may shutdown while students are taking the online exam. As a result, some students may not prefer online exams.

3.4 Financial pressure

Financial anxiety is considered an issue many students face when studying at higher education intuitions because they are responsible for paying for their studies, especially those at private institutions. Some of them may be in debt. This financial pressure leads students to feel anxiety, which causes them psychological distress [ 26 ] that results in a lower grade point average (GPA). Some students who face difficulties in understanding difficult subjects depend on costly private instructors. Students also need money for daily expenses such as food and transportation. When students feel they do not have enough money, they may be anxious and ask friends for money, which negatively affects students’ feelings, leading to stress and anxiety.

3.5 Culture shock

Many international students suffer from alienation and culture shock when they seek to continue studying in other countries. This shock causes them psychological crises such as anxiety because of the distance from their family and country, the difficulty of a new language and speaking with others, and the difficulty of adapting to the new culture, which requires patience.

3.6 Family responsibilities

Some university students have many family problems that cause them anxiety while they are studying. One of these problems is the illness of a family member, such as a parent, which requires the student to remain close to the patient. If the students are they have responsibility of the children and the husband. These problems affect the student academically because it hinders him in preparing well for tests, performing academic duties, and attending lectures, which causes him concern about how to balance his family and academic responsibilities.

3.7 Illness

Some students who suffer from a chronic illness have anxiety [ 27 ]. For example, a student who suffers diabetes is required to take daily insulin injections and go to the health center, and some may experience dizziness while at the university campus. Diabetes may hinder a student if his health condition relapses, and he may be absent from the university. Other illnesses include cancer, anemia, heart problems, asthma, and obesity. These diseases may negatively affect the academic level of the students, which causes them anxiety.

3.8 Employment

Some students who attend higher education institutions work off campus part or full time. Some of them may not be able to attend classes, specifically if they work in the mornings and their classes are scheduled for the same time. Some of them face anxiety if they cannot manage their time. They may fail their courses because of their absences or fail exams because they do not prepare well. As a result, they receive a lower GPA and an academic warning.

3.9 Discrimination

Discrimination is a concern that many students face at higher education institutions. The institutions gather students and staff from different backgrounds, nationalities, ethnicities, religions, colors, cultures, and levels of intelligence. Some students feel anxiety because of unfair treatment from classmates, friends, and staff and faculty members. When the students feel they are not welcome and they are treated badly, this causes them to feel anxiety [ 28 , 29 ].

3.10 Disabilities

Some disabled students enter higher education institutions to further their studies, despite the challenges they face. Some disabled students have anxiety because they are unable to major in some specializations they may need. Some specializations such as geology and biology need students to participate in course activities using labs and instruments, which is difficult for some disabled students. Also, they may not be able to socialize with other students and may prefer isolation. Some students are visually impaired and require specific resources like certain textbooks, handouts, and software [ 30 ]. However, these resources are not available for some courses. Also, some students may have psychiatric disabilities [ 31 ] and need special attention from the mental health center to help them succeed academically. Some students who have autism spectrum disorder have anxiety when trying to achieve success in their academic studies [ 32 ].

4. Anxiety symptoms

When students have anxiety, it affects their mind and body and causes several symptoms: cognitive, physical, and emotional.

4.1 Cognitive symptoms

Many students could experience symptoms along with their feelings of anxiety. They may have many negative and painful experiences, such as insomnia and other sleep disorders. Anxiety also negatively affects students’ memories. They may become unable to think correctly and make decisions; therefore they cannot participate in classroom discussions and cannot share their opinions and ideas with other classmates and faculty members. Some of them feel sadness, fear, and panic. As a result, their academic achievements are negatively affected.

4.2 Physical symptoms

Students with anxiety can experience significant pain with symptoms like breathing problems, stomachaches, headaches, joint and muscle pain, and fatigue. These symptoms could make them unable to come to the university. Some students may fear from the faculty, so they upsent from the institution. Some students become fatigued when they have a lot of academic work to do on campus and feel anxious because they do not know how to manage this work. This academic anxiety badly affects students’ health; however, they should cope with anxiety. Some students have sensitive behaviors, which play a role in their tendency to become overwhelmed with anxiety. This needs change for students to cope with academic life.

4.3 Emotional symptoms

Some students who suffer from anxiety experience painful emotional symptoms such as depression, sadness, nervousness, anger, and loneliness. Students may feel unhappy about coming to a university or become very nervous around people, such as friends and peers. Others feel worried and sad when they cannot overcome the challenges they face, such as having a low GPA. Some of them like to be alone when they have stressful feelings, which can lead to depression. Sometimes, these students may have negative thoughts about withdrawing from college, especially when their friends are successful and have good GPAs. Some students may feel tired from studying, the tough curriculum, and attending the university. They may have expected to be happy with university life but instead found it to be the opposite of what they wanted.

5. Alleviating students’ anxiety

Mental health problems are a social phenomenon that requires attention in many societies [ 33 ]. Anxiety is an aspect of a college student’s mental health. Attention must be paid to the means that help prevent students from falling into anxiety and reducing its severity and psychological impact on the students continuing their studies.

5.1 Improving relationships between faculty members and students

Having human relationships between faculty members and students is very important. Communication between these groups plays an important role in reducing students’ anxiety. The faculty members should ask students if they need help to understand the curriculum, do assignments, and write reports. The faculty can design class activities related to the curriculum to make sure the students understand the content. For example, in teaching a language, the faculty should encourage group work in class to help students participate in communication to improve their speaking and their relationships with classmates to reduce the anxiety. Also, video games online can improve communication among students, increase students’ confidence, and reduce their anxiety [ 34 ]. Collaborative work among students can also reduce students’ anxiety, and they should be encouraged to use the strategies they prefer in learning [ 6 ].

The faculty members can encourage students to answer questions, whether they are right or wrong, to reduce students’ fear. They should provide students with steps to do the assignments, write reports, and create projects. The faculty members should also understand that students differ in abilities and intelligence when they design exam questions. The exams should meet different students’ abilities to reduce their anxiety. Hull et al. [ 35 ] indicate the importance of changing evaluation tools and improving students’ self-efficacy.

The faculty members have important roles in guiding students and giving them advice related to academic decisions, such as selecting a college or selecting a major, and helping them manage their time for studying multiple curriculums. In helping them deal with anxiety, adapt to the challenges, and improve their thinking, faculty members can reduce anxiety’s psychological side effects on students’ bodies and minds.

5.2 Using mental health services

Many institutions provide students with mental health workers to help them cope with their negative feelings while they pursue their studies. To provide students with efficient mental services, the institutions should hire workers who specialize in psychology and counseling. This will provide students with effective strategies to reduce their anxiety. These mental health workers should be available to meet students on campus and, most importantly, give students a trusted and secure setting in which to improve their self-confidence when they seek help. They should provide students with a socialized atmosphere to reduce their isolation and fear, specifically students with psychiatric disabilities and autism spectrum disorder. These students need to manage their social emotions to adapt to university life [ 32 ]. Online social networks should be improved to help them socially [ 36 ].

Also, it’s important to provide international students with mental health workers who can deal with their culture shock, speak different languages to meet various students’ needs, and provide them with social support and a friendly relationship. Different social and cultural activities on campus can help students communicate with others and reduce their anxiety as well. Shelton, Wang, and Zhu [ 37 ] indicate that cultural orientation is an appropriate way to keep students mentally healthy.

5.3 Improving academic services

To reduce students’ academic anxiety, institutions should provide students with effective academic services like a writing center, which helps students write in many subjects and improves their efficiency in using vocabulary and grammar. Tutorial services are also important in helping students reduce their anxiety. The tutorial should be provided by an effective staff specializing in the curriculum to help students understand the content of the courses, specifically in language courses, mathematics, statistics, and biology.

Improving library services can help students do research and experiments. According to Grandy [ 38 ], some adult students have library anxiety because they are unable to use the library services. Therefore, these students should be provided with information literacy courses to reduce their anxiety about using library resources, technology resources, and searching strategies.

5.4 Physical exercise and relaxation activities

Many students who suffer from anxiety can manage their negative feelings through exercises like walking, swimming, and playing tennis. Some students join sports clubs, which results in an improvement in their positivity and happiness. Taking part in physical activities can greatly reduce the negative psychological health impacts on a student’s body and mood [ 17 ], reduce mental difficulties, and improve their health well-being [ 39 ]. Other students may prefer to reduce their anxiety by reading books about mental health and dealing with anxiety to be aware of how they can manage their symptoms. Some of them may also read novels and magazines for relaxation [ 17 ].

6. Discussion

Students at higher education institutions face many kinds of challenges that affect negatively their psychology feeling, causing them anxiety. Its effects vary among students in how to deal with it, overcome its symptoms, and find solutions. The study found that the most important factor that causes anxiety is related to academic study. For example, many students who study a new language face challenges in understanding language words, speaking, and writing. This issue was found in many students in different countries such as Korea, Thailand, Indonesia, China, and Saudi Arabia, which reflect that this issue is widespread internationally among higher education institutions. This finding needs a special issue from the faculty members to dedicate efforts in helping students understand a new language without the feeling of anxiety.

The finding also highlights that students in different colleges and specialization face anxiety. This finding may reflect that some students may have low academic competency to study in a specific specialization and not qualify to study high level of curriculum contents. However, some faculty members may not spend adequate time to explain to students the curriculums’ contents, which may affect negatively students’ feeling in searching and understanding. As it known that students vary in competencies, therefore the faculty members should focus in insuring that students understand the curriculum and how doing the assignments.

In addition, most students in higher education institutions face anxiety of midterm and final exams in most specialization. This issue needs faculty members’ attention to focus on preparing students for exam and reduce their anxiety. As it is known, students attend the university or college to get knowledge and improve their abilities, not to feel anxiety and have a psychological sickness.

Besides academic factors, some students feel anxiety because of financial pressure and not being able to pay the tuitions and fees. This issue is widely spread among students specifically who study in a private institution. Actually the institution should consider the students’ financial level before admitting them in the institution to insure their ability to pay to reduce their anxiety.

Some students feel anxiety because of illness and not being able to attend class daily and doing assignments and preparing for exams. The medical staff in the institution should consider students with illness and disabilities and follow their health situations to insure their capability to follow their study and provide them with support services to reduce their anxiety. Most importantly some students hesitate to inform the support staff with their illness or disability situation; as a result they may not able to deal with the problems that they might face, and as a result they feel anxiety.

Because the anxiety have several negative cognitive, physical, and emotional symptoms which negatively affect students’ mental health. Several solutions were suggested to alleviate students’ anxiety through developing human relationship between students and faculty members, which is an important factor in helping students reduce their anxiety feelings. It is important that faculty members recognize the importance of helping students in gaining knowledge and understanding curriculum contents to help them become qualified graduates in the work market with well body and mental health. As several researchers indicated, the negative effects of anxiety could have a continuous side effect on the students when they graduate and join the workforce. The workforce needs healthy and qualified graduates who can work without anxiety and stress.

7. Conclusion

Most students studying at higher education institutions face many challenges that give them a negative feeling and lead them to have anxiety. The most important factor that affects students is the academic challenges which are related to their persistence or retention in the institutions. Some of these academic factors include difficulty of studying new language, midterm and final exams, and curriculum. Some other students also face anxiety related to different reasons such as being ill or disabled students. Also some students feel discrimination which affects them negatively. International students also face culture shock anxiety which affects their persistence in the institutions. Students should learn managing this feeling to prevent their mental health from unwanted symptoms which may negatively affect their psychological behaviors. Also, students should understand how to improve their thinking and mind to adapt to the different challenges they face in their academic life. More importantly the university and college leadership should consider the importance of providing students with academic environment that encourage students’ learning and persistence at the institution and protect them from an unwanted anxiety feeling which probably may change students’ behaviors and attitudes negatively after graduation.

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Adolescent Anxiety Is Hard to Treat. New Drug-Free Approaches May Help

Research on the developing brain points to new ways to help young people with anxiety disorders

By BJ Casey & Heidi Meyer

Ellen Weinstein

A dolescence is a remarkable period of development and learning, a time when youths explore and adapt to changes in their social worlds and begin to form a sense of who they are and hope to be. It is a time when they first demonstrate a dramatic adaptability to the unique cognitive, emotional, physical, social and sexual demands placed on them as they transition from dependence on their parents or caregivers to relative independence. It is also, unfortunately, a time when the emergence of most mental health problems peaks.

The most common mental health concerns facing adolescents today are anxiety disorders, and their prevalence has been increasing for the past decade. A survey of tens of thousands of teens showed that this prevalence increased roughly 30 to 40 percent between 2012 and 2018, and based on evidence from teens from Germany, it rose another 70 percent during the first few years of the COVID pandemic. Yet anxiety disorders in young people are largely undertreated.

The only evidence-based behavioral treatments for anxiety are cognitive-behavioral therapies (CBTs). They involve identifying triggers of anxiety and then desensitizing the affected person to them through coping strategies such as positive thought reframing or breathing exercises, along with repeated exposure to the triggers in a safe environment. Although CBT is the most established treatment for adolescent anxiety, not all youths who try it experience relief. Among those who do, many fail to maintain improvements over time. A mere 20 to 50 percent of patients treated for anxiety without medication during adolescence remain in remission six years after initial CBT. The consequences can be long-lasting and severe. Left untreated, anxiety can lead to more serious chronic illnesses such as depression and substance use disorder later in life, greater susceptibility to physical illnesses and, in extreme cases, suicide.

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Fortunately, new discoveries about the adolescent brain are showing promising paths forward for the treatment of anxiety. Current research benefits from rapidly advancing imaging technologies that can reveal patterns of neural activity and exciting potential avenues for intervention. These modalities have already provided access to the inner workings of the developing brain in laboratory animals and teens, and scientists hope they will lead to new approaches in clinical practice that take into account the unique changes in the human brain during adolescence. By focusing on the developing brain and the behaviors it generates early on in life, we may be better able to alter anxiety-related memories, identify cues and situations that help to reduce symptoms, and mitigate the adverse effects of anxiety for young people before they become a more chronic affliction in adulthood.

In the past two decades we have learned that the adolescent brain undergoes notable changes in structure and function, and these changes are distinct from those observed during early childhood and adulthood. They are localized, meaning certain brain areas change earlier in development than others. Regions involved in emotions, such as the amygdala and the hippocampus, show peak structural and functional changes during the teen years. For example, during adolescence the amygdala’s volume increases (a structural change), and so does the way the amygdala is activated by certain emotional experiences (a functional change). In contrast, brain regions and circuitry associated with the regulation of emotions, thoughts and actions—the prefrontal cortex, for instance—change more gradually, with development continuing well into adulthood. These differences in developmental timing may lead to an imbalance in communication among brain regions, allowing one area to prevail over another in an adolescent’s decision-making. Accordingly, in emotionally charged or threatening situations, early-developing emotional areas “win out” over later-­devel­op­ing ones, driving some of the reactions and responses linked with the behaviors of anxious and volatile teens. These regional differences might have served an evolutionary purpose. They have been linked to heightened sensitivity to emotional and social information that may be essential for reproductive success and the survival of the human species. Unfortunately, these same imbalances have also been associated with increased reactivity to stress and greater susceptibility to anxiety disorders.

A core emotion associated with anxiety disorders is fear. Although fear is an adaptive response to threats and therefore essential for survival, persistent fear long after a threat has been removed can lead to a pathological state of anxiety. People with anxiety disorders have difficulty identifying when previously threatening situations have become safe, and they may overgeneralize by thinking that a negative experience in one situation will recur in other scenarios.

Decades of animal and human research have identified the basic brain circuitry for remembering an acquired fear in adults. The amygdala is key to developing a fear memory, and parts of the prefrontal cortex are involved in decreasing the strength of fear memories—a process known as extinction. Both the amygdala and the prefrontal cortex are highly interconnected with a third region, the hippocampus, which plays a role not only in fear extinction but also in determining how we experience fear in different situations. In particular, the hippocampus provides information about the surrounding environment to help an individual decide whether a given situation is more likely to present a threat (for example, a bear in the woods) or an absence thereof (a bear at the zoo). Much of this circuitry is conserved across different species, enabling the translation of basic animal research to treatments in humans.

Recently researchers have focused attention on fear memory and extinction during adolescence. These studies show that adolescents, like preadolescents and adults, are capable of acquiring a fear memory, but they are less able to extinguish those memories than people in other age groups. After being exposed to a few simple pairings of a neutral stimulus (a colored square) with an aversive stimulus (a loud noise), children, adolescents and adults alike show a fear response, measured by sweat gland activity, to the colored square even when the loud noise no longer happens. When preteen children and adults are then presented repeatedly with the colored square without the loud noise, they begin to see the square not as something predicting the threat of the loud noise but rather as a safe refuge from it—the fear memory is extinguished. Adolescents, however, continue to react fearfully to the colored square.

In cases when fear does get diminished for adolescents, it regularly returns with the passage of time. The finding that adolescents “learn” to extinguish fear less readily than younger or older people has been replicated in studies across species (mice, rats and humans). Most notably, during this developmental period, the amygdala is much more involved in sustaining the fear memory than the prefrontal cortex is in initiating the extinction process. A lower ability to initiate fear-­extinction learning is thought to confer a risk for anxiety. Thus, adolescents may innately be at higher risk.

