stress and anxiety research paper

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• April 01, 2024 | VOL. 181, NO. 4 CURRENT ISSUE pp.255-346
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The Critical Relationship Between Anxiety and Depression

• Ned H. Kalin , M.D.

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Anxiety and depressive disorders are among the most common psychiatric illnesses; they are highly comorbid with each other, and together they are considered to belong to the broader category of internalizing disorders. Based on statistics from the Substance Abuse and Mental Health Services Administration, the 12-month prevalence of major depressive disorder in 2017 was estimated to be 7.1% for adults and 13.3% for adolescents ( 1 ). Data for anxiety disorders are less current, but in 2001–2003, their 12-month prevalence was estimated to be 19.1% in adults, and 2001–2004 data estimated that the lifetime prevalence in adolescents was 31.9% ( 2 , 3 ). Both anxiety and depressive disorders are more prevalent in women, with an approximate 2:1 ratio in women compared with men during women’s reproductive years ( 1 , 2 ).

Across all psychiatric disorders, comorbidity is the rule ( 4 ), which is definitely the case for anxiety and depressive disorders, as well as their symptoms. With respect to major depression, a worldwide survey reported that 45.7% of individuals with lifetime major depressive disorder had a lifetime history of one or more anxiety disorder ( 5 ). These disorders also commonly coexist during the same time frame, as 41.6% of individuals with 12-month major depression also had one or more anxiety disorder over the same 12-month period. From the perspective of anxiety disorders, the lifetime comorbidity with depression is estimated to range from 20% to 70% for patients with social anxiety disorder ( 6 ), 50% for patients with panic disorder ( 6 ), 48% for patients with posttraumatic stress disorder (PTSD) ( 7 ), and 43% for patients with generalized anxiety disorder ( 8 ). Data from the well-known Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study demonstrate comorbidity at the symptom level, as 53% of the patients with major depression had significant anxiety and were considered to have an anxious depression ( 9 ).

Anxiety and depressive disorders are moderately heritable (approximately 40%), and evidence suggests shared genetic risk across the internalizing disorders ( 10 ). Among internalizing disorders, the highest level of shared genetic risk appears to be between major depressive disorder and generalized anxiety disorder. Neuroticism is a personality trait or temperamental characteristic that is associated with the development of both anxiety and depression, and the genetic risk for developing neuroticism also appears to be shared with that of the internalizing disorders ( 11 ). Common nongenetic risk factors associated with the development of anxiety and depression include earlier life adversity, such as trauma or neglect, as well as parenting style and current stress exposure. At the level of neural circuits, alterations in prefrontal-limbic pathways that mediate emotion regulatory processes are common to anxiety and depressive disorders ( 12 , 13 ). These findings are consistent with meta-analyses that reveal shared structural and functional brain alterations across various psychiatric illnesses, including anxiety and major depression, in circuits involving emotion regulation ( 13 ), executive function ( 14 ), and cognitive control ( 15 ).

Anxiety disorders and major depression occur during development, with anxiety disorders commonly beginning during preadolescence and early adolescence and major depression tending to emerge during adolescence and early to mid-adulthood ( 16 – 18 ). In relation to the evolution of their comorbidity, studies demonstrate that anxiety disorders generally precede the presentation of major depressive disorder ( 17 ). A European community-based study revealed, beginning at age 15, the developmental relation between comorbid anxiety and major depression by specifically focusing on social phobia (based on DSM-IV criteria) and then asking the question regarding concurrent major depressive disorder ( 18 ). The findings revealed a 19% concurrent comorbidity between these disorders, and in 65% of the cases, social phobia preceded major depressive disorder by at least 2 years. In addition, initial presentation with social phobia was associated with a 5.7-fold increased risk of developing major depressive disorder. These associations between anxiety and depression can be traced back even earlier in life. For example, childhood behavioral inhibition in response to novelty or strangers, or an extreme anxious temperament, is associated with a three- to fourfold increase in the likelihood of developing social anxiety disorder, which in turn is associated with an increased risk to develop major depressive disorder and substance abuse ( 19 ).

It is important to emphasize that the presence of comor‐bid anxiety symptoms and disorders matters in relation to treatment. Across psychiatric disorders, the presence of significant anxiety symptoms generally predicts worse outcomes, and this has been well demonstrated for depression. In the STAR*D study, patients with anxious major depressive disorder were more likely to be severely depressed and to have more suicidal ideation ( 9 ). This is consistent with the study by Kessler and colleagues ( 5 ), in which patients with anxious major depressive disorder, compared with patients with nonanxious major depressive disorder, were found to have more severe role impairment and more suicidal ideation. Data from level 1 of the STAR*D study (citalopram treatment) nicely illustrate the impact of comorbid anxiety symptoms on treatment. Compared with patients with nonanxious major depressive disorder, those 53% of patients with an anxious depression were less likely to remit and also had a greater side effect burden ( 20 ). Other data examining patients with major depressive disorder and comorbid anxiety disorders support the greater difficulty and challenge in treating patients with these comorbidities ( 21 ).

This issue of the Journal presents new findings relevant to the issues discussed above in relation to understanding and treating anxiety and depressive disorders. Drs. Conor Liston and Timothy Spellman, from Weill Cornell Medicine, provide an overview for this issue ( 22 ) that is focused on understanding mechanisms at the neural circuit level that underlie the pathophysiology of depression. Their piece nicely integrates human neuroimaging studies with complementary data from animal models that allow for the manipulation of selective circuits to test hypotheses generated from the human data. Also included in this issue is a review of the data addressing the reemergence of the use of psychedelic drugs in psychiatry, particularly for the treatment of depression, anxiety, and PTSD ( 23 ). This timely piece, authored by Dr. Collin Reiff along with a subgroup from the APA Council of Research, provides the current state of evidence supporting the further exploration of these interventions. Dr. Alan Schatzberg, from Stanford University, contributes an editorial in which he comments on where the field is in relation to clinical trials with psychedelics and to some of the difficulties, such as adequate blinding, in reliably studying the efficacy of these drugs ( 24 ).

In an article by McTeague et al. ( 25 ), the authors use meta-analytic strategies to understand the neural alterations that are related to aberrant emotion processing that are shared across psychiatric disorders. Findings support alterations in the salience, reward, and lateral orbital nonreward networks as common across disorders, including anxiety and depressive disorders. These findings add to the growing body of work that supports the concept that there are common underlying factors across all types of psychopathology that include internalizing, externalizing, and thought disorder dimensions ( 26 ). Dr. Deanna Barch, from Washington University in St. Louis, writes an editorial commenting on these findings and, importantly, discusses criteria that should be met when we consider whether the findings are actually transdiagnostic ( 27 ).

Another article, from Gray and colleagues ( 28 ), addresses whether there is a convergence of findings, specifically in major depression, when examining data from different structural and functional neuroimaging modalities. The authors report that, consistent with what we know about regions involved in emotion processing, the subgenual anterior cingulate cortex, hippocampus, and amygdala were among the regions that showed convergence across multimodal imaging modalities.

In relation to treatment and building on our understanding of neural circuit alterations, Siddiqi et al. ( 29 ) present data suggesting that transcranial magnetic stimulation (TMS) targeting can be linked to symptom-specific treatments. Their findings identify different TMS targets in the left dorsolateral prefrontal cortex that modulate different downstream networks. The modulation of these different networks appears to be associated with a reduction in different types of symptoms. In an editorial, Drs. Sean Nestor and Daniel Blumberger, from the University of Toronto ( 30 ), comment on the novel approach used in this study to link the TMS-related engagement of circuits with symptom improvement. They also provide a perspective on how we can view these and other circuit-based findings in relation to conceptualizing personalized treatment approaches.

Kendler et al. ( 31 ), in this issue, contribute an article that demonstrates the important role of the rearing environment in the risk to develop major depression. Using a unique design from a Swedish sample, the analytic strategy involves comparing outcomes from high-risk full sibships and high-risk half sibships where at least one of the siblings was home reared and one was adopted out of the home. The findings support the importance of the quality of the rearing environment as well as the presence of parental depression in mitigating or enhancing the likelihood of developing major depression. In an accompanying editorial ( 32 ), Dr. Myrna Weissman, from Columbia University, reviews the methods and findings of the Kendler et al. article and also emphasizes the critical significance of the early nurturing environment in relation to general health.

This issue concludes with an intriguing article on anxiety disorders, by Gold and colleagues ( 33 ), that demonstrates neural alterations during extinction recall that differ in children relative to adults. With increasing age, and in relation to fear and safety cues, nonanxious adults demonstrated greater connectivity between the amygdala and the ventromedial prefrontal cortex compared with anxious adults, as the cues were being perceived as safer. In contrast, neural differences between anxious and nonanxious youths were more robust when rating the memory of faces that were associated with threat. Specifically, these differences were observed in the activation of the inferior temporal cortex. In their editorial ( 34 ), Dr. Dylan Gee and Sahana Kribakaran, from Yale University, emphasize the importance of developmental work in relation to understanding anxiety disorders, place these findings into the context of other work, and suggest the possibility that these and other data point to neuroscientifically informed age-specific interventions.

Taken together, the papers in this issue of the Journal present new findings that shed light onto alterations in neural function that underlie major depressive disorder and anxiety disorders. It is important to remember that these disorders are highly comorbid and that their symptoms are frequently not separable. The papers in this issue also provide a developmental perspective emphasizing the importance of early rearing in the risk to develop depression and age-related findings important for understanding threat processing in patients with anxiety disorders. From a treatment perspective, the papers introduce data supporting more selective prefrontal cortical TMS targeting in relation to different symptoms, address the potential and drawbacks for considering the future use of psychedelics in our treatments, and present new ideas supporting age-specific interventions for youths and adults with anxiety disorders.

Disclosures of Editors’ financial relationships appear in the April 2020 issue of the Journal .

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Research Article

Anxiety, Affect, Self-Esteem, and Stress: Mediation and Moderation Effects on Depression

Affiliations Department of Psychology, University of Gothenburg, Gothenburg, Sweden, Network for Empowerment and Well-Being, University of Gothenburg, Gothenburg, Sweden

Affiliation Network for Empowerment and Well-Being, University of Gothenburg, Gothenburg, Sweden

Affiliations Department of Psychology, University of Gothenburg, Gothenburg, Sweden, Network for Empowerment and Well-Being, University of Gothenburg, Gothenburg, Sweden, Department of Psychology, Education and Sport Science, Linneaus University, Kalmar, Sweden

* E-mail: [email protected]

Affiliations Network for Empowerment and Well-Being, University of Gothenburg, Gothenburg, Sweden, Center for Ethics, Law, and Mental Health (CELAM), University of Gothenburg, Gothenburg, Sweden, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

• Ali Al Nima, 
• Patricia Rosenberg, 
• Trevor Archer, 
• Danilo Garcia

• Published: September 9, 2013
• https://doi.org/10.1371/journal.pone.0073265
• Reader Comments

23 Sep 2013: Nima AA, Rosenberg P, Archer T, Garcia D (2013) Correction: Anxiety, Affect, Self-Esteem, and Stress: Mediation and Moderation Effects on Depression. PLOS ONE 8(9): 10.1371/annotation/49e2c5c8-e8a8-4011-80fc-02c6724b2acc. https://doi.org/10.1371/annotation/49e2c5c8-e8a8-4011-80fc-02c6724b2acc View correction

Mediation analysis investigates whether a variable (i.e., mediator) changes in regard to an independent variable, in turn, affecting a dependent variable. Moderation analysis, on the other hand, investigates whether the statistical interaction between independent variables predict a dependent variable. Although this difference between these two types of analysis is explicit in current literature, there is still confusion with regard to the mediating and moderating effects of different variables on depression. The purpose of this study was to assess the mediating and moderating effects of anxiety, stress, positive affect, and negative affect on depression.

Two hundred and two university students (males  = 93, females  = 113) completed questionnaires assessing anxiety, stress, self-esteem, positive and negative affect, and depression. Mediation and moderation analyses were conducted using techniques based on standard multiple regression and hierarchical regression analyses.

Main Findings

The results indicated that (i) anxiety partially mediated the effects of both stress and self-esteem upon depression, (ii) that stress partially mediated the effects of anxiety and positive affect upon depression, (iii) that stress completely mediated the effects of self-esteem on depression, and (iv) that there was a significant interaction between stress and negative affect, and between positive affect and negative affect upon depression.

The study highlights different research questions that can be investigated depending on whether researchers decide to use the same variables as mediators and/or moderators.

Citation: Nima AA, Rosenberg P, Archer T, Garcia D (2013) Anxiety, Affect, Self-Esteem, and Stress: Mediation and Moderation Effects on Depression. PLoS ONE 8(9): e73265. https://doi.org/10.1371/journal.pone.0073265

Editor: Ben J. Harrison, The University of Melbourne, Australia

Received: February 21, 2013; Accepted: July 22, 2013; Published: September 9, 2013

Copyright: © 2013 Nima 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.

Funding: The authors have no support or funding to report.

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

Introduction

Mediation refers to the covariance relationships among three variables: an independent variable (1), an assumed mediating variable (2), and a dependent variable (3). Mediation analysis investigates whether the mediating variable accounts for a significant amount of the shared variance between the independent and the dependent variables–the mediator changes in regard to the independent variable, in turn, affecting the dependent one [1] , [2] . On the other hand, moderation refers to the examination of the statistical interaction between independent variables in predicting a dependent variable [1] , [3] . In contrast to the mediator, the moderator is not expected to be correlated with both the independent and the dependent variable–Baron and Kenny [1] actually recommend that it is best if the moderator is not correlated with the independent variable and if the moderator is relatively stable, like a demographic variable (e.g., gender, socio-economic status) or a personality trait (e.g., affectivity).

Although both types of analysis lead to different conclusions [3] and the distinction between statistical procedures is part of the current literature [2] , there is still confusion about the use of moderation and mediation analyses using data pertaining to the prediction of depression. There are, for example, contradictions among studies that investigate mediating and moderating effects of anxiety, stress, self-esteem, and affect on depression. Depression, anxiety and stress are suggested to influence individuals' social relations and activities, work, and studies, as well as compromising decision-making and coping strategies [4] , [5] , [6] . Successfully coping with anxiety, depressiveness, and stressful situations may contribute to high levels of self-esteem and self-confidence, in addition increasing well-being, and psychological and physical health [6] . Thus, it is important to disentangle how these variables are related to each other. However, while some researchers perform mediation analysis with some of the variables mentioned here, other researchers conduct moderation analysis with the same variables. Seldom are both moderation and mediation performed on the same dataset. Before disentangling mediation and moderation effects on depression in the current literature, we briefly present the methodology behind the analysis performed in this study.

Mediation and moderation

Baron and Kenny [1] postulated several criteria for the analysis of a mediating effect: a significant correlation between the independent and the dependent variable, the independent variable must be significantly associated with the mediator, the mediator predicts the dependent variable even when the independent variable is controlled for, and the correlation between the independent and the dependent variable must be eliminated or reduced when the mediator is controlled for. All the criteria is then tested using the Sobel test which shows whether indirect effects are significant or not [1] , [7] . A complete mediating effect occurs when the correlation between the independent and the dependent variable are eliminated when the mediator is controlled for [8] . Analyses of mediation can, for example, help researchers to move beyond answering if high levels of stress lead to high levels of depression. With mediation analysis researchers might instead answer how stress is related to depression.

In contrast to mediation, moderation investigates the unique conditions under which two variables are related [3] . The third variable here, the moderator, is not an intermediate variable in the causal sequence from the independent to the dependent variable. For the analysis of moderation effects, the relation between the independent and dependent variable must be different at different levels of the moderator [3] . Moderators are included in the statistical analysis as an interaction term [1] . When analyzing moderating effects the variables should first be centered (i.e., calculating the mean to become 0 and the standard deviation to become 1) in order to avoid problems with multi-colinearity [8] . Moderating effects can be calculated using multiple hierarchical linear regressions whereby main effects are presented in the first step and interactions in the second step [1] . Analysis of moderation, for example, helps researchers to answer when or under which conditions stress is related to depression.

Mediation and moderation effects on depression

Cognitive vulnerability models suggest that maladaptive self-schema mirroring helplessness and low self-esteem explain the development and maintenance of depression (for a review see [9] ). These cognitive vulnerability factors become activated by negative life events or negative moods [10] and are suggested to interact with environmental stressors to increase risk for depression and other emotional disorders [11] , [10] . In this line of thinking, the experience of stress, low self-esteem, and negative emotions can cause depression, but also be used to explain how (i.e., mediation) and under which conditions (i.e., moderation) specific variables influence depression.

Using mediational analyses to investigate how cognitive therapy intervations reduced depression, researchers have showed that the intervention reduced anxiety, which in turn was responsible for 91% of the reduction in depression [12] . In the same study, reductions in depression, by the intervention, accounted only for 6% of the reduction in anxiety. Thus, anxiety seems to affect depression more than depression affects anxiety and, together with stress, is both a cause of and a powerful mediator influencing depression (See also [13] ). Indeed, there are positive relationships between depression, anxiety and stress in different cultures [14] . Moreover, while some studies show that stress (independent variable) increases anxiety (mediator), which in turn increased depression (dependent variable) [14] , other studies show that stress (moderator) interacts with maladaptive self-schemata (dependent variable) to increase depression (independent variable) [15] , [16] .

The present study

In order to illustrate how mediation and moderation can be used to address different research questions we first focus our attention to anxiety and stress as mediators of different variables that earlier have been shown to be related to depression. Secondly, we use all variables to find which of these variables moderate the effects on depression.

The specific aims of the present study were:

• To investigate if anxiety mediated the effect of stress, self-esteem, and affect on depression.
• To investigate if stress mediated the effects of anxiety, self-esteem, and affect on depression.
• To examine moderation effects between anxiety, stress, self-esteem, and affect on depression.

Ethics statement

This research protocol was approved by the Ethics Committee of the University of Gothenburg and written informed consent was obtained from all the study participants.

Participants

The present study was based upon a sample of 206 participants (males  = 93, females  = 113). All the participants were first year students in different disciplines at two universities in South Sweden. The mean age for the male students was 25.93 years ( SD  = 6.66), and 25.30 years ( SD  = 5.83) for the female students.

In total, 206 questionnaires were distributed to the students. Together 202 questionnaires were responded to leaving a total dropout of 1.94%. This dropout concerned three sections that the participants chose not to respond to at all, and one section that was completed incorrectly. None of these four questionnaires was included in the analyses.

Instruments

Hospital anxiety and depression scale [17] ..

The Swedish translation of this instrument [18] was used to measure anxiety and depression. The instrument consists of 14 statements (7 of which measure depression and 7 measure anxiety) to which participants are asked to respond grade of agreement on a Likert scale (0 to 3). The utility, reliability and validity of the instrument has been shown in multiple studies (e.g., [19] ).

Perceived Stress Scale [20] .

The Swedish version [21] of this instrument was used to measures individuals' experience of stress. The instrument consist of 14 statements to which participants rate on a Likert scale (0 =  never , 4 =  very often ). High values indicate that the individual expresses a high degree of stress.

Rosenberg's Self-Esteem Scale [22] .

The Rosenberg's Self-Esteem Scale (Swedish version by Lindwall [23] ) consists of 10 statements focusing on general feelings toward the self. Participants are asked to report grade of agreement in a four-point Likert scale (1 =  agree not at all, 4 =  agree completely ). This is the most widely used instrument for estimation of self-esteem with high levels of reliability and validity (e.g., [24] , [25] ).

Positive Affect and Negative Affect Schedule [26] .

This is a widely applied instrument for measuring individuals' self-reported mood and feelings. The Swedish version has been used among participants of different ages and occupations (e.g., [27] , [28] , [29] ). The instrument consists of 20 adjectives, 10 positive affect (e.g., proud, strong) and 10 negative affect (e.g., afraid, irritable). The adjectives are rated on a five-point Likert scale (1 =  not at all , 5 =  very much ). The instrument is a reliable, valid, and effective self-report instrument for estimating these two important and independent aspects of mood [26] .

Questionnaires were distributed to the participants on several different locations within the university, including the library and lecture halls. Participants were asked to complete the questionnaire after being informed about the purpose and duration (10–15 minutes) of the study. Participants were also ensured complete anonymity and informed that they could end their participation whenever they liked.

Correlational analysis

Depression showed positive, significant relationships with anxiety, stress and negative affect. Table 1 presents the correlation coefficients, mean values and standard deviations ( sd ), as well as Cronbach ' s α for all the variables in the study.

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https://doi.org/10.1371/journal.pone.0073265.t001

Mediation analysis

Regression analyses were performed in order to investigate if anxiety mediated the effect of stress, self-esteem, and affect on depression (aim 1). The first regression showed that stress ( B  = .03, 95% CI [.02,.05], β = .36, t  = 4.32, p <.001), self-esteem ( B  = −.03, 95% CI [−.05, −.01], β = −.24, t  = −3.20, p <.001), and positive affect ( B  = −.02, 95% CI [−.05, −.01], β = −.19, t  = −2.93, p  = .004) had each an unique effect on depression. Surprisingly, negative affect did not predict depression ( p  = 0.77) and was therefore removed from the mediation model, thus not included in further analysis.

The second regression tested whether stress, self-esteem and positive affect uniquely predicted the mediator (i.e., anxiety). Stress was found to be positively associated ( B  = .21, 95% CI [.15,.27], β = .47, t  = 7.35, p <.001), whereas self-esteem was negatively associated ( B  = −.29, 95% CI [−.38, −.21], β = −.42, t  = −6.48, p <.001) to anxiety. Positive affect, however, was not associated to anxiety ( p  = .50) and was therefore removed from further analysis.

A hierarchical regression analysis using depression as the outcome variable was performed using stress and self-esteem as predictors in the first step, and anxiety as predictor in the second step. This analysis allows the examination of whether stress and self-esteem predict depression and if this relation is weaken in the presence of anxiety as the mediator. The result indicated that, in the first step, both stress ( B  = .04, 95% CI [.03,.05], β = .45, t  = 6.43, p <.001) and self-esteem ( B  = .04, 95% CI [.03,.05], β = .45, t  = 6.43, p <.001) predicted depression. When anxiety (i.e., the mediator) was controlled for predictability was reduced somewhat but was still significant for stress ( B  = .03, 95% CI [.02,.04], β = .33, t  = 4.29, p <.001) and for self-esteem ( B  = −.03, 95% CI [−.05, −.01], β = −.20, t  = −2.62, p  = .009). Anxiety, as a mediator, predicted depression even when both stress and self-esteem were controlled for ( B  = .05, 95% CI [.02,.08], β = .26, t  = 3.17, p  = .002). Anxiety improved the prediction of depression over-and-above the independent variables (i.e., stress and self-esteem) (Δ R 2  = .03, F (1, 198) = 10.06, p  = .002). See Table 2 for the details.

https://doi.org/10.1371/journal.pone.0073265.t002

A Sobel test was conducted to test the mediating criteria and to assess whether indirect effects were significant or not. The result showed that the complete pathway from stress (independent variable) to anxiety (mediator) to depression (dependent variable) was significant ( z  = 2.89, p  = .003). The complete pathway from self-esteem (independent variable) to anxiety (mediator) to depression (dependent variable) was also significant ( z  = 2.82, p  = .004). Thus, indicating that anxiety partially mediates the effects of both stress and self-esteem on depression. This result may indicate also that both stress and self-esteem contribute directly to explain the variation in depression and indirectly via experienced level of anxiety (see Figure 1 ).

Changes in Beta weights when the mediator is present are highlighted in red.

https://doi.org/10.1371/journal.pone.0073265.g001

For the second aim, regression analyses were performed in order to test if stress mediated the effect of anxiety, self-esteem, and affect on depression. The first regression showed that anxiety ( B  = .07, 95% CI [.04,.10], β = .37, t  = 4.57, p <.001), self-esteem ( B  = −.02, 95% CI [−.05, −.01], β = −.18, t  = −2.23, p  = .03), and positive affect ( B  = −.03, 95% CI [−.04, −.02], β = −.27, t  = −4.35, p <.001) predicted depression independently of each other. Negative affect did not predict depression ( p  = 0.74) and was therefore removed from further analysis.

The second regression investigated if anxiety, self-esteem and positive affect uniquely predicted the mediator (i.e., stress). Stress was positively associated to anxiety ( B  = 1.01, 95% CI [.75, 1.30], β = .46, t  = 7.35, p <.001), negatively associated to self-esteem ( B  = −.30, 95% CI [−.50, −.01], β = −.19, t  = −2.90, p  = .004), and a negatively associated to positive affect ( B  = −.33, 95% CI [−.46, −.20], β = −.27, t  = −5.02, p <.001).

A hierarchical regression analysis using depression as the outcome and anxiety, self-esteem, and positive affect as the predictors in the first step, and stress as the predictor in the second step, allowed the examination of whether anxiety, self-esteem and positive affect predicted depression and if this association would weaken when stress (i.e., the mediator) was present. In the first step of the regression anxiety ( B  = .07, 95% CI [.05,.10], β = .38, t  = 5.31, p  = .02), self-esteem ( B  = −.03, 95% CI [−.05, −.01], β = −.18, t  = −2.41, p  = .02), and positive affect ( B  = −.03, 95% CI [−.04, −.02], β = −.27, t  = −4.36, p <.001) significantly explained depression. When stress (i.e., the mediator) was controlled for, predictability was reduced somewhat but was still significant for anxiety ( B  = .05, 95% CI [.02,.08], β = .05, t  = 4.29, p <.001) and for positive affect ( B  = −.02, 95% CI [−.04, −.01], β = −.20, t  = −3.16, p  = .002), whereas self-esteem did not reach significance ( p < = .08). In the second step, the mediator (i.e., stress) predicted depression even when anxiety, self-esteem, and positive affect were controlled for ( B  = .02, 95% CI [.08,.04], β = .25, t  = 3.07, p  = .002). Stress improved the prediction of depression over-and-above the independent variables (i.e., anxiety, self-esteem and positive affect) (Δ R 2  = .02, F (1, 197)  = 9.40, p  = .002). See Table 3 for the details.

https://doi.org/10.1371/journal.pone.0073265.t003

Furthermore, the Sobel test indicated that the complete pathways from the independent variables (anxiety: z  = 2.81, p  = .004; self-esteem: z  =  2.05, p  = .04; positive affect: z  = 2.58, p <.01) to the mediator (i.e., stress), to the outcome (i.e., depression) were significant. These specific results might be explained on the basis that stress partially mediated the effects of both anxiety and positive affect on depression while stress completely mediated the effects of self-esteem on depression. In other words, anxiety and positive affect contributed directly to explain the variation in depression and indirectly via the experienced level of stress. Self-esteem contributed only indirectly via the experienced level of stress to explain the variation in depression. In other words, stress effects on depression originate from “its own power” and explained more of the variation in depression than self-esteem (see Figure 2 ).

https://doi.org/10.1371/journal.pone.0073265.g002

Moderation analysis

Multiple linear regression analyses were used in order to examine moderation effects between anxiety, stress, self-esteem and affect on depression. The analysis indicated that about 52% of the variation in the dependent variable (i.e., depression) could be explained by the main effects and the interaction effects ( R 2  = .55, adjusted R 2  = .51, F (55, 186)  = 14.87, p <.001). When the variables (dependent and independent) were standardized, both the standardized regression coefficients beta (β) and the unstandardized regression coefficients beta (B) became the same value with regard to the main effects. Three of the main effects were significant and contributed uniquely to high levels of depression: anxiety ( B  = .26, t  = 3.12, p  = .002), stress ( B  = .25, t  = 2.86, p  = .005), and self-esteem ( B  = −.17, t  = −2.17, p  = .03). The main effect of positive affect was also significant and contributed to low levels of depression ( B  = −.16, t  = −2.027, p  = .02) (see Figure 3 ). Furthermore, the results indicated that two moderator effects were significant. These were the interaction between stress and negative affect ( B  = −.28, β = −.39, t  = −2.36, p  = .02) (see Figure 4 ) and the interaction between positive affect and negative affect ( B  = −.21, β = −.29, t  = −2.30, p  = .02) ( Figure 5 ).

https://doi.org/10.1371/journal.pone.0073265.g003

Low stress and low negative affect leads to lower levels of depression compared to high stress and high negative affect.

https://doi.org/10.1371/journal.pone.0073265.g004

High positive affect and low negative affect lead to lower levels of depression compared to low positive affect and high negative affect.

https://doi.org/10.1371/journal.pone.0073265.g005

The results in the present study show that (i) anxiety partially mediated the effects of both stress and self-esteem on depression, (ii) that stress partially mediated the effects of anxiety and positive affect on depression, (iii) that stress completely mediated the effects of self-esteem on depression, and (iv) that there was a significant interaction between stress and negative affect, and positive affect and negative affect on depression.

Mediating effects

The study suggests that anxiety contributes directly to explaining the variance in depression while stress and self-esteem might contribute directly to explaining the variance in depression and indirectly by increasing feelings of anxiety. Indeed, individuals who experience stress over a long period of time are susceptible to increased anxiety and depression [30] , [31] and previous research shows that high self-esteem seems to buffer against anxiety and depression [32] , [33] . The study also showed that stress partially mediated the effects of both anxiety and positive affect on depression and that stress completely mediated the effects of self-esteem on depression. Anxiety and positive affect contributed directly to explain the variation in depression and indirectly to the experienced level of stress. Self-esteem contributed only indirectly via the experienced level of stress to explain the variation in depression, i.e. stress affects depression on the basis of ‘its own power’ and explains much more of the variation in depressive experiences than self-esteem. In general, individuals who experience low anxiety and frequently experience positive affect seem to experience low stress, which might reduce their levels of depression. Academic stress, for instance, may increase the risk for experiencing depression among students [34] . Although self-esteem did not emerged as an important variable here, under circumstances in which difficulties in life become chronic, some researchers suggest that low self-esteem facilitates the experience of stress [35] .

Moderator effects/interaction effects

The present study showed that the interaction between stress and negative affect and between positive and negative affect influenced self-reported depression symptoms. Moderation effects between stress and negative affect imply that the students experiencing low levels of stress and low negative affect reported lower levels of depression than those who experience high levels of stress and high negative affect. This result confirms earlier findings that underline the strong positive association between negative affect and both stress and depression [36] , [37] . Nevertheless, negative affect by itself did not predicted depression. In this regard, it is important to point out that the absence of positive emotions is a better predictor of morbidity than the presence of negative emotions [38] , [39] . A modification to this statement, as illustrated by the results discussed next, could be that the presence of negative emotions in conjunction with the absence of positive emotions increases morbidity.

The moderating effects between positive and negative affect on the experience of depression imply that the students experiencing high levels of positive affect and low levels of negative affect reported lower levels of depression than those who experience low levels of positive affect and high levels of negative affect. This result fits previous observations indicating that different combinations of these affect dimensions are related to different measures of physical and mental health and well-being, such as, blood pressure, depression, quality of sleep, anxiety, life satisfaction, psychological well-being, and self-regulation [40] – [51] .

Limitations

The result indicated a relatively low mean value for depression ( M  = 3.69), perhaps because the studied population was university students. These might limit the generalization power of the results and might also explain why negative affect, commonly associated to depression, was not related to depression in the present study. Moreover, there is a potential influence of single source/single method variance on the findings, especially given the high correlation between all the variables under examination.

Conclusions

The present study highlights different results that could be arrived depending on whether researchers decide to use variables as mediators or moderators. For example, when using meditational analyses, anxiety and stress seem to be important factors that explain how the different variables used here influence depression–increases in anxiety and stress by any other factor seem to lead to increases in depression. In contrast, when moderation analyses were used, the interaction of stress and affect predicted depression and the interaction of both affectivity dimensions (i.e., positive and negative affect) also predicted depression–stress might increase depression under the condition that the individual is high in negative affectivity, in turn, negative affectivity might increase depression under the condition that the individual experiences low positive affectivity.

Acknowledgments

The authors would like to thank the reviewers for their openness and suggestions, which significantly improved the article.

Author Contributions

Conceived and designed the experiments: AAN TA. Performed the experiments: AAN. Analyzed the data: AAN DG. Contributed reagents/materials/analysis tools: AAN TA DG. Wrote the paper: AAN PR TA DG.

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• Research Article
• Open access
• Published: 06 April 2021

Health anxiety, perceived stress, and coping styles in the shadow of the COVID-19

• Szabolcs Garbóczy 1 , 2 ,
• Anita Szemán-Nagy 3 ,
• Mohamed S. Ahmad 4 ,
• Szilvia Harsányi 1 ,
• Dorottya Ocsenás 5 , 6 ,
• Viktor Rekenyi 4 ,
• Ala’a B. Al-Tammemi 1 , 7 &
• László Róbert Kolozsvári   ORCID: orcid.org/0000-0001-9426-0898 1 , 7  

BMC Psychology volume  9 , Article number:  53 ( 2021 ) Cite this article

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Metrics details

In the case of people who carry an increased number of anxiety traits and maladaptive coping strategies, psychosocial stressors may further increase the level of perceived stress they experience. In our research study, we aimed to examine the levels of perceived stress and health anxiety as well as coping styles among university students amid the COVID-19 pandemic.

A cross-sectional study was conducted using an online-based survey at the University of Debrecen during the official lockdown in Hungary when dormitories were closed, and teaching was conducted remotely. Our questionnaire solicited data using three assessment tools, namely, the Perceived Stress Scale (PSS), the Ways of Coping Questionnaire (WCQ), and the Short Health Anxiety Inventory (SHAI).

A total of 1320 students have participated in our study and 31 non-eligible responses were excluded. Among the remaining 1289 participants, 948 (73.5%) and 341 (26.5%) were Hungarian and international students, respectively. Female students predominated the overall sample with 920 participants (71.4%). In general, there was a statistically significant positive relationship between perceived stress and health anxiety. Health anxiety and perceived stress levels were significantly higher among international students compared to domestic ones. Regarding coping, wishful thinking was associated with higher levels of stress and anxiety among international students, while being a goal-oriented person acted the opposite way. Among the domestic students, cognitive restructuring as a coping strategy was associated with lower levels of stress and anxiety. Concerning health anxiety, female students (domestic and international) had significantly higher levels of health anxiety compared to males. Moreover, female students had significantly higher levels of perceived stress compared to males in the international group, however, there was no significant difference in perceived stress between males and females in the domestic group.

The elevated perceived stress levels during major life events can be further deepened by disengagement from home (being away/abroad from country or family) and by using inadequate coping strategies. By following and adhering to the international recommendations, adopting proper coping methods, and equipping oneself with the required coping and stress management skills, the associated high levels of perceived stress and anxiety could be mitigated.

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Introduction

On March 4, 2020, the first cases of coronavirus disease were declared in Hungary. One week later, the World Health Organization (WHO) declared COVID-19 as a global pandemic [ 1 ]. The Hungarian government ordered a ban on outdoor public events with more than 500 people and indoor events with more than 100 participants to reduce contact between people [ 2 ]. On March 27, the government imposed a nationwide lockdown for two weeks effective from March 28, to mitigate the spread of the pandemic. Except for food stores, drug stores, pharmacies, and petrol stations, all other shops and educational institutions remained closed. On April 16, a week-long extension was further announced [ 3 ].

The COVID-19 pandemic with its high morbidity and mortality has already afflicted the psychological and physical wellbeing of humans worldwide [ 4 , 5 , 6 , 7 , 8 , 9 ]. During major life events, people may have to deal with more stress. Stress can negatively affect the population’s well-being or function when they construe the situation as stressful and they cannot handle the environmental stimuli [ 10 ]. Various inter-related and inter-linked concepts are present in such situations including stress, anxiety, and coping. According to the literature, perceived stress can lead to higher levels of anxiety and lower levels of health-related quality of life [ 11 ]. Another study found significant and consistent associations between coping strategies and the dimensions of health anxiety [ 12 ].

Health anxiety is one of the most common types of anxiety and it describes how people think and behave toward their health and how they perceive any health-related concerns or threats. Health anxiety is increasingly conceptualized as existing on a spectrum [ 13 , 14 ], and as an adaptive signal that helps to develop survival-oriented behaviors. It also occurs in almost everyone’s life to a certain degree and can be rather deleterious when it is excessive [ 13 , 14 ]. Illness anxiety or hypochondriasis is on the high end of the spectrum and it affects someone’s life when it interferes with daily life by making people misinterpret the somatic sensations, leading them to think that they have an underlying condition [ 14 ].

According to the American Psychiatric Association—Diagnostic and Statistical Manual of Mental Disorders (fifth edition), Illness anxiety disorder is described as a preoccupation with acquiring or having a serious illness, and it reflects the high spectrum of health anxiety [ 15 ]. Somatic symptoms are not present or if they are, then only mild in intensity. The preoccupation is disproportionate or excessive if there is a high risk of developing a medical condition (e.g., family history) or the patient has another medical condition. Excessive health-related behaviors can be observed (e.g., checking body for signs of illness) and individuals can show maladaptive avoidance as well by avoiding hospitals and doctor appointments [ 15 ].

Health anxiety is indeed an important topic as both its increase and decrease can progress to problems [ 14 ]. Looking at health anxiety as a wide spectrum, it can be high or low [ 16 ]. While people with a higher degree of worry and checking behaviors may cause some burden on healthcare facilities by visiting them too many times (e.g., frequent unnecessary visits), other individuals may not seek medical help at healthcare units to avoid catching up infections for instance. A lower degree of health anxiety can lead to low compliance with imposed regulations made to control a pandemic [ 17 ].

The COVID-19 pandemic as a major event in almost everyone’s life has posed a great impact on the population’s perceived stress level. Several studies about the relation between coping and response to epidemics in recent and previous outbreaks found higher perceived stress levels among people [ 18 , 19 , 20 , 21 ]. Being a woman, low income, and living with other people all were associated with higher stress levels [ 18 ]. Protective factors like being emotionally more stable, having self-control, adaptive coping strategies, and internal locus of control were also addressed [ 19 , 20 ]. The findings indicated that the COVID-19 crisis is perceived as a stressful event. The perceived stress was higher amongst people than it was in situations with no emergency. Nervousness, stress, and loss of control of one’s life are the factors that are most connected to perceived stress levels which leads to the suggestion that unpredictability and uncontrollability take an important part in perceived stress during a crisis [ 19 , 20 ].

Moreover, certain coping styles (e.g., having a positive attitude) were associated with less psychological distress experiences but avoidance strategies were more likely to cause higher levels of stress [ 21 ]. According to Lazarus (1999), individuals differ in their perception of stress if the stress response is viewed as the interaction between the environment and humans [ 22 ]. An Individual can experience two kinds of evaluation processes, one to appraise the external stressors and personal stake, and the other one to appraise personal resources that can be used to cope with stressors [ 22 , 23 ]. If there is an imbalance between these two evaluation processes, then stress occurs, because the personal resources are not enough to cope with the stressor’s demands [ 23 ].

During stressful life events, it is important to pay attention to the increasing levels of health anxiety and to the kind of coping mechanisms that are potential factors to mitigate the effects of high anxiety. The transactional model of stress by Lazarus and Folkman (1987) provides an insight into these kinds of factors [ 24 ]. Lazarus and Folkman theorized two types of coping responses: emotion-focused coping, and problem-focused coping. Emotion-focused coping strategies (e.g., distancing, acceptance of responsibility, positive reappraisal) might be used when the source of stress is not embedded in the person’s control and these strategies aim to manage the individual’s emotional response to a threat. Also, emotion-focused coping strategies are directed at managing emotional distress [ 24 ]. On the other hand, problem-focused coping strategies (e.g., confrontive coping, seeking social support, planful problem-solving) help an individual to be able to endure and/or minimize the threat, targeting the causes of stress in practical ways [ 24 ]. It was also addressed that emotion-focused coping mechanisms were used more in situations appraised as requiring acceptance, whereas problem-focused forms of coping were used more in encounters assessed as changeable [ 24 ].

A recent study in Hunan province in China found that the most effective factor in coping with stress among medical staff was the knowledge of their family’s well-being [ 25 ]. Although there have been several studies about the mental health of hospital workers during the COVID-19 pandemic or other epidemics (e.g., SARS, MERS) [ 26 , 27 , 28 , 29 ], only a few studies from recent literature assessed the general population’s coping strategies. According to Gerhold (2020) [ 30 ], older people perceived a lower risk of COVID-19 than younger people. Also, women have expressed more worries about the disease than men did. Coping strategies were highly problem-focused and most of the participants reported that they listen to professionals’ advice and tried to remain calm [ 30 ]. In the same study, most responders perceived the COVID-19 pandemic as a global catastrophe that will severely affect a lot of people. On the other hand, they perceived the pandemic as a controllable risk that can be reduced. Dealing with macrosocial stressors takes faith in politics and in those people, who work with COVID-19 on the frontline.

Mental disorders are found prevalent among college students and their onset occurs mostly before entry to college [ 31 ]. The diagnosis and timely interventions at an early stage of illness are essential to improve psychosocial functioning and treatment outcomes [ 31 ]. According to research that was conducted at the University of Debrecen in Hungary a few years ago, the students were found to have high levels of stress and the rate of the participants with impacted mental health was alarming [ 32 ]. With an unprecedented stressful event like the COVID-19 crisis, changes to the mental health status of people, including students, are expected.

Aims of the study

In our present study, we aimed at assessing the levels of health anxiety, perceived stress, and coping styles among university students amidst the COVID-19 lockdown in Hungary, using three validated assessment tools for each domain.

Methods and materials

Study design and setting.

This study utilized a cross-sectional design, using online self-administered questionnaires that were created and designed in Google Forms® (A web-based survey tool). Data collection was carried out in the period April 30, 2020, and May 15, 2020, which represents one of the most stressful periods during the early stage of the COVID-19 pandemic in Hungary when the official curfew/lockdown was declared along with the closure of dormitories and shifting to online remote teaching. The first cases of COVID-19 were declared in Hungary on March 4, 2020. On April 30, 2020, there were 2775 confirmed cases, 312 deaths, and 581 recoveries. As of May 15, 2020, the number of confirmed cases, deaths, and recovered persons was 3417, 442, and 1287, respectively.

Our study was conducted at the University of Debrecen, which is one of the largest higher education institutions in Hungary. The University is located in the city of Debrecen, the second-largest city in Hungary. Debrecen city is considered the educational and cultural hub of Eastern Hungary. As of October 2019, around 28,593 students were enrolled in various study programs at the University of Debrecen, of whom, 6,297 were international students [ 33 ]. The university offers various degree courses in Hungarian and English languages.

Study participants and sampling

The target population of our study was students at the University of Debrecen. Students were approached through social media platforms (e.g., Facebook®) and the official student administration system at the University of Debrecen (Neptun). The invitation link to our survey was sent to students on the web-based platforms described earlier. By using the Neptun system, we theoretically assumed that our survey questionnaire has reached all students at the University. The students who were interested and willing to participate in the study could fill out our questionnaire anonymously during the determined study period; thus, employing a convenience sampling approach. All students at the University of Debrecen whose age was 18 years or older and who were in Hungary during the outbreak had the eligibility to participate in our study whether undergraduates or postgraduates.

Study instruments

In our present study, the survey has solicited information about the sociodemographic profile of participants including age (in years), gender (female vs male), study program (health-related vs non-health related), and whether the student stayed in Hungary or traveled abroad during the period of conducting our survey in the outbreak. Our survey has also adopted three international scales to collect data about health anxiety, coping styles, and perceived stress during the pandemic crisis. As the language of instruction for international students at the University of Debrecen is English, and English fluency is one of the criteria for international students’ admission at the University of Debrecen, the international students were asked to fill out the English version of the survey and the scales. On the other hand, the Hungarian students were asked to fill out the Hungarian version of the survey and the validated Hungarian scales. Also, we provided contact information for psychological support when needed. Students who felt that they needed some help and psychological counseling could use the contact information of our peer supporters. Four International students have used this opportunity and were referred to a higher level of care. The original scales and their validated Hungarian versions are described in the following sections.

Perceived Stress Scale (PSS)

The Perceived Stress Scale (PSS) measures the level of stress in the general population who have at least completed a junior high school [ 34 ]. In the PSS, the respondents had to report how often certain things occurred like nervousness; loss of control; feeling of upset; piling up difficulties that cannot be handled; or on the contrary how often the students felt they were able to handle situations; and were on top of things. For the International students, we used the 10-item PSS (English version). The statements’ responses were scored on a 5-point Likert scale (from 0 = never to 4 = very often) as per the scale’s guide. Also, in the 10-item PSS, four positive items were reversely scored (e.g. felt confident about someone’s ability to handle personal problems) [ 34 ]. The PSS has satisfactory psychometric properties with a Cronbach’s alpha of 0.78, and this English version was used for international students in our study.

For the Hungarian students, we used the Hungarian version of the PSS, which has 14 statements that cover the same aspects of stress described earlier. In this version of the PSS, the responses were evaluated on a 5-point Likert scale (0–4) to mark how typical a particular behavior was for a respondent in the last month [ 35 ]. The Hungarian version of the PSS was psychometrically validated in 2006. In the validation study, the Hungarian 14-item PSS has shown satisfactory internal consistency with a Cronbach’s alpha of 0.88 [ 35 ].