Jen Christiansen

The discovery of differences in fear-extinction behavior and brain circuitry during adolescence has important implications not only for understanding the potential for increased susceptibility to anxiety disorders but also for choosing treatment options. Behavioral therapies such as CBT entail identifying triggers of anxiety, finding coping strategies and undergoing a process of desensitization built on the principles of fear extinction. But during adolescent fear extinction, the involvement of the prefrontal cortex, which is associated with the planning and control of behavior, is diminished—which implies that for adolescents, the effectiveness of conventional exposure-based CBT might also be diminished. Together, these facts raise the question of how we should tailor treatments for the developing brain. Specifically, how might we use what we know about the brain’s fear circuitry and the development of fear learning during adolescence to guide interventions that may be more successful in altering teens’ fear memories?

One strategy involves conceding the delayed maturation of the prefrontal cortex and circumventing the region in treatment. Rather than relying on prefrontal-based extinction learning, we have tested an alternative method called memory reconsolidation updating. Memory reconsolidation is based on the principle that memories are dynamic, not static. Every time a memory is retrieved, it gets modified. Reactivating a fear memory by presenting a reminder of the fear stimulus opens a time-limited window during which the memory itself becomes prone to disruption and change.

Studies in both humans and rodents suggest that fear-­memory updating is mediated by changes to the memory in the amygdala. Unlike the prefrontal circuitry, which continues to show developmental changes into young adulthood, the amygdala undergoes peak maturation during midadolescence.

These findings suggest that one way to help adolescents overcome pathological fear is to introduce what is called a reminder cue to retrieve the memory, followed by a delay before subsequently extinguishing it. In our lab, we tested this idea in both healthy adolescents and adults by comparing their retention of a fear memory after extinction with and without a preceding reminder cue. We found that even though adolescents typically show diminished fear extinction relative to adults, those who were prompted to retrieve the fearful memory several minutes before extinction learning showed a dramatic reduction in fear the next day compared with those who underwent only extinction learning. In fact, those adolescents’ fear memories diminished to the same degree as observed in adults.

Traditionally, extinction learning involves forming a new, competing, safe memory that leaves the original fear memory intact, meaning it is possible for those fearful thoughts to return later. The current findings, however, suggest that with memory reconsolidation updating, the original fear memory is altered. Thus, the reconsolidation approach has the potential to both reduce fear at the time of treatment and lessen the likelihood that it will return.

This research is exciting because it suggests a path to the clinical use of reconsolidation updating. Simple modifications to existing exposure-based CBT techniques might prove effective in reducing triggers of fear and anxiety in adolescent patients. This method could entail a step as simple as the therapist reminding patients why they are there when they arrive for their appointment—the equivalent of the reminder cue and fear-­memory retrieval in the lab setting. Then the therapist could spend several minutes establishing a safe rapport with the patient while waiting for the memory to enter a labile state during the reconsolidation-updating window. Desensitization with exposure therapy could then begin during the time in which the updating process takes place. The current variable efficacy of CBT in adolescents with anxiety disorders may be explained by the fact that some clinicians already use procedures that inadvertently tap into components of reconsolidation updating.

Recent attempts to incorporate reconsolidation-­updating approaches in treating adult patients with anxiety and trauma-related disorders have yielded some success, but to date they have not been used with adolescent patients. The studies in adults show short- and long-term reduction of symptoms, especially for patients with specific phobias and post-traumatic stress disorder. Although more basic and clinical research is needed, this method seems promising.

A nother strategy that may help adolescents extinguish a fear memory involves the use of safety cues that signal there is nothing to be afraid of. In an experimental setting, a safety cue can be a simple stimulus—a symbol or a sound—that is distinguishable from and repeatedly contrasted with a fear cue. Outside the lab, safety cues come in many forms and are likely to be a stimulus unique to the individual: a small personal object, a photograph of a loved one, a specific scent. We and others have shown that in humans and rodents alike, safety cues act by recruiting brain regions that show elevated activity during adolescence, including the amygdala and the hippocampus. The anterior part of the hippo­campus in particular shows a strong increase in activity when a safety cue is presented alongside a fear cue; the degree of activity corresponds to the reduction in fear. Furthermore, safety cues rely less on the prefrontal cortex than do other forms of fear regulation , such as extinction, highlighting the possible advantage of using a safety cue–based approach for anxiety during adolescence.

It is not feasible to avoid all triggers of excessive fear and anxiety, so it’s important that patients do not become overly reliant on safety cues to the detriment of learning other coping skills. Safety cues may be a valuable tool for increasing the tolerability of the early stages of treatment so that patients do not drop out. Early treatment sessions could include guidance from the clinician on how to identify and properly deploy a safety cue.

As treatment progresses, cues can give patients a way to reduce their fear response long enough to evaluate the situation and use tools from CBT practice. Although research on integrating safety cues into treatment is in its earliest stages, the method shows great promise, particularly for adolescents. Our group recently demonstrated in mice that intermittently presenting a safety cue during an extinction protocol led to better fear extinction in adolescent mice than observed in either adolescent (28 to 50 days) or adult rodents trained without a safety cue.

The hope for these emerging therapeutic approaches is that we can tailor current anxiety treatments for young people by targeting the developing brain. It is important to be mindful of the fact that the magnitude and intensity of the fear response in people diagnosed with anxiety are probably much greater than the fear evoked by aversive stimuli in lab experiments, which are often mild, narrowly targeted and transient. It is also important to remember that CBT and antidepressants can treat anxiety effectively in many people. Unfortunately, though, for some, these solutions offer only limited or brief benefits. Therefore, the most effective forms of treatment may require a combination of approaches, including desensitization techniques modified to incorporate reconsolidation updating or safety cues, possibly in conjunction with antidepressants.

The ultimate aim is for us to optimize current treatments for youths with anxiety by targeting the brain during a period of development accompanied by intensive learning and, in so doing, improve the quality of life for adolescents both in the immediate future and later in life.

BJ Casey is Christina L. Williams Professor of Neuroscience at Barnard College.

Heidi Meyer is an assistant professor in psychological and brain sciences at Boston University’s Center for Systems Neuroscience.

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Student Anxiety and Perception of Difficulty Impact Performance and Persistence in Introductory Biology Courses

• Benjamin J. England
• Jennifer R. Brigati
• Elisabeth E. Schussler
• Miranda M. Chen

*Address correspondence to: Benjamin J. England ( E-mail Address: [email protected] ).

Division of Biology, University of Tennessee, Knoxville, TN 37996

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Department of Biology, Maryville College, Maryville, TN 37804

Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN 37996

Students respond to classroom activities and achievement outcomes with a variety of emotions that can impact student success. One emotion students experience is anxiety, which can negatively impact student performance and persistence. This study investigated what types of classroom anxiety were related to student performance in the course and persistence in the major. Students in introductory biology classes self-reported their general class, test, communication, and social anxiety; perceived course difficulty; intention to stay in the major; and demographic variables. Final course grades were acquired from instructors. An increase in perception of course difficulty from the beginning to the end of the semester was significantly associated with lower final course grades ( N = 337), particularly for females, non-Caucasians, and students who took fewer Advanced Placement (AP) courses. An increase in communication anxiety slightly increased performance. Higher general class anxiety at the beginning of the semester was associated with intention to leave the major ( N = 122) at the end of the semester, particularly for females. Females, freshmen, and those with fewer AP courses reported higher general class anxiety and perceived course difficulty. Future research should identify which factors differentially impact student anxiety levels and perceived difficulty and explore coping strategies for students.

INTRODUCTION

Emotions are human reactions to future, current, and past events, and are a constant presence in academic classrooms ( Pekrun, 1992 ; Mazer, 2017 ). These course-related emotions can be beneficial by promoting actions or reflections that increase student motivation, learning, and performance ( Kim and Pekrun, 2014 ). However, not all emotions have positive impacts on student success. Positive emotions experienced by university students include enjoyment, interest, hope, and pride, while negative emotions can be anger, anxiety, frustration, and boredom ( Pekrun and Stephens, 2010 ). Anxiety has been of interest to undergraduate education researchers in recent years because of the increasing prevalence of this emotion in students ( Bitsko et al. , 2018 ; Castillo and Schwartz, 2013 ) and student reports of anxiety associated with active-learning pedagogies in undergraduate science classrooms ( Broeckelman-Post et al. , 2016 ; England et al. , 2017 ; Cooper et al. , 2018 ).

Pedagogical approaches at the postsecondary level have been undergoing a transformation, including a noticeable shift toward the implementation of empirically validated teaching practices in science, technology, engineering, and mathematics (STEM) classrooms ( Armbruster et al. , 2009 ; American Association for the Advancement of Science [AAAS], 2011 , 2015 ). This shift is not without cause: these teaching practices, including active-learning pedagogies, increase exam performance and lower course failure rates on average ( Springer et al. , 1999 ; Freeman et al. , 2007 , 2011 , 2014 ; Armbruster et al. , 2009 ; Haak et al. , 2011 ). The use of active-learning pedagogies has also been suggested as a mechanism to improve student persistence in STEM undergraduate majors ( Graham et al. , 2013 ). These results are encouraging, because fewer than 40% of freshman STEM majors persist to earn a STEM degree, yet the President’s Council of Advisors on Science and Technology ( PCAST; 2012 ) projected the need for an additional one million STEM graduates over the next decade.

The use of active learning typically comes with the expectation that students will respond in electronic, verbal, and/or written formats to questions that the instructor provides. For some students, the expectation to respond (and potentially be judged on that response) is associated with feelings of anxiety ( Cooper et al ., 2018 ). Students, for example, have reported anxiety associated with cold calling ( Broeckelman-Post et al. , 2016 ). While students liked that this practice encouraged them to pay attention, they did not enjoy having the spotlight on them, and the use of this practice discouraged some students from attending class on non–exam days. England et al. (2017) found that students in introductory biology reported different average levels of anxiety for different active-learning practices; responding to verbal questions produced higher average anxiety than group work or clicker questions. Average levels of student general class anxiety were higher with lower student self-reported letter grade, and student intention to persist in the major was associated with lower class anxiety levels ( England et al. , 2017 ). Active learning does not always increase student anxiety, however. Cooper et al. (2018) interviewed 52 students in large-enrollment courses and provided evidence that clicker questions and group work had the potential to either increase or decrease anxiety depending on teacher implementation and perceived benefit to the student, while random/cold call was always viewed negatively.

The current study focused on introductory biology courses at a large southeastern public research university that had incorporated active learning into its classrooms. Given the student reports of anxiety in these active-learning classrooms, we asked what types of anxiety might be related to student performance and persistence in these courses, at what times of the semester, and for which students in the classes. This research responds to a growing interest in understanding how undergraduate anxiety may impact motivation, performance, and persistence, particularly for demographic subsets of students ( Bledsoe and Baskin, 2014 ; Eddy et al. , 2014 , 2015a,b ; Broeckelman-Post et al. , 2016 ; Cooper and Brownell, 2016 ; Cooper et al ., 2018 ). Performance and/or persistence differentials have been observed between different genders ( Eddy et al. , 2014 ; Eddy and Brownell, 2016 ), ethnicities ( Eddy and Hogan, 2014 ), and those who completed different numbers of Advanced Placement (AP) courses ( Ackerman et al. , 2013 ). These demographic groups were of particular interest to this study.

Theoretical Framework

This study used the control-value theory of achievement emotions ( Pekrun, 2006 ; Pekrun et al ., 2007 ) as the framework for the investigation. This theory proposes that students appraise the value of their achievement, and their perception of the control they have over their achievement, as antecedents to the emotions they feel in the classroom. Emotional feelings can be prospective (in anticipation of an activity or outcome) or retrospective (after the activity or outcome occurred) or can be related to activities currently occurring in the class. The emotions are termed “achievement emotions,” because they are student responses to their perceptions of being judged on their performance in the class.

Achievement emotions have cascading impacts on student achievement through their interaction and influence on cognition and metacognition ( Zeidner and Matthews, 2005 ; Grossberg, 2009 ; Bledsoe and Baskin, 2014 ), motivation ( Kim and Pekrun, 2014 ), and engagement ( Pekrun and Linnenbrink-­Garcia, 2012 ). Achievement outcomes feed back to the student perceptions of control and value, which are themselves impacted by factors such as class curriculum and context, past educational performance, coping strategies, and genetic emotional predisposition ( Pekrun, 2006 ). Instructor communication practices in particular have been strongly linked with student emotional responses in the classroom ( Mazer, 2013 ), including increased student anxiety when instructors are rated lower in immediacy (closeness between teacher and students), clarity, and communication skills ( Mazer et al. , 2014 ).

Anxiety is a negative, prospective emotion that students experience when they are worried about failure (value) and feel only partially certain about their ability to control the outcome ( Pekrun et al ., 2007 ). Despite its assignment as a negative emotion, anxiety is also considered an activating emotion in terms of its impact on student interest and motivation (in contrast, hopelessness is an example of a deactivating emotion). Therefore, although the impact of anxiety is generally negative on average, its outcome varies by individual students based on their interest and motivation levels ( Pekrun et al ., 2007 ).

Several studies have revealed lower student performance associated with higher anxiety ( Zusho et al. , 2003 ; Akgun and Ciarrochi, 2010 ; England et al ., 2017 ); however, students with midlevel anxiety earned the highest exam scores in a statistics course ( Keeley et al. , 2008 ). This follows the Yerkes–Dodson law ( Yerkes and Dodson, 1908 ), which shows a bell curve–type relationship between anxiety and performance, with very low and very high anxiety impeding performance, but midrange anxiety heightening performance.

Anxiety can also be related to persistence. England et al . (2017) identified a difference between the average general class anxiety levels of undergraduate students who intended to stay in or leave the biology major, with those intending to leave having higher anxiety. Witt et al. (2014) found that receiver apprehension (students’ fear that they may not be able to understand the presented material) was negatively associated with the intent to persist in students’ respective majors. A study of 883 undergraduates at a STEM-focused German university reported that increased student anxiety was related to intent to drop out, but not to academic achievement ( Respondek et al ., 2017 ). In a study on course climate, Barthelemey et al. (2015) found that academic stress was negatively related to persistence (stress has the same physiological reactions as anxiety, but the cause of the reaction is considered more specifically identifiable [ Endler and Parker, 1990 ]; however, stress and anxiety are very closely related).

Just as anxiety can differ by individual, anxiety also differs among demographic subsets of students in academic environments. The most widely studied differences have been between females and males, with females having consistently higher anxiety then males ( Misra and McKean, 2000 ; Bayram and Bilgel, 2008 ; Bryant et al ., 2013 ). Anxiety is also higher in freshmen compared with upper-level students ( Bayram and Bilgel, 2008 ). Anxiety in terms of underrepresented minorities has been studied in the context of stereotype threat ( Steele and Aronson, 1995 ), in which invoking membership in a group with a negative stereotype causes lower student performance compared with peers. This effect is hypothesized to be mediated by increased anxiety among these students ( Steele et al. , 2002 ).

Study Rationale

Given the increasing prevalence of anxiety among undergraduates ( Castillo and Schwartz, 2013 ) and use of pedagogies that students say can either increase or decrease their class anxiety ( Broeckelman-Post et al. , 2016 ; England et al ., 2017 ; Cooper et al ., 2018 ), it is important to investigate any potential links between anxiety and student performance and persistence in introductory science courses. As stated in multiple national reports ( AAAS, 2011 ; PCAST, 2012 ), the retention of students in science is a priority for undergraduate education reform efforts. Investigations linking anxiety with persistence and performance in science, however, typically only use one measure of anxiety (such as general class anxiety), without probing other types of anxiety that may arise from active learning or assessment practices in today’s increasingly complicated active-­learning environments. Although a controlled design is needed to make clear connections between particular course practices and types of anxiety and student success, this study takes a first step by looking for relationships between different types of student anxiety, some of which may occur in response to active-learning pedagogies, and student performance and persistence in several introductory biology classes.

For this study, the specific types of anxiety that may be related to active-learning practices in undergraduate classrooms are communication anxiety and social anxiety ( Zeidner and Matthews, 2005 ). We also examined test anxiety, because it has been a common type of anxiety investigated in relation to student performance ( Culler and Holahan, 1980 ; Chapell et al ., 2005 ). Communication anxiety in the classroom setting, known as classroom communication apprehension or participation anxiety, is a type of situational anxiety that occurs when students fear that they will perform inadequately in front of the instructor or their peers, such as when answering a question in front of the class (either by volunteering or in a cold-call response; Rocca, 2010 ; Karim and Shah, 2012 ). Classroom communication apprehension is fairly prevalent among undergraduates, with one study documenting that 70% experienced it at least once ( Bowers, 1986 ). Social anxiety refers to the “marked and persistent fear of social or performance situations in which embarrassment may occur,” and often manifests itself when students are expected to interact with others, for example, in classroom-based group work ( Jefferson, 2001 ). A student with social anxiety would be expected to also suffer from classroom communication anxiety, but not all students who experience situational classroom communication anxiety have persistent social anxiety. Test anxiety is a fear of not performing well on assessments and is a commonly experienced anxiety in undergraduates. Gerwing et al. (2015) reported that 38.5% of undergraduates experience test anxiety at some point in their academic careers. Test anxiety is not limited to high-stakes exams—it has also been reported with low-stakes quizzes ( Khanna, 2015 ). Measures of general class anxiety were also included to capture overall feelings of classroom anxiety that may have been different from specific anxieties that arose from particular classroom practices (e.g., questioning techniques or assessment style).