Ways of Coping Questionnaire (WCQ)

The second scale we used was the 26-Item Ways of Coping Questionnaire (WCQ) which was developed by Sørlie and Sexton [ 36 ]. For the international students, we used the validated English version of the 26-Item WCQ that distinguished five different factors, including Wishful thinking (hoped for a miracle, day-dreamed for a better time), Goal-oriented (tried to analyze the problem, concentrated on what to do), Seeking support (talked to someone, got professional help), Thinking it over (drew on past experiences, realized other solutions), and Avoidance (refused to think about it, minimized seriousness of it). The WCQ examined how often the respondents used certain coping mechanisms, eg: hoped for a miracle, fantasized, prepared for the worst, analyzed the problem, talked to someone, or on the opposite did not talk to anyone, drew conclusions from past things, came up with several solutions for a problem or contained their feelings. As per the 26-item WCQ, responses were scored on a 4-point Likert scale (from 0 = “does not apply and/or not used” to 3 = “used a great deal”). This scale has satisfactory psychometric properties with Cronbach's alpha for the factors ranged from 0.74 to 0.81[ 36 ].

For the Hungarian students, we used the Hungarian 16-Item WCQ, which was validated in 2008 [ 37 ]. In the Hungarian WCQ, four dimensions were identified, which were cognitive restructuring/adaptation (every cloud has a silver lining), Stress reduction (by eating; drinking; smoking), Problem analysis (I tried to analyze the problem), and Helplessness/Passive coping (I prayed; used drugs) [ 37 ]. The Cronbach’s alpha values for the Hungarian WCQ’s dimensions were in the range of 0.30–0.74 [ 37 ].

Short Health Anxiety Inventory (SHAI)

The third scale adopted was the 18-Items Short Health Anxiety Inventory (SHAI). Overall, the SHAI has two subscales. The first subscale comprised of 14 items that examined to what degree the respondents were worried about their health in the past six months; how often they noticed physical pain/ache or sensations; how worried they were about a serious illness; how much they felt at risk for a serious illness; how much attention was drawn to bodily sensations; what their environment said, how much they deal with their health. The second subscale of SHAI comprised of 4 items (negative consequences if the illness occurs) that enquired how the respondents would feel if they were diagnosed with a serious illness, whether they would be able to enjoy things; would they trust modern medicine to heal them; how many aspects of their life it would affect; how much they could preserve their dignity despite the illness [ 38 ]. One of four possible statements (scored from 0 to 3) must be chosen. Alberts et al. (2013) [ 39 ] found the mean SHAI value to be 12.41 (± 6.81) in a non-clinical sample. The original 18-item SHAI has Cronbach’s alpha values in the range of 0.74–0.96 [ 39 ]. For the Hungarian students, the Hungarian version of the SHAI was used. The Hungarian version of SHAI was validated in 2011 [ 40 ]. The scoring differs from the English version in that the four statements were scored from 1 to 4, but the statements themselves were the same. In the Hungarian validation study, it was found that the SHAI mean score in a non-clinical sample (university students) was 33.02 points (± 6.28) and the Cronbach's alpha of the test was 0.83 [ 40 ].

Data analyses

Data were extracted from Google Forms® as an Excel sheet for quality check and coding then we used SPSS® (v.25) and RStudio statistical software packages to analyze the data. Descriptive and summary statistics were presented as appropriate. To assess the difference between groups/categories of anxiety, stress, and coping styles, we used the non-parametric Kruskal–Wallis test, since the variables did not have a normal distribution and for post hoc tests, we used the Mann–Whitney test. Also, we used Spearman’s rank correlation to assess the relationship between health anxiety and perceived stress within the international group and the Hungarian group. Comparison between international and domestic groups and different genders in terms of health anxiety and perceived stress levels were also conducted using the Mann–Whitney test. Binary logistic regression analysis was also employed to examine the associations between different coping styles/ strategies (treated as independent variables) and both, health anxiety level and perceived stress level (treated as outcome variables) using median splits. A p-value less than 5% was implemented for statistical significance.

Ethical considerations

Ethical permission was obtained from the Hungarian Ethical Review Committee for Research in Psychology (Reference number: 2020-45). All methods were carried out following the institutional guidelines and conforming to the ethical standards of the declaration of Helsinki. All participants were informed about the study and written informed consent was obtained before completing the survey. There were no rewards/incentives for completing the survey.

Sociodemographic characteristics of respondents

A total of 1320 students have responded to our survey. Six responses were eliminated due to incompleteness and an additional 25 responses were also excluded as the students filled out the survey from abroad (International students who were outside Hungary during the period of conducting our study). After exclusion of the described non-eligible responses (a total of 31 responses), the remaining 1289 valid responses were included in our analysis. Out of 1289 participants (100%), 73.5% were Hungarian students and around 26.5% were international students. Overall, female students have predominated the sample (n = 920, 71.4%). The median age (Interquartile range) among Hungarian students was 22 years (5) and for the international students was 22 years (4). Out of the total sample, most of the Hungarian students were enrolled in non-health-related programs (n = 690, 53.5%), while most of the international students were enrolled in health-related programs (n = 213, 16.5%). Table 1 demonstrates the sociodemographic profile of participants (Hungarian vs International).

Perceived stress, anxiety, and coping styles

For greater clarity of statistical analysis and interpretation, we created preferences regarding coping mechanisms. That is, we made the categories based on which coping factor (in the international sample) or dimension (in the Hungarian sample) the given person reached the highest scores, so it can be said that it is the person's preferred coping strategy. The four coping strategies among international students were goal-oriented, thinking it over, wishful thinking, and avoidance, while among the Hungarian students were cognitive restructuring, problem analysis, stress reduction, and passive coping.

The 26-item WCQ [ 31 ] contains a seeking support subscale which is missing from the Hungarian 16-item WCQ [ 32 ]; therefore, the seeking support subscale was excluded from our analysis. Moreover, because the PSS contained a different number of items in English and Hungarian versions (10 items vs 14 items), we looked at the average score of the answers so that we could compare international and domestic students.

In the evaluation of SHAI, the scoring of the two questionnaires are different. For the sake of comparability between the two samples, the international points were corrected to the Hungarian, adding plus one to the value of each answer. This may be the reason why we obtained higher results compared to international standards.

Among the international students, the mean score (± standard deviation) of perceived stress among male students was 2.11(± 0.86) compared to female students 2.51 (± 0.78), while the mean score (± standard deviation) of health anxiety was 34.12 (± 7.88) and 36.31 (± 7.75) among males and females, respectively. Table 2 shows more details regarding the perceived stress scores and health anxiety scores stratified by coping strategies among international students.

In the Hungarian sample, the mean score (± standard deviation) of perceived stress among male students was 2.06 (± 0.84) compared to female students 2.18 (± 0.83), while the mean score (± standard deviation) of health anxiety was 33.40 (± 7.63) and 35.05 (± 7.39) among males and females, respectively. Table 3 shows more details regarding the perceived stress scores and health anxiety scores stratified by coping strategies among Hungarian students.

Concerning coping styles among international students, the statements with the highest-ranked responses were “wished the situation would go away or somehow be finished” and “Had fantasies or wishes about how things might turn out” and both fall into the wishful thinking coping. Among the Hungarian students, the statements with the highest-ranked responses were “I tried to analyze the problem to understand better” (falls into problem analysis coping) and “I thought every cloud has a silver lining, I tried to perceive things cheerfully” (falls into cognitive restructuring coping).

On the other hand, the statements with the least-ranked responses among the international students belonged to the Avoidance coping. Among the Hungarians, it was Passive coping “I tried to take sedatives or medications” and Stress reduction “I staked everything upon a single cast, I started to do something risky” to have the lowest-ranked responses. Table 4 shows a comparison of different coping strategies among international and Hungarian students.

To test the difference between coping strategies, we used the non-parametric Kruskal–Wallis test, since the variables did not have a normal distribution. For post hoc tests, we used Mann–Whitney tests with lowered significance levels ( p  = 0.0083). Among Hungarian students, there were significant differences between the groups in stress ( χ 2 (3) = 212.01; p < 0.001) and health anxiety ( χ 2 (3) = 80.32; p  < 0.001). In the post hoc tests, there were significant differences everywhere ( p  < 0.001) except between stress reduction and passive coping ( p  = 0.089) and between problem analysis and passive coping ( p  = 0.034). Considering the health anxiety, the results were very similar. There were significant differences between all groups ( p  < 0.001), except between stress reduction and passive coping ( p  = 0.347) and between problem analysis and passive coping ( p  = 0.205). See Figs.  1 and 2 for the Hungarian students.

Perceived stress differences between coping strategies among the Hungarian students

Health anxiety differences between coping strategies among the Hungarian students

Among the international students, the results were similar. According to the Kruskal–Wallis test, there were significant differences in stress ( χ 2 (3) = 73.26; p  < 0.001) and health anxiety ( χ 2 (3) = 42.60; p  < 0.001) between various coping strategies. The post hoc tests showed that there were differences between the perceived stress level and coping strategies everywhere ( p  < 0.005) except and between avoidance and thinking it over ( p  = 0.640). Concerning health anxiety, there were significant differences between wishful thinking and goal-oriented ( p  < 0.001), between wishful thinking and avoidance ( p  = 0.001), and between goal-oriented and avoidance ( p  = 0.285). There were no significant differences between wishful thinking and thinking it over ( p  = 0.069), between goal-oriented and thinking it over ( p  = 0.069), and between avoidance and thinking it over ( p  = 0.131). See Figs.  3 and 4 .

Perceived stress differences between coping strategies among the international students

Health anxiety differences between coping strategies among the international students

The relationship between coping strategies with health anxiety and perceived stress levels among the international students

We applied logistic regression analyses for the variables to see which of the coping strategies has a significant effect on SHAI and PSS results. In the first model (model a), with the health anxiety as an outcome dummy variable (with median split; median: 35), only two coping strategies had a statistically significant relationship with health anxiety level, including wishful thinking (as a risk factor) and goal-oriented (as a protective factor).

In the second model (model b), with the perceived stress as an outcome dummy variable (with median split; median: 2.40), three coping strategies were found to have a statistically significant association with the level of perceived stress, including wishful thinking (as a risk factor), while goal-oriented and thinking it over as protective factors. See Table 5 .

The relationship between coping strategies with health anxiety and perceived stress levels among domestic students

By employing logistic regression analysis, with the health anxiety as an outcome dummy variable (with median split; median: 33.5) (model a), three coping strategies had a statistically significant relationship with health anxiety level among domestic students, including stress reduction and problem analysis (as risk factors), while cognitive restructuring (as a protective factor).

Similarly, with the perceived stress as an outcome dummy variable (with median split; median: 2.1429) (model b), three coping strategies had a statistically significant relationship with perceived stress level, including stress reduction and problem analysis (as risk factors), while cognitive restructuring (as a protective factor). See Table 6 .

Comparisons between domestic and international students

We compared health anxiety and perceived stress levels of the Hungarian and international students’ groups using the Mann–Whitney test. In the case of health anxiety, the results showed that there were significant differences between the two groups ( W  = 149,431; p  = 0.038) and international students’ levels were higher. Also, there was a significant difference in the perceived stress level between the two groups ( W  = 141,024; p  < 0.001), and the international students have increased stress levels compared to the Hungarian ones.

Comparisons between genders within students’ groups (International vs Hungarian)

Firstly, we compared the international men’s and women’s health anxiety and stress levels using the Mann–Whitney test. The results showed that the international women’s health anxiety ( W  = 11,810; p  = 0.012) and perceived stress ( W  = 10,371; p  < 0.001) levels were both significantly higher than international men’s values. However, in the Hungarian sample, women’s health anxiety was significantly higher than men’s ( W  = 69,643; p  < 0.001), but there was no significant difference in perceived stress levels among between Hungarian women and men ( W  = 75,644.5; p  = 0.064).

Relationship between health anxiety and perceived stress

We correlated the general health anxiety and perceived stress using Spearman’s rank correlation. There was a significant moderate positive relationship between the two variables ( p  < 0.001; ρ  = 0.446). Within the Hungarian students, there was a significant correlation between health anxiety and perceived stress ( p  < 0.001; ρ  = 0.433), similarly among international students as well ( p  < 0.001; ρ  = 0.465).

In our study, we found that individuals who were characterized by a preference for certain coping strategies reported significantly higher perceived stress and/or health anxiety than those who used other coping methods. These correlations can be found in both the Hungarian and international students. In the light of our results, we can say that 48.4% of the international students used wishful thinking as their preferred coping method while around 43% of the Hungarian students used primarily cognitive restructuring to overcome their problems.

Regulation of emotion refers to “the processes whereby individuals monitor, evaluate, and modify their emotions in an effort to control which emotions they have, when they have them, and how they experience and express those emotions” [ 41 ]. There is an overlap between emotion-focused coping and emotion regulation strategies, but there are also differences. The overlap between the two concepts can be noticed in the fact that emotion-focused coping strategies have an emotional regulatory role, and emotion regulation strategies may “tax the individual’s resources” as the emotion-focused coping strategies do [ 23 , 42 ]. However, in emotion-focused coping strategies, non-emotional tools can also be used to achieve non-emotional goals, while emotion regulation strategies may be used for maintaining or reinforcing positive emotions [ 42 ].

Based on the cognitive-behavioral model of health anxiety, emotion-regulating strategies can regulate the physiological, cognitive, and behavioral consequences of a fear response to some degree, even when the person encounters the conditioned stimulus again [ 12 , 43 ]. In the long run, regular use of these dysfunctional emotion control strategies may manifest as functional impairment, which may be associated with anxiety disorders. A detailed study that examined health anxiety in the view of the cognitive-behavioral model found that, regardless of the effect of depression, there are significant and consistent correlations between certain dimensions of health anxiety and dysfunctional coping and emotional regulation strategies [ 12 ].

Similar to our current study, other studies have found that health anxiety was positively correlated with maladaptive emotion regulation and negatively with adaptive emotion regulation [ 44 ], and in the case of state anxiety that emotion-focused coping strategies proved to be less effective in reducing stress, while active coping leads to a sense of subjective well-being [ 17 , 27 , 45 , 46 , 47 ]

SHAI values were found to be high in other studies during the pandemic, and the SHAI results of the international students in our study were found to be even slightly higher compared to those studies [ 44 , 48 ]. Besides, anxiety values for women were found to be higher than for men in several studies [ 44 , 48 , 49 , 50 ]. This was similar to what we found among the international students but not among the Hungarian ones. We can speculate that the ability to contact someone, the closeness of family and beloved ones, familiarity with the living environment, and maybe less online search about the coronavirus news could be factors counting towards that finding among Hungarian students. Also, most international students were enrolled in health-related study programs and his might have affected how they perceived stress/anxiety and their preferred coping strategies as well. Literature found that students of medical disciplines could have obstacles in achieving a healthy coping strategy to deal with stress and anxiety despite their profound medical knowledge compared to non-health-related students [ 51 , 52 ]. Literature also stressed the immense need for training programs to help students of medical disciplines in adopting coping skills and stress-reducing strategies [ 51 ].

The findings of our study may be a starting point for the exploration of the linkage between perceived stress, health anxiety, and coping strategies when people are not in their domestic context. People who are away from their home and friends in a relatively alien environment may tend to use coping mechanisms other than the adequate ones, which in turn can lead to increased levels of perceived stress.

Furthermore, our results seem to support the knowledge that deep-rooted health anxiety is difficult to change because it is closely related to certain coping mechanisms. It was also addressed in the literature that personality traits may have a significant influence on the coping strategy used by a person [ 53 ], revealing sophisticated and challenging links to be considered especially during training programs on effective coping and management skills. On the other hand, perceived stress which has risen significantly above the average level in the current pandemic, can be most effectively targeted by the well-formulated recommendations and advice of major international health organizations if people successfully adhere to them (e.g. physical activity; proper and adequate sleep; healthy eating; avoiding alcohol; meditation; caring for others; relationships maintenance, and using credible information resources about the pandemic, etc.) [ 1 , 54 ]. Furthermore, there may be additional positive effects of these recommendations when published in different languages or languages that are spoken by a wide range of nationalities. Besides, cognitive behavioral therapy techniques, some of which are available online during the current pandemic crisis, can further reduce anxiety. Also, if someone does not feel safe or fear prevails, there are helplines to get in touch with professionals, and this applies to the University of Debrecen in Hungary, and to a certain extent internationally.

Naturally, our study had certain limitations that should be acknowledged and considered. The temporality of events could not be assessed as we employed a cross-sectional study design, that is, we did not have information on the previous conditions of the participants which means that it is possible that some of these conditions existed in the past, while others de facto occurred with COVID-19 crisis. The survey questionnaires were completed by those who felt interested and involved, i.e., a convenience sampling technique was used, this impairs the representativeness of the sample (in terms of sociodemographic variables) and the generalizability of our results. Also, the type of recruitment (including social media) as well as the online nature of the study, probably appealed more to people with an affinity with this kind of instrument. Besides, each questionnaire represented self-reported states; thus, over-reporting or under-reporting could be present. It is also important to note that international students were answering the survey questionnaire in a language that might not have been their mother language. Nevertheless, English fluency is a prerequisite to enroll in a study program at the University of Debrecen for international students. As the options for gender were only male/female in our survey questionnaire, we might have missed the views of students who do not identify themselves according to these gender categories. Also, no data on medical history/current medical status were collected. Lastly, we had to make minor changes to the used scales in the different languages for comparability.

The COVID-19 pandemic crisis has imposed a significant burden on the physical and psychological wellbeing of humans. Crises like the current pandemic can trigger unprecedented emotional and behavioral responses among individuals to adapt or cope with the situation. The elevated perceived stress levels during major life events can be further deepened by disengagement from home and by using inadequate coping strategies. By following and adhering to the international recommendations, adopting proper coping strategies, and equipping oneself with the required coping and stress management skills, the associated high levels of perceived stress and anxiety might be mitigated.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to compliance with institutional guidelines but they are available from the corresponding author (LRK) on a reasonable request.

Abbreviations

Centers for Disease Control and Prevention

Coronavirus Disease 2019

Perceived Stress Scale

Short Health Anxiety Inventory

Middle East Respiratory Syndrome

Severe Acute Respiratory Syndrome

Ways of Coping Questionnaire

World Health Organization

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Acknowledgments

We would like to provide our extreme thanks and appreciation to all students who participated in our study. ABA is currently supported by the Tempus Public Foundation’s scholarship at the University of Debrecen.

This research project did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Doctoral School of Health Sciences, University of Debrecen, Debrecen, Hungary

Szabolcs Garbóczy, Szilvia Harsányi, Ala’a B. Al-Tammemi & László Róbert Kolozsvári

Department of Psychiatry, Faculty of Medicine, University of Debrecen, Debrecen, Hungary

Szabolcs Garbóczy

Department of Personality and Clinical Psychology, Institute of Psychology, University of Debrecen, Debrecen, Hungary

Anita Szemán-Nagy

Faculty of Medicine, University of Debrecen, Debrecen, Hungary

Mohamed S. Ahmad & Viktor Rekenyi

Department of Social and Work Psychology, Institute of Psychology, University of Debrecen, Debrecen, Hungary

Dorottya Ocsenás

Doctoral School of Human Sciences, University of Debrecen, Debrecen, Hungary

Department of Family and Occupational Medicine, Faculty of Medicine, University of Debrecen, Móricz Zs. krt. 22, Debrecen, 4032, Hungary

Ala’a B. Al-Tammemi & László Róbert Kolozsvári

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All authors SG, ASN, MSA, SH, DO, VR, ABA, and LRK have worked on the study design, text writing, revising, and editing of the manuscript. DO, SG, and VR have done data management and extraction, data analysis. Drafting and interpretation of the manuscript were made in close collaboration by all authors SG, ASN, MSA, SH, DO, VR, ABA, and LRK. All authors read and approved the final manuscript.

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Garbóczy, S., Szemán-Nagy, A., Ahmad, M.S. et al. Health anxiety, perceived stress, and coping styles in the shadow of the COVID-19. BMC Psychol 9 , 53 (2021). https://doi.org/10.1186/s40359-021-00560-3

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• Published: 09 January 2023

Effect of breathwork on stress and mental health: A meta-analysis of randomised-controlled trials

• Guy William Fincham 1 ,
• Clara Strauss 1 , 2 ,
• Jesus Montero-Marin 3 , 4 , 5 &
• Kate Cavanagh 1 , 2  

Scientific Reports volume  13 , Article number:  432 ( 2023 ) Cite this article

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Deliberate control of the breath (breathwork) has recently received an unprecedented surge in public interest and breathing techniques have therapeutic potential to improve mental health. Our meta-analysis primarily aimed to evaluate the efficacy of breathwork through examining whether, and to what extent, breathwork interventions were associated with lower levels of self-reported/subjective stress compared to non-breathwork controls. We searched PsycInfo, PubMed, ProQuest, Scopus, Web of Science, ClinicalTrials.gov and ISRCTN up to February 2022, initially identifying 1325 results. The primary outcome self-reported/subjective stress included 12 randomised-controlled trials ( k  = 12) with a total of 785 adult participants. Most studies were deemed as being at moderate risk of bias. The random-effects analysis yielded a significant small-to-medium mean effect size, g  = − 0.35 [95% CI − 0.55, − 0.14], z  = 3.32, p  = 0.0009, showing breathwork was associated with lower levels of stress than control conditions. Heterogeneity was intermediate and approaching significance, χ 2 11  = 19, p  = 0.06, I 2  = 42%. Meta-analyses for secondary outcomes of self-reported/subjective anxiety ( k  = 20) and depressive symptoms ( k  = 18) showed similar significant effect sizes: g  = − 0.32, p  < 0.0001, and g  = − 0.40, p  < 0.0001, respectively. Heterogeneity was moderate and significant for both. Overall, results showed that breathwork may be effective for improving stress and mental health. However, we urge caution and advocate for nuanced research approaches with low risk-of-bias study designs to avoid a miscalibration between hype and evidence.

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Introduction.

Breathwork comprises various practices which encompass regulating the way that one breathes, particularly in order to promote mental, emotional and physical health (Oxford English Dictionary) 1 . These techniques have emerged worldwide with complex historical roots from various traditions such as yoga (i.e., alternate nostril breathing) and Tibetan Buddhism (i.e., vase breathing) along with psychedelic communities (i.e., conscious connected breathing) and scientific/medical researchers and practitioners (i.e., coherent/resonant frequency breathing). Recently, breathwork has been garnering public attention and popularity in the West due to supposed beneficial effects on health and well-being 2 in addition to the breathing-related pathology of covid-19, however it has only been partly investigated by clinical research and psychiatric medical communities.