Is student performance impacted by different types of initial anxiety and/or changes in these anxieties over the semester?

Is student persistence impacted by different types of initial anxiety and/or changes in these anxieties over the semester?

Do student subsets (e.g., different demographic groups) in the classes experience differential anxieties?

To answer these questions, we probed four types of student anxiety: general class anxiety, communication anxiety, social anxiety, and test anxiety. Identifying the types of anxiety that may impact performance and persistence for some students at certain times of the semester is critical to understanding student emotional experiences in undergraduate classrooms and developing potential interventions to promote student success.

Courses and Instructors

Measurements of student anxiety, course performance, and persistence in the major were collected in Fall 2016 from students enrolled in majors’ introductory biology lecture classes at a large southeastern public research university. The introductory biology sequence includes an Organismal and Ecological Biology (OEB) class and a Cellular and Molecular Biology (CMB) class that can be taken in either order. There were four OEB courses and three CMB courses offered in the Fall semester. The four OEB courses had enrollments of 214, 214, 221, and 70 students. The three CMB courses had enrollments of 221, 206, and 144 students. Students in each course were mostly freshmen and sophomore biology or pre-professional majors. Data were collected from all OEB and CMB courses offered in the Fall semester for a total of seven possible courses with seven separate instructors as sources of data.

Each OEB and CMB class is structured as 2 hours of lecture per week and 1 hour of graduate teaching assistant–led small-group discussion. Students were prompted to only answer our survey questions about anxiety for the lecture portion of the class. Final grades included their performance in both the lecture (75% of the grade) and the discussion (25% of their grade). The introductory courses at this university used the main tenets of Vision and Change in Undergraduate Biology Education ( AAAS, 2011 ) to guide their delivery, including the use of active-­learning pedagogies. The lecture instructors of the courses all held PhDs in the relevant fields and were part of a community of instructors who met regularly to talk about course implementation.

All procedures for this study were approved by the human subjects review board at the University of Tenneessee, Knoxville, before the start of the research (IRB-16-03181-XP).

Data Collection

Data were collected through the use of two online surveys sent as links via an email from each course instructor. For each course (OEB and CMB), surveys were disseminated at two points during the semester: an initial survey within the first month of the semester and a final survey at about week 14 of the semester. Surveys were disseminated by all instructors within a 24-hour period, so all students received access to the surveys within the same time frame. The initial survey dissemination was planned for week 4 to ideally have students complete it just before a first exam; however, classes had different exam schedules and some had quizzes, so a few courses may have had an assessment before students completed the survey. The final survey was given week 14 of the semester. This survey closed before the final exam period started, so all surveys were completed before course final assessments and with students having completed about 95% of the course. The total time for survey completion ranged from 10 to 15 minutes. Instructors offered their students an incentive to complete the surveys in the form of 1–3 bonus points (decided by the instructor) per survey the student completed. The total points in each class were 1000.

Students’ perceptions of their general class anxiety were captured through a seven-item, 7-point Likert-scale instrument adapted from Papanastasiou and Zembylas (2008) to measure anxiety levels toward research. The factor structure of this scale was delineated by Papanastasiou (2005) . The scale was 7 points, with 1 being no anxiety and 7 being high anxiety. For this study, the word “research” in each item was replaced with the words “Biology lecture”; this was the only change made to the instrument. The seven items were as follows: “Biology lecture…makes me nervous, is stressful, makes me anxious, scares me, is complex, is complicated, is difficult” (Supplemental Table 1).

To assess the validity of this scale on our population, factor analyses were performed. Partial confirmatory factor analysis on the general class anxiety scale resulted in a best-fit solution of two factors: one for general class anxiety (four items), and one for perceived difficulty (three items). Full factor analyses results are reported in Supplemental Tables 2 and 3. This two-factor solution is noteworthy, as it conflicts with the proposed factor solution reported in Papanastasiou (2005) ; this could be explained by the different population of our sample or because students perceive anxiety toward research as different from anxiety toward a class.

Perception of difficulty and general class anxiety should be related via the “control” antecedent to anxiety as described in the control-value theory of achievement emotions ( Pekrun, 2006 ). To make judgments about control, students would assess the demand that the class is placing on them (its perceived difficulty) and the resources that they have to meet that demand, which would then lead to emotional outcomes like anxiety. Because of the close theoretical relationship between perceived difficulty and anxiety, we retained the perceived difficulty measure for this study, but acknowledge that perception of difficulty is not a measure of anxiety.

Student responses to the first four items were averaged to arrive at a mean general class anxiety score for each student; student responses to the final three items were averaged to arrive at a mean perceived difficulty score for each student. Mean scores ranged from 1 to 7: the higher the mean, the higher the anxiety or difficulty. The researchers use the word “general” to indicate that the anxiety measure was not specifically in response to any one aspect of the course, but instead a measure of overall perception of anxiety in the course.

Also included were three scales intended to measure student test anxiety, communication anxiety, and social anxiety (survey items included as Supplemental Table 4). The test anxiety scale was from the Motivated Strategies for Learning Questionnaire ( Pintrich et al. , 1991 ). The scale is composed of five items, measured via a 7-point Likert scale. Student responses to the test anxiety scale were averaged to arrive at a mean test anxiety score for each student. Mean scores range from 1 to 7, with the higher numbers indicative of higher anxiety.

Both the communication and social anxiety scales were taken from the Personal Report of Communication Apprehension-24 ( McCroskey, 1982 ). These scales are both composed of six items, measured via 5-point Likert scales. Final scores for these instruments were calculated based on the protocol provided by McCroskey (1982) : 18 + (scores for positively worded items) − (scores for negatively worded items). Each final score ranges from 6 to 30, and the higher the final score, the higher the anxiety. Partial confirmatory factor analyses on the test, communication, and social anxiety scales all resulted in one-factor solutions. Full factor analysis results, including calculated best-fit indexes and Cronbach’s reliability values (Cronbach’s α all ≥ 0.859) can be found in Supplemental Tables 5 and 6.

In addition to measures of student anxiety, self-reported demographic information regarding year in school (freshman, sophomore, junior, senior, super senior), gender identity (male, female, other, or prefer not to respond), racial/ethnic identity (open response), and number of AP courses completed in high school (0, 1, 2, 3, or 3+) was also collected. The number of AP courses taken was used as a measure of college preparation and potential confidence to succeed, not as a measure of academic ability. Students also self-reported the names of their course instructors.

For the dependent variable of persistence, students were asked on the final survey whether they had changed their intended majors since the beginning of the semester (yes or no). To collect the dependent variable of performance, with student permission, students’ final course grades were collected from instructors after grade submission had closed for the semester. Thus, the dependent variables of persistence and performance were collected once at the end of the semester, the demographic independent variables were collected once at the beginning of the semester, and the independent variables related to anxiety and difficulty were collected twice over the semester (initial and final surveys).

Data Analysis

Data from students who indicated they were under the age of 18 or students who did not consent to the use of their data were removed. The Fall initial survey had a total 861 responses across all sections of OEB and CMB (67% completion rate). The Fall final survey had a total of 677 responses across all sections of OEB and CMB (52% completion rate). For all data analyses, we included only students for whom we had matched initial and final survey responses and for whom we had permission to acquire their final course grades (final N = 337).

Because the scales varied among the anxiety and perceived difficulty instruments, raw scores were converted to z -scores to facilitate comparisons across instruments. We also created a correlation matrix to examine the relationships among the variables (see Supplemental Table 7). There was only one strong correlation between final general class anxiety and final perception of difficulty at 0.751; all other correlations were relatively weak or low. Given the lack of strong correlations among almost all of the variables, we retained all variables in the analyses. However, to eliminate any concerns about correlations between initial and final measures of the same anxiety variables, we decided to test the initial and change in anxiety from the beginning to the end of the semester (delta; final minus initial measure) as the independent anxiety variables.

For data analysis, gender was pared down to male and female, as the other two categories had very few respondents. Ethnicity was coded into Caucasian versus non-Caucasian based on student free responses to this question. Responses such as “white,” “Caucasian,” and “European American” were coded as Caucasian. All other demographic groups were coded as non-Caucasian. Further parsing of the non-Caucasian category was not possible due to limited diversity in the sample. The survey was disseminated to students across seven sections (each with a unique instructor), but there were no students in the seventh course section who completed both surveys, so that course instructor was excluded from the analysis.

The introductory courses that were the focus of this study included both biology majors, and pre-professional and other majors. Because we were only interested in the persistence of biology majors as a function of these introductory biology courses for the persistence analyses, we reduced this sample of students to include only those who took both surveys and indicated at the beginning of the semester that they were biology majors ( N = 122). At the end of the Fall semester, there were 80 biology majors who indicated they were remaining, and there were 42 biology majors who indicated they were leaving.

Modeling the Relationships of Anxiety, Difficulty, Demographics, Instructor, and Student Performance

To assess the impacts of the anxiety, difficulty measures, demographics, and instructor on final student performance (letter grade earned), four models were developed. One model was an ordinal regression with all independent variables included (full model), and the second was an ordinal regression using a backward-selection procedure (best-fit model). To account for nonindependence among students within the same class, two mixed-effects models were also developed ( Theobald, 2018 ). When evaluated with the ordinal package in R ( Christensen, 2018 ), the latter two models replicated the fixed variables selected in the previous full and best-fit ordinal models, with the addition of instructor as a random effect. For determination of the most parsimonious performance model, measures of Akaike’s information criterion (AIC) were calculated and compared between models. All modeling was conducted in the R language environment (v. 3.3.1; R Core Team, 2018 ).

For all models, the dependent variable was letter grade earned (“A” = 5, “B” = 4, “C” = 3, “D” = 2, “F” = 1; note that plus and minus grade designations were not used for this analysis). The independent variables included initial and delta (final survey measures minus initial survey measures) Z-scores of the following: general class anxiety (GA), perception of difficulty (PD), test anxiety (TA), communication anxiety (CA), and social anxiety (SA). Independent variables of gender, year, ethnicity, and number of AP courses (0–1 AP courses vs. 2 or more AP courses) were also included. Instructor was treated as an independent variable or random effect, depending on the model. In the ordinal regression model, Instructor 6 was chosen as the comparison instructor against which all other instructors were compared; Instructor 6 had a normal grade distribution. Thus, the full initial model was as follows: letter grade earned ∼ initial GA + initial PD + initial TA + initial CA + initial SA + delta GA + delta PD + delta TA + delta CA + delta SA + gender + year + ethnicity + AP + instructor.

Modeling the Relationships of Anxiety, Difficulty, Demographics, Instructor, and Student Persistence

To assess the impacts of anxiety, difficulty measures, demographics, and instructor on student persistence (a student’s intention to remain in or leave the major at the end of the semester), four models were again developed. A logistic regression with all independent variables (full model) was first constructed, followed by a second logistic regression using a backward-selection procedure (best-fit model). Similar to the student performance models, the third and fourth models accounted for instructor as a random effect ( Theobald, 2018 ). These models replicated the variables found in the full and best-fit logistic regressions, respectively, with the addition of instructor as a random effect. Again, AICs for each model were calculated and compared to determine the best-fit model. The dependent variable was whether students indicated they were either leaving or remaining in the biology major. The same independent variables were used as in the ordinal regression model for student performance, again with both initial and delta measures (as z -scores) included. Thus, the full initial model was as follows: leaving or remaining in the major ∼ initial GA + initial PD + initial TA + initial CA + initial SA + delta GA + delta PD + delta TA + delta CA + delta SA + gender + year + ethnicity + AP + instructor.

Investigating Differences in Anxiety and Difficulty among Demographic Subsets of Students

We also investigated the relationships between student demographic variables (gender, year in school, ethnicity, and number of AP courses) and general class anxiety, perceived difficulty, test anxiety, social anxiety, and communication anxiety measures. Two multiple linear regression models were created for each anxiety and difficulty measure, one with the initial value and one with the delta value ( z -scores were used for each; SPSS v.22; IBM Corporation, 2013 ). All predictor variables for this model were categorical: freshman versus nonfreshman, Caucasian versus non-Caucasian, male versus female, and 0–1 AP courses versus 2 or more AP courses. Predictors were considered significant at p < 0.05. All predictors were dummy coded as 0 or 1: 0 for freshmen, 0 for females, 0 for non-Caucasians, and 0 for those with 0–1 AP courses. Hierarchical forward regression was used, meaning variables were entered into the model based on greatest increment to the R 2 value. AIC and Bayesian information criterion (BIC) values were used to determine the best-fit model produced. The full initial models all followed the same format: [type of initial anxiety or perceived difficulty] ∼ gender + year + ethnicity + AP. The full delta models all followed the same format: [type of delta anxiety or perceived difficulty] ∼ gender + year + ethnicity + AP.

Of the 337 students who completed both surveys and allowed the researchers to access their final grades, 134 students earned an “A,” 142 earned a “B,” and 61 earned lower than a “B.” Of these 337 students, 242 were female, 140 were freshmen, and 280 were Caucasian. There were 134 students who had either taken no or only one AP course in high school; 203 students had taken more than one AP course. For the 122 students who completed both surveys and started the semester as biology majors, 80 students indicated they were continuing in the major, but 42 indicated they were not. In terms of distribution of students across courses, the sample sizes for Instructors 1–6 were as follows, respectively: 78, 36, 81, 37, 32, and 73.

Before z -score conversion, means were calculated for all anxiety and difficulty scales. Of the constructs measured using a 7-point Likert scale, student test anxiety was consistently higher than perceived difficulty and general class anxiety for both the initial and final surveys ( Table 1 ) and was the only measure above the midpoint of the Likert-scale average. For the anxiety measures ranging from 6 to 30, communication anxiety was consistently higher than social anxiety, and always above the midpoint on the measurement scale on both the initial and final surveys ( Table 1 ). There were no significant differences between the overall means on the initial and final surveys for any of the variables measured.

a Communication and social anxieties are on a scale ranging from 6 to 30; all others are on a scale ranging from 1 to 7. In all cases, the higher the mean, the higher the anxiety. There were no significant differences between the means on the initial and final surveys for any of the variables measured.

Backward selection produced the best-fit ordinal model with an AIC of 708.440 and produced the best-fit mixed-effects model with an AIC of 708.040 (Supplemental Table 8). The ordinal regression model is presented as the best-fitting model given the nearly identical AIC values.

The ordinal regression model estimated which grades students were more or less likely to earn based on the independent variable predictors. The estimates and standard errors for the model are provided in Table 2 . In the model, the dependent variable was letter grade, with “A” = 5 and “F” = 1. The estimates suggest which direction student grades will move based on those predictors. For example, a negative estimate (coefficient) pulls the score closer to 1, which is equal to a letter grade of “F”; conversely, a positive estimate pulls the score closer to 5, or an “A.” The model produced two significant scale predictors: delta perceived difficulty and delta communication anxiety. An increase in perceived difficulty over the semester was associated with lower course performance (estimate of −0.442, SE ±0.198, p = 0.023); interestingly, an increase in communication anxiety was associated with higher performance (estimate 0.38, SE ±0.177, p = 0.03). Three demographic variables were significant in the model. Females (estimate −0.734, SE ±0.267, p = 0.006), non-Caucasians (estimate −0.614, SE ±0.293, p = 0.03), and those with 0–1 AP courses (estimate −1.093, SE ±0.241, p < 0.001) were negatively associated with performance, meaning lower grades. Students who had Instructor 5 showed negative associations with performance, meaning earning lower letter grades as compared with students who had Instructor 6; however, this was the only significant result found among all instructors. To visualize average perceived difficulty measures by student performance categories, a bar chart of average initial and final (not delta) perceived difficulty scores by students’ final earned grade was created ( Figure 1A ).

a In the model, “A” = 5 and “F” = 1. A negative estimate would indicate a student is more likely to earn a lower grade as the independent variable increases; a positive estimate brings students to a higher grade as the independent variable increases. For example, a 1-point increase in the change in perceived difficulty across the semester would lower a student’s grade by ∼0.442 points on a 5-point letter grade scale. Identifying as female would lower one’s final grade by ∼0.734 points on a 5-point letter grade scale. Nagelkerke (pseudo R 2 ) = 0.262.

b CA, communication anxiety; GA, general class anxiety; I1–I6, Instructors 1–6; PD, perception of difficulty; SA, social anxiety; TA, test anxiety.

* p < 0.05.

** p < 0.01.

FIGURE 1. (A) Students ( N = 337) who perceived the class as less difficult, even at the start of the semester, earned higher final letter grades. However, only change in perceived difficulty over the semester (delta) was predictive of course performance in our model. (B) Biology majors ( N = 122) who reported leaving the major had higher initial and final general class anxiety. However, only initial general class anxiety levels were predictive of intention to persist in the major. The Likert scale is 1–7, with 1 being no general class anxiety/perceived difficulty and 7 being high general class anxiety/perceived difficulty. Data are mean general class anxiety or perceived difficulty ± SEM.

We initially produced a full logistic regression model in SPSS (AIC 138.490); treating instructor as a random effect using mixed-effects models in R produced a model with an AIC of 134.046. Using backward selection, we resolved on the full logistic regression model with an AIC 138.490 as the best-fit model (Supplemental Table 8). Lower AIC values are considered an indication of better fit; however, there exists no statistical test for determining whether two AIC values are truly different. It has been argued that, when two AIC values show a difference of less than 5 units, this constitutes less than certain evidence of greater model fit ( Burnham and Anderson, 1998 ; Burnham et al. , 2011 ). Occasionally, interpretability of results can be a factor in model selections, and models with slightly higher AICs may be preferable due to ease of interpretation. For ease of interpretability, the logistic regression model without random effect is the model presented here.

The logistic regression model produced an odds ratio indicating the likelihood that a student will either remain in the major or not. The odds ratio is the exponent of the regression coefficient calculated in the model and ranges from zero to infinity. An odds ratio greater than 1 indicates that a student is more likely to remain in a certain category as opposed to a reference category against which it is compared. The logistic regression results are reported in Table 3 . A 1-point increase in the initial general class anxiety score of a student indicates that, for every student who reported staying in the major, 4.446 times as many students reported leaving the major ( p < 0.001, 95% CI [1.794, 11.019]). The odds of females reporting leaving the major were 5.779 times greater than the odds of males leaving the major ( p = 0.007, 95% CI [1.616, 20.667]). To visualize general class anxiety scores (initial and final values from the surveys) by intention to persist, a bar chart of general class anxiety scores by students’ intention to persist in the major was created ( Figure 1B ).

a An odds ratio of 1 indicates a student is equally likely to report remaining in or leaving the major. A higher odds ratio indicates one group is more likely than the other to report leaving the major; a lower odds ratio indicates that group is less likely to report leaving the major compared with the other group. Nagelkerke (pseudo R 2 ) = 0.480.

For the multiple linear regression analyses for general class anxiety, the initial best-fit model (based on the initial general class anxiety measures) was significant ( F = 6.355, p < 0.001), as was the delta best-fit model ( F = 2.409, p = 0.049). These significant models indicated that they were better predictors than a null model with no predictors. At the beginning of the semester, freshmen, females, and those with fewer than 2 AP courses were more likely to have higher perceptions of general class anxiety ( Table 4 ). When examining the change in general anxiety, this trend continued for freshmen.

a Negative coefficient values indicate a negative relationship between general class anxiety and the corresponding demographic variable. For initial: AIC = 281.979, BIC = 297.294; for final: AIC = 323.027, BIC = 334.514.

For the multiple linear regression analyses for perceived difficulty, the initial best-fit model was significant ( F = 6.010, p < 0.001), but the delta best-fit model was not ( F = 1.199, p = 0.311). At the beginning of the semester, freshmen, females, and those with fewer than two AP courses were more likely to perceive higher levels of difficulty regarding the course ( Table 5 ). Change in perceptions of difficulty (delta) showed no measurable difference between demographic subsets.

a Negative coefficient values indicate a negative relationship between perceived difficulty and the corresponding demographic variable. For initial: AIC = 344.683, BIC = 359.998.

The multiple linear regression analyses for communication anxiety were significant for both the initial ( F = 6.619, p < 0.01) and delta ( F = 2.512, p = 0.042) models. At the start of the semester, females and those with 0–1 AP courses were more likely to perceive higher levels of communication anxiety in the course. When change in communication anxiety was measured, freshman communication anxiety remained relatively stable from the initial to the final survey, while communication anxiety in nonfreshmen decreased slightly ( Table 6 ).

a Negative coefficient values indicate a negative relationship between communication anxiety and the corresponding demographic variable. For initial: AIC = 1178.398, BIC = 1189.884; for final: AIC = 1126.082, BIC = 1137.560.

The multiple linear regression models for test and social anxiety can be found in Supplemental Tables 9 and 10. There were gender differences and year differences for test anxiety; social anxiety differences existed between genders, years, and ethnicity, but only at the start of the semester. These detailed results are included in the Supplemental Material, because social and test anxiety were not found to impact persistence or performance in the course.

This study investigated the relationships among different types of anxiety and perceived course difficulty at different times of the semester, student demographics, instructor, and student course performance and persistence in the major. We found that an increase in student perception of difficulty by the end of the semester was related to decreased course performance, and an increase in communication anxiety was associated with increased course performance. General class anxiety at the start of the semester was inversely related to persistence in the major. Test and social anxiety were not related to performance or persistence in this study. Our study also revealed that females, freshmen, and students with fewer AP courses typically had higher general class anxiety and perceptions of difficulty, but it was only females who were also more likely to have lower grades and leave the major (those with fewer AP courses had lower performance only). Conversely, non-Caucasian students were more likely to receive lower grades, but did not have different levels of general class anxiety or perceptions of difficulty compared with Caucasian students. Freshmen had higher general class anxiety and perceptions of difficulty compared with nonfreshmen, but had no differentials in performance or persistence. Taken together, these results suggest that some student emotions do impact performance and persistence but certain groups of students in introductory biology are more negatively impacted than others.

Perceived Difficulty Related to Student Performance

It is perhaps not surprising that changes in perceived course difficulty by the end of the semester were related to performance in the class. While perceived difficulty is not synonymous with anxiety, we hypothesize it is a direct antecedent to emotions such as anxiety. The difficulty scale asked students to rate their perceptions of whether the course was complex, complicated, and difficult, perceptions that would be used to appraise the demands of the course and judge the resources needed to succeed. Students overall consistently rated perceived difficulty higher than the general class anxiety items, suggesting that most students were able to meet the course demands and manage their anxiety. However, at week 14, the students who were going to earn lower grades in the class perceived the class as more difficult. This aligns with the expectancy-value theory of achievement motivations ( Wigfield and Eccles, 2000 ), which posits that students’ performances are partially explained by how well students feel they can perform on a task. It would be interesting to capture perceived difficulty more frequently through the semester to determine at what point in the semester perceived difficulty becomes predictive of performance.

If students anticipate performing poorly on a task, their goal may be to avoid the need to achieve altogether, a phenomenon known as performance avoidance ( Elliot and Harackiewicz, 1996 ). Performance avoidance has been linked to reduced motivation and achievement ( Elliot and Church, 1997 ; Richardson et al. , 2012 ). It may also be worth investigating what classroom emotions were associated with higher perceived difficulty ratings, with the presumption that, instead of anxiety, students may feel a nonactivating emotion such as hopelessness ( Pekrun and Stephens, 2010 ). Given that perceptions of difficulty were not related to persistence, it may be that students attribute difficulty to a particular instructor or course topic and are less likely to judge their future success in the major based on one course. For example, students found Instructor 5 more difficult, as seen in the ordinal regression model. Students in that instructor’s section were more likely to earn lower grades compared with the reference variable, which in this case was Instructor 6. All instructors within the same course (OEB or CMB) taught the same content, but not all students perceived the courses as equally difficult. Because there were no shared exams in these courses, these results may be explained by differences in exam difficulty among instructors (e.g., Instructor 5 vs. other instructors), which may have differentially impacted students.

General Class Anxiety Related to Student Persistence

General class anxiety was not predictive of student performance in our model, but it was predictive of student persistence. Higher levels of general class anxiety were positively associated with—and predictive of—a student’s intention to leave the major. In our model, this was found only at the beginning of the semester, indicating that prospective anxiety early in one course can impact students’ persistence in the major. This suggests that, just as students may perceive difficulty to be a function of a particular course, general class anxiety may be perceived as a future judgment on success in a degree program. There is a dearth of literature regarding the reasons why some students may come into the classroom with higher anxiety. There have been anecdotal reports of negative experiences with previous science classes and/or teachers, negative stereotypes of scientists, lack of role models, poor academic advising, and perpetuation of the myth that only a select few are capable of being scientists ( Mallow and Greenburg, 1982 ; Mallow, 2006 ). Some of these reasons correlate with recent work in our lab that indicates students’ presemester anxiety is driven by previous science course experiences, perceptions of difficulty of the subject, the length of time since their last biology course, not knowing what to expect, and concern about instruction and size of class (E.E.S., B.J.E., and J.R.B., unpublished data). However, these reasons remain untested and bound by our institutional context and should be considered tentative explanations.

The finding that anxiety impacts persistence is aligned with other recent studies ( Witt et al ., 2014 ; Barthelemey et al ., 2015 ; England et al ., 2017 ; Respondek et al ., 2017 ). Respondek et al . (2017) found that anxiety was negatively related to persistence, but not academic performance, supporting the idea that anxiety has more long-term versus short-term impacts on student perceptions of success. One potential explanation is that students may be able to deploy effective coping strategies to deal with their anxiety related to performance but are unable to extend this to persistence ( Boekaerts and Pekrun, 2015 ). Previous studies have indicated that the use of coping is correlated with persistence, but not necessarily with the amount of anxiety ( Shields, 2001 ). How students employ coping in regard to performance versus persistence is an area that needs further exploration.

Differential Performance and Persistence of Students Subsets

The findings that several demographic groups had differential performance and persistence support an extensive literature base on differential outcomes for subsets of students in science. Studies identifying gender differences in performance and persistence are common in the biology education literature ( Eddy et al. , 2014 ; Eddy and Brownell, 2016 ) and are thought to contribute to the lower than expected numbers of females in science ( National Science Foundation [NSF], 2011 ). Academic performance differences between Caucasians and non-Caucasians are consistent with other studies ( Greene et al. , 2008 ; Eddy and Hogan, 2014 ), and higher numbers of completed AP courses were linked with higher student grade point averages (GPAs) and graduation rates in a 10-year-long study at one institution ( Ackerman et al. , 2013 ).

An opportunity in this study was to examine potential links between general class anxiety and/or perception of difficulty and student performance and/or persistence for particular demographic groups. When the results of all the models were looked at collectively, the following trends emerged. Females had lower performance and persistence than males and reported higher perceived class difficulty and general class anxiety. However, higher levels of general class anxiety and perceived class difficulty only decreased performance in students with fewer AP courses and did not decrease performance or persistence in freshmen, and non-Caucasians had decreased performance without differentials in perceived difficulty or general class anxiety. Thus, differentials in general class anxiety and perceived difficulty are not always aligned with decreased student success in these groups, or vice versa. This could be explained by different emotional regulation within each subset. Females and males, for example, may have different mechanisms of coping, with female students consistently reporting more anxiety than male students ( Misra and McKean, 2000 ; Bayram and Bilgel, 2008 ; Bryant et al ., 2013 ) and higher use of emotion-focused coping ( Brougham et al. , 2009 ). In this sense, it is possible that females and students with fewer AP courses may feel that they do not have the cognitive and affective resources to manage the sources of anxiety, non-Caucasians may use emotion-focused coping (such as avoidance) to decrease their anxiety but without helping their performance, and freshmen may use their confidence generated from their recent high school successes to ameliorate some of the negative impacts of anxiety. Analyzing coping strategies within each of these subsets in introductory biology would be a worthy follow-up to this study.

Many of the impacted subsets of students in this study come from traditionally marginalized groups in science who sometimes feel they do not belong in the science classroom ( Grunspan et al. , 2016 ). This has negative impacts on self-efficacy, which is inversely related to anxiety ( Bandura, 1989 ). Although there are many potential causes of these differentials, recent work has highlighted classroom climate issues that could impact emotional responses. For example, factors such as professor–student interactions, student–student interactions, and classroom pedagogical practices may influence how these groups perceive the course and experience anxiety ( Barthelemey et al ., 2015 ). Classroom differentials, such as professors being more likely to call on males in class ( Eddy et al. , 2014 ) or females not being conferred the same respect for intellectual abilities that male students are in introductory biology ( Grunspan et al ., 2016 ), may lead to disenfranchisement and decreases in self-efficacy. This fits well with the control-value theory of achievement emotion ( Pekrun, 2006 ) because of the impact of classroom context, including instructor, on student emotion and the iterative nature of student experiences that may solidify particular emotions in particular student subsets based on their common classroom experiences.

Active Learning and Anxiety

This study originated as a follow-up to work showing student anxiety toward particular active-learning practices in introductory classes ( England et al ., 2017 ). Thus, we investigated types of anxiety that may be related to active-learning practices and their potential impacts on student performance and persistence. Neither communication nor social anxiety measures were predictive of either student performance or persistence, with one notable exception: An increase in communication anxiety over the semester was associated with higher grades. This counters work by McCroskey et al. (1989) , who found that higher communication apprehension lowered student GPAs, although this study was a longitudinal design and not focused on active-learning classrooms. The items designed to measure communication anxiety were focused on answering questions in front of peers. Although most anxiety impacts are negative, they can vary by individual ( Pekrun et al ., 2007 ) and have a modulating effect on performance ( Yerkes and Dodson, 1908 ; Keeley et al ., 2008 ). It is possible that a high level of communication anxiety “kept students on their toes” and made them more engaged in anticipation of possibly being asked a question. It is possible that certain instructors invoked a level of communication anxiety in their students that fostered higher engagement ( Mazer et al ., 2014 ).

The lack of association between higher social and communication anxieties and decreased performance or persistence supports the body of evidence on the positive impacts of active-learning practices on student performance ( Freeman et al. , 2014 ) and once again invokes an explanation suggested by the Yerkes–Dodson law ( Yerkes and Dodson, 1908 ) that, even if active learning causes anxiety, its impact is more likely to activate performance rather than inhibit it. Cooper et al . (2018) found that active-learning practices can both increase and decrease student anxiety in the classroom. It also may not be the increase or decrease in anxiety that matters for student success, but the presence of an optimal amount of anxiety overall. Both communication anxiety and social anxiety are known to exist in introductory classrooms ( Broeckelman-Post et al. , 2016 ; England et al ., 2017 ; Cooper et al ., 2018 ) and were found to vary by demographic group in this study, yet they were not factors in differential student success. Although not related to active learning per se, test anxiety was also found to be relatively high in students in this study, and differed for females and students with fewer AP courses, yet was not related to performance or persistence in this sample. This is counter to many findings that test anxiety is related to performance in college classes ( Culler and Holahan, 1980 ; Chapell et al ., 2005 ); however, the impacts of test anxiety likely vary based on the specific courses and assessment practices in those courses. Overall, it appears that the anxiety that impacts student success in these introductory biology classes is broader than individual active-learning practices or test anxiety. Further research is needed to investigate what classroom aspects are driving measures of general class anxiety and perceived difficulty in these classes.

Implications, Limitations, and Conclusion

The control-value theory of achievement emotions ( Pekrun, 2006 ; Pekrun et al ., 2007 ) indicates that emotions that students experience in the classroom are the manifestations of their appraisal of the value and control they feel in the class. However, the control-value theory also suggests that these appraisals and reactions can be regulated ( Pekrun, 2006 ; Boekaerts and Pekrun, 2015 ). Students, for example, can learn to adjust their appraisals of value and control or cope with their emotional responses ( Pekrun, 2006 ; Carter, 2010 ). Thus, there are strategies that could be tested to help females, for example, appraise their perception of control over their achievement outcomes or perhaps cope with anxiety they may be feeling in the classroom. Active coping in particular has been shown to help with student persistence and better college adjustment, although not with GPA ( Leong et al. , 1997 ; Shields, 2001 ). These findings align with Dweck’s (1986) and Bandura’s (1989) work on motivation and self-efficacy, suggesting that resilience in the face of challenge and high self-efficacy maintains student persistence and effort.

These coping strategies could be tested in a quasi-experimental manner in introductory biology classrooms to see whether they can equalize some of the performance and persistence impacts that some groups experience in these classes. These coping strategies should also be generally useful in other STEM courses, perhaps helping student success in courses outside biology ( Conley et al. , 2013 ). Alternatively, interventions that modify the curriculum or instructor practices may also be pivotal in helping subsets of students equalize their performances in the class ( Eddy and Hogan 2014 ; Mazer et al ., 2014 ). While documenting anxiety differentials and their potential impacts on performance and persistence is an important step, interventions to address these imbalances need to be tested.

There are several limitations to the findings we report in this study. The results were from a sample of students from one set of introductory courses at one university; thus, these results may not be generalizable to other institutions. The sample was also voluntary and heavily female and Caucasian. Although we intended to use the number of AP classes as a measure of college preparation and confidence, it is also likely conflated with disparity in academic access for many students. We were unable to collect a variable that would capture prior academic performance or ability to serve as a covariate in this study. Because students opted to respond to the survey or not, the sample may be biased toward those students who are more likely to share their anxiety experiences. We also do not have information on performance or persistence of the students who did not take the survey and thus cannot judge the potential bias of the sample. Furthermore, we do not know the specific course factors that were causing students to feel anxiety, or even if they were similar from course to course. Instructor was not a major factor in our models, but should be further explored in regard to impacts on particular subsets of students. Although mostly similar in size, one class was smaller than the others, and the impacts of different class sizes on anxiety and student outcomes could be a factor as well. There could also be many other factors outside the introductory biology courses of interest that would affect student performance and persistence, such as course load, student employment, or personal issues, none of which were explored in this study. Finally, the links between measures of general class anxiety or perceived difficulty and performance and persistence for the different demographic groups were only correlations. There are no causative links that can be drawn from this work.

This study investigated the relationships among different types of anxieties, demographic subsets of students, instructor, and student performance and persistence in introductory biology at one university. We found that general class anxiety, perceived difficulty, and communication anxiety had complicated links with performance and persistence, with higher levels of each at certain times of the semester impacting some measures of student success, particularly for females. It is likely that students appraise multiple aspects of the course to arrive at perceptions of anxiety and difficulty, yet these aspects are currently unknown. For certain subsets of students in the classes, their emotional experiences, appraisals, and coping are different from the rest of the class, leaving them vulnerable in terms of STEM retention and success. Further research on anxiety in the classroom is imperative to determine the actions instructors can take to address the STEM attrition crisis.