Slow-paced breathing practices have gained most research attention thus far. Several psychophysiological mechanisms of action are proposed to underpin such techniques: from polyvagal theory and interoception literature 3 along with enteroception, central nervous system effects, and increasing heart-rate variability (HRV) via modulation of the autonomic nervous system (ANS) and increased parasympathetic activity 4 . ANS activity can be measured using HRV, the oscillations in heart rate connected to breathing (i.e., the fluctuation in the interval between successive heart beats) 5 . Fundamentally, as one inhales and exhales, heart rate increases and decreases, respectively. Higher HRV, arising from respiratory sinus arrhythmia 6 , is typically beneficial as it translates into robust responses to changes in breathing and thus a more resilient stress-response system 7 .

Stress-response dysfunction, associated with impaired ANS activity, and low HRV are common in stress, anxiety, and depression 8 , 9 , 10 , 11 , 12 . This may explain why techniques like HRV biofeedback can be helpful 13 , however, it is possible that simply pacing respiration slowly at approximately 5–6 breaths/minute, requiring no monitoring equipment, can elicit similar effects 14 . Polyvagal Theory 3 , for instance, posits that vagal nerves are major channels for bidirectional communication between body and brain. Bodily feedback has profound effects on mental states as 80% of vagus nerve fibres transmit messages from body to brain 15 . Further, the neurovisceral integration model states that high vagal tone is associated with improved health along with emotional and cognitive functioning 16 , 17 . Vagal nerves form the main pathway of the parasympathetic nervous system, and high HRV indicates greater parasympathetic activity 7 .

Modifying breathing alters communication sent from the respiratory system, rapidly influencing brain regions regulating behaviour, thought and emotion 18 . Likewise, respiration may entrain brain electrical activity 19 , with slow breathing resulting in synchrony of brain waves 20 , thereby enabling diverse brain regions to communicate more effectively 21 . It has been observed that adept long-term Buddhist meditation practitioners can achieve states where brain waves are synchronised continuously 22 .

Breathwork and stress

Stress, anxiety and depression have markedly exceeded pre-covid-19 pandemic population norms 23 . Thus, research is needed to address how this can be mitigated 24 . A recent survey based on more than 150,000 interviews in over 100 countries suggested that 40% of adults had experienced stress the day preceding the survey (Gallup, US) 25 . Prior to the pandemic, mental health difficulties were already a significant issue. For instance, stress has been identified by the World Health Organisation as contributing to several non-communicable diseases 26 and a 2014 survey, led in collaboration with Harvard, of over 115 million adults showed that 72% and 60% frequently experienced financial and occupational stress, respectively (Robert Wood Johnson Foundation, US) 27 .

Chronic stress is associated with, and can significantly contribute to, many physical and mental health conditions, from hypertension and cardiovascular disease to anxiety and depression 28 . For common mental health problems such as anxiety and depression, cognitive behavioural therapy (CBT) is widely recommended in treatment guidelines worldwide 29 , 30 , yet many do not recover and waiting times can be long 31 , 32 , in addition to extensive professional training and ongoing supervision being required for therapists. Moreover, such treatment is typically individualised and offered on a one-to-one basis making it resource intensive. The present state of global mental health coupled with the access barriers to psychological therapies requires interventions that are easily accessible and scalable 7 , and manualised practices such as breathwork may meet this remit.

Breathing exercises can be easily taught to both trainers and practitioners, and learned in group settings, increasingly via synchronous and asynchronous methods remotely/online. Therefore, given the need for effective treatments that can be offered at scale with limited resources, interventions focusing on deliberately changing breathing might have significant potential. Indeed, some government public health platforms already recommend deep breathing for stress, anxiety and panic symptoms (NHS and IAPT, UK) 33 , 34 . However, the evidence underlying this recommendation has not been scrutinised in a comprehensive systematic review and meta-analysis and this is the aim of the current study.

Moreover, it is not only slow-paced breathing which may help reduce stress. Fast-paced breathwork may also offer therapeutic benefit as temporary voluntarily induced stress is also known to be beneficial for health and stress resilience. For example, regular physical exercise can improve stress, anxiety and depression levels 35 , along with HRV 36 . Similarly, fast-paced breathing techniques can induce short-term stress that may improve mental health 37 , and have also been shown to volitionally influence the ANS, promoting sympathetic activity 38 . There are countless breathwork techniques—and such variation in their potential modalities and underlying principles warrants exploration.

Review aims

It is important that hype around breathwork is grounded in evidence for efficacy—and effects are not overstated to the public. Whilst some previous reviews of breathwork have been published, it is not possible to conclude the effectiveness of breathwork for stress (nor mental health in general) based on previous meta-analyses, since they have been restricted by certain factors. These include focusing on populations with impaired breathing (i.e., chronic obstructive pulmonary disease—COPD, and Asthma) 39 , 40 , insufficient focus on the breathwork intervention itself (i.e., including interventions where breathwork is combined with several other intervention components) 41 making it hard to elicit separate effects, along with spanning more literature on self-reported/subjective anxiety and depression compared to stress 14 . On the other hand, systematic reviews with narrative syntheses of quantitative data may have overlooked key studies because of too much focus on a specific technique (i.e., slow breathing or diaphragmatic breathing) 4 , 42 , an absence of randomised-controlled trials (RCTs), scanter literature on self-reported/subjective stress compared to self-reported/subjective symptoms of anxiety and depression, along with limited databases 4 , or exclusion of unpublished studies and grey literature (i.e., theses/dissertations) 43 .

Furthermore, in keeping with the participant, intervention, control, outcome and study design (PICOS) framework 44 , there is an absence of examining dose–response correlates with effects and subgroup analyses evaluating differential effects of different breathwork interventions and how they were delivered, what controls were used, effects on populations with differing health statuses and, finally, the psychological outcome measures used. All of these are crucial for an adequate ethical, precautional and practical implementation of breathwork interventions. Accordingly, subgroup analyses were explored to account for these, for the primary outcome of stress. It could be relevant to investigate potential sources of heterogeneity in terms of effects on stress, and this might be related to how some subgroups (such as mental/physical health populations, along with nonclinical/general populations) receive the intervention. Moreover, other subgroups such as the type of breathwork intervention (i.e., slow/fast) and how it is delivered (i.e., online/in-person or individual/group-based), along with the type of comparator (active/inactive control) and outcome measure (questionnaire) used to self-report on stress, may be sources of heterogeneity and thus warrant investigation.

So far, there is no existing meta-analysis of RCTs on the effect of breathwork on psychological stress. Thus, to fill this research gap, the aim of our meta-analysis was to estimate the effect of breathwork in targeting stress. Because prolonged stress can significantly contribute to anxiety and depressive symptoms and there is considerable overlap between them 45 , 46 , we included these two common mental health issues as secondary outcomes, to provide a bigger picture and greater context around the findings on stress. The primary outcome was pre-registered as stress since it is a transdiagnostic variable, relevant in a variety of disorders, and also in people without a diagnosis but suffering from high levels of psychological distress 47 . This makes stress a very interesting target for breathwork-based interventions.

In brief, our research question was the following: do breathwork interventions lead to lower self-reported/subjective stress (primary outcome), anxiety, and depression (secondary outcomes) in comparison to non-breathwork control conditions? We propose this work as a first comprehensive systematic review and meta-analysis exploring the effects of breathwork on stress and mental health, to help lay a solid foundation for the field to grow and evolve in an evidence-based manner.

We focused solely on RCTs reporting psychological measures, to gauge any potential efficacy or effectiveness of breathwork. We also explored sub-analyses for stress outcomes depending on the health status of the study population, technique, and delivery of breathwork, along with types of control groups and stress outcome measures used. Finally, we examined dose–response effects of breathwork on stress.

Pre-registration and search strategy

Our meta-analysis was pre-registered on the international prospective register of systematic reviews PROSPERO (2022 CRD42022296709). Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards were applied throughout. We searched published, unpublished, and grey literature in the following five databases: PsycInfo, PubMed, ProQuest, Scopus, and Web of Science, along with two clinical trial registers: ClinicalTrials.gov and ISRCTN. The search was run up to February 2022 for all seven electronic repositories, with no date restrictions, in line with the search criteria pre-registered on Prospero, including keywords such as: breath*, respir*, random*, RCT, and stress (see Online Appendix A for the detailed search). For purposes of feasibility in conducting the search, we maintained our focus on the pre-registered primary outcome, following Cochrane Collaboration guidelines to meet the highest criteria for self-reported/subjective stress outcomes by searching trial registers for unpublished studies. There is limited search functionality on trial registers and time involved in contacting researchers for trial data. Moreover, as mentioned above, some previous reviews have not searched unpublished, grey literature before and there are less data available on breathwork and self-reported/subjective stress, in comparison to self-reported/subjective anxiety and depression. In brief, given our focus on stress (paired with time and resource constraints), we conducted the most robust search possible for the primary outcome whilst secondary outcomes only included published data—and we were explicit about this from pre-registration onwards.

Inclusion and exclusion criteria

Inclusion criteria were that studies: (1) were published in the English language, (2) included a breathwork intervention where breathwork formed 50% or more of the intervention (and home practice/self-practice, if any), (3) were RCTs, (4) included an outcome measure of self-reported/subjective stress, anxiety, or depression, (5) included an adult participant sample 18 + years of age. For the five databases, studies with abstracts that did not include either the primary outcome keyword (stress), or a secondary outcome keyword (anxiety or depression), were excluded. For the two registers, if it was clear from the summary information that trials did not comprise the primary outcome of stress, they were excluded. As mentioned above, stress is a transdiagnostic health variable, relevant across various (clinical and nonclinical) populations and conditions, hence it was our primary interest. Additional rationale included the fact that there is far more limited research literature available on self-reported/subjective stress and breathwork (as opposed to anxiety and depression) and, since this was the primary outcome, because fewer (published) data were available, and to make the secondary search (which was only used in the present study to contextualise findings) more feasible, we used the referred search strategy, as this allowed us to find more information on stress from unpublished sources.

For all electronic repositories, studies with control conditions that comprised components of breathwork were excluded, except for studies which had time-points wherein data were collected before controls participated in breathwork (i.e., crossover RCTs). Only non-breathwork controls were used as post-intervention comparisons. Studies with interventions that comprised of equipment (oronasal or otherwise) which physically altered and/or assisted breathing activity were excluded. Breathwork was operationalised as techniques which involved conscious and volitional control or manipulation of one's breath (depth, pattern, speed or otherwise) through deliberate breathing practices. Interventions that affected breathing as a by-product, e.g., mindfulness, singing, and aerobic exercise, were excluded.

Review strategy and study selection

The first author conducted the search and initial screening against eligibility criteria along with full-text screening. Records were then screened, excluding reports based on review of titles and keywords in abstracts or summary information (for trials), or if the inclusion criteria were not met. Remaining reports were sought for retrieval and the full-text reports assessed for eligibility, before final eligibility decisions were made. Further identification of studies comprised forward and backward citation searching via Google Scholar and reference lists, respectively, of the final reports included from the database/registry search. For inter-rater consistency purposes, one of the authors (JMM) checked a random sample (10% of reports) after duplicates had been removed. Furthermore, where GWF was unsure after full-text screening, they consulted authors KC and CS to come to a collective decision on eligibility. Any discrepancies between authors were resolved by discussion and reaching consensus.

Data extraction

Our primary outcome was self-reported/subjective stress. Secondary outcomes were self-reported/subjective anxiety, depression, and global mental health (where two or more of stress, anxiety and depression were combined into a total measure without providing subscale data). We extracted the following data across the studies’ conditions: sample sizes, means, and standard deviations of outcome scores post-intervention (timepoint 1—T1, where T0 is pre-intervention/baseline) along with at latest follow-up where possible (a true follow-up was classed as when participants no longer received any instruction for the breathwork intervention). Where studies involved crossover designs, the midpoints were categorised as post-intervention (before the control group started the breathwork given initially to the intervention group). For studies which required multiple groups’ mean and standard deviation (M ± SD) scores to be combined, or for just SDs to be calculated, these were calculated in accordance with the Cochrane Collaboration handbook 48 . For example, calculating SDs from Ms and 95% confidence intervals (CIs) or combining multiple groups’ M ± SD scores if two or more groups completed an intervention that involved breathwork (but the study still comprised a non-breathwork control).

Risk of bias and quality assessment

The most recent, revised Cochrane Collaboration’s tool for assessing risk of bias in randomised trials (RoB 2) 49 was used for analysing studies on the primary outcome measure of self-reported/subjective stress. The studies were analysed across the following five domains for the stress outcomes: randomisation process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. Each domain produced an algorithmic judgement of “low risk of bias”, “some concerns”, or “high risk of bias”, resulting in an overall risk of bias judgement. For further inter-rater consistency purposes, both JMM and GWF completed bias scoring using RoB 2 on all included studies for stress, with any discrepancies resolved via discussion.

Data synthesis and analysis

To evaluate whether breathwork can effectively lower stress compared to non-breathwork controls and to quantify the estimation we ran a quantitative synthesis meta-analysis using standardised mean differences and a random-effects model. This used aggregate participant data of M ± SD scores on stress outcome measures for intervention and control conditions of each study at post-intervention (T1), along with the groups’ sample sizes. We also conducted a sensitivity analysis by removing one study at a time, to evaluate the robustness of effects. Separate random-effects meta-analyses were run for the secondary outcomes. The software Review Manager (RevMan) version 5.4 50 was used. For the between-group effect sizes (ESs) we computed Hedges’ g , based on the standardised between-group difference at post-intervention considering sampling variance among groups; an ES of 0.2 is classed as small, 0.5 medium and 0.8 large 51 . For each separate outcome, the ESs were calculated via comparison of post-breathwork intervention scores between the conditions. Intention-to-treat data were chosen over per-protocol data where available, since the former provides a more conservative estimate of between-group differences.

Heterogeneity of ESs variance was assessed using Cochran’s Q 52 based on a chi-square distribution ( χ 2 ) and Higgins’ I 2 53 . If χ 2 is significant and an I 2 index value is around 50%, this implies variance may be explained by variables other than breathwork and such statistical heterogeneity is moderate, respectively. A funnel plot was produced to examine publication bias for the primary outcome, and the software R (version 4) 54 was used to explore asymmetry of the funnel plot via the Egger’s test 55 (i.e., correlations between standard error and ESs). Moreover, Rosenthal’s fail-safe N was calculated (to estimate how many further studies yielding zero effect would be required to make the overall ES non-significant for stress) 56 . Kendall's tau-b (τ B ) correlations were used to detect any potential relationships between ESs of breathwork on stress and: estimated total duration of intervention/home practice, total number of intervention/home practice sessions, and intervention/home practice session frequency. If intervention time was not provided by a study (where participants only had home practice), we used the minimum estimated home practice duration (recommended in the study) to gauge the approximate time taken for participants to ‘learn’ the breathwork technique. Minimum recommended duration was used for most conservative estimates, helping account for common attrition found across behavioural studies.

Lastly, subgroup analyses were run for stress, again using a random-effects model. These subsets included: health status of population (physical, nonclinical, or mental health), technique type (fast or slow-paced breathing) and delivery method of the breathwork intervention (individual, group, or a combination of both, and remote (self-help), in-person, or combination) along with the type of control group (active or inactive; in line with Cochrane Collaboration guidelines 48 ), and outcome measure used (scale).

Search results

As shown in Fig.  1 , the search produced 1325 results: 1175 and 150 records from databases and registers, respectively. After duplicates were removed, the titles and abstracts (or summary information for registers) of 679 records were screened. During screening, the eligibility of 11% of reports were decided collectively among GWF, KC, and CS. All studies included by GWF were checked by KC and CS to ensure none were incorrectly included. One particular study 57 that comprised a global mental health measure only had to be excluded as there were insufficient studies to reliably interpret results ( n  < 5) 58 —the only other available was Goldstein et al. 59 (which also included a measure of self-reported/subjective stress). Accordingly, the global mental health secondary outcome was dropped from the analysis.

PRISMA flow diagram showing the identification of eligible studies via databases, registers, and citation searching. Self-reported/subjective stress was the primary outcome for the quantitative synthesis random-effects meta-analysis. Total number of included studies was 26. Trial registries searched primary outcome only.

Further data were required for eight reports; corresponding authors were contacted, and data from four studies were retrieved, but not the remaining half 60 , 61 , 62 , 63 subsequently excluded from the analysis. Thus, a total of 104 reports were screened and 81 were excluded, leaving 23. As a result of citation searching, a further three studies were included. Of the 26 total reports included in the quantitative synthesis meta-analyses, stress comprised 12 studies 59 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 . Secondary outcomes of self-reported/subjective anxiety and depression comprised of 20 studies 64 , 65 , 66 , 67 , 68 , 69 , 70 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 and 18 studies 64 , 65 , 66 , 67 , 69 , 70 , 71 , 72 , 74 , 78 , 79 , 80 , 81 , 82 , 85 , 86 , 87 , 88 , respectively. Please see Online Appendix B for more information on the secondary outcomes.

Summary of findings for stress

In terms of data extraction, all studies provided raw M ± SD scores apart from two 55 , 56 where estimated marginal M ± SDs were given (raw data was requested from corresponding authors but could not be obtained). One study 65 required SDs from Ms and 95% confidence intervals (CIs) provided, both of which were calculated in accordance with Cochrane Collaboration guidelines 48 . Furthermore, another study 70 required two groups’ M ± SD scores (there was one control group and two intervention groups) to be combined and two further studies 64 , 71 involved crossover designs (hence data were extracted at the midpoints of each study before controls started the breathwork intervention). Analyses of follow-up scores were not possible for self-reported/subjective stress as there were insufficient studies for results to be reliably interpreted 58 .

The 12 studies included in the meta-analysis for the primary outcome of stress were completed from 2012 to 2021 (seven, or 60%, were conducted from 2020 onwards). Half of these studies were conducted in the US 59 , 64 , 65 , 66 , 68 , 74 , two in India 71 , 72 , one globally 73 , and one each in: Israel 70 , Turkey 67 , and Canada 69 . The average age was 41.7 (± 8.47) and 75% identified as female, since the largest study 68 was for women only. Attrition rates (after the breathwork intervention began) ranged from 3 to 40%. Participant sample sizes ranged from 10 to 150, with the total number of participants analysed being 785. The number of participants randomised to a breathwork intervention or control condition was 417 and 368, respectively. The minimum total estimated durations of an intervention/home practice ranged from 80 to 5625 min.

Half of the studies comprised physical health, five nonclinical, and one mental health samples. Ten and two studies comprised interventions with a primary focus on slow-paced breathing and fast-paced breathing, respectively. Seven were individual-based interventions, four taught to groups, and one a combination of both modes. Half were remote/self-help interventions, five in-person, and one combination. Seven and five studies had inactive and active control groups, respectively. Eight studies used the perceived stress scale (PSS) 89 , three used the stress subscale from the depression anxiety stress scale (DASS) 90 , and one used the perceived stress questionnaire (PSQ) 91 .

Risk of bias for stress

Risk of bias scoring for the 12 studies on the primary outcome is reported using RoB 2 in Fig.  2 . Three studies’ overall assessment were algorithmically scored as being at high risk of bias, with domain two (deviations from the intended interventions) contributing to most bias. The remaining nine studies’ overall risk of bias were algorithmically scored as having some concerns. Only one study did not disclose how randomisation was conducted. Most of the domains, from randomisation to selection of the reported result, were scored as having some concerns or low risk of bias. We did not find reported adverse events or lasting bad effects directly attributed to breathwork interventions; four studies (six in total including secondary outcome studies) actively reported on this. Nonetheless, regarding safety and tolerability, a small subgroup of participants in Ravindran et al.’s study 71 focusing on fast-paced breathwork in unipolar and bipolar depression reported side effects such as hot flushes, shortness of breath and/or sweating. However, these participants opted to continue the intervention and no participants dropped out of the breathwork group due to adverse effects.

Risk of bias scoring using Cochrane Collaboration’s RoB 2 tool. Green and red colours correspond to low and high risk of bias, respectively. Yellow represents some concerns. D1 Randomisation process, D2 Deviations from the intended interventions, D3 Missing outcome data, D4 Measurement of the outcome, D5 Selection of the reported result.

As shown in Fig.  3 , the random-effects meta-analysis (k  = 12) displayed a small-medium but significant post-intervention between-group ES, g  = − 0.35 [95% CI − 0.55, − 0.14], z  = 3.32, p  = 0.0009, denoting breathwork was associated with lower levels of self-reported/subjective stress at post-intervention than controls. There were insufficient studies including follow-up measures for a meta-analysis. Heterogeneity was moderate but non-significant, χ 2 11  = 19, p  = 0.06, I 2  = 42%. Via removing one individual study at a time, the ES of breathwork on stress ranged from − 0.27 to − 0.39 and remained significant in all cases. Initial visual inspection of the funnel plot in Online Appendix  C suggested some skew due to studies with small samples; however, the Egger’s test was non-significant, z  = 0.03, p  = 0.947, indicating a low chance of publication bias. Fail-safe N  analysis denoted that a further 69 studies yielding zero effect would need to be added to make the overall ES non-significant for stress. On removal of the one potential outlier 67 the ES remained significant but became smaller: − 0.27. On removal of the two studies using estimated marginal M ± SDs, the ES remained significant and became larger: − 0.40.

Forest plot comparing breathwork interventions to non-breathwork control groups on primary outcome of self-reported/subjective stress at post-intervention. Squares and their size represent individual studies and their weight, respectively. Lines through squares are 95% CIs and diamond is the overall effect size with 95% CIs. More negative values denote larger effect of breathwork on self-reported/subjective stress in comparison to control condition. Effect sizes calculated using Hedges’ g . Figure produced using RevMan v5.4.

Subgroup analyses for stress

As displayed by Table 1 , we conducted five sub-analyses for the primary outcome self-reported/subjective stress. There were no significant differential effects between subgroups.

There was a significant effect of breathwork on stress in nonclinical samples, but not in mental (only one study) or physical health populations. Moreover, significant effects were yielded when breathwork was primarily focused on slow-paced breathing (but not for fast-paced breathing), taught to individuals alone, and when taught to groups (but not in combination, which comprised only one study). There were also significant effects of breathwork on stress when the intervention was taught remotely, in-person, and using a combination of these two delivery methods. Significant effects existed for both active and inactive control groups. There were significant effects for studies which used PSS and DASS measures (but not the PSQ, used by only one study).

Heterogeneity was high for studies with physical health samples, slow-paced breathwork, when breathwork was taught to groups and in-person, plus those studies with inactive controls, and when stress was measured by using the DASS, suggesting potential moderating factors that were not accounted for by the subgroup analyses. There was no significant correlation between estimated total duration of breathwork intervention/home practice and ES ( n  = 12) τ B  = − 0.05, p  = 0.418, number of intervention/home practice sessions and ES for stress ( n  = 12) τ B  = − 0.28, p  = 0.107, nor for intervention/home practice session frequency and ES ( n  = 12) τ B  = − 0.17, p  = 0.224.

Breathwork and secondary outcomes

In terms of data extraction, one study 79 had a measure with positively scored anxiety and depression subscales; accordingly, we subtracted the subscale score from the maximum score to reverse the polarity of the measure without changing the magnitude of difference. Another study 88 required two groups’ M ± SD scores to be combined. Analysis of follow-up scores were not possible for secondary outcomes as there were insufficient studies 58 ( n  < 5). Forest plots for the secondary outcomes are reported in Online Appendix  D . Random-effects analysis for anxiety ( k  = 20) showed a significant small-medium between-group ES in favour of breathwork, g  = − 0.32 [95% CI − 0.48, − 0.16], z  = 3.90, p  < 0.0001, with moderate and significant heterogeneity, χ 2 19  = 38.62, p  = 0.005, I 2  = 51%. Sensitivity analysis showed ESs ranging from − 0.29 to − 0.34, significant in all cases. No individual study was responsible for the significant heterogeneity. Random-effects analysis for depression ( k  = 18) displayed a significant small-medium ES in favour of breathwork, g  = − 0.40 [95% CI − 0.58, − 0.22], z  = 4.27, p  < 0.0001, and heterogeneity was moderate and significant, χ 2 17  = 40.5, p  = 0.001, I 2  = 58%. Sensitivity analysis showed ESs ranging from − 0.35 to − 0.44, significant in all cases. On removal of two potential outliers 85 , 88 , the ES remained the same. No single study was responsible for the significant heterogeneity.

We conducted the first comprehensive systematic review and meta-analysis of RCTs on the effect of breathwork on self-reported/subjective stress, analysing 12 studies which comprised a total of 785 participants. Breathwork yielded a significant post-intervention between-group effect of breathwork on stress compared to non-breathwork controls, denoting breathwork was associated with lower levels of stress than controls.

Statistical heterogeneity was moderate but not significant, meaning variance in ESs was likely explained by breathwork rather than other variables, although this non-significance could also be a consequence of the low number of studies included. This small-medium ES should be interpreted in the light of moderate risk of bias overall for the 12 studies. More than half of the studies included in our meta-analysis for stress were completed from 2020 onwards, suggesting a recent emergence of research into breathwork, which may have been accelerated by the covid-19 pandemic. Research on breathwork could be likened to that of meditation, which received an unprecedented surge in scientific exploration two decades ago 92 . We may be at a similar cusp with breathwork and anticipate considerable growth in the field. Given the close ties of breathwork to psychedelic research 93 , which is growing rapidly, this could accelerate growth further.

Regarding subgroup analyses for self-reported/subjective stress, heterogeneity was significant for studies with physical health samples, slow-paced breathwork interventions, inactive control groups, along with studies when breathwork was group-based and in-person. At present, there are too few studies within the sub-analyses to address this issue of statistical heterogeneity. Overall, point estimates were similar and sample sizes were small, hence where results were non-significant, it is unclear whether there was genuinely no effect, or lack of statistical power. Furthermore, no significant differential effects across subgroups were observed, but this could also be the result of the scarce number of studies.

While nonclinical samples showed a significant effect on self-reported/subjective stress outcomes and physical and mental health samples did not, between-subgroup differences were non-significant and the point estimates for these subgroups were similar (ranging from ES = 0.26–0.38). These findings could mean that breathwork is not effective for physical/mental health populations, however, it is also possible that this analysis was underpowered to detect effects given the relatively small number of studies contributing to the subgroups, as we have already mentioned. There were only two studies primarily focused on fast-paced breathwork and stress, insufficient to make a meaningful comparison with the ten studies primarily focused on slow-paced breathwork. Interestingly, delivery modes and styles did not seem to influence the results, which may suggest breathwork can be learned through several different formats. Half of the studies’ interventions were delivered remotely without instructors (self-help), hence breathwork could potentially be widely disseminated and thus accessible and probably scalable. The results were significant for both active and inactive controls, although it would be expected that breathwork would have less effect compared to active controls. This could be due to poor quality of the active controls. Lastly, results were significant for two of three stress outcome measures, most likely due to them being psychometrically well-validated—only one study used the third measure (PSQ).

Concerning dose–response, although associations were in the expected direction, there were no significant correlations between the minimum estimated durations of breathwork intervention/home practice and ES, for all outcomes. This apparent absence of dose–response effects was surprising as increased practice time might be expected to be associated with greater benefit, however compliance to intervention home practice was not reported for many studies and so true dose–response analysis was not possible. Moreover, intention-to-treat analysis data were used for the most conservative estimates of effect. Dhruva et al.’s study 64 included in our meta-analysis specifically investigated dose–response effects, finding a positive relationship between total amount of breathwork intervention/home practice and improvement in quality of life and chemotherapy-associated symptomology—there was a significant decrease in anxiety for each hour increase in breathwork. Alternatively, this could be indicative of breathwork being possibly able to help quickly, as suggested in very recent literature whereby just one session of slow, deep breathing had beneficial effects on anxiety and vagal tone in adults 94 , with vagal tone being measured, albeit indirectly, through HRV 6 . This may be likened to ‘micro dosing’ breathwork, similar to single session mindfulness meditation practices 95 .

The meta-analysis results are largely consistent with and extend upon previous work. For instance, our findings are somewhat in line with Malviya et al.’s recent review which provides some support for breathwork’s effectiveness in alleviating stress 43 . However, this review only included two studies for stress, one of which comprised of both groups incorporating breathing practices (and was thus excluded from our meta-analysis). Hopper et al.’s systematic review on diaphragmatic breathing found just one RCT for stress, however this used physiological measures 42 . Nonetheless, this study showed that the stress hormone cortisol was lower in people undergoing slow-paced breathwork compared to controls 96 . In a different study 38 , participants administered with bacterial endotoxin ( E. coli ) who performed fast-paced breathwork had higher spikes of cortisol compared to non-breathwork controls, during the intervention, but a quicker recovery and stabilisation of cortisol levels after cessation of breathwork. This could be another mechanism of action warranting further investigation.

Breathwork, anxiety and depression

Furthermore, meta-analyses comprising 20 and18 studies run for secondary outcome measures of self-reported/subjective anxiety and depressive symptoms, showed that breathwork interventions also yielded significant small-medium ESs in comparison to controls, favouring breathwork (see Online Appendix  D for results). However, heterogeneity was significant for both outcomes, meaning the variance in ESs may be due to other variables apart from breathwork. Thus, these ESs should be interpreted with caution and need further research. As per Malviya et al.’s review 43 , greater support was offered for breathwork in alleviating anxiety and depressive symptoms (eight studies for both outcomes). The review deemed findings pertaining to the efficacy of breathwork in decreasing anxiety and depression as promising. This was also consistent with Zaccaro et al.’s review findings on slow breathing (15 studies—no RCTs), that had lower self-reported anxiety and depression, possibly linked to increased HRV measured during interventions 4 . Ubolnuar et al.’s review of breathing exercises for COPD found no significant effect on anxiety and depression from a subgroup meta-analysis of two RCTs, however the interventions used for both were singing classes 39 . Nonetheless, a recent meta-analysis by Leyro et al. of 40 RCTs on interventions for anxiety, which comprised a respiratory component (ranging from diaphragmatic breathing to capnometry assisted respiratory training), showed such treatments were associated with significantly lower symptoms of anxiety compared to control groups 41 . Though non-respiratory controls were used, respiratory components did not have to form a significant part of the intervention, thus it is less possible to tease out the effects of such techniques. While some interventions used physically altering equipment such as training of musculature involved in respiration, this might provide further potential for breathwork-related work in clinical conditions.

Comparison to stress-reduction interventions

Through estimating statistically significant differences and 95% CIs among studies 97 , in comparison to interventions for stress, our findings suggest that breathwork might be associated with similar—and non-significantly different—effects. For instance, Heber et al.’s meta-analysis on computer- and online-based stress interventions, including CBT and third-wave CBT (e.g., inclusion of meditation, mindfulness, or acceptance of emotions) compared to controls in adults, found moderate effects on stress, d  = 0.43 [95% CI 0.31, 0.54], anxiety, d  = 0.32 [95% CI 0.17, 0.47], and depression, d  = 0.34 [95% CI 0.21, 0.48] 98 . Each of these effects overlap more than 25% with the width of either interval in our results for breathwork, denoting no indication of a clinically relevant difference between the interventions. Similar meta-analytic findings concerning effects on stress, anxiety and depression have been found for related and more analogous techniques such as mindfulness-based cognitive therapy and stress reduction (MBCT/MBSR) 99 along with self-help (MBSH) 100 . While Pizzoli et al.’s recent post-intervention HRVB meta-analysis (14 published RCTs) 13 found a significant effect on depression, another meta-analysis did not find a significant effect on stress, with the smallest ES being yielded for self-reported stress out of myriad outcomes 14 . Lastly, a meta-analysis of eight meta-analytic outcomes of RCTs on physical activity 99 showed similar significant effects on depression and anxiety. While we are not proposing breathwork as a substitute for other treatments, it could complement other therapeutic interventions, potentially leading to additive effects of such health behaviours.

People with stress and anxiety disorders tend to chronically breathe faster and more erratically, yet with increased meditation practice, respiration rate can become gradually slower, potentially translating into better health and mood, along with less autonomic activity 92 . Positive impacts on HRV may partially explain some of the mechanisms behind mindfulness meditation 101 , 102 . However, the above approaches like MBCT/MBSR and HRVB may be less accessible. MBCT/MBSR teacher training takes at least one year while HRVB is routinely taught by a qualified healthcare professional; this is usually a prerequisite and most certified biofeedback therapists are habitually licensed medical providers, including general practitioners, psychiatrists, dentists, nurses, and psychologists 103 . MBCT/MBSR and HRVB therapist training includes theoretical/practical curricula, while breathwork teacher training can be more quickly and easily taught (i.e., over days and weeks) online and remotely to both healthcare professionals and the general population, thus potentially proving cost-effective.

Two of our studies used the only Food and Drug Administration-approved portable electronic biofeedback device, which encourages deep, slow breathing 103 . However, HRV can be improved in the same way (tenfold) by simply breathing at a rate around 5–6 breaths/min 104 and some Zen Buddhist monks have been found to naturally respire around this rate during deep meditation 105 . It may be possible that breathing rate forms a key component of meditation’s known positive effects. Indeed, it has been shown that HRV can be modulated during the practice of meditation 106 . However, a recent meta-analysis on this exact matter found insufficient evidence suggesting mindfulness/meditation led to improvements in vagally mediated HRV, and more well-designed RCTs without high risk of bias are needed to clarify any such contemplative practices’ impact on this physiological metric 107 , along with potential mechanisms related to cortisol.

Traditional mindfulness-based programmes frequently involve meditation requiring observation of the breath, using it as an object of awareness, not voluntary regulation of respiration like in breathwork. Such breath-focus may be a key active ingredient and potential mechanism of action of the former contemplative practices, since highly experienced meditators have been found to breathe at over 1.5 times slower than nonmeditators, during meditation and at rest 108 . This translates into approximately 2000 less daily breaths for the former group of adept meditation practitioners (i.e., around 700,000 less breaths in a year), placing less demand on the ANS 92 . Meditation could also be complementary; voluntary upregulation of HRV through biofeedback may be improved by mental contemplative training 109 . While there is a possibility that it could simply be the cognitive-attentional components of both meditation and breathing practices that explain their effects, observation of the breath (i.e., most practices within mindfulness curricula) versus control of the breath (i.e., breathwork) warrants nuanced investigation.

Strengths, limitations and future directions

Our systematic review searched published, unpublished and grey literature across numerous electronic databases and the meta-analysis comprised several very recent RCTs with well-validated measures of self-reported/subjective stress. However, like most systematic reviews in this field, given the small sample size (likely due to the recent phenomena of breathwork in the West) and moderate risk of bias across the studies included in our meta-analysis, our results should be interpreted cautiously. Future studies exploring breathwork’s effectiveness should aim for research designs with low risk of bias. While this review attempted to bridge the gap and unify both old and new research, future low risk-of-bias studies are now needed in order to draw definitive conclusions of breathwork’s impact on mental health. There were also not enough studies for valuable subgroup comparisons, and therefore we did not identify any potential sources of heterogeneity. Furthermore, secondary outcomes were not scrutinised with the same level of detail as the primary outcome, as they were only used to provide complementary context and a bigger picture around stress and mental health in general.

Our meta-analysis is the first review of breathwork’s impact on self-reported/subjective stress and its therapeutic potential, and combining this quantitative synthesis of psychological effects of breathwork with other syntheses, i.e., of physiological effects 4 , could help build a stronger psychophysiological model of breathwork’s efficacy along with more robust mechanisms of action. Studies could use stress subscales in DASS as standard in addition to the anxiety/depression scales, as this could be important for nonclinical and subclinical populations experiencing stress and allow for direct comparison of effects across clinical/nonclinical populations. Additionally, psychophysiological RCTs combining both subjective and objective measures in line with proposed mechanisms of action (i.e., self-reported stress and ECG HRV/respiration rate measurements) should be conducted, along with further imaging (MRI, EEG, NIRS, etc.) studies on various breathwork techniques (only one fMRI study was available in Zaccaro et al.’s review 4 ). This could help better determine modalities and underlying principles of different breathwork techniques. Though validated scales were used for stress in the meta-analysis, our review lacks objective outcomes, which increases risk of bias further.

Comparison groups promoting observation versus control of the breath could yield interesting findings when exploring any differences between the effects of meditation and breathwork. However, robust scientific methods that align well with current methodological demands on meditation and contemplative psychological science 110 should be implemented. There was also limited scope to report on follow-up effects, hence more studies could include true follow-up timepoints and longitudinal designs, now more common in meditation and contemplative science research. On top of this, there could be cross-cultural differences in response to breathwork (i.e., between Eastern and Western modalities) which could be explored by future research, along with searching non-English language literature. There could also be differences between age categories (including children); this meta-analysis focused solely on adults across a broad age-range. Lastly, more studies should report on adverse events and lasting bad effects, with further research needed to gauge the safety profile of fast-paced breathwork in particular, so it not administered blindly to potentially vulnerable populations.

Clinical implications

For stress, though not many studies monitored home practice/self-practice, engagement with interventions appeared good, none reporting adverse effects directly attributed to breathwork. This suggests breathwork has a high safety profile and slow-paced breathing techniques can be recommended to subclinical populations or those experiencing high stress. However, regarding clinical populations, the findings from our meta-analysis show non-significant effects for mental and physical health populations, hence it could be premature to recommend breathwork in these contexts. If breathwork can indeed provide therapeutic benefit to specific populations, conducting research with strong, low risk-of-bias design is essential to understanding if breathwork is genuinely effective or not. Ethicality should always take centre stage, with first doing no harm being the priority. Nonetheless, in nonclinical settings (excluding those predisposed to mental and physical health conditions), the low cost and risk profiles make breathwork (primarily focused on slow-paced breathing), scalable, with evidence from this meta-analysis that some techniques can potentially be self-learned, not requiring an instructor in real-time. Providing future robust research shows positive effects of breathwork, only then can an evidence-based canon be borne out of breathwork, using standardised and manualised materials for both training and practicing various secular, accessible techniques. However, there is a possibility rigorous research demonstrates that breathwork is not effective. Moreover, precaution must be exercised at all times; clinicians should consider for the individual whether breathwork may exacerbate the symptoms of certain mental and/or physical health conditions (cf. Muskin et al. 111 ).

Conclusions

More accessible therapeutic approaches are needed to reduce, or build resilience to, stress worldwide. While breathwork has become increasingly popular owing to its possible therapeutic potential, there also remains potential for a miscalibration, or mismatch, between hype and evidence. This meta-analysis found significant small-medium effects of breathwork on self-reported/subjective stress, anxiety and depression compared to non-breathwork control conditions. Breathwork could be part of the solution to meeting the need for more accessible approaches, but more research studies with low risk-of-bias designs are now needed to ensure such recommendations are grounded in research evidence. Robust research will enable a better understanding of breathwork’s therapeutic potential, if any. The scientific research community can build on the preliminary evidence provided here and thus, potentially pave the way for effective integration of breathwork into public health.

Data availability

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

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Acknowledgements

G.W.F. has a doctoral scholarship from—and is a Fellow of—The Ryoichi Sasakawa Young Leaders Fellowship Fund, Sylff Association, Tokyo. J.M.M. has a “Miguel Servet” research contract from the ISCIII (CP21/00080). J.M.M. is grateful to the CIBER of Epidemiology and Public Health (CIBERESP CB22/02/00052; ISCIII) for its support. Authors thank Dr. Patricia L. Gerbarg, M.D., and Dr. Frances Meeten for reading the manuscript and providing feedback prior to submission for publication. Thank you Dr. Daron A. Fincham for proofreading a final copy of the manuscript.

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G.W.F. was responsible for securing funding for the programme of work to which this contributes, conceived the initial idea, and was responsible for leading the meta-analysis. G.W.F. and J.M.M. conducted the literature search. C.S. and K.C. supervised the entire process. G.W.F. conducted the analysis with support from C.S., K.C., and J.M.M. All authors discussed the data and clinical implications of the study. G.W.F. and J.M.M. conducted the risk-of-bias evaluations. G.W.F. drafted the manuscript, with input from C.S., K.C., and J.M.M. All authors read and revised drafts and approved the final manuscript. Each section of the manuscript was discussed among all authors.

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Fincham, G.W., Strauss, C., Montero-Marin, J. et al. Effect of breathwork on stress and mental health: A meta-analysis of randomised-controlled trials. Sci Rep 13 , 432 (2023). https://doi.org/10.1038/s41598-022-27247-y

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Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis

• Nader Salari 1 , 2 ,
• Amin Hosseinian-Far 3 ,
• Rostam Jalali 4 ,
• Aliakbar Vaisi-Raygani 4 ,
• Shna Rasoulpoor 5 ,
• Masoud Mohammadi   ORCID: orcid.org/0000-0002-5722-8300 4 ,
• Shabnam Rasoulpoor 4 &
• Behnam Khaledi-Paveh 2  

Globalization and Health volume  16 , Article number:  57 ( 2020 ) Cite this article

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The COVID-19 pandemic has had a significant impact on public mental health. Therefore, monitoring and oversight of the population mental health during crises such as a panedmic is an immediate priority. The aim of this study is to analyze the existing research works and findings in relation to the prevalence of stress, anxiety and depression in the general population during the COVID-19 pandemic.

In this systematic review and meta-analysis, articles that have focused on stress and anxiety prevalence among the general population during the COVID-19 pandemic were searched in the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI) and Google Scholar databases, without a lower time limit and until May 2020. In order to perform a meta-analysis of the collected studies, the random effects model was used, and the heterogeneity of studies was investigated using the I 2 index. Moreover. data analysis was conducted using the Comprehensive Meta-Analysis (CMA) software.

The prevalence of stress in 5 studies with a total sample size of 9074 is obtained as 29.6% (95% confidence limit: 24.3–35.4), the prevalence of anxiety in 17 studies with a sample size of 63,439 as 31.9% (95% confidence interval: 27.5–36.7), and the prevalence of depression in 14 studies with a sample size of 44,531 people as 33.7% (95% confidence interval: 27.5–40.6).

COVID-19 not only causes physical health concerns but also results in a number of psychological disorders. The spread of the new coronavirus can impact the mental health of people in different communities. Thus, it is essential to preserve the mental health of individuals and to develop psychological interventions that can improve the mental health of vulnerable groups during the COVID-19 pandemic.

In December 2019, in the city of Wuhan, China, unusual cases of patients with pneumonia caused by the new Coronavirus (COVID-19) were reported [ 1 ], and the spread of the virus swiftly became a global health threat [ 2 ]. There have been several viral diseases in the past 20 years including Severe Acute Respiratory Syndrome (SARS) in 2003, influenza virus with the H1N1 subtype in 2009, Middle East Respiratory Syndrome (MERS) in 2012, and Ebola virus in 2014 [ 3 , 4 , 5 ].

Although COVID-19 is a new strain of coronaviruses, it is known to cause diseases ranging from cold to more severe illnesses such as SARS and MERS [ 5 ]. Symptoms of the Coronavirus infection include fever, chills, cough, sore throat, myalgia, nausea and vomiting, and diarrhea. Men with a history of underlying diseases are more likely to be infected with the virus and would experience worse outcomes [ 6 ]. Severe cases of the disease can lead to heart, and respiratory failure, acute respiratory syndrome, or even death [ 7 ]. In addition to the physical impacts, COVID-19 can have serious effects on people’s mental health [ 8 ]. A wide range of psychological outcomes have been observed during the Virus outbreak, at individual, community, national, and international levels. At the individual level, people are more likely to experience fear of getting sick or dying, feeling helpless, and being stereotyped by others [ 9 ]. The pandemic has had a harmful effect on the public mental health which can even lead to psychological crises [ 10 ]. Early identification of individuals in the early stages of a psychological disorder makes the intervention strategies more effective. Health crises such the COVID-19 pandemic lead to psychological changes, not only in the medical workers, but also in the citizens, and such psychological changes are instigated by fear, anxiety, depression, or insecurity [ 11 ].

Nervousness and anxiety in a society affect everyone to a large extent. Recent evidence suggests that people who are kept in isolation and quarantine experience significant levels of anxiety, anger, confusion, and stress [ 12 ]. At large, all of the studies that have examined the psychological disorders during the COVID-19 pandemic have reported that the affected individuals show several symptoms of mental trauma, such as emotional distress, depression, stress, mood swings, irritability, insomnia, attention deficit hyperactivity disorder, post-traumatic stress, and anger [ 12 , 13 , 14 ]. Research has also shown that frequent media exposure may cause distress [ 15 ]. Nevertheless, in the current situation, it is challenging to accurately predict the psychological and emotional consequences of COVID-19. Studies conducted in China, the first country that was affected by this recent Virus spread, show that people’s fear of the unknown nature of the Virus can lead to mental disorders [ 16 ].

Due to the pathogenicity of the virus, the rate of spread, the resulting high mortality rate, COVID-19 may affect the mental health of individuals at several layers of society, ranging from the infected patients, and health care workers, to families, children, students, patients with mental illness, and even workers in other sectors [ 17 , 18 , 19 ].

Considering several reported psychological consequences of COVID-19 and its spread (Fig.  1 ), and the lack of general statistics on the topic globally, we decided to conduct a systematic review of the existing studies in this field, with a view to providing a holistic, yet comprehensive statistics on the impact of the Virus on general population mental health. The aim of this study is to examine and systematically review and analyze the literature and their reported results related to the impacts of COVID-19 on the prevalence of stress, anxiety, and depression.