ACKNOWLEDGMENTS

We thank the instructors who helped us conduct this research, the students who participated in the surveys, and peer reviewers who improved this work. Liam Mueller and Elli Theobald provided invaluable assistance with the statistical analyses. This research was funded in part by an NSF TUES grant (DUE 1245215; E.E.S., principal investigator).

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Submitted: 29 December 2017 Revised: 18 January 2019 Accepted: 6 February 2019

© 2019 B. J. England et al. CBE—Life Sciences Education © 2019 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

• Open access
• Published: 23 May 2024

The effect of replacing sedentary behavior with different intensities of physical activity on depression and anxiety in Chinese university students: an isotemporal substitution model

• Yulan Zhou 1 ,
• Zan Huang 1 ,
• Yanjie Liu 1 &
• Dongao Liu 2  

BMC Public Health volume  24 , Article number:  1388 ( 2024 ) Cite this article

Metrics details

Previous research has suggested that engaging in regular physical activity (PA) can help to reduce symptoms of depression and anxiety in university students. However, there is a lack of evidence regarding the impact of reducing sedentary behavior (SB) and increasing light-intensity PA (LPA) on these symptoms. This study aims to address this gap by using isotemporal substitution (IS) models to explore how substituting SB with LPA or moderate-to-vigorous PA (MVPA) affects depression and anxiety symptoms among university students.

The study recruited 318 university students with a mean age of 21.13 years. Accelerometers were used to objectively measure the time spent on SB, LPA, and MVPA, while depression and anxiety symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) and the Self-rating Anxiety Scale (SAS). IS models using multivariable linear regression were employed to estimate the associations between different behaviors and depression and anxiety symptoms when 30 min of one behavior was substituted with another.

In the single-activity model, less SB (β = 0.321, 95% CI: 0.089, 1.297) and more MVPA (β = −0.142, 95% CI: −1.496, − 0.071) were found to be significantly and negatively associated with depression scores, while less SB (β = 0.343, 95% CI: 0.057, 1.014), LPA (β = 0.132, 95% CI: 0.049, 1.023), and more MVPA (β = −0.077, 95% CI: −1.446, − 0.052) were significantly and negatively correlated with anxiety scores. The IS analysis revealed that substituting 30 min of SB with LPA (β = −0.202, 95% CI: −1.371, − 0.146) or MVPA (β = −0.308, 95% CI: −0.970, − 0.073) was associated with improvements in depressive symptoms. Substituting 30 min of SB with MVPA (β = −0.147, 95% CI: −1.863, − 0.034) was associated with reduced anxiety symptoms.

Replacing 30 min of SB with MVPA may alleviate depression and anxiety symptoms in university students. Further research is needed to explore the long-term effects of PA interventions on the mental health disorders of this population.

Peer Review reports

The World Health Organization (WHO) estimates that people worldwide will be affected by mental health disorders at some point in their lives. Around one billion people currently suffer from such conditions, placing mental health disorders among the leading causes of disease burden and disability worldwide [ 1 ]. Furthermore, individuals with moderate to severe mental health disorders have a reduced life expectancy of 10–20 years and a 2–3 times higher risk of mortality compared to the general population [ 2 ]. Depression and anxiety are globally prevalent mental health disorders and increased by a massive 25% in the first year of the COVID-19 pandemic [ 1 ]. Both disorders are negative emotions and often co-occur, with 62% of adults with anxiety experiencing depressive episodes as well [ 3 ]. The pressures of academics, interpersonal interactions, and employment make university students particularly vulnerable to mental health disorders, including depression and anxiety [ 4 ]. Consequently, the annual detection rate of these conditions among university students is on the rise globally [ 5 ]. A survey conducted in China has revealed that a significant number of university students are at high risk of developing depression and anxiety. Specifically, 6.6% of students face a high risk of depression, while 5.4% exhibit severe anxiety disorders [ 6 ]. The presence of depression and anxiety during university can persist into adulthood and adversely affect many aspects of personal life, such as personal relationships, academic performance, and work productivity [ 7 ]. It is essential to address the mental health needs of university students who experience depression and anxiety.

Research has shown that engaging in regular physical activity (PA) can provide numerous health benefits for university students, including a reduced risk of depression and anxiety [ 8 , 9 ]. Studies have found a strong connection between moderate-to-vigorous PA (MVPA) and lower levels of both depression and anxiety symptoms in this population. Two recent systematic reviews, which encompassed evidence from prospective cohort studies and intervention studies, have converged on the conclusion that engaging in regular MVPA is linked to a reduction in depressive and anxiety symptoms [ 10 , 11 ]. Moreover, while the relationship between light-intensity PA (LPA) and sedentary behavior (SB) with depression and anxiety is not entirely conclusive, some studies have found links between these factors [ 12 , 13 ]. Given the health-promoting relationship between PA and health, public health organizations worldwide encourage individuals to “sit less and move more” [ 14 , 15 ]. However, individuals are limited in the amount of time they can engage in PA each day, and changes in the duration of one behavior inevitably led to compensatory changes in the duration of other behaviors. Therefore, a more comprehensive approach should be used to explore the combined effects of different intensities of PA and SB on health outcomes [ 16 ].

The Isotemporal Substitution (IS) Model as suggested by Mekary et al. [ 17 ] simultaneously simulates the specific activity being performed and the specific behavior being replaced in an equal time-exchange manner. The model controls for the confounding effect of total activity time and the heterogeneity of participation or substitution activities. Thus, one can estimate associations between theoretically substituting one type of PA for others and health outcomes. Several recent studies have explored the associations of SB, LPA, and MVPA with symptoms of depression and anxiety in older adults using the IS modeling method. For example, two cross-sectional studies have shown that reallocating 30 min of SB with an equal amount of either LPA or MVPA is significantly associated with a reduced risk of developing depression symptoms among older adults [ 18 , 19 ]. The studies by Dillon et al. [ 20 ] and Tully et al. [ 21 ] demonstrated that reallocating 30 min of SB with LPA or MVPA was associated with improved anxiety symptoms among older adult. In a study by Chao et al. [ 22 ], Chinese university students experienced a noteworthy reduction in anxiety symptoms by replacing 15 min of SB with LPA. Nonetheless, the impact of substituting SB with various intensities of PA on depression among university students remains an area that requires further exploration. Moreover, unlike depression, anxiety often presents with distinct physiological symptoms such as a racing heart, muscle tension, sweaty palms, and dry mouth [ 23 ]. Given these distinctions, it becomes imperative to explore whether the substitution relationship between various activity behaviors differs in its effects on depression and anxiety among university students.

Therefore, this study aimed to investigate the cross-sectional associations between SB, LPA, and MVPA with depression and anxiety among university students, and to explore the difference in the effects of replacing 30 min of SB with different intensity PA (LPA and MVPA) on depression and anxiety among university students. The outcomes of this study hold the potential to enrich our comprehension of the intricate connection between PA and the prevalence of depression and anxiety among university students. Furthermore, they offer valuable practical insights that can inform the development of effective interventions aimed at promoting PA and mitigating these mental health disorders within this population.

Participants and data collection

For this study, participants were university students recruited via a convenience-based sampling method. Recruitment efforts were concentrated on one sizable public university in each of the regions: Hubei Province, Zhejiang Province, and Shanghai, China. A multistage cluster sampling approach was employed to select participants. In the first stage, one college (e.g., Humanities, social sciences, engineering, and information sciences) was chosen from each of the selected universities. Following that, two classes were selected from each of the selected colleges. To be eligible for participation in the study, individuals needed to meet the following criteria: They had to be full-time university students between the ages of 18 and 25 years old. Participants who reported any physical or mental condition that would hinder their ability to engage in PA were excluded from the study. Ethical approval from the Ethics Committee of Zhejiang Normal University was obtained before the commencement of data collection for our study. Informed consent was obtained from all participants before they completed the questionnaire. To ensure confidentiality, participants were assigned a unique identification number and all data collected were kept secure and anonymous. Participants were informed that they could withdraw from the study at any time without penalty. The study’s required number of participants was estimated using G*Power 3.1 software, considering a 5% maximum tolerable error and a power of 0.8. The estimated number of subjects needed was 343. To accommodate potential losses such as dropouts and hardware failures, this number was increased by 20%. Therefore, a total of 463 university students from 6 classes were invited to participate in the survey. Ten students declined to cooperate with the survey, and an additional seven students were excluded due to recent psychological dysfunction, defined as having received a psychological disorder diagnosis within the past 6 months. Consequently, a total of 446 students actively participated in this study. An initial inspection of the raw data showed that 104 participants did not provide valid accelerometry data (at least 10 h of wear per day was considered one valid day, and at least one valid weekend day and two valid weekdays), and a further 24 participants did not provide valid survey data for the outcome variables. Thus, the study included a total of 318 participants, with 107 from Hubei Province, 93 from Zhejiang Province, and 118 from Shanghai.

Data collection took place during the middle of the Fall semester, spanning from October to December 2022. The primary author, alongside two research assistants who were postgraduates specializing in physical education, conducted the data collection. Participants were equipped with accelerometers and instructed to maintain their regular daily routines during the monitoring period. To ensure adherence, the research assistants made daily visits to the universities in the mornings to remind students to wear the accelerometers. To ensure the collection of data for a complete seven days, students were instructed to return the accelerometers after eight days. Subsequently, all participants were requested to complete a self-administered questionnaire in a classroom environment. This questionnaire covered various socio-demographic factors (e.g., age and gender), lifestyle aspects (e.g., alcohol consumption and smoking habits), sleep patterns, and details regarding mental health disorders. Throughout this process, the research assistants were present to offer support to the participants and ensure order in the classrooms.

Measurements

Sedentary behavior and physical activity.

SB, LPA, and MVPA were measured using the triaxial accelerometer (ActiGraph wGT3X-BT). The technical reliability and validity of the accelerometer device have been described elsewhere [ 24 ]. Participants were instructed to wear the accelerometers on their right hipbone for at least seven consecutive days and only remove it for sleeping and water-based activities (e.g., swimming and bathing). The accelerometer started recording data at 0:00 a.m. on the second day of distribution and continued until the researcher retrieved it at the end of the eighth day. After the test was conducted, data were extracted using Actilife 6.5 software and then collapsed into a specific time interval (epoch), for example, a 60 s epoch. The inclusion criteria of wearing the accelerometer for at least one valid weekend day and two valid weekdays, with at least 10 h per day of wear, helps to ensure that the data is representative of the participants’ typical PA levels [ 25 ]. Non-wear time was defined as a period of at least consecutive 60 min during which the accelerometer recorded 0 counts per minute (cpm) [ 26 ]. Activity counts were classified using a set of cut points to calculate the intensity and amount of SB, LPA, and MVPA. SB was classified as < 100 cpm, LPA was 100–1952 cpm, and MVPA was > 1952 cpm [ 27 ].

Depression and anxiety

The Center for Epidemiologic Studies Depression (CES-D) 20-item symptom scale was used to assess symptoms of depression [ 28 ]. The CES-D is a widely used and well-established measure for assessing symptoms of depression in research studies [ 29 ]. Participants were asked to report how often over the past week they have experienced each of the 20 symptoms associated with depression such as restless sleep, poor appetite, and feeling lonely. The score of each item ranges from 0 (rarely or none of the time) to 3 (most or all of the time). The total score ranged from 0 to 60, with higher scores indicative of higher levels of depressive symptoms. A score of 16 points or more is indicative of depression in this assessment [ 30 ]. The reliability of the CES-D in this study, as indicated by a Cronbach’s alpha coefficient of 0.865, is well above the acceptable threshold of 0.70, indicating that the scale is consistent in measuring symptoms of depression.

The Self-rating Anxiety Scale (SAS), which was compiled by Zung et al. [ 31 ] was employed in this study to allow university students to self-report anxiety symptoms, which can provide insight into the subjective experience of anxiety. The survey consists of 20-item scale and covers a range of potential anxiety symptoms, including psychological and somatic symptoms. Each item is score on a four-point Likert scale according to the frequency of the status in the previous week. Participants choose responses ranging from 1 to 4 (1 = no or a little of the time, 2 = some of the time, 3 = good part of the time, 4 = most of the time or all the time) with summed scores ranging from 20 to 80. Higher scores indicate a higher level of anxiety symptoms. A cut-off value of 50 for the total score was established to indicate the presence of anxiety symptoms [ 7 ]. This scale has been shown to have good reliability and validity in a variety of populations [ 32 ]. In this study, the internal consistency of the scale was also found to be high, with a Cronbach’s alpha coefficient of 0.782, indicating that the scale is reliable in measuring this construct in the study population.

Covariates were selected based on previous studies and included socio-demographic characteristics (i.e., weight, height, age, gender, years of university, and residential background), lifestyle aspects (i.e., alcohol consumption and smoking information), and sleep pattern [ 33 ]. Participants were asked to report their socio-demographic and lifestyle information through questionnaires. The Pittsburgh sleep quality index (PSQI) questionnaire were used to assess students’ sleep patterns [ 34 ]. The researchers also calculated the participants’ body mass index (BMI) using their reported weight and height (weight in kilograms divided by height in meters squared).

Data analysis

Statistical analyses were performed using IBM SPSS Statistics, Version 26.0 for Windows and the level of significance was set at P  < 0.05. Descriptive statistics like frequencies, percentages, means, and standard deviations were used to summarize the data. Categorical variables like gender, drinking alcohol, and smoking status were presented as frequencies and percentages. Continuous variables like age and BMI were presented as means and standard deviations. Person correlations were used to assess the associations among SB, LPA, MVPA, and mental health disorders. Three multiple linear regression models including a single-activity, a partition, and an IS models were utilized to examine the relationship between SB, LPA, and MVPA with both depression and anxiety. Prior to conducting the analysis using three distinct linear regression models, it was ensured that there existed linear relationships between SB, LPA, MVPA, and the scores for depression and anxiety. Additionally, it was confirmed that there was no evidence of multicollinearity among the independent variables. In current study, we focused on modeling the effects of reallocating 30 min from one behavior to another. This approach was chosen for its practicality, especially considering that in China, university students tend to be generally physically inactive [ 30 ]. Reallocating 30 min is a more feasible and realistic scenario than longer durations. In addition, previous studies among adults have interpreted the association between replacing of 30 min with different activity intensities and mental health disorder [ 18 , 19 , 20 , 21 ]. We chose the replacing 30 min in the present study to improve the interpretability of the results. SB, LPA, and MVPA were standardized using 30 min as a unit for activity in analyses.

First, a series of single-activity models were computed to investigate the independent associations between each behavior (i.e., SB, LPA, MVPA) and mental health disorder (i.e., depression, anxiety), adjusted for covariates that are known to be associated with both activity and mental health disorder (e.g., age, gender, smoking status, and alcohol consumption). One type of single activity model (in the case of SB) is shown as follows: Mental health disorder = (β1) SB + (β5) covariates.

Second, partition models were used to estimate the effects of increasing each behavior on mental health disorder while holding the duration of each of the other behavior variables constant. Partition model represents the effects of adding, not substituting an activity type because total wear time is excluded in the model (thus is not held constant). Partition models were expressed as: Mental health disorder = (β1) SB + (β2) LPA + (β3) MVPA + (β5) covariates.

Finally, IS models were applied to explore the effects of reallocating time between SB, LPA, and MVPA on indicators of mental health disorder. IS models estimate the effects of replacing time spent engaging in one behavior with another behavior for the same amount of time, while holding total time constant. The following equation describes the effects of replacing 30 min of SB with 30 min of LPA (β2), or MVPA (β3): Mental health disorder = (β2) LPA + (β3) MVPA + (β4) total wear time + (β5) covariates. β1-β5 are the coefficients of respective activities or covariates.

Descriptive characteristics of study sample

Table  1 shows the characteristics of study participants. The study included a final sample of 318 participants, of which 127 (39.9%) were male and 191 (60.1%) were female. The mean age was 21.13 (SD = 3.53) years. The mean BMI and total PSQI score were 19.48 (SD = 1.03) and 6.93 (SD = 2.13), respectively. On average, participants wore accelerometers for 823.89 (SD = 111.75) minutes/day. The mean proportion of SB, LPA, and MVPA time to total accelerometer wearing time were 72.5%, 21.5%, and 6.0%, respectively. Participants reported an average score of 13.85 (SD = 8.21) for depressive symptoms, with 17.3% of participants falling into the category of experiencing depressive symptoms (a total score of CES-D ≥ 16). In terms of anxiety symptoms, the mean score was 39.03 (SD = 6.20), and 26.1% of the sample met the criteria for anxiety symptoms (a total score of SAS ≥ 50). The correlation among SB, LPA, MVPA, and mental health problems presented in the supplemental Table 1 .