Impacts of the COVID-19 pandemic on mental health

As the first step of this systematic review and meta-analysis, the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI) and Google Scholar databases were searched. To identify the articles, the search terms of Coronavirus, COVID-19, 2019-ncov, SARS-cov-2, Mental illness, Mental health problem, Distress, Anxiety, Depression, and all the possible combinations of these keywords were used.

(((((((((((((Coronavirus [Title/Abstract]) OR (COVID-19[Title/Abstract])) OR (2019-ncov [Title/Abstract])) AND (SARS-cov-2[Title/Abstract])) AND (Mental illness [Title/Abstract])) OR (Mental health problem [Title/Abstract])) AND (Anxiety [Title/Abstract])) AND (Social Anxiety [Title/Abstract])) OR (Anxiety Disorders [Title/Abstract])) AND (Depression [Title/Abstract])) OR (Emotional Depression [Title/Abstract])) OR (Depressive Symptoms [Title/Abstract]))))))))))))

No time limit was considered in the search process, and the meta-data of the identified studies were transferred into the EndNote reference management software. In order to maximize the comprehensiveness of the search, the lists of references used within all the collected articles were manually reviewed.

Inclusion and exclusion criteria

The criteria for entering the systematic review included: 1- Studies that examined the prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic. 2- Studies that were observational (i.e. non-interventional studies) 3- Studies that their full text was available. The criteria for excluding a study were: 1- Unrelated research works, 2- Studies without sufficient data, 3- Duplicate sources, 4-Pieces of research with unclear methods 5- Interventional studies 6- Case reports, and 7- Articles that their full text was not available.

Study selection

Initially, duplicate articles that were repeatedly found in various databases were removed. Then, a title list of all the remaining articles was prepared, so that the articles could be filtered out during the evaluation phase in a structured way. As part of the first stage of the systematic review process, i.e. screening, the title and abstract of the remaining articles were carefully examined, and a number of articles were removed considering the inclusion and exclusion criteria. In the second stage, i.e. eligibility evaluation, the full text of the studies, remaining from the screening stage, were thoroughly examined according to the criteria, and similarly, a number of other unrelated studies were excluded. To prevent subjectivity, article review and data extraction activities were performed by two reviewers, independently. If an article was not included, the reason for excluding it was mentioned. In cases where there was a disagreement between the two reviewers, a third person reviewed the article. Seventeen studies entered the third stage, i.e. quality evaluation.

Quality evaluation

In order to examine the quality of the remaining articles (i.e. methodological validity and results), a checklist appropriate to the type of study was adopted. STROBE checklists are commonly used to critique and evaluate the quality of observational studies. The checklist consists of six scales/general sections that are: title, abstract, introduction, methods, results, and discussion. Some of these scales have subscales, resulting in a total of 32 fields (subscales). In fact, these 32 fields represent different methodological aspects of a piece of research. Examples of subscales include title, problem statement, study objectives, study type, statistical population, sampling method, sample size, the definition of variables and procedures, data collection method(s), statistical analysis techniques, and findings. Accordingly, the maximum score that can be obtained during the quality evaluation phase and using the STROBE checklist is 32. By considering the score of 16 as the cut-off point, any article with a score of 16 or above is considered as a medium or a high-quality article [ 20 ]. Sixteen papers obtained a score below 16, denoting a low methodological quality, and were therefore excluded from the study. In the present study, following the quality evaluation by means of the STROBE checklist, 17 papers, with a medium or high quality, entered the systematic review and meta-analysis phases.

Data extraction

Data of from all the final studies were extracted using a different pre-prepared checklist. The items on the checklist included: article title, first author’s name, year of publication, place of study, sample size, assessment method, gender, type of study, the prevalence of depression, anxiety, and stress.

Statistical analysis

The I 2 (%) test was used to assess the heterogeneity of the selected research works. In order to assess publication bias, due to the high volume of samples that entered the study, the Egger’s test was conducted with the significance level of 0.05, and the corresponding Forest plots were drawn. Data analysis was performed using the Comprehensive Meta-Analysis (CMA version 2.0) software.

In this work, the prevalence of stress and anxiety among general population during the COVID-19 pandemic was assessed. Articles with this focus were collected with no lower time limit and until May 2020 and were systematically reviewed according to the PRISMA guidelines. Following the initial search, 350 possible related articles were identified and transferred to the reference management software, EndNote. Of the 350 studies identified, 100 were duplicates, and therefore excluded. At the screening stage, out of the remaining 250 studies, 170 articles were removed after assessing their title and abstract and considering the inclusion and exclusion criteria. At the eligibility evaluation phase, out of the remaining 80 studies, 60 articles were removed after the examination of their full text, and similarly by considering the inclusion and exclusion criteria. At the quality evaluation stage, through the evaluation of the full text of the articles, and based on the score obtained from the STROBE checklist for each paper, out of the remaining 20 studies, 3 studies, that were assessed as low methodological quality works, were eliminated, and finally 17 cross-sectional studies reached the final analysis stage (please see Fig.  2 ). Details and characteristics of these articles are also provided in Table  1 .

PRISMA (2009) flow diagram demonstrating the stages for sieving articles in this systematic review and meta-analysis

Investigating heterogeneity and publication Bias

To investigate the heterogeneity of the studies, the I 2 (%) indices for the prevalence of stress (I 2 : 96.8%), anxiety (I 2 : 99.3%) and depression (I 2 : 99.4%) were obtained. Due to the high heterogeneity in the studies, the random effects model was used in the analysis of findings. To examine publication bias in the collected articles, the Egger’s test indices were obtained for the prevalence of stress (p: 0.304) (Fig.  3 ), anxiety (p: 0.064) (Fig.  4 ), and depression (p: 0.073) (Fig.  5 ), indicating that publication bias was not significant for any of the three clinical symptoms.

Funnel plot of results of prevalence of stress among the general population during the COVID-19 pandemic

Funnel plot of results of prevalence of anxiety among the general population during the COVID-19 pandemic

Funnel plot of results of prevalence of depression among the general population during the COVID-19 pandemic

• Meta-analysis

The prevalence of stress in 5 of the studies with a sample size of 9074 was 29.6% (95% CI: 24.3–35.4). Results of the 5 studies are evaluated by the Depression, Anxiety and Stress Scale (DASS-21) instrument (Fig.  6 ). The prevalence of anxiety in 17 studies with a sample size of 63,439 was obtained as 31.9% (95% CI: 27.5–36.7) (Fig.  7 ). Moreover, the prevalence of depression in 14 studies with a sample size of 44,531 was 33.7% (95% CI: 27.5–40.6) (Fig.  8 ).

The prevalence of stress in the studies based on the random effects model

The prevalence of anxiety in the studies based on the random effects model

The prevalence of depression in the studies based on the random effects model

Figures 3 , 4 and 5 present the Forest plots for the prevalence of stress, anxiety, and depression based on the random effects model, in which each black square is the prevalence rate, and the length of the line on which the square is located denotes 95% confidence interval. The black diamond shape represents the overall prevalence rate for the symptoms.

Subgroup analysis

Table  2 , reports the prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic in different continents. The highest prevalence of anxiety in Asia is 32.9 (95% CI: 28.2–37.9), the highest prevalence of stress in Europe is 31.9 (95% CI: 23.1–42.2), and the highest prevalence of depression in Asia is 35.3 (95% CI: 27.3–44.1) (Table 2 ).

This work is the first systematic review and meta-analysis on the prevalence of stress, anxiety and depression in the general population following the COVID-19 pandemic. This study has followed the appropriate methods of secondary data analysis for examining 17 related research works. The articles used in this study were all cross-sectional. According to our analysis, the prevalences of stress, anxiety, and depression, as a result of the pandemic in the general population, are 29.6, 31.9 and 33.7% respectively.

The emergence of COVID-19, with its rapid spread, has exacerbated anxiety in populations globally, leading to mental health disorders in individuals. This has even caused cases of stereotyping and discrimination [ 37 , 38 ]. Therefore, it is necessary to examine and recognize people’s mental states in this challenging, destructive and unprecedented time. Evidence suggests that individuals may experience symptoms of psychosis, anxiety, trauma, suicidal thoughts, and panic attacks [ 39 , 40 ]. Recent studies have similarly shown that COVID-19 affects mental health outcomes such as anxiety, depression, and post-traumatic stress symptoms [ 22 , 24 , 31 ]. COVID-19 is novel and unexplored, and its rapid transmission, its high mortality rate, and concerns about the future can be the causes of anxiety [ 41 ]. Anxiety, when above normal, weakens body’s immune system and consequently increases the risk of contracting the virus [ 39 ].

Research shows that people who follow COVID-19 news the most, experience more anxiety [ 39 ]. Most of the news published on COVID-19 are distressing, and sometimes news are associated with rumors, which is why anxiety levels rise when a person is constantly exposed to COVID-19 news [ 21 ]. Misinformation and fabricated reports about COVID-19 can exacerbate depressive symptoms in the general population [ 23 ]. The latest and most accurate information, such as the number of people who have improved and the progress of medications and vaccines, can reduce anxiety levels [ 42 ]. In this regard, mental health professionals recommend promoting healthy behaviors, avoiding exposure to negative news, and using alternative communication methods such as social networks and digital communication platforms to prevent social isolation [ 41 ].

Such conditions are even more significant for populations with poorer health conditions. In the under-developed and developing countriesthe epidemic conditions of COVID-19 impose greater psychological effects on the population, given that these countries are also affected by many other infectious diseases. Uncertainty about health status, follow-up of patients, treatment care, and inefficiency in these communities can also increase the vulnerability of such communities to the psychological effects of COVID-19 [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ].

The results of epidemiological studies show that women are at a higher risk of depression [ 43 ]. Women are more vulnerable to stress and post-traumatic stress disorder than men [ 44 ]. In recent studies, the prevalence of anxiety and depression and stress during COVID-19 pandemic is shown to be higher in women than in men [ 21 , 23 , 27 , 31 ].

Aging increases the risk of COVID-19 infection and mortality, however, the results of existing studies show that during the pandemic, the levels of anxiety, depression and stress are significantly higher in the age group of 21–40 years. The main reason for this seems to be that this age group are concerned over the future consequences and economic challenges caused by the pandemic, as they are key active working forces in a society and are, therefore, mostly affected by redundancies and business closures [ 21 , 22 , 25 ]. Some researchers have argued that a greater anxiety among young people may be due to their greater access to information through social media, which can also cause stress [ 45 ].

During the COVID-19 pandemic, people with higher levels of education had greater levels of anxiety, depression, and stress. According to recent studies, during the COVID-19 pandemic, there is an association between education levels, and anxiety and depression levels [ 21 , 31 ]. According to a study which was conducted in China, the higher prevalence of mental symptoms among people with higher levels of education is probably due to this group’s high self-awareness in relation to their own health [ 46 ]. In addition, anxiety levels are significantly higher in people with at least one family member, relative, or a friend with the COVID-19 disease [ 21 , 24 , 42 ].

Recent studies have revealed an association between medical history and increased anxiety and depression caused by the COVID-19 spread [ 36 ]. Previous research works had shown that medical history and chronic illnesses are associated with increased psychiatric distress levels [ 42 , 47 ]. People who have a history of medical problems and are also suffering from poor health may feel more vulnerable to a new disease [ 48 ].

Governments and health officials must provide accurate information on the state of the pandemic, refute rumors in a timely manner, and reduce the impact of misinformation on the general public’s emotional state. These high level activities result in a sense of public security and potential psychological benefits. Governments and health authorities need to ensure that infrastructure is provided to produce and supply adequate amounts of personal protective equipment (PPE), e.g. masks, hand sanitizers and other personal hygiene products during the COVID-19 pandemic. Optimistic and positive thoughts and attitude toward the COVID-19 spread are also protective factors against depression and anxiety [ 23 ]. The use of electronic devices and applications to provide counseling can reduce the psychological damages caused by COVID-19, and can consequently promote social stability [ 31 ]. The rise in the number of infections and mortalities are likely to affect the symptoms of depression and anxiety. During the H1N1 epidemic, anxiety reached the highest point at the peak of the epidemic and decreased with its decline [ 49 ].

Our research has a few limitations; All of the studies in our analysis were periodic, which could reflect the psychological state of the population over a period of time. However, psychological states change with the passage of time and with the alterations in one’s surrounding environment. Therefore, it is necessary to portray the psychological impacts of the COVID-19 catastrophe over a longer and more forward-looking period. Follow-up studies can be helpful in clarifying the mental state of the population in future. Although several research works in this meta-analysis have used the same tests for population screening, yet there were a few studies that followed different scales to assess stress, anxiety and depression.

In less than a few months, the COVID-19 pandemic has created an emergency state globally. This contagious virus has not only raised concerns over general public health, but has also caused a number of psychological and mental disorders. According to our analysis, it can be concluded that the COVID-19 pandemic can affect mental health in individuals and different communities. Therefore, in the current crisis, it is vital to identify individuals prone to psychological disorders from different groups and at different layers of populations, so that with appropriate psychological strategies, techniques and interventions, the general population mental health is preserved and improved.

Availability of data and materials

Datasets are available through the corresponding author upon reasonable request.

Abbreviations

Severe Acute Respiratory Syndrome

Middle East Respiratory Syndrome

Strengthening the Reporting of Observational studies in Epidemiology

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

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Salari, N., Hosseinian-Far, A., Jalali, R. et al. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Global Health 16 , 57 (2020). https://doi.org/10.1186/s12992-020-00589-w

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ORIGINAL RESEARCH article

The relationship of anxiety and stress with working memory performance in a large non-depressed sample.

• 1 Department of Psychology, Åbo Akademi University, Turku, Finland
• 2 Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
• 3 Center for Multilingualism in Society Across the Lifespan, Department of Linguistics and Scandinavian Studies, University of Oslo, Oslo, Norway
• 4 Turku Brain and Mind Center, University of Turku, Turku, Finland

Clinical anxiety and acute stress caused by major life events have well-documented detrimental effects on cognitive processes, such as working memory (WM). However, less is known about the relationships of state anxiety or everyday stress with WM performance in non-clinical populations. We investigated the associations between these two factors and three WM composites (verbal WM, visuospatial WM, and n-back updating performance) in a large online sample of non-depressed US American adults. We found a trend for a negative association between WM performance and anxiety, but not with stress. Thus, WM performance appears rather robust against normal variation in anxiety and everyday stress.

Introduction

Cognitive performance can be affected by a number of factors, including non-cognitive ones like the emotional state of the test-taker ( Gray, 2001 ; Owens et al., 2012 ; Storbeck, 2012 ; Luck and Vogel, 2013 ). In the emotional sphere, major factors that can affect demanding cognitive performance include stress and anxiety. While more pronounced symptoms on each of these two factors are clearly linked to impaired cognitive performance ( Sandi, 2013 ; Maloney et al., 2014 ; Moran, 2016 ), their effects are less clear when testing cognition in non-clinical populations. However, for both theoretical and practical purposes, it is essential to know whether even normal variability in stress and anxiety has an impact on cognition. This is relevant also for working memory (WM) that represents a core cognitive function. It is a limited-capacity temporary memory storage system that is constantly updated ( Baddeley, 2003 ). It serves as a mental platform for ongoing activities, being crucial for purposeful behavior and flexible interaction with the environment. WM is an object of extensive study both for basic research and for clinical assessment, and it is thus important to clarify factors that affect WM performance. In the present study, we examined the relationships between WM performance and stress and anxiety in a large non-depressed adult sample.

Working Memory and Anxiety

Anxiety is a state of heightened vigilance ( Grillon, 2002 ) that is associated with an increase in overall sensory sensitivity due to uncertainty or conflict ( Gray, 2001 ; Cornwell et al., 2007 ; Eysenck et al., 2007 ; Grupe and Nitschke, 2013 ). A characteristic feature of anxiety is the limited control over worrying thoughts and attentional biases, contributing to a greater focus on negative stimuli ( Matthews and Wells, 1996 ). It has been shown that anxiety disrupts cognitive performance ( Maloney et al., 2014 ), including WM ( Moran, 2016 ). This relationship works both ways, as cognitive impairment can lead to increased anxiety ( Petkus et al., 2017 ).

In this study, we focused on self-reported state anxiety, the immediate sensation of feeling anxious, rather than temporally stable trait anxiety. The attentional control theory, proposed by Eysenck et al. (2007) , suggests that state anxiety impairs cognitive performance by giving greater influence to the stimulus-driven (bottom-up) attentional system. The greater the anxiety, the more disruption this causes. A later paper on attentional control theory suggests that anxiety might affect only the executive component of WM ( Eysenck and Derakshan, 2011 ): in a dual-task study of anxiety, the primary WM task performance in high anxious individuals decreased only if the additional task required executive control ( Eysenck et al., 2005 ; see also Christopher and MacDonald, 2005 ). A study by Gustavson and Miyake (2016) showed that worry is also associated with impaired WM updating.

A recent meta-analysis by Moran (2016) examined the relationship between anxiety and WM capacity. Based on 177 samples, this meta-analysis on correlative studies found a moderate but reliable association so that higher anxiety was related to lower WM performances (overall Hedge’s g = -0.334). This held across anxiety type (state, trait), sample type (clinical, non-clinical), WM task paradigm (simple span, complex span, n-back), and WM content (spatial, phonological, visual). These findings speak for a rather general relationship that could be fitted to the attentional control account. However, Moran (2016) highlights various limitations of this research, including reliance on single measures of WM that makes it impossible to separate task-specific and task-general effects. Thus far, there have been no studies examining the relationships between state anxiety and WM domains at latent variable level.

WM and Stress

In terms of both emotional components and the underlying neurocircuitry, there is a significant overlap between stress and anxiety, but stress encompasses both avoidant (anxious) and proactive responses. In turn, fear and anxiety can be experienced even in the absence of the neuroendocrine cascade that is related to stress reaction ( Miller and O’Callaghan, 2002 ), just as stress does not necessarily entail experiencing fear or anxiety ( Shin and Liberzon, 2010 ). As regards cognitive effects, it appears that stress and anxiety behave in similar ways: it has been shown that under stress, controlled attention resources are reduced as they are allocated to the potential threat ( Klein and Boals, 2001 ). However, the inverted-U theory of acute stress ( Mizoguchi et al., 2000 ; Muse et al., 2003 ; Sandi, 2013 ; Sapolsky, 2015 ) states that this effect depends on stress levels related to the test situation: moderate stress may enhance cognitive performance, while both low (unmotivating) and high (overwhelming) stress are associated with a decline in performance. Indeed, Lewis et al. (2008) observed improvement in WM performance in the presence of mild acute stressors.

The experimental studies cited above investigated the role of acute stress, but research addressing the cognitive implications of self-reported daily stress has primarily reported negative effects. Wu and Yan (2017) state that chronic stress may negatively affect neuroplasticity and learning. Sliwinski et al. (2006) studied within-person variability of everyday stressors (as opposed to major stressful life events, see Klein and Boals, 2001 ) and their effect on cognition in young and older adults on six separate occasions. Daily stress predicted variability in response times on a WM updating task in both groups, while only the older group showed negative effects of heightened stress on an attention task. Moreover, stress affected only the more difficult and demanding attention task variant. These findings support the attention depletion hypothesis, suggesting that even everyday stressors may decrease WM and attentional resources. Stawski et al. (2006) conducted a similar study with older adults, arguing that stress impaired cognition through intrusive thoughts and avoidant thinking that appear in response to stressful situations. In young adults, decreased performance on WM updating has been related to negative affect, motivational problems, and reduced attentional control, which are key features in experiencing anxiety or negative stress ( Brose et al., 2012 ). Similarly, work-related stress negatively affected cognitive performance in a sample of Latino workers ( Nguyen et al., 2012 ). A cohort study by Tun et al. (2013) revealed that social strain had the greatest effect on the cognitive performance of those who had low baseline cognitive abilities. Petrac et al. (2009) reported a moderate positive correlation between everyday stress and error rates on attention tasks (both auditory and visual) in undergraduate students, but also a negative correlation between state anxiety and error rates. Thus, while even mild everyday stressors can have an impact on cognitive functioning, there seem to be moderating factors that we are only beginning to understand.

Aims of the Present Study

The short literature review above indicates that WM performance can be sensitive to stress- or anxiety-related interference. These effects have been extensively studied in clinical and older adult populations. However, less is known about the effects of stress and anxiety on WM in non-depressed adult populations. This lack of research is baffling given the increasing prevalence of stress in a working age population ( Wiegner et al., 2015 ). Experiencing stress and feelings of anxiety are common in otherwise healthy populations, but we know very little about how these mental states are associated with cognitive performance. Many previous studies are also hampered by the fact that they have used only single WM measures (e.g., Moran, 2016 ). Therefore, the present exploratory study investigated the relationships between WM performance and stress and state anxiety in a large non-depressed adult sample by using questionnaires and an extensive WM test battery including both verbal and visuospatial task variants.

Materials and Methods

Participants.

Our adult US American participants were recruited online via the Amazon Mechanical Turk (MTurk) crowdsourcing site, and the sample was a sub-set of the sample in the study by Waris et al. (2017) ; see that paper for more details on recruitment. The participants were selected on the basis of their previous MTurk ratings (95% work approval rating or higher, see also Peer et al., 2014 ) and the number of completed tasks (more than 100, but less than 1000 task assignments to avoid both inexperienced and very experienced MTurk users). They were also asked whether they had previously participated in similar studies, and 83.9% reported never having done so. Of the 711 participants who completed the study, 159 were excluded due to having more than 10 points on the QIDS-SR16 scale, indicating moderate, severe or very severe depressive symptoms ( Rush et al., 2003 ), since our focus lay on people who according to this cutoff did not currently suffer from depression. Thus, the included participants exhibited at most subclinical levels of depressive symptoms. As the STAI-6 and PSS-4 measures we used (see below) are not clinical diagnostic tools, we did not exclude anyone due to high anxiety or stress scores. Furthermore, we excluded 36 people due to having missing values on the tasks, admitting the use of external aids on WM tasks when probed afterwards, and/or having spent over 24 h on completing the study. We also removed participants who were multivariate outliers on WM task performance ( n = 13) according to Mahalanobis distance [χ 2 cutoff = 32.909, df = 12]. The final sample included 503 participants.

To investigate the representativeness of our sample vis-à-vis the US adult population, we compared the present sample to the 2015 statistics reported by the U.S. Bureau of Labor Statistics (2016) , U.S. Census Bureau Database (2016) , and U.S. National Institute of Mental Health (2016) . In line with previous MTurk studies ( Chandler et al., 2014 ; Paolacci and Chandler, 2014 ; Arditte et al., 2016 ; Keith and Harms, 2016 ), this comparison indicates that our sample was younger, more highly educated, included more females, exhibited a higher unemployment rate, and had an overrepresentation of people of Caucasian and Asian descent while Hispanic and Black Americans were underrepresented (see Table 1 ). Several of these features are most likely linked to Internet use in general.

Table 1. Demographics of the present sample compared with US adult population statistics.

The study measures consisted of questionnaires assessing anxiety and stress as well as ten WM tests (see below) that were administered online using an in-house developed web-based programmable testing platform. The platform employs a domain-specific programming language, and it allows researchers to create, distribute, and manage psychological experiments. The MTurk users who were willing to participate received a link through which they accessed and completed the experiment on a computer of their choosing. All participants started with the background questionnaire, after which they completed the ten WM tests. The order of the WM tests was randomized for each participant in order to control for possible test order effects. The only exception to this rule was that the forward single span task (SST) was always administered immediately before the respective numerical-verbal or visuospatial backward SST. On average, the participants completed the entire study in 1 h and 34 min.

Questionnaires for Stress and Anxiety

The short form perceived stress scale (pss-4).

The Short Form Perceived Stress Scale (PSS-4) is an abbreviated version of the self-report Perceived Stress Scale ( Cohen et al., 1983 ). It provides the subjective assessment of stressful life events within the previous month. The PSS-4 consists of four items (items 2, 6, 7, 14 from the original questionnaire) in which the frequency of stressful events is rated on a 5-point Likert scale ( never to very often ). The stress dimensions measured are unpredictability, uncontrollability, and sense of overload in everyday life. Individual scores are compared to normative values. The complete 14-item scale has higher reliability than the PSS-4 ( r = 0.85 as compared to r = 0.60) ( Cohen and Williamson, 1988 ), but the brevity of PSS-4 makes it an attractive tool for research.

PSS-4 population norms for non-clinical samples have been gathered in the 1983 Harris Poll in the United States ( N = 2,387) ( Cohen and Williamson, 1988 ) and in the United Kingdom ( N = 1,484) ( Warttig et al., 2013 ). When comparing our data to the more recent norms established by Warttig et al. (2013) , the total PSS-4 score of our sample is very similar (Table 2 ). Also the internal consistency of the scale in our sample (α = 0.76) was comparable to that reported by Warttig et al. (2013) (α = 0.77).

Table 2. Comparison of average PSS-4 scores in the present sample and in the normative sample collected by Warttig et al. (2013) .

The Six-Item Form of the Spielberger State-Trait Anxiety Inventory (STAI-6)

STAI-6 comprises six items from the State scale of the original State-Trait Anxiety Inventory Y form ( Spielberger et al., 1983 ) that had the highest item-remainder correlations ( Marteau and Bekker, 1992 ). STAI-6 includes three anxiety-present and three anxiety-absent items (respectively, tense, upset, worried and calm, relaxed, content ). The items are formed as statements (e.g., I feel calm, I am tense ) and each of them is rated on a four-point Likert scale ( not at all to very much ). On average, our participants received 10.7 points total in STAI-6. Marteau and Bekker (1992) reported that Cronbach’s alpha of the six-item scale was α = 0.82, as compared to an internal reliability coefficient of α = 0.91 for the 20-item STAI. A later study reported a reliability of α = 0.79 in a study of parental dyads ( Tluczek et al., 2009 ). We obtained an alpha of 0.82, the same as in Marteau and Bekker’s study.

The WM Measures

Our WM test battery employed four commonly used task paradigms: simple span tasks (forward and backward), complex span tasks, running memory tasks, and n-back tasks. In all task paradigms, two isomorphic variants were administered, namely numerical-verbal (with digits 1–9 as stimuli) and visuospatial (with spatial locations in a 3 × 3 grid as stimuli). Test scores were calculated separately for each test and test variant. Brief descriptions of each task are given below.

Simple Span Tasks

In the simple span tasks, the participants were shown stimulus item lists of unpredictable length. In the forward span tasks, the participants reported the presented items in the exact order of appearance, while in the backward span tasks they listed the stimuli in the reverse order. Each test included three- and four-item practice sequences that were administered prior to the task. The proper task involved seven trials comprising stimulus lists that ranged from three to nine items.

The lists were pseudo-randomly generated. All participants were presented with the same set of lists, but the order was randomized. Each item was presented for 1000 ms. In the two verbal versions, an asterisk appeared for 500 ms between digits. In the two spatial versions, the matrix was empty for 500 ms before a new item appeared. There was no time limit set on list recall. The dependent measure was the total number of correctly recalled items, regardless of span length. It was calculated separately for each of the four simple span tasks.

Complex Span Tasks

Similarly to the forward simple span tasks, in the complex span tasks the participants were presented with stimulus item lists of unpredictable length, and these lists were to be recalled in the exactly same order. However, after each item, the participants had to make a true/false judgment on a distractor item (see Figure 1 ). In the verbal version, the distractors consisted of arithmetic problems. In the visuospatial version, the participants had to combine two 3 × 3 matrix patterns in their mind and decide whether this combination corresponded to a third pattern. There was a six-second time limit on solving each distractor item.

Figure 1. Examples of distractor items in the complex span tasks. Numerical-verbal example item on the left, visuospatial on the right. A timer bar above each item depicts the remaining response time.

All participants were presented with the same set of lists, but the order of presentation was randomized. The task comprised five trials consisting of sequences of three to seven items. As in the simple span tasks, item lists were pseudo-randomly generated. Prior to the to-be-remembered item, a fixation point appeared on-screen for 500 ms, followed by the item (1000 ms), fixation point (500 ms) and distractor item (up to 6000 ms). The tests were preceded by a training sequence of three-item and four-item lists. The dependent measure was the total number of correctly recalled items, regardless of span length.

Running Memory Tasks

In the running memory tasks, stimulus item lists of unpredictable length were shown. After each list, the participants were asked to report the four last items in the order of presentation. The actual test included eight lists (containing 4–11 items) that were preceded by a practice session.

As in the previous tasks, the stimulus lists were pseudorandomized. Each item appeared on-screen for 1000 ms. In the verbal version, list items were separated by a fixation point (asterisk) that was visible for 500 ms, while in the visuospatial version, the matrix remained empty for 500 ms between items. The number of correctly reported items was used as the dependent measure. The four-item list was excluded from the analyses as it did not require any updating.

N-Back Tasks

In the n-back tasks, the participants had to decide for each item whether or not it was the same (target) or not (non-target) as the n th item back. In this study, 2-back versions of the task were used. Before each task, the participants had to complete a corresponding practice block. Item lists in the tasks were pseudorandomized, and they included 16 target items, 16 standard non-target items and 16 so-called lure items. The lures were non-targets that would have been targets for the adjacent n-back levels (1-back or 3-back). The potentially distracting lure items were included to avoid test performances that would be based merely on item familiarity.

Each item was presented for 1500 ms. In the verbal version, items were separated by an asterisk presented for 450 ms, and in the visuospatial version, the matrix was empty for 450 ms between items. Overall, the participants had 1950 ms to respond to each item. The dependent measure was the proportion of false alarms (“same” response on non-target items) subtracted from the proportion of hits (correct targets) on the 2-back task.

Descriptive Data

Raw accuracy scores obtained from the WM tasks were Box-Cox transformed to decrease skewness and thus improve normality ( Osborne, 2010 ). The overall WM results were comparable to those obtained in laboratory-based studies ( Engle et al., 1999 ).

Table 3 depicts mean accuracy measures prior to the Box-Cox transformation. All scores except the n-back scores denote percentage of correct items; for the n-back tasks, we used the corrected recognition score (i.e., we subtracted the proportion of false alarms from the proportion of correctly recalled target items).

Table 3. Mean accuracy rates (SD) on the WM tasks.

In the subsequent analyses, we employed composite WM variables as they represent more reliable measures than single task scores. Correlations between single tasks are depicted in Supplementary Table S1 . Our WM composites were derived from the exploratory factor analysis performed by Waris et al. (2017) . We used a data-driven approach rather than a priori categorization of the WM tasks to come up with the composites. The reason for this is that previous factor analytic studies on WM tasks have yielded variable results ( Waris et al., 2017 ), and the outcomes of such analyses depend on the particular constellation of tasks used. The exploratory factor analysis by Waris et al. yielded two alternative factor solutions that provided the best fit for the present data: a two-factor model (numerical-verbal factor; visuospatial + n-back factor) and a three-factor model (numerical-verbal factor; visuospatial factor; n-back factor). To retain content-specificity (verbal/visuospatial) that has been considered as the main dimension in the mental organization of WM (e.g., Nee et al., 2012 ), we used the three-factor solution. Thus, we compiled composite scores for the three latent factors using z -transformed task scores. Table 4 shows the bivariate correlations between scores on stress, anxiety and the three WM composites.

Table 4. Correlations (Pearson’s r ) between STAI-6 (anxiety), PSS-4 (stress), verbal WM composite, visuospatial WM composite, and n-back composite ( N = 503).

Factor Analyses of the Stress and Anxiety Measures

Following the factor analyses on the WM tasks, we also conducted separate exploratory factor analyses on PSS-4 and STAI-6. Factorability for both scales was adequate. For PSS-4, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.74, Bartlett’s test of sphericity was significant [χ 2 (6, N = 503) = 487.37, p < .001], and the diagonal values of the anti-image correlation matrix were in the 0.72–0.77 range. The exploratory factor analysis on PSS-4 using principal axis factor extraction method with oblique Promax rotation yielded a one-factor solution. The one-factor model accounted for 58,22% of the variance. The model is summarized in Table 5 below.

Table 5. Factor loadings on the single-factor model of PSS-4.

For STAI-6, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.79, Bartlett’s test of sphericity was significant [χ 2 (15, N = 503) = 1165.4, p < 0.001], and the diagonal values of anti-image correlation matrix were in the 0.78–0.83 range. On the basis of principal axis factoring with oblique Promax rotation, a two-factor solution accounting for 72% of the variance was chosen for STAI-6. This model that is summarized in Table 6 shows that anxiety-absent items ( I am calm, relaxed, content ) loaded on factor 1, while anxiety-present items ( I am tense, upset, worried ) loaded on factor 2. As expected, these two factors, mirroring each other through presence vs. absence of anxiety, correlated quite strongly (Pearson’s r = 0.57). Moreover, an analysis of bivariate correlations showed similar negative and small associations between the two factors and WM measures (range –0.12 to –0.04). Based on these findings, we decided to employ a single summative score for STAI-6 in the subsequent analyses. It should also be noted that there were no conceptual grounds for the present two-factor solution that might instead be linked to the formulation of the questions and the fact that anxiety-absent items seem to be more strongly correlated with the scale overall ( Marteau and Bekker, 1992 ).

Table 6. Factor matrix and factor correlation for STAI-6.

Linear Mixed Models (LME)

For the statistical analyses, we used the lme4 package ( Bates et al., 2015 ) to compare three models: the null model (including only participant random effects), Model 1 (including the three background variables: age, education, and childhood SES, as well as participant random effects) and Model 2, which also included the variables of interest (PSS-4 and STAI-6 scores). This choice of method allowed us to examine the specific interactions between the variables of interest and WM domains. We decided to keep the background variables in Model 2, as they are known to interact with WM, and because this interaction may differ between WM domains ( Myerson et al., 1999 ; Reuter-Lorenz et al., 2000 ; Evans and Schamberg, 2009 ). The formulas of the three models are as follows:

Y = f ( e )

Y = f ((age, education, childhood SES) ∗ domain) + e

Y = f ((age, education, childhood SES, anxiety, stress) ∗ domain) + e

Here Y is the WM performance score, domain is the WM domain (verbal, visuospatial, or n-back), and e is the participant random effect. Since WM domain is a categorical variable, we used a deviation coding scheme, in which the mean of each WM domain was compared to the grand mean of overall WM performance. A likelihood ratio test showed that Model 2 was a better fit for the data than the null model [χ 2 (17) = 48.36, p < 0.001], or Model 1 [χ 2 (6) = 17.73, p = 0.007]. Marginal R 2 GLMM for Model 1 was 0.028, meaning that this model explained 2.8% of the variance. Marginal R 2 GLMM for Model 2 was 0.045 ( Nakagawa and Schielzeth, 2013 ).

A closer look at Model 2 revealed statistically significant main effects of age, education and domain as well as a trend toward a main effect of anxiety. The model is summarized in Table 7 , and regression plots with age and anxiety as predictors are shown in Supplementary Figures S1 , S2 . We calculated p -values using the lmerTest package ( Kuznetsova et al., 2017 ).

Table 7. Summary of Model 2 ( N = 503).

Participants who were younger tended to score higher on the WM tasks. Those who reported a higher level of education also scored higher on the tasks. The main effect of domain was driven by the fact that the mean scores in verbal WM were lower than in visuospatial WM ( z = 2.63, p = 0.02) and in n-back ( z = 2.15, p = 0.06). With regard to anxiety, participants who obtained higher STAI-6 scores also performed worse on the WM tasks (zero-order correlations between WM performance, age, and anxiety scores are presented in Supplementary Figures S1 , S2 ). It is, however, important to note that there was only a trend toward an effect of anxiety, and the WM domain did not interact with anxiety. We also observed an age ∗ domain interaction, as higher age was related to lower performance on visuospatial WM and n-back tasks, but not on verbal WM tasks.

In the present study, we examined how anxiety and stress were associated with WM performance in a large, non-depressed adult sample. Using linear mixed models, we tested the predictive power of the self-report measures of these two factors, as well as background factors (age, education, childhood SES) on WM performance in three different domains (verbal, visuospatial, and n-back). Our analyses revealed main effects of age and education. We also observed a trend toward a main effect of anxiety. Moreover, we found an interaction between WM domain and age: visuospatial WM and n-back performance were negatively associated with age, while verbal WM performance was not. We did not observe significant relationships between stress and the WM measures.

As regards anxiety, our findings are in line with Moran’s (2016) meta-analysis that indicates that increased anxiety (both state and trait) is related to worse WM performance across task paradigms and contents. Our results show that anxiety correlated negatively with both verbal and visuospatial WM performance as well as with n-back task performance, which Moran (2016) calls the dynamic span measure.

We did not find a relationship between stress and WM performance. Our stress measure, PSS-4, focuses on stressful life events experienced during the past month, instead of acute stress linked to the testing situation. In previous research, chronic stress has been reported to show primarily negative effects on cognition ( Sliwinski et al., 2006 ; Stawski et al., 2006 ). As noted in the Introduction, stress and anxiety are partly overlapping constructs, which is also reflected in the notable correlation between these measures (see Table 4 ). Hence, it is possible that the STAI-6 score, which we operationalized as transient anxiety, also encompassed feelings of stress.

As regards the limitations of the present study, one concern often discussed in the context of online studies is data quality, as the researcher cannot observe participants’ behavior and performance during the study. However, empirical research on Internet-based cognitive studies shows that their results are comparable to those obtained in traditional experiments, offering good data quality and greater diversity than studies conducted on college samples ( Berinsky et al., 2012 ; Casler et al., 2013 ; Crump et al., 2013 ; Goodman et al., 2013 ; Shapiro et al., 2013 ; Paolacci and Chandler, 2014 ). We were careful in taking into account our MTurk participants’ level of experience, quality of previous work, and possible cheating. These control procedures should help to counter several potential pitfalls in Internet data collection. At the same time, recruitment of participants from diverse backgrounds, such as in MTurk, contributes to a greater representativeness of the obtained results. Secondly, there are some possible limitations stemming from our choice of methods. Due to the correlative nature of our data, we cannot make any causal inferences about the interactions between WM, stress, and anxiety. Nevertheless, as our approach allowed the participants to estimate their subjective experience, it can be considered as ecologically more valid than laboratory-induced stress and anxiety. Finally, the questionnaires that we used to measure subjective experience of stress and anxiety have operated on varying time scales: while the PSS-4 asks the participant to assess stress experienced during the previous month, STAI-6 addresses the current mental state. This limits the comparisons between the two measures. Furthermore, one significant limitation of our study is the use of STAI-6 instead of more nuanced measures that could discriminate between components of anxiety (such as worry and arousal). However, we chose the questionnaires on the grounds that they are validated, commonly used, and possess good internal consistency and sufficient discriminatory power despite their brevity.

Future research would benefit from using more detailed measures of anxiety and stress. Another aspect important to address is using measures that operate on comparable time spans. Furthermore, older samples as well as longitudinal studies would also shed some light on the interaction and fluctuation of cognitive performance with anxiety and stress.

In summary, our results showed only a trend toward a negative association between transient anxiety and WM performance. Thus, even demanding WM performance appears to be rather robust against normal variation in everyday stress and anxiety. These findings are relevant for research on cognition-emotion interfaces as well as for testing practices.

Ethics Statement

This study was carried out in accordance with the recommendations of the Ethics Board of the Åbo Akademi University with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Participation was anonymous and all participants were informed of their right to stop at any time. The protocol was approved by the Joint Ethics Committee at the Departments of Psychology and Logopedics, Åbo Akademi University.

Author Contributions

OW, AS, MiL, and MaL conceived and designed the research. OW aggregated the data. KML, OW, and MiL analyzed the data. KML wrote the original draft. All authors provided critical revisions and approved the final version of the manuscript for submission.

MaL was financially supported by grants from the Academy of Finland (project #260276) and the Abo Akademi University Endowment (the BrainTrain project). MiL was supported by the Academy of Finland (grant #288880).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2019.00004/full#supplementary-material

FIGURE S1 | Regression plots from Model 1 with age as the predictor ( x -axis) and the working memory composite scores (n-back, verbal, visual; y -axis) as dependent measures.

FIGURE S2 | Regression plots from Model 2 with STAI-6 summative score as the predictor ( x -axis) and the working memory composite scores (n-back, verbal, visual; y -axis) as dependent measures.

TABLE S1 | Zero-order correlations between the WM tasks.

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Keywords : anxiety, stress, working memory, cognition, healthy adults

Citation: Lukasik KM, Waris O, Soveri A, Lehtonen M and Laine M (2019) The Relationship of Anxiety and Stress With Working Memory Performance in a Large Non-depressed Sample. Front. Psychol. 10:4. doi: 10.3389/fpsyg.2019.00004

Received: 01 August 2018; Accepted: 03 January 2019; Published: 23 January 2019.

Reviewed by:

Copyright © 2019 Lukasik, Waris, Soveri, Lehtonen and Laine. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Matti Laine, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Financial stress and depression in adults: A systematic review

Naijie guan.

1 Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, United Kingdom

Alessandra Guariglia

2 Department of Economics, University of Birmingham, Edgbaston, Birmingham, United Kingdom

Patrick Moore

Fangzhou xu, hareth al-janabi, associated data.

All relevant data are within the paper and its Supporting Information files.

Financial stress has been proposed as an economic determinant of depression. However, there is little systematic analysis of different dimensions of financial stress and their association with depression. This paper reports a systematic review of 40 observational studies quantifying the relationship between various measures of financial stress and depression outcomes in adults. Most of the reviewed studies show that financial stress is positively associated with depression. A positive association between financial stress and depression is found in both high-income and low-and middle-income countries, but is generally stronger among populations with low income or wealth. In addition to the “social causation” pathway, other pathways such as “psychological stress” and “social selection” can also explain the effects of financial stress on depression. More longitudinal research would be useful to investigate the causal relationship and mechanisms linking different dimensions of financial stress and depression. Furthermore, exploration of effects in subgroups could help target interventions to break the cycle of financial stress and depression.

Introduction

Depression is one of the most common mental health problems and is marked by sadness, loss of interest or pleasure, feelings of guilt or low self-worth, disturbed sleep or appetite, feelings of tiredness, and poor concentration [ 1 ]. Depression is a leading cause of disability and poor health worldwide [ 1 ] and is expected to rank first worldwide by 2030 [ 2 ]. According to a survey from the World Health Organization, more than 322 million people, which accounted for approximately 4.4% of the world population, suffered from depressive disorders in 2015 [ 3 ]. The lifetime risk of developing depression was estimated to be 15%-18% [ 4 ]. Mental health problems including depression have imposed a heavy economic burden on individuals and households who are suffering from mental disorders and even on society [ 5 – 7 ]. Specifically, the global costs of mental health problems are increasing each year in every country. Those costs are estimated to reach approximately 16 trillion dollars by 2030 [ 8 , 9 ]. There is a considerable need to explore the risk factors of mental disorders or the determinants of mental health, which will inform preventive strategies and actions aimed at reducing the risk of getting mental disorders and thereby promoting public mental health.

Many social and economic determinants of depression have been identified. These include proximal factors like unemployment, low socioeconomic status, low education, low income and not being in a relationship and distal factors such as income inequality, structural characteristics of the neighbourhood and so on [ 10 – 12 ]. Research has emerged in the past two decades focusing on the association between the individual or household financial stressors and common mental disorders such as depression and anxiety. However, findings regarding the relationship between different indicators of financial stress and depression are inconclusive in the previous literature. Studies have shown positive associations between depression and various indicators of financial stress such as debt or debt stress, financial hardship, or difficulties [ 13 – 15 ]. Some other studies find no relationships when financial stress was indicated by low income. For example, Zimmerman and Katon [ 16 ] found that when other socioeconomic confounders were considered, no relationship between low income and depression was observed. Besides, there is evidence showing a negative association between low income and major depressive disorder in South Korea [ 17 ]. A 2010 review on poverty and mental disorders also finds that the association between income and mental disorders (including depression) was still unclear [ 18 ].

The social causation theory is one of the theories that has been proposed to explain possible mechanisms underlying the effect of poverty on mental disorders [ 18 , 19 ]. It states that stressful financial circumstances might lead to the occurrence of new depressive symptoms or maintain previous depression. This might be due to exposure to worse living conditions, malnutrition, unhealthy lifestyle, lower social capital, social isolation, or decreased coping ability with negative life events. Individuals or households with limited financial resources are more vulnerable to stressful life events (e.g., economic crises, public-health crises), which might increase the risk of mental health problems [ 18 – 20 ]. However, practically, social causation might not be applicable to situations where individuals are not in poverty or deprivation but still can experience depression due to financial stress.

Reviews to date have examined the relationship between debt specifically and broader mental health outcomes with depression being one of them. For example, two reviews published in 2013 and 2014 reviewed the literature on the relationship between debt and both mental health and physical health [ 21 , 22 ]. They concluded that there was a significant relationship between personal unsecured debt or unpaid debt obligations and the increased risk of common mental disorders, suicidal ideation and so on [ 21 , 22 ]. In terms of depression, they found that there was a strong and consistent positive relationship between debt and depression. Another focus of the literature is on the relationship between poverty and mental health problems including depression in low-and middle-income countries (LMIC). In those reviews, indicators of poverty include low socioeconomic status, low income, unemployment, low levels of education, food insecurity and low social class [ 18 , 23 ]. Both reviews find a positive relationship between poverty and common mental disorders, which exists in many LMIC societies regardless of their levels of development. Being related to low income, factors such as insecurity, low levels of education, unemployment, and poor housing were found to be strongly associated with mental disorders, while the association between income and mental disorders was unclear.