Effects of reallocating time between the different intensities of PA and SB on depression symptoms

Table  2 displays single-activity, partition, and IS models for the relationship between different intensities of PA, SB, and university students’ scores of depressive symptoms. In the single-activity models, SB time tended to be significantly and positively associated with depression scores (β = 0.321, 95% CI: 0.089 to 1.297), whereas MVPA was significantly and negatively associated with scores of depressive symptoms (β = −0.142, 95% CI: −1.496 to − 0.071). In the partition models, increasing SB by 30 min while holding the other variables constant was associated with a significant increase in depression scores among university students (β = 0.326, 95% CI: 0.098 to 1.315). In the IS models, replacing 30 min/day of SB with LPA (β = −0.202, 95% CI: −1.371 to − 0.146) and MVPA (β = −0.308, 95% CI: −0.970 to − 0.073) resulted in a significant decrease in depression scores.

Effects of reallocating time between the different intensities of PA and SB on anxiety symptoms

Table  3 presents the results for the single-activity, partition, and IS models adjusted for covariates. The single-activity model shows that higher levels of both SB (β = 0.343, 95% CI: 0.057 to 1.014) and LPA (β = 0.132, 95% CI: 0.049 to 1.023) were significantly associated with higher anxiety scores. Conversely, a higher level of MVPA was associated with a lower anxiety score (β = −0.077, 95% CI: −1.446 to − 0.052). The partition model showed that increasing SB by 30 min was associated with higher symptoms of anxiety (β = 0.325, 95% CI: 0.085 to 0.983). The IS model demonstrated that a 30 min unit of SB replaced with MVPA was significantly and negatively associated with anxiety scores (β = −0.147, 95% CI: −1.863 to − 0.034). No statistically significant change in scores of anxiety symptoms was observed when SB was substituted by LPA (β = −0.095, 95% CI: −0.982 to 0.281).

In the current study, the prevalence rates of depression and anxiety among university students were determined to be 17.3% and 26.1%, respectively. These findings align with surveys conducted among university students in various other countries [ 35 , 36 ]. This highlights that depression and anxiety are major mental health concerns not confined to Chinese students but prevalent among university students worldwide [ 37 , 38 ]. Furthermore, university students face unique challenges in terms of PA and SB. They often have demanding schedules and spend long periods of time sitting in lectures or studying. A comprehensive body of evidence has found PA to reduce depression and anxiety in both clinical and non-clinical populations [ 39 , 40 ]. However, the beneficial effects of LPA and MVPA, as well as the impact of substituting SB with light activity or MVPA on mental health disorders, are less known. The IS model is likely to show more accurate results of associations of SB and PA with mental health disorders, since it takes the finite amount of time in a day into account, allowing for estimating the effect of replacing one type of PA with another. This study demonstrated the usefulness of the IS model approach in examining the relationship between PA and mental health disorders in Chinese university students. By estimating the effects of substituting SB with different intensities of PA, the study found that replacing 30 min of SB with MVPA was associated with decreased depression and anxiety scores. Additionally, replacing 30 min of SB with LPA was associated with lower depression scores.

The available evidence, specifically within the domain of IS modeling, is notably limited when it comes to addressing depressive symptoms among university students in comparison to studies conducted on other age demographics. Consistent with two cross-sectional studies among older adults [ 18 , 19 ], the current study found a significant decrease in depressive symptoms when 30 min of SB was substituted with LPA or MVPA. These results suggest that the benefits of PA on depressive symptoms are not limited to older adults and can also be observed in younger populations. A larger number of intervention studies have also confirmed that PA can significantly reduce depressive symptoms [ 41 ]. Multiple mechanisms of action have been proposed to explain associations between PA and depressive symptoms. Depression is a negative mood that can be impacts how people think, feel, and go about daily activities. Typical symptoms of depression include sadness, emptiness, hopelessness, feeling of worth-lessness, and loss of interest in activities [ 42 , 43 ]. Compared with SB, LPA or MVPA can increase the production and release of mood-related neurotransmitters such as serotonin and endorphins, which can help promote pleasure and positive feeling, thereby alleviating depressive symptoms [ 44 , 45 ]. In addition, participation in PA can lead to improved social relationships and increased social support, which can help reduce psychological stress and improve depressive symptoms [ 46 , 47 ]. Our study further revealed that compared to replacing 30 min of SB with LPA (− 0.202), a more substantial benefit was observed when replacing 30 min of SB with MVPA (− 0.308) concerning depressive symptoms in university students. A recent systematic review of IS studies also demonstrated that the strongest association with health outcomes is observed when time is reallocated from SB to MVPA [ 48 ]. These results imply that university students who spent most of their day sedentary (72.5%) should be encouraged to sit less and move more for a range of health benefits, including improvements in mental and cardiovascular health and reduced risk of chronic diseases. Furthermore, incorporating reduced SB and increased MVPA into daily life may be a more effective strategy for improving depressive symptoms in university students.

The findings derived from this study emphasized the positive consequences of replacing 30 min of SB with MVPA in mitigating anxiety among university students. Conversely, there was no observable effect when substituting 30 min of SB with LPA, marking a distinction from the ameliorative impact of LPA on depression. Anxiety is characterized as a distinct, unpleasant emotional state or condition encompassing apprehension, tension, worry, and physiological arousal [ 49 ]. It is essential to note that anxiety and depression represent two distinct and valid constructs that frequently occur simultaneously. Alternatively, they could be regarded as different expressions of the same underlying vulnerability [ 23 ]. In the model formulated by Clark et al. [ 50 ] symptoms of depression and anxiety are classified into three subtypes: negative affectivity, positive affectivity, and physiological hyperarousal. Negative affectivity is linked to both depression and anxiety. The deficiency in positive affectivity is hypothesized to be solely connected to depression, while physiological hyperarousal is suggested to be specific to anxiety. Consequently, consistent with the approach for addressing depression, substituting SB with PA holds promise for ameliorating the negative emotional dimensions of both depression and anxiety. This is believed to occur through the modulation of neuroplasticity and the reduction of inflammation [ 47 , 51 ]. However, it’s worth noting that LPA may not be as effective in attenuating the physiological hyperarousal associated with anxiety. In contrast, engaging in a moderate or high level of PA has the potential to induce relaxation in the central nervous response and decrease the sensitivity of physiological arousal tied to anxiety, ultimately resulting in a reduction in anxiety [ 52 ]. Moreover, PA serves as a valuable form of distraction from the daily stressors that individuals encounter. Conversely, engaging in LPA may curtail students’ ability to divert their attention away from the stress-inducing factors of everyday life, leading to an escalation in the severity of anxiety symptoms [ 53 ].

Consistent with the established literature, current study provided further evidence that replacing SB with either LPA or MVPA yielded favorable effects on the depressive symptoms of participants [ 18 , 19 ]. However, it’s important to emphasize that our investigate did not uncover any beneficial effects of LPA substituting SB on anxiety among university students. This outcome stands in contrast to the findings of studies conducted by Dillon et al. [ 20 ] and Chao et al. [ 22 ]. The inconsistent findings may be related to the use of different measures of physical behavior in these studies. Dillon et al. [ 20 ] used objective measures of physical behavior, the GENEActiv. This accelerometer measures acceleration at the wrist, while the ActiGraph, which used in present study, measures acceleration of the body at the hip. The movement or acceleration of the body differs significantly at these two positions and thus affect the comparability of the current findings to previous research. In an investigation led by Chao et al. [ 22 ], the central focus was on examining the connection between self-reported PA and anxiety among college students. It’s worth noting that when PA levels are assessed using self-reported measurement tools, there is a tendency for individuals to overestimate their activity levels [ 54 ]. This could offer an explanation for why previous research often demonstrated positive associations between LPA and anxiety, while our current study did not. Additionally, it is important to consider the context of LPA when examining its relationship with anxiety. Different contexts of LPA may have different effects on anxiety levels due to factors such as the level of mental stimulation they provide and the social context in which they are performed [ 55 ]. For example, household and occupational LPA may reduce anxiety by providing a sense of accomplishment and control, while transport LPA may be associated with anxiety due to the stress and time pressure involved [ 56 ]. The single model of this study also demonstrated that a more time spent in LPA was associated with higher anxiety scores. It is premature to entirely negate the potential effects of replacing SB with LPA. To gain a more thorough understanding of whether and to what extent LPA can be beneficial for the mental health of university students, additional research utilizing comprehensive measurement tools is essential.

Given the escalating prominence of depression and anxiety as significant public health concerns, it is paramount that we pinpoint cost-effective strategies to address these challenges. Our findings indicate that replacing 30 min of SB with LPA (β = −0.202, 95% CI: −1.371, − 0.146) or MVPA ((β = −0.308, 95% CI: −0.970, − 0.073) significantly improved depression symptoms, while only 30 min of MVPA (β = −0.147, 95% CI: −1.863, − 0.034) substitution for SB was effective in reducing anxiety symptoms among university students. Although the relatively small β coefficients and wide confidence intervals may indicate that the actual effect size is insufficient to confidently assert that such behavior substitution has a substantive improvement in university students’ depression and anxiety symptoms, the findings offer insights into optimizing PA implementation and highlight the challenges one may encounter in making such changes. For university students who are relatively physically robust, targeting substituting SB with MVPA may be a more feasible, attractive, or realistic behavior change to target in the first instance. However, for students who are not used to regular PA, attempting to switch from SB to MVPA may be too daunting and overwhelming. Encouraging students to find PA that they enjoy and can easily incorporate into their daily routine is key. Embarking on the journey with smaller, realistic goals can be instrumental in building both confidence and motivation. Following this, it is prudent to undergo exercise testing to tailor a PA program that aligns with these goals, preventing an initial overexertion. The use of an activity diary is strongly encouraged, and documenting daily life PA can enhance students’ commitment to the PA program. Finally, maintaining social connections, whether with parents or classmates, while engaging in PA can be a valuable factor in facilitating students’ achievement or maintenance of this new behavior [ 57 , 58 ]. Furthermore, Ministries of Education and Health should place a strong emphasis on heightening public health awareness concerning the pivotal role of MVPA for individuals with mental health disorders. They should offer guidance on indispensable preventive measures for university students who are beginning to adopt a lifestyle of physical inactivity. Additionally, these ministries should actively adopt and implement effective policies and interventions related to these pertinent issues.

There are several potential limitations presents in this study. First, due to its cross-sectional design, the study cannot establish causal associations, and there remains the potential for confounding by unmeasured covariates. Second, the IS method merely indicated the theoretical effect of substituting one behavior for another, it may not fully encapsulate the complexity and dynamism of behavior changes in everyday life. Third, the use of accelerometers fails to capture certain types of activities (e.g., swimming, cycling) and the placement of the device (hipbone vs. wrist) may affect data accuracy. Fourth, depression and anxiety outcomes were self-reported. In spite of self-reported measures are more cost effective and convenient, there is a possibility of social expectation bias as respondents may conceal their true situation. Finally, since all the participants were restricted to three provinces in China, representation of the general population is limited. Future research should address these limitations to provide a more comprehensive understanding of the relationship between time-use compositions and mental health disorders in the university students.

This study revealed that substituting 30 min of SB with LPA or MVPA significantly improved depression symptoms in university students. Greater benefits were observed when shifting SB to MVPA. Moreover, substituting 30 min of SB with MVPA was associated with reduced anxiety symptoms. These findings contribute valuable and novel information to our comprehension of how various intensities of PA impact mental health disorders. Future research should delve into the potential of PA as a cost-effective and readily accessible strategy to alleviate the burden of mental health disorders among university students.

Data availability

The datasets used and/or analyzed during the present study are available from the Y. Zhou ([email protected]) on reasonable request.

Abbreviations

Sedentary Behavior

Light-intensity Physical Activity

Moderate-to-Vigorous Physical Activity

Isotemporal Substitution

Body Mass Index

Center for Epidemiologic Studies Depression Scale

Self-rating Anxiety Scale

Counts Per Minute

Pittsburgh Sleep Quality Index

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Acknowledgements

We would like to thank the team who have collaborated in data collection and to all the students and the teachers for their participation.

This study was supported by Zhejiang Federation of Humanities and Social Sciences Circles (No. 2023N014).

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Zhou, Y., Huang, Z., Liu, Y. et al. The effect of replacing sedentary behavior with different intensities of physical activity on depression and anxiety in Chinese university students: an isotemporal substitution model. BMC Public Health 24 , 1388 (2024). https://doi.org/10.1186/s12889-024-18914-y

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Female students could be passing on STEM due to experiencing higher levels of maths anxiety than boys in primary school

Wednesday, 22 May, 2024

Posted 22 May, 2024

STEM uptake by female students in Ireland could be being hampered by them experiencing higher levels of maths anxiety during primary school.

(opens in a new window) A new report outlining new recommendations to parents, teachers, and policymakers to better promote mathematics education warns a lack of confidence at such an early age could discourage young girls from pursuing an interest in science, technology, engineering, and mathematics (STEM).

Reporting the results of the (opens in a new window) Arithmós Project , collaborative between University College Dublin and Technological University Dublin, supported by funding from the Irish Research Council, it found at primary school level boys outperformed girls in most maths tasks and that the latter experienced higher levels of maths anxiety.

This unease in their mathematical ability means even the highest-achieving female students may shy away from maths tasks because they think they might be too difficult.

“It is striking that girls underperformed in some maths tasks despite having the same teachers and attending co-ed school,” said Arithmós Project lead, (opens in a new window) Dr Flavia H. Santos , UCD School of Psychology and Research Fellow at the UCD Geary Institute for Public Policy .

“Our data indicated that parental maths anxiety and negative attitudes towards maths had an impact on homework activities and in turn widening disparities. We as a society, educators and parents must change the way we introduce maths to young children.”

The report underscores gender disparities in attitudes toward mathematics, with boys demonstrating significantly higher motivation and perseverance, along with lower levels of mathematics anxiety. The impact of mathematics attitudes on education, it says, demands immediate attention, prompting educators and policymakers to identify effective strategies to foster positive mathematics attitudes from an early age. 

A very insightful and informative round table to discuss the importance of talking about maths anxiety and how to transform children’s mathematics education through digital games. (opens in a new window) #ArithmosProject (opens in a new window) #GirlsInSTEM (opens in a new window) pic.twitter.com/W08QqfwzuE — UCD Geary Institute for Public Policy (@ucdgearyinst) (opens in a new window) May 21, 2024

One potential approach undertaken as part of the Arithmós Project involved the integration of educational digital games into the classroom. The Seven Spells - an awarded digital card game that stimulates mathematical skills and strategic thinking developed by Dr Pierpaolo Dondio and Dr Mariana Rocha from Technological University Dublin – was introduced to 403 third and fourth graders (217 girls and 186 boys) across 23 classrooms in Ireland. A number of children from each classroom played the Seven Spells games for five weeks, 45 minutes per week, during regular school hours.  Others in the class were engaged with educational mathematics videos, while those in the ‘passive groups’ did only their regular classroom activities. 

Those children allowed to play Seven Spells showed a significant improvement in their maths skills, and after the intervention, many children, including highly maths anxious students, spontaneously continued playing Seven Spells on their own. It means that gameplay reduced students' avoidance of the maths implied in the game, said Dr Santos. “The gameplay had a positive effect on the avoidance towards maths as even highly maths anxious students spontaneously continue to play after the intervention,” she added.

By:   David Kearns , Digital Journalist / Media Officer, UCD University Relations

To contact the UCD News & Content Team, email: [email protected]

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Four Psychology Students Present Research at External Conferences in April 2024

Four psychology majors presented research to outside audiences in April 2024. Alongside former SMCM Visiting Assistant Professor of Psychology Amie Severino ‘11 (now at Mount Saint Mary’s University), Briana Corbin ’24 presented at the Anxiety and Depression Association of America conference in Boston, MA. Gabrielle Blew ’24, Angelie Roche ’24 and Katya Scott ’24 presented their individual St. Mary’s Projects (all conducted under the mentorship of Professor of Psychology Libby Nutt Williams) at the L. Starling Reid Undergraduate Psychology Conference held at the University of Virginia in Charlottesville, VA on April 26.

Corbin’s poster, “Risk Factors Associated with the Etiology of Adolescent Mood Disorders,” was based on work conducted as part of her 2023 Summer Undergraduate Research Fellowship (SURF) project with Severino. Abstract: Risk factors in a young person’s environment can lead to the expression of mood disorders such as anxiety and depression in adolescence. These mental health disorders often lead to detrimental psychosocial and academic impacts during adolescence and may persist throughout adulthood. It is important to identify the risk factors of adolescent mood disorders to allow for the implementation of early interventions. We implemented a rigorous methodological strategy to synthesize the existing evidence of environmental risk factors for anxiety and depression of adolescents in the United States (age 13-19) from quantitative, empirical sources that were published in the last 10 years (2013-2023) in peer-reviewed journals. After reviewing 29 articles from an initial search query of 16,363 articles, we identified common themes of risk factors associated with the development of anxiety and depression in adolescence. These themes were trauma, factors relating to the neighborhood and community the youth resided in, negative life events, victimization, peer rejection, factors related to the income level of the youths' environment, discrimination, family factors, and substance use. Only one of the 29 studies examined puberty as an indication of adolescence. Additionally, a few studies indicated the interaction of mood disorders with biological implications, such as anxiety being associated with sleep disturbances. Understanding how to effectively address these identified risk factors is essential to clinical interventions for adolescent mood disorders and the potential life-long biological consequences.

Blew presented a poster entitled “To Gift or Not to Gift: Examining the Impact of Telehealth on Client Gift Giving.” Abstract: Gift-giving is an ethical dilemma in psychotherapy, often discussed by many psychotherapists. There is no clear answer on how therapists should respond when they are presented with a gift by a client, which is likely due to the controversy of gift-giving within psychotherapy more broadly. There is a lack of understanding of how other morally gray areas of psychology, such as the introduction of telehealth, have impacted the overall issue of client gift-giving. Zoom interviews were conducted to better understand therapists’ views and experiences with client gift-giving, and how their experiences differed between in-person and telehealth settings. Eight therapists who had been practicing for a minimum of five years, have provided telehealth services and who have been offered at least one gift by a client were interviewed. These interviews were analyzed using Consensual Qualitative Research methods (Hill, Thompson, & Williams, 1997; Hill et al., 2005). Major findings include that only four of the eight therapists had received gifts while seeing clients via telehealth, with these gifts usually being delivered through electronic means. Additionally, most of these therapists had only one experience where a telehealth client had attempted to give them a gift. Typically, the therapists felt like gift-giving is less prevalent via telehealth, which may be related to how it fosters a less personal relationship. Overall, these results suggest that telehealth may have minimized the ethical dilemma of gift-giving within the realm of psychotherapy.

Roche presented a poster entitled “Summer Camp Counselor Experiences: The Influence of Training, Self-Efficacy, and Organizational Cohesion.” Abstract: Many overnight camps use Counselor-in-Training (CIT) programs to prepare adolescent campers for the counselor role. Although research has investigated the efficacy of individual CIT programs, studies have not compared the experiences of previous CITs to the experiences of new, non-CIT counselors across camp types. We recruited 314 camp counselors (130 previous CITs and 185 non-CITs) from camps across the US and Canada for an online survey assessing their self-efficacy and organizational cohesion (Chen et al., 2001; Ruga, 2014) and other items related to their experiences as first-year counselors. The majority of the sample reported positive experiences. Although CIT completion did not correlate significantly with any variable, individual and camp factors such as job fit, satisfaction with training and comfort talking to administrators correlated with self-efficacy and organizational cohesion. More between-camps research is needed to examine training, overall climate and the true efficacy of CIT programs.

Scott presented a talk entitled “Art Therapy with Ukrainian Refugees: A Pilot Program”. Abstract: There is little empirical research available that explores art therapy with adult refugees. Refugees face increased risk for mental health conditions such as depression, anxiety, and post-traumatic stress disorder; post-migration, they also often face challenges such as cultural isolation and a loss of community. As the number of refugees worldwide increases at an alarming rate, well-researched, culturally sensitive systems of mental health support must be made available to anyone who requires them. Group art therapy may be a particularly helpful intervention due to a focus on social connections and a de-emphasis on verbal processing, which might help lessen the impact of language barriers and support those with difficulty expressing painful experiences in words. To ensure programs are culturally sensitive, adopting the Multicultural Orientation (MCO) framework is an ideal approach. Current research on art therapy with refugees is extremely limited, particularly with adults. However, incorporating culturally significant materials into art therapy programs may be a promising route for future work. To address this gap in research, we conducted a mixed-methods study of a five-week group art therapy program incorporating culturally significant materials with adult Ukrainian refugees. Via pre- and post-intervention surveys, we analyzed the program’s potential influence on anxiety, resilience, and connection to community. We also conducted brief interviews with participants about their experiences in the program and analyzed these using Consensual Qualitative Research (CQR). The results offer potential directions for research in this area that should be further explored in future studies.

For her SMP work, Katya Scott was presented with the Myron G. Marlay Award for Science at this year's Awards Convocation. Angelie Roche was selected as one of two winners of the 2024 Department of Psychology's SMP Award.

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Can dogs soothe stressed students following simulation learning?

Flynn the therapy dog enlisted to improve student learning and future patient care.

Sometimes, it’s an instructor’s job to stress students out. That’s particularly true in health-care training where simulations can be the main approach to learning.

A group of NAIT staff are experts in generating that educational adrenaline rush. At the Centre for Advanced Simulation (CAMS), technicians recreate unfortunate, though artificial, scenes of patient distress by way of images, sounds and even smells. The scenarios help students see how they’ll perform under pressure before the pressure is real, and find areas for improvement.

But a balance has to be struck for improvement to happen. CAMS research associate Efrem Violato says that if a student experiences too much stress, “it's going to impair their learning.” A post-simulation debrief, for example, is lost on a student who’s unable to calm down.

That’s where the process goes to the dogs – to the benefit of students and instructors alike.

In a recent paper in the International Journal of Healthcare Simulation , Violato, simulation technologist Michele Edwards and counsellor Linda Shaw, demonstrated the positive impact of animal-assisted therapy on the well-being of students following the emotional intensity of a simulation.

Read the full paper

Stress reduction is a common part of simulation education at NAIT. While it is often accomplished by focused breathing exercises, Edwards wondered if the introduction of a dog – at first her own, a mixed-breed named Riggs – might bring even greater benefit to students. After a pilot study showed promise, she and Violato asked Shaw, owner of NAIT’s official therapy dog, a nine-year-old Australian labradoodle named Flynn, to help conduct a formal experiment in late 2023.

Forty-five Primary Care Paramedic , Animal Health and Respiratory Therapy students participated in the study. Following separate simulations tailored to each discipline, they scanned a QR code for a survey to rate feelings of stress, anxiety and more. After that, a random selection was directed to a room for breathing exercises while others joined Flynn in another room for petting and playing.

Two-and-a-half minutes later, both groups took the survey again.

“Based on the pilot study and existing literature, we did expect that there would be a positive effect,” says Violato of time spent with Flynn. And that’s how playing with the dog played out in the results: “Animal-assisted intervention reduced anxiety to a greater extent.”

The data supports the case for Flynn to be a fixture at simulations – and not just at the end. Students can experience anxiety at orientations before events, says Violato, suggesting that there may be value in bookending exercises with assisted therapy. What’s more, he sees no reason that other post-secondary institutes, many of which also have resident canines, couldn’t do the same.

“If this can work here then it can work almost anywhere else that's running simulations,” says Violato.

Ultimately, the three researchers hope the benefits of animal-assisted therapy extend beyond the students. Positioning them for success in school, by ensuring that they can learn effectively even during its most stressful times, prepares them for success in the field.

“The better we can educate students here, the better that patient outcomes will be.”

If the researchers could have improved upon their self-funded study, it would have been to involve more participants to generate a more robust dataset. Also, while all students were excited to participate, those who were assigned breathing exercises post-simulation “expressed some disappointment,” says Violato, which possibly affected outcomes.

But once the data was collected, and the hypothesis nonetheless validated, the “disappointed” group was rewarded with a chance to destress even further, this time with a furry friend. “They were able to play with the dog after the study was concluded,” Violato assures.

Where can you find Flynn?

Flynn, NAIT's therapy dog, makes his way across campuses throughout the academic year to help improve the mental well-being of students and staff. For more information, reach out to Student Counselling .

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The Prevalence of Depression, Anxiety and Stress and Their Associated Factors in College Students

Enrique ramón-arbués.

1 Faculty of Health Sciences, Campus Universitario Villanueva de Gállego, Universidad San Jorge, 50830 Villanueva de Gállego, Zaragoza, Spain; se.jsu@nomare

Vicente Gea-Caballero

2 Nursing School La Fe, Adscript Center of University of Valencia, 46026 Valencia, Spain

3 Research Group GREIACC, Health Research Institute La Fe, 46026 Valencia, Spain

José Manuel Granada-López

4 Faculty of Health Sciences, Zaragoza University, 50009 Zaragoza, Spain

Raúl Juárez-Vela

5 Faculty of Health Sciences, La Rioja University, 26006 Logroño, Spain; [email protected]

Begoña Pellicer-García

6 Servicio Aragonés de Salud, Sector Alcañiz Atención Primaria, Centro de Salud Andorra Calle Huesca, 44500 Andorra, Spain; se.liamtoh@8002geb

Isabel Antón-Solanas

7 Department of Physiatry and Nursing, Faculty of Health Sciences, University of Zaragoza, 50009 Zaragoza, Spain; se.razinu@notnai

Aim: To estimate the prevalence of symptoms of depression, anxiety, stress and associated factors in a population of college students. Method: Cross-sectional study of psychological distress measured through the Depression, Anxiety and Stress Scale (DASS-21) in a sample of 1074 college students. Results: We found a moderate prevalence of depression (18.4%), anxiety (23.6%) and stress (34.5%) symptoms in our study population. Being <21, having problematic Internet use behavior, smoking, presenting insomnia and having a low self-esteem were independently associated with symptoms of depression, anxiety and stress. Being a woman, living with their family, having a stable partner, consuming alcohol frequently and having poor nutritional habits were significantly associated with symptoms of stress; lacking a stable partner was significantly associated with depressive symptoms; and frequent consumption of alcohol was significantly associated with symptoms of anxiety. Conclusion: We found a moderate prevalence of depression, anxiety and stress symptoms in our population. Interventions aimed at promoting mental health among college students should be implemented.

1. Introduction

In its plan for the prevention, treatment and overcoming of mental health disorders, the World Health Organization described mental health as fundamental to human health [ 1 ]. Yet, mental health problems are the first cause of disability and a major public health issue worldwide due to disease progression, difficulties in therapeutic management and increasing prevalence [ 2 , 3 ]. Specifically, depression, anxiety and stress are considered important indicators for mental health which, if untreated, can have a negative effect on individuals [ 4 , 5 ]. According to the American Psychological Association, anxiety and depression are both emotional responses leading to a very similar set of symptoms, including difficulty sleeping, fatigue, muscle tension and irritability. Whereas stress is usually caused by an external factor and can be short-term, anxiety is persistent, even in the absence of a stressor [ 6 ]. Depression is characterized by a set of symptoms including a lack of interest in daily activities, significant weight loss or gain, sleep pattern alterations, lack of energy, loss of concentration, feelings of worthlessness or guilt and even recurrent thoughts of death or suicide [ 7 ].

Most mental health problems appear by early adulthood, yet young adults rarely get any support for their mental health [ 8 ]. Furthermore, mental health issues in this population are associated with higher incidence of physical and emotional problems in the mid to long term [ 9 ], labor market marginalization [ 10 ], worse quality of sleep [ 11 ] and dysfunctional relationships [ 12 ], among others. College students are at risk of experiencing stress, anxiety and depression, which cause psychological distress and may impact on their academic performance [ 13 ]. Worldwide, it is estimated that 12–50% of college students present at least one diagnostic criterion for one or more mental disorders [ 14 ]. Causes of stress during college life include academic pressure stemming from factors such as exams and workload, lack of leisure time, competition, concerns about not meeting parents’ expectations, establishing new personal relationships and moving to a strange location [ 15 ]; biological factors such as age and gender, specifically being female [ 16 ]; and financial burden [ 17 ].

Globally, studies conducted on different samples of undergraduate students have identified a moderate to high prevalence of depression, anxiety and stress in this population [ 18 , 19 , 20 , 21 , 22 , 23 ]. Early diagnosis and management of psychological distress lead to better management and patient outcomes [ 24 ]. Thus, it is necessary to identify those students who are at a higher risk of developing mental health problems during college life.

In Spain, mental health problems are highly prevalent in the general population [ 25 ], as well as in specific groups [ 26 , 27 ]. However, little is known about the mental health of college students. Previous studies have reported a high prevalence of anxiety and depression in this population [ 28 , 29 ], but sample size was small and they did not measure stress. Furthermore, since psychological health status was not the main research variable, predictive factors were not reported. Based on the above, we aim to determine the prevalence of anxiety, depression and stress, and their associated factors in a sample of Spanish college students.

2. Materials and Methods

2.1. design.

A cross-sectional study of the prevalence of symptoms of depression, anxiety, stress and associated factors was carried out in a population of college students registered in San Jorge University (SJU) in Zaragoza (Spain).

2.2. Sample

Our participants were undergraduate students registered in one of the bachelor’s degrees offered by SJU’s Faculty of Health Sciences, Faculty of Communication and School of Architecture and Technology during the academic year 2018–2019. Participant recruitment took place from September 2018 to May 2019. The students were informed about the aims of the study and the methods of data collection by a researcher in the classroom; a copy of the participant information leaflet and consent form was given to the students at this time. The students were assured that privacy and confidentiality would be maintained, and that they had a right to refuse to participate in the study or to withdraw consent to participate at any time without reprisal. Of a total population of 1341, 1074 students gave their consent to participate in this study and completed the questionnaire.

2.3. Data Collection

The questionnaire was divided into two sections, namely sociodemographic characteristics (including anthropometry and habits) and psychological health. Sociodemographic data included a list of variables generally associated with psychological distress in younger populations [ 20 , 21 ], namely age, gender, bachelor’s degree, place of residence, personal relationship, height, weight, financial status, tobacco and alcohol consumption, physical activity, diet and Internet use. As in a previous study by Mahroon et al. [ 30 ], the variable age was dichotomized into <21 and ≥21. This allowed us to estimate differences in the prevalence of anxiety, stress and depression in relation to the students’ age group and year of study. The variable body mass index (BMI) was calculated from weight and height self-report (BMI = kg/m 2 ). BMI was trichotomized to: (1) low BMI (≤18.5 kg/m 2 ), (2) normal BMI (18.5–24.9 kg/m 2 ) and (3) high BMI/obese (≥25 kg/m 2 ).

Physical activity was measured using the short form of the International Physical Activity Questionnaire (IPAQ-SF). This tool measures the intensity, frequency and duration of physical activity over the last seven-day period. Metabolic equivalents (METs), defined as “the ratio of a person’s working metabolic rate relative to their resting metabolic rate” [ 31 ], are then calculated for each of the physical activities undertaken including walking, moderate and vigorous physical activity. The results are then added up to obtain a measure of the total physical activity undertaken in the previous seven days and the participants are subsequently classified into one of three categories of physical activity (low, medium and high) [ 32 ].

Internet use was measured using Young’s Internet Addiction Test (IAT). This tool comprises 20 items answered on a 5-point Likert scale ranging from 1 to 5, indicating the extent to which they endorse each particular behavior. The IAT total score ranges from 0 to 100, where higher scores represent higher levels of severity of Internet compulsivity and addiction. Specifically, scores <50 suggest a controlled use of the Internet, scores between 50 and 79 suggest excessive Internet use and scores ≥80 imply that the participant is experiencing severe Internet addition, which impacts on his or her personal as well as social life [ 32 ]. In this study, Internet use was analyzed as a dichotomous variable where IAT <50 indicated no problematic Internet use (PIU) and IAT ≥50 indicated PIU [ 33 ].

We assessed the quality of the participants’ diet through the Healthy Eating Index (HEI) in its Spanish version [ 34 ]. The HEI uses a scoring system ranging from 1 to 10 to evaluate a set of foods. Total score ranges from 0 to 100, with higher scores suggesting healthier dietary habits. HEI scores >80 indicate a good or healthy diet, scores ranging from 50 to 80 suggest a diet that needs improvement and scores <50 imply a poor or unhealthy diet.

The presence and severity of insomnia was assessed through the Insomnia Severity Index (ISI). The ISI is a 7-item self-report questionnaire assessing the nature, severity and impact of insomnia experienced in the last month. A 5-point Likert scale is used to rate each item (0 = no problem; 4 = very severe problem) yielding a total score ranging from 0 to 28. The ISI total score is interpreted as follows: absence of insomnia (0–7); sub-threshold insomnia (8–14); moderate insomnia (15–21); and severe insomnia (22–28) [ 35 ].

The participants’ self-esteem was assessed using the Rosenberg Self-Esteem Scale (RSES). This tool consists of 10 items, five of which are expressed in positive statements and the other five in negative statements. Negative items were reverse-scored prior to analysis. The RSES uses a 4-point response scale (1 = strongly disagree; 2 = disagree; 3 = agree; 4 = strongly agree) with total scores ranging from 10 to 40. Respondents are classified into three levels of self-esteem: high self-esteem (≥30 points), medium self-esteem (26–29 points) and low self-esteem (≤25 points) [ 36 ].

Finally, symptoms of anxiety, stress and depression were measured through the Depression Anxiety Stress Scales (DASS-21). The DASS-21 consists of 21 items, 7 items per subscale: DASS-D (depression), DASS-A (anxiety) and DASS-S (stress). Respondents must rate the extent to which each statement applies during the past week on a 4-point Likert scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much). Because the DASS-21 is a short-form version of the DASS (42 items), the final score for each sub-scale is multiplied by two and evaluated according to its severity rating index. Depression, anxiety and stress scores are calculated by adding up the scores of the items in each separate subscale. The results are interpreted as follows: DASS-A (>19 = extremely severe depression; 19–15 = severe anxiety; 14–10 = moderate anxiety; 9–8 = mild anxiety; 7–0 = no anxiety/normal), DASS-D (>27 = extremely severe depression; 27–21 = severe depression; 20–14 = moderate depression; 13–10 = mild depression; 9–0 = no depression/normal), DASS-S (>33 = extremely severe stress; 33–26 = severe stress; 25–19 = moderate stress; 18–15 = mild stress; 14–0 = no stress/normal). This tool has been previously validated in a population of Spanish college students showing high levels of consistency for the three subscales [ 37 ].

2.4. Data Analysis

The sample characteristics were analyzed using frequency and percentage for qualitative variables and mean and standard deviation for quantitative ones. We used a Kolmogorov–Smirnov test to test for normality of distribution in our data. Bivariant analyses were carried out using Chi-Square, Mann–Whitney or t-test, as applicable. In addition, we carried out a binary logistic regression (backward stepwise method with a probability value for the entry of p = 0.05 and removal of p = 0.10) analysis in order to determine the predictive factors of psychological health in our sample (presence of anxiety, stress, depression). We performed a collinearity analysis in order to detect variables which showed a condition index ≥30. Subsequently, two analyses were carried out with and without outliers, with minimal differences between them. In this manuscript, we present the results from the analysis including outliers. Data codification, processing and analysis were completed using the statistical software Statistical Package for the Social Science (SPSS version 21 for Windows, IBM Corp., Chicago, IL, USA) accepting a level of significance of p < 0.05.

2.5. Ethical Considerations

This study was reviewed and approved by the Clinical Research Ethics Committee of Aragón (IRB Ref: CP-CI.PI09/93) prior to the start of this investigation. We confirm that each and every one of the national and international standards for ethical research with human subjects were respected and adhered to.

A total of 1074 undergraduate students (71% women and 29% men) took part in this investigation. Age ranged from 18 to 42, with an average of 21.73 ± 5.12 years. The majority of our students were enrolled in a healthcare program (57.3%), had a normal BMI (69.8%), perceived their financial status to be medium (74.7%), did not have a stable partner (53.2%) and lived with their family (66.3%). In addition, 24.9% consumed tobacco habitually, 28% had a low level of physical activity, 23% experienced PIU, 42.9% had some degree of insomnia and 82.6% needed diet improvement (see Table 1 ).

Participants’ characteristics ( n = 1074).

* Metabolic equivalents (MET) are defined as “the ratio of a person’s working metabolic rate relative to their resting metabolic rate”.

Of our participants, 23.6% and 34.5% had symptoms of anxiety and stress above the normal range, respectively. In both cases, women’s levels of anxiety and stress were higher than men’s ( p < 0.05). The symptoms of depression, on the other hand, were evenly distributed between our male and female participants (19.3% men and 18.1% women) (see Table 2 ).

Total scores from the DASS-21 and by gender.

* NS: Non-significant. ( a ) DASS-21: 21 item Depression, Anxiety Stress Scale. ( b ) DASS-D: 7-item DASS-21 Depression subscale. ( c ) DASS-A: 7-item DASS-21 Anxiety subscale. ( d ) DASS-S: 7-item DASS-21 Stress subscale.

Of our participants, 22.5% presented symptoms of two mental disorders according to the DASS-21 questionnaire, and up to 9.7% of our sample experienced symptoms of anxiety, depression and stress simultaneously (see Figure 1 ).

Coexistence of symptoms of depression, anxiety and stress according to the results from DASS-21 ( n = 1074).

We performed a collinearity analysis in order to detect variables which showed a condition index ≥30. None of them reached this value. Subsequently, two analyses were carried out with and without outliers, with minimal differences between them. In this manuscript, we present the results from the analysis including outliers. The final models of binary logistic regression evidenced that being <21, experiencing PIU, smoking, having insomnia and reporting a low level of self-esteem were associated with depression, anxiety and stress ( p < 0.05). In addition, being a woman, living with their family, having a stable partner, consuming alcohol regularly and following an inadequate or unhealthy diet were significantly associated with stress; not having a stable partner was associated with depression; and frequent alcohol consumption was associated with anxiety (see Table 3 ).

Factors associated with symptoms of depression, anxiety and stress in Spanish college students. OR a (IC95%).

* p < 0.01; † p < 0.05. Non-significant variables according to the following criterion: probability value for the entry of p = 0.05 and removal of p = 0.1, were removed from the model after each step. The values from the variables that remained after the last step of the model are shown in the multivariable model columns. ( a ) OR: Odds ratio. ( b ) PUI: Problematic use of the internet.

4. Discussion

This, to our knowledge, is the first report of the prevalence of symptoms of anxiety, depression and stress, and their associated factors, in a sample of Spanish college students. Although the DASS-21 questionnaire cannot be considered as a tool for the diagnosis of psychological pathology, it is useful to identify the prevalence of symptoms of anxiety, depression and stress. We identified a significant prevalence of symptoms of stress (34.5%), anxiety (23.6%) and depression (18.4%) in our population. Previous studies carried out in Spain involving smaller samples have reported an even greater prevalence of psychological distress in our population [ 28 , 29 ]. Specifically, Balanza et al. [ 28 ] reported a prevalence of anxiety and depression of 41.7% and 55.6% respectively using the Goldberg Anxiety and Depression Scale. Fernández et al. [ 29 ] identified an even higher percentage of students with anxiety symptoms (44.7%) and a lower prevalence of depressive symptoms (23.5%) using the Hospital Anxiety and Depression Scale (HADS). Unfortunately, the use of different screening tools does limit the comparability of the findings.

Worldwide, there is variation in the reported prevalence of psychological distress among college students. A systematic review of 24 studies estimated an average prevalence of depression of 30.5%, with results ranging between 10.4 and 80.5% [ 38 ]. The same level of variation was observed in previous studies which used the DASS questionnaire to assess psychological distress. This may be explained by differences in the selection criteria, as well the presence of confounding factors such as the influence of the environment on the mental health of our participants, modulating both the individual’s subjective perception and the expression of symptoms of psychological discomfort. That is, it is possible that external factors including the participants’ geographical location as well as their sociocultural context can significantly affect the prevalence of psychological distress in this population.

Of our participants, 37.4% presented symptoms of two or more psychological disorders. This association has been previously described both in the general population [ 39 ], as well as in college students [ 11 ]. In fact, Long et al. [ 40 ] suggest that there is a bidirectional, systematic pattern between the development of depressive and anxious syndromes in young adults. In addition, previous studies [ 41 , 42 , 43 ] have identified similarities in the neurobiology and genetic structure of depression and anxiety. Another possible explanation for the association between depression, anxiety and stress is the fact that they share a significant number of risk factors and symptoms. Nevertheless, the reason for the association between these psychological syndromes is yet to be established.

In our sample, female students presented a higher prevalence of symptoms of stress and anxiety compared to male students. This is in agreement with previous studies, which also reported a higher prevalence of anxiety, stress and depression among women [ 39 ].

The relationship between lifestyle habits and mental health has been studied in detail. Thus, physical activity has often been associated with psychological wellbeing through a range of mechanisms including the secretion of endogen substances such as endorphins, the activity of the regulation of stress responses through the hypothalamic-pituitary-adrenal (HPA) axis, the improvement of sleep quality and the development of self-regulation and other coping mechanisms [ 44 , 45 ]. However, physical activity was not clearly associated with psychological distress in our sample. It seems reasonable to suggest that other factors, such as those linked to socialization, may have a bigger impact on mental health in adolescence and early adulthood, when personality is shaped and an adult role is gradually acquired.

Various possible explanations have been proposed to explain the relationship between tobacco and psychological distress in our population. First, it is likely that tobacco use and mental health problems have a common root; people who experience mental health problems may smoke to regulate feelings of low mood, stress and anxiety; smoking could also cause or exacerbate existing mental health problems [ 46 ]. With regard to the association between alcohol and psychological distress, several studies [ 47 , 48 ] have reported strong behavioral and neurologic interactions. In addition, drinking alcohol carries a heavy social component which, in part, may explain its intense consumption in youth and adolescence.

PIU has been studied in the past few years, both in the general population and, especially, in the young and adolescent. The negative consequences of PIU include, among others, a possible increase in stress and anxiety, as well as a lack of social communication and interaction [ 49 , 50 ]. This may seem paradoxical. However, even when used for social interaction, digital screen time can reduce the time spent developing skills to read and interpret cues of human emotion.

Recent studies [ 51 , 52 ] have reported a positive association between dietary quality and mental health problems. However, two previous systematic reviews [ 53 , 54 ] found limited and contradictory evidence to support the association between dietary patterns and mental health problems in adults.

As has been reported in previous studies involving similar samples, we found an association between certain habits such as tobacco and alcohol consumption [ 32 ], presenting PIU [ 11 ] and eating unhealthily [ 43 , 55 ] with higher levels of psychological distress. It is important to highlight that, although we were not able to establish the direction of these associations, it seems reasonable to hypothesize that a bidirectional relationship exists between these variables. Thus, we suggest that future mental health promoting strategies in this population include an evaluation of lifestyle.

It is also worth highlighting that being enrolled in a healthcare program did not increase the risk of experiencing symptoms of depression, anxiety and stress in our sample. This is contrary to previous studies [ 56 , 57 ], which identified that being enrolled in a health-related program of study was a risk factor for psychological distress. They argue that health-related programs pose unique challenges including excessive academic pressure, peer competition, working under minimal supervision in unfamiliar clinical settings and witnessing pain, suffering and death [ 30 ].

It is possible that college students perceive vital events associated with college life as threatening to them, thus negatively affecting their mental health. For example, being under 21 was significantly associated with symptoms of depression, anxiety and stress [ 58 ]. This is an interesting finding, which may suggest that younger students may be more likely to experience uncertainty related to their studies than mature students. This observation is supported by previous studies [ 1 , 2 ], which reported higher levels of anxiety in the initial years of study.

This, to our knowledge, is the first attempt to establish an association between the symptoms of anxiety, depression and stress, and a large range of socioeconomic and behavioral variables, in a sample of Spanish college students. Furthermore, we believe that our results are highly representative of, and generalizable to, the population of college students in Spain, owing not only to our sample size but also to the standardized procedures of data collection and plausibility of the associations established. This has allowed us to draw a reliable picture of the psychological health and habits of our population of college students, which may serve as a starting point for the development and implementation of preventative and diagnostic interventions, and treatment services, to promote mental health and well-being.

Some authors [ 59 , 60 ] have argued that educational settings are ideal to implement measures and interventions leading to the promotion of a healthy lifestyle and prevention of mental health problems. Higher education institutions must attempt to train not only excellent professionals but also healthy individuals. Some Spanish universities, known as Health Promoting Universities, have accepted this challenge and are systematically implementing measures to promote health and wellbeing among the university population [ 61 ]. Future studies should compare the prevalence and level of psychological distress between the staff and the students enrolled in these universities, and those working and studying in non-health promoting ones.

This study has some limitations that need to be acknowledged. Firstly, the DASS-21 questionnaire is a suitable tool to screen for anxious, depressive and stress disorders, and may be useful to identify patients who are at risk of being affected by these conditions. However, additional tools should be used to establish a formal diagnosis. Secondly, the methodology employed in this investigation does allow for the establishment of associations between variables, but not causality. Finally, we would like to highlight that data collection took place over a period of eight months and, consequently, it is likely that academic life conditions were different for some of the students. For example, it is well known that factors such as the proximity of deadlines and exams may become a significant source of stress and anxiety for college students [ 62 ]. Furthermore, the generalizability of our results to the general population of Spanish college students may be limited. Specifically, this study was carried out in a private college in Spain. Such private higher education institutions are less numerous than public ones in Spain and tuition fees tend to be significantly higher. Having said this, the sociodemographic characteristics of our sample are similar to those of similar studies conducted in Spain involving college students enrolled in public universities [ 63 , 64 ]

Future investigations in this area should attempt to address these limitations. In any case, we argue that our results bring to light the need to implement strategies to protect and, if applicable, improve the mental health and wellbeing of college students.

5. Conclusions

We found a considerable prevalence of symptoms of depression, anxiety and stress in our population which, in some cases, do not occur in isolation, but coexist. In addition, we identified a number of factors associated with these symptoms. Factors including age, gender, self-esteem, sleep quality and living arrangements of college students, as well as specific behaviors relating to alcohol, tobacco and Internet use seem to be strongly associated with psychological distress in the college student population. We argue that our results can be helpful to design strategies for the early identification of mental health disorders, as well as psychological and other interventions leading to mental health promotion and wellbeing in the population of college students.

Author Contributions

All the authors have made a substantial contribution to this work, have approved the submitted version and agree to be personally accountable for their own contribution to the study and for ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated, resolved and documented in the literature. Conceptualization, E.R.-A. and I.A.-S.; methodology, E.R.-A., I.A.-S. and J.M.G.-L.; data collection, E.R.-A. and B.P.-G.; formal analysis, E.R.-A., V.G.-C. and R.J.-V.; resources, E.R.-A. and B.P.-G., writing—original draft preparation, E.R.-A. and B.P.-G.; writing—review and editing, R.J.-V., V.G.-C., J.M.G.-L. and I.A.-S.; supervision, I.A.-S.; project administration, V.G.-C. All authors have read and agreed to the published version of the manuscript.

We declare that we have not received any funding in support of this research or to cover publication costs.

Conflicts of Interest

The authors declare no conflict of interest.

The federal government’s proposed international education policy is flawed, and will likely have unintended consequences

Ly Tran and George Tan

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The overarching priority of the newly released draft International Education and Skills Strategic Framework — to ensure the quality and integrity of the international education sector — is laudable. But its approach to is flawed: micro-managing international student numbers across courses, providers, and locations.

The proposed policy aims to align international student enrolments closely with Australia’s skills needs. It demonstrates the importance of addressing particular skills demands in regional Australia by attracting international students to study at regional universities in the hope that they will remain to work in those communities after they graduate.

But an analysis of the proposed policy alongside census data shows the premise of this policy is not just contradictory, but difficult to achieve.

The proposed policy’s evident shortcomings

To begin with, the proposed policy does not recognise the increased difficulty that international students face in finding jobs in regional Australia. Our joint Deakin University and University of Adelaide research highlights the misunderstandings that occur in regional communities about international graduates’ work rights, skill level, English language proficiency and other prejudices, thus making it harder for these job seekers to find work than their Australian counterparts.

Any initiative that seeks to funnel international students to Australia’s regions must therefore address xenophobia and discrimination, and should include educating employers about international students and temporary graduate visas.

Furthermore, census data shows regional Australia, including the Northern Territory and Tasmania, had the lowest likelihood of international graduates being employed in a Skill Level 1 job . This is a skill level commensurate with a bachelor’s degree or higher tertiary qualification. International graduates in these locations had a higher probability to be employed in low-skilled jobs.

Census data also shows us that, among all of Australia’s states and territories, the Northern Territory had the lowest percentage of international graduate visa holders earning $1,000 or more per week in 2021. In other words, international students and graduates are more likely to be paid less in the regions.

There is therefore a gap between skills shortages and the realities of employer practices and employment outcomes.

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Let’s be clear: skilled international graduates who cannot find jobs matched to their qualifications or effectively integrate will leave the regions to relocate back to Australian cities. It’s not just a matter of wages or career ambitions that will drive this. International graduates’ visa conditions stipulate they must find skilled work in an area of skills demand to secure permanent residency in Australia.

It is also important to point out that the proposed policy change stands in glaring contradiction to the new migration policy released in December last year. This policy explicitly indicated that most international students are expected to leave Australia after they complete their studies.

What’s more, the government has tried to decouple the international education-migration nexus over the past fifteen years. But the proposed policy dictates what international students should study to meet the skills demands in Australia, which is the most feasible pathway to migration in the current context.

Likely consequences of poor policy design

It’s for these reasons — among others — that the proposed policy change needs careful reconsideration. At best, it’s a half-baked plan to attract international student revenue while trying to address the nation’s skill shortages. At worst, it reveals the self-interested and one-sided approach of the Australian government in its diplomatic approach to international education.

Ultimately, most international students pay sky-high tuition fees for their Australian education, and they should have the freedom to choose a course that aligns with their capabilities and aspirations — just like Australian students. Their course choices and career pathways should not be informed or shaped by the skills demands of their host country. Otherwise, many students may enrol in courses of in-demand skills for the purpose of securing permanent residency , and end up uninterested in learning, resulting in disengagement or else graduate with skills they may not wish to pursue.

The proposed policy, where enrolments are capped to benefit Australia’s interest, also tends to disregard the skills demands in their own home countries, where around 80 per cent  of our international students will one day return to work.

Universities and related stakeholders have been working very hard to restore and sustain international education after the devastating effects of COVID-19 on student mobility. But the government’s heavy-handed approach to managing current rates of overseas migration by scapegoating international education will cause long term damage.

Although the rebound in student numbers and the recovery of the international education sector contributed to high Net Overseas Migration (NOM), efforts to bring down NOM through the proposed policy approach risks unintended consequences for the international education sector with ramifications across the wider economy.

Safeguarding the benefits of international students

We believe that Australia’s proposed policy will not only compromise the study needs of international students, but also jeopardise the nation’s competitiveness as a destination for international students in the long term.

International students have an increased range of study options, including:

• studying abroad with major traditional study destinations;
• “studying nearby” with emerging destinations — such as Hong Kong, Singapore, Japan, Korea, and China; and
• “international education at home” through an unprecedented growing number of transnational programs in their home countries.

International students and graduates are major contributors to Australia’s economy, society, and international relationships. It’s therefore both good education policy and a sensible long-term economic strategy to have certainty in policy and reciprocity towards international students and their home countries.

Any policy changes meant to manage growth in the international education sector must take into consideration the needs of international students and be developed in close consultation with education providers and related stakeholders, with a view towards a long-term sustainable growth and ensuring quality in student experiences.

Ly Tran is a Professor in the School of Education at Deakin University. Her work focuses on international education and the education-work-migration nexus.

George Tan is a Senior Lecturer in the School of Social Sciences at the University of Adelaide. His research focuses on international students and skilled migration.

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