The reviews discussed above focus mainly on the relationship between debt or poverty and mental health outcomes. As sources of financial stress are complex and multidimensional, indicators such as low income or debt are not the only economic risk factor of mental health problems. Other sources of financial stress such as lack of assets, economic hardship or financial difficulties (e.g., whether an individual finds it difficult to meet standard living needs like buying food, clothes, paying bills and so on) might also relate to depression. In addition, various sources of financial stress might be related to mental health problems in different ways. Based on the existing reviews, it is still unknown which domains of financial stress have clearer associations with depression and whether there is heterogeneity in the relationship between financial stress and depression for different populations and contexts. Moreover, the existing reviews do not discuss the possible mechanisms underpinning the association between financial stress and depression. To better understand the association between financial stress and depression and the possible mechanisms underlying it, a systematic review was conducted bringing together a wide range of indicators of financial stress. The eligible economic indicators of financial stress in this review include objective financial variables like income, assets, wealth, indebtedness; as well as measures that capture subjective perceptions of financial stress, such as perceived financial hardship (e.g., subjective feelings of sufficiency regarding food, clothes, medical care, and housing), subjective financial situation (e.g., individuals’ feelings about their overall financial situation), subjective financial stress, subjective financial position, and financial dissatisfaction.

This study aims at providing a comprehensive review of the association between different financial stressors and depression considering the characteristics of the associations of interest and discussing the proposed mechanisms underlying the associations. An understanding of the relationship between financial stress and depression would not only advance our understanding and knowledge of the economic risk factors of mood disorders but also provide policymakers with more understanding of additional public mental health benefits of intervention aimed at alleviating poverty and/or at improving people’s financial conditions.

Search strategies

A systematic review of published literature was conducted using online searches on bibliographic databases. At the first stage, six bibliographic databases including CINAHL, PsycINFO, EMBASE, EconLit, AMED, and Business Source Premier were searched for related peer-reviewed journal articles to April 2019. The search terms are listed in Table 1 . The broad strategy was to combine terms related to finances, with terms related to depression, and terms related to the unit of analysis (individual, household etc). Several key studies that were eligible for inclusion criteria were pre-identified. Before the formal search, pilot searches were performed to make sure the pre-identified key studies can be found by the search. More details of search strategies are displayed in S1 Appendix . All the search results were limited to the English language. No time restriction was added to the search. The reference lists of the eligible studies and several relevant review papers were checked manually to supplement the main electronic searching.

Eligibility criteria

The inclusion and exclusion criteria for study selection were designed to ensure a focus on primary studies and secondary studies conducted on adults, using measures of financial stress (exposure) and depression (outcome). The eligibility criteria were tested on a selection of papers by multiple members of the study team to ensure that studies were categorised accurately. Based on the piloting process, the eligibility criteria were further modified. The following is the list of the final inclusion and exclusion criteria applied in this study.

Studies were included if they met the following criteria: (1) observational and experimental studies on the relationship between the individual or household financial stress and depression or depressive symptoms; (2) original research in peer-reviewed journals; (3) conducted on general population samples aged 18 and over; (4) used indicators which capture different dimensions of an individual or household financial stress, such as income, assets, debt, wealth, economic hardship, financial strain, financial stress, and financial satisfaction; (5) studies that measure depression through both non-clinical and clinical techniques (e.g., Centre for Epidemiologic Studies Depression Scale), were eligible for this review.

Studies were excluded if they were: (1) systematic reviews, dissertations, conference abstracts, or study protocols; (2) studies focusing on a specific population including female-only, male-only, people with a special occupation (e.g., soldiers), migrants, or people with specific illnesses; (3) studies relating to societal (as opposed individual) economic circumstances (e.g., income inequality measured at the community level or the country level) or shocks to the macroeconomy (e.g., stock market crashes, hyperinflation, banking crises, economic depressions, and financial crisis); (4) studies that only reported the joint association between several socioeconomic determinants and depression. These were included only if the association related to individual or household finances were reported and explained individually; (5) studies based on overall mental health, or other types of mental disorders (e.g., anxiety, suicide, self-harm, bipolar disorder, schizophrenia, dementia) where the association between household financial stress and depression was not reported and explained independently.

Study selection

Search strategies were applied to six databases (i.e., CINAHL, PsycINFO, EMBASE, EconLit, AMED, and Business Source Premier EBSCO) to generate a long list of candidate studies. All the search results were exported into Mendeley. Search results, after the removal of duplicates, were screened for relevance using the title and abstract information. The full texts of relevant articles were then checked for eligibility based on the selection criteria. Screening and selection were undertaken by two reviewers independently. All authors were consulted when a disagreement arose. The study selection process and reasons for study exclusions were recorded in a flow chart shown in Fig 1 .

Data analysis

A pre-designed data collection form (listed in S2 Appendix ) was used for the data extraction process. The data extracted from the eligible studies covered the following categories: (1) characteristics of studies: year, author, journal, aim of study, countries, study type, data sources, responsible rate, level of study, eligibility of ethical approval; (2) characteristics of the population: sample size, age group, mean age of the participants, gender; (3) depression measures, definition of depression, validity of the measures; (4) measures of financial stress (exposures) used, measures of the exposure, definition of the exposure, validity of the measures; (5) statistical analysis: econometric methodologies, covariates, whether reverse causality was taken into account, whether there are subgroup analyses and methods (6) main results. The key information being extracted is presented in S1 and S3 Tables, which is a simplified version of the extracted data.

This review focused on the association between each dimension of financial stress and depression and analysed the heterogeneity of the association in different contexts. The eligible studies were reviewed narratively, and the results were stratified by different indicators of financial stress (e.g., low income, low assets, low wealth, debt, financial difficulties and so on). Causal inferences and proposed mechanisms underlying the association between financial stress and depression based on the reviewed evidence were discussed in the discussion section. No meta-analysis was conducted to pool the reviewed evidence due to the substantial heterogeneity in the measurements and definitions of the exposure and outcome variables, study context, and methodologies.

Quality assessment

The quality of the included studies was assessed using an adapted version of the Quality Assessment Tool for Quantitative Studies used in Glonti et al. [ 24 ] (see S3 Appendix ). The original version of this tool is developed by the Effective Public Health Practice Project (EPHPP) [ 25 ]. Seven key domains relating to study design, selection bias, withdrawals, confounders, data collection, data analysis and reporting were considered. Studies can have between six and seven component ratings. The score of each domain equals 1 if the quality is high, 2 if the quality is moderate and 3 if the quality is low. An overall rating for each study was determined based on the ratings for all domains. The overall rating of studies’ quality was classified as high, moderate, or low. Full details of the design and usage of the quality assessment tool can be found in Glonti et al. [ 24 ]. The quality assessment of the included studies was conducted by two reviewers independently. The results of the quality assessment were based on consensus between the two reviewers.

5,134 papers were identified after searching online databases including CINAHL, PsycINFO, EMBASE, EconLit, AMED, and Business Source Premier. The flow chart for the study selection process is displayed in Fig 1 . The total number of papers after removing duplicates was 4,035. Both titles and abstracts of the identified 4,035 papers were screened. 3,763 papers were removed since they did not satisfy the eligibility criteria. The full texts of the remaining 272 papers were accessed and further screened separately by the two reviewers based on the eligible criteria. 235 papers were further excluded leading to 37 studies for consideration. The main reasons for exclusion were that the exposure, the outcome variables of interest, or the targeted population of those studies did not meet the inclusion criteria. Three additional articles were further added after checking the reference lists of all the eligible papers and those of the past relevant review papers [e.g., 18 , 21 ]. 40 articles were finally identified for the data extraction.

Study characteristics

Regarding the number of reviewed studies by years of publication, most of the reviewed studies were published in the past two decades, with a noticeable spike in the last five years. The majority of studies (32 out of 40) reported evidence from high-income European countries and the USA, Australia, Japan, and South Korea. Eight studies were based on low- and middle-countries including China, Chile, and South Africa. In terms of study design, 17 studies were cross-sectional, and 23 studies were longitudinal. The age groups considered in the 40 studies vary: 17 studies focused on the general adult population including young adults, middle-aged adults, and older adults, while 23 studies focused specifically on working-age, young adults, middle-aged, or older adults. Data of study characteristics were displayed in the data extraction form in S1 Table .

Measures of depression

The most commonly used measure for depression was the Centre for Epidemiological Studies Depression Scale (CES-D) [e.g., 26 – 28 ]. Various versions of the CES-D were used in the reviewed studies: six studies used the full version, that is, the 20-item CES-D; 19 studies used the shortened version of the CES-D scale. Other measures were also used to assess the individual’s depressive symptoms such as the Hospital Anxiety and Depression Scale (HADS) [ 29 ], the World Mental Health Composite International Diagnostic Interview (WMH-CIDI) [ 30 , 31 ], a subsection of the General Health Questionnaire (GHQ depression) [ 31 – 33 ], the Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS-IV) [ 34 ], the 21-item Beck Depression Inventory (BDI) [ 35 ] and the Geriatric Depression Scale (GDS) [ 15 , 36 , 37 ]. Two studies used self-reported depression by asking participants whether or not they had any experience of depression [ 13 , 38 ].

Measures of financial stress

A wide variety of concepts and measures of financial stress were used across the reviewed studies. The financial exposure in the reviewed studies can be divided roughly into two categories. First, personal or household finances, which include income, assets or wealth, debt or hardship were investigated. These economic indicators were measured in different ways. Some studies measured the total amount of assets while other studies measured assets by counting the number of durable items owned by an individual (such as motor vehicles, bicycles, computers, or cameras) or a household (such as fridges, microwaves, TV, cameras). The measures of debt were more diversified: the onset of debt, the amount of debt in general and of different types of debt, the debt-to-asset ratio, debt problems like over-indebtedness, debt arrears, and debt stress. Financial hardship was defined as difficulties in meeting the basic requirements of daily life due to a lack of financial resources. For example, not having enough money for food, clothes, shelter and medical expenses; being unable to pay bills on time or heat the home; having to sell assets; going without meals; or asking for financial help from others were used by these studies as proxies for financial hardship [ 30 , 39 ]. Second, some studies examined the associations between depression and subjective perceptions of financial stress such as perceived financial hardship (e.g., subjective feelings of insufficiency regarding food, clothes, medical care, etc.), subjective financial situation (e.g., individual’s feelings of their overall financial situation), subjective financial stress, subjective financial position, financial dissatisfaction and so on.

Quality of reviewed studies

Full details of the quality assessment of the 40 included studies are displayed in S2 Table and S1 Fig . An observational study design was utilised in all of the included papers. 29 (72.5%), and 11 (27.5%) studies were rated as methodologically strong [ 6 , 13 – 16 , 20 , 26 – 28 , 30 , 31 , 34 , 37 – 53 ] and moderate [ 29 , 31 , 32 , 35 , 36 , 54 – 59 ], respectively. Among the 40 included studies, 34 (85%) had a low risk of selection bias, five (12.5%) had a moderate risk and one had a high risk of selection bias. Eight studies were able to be rated on withdrawals and drop-outs: two of them were rated as “strong”, four achieved a “moderate” rating and one received a “weak” rating. We found that 15 studies (37.5%) had a low risk while 25 (62.5%) had a moderate risk of confounding bias. Regarding the data collection, two studies were rated as ‘strong’, 37 received a ‘strong’ score, and one study was rated as ‘weak’. All the studies received a ‘strong’ rating for data analysis except for one study that was rated as ‘weak’. 35 (87.5%) studies received a ‘strong’ rating for reporting, while five studies had a ‘moderate’ quality of reporting.

Association between income and depression

Eleven studies were identified examining the relationship between individual or household income levels and depression. All controlled for other socioeconomic confounders or/and health status. Seven studies found a statistically significant association between low income and a higher risk of depressive symptoms after adjustment. The positive association between low income and depression was reported in both high-income countries and low- and middle-income countries and found in different age groups (i.e., younger adults, middle-aged adults, and older adults).

The intertemporal relationship between individual or household income and depression was investigated in three longitudinal studies [ 20 , 34 , 58 ]. Osafo et al. found that in the UK, an increase in household relative income (i.e., income rank) was statistically related to a decreased risk of depression at a given time point [ 58 ]. The effect of household income at baseline on the risk of showing depressive symptoms in the following time point was weakened but still statistically significant, controlling for the baseline depression level. Lund and Cois reported similar results: they found that lower household income at baseline could predict a worse depression status during the follow-up period in South Africa [ 20 ]. Based on evidence from the US, Sareen et al. found that individuals with lower levels of household income faced an increased risk of depression compared to those with higher levels of household income [ 34 ]. Furthermore, a reduction in income was also related to an increased risk of depression [ 34 ].

Focusing on pension income, which is one of the main sources of household income for the retired population, Chen et al. found that pension enrolment and pension income were significantly associated with a reduction in CESD scores among Chinese older adults, controlling for other socioeconomic factors and health status [ 43 ].

The strength of the relationship between income and depression varies and can be affected by how income is measured. For example, compared to absolute income, a household’s relative income level within a reference group was found to be a more consistent household financial predictor of depression [ 58 ]. Osafo et al. compared the effect of relative income with that of the absolute value of household income [ 58 ]. They found that a deterioration in the rank of household income was associated with a higher possibility of showing depression at a given time point, as well as the subsequent time point [ 58 ].

The relationship between income and depression holds for all income groups but is more pronounced among lower-income groups. According to Zimmerman and Katon, the association between depression and income is stronger among people with income levels below the median [ 16 ]. Based on a quasi-natural experiment, Reeves et al. also found that the reduction in housing benefits significantly increased the prevalence of depression for low-income UK households [ 38 ]. Additionally, the association between pension income and depressive symptoms in older adults were more pronounced among lower-income groups [ 43 ]. More broadly, the income-depression relationship might be influenced by the economic status of the regions where households live. For example, Jo et al. found that the association between income and depression was significant among participants from low-economic-status regions, while it was insignificant among participants from high-economic-status regions [ 55 ].

Association between material assets and depression

Two studies on the relationship between assets and depression were identified: one cross-sectional study [ 29 ] and one longitudinal study [ 20 ]. Those studies showed that assets were a significant predictor of depression after controlling for demographic and other socioeconomic confounders. Furthermore, the household assets-depression association was found to be stronger for individuals with lower levels of assets at baseline [ 20 , 29 ]. The directions of the assets-depression relationship were investigated in one study. Lund and Cois simultaneously examined both directions of the relationships using a nationally representative survey on South Africa [ 20 ]. They found that low levels of individual and household material assets were significantly related to depression in the follow-up period after controlling for age, gender, race and education. Conversely, having more depression symptoms at baseline was significantly associated with lower levels of individual assets in the follow-up period [ 20 ].

Association between wealth and depression

Three studies explored the relationship between wealth and depression in adults. All of them were based on high-income country contexts including the UK and the US and suggested a positive relationship between individual or household low wealth and depression among middle-aged and older adults. Two longitudinal studies examined the association between wealth and depression. Specifically, Pool et al. found that an increase in household wealth was statistically related to a decrease in the risk of depressive symptoms [ 50 ]. Osafo et al. compared the effect of relative wealth (i.e., wealth rank) and absolute wealth on depressive symptoms [ 58 ]. Their results showed that, instead of the absolute wealth, the wealth rank within a social comparison group was the primary driver of the association between wealth and depressive symptoms [ 58 ]. The strength of the relationship between wealth and depression varies according to the level of wealth at baseline [ 58 ]. For example, Martikainen et al. found the association between household wealth and depression was most pronounced among the lowest wealth group [ 33 ].

Association between debt and depression

Fourteen studies investigated the association between debt and depression and provided empirical evidence based on high-income countries (Europe and the US) and Chile. Three studies were cross-sectional and all of them reported a positive association between debt (assessed by student debt, the occurrence of any debt, or unsecured debt) and depressive symptoms after controlling for demographic and other socioeconomic factors [ 6 , 27 , 53 ]. Eleven longitudinal studies identified by this review investigated the association between debt and depression over time. The definitions and measures of debt vary across studies. Associations between the occurrence and/or amount of financial debt, the occurrence and/or amount of housing debt, excessive mortgage debt, the occurrence of any debt, the debt-to-asset ratio, and the debt-to-income ratio, on the one hand, and depression, on the other, were investigated in the reviewed studies.

The association between changes in debt status and changes in depressive symptoms was investigated in six studies. Specifically, using five waves of data from the Survey of Health, Ageing and Retirement in Europe (SHARE), Hiilamo and Grundy found that both men and women switched from having no financial debt to having substantial financial debt suffered from a deterioration in depressive symptoms [ 28 ]. Also, switching from no mortgage debt to having substantial mortgage debt was positively associated with the deterioration in depressive symptoms among women [ 28 ]. Using a large nationally representative dataset from the Chilean Social Protection Survey (SPS), Hojman et al. also found that individuals who were always over-indebted or switch from having moderate levels of debt to over-indebtedness had more depressive symptoms than those who were never over-indebted [ 46 ]. Additionally, they found that those who were not over-indebted, regardless of the previous debt status, did not experience a worsening in depression, showing that the effect of over-indebtedness on depressive symptoms faded away as the debt levels decreased [ 46 ].

Various measures of debt were used in the reviewed studies such as the occurrence of debt [ 26 , 27 , 53 ], the amount of debt [ 6 , 14 , 26 , 28 , 53 ], and the debt-to-income ratio or debt-to-asset ratio [ 14 , 46 , 48 ]. The debt-depression relationship varies with different operationalisations of debt with the debt to asset ratio being a more reliable predictor of depression than the total debt. Both Sweet et al. and Hojman et al. found that only the debt-to-assets ratio or debt-to-income ratio (rather than the absolute amount of debt) were consistently and positively associated with higher depression scores before and after adjustment (see S1 and S3 Tables for details of the covariates used) [ 14 , 46 ].

Different types of debt such as secured debt (e.g., mortgage debt) and unsecured debt (e.g., consumer debt) might be related to the depression in different ways. The reviewed studies reported a positive association between high levels of mortgage debt and high unsecured consumer debt (regardless of the amount) and depression [ 14 , 48 , 53 ]. For example, Leung and Lau examined the causal relationship between mortgage debt and depressive symptoms and found that a high level of mortgage indebtedness (defined as a mortgage loan to home value ratio over 80%) was associated with more depressive symptoms among mortgagors [ 48 ]. Both Zurlo et al. and Sweet et al. found that unsecured debt (e.g., consumer debt) was a significant predictor of more depressive symptoms [ 14 , 53 ]. Three studies compared the effect of different types of household debts on depression [ 26 , 28 , 46 ]. The results of those three studies suggested that the association between household debt and depressive symptoms was predominantly driven by short-term debt. Specifically, unsecured debt (e.g., financial debt), or short-term debt were associated with a higher risk of experiencing depression, while secured debt itself (e.g., mortgage debt) or long-term debt were not related to depressive symptoms. For example, using longitudinal data from the Survey of Health, Ageing and Retirement in Europe (SHARE), Hiilamo and Grundy found that household financial debt was positively and significantly associated with more depressive symptoms, while the effect of household housing debt on depression was weak or even insignificant [ 28 ]. Berger et al. found a similar result using longitudinal data from the US. Their results (controlling for baseline characteristics and socioeconomic factors) showed that only short-term debt (i.e., unsecured debt) was positively and statistically significantly associated with depressive symptoms, while the effects of mid-term and long-term debt (e.g., mortgage loan) on depressive symptoms were not significant [ 26 ].

However, it is not always the case that the association between debt and depressive symptoms is only driven by consumer debt. As reported in two longitudinal studies by Hiilamo and Grundy and by Gathergood, a secured debt like mortgage might be associated with depression when the secured debt becomes a problem debt [ 28 , 32 ]. Hojman et al. found that mortgage debt had no association with depressive symptoms, while consumer debt was positively and significantly related to more depressive symptoms [ 28 ]. Nevertheless, both Hojman et al. and Alley et al. found that mortgage arrears had a significant effect on more severe depression, even when the effect of consumer debt on depression was controlled [ 40 , 46 ]. In line with their study, Gathergood also found that housing payment problems were strongly associated with a higher depression score [ 32 ].

Association between financial hardship and depression

The association between financial hardship and depression was reported in four studies, all of which were based on high-income countries such as the US and Australia [ 15 , 30 , 39 , 52 ]. They all observed a cross-sectionally positive relationship between financial hardship and depression, which holds after adjustments (see S1 and S3 Tables for details of the covariates used). The intertemporal association between financial hardship and depressive symptoms was reported in two longitudinal studies [ 15 , 39 ]. However, the consistency of the findings is sensitive to the statistical methods applied. Mirowsky and Ross found that current financial hardship was associated with a subsequent increase in depression in the US [ 39 ]. The other study only observed an association between financial hardship at baseline and baseline depression, as well as a weak or even no association between prior financial hardship and current depression [ 15 ]. When the same statistical strategy was applied, the findings from Butterworth et al. were consistent with those were observed in Mirowsky and Ross’s study [ 15 , 39 ].

Furthermore, the reviewed studies showed that the effect of past financial hardship on depressive symptoms decayed with time. In other words, current financial hardship mattered the most for current depressive symptoms. Following Mirowsky and Ross, changes in financial hardship were stratified into four types [ 39 ]. An individual experiencing (not experiencing) current financial hardship and hardship in the past belongs to the always hardship group (no hardship group). An individual experiencing only current (past) financial hardship belongs to the new hardship group (resolved hardship group). Mirowsky and Ross found that the effects of consistent hardship and new financial hardship (3 years later) on depressive symptoms were positive and significant [ 39 ]. Moreover, there was no significant difference in the follow-up depressive symptoms between the consistent hardship group and the new hardship group [ 39 ]. Furthermore, the association between both resolved hardship and no hardship on depressive symptoms was not significant [ 39 ]. Consistent with this, Butterworth et al. also found that the individuals who currently experienced financial hardship were more likely to have depression than those who only experienced financial hardship in the past or never experienced it [ 15 ].

Age was the most analysed moderator of the association between financial hardship and depressive symptoms among the reviewed studies. This review found that there is no consistency in terms of the association between financial hardship and depression across different age groups. Butterworth et al. reported that the effect of financial hardship on depressive symptoms increased with age among Australian adults [ 30 ]. However, Butterworth et al. and Mirowsky and Ross reported different results [ 15 , 39 ]. Specifically, they found that the positive association between financial hardship and depressive symptoms decreased with age in the US. In contrast to the two studies listed above, Butterworth et al. did not find any statistically significant differences regarding this association among different age cohorts in Australia [ 15 ].

Association between subjective financial strain and depression

Eleven studies examined the association between subjective financial indicators (i.e., subjective financial strain, financial dissatisfaction or financial stress) and depression, providing empirical evidence based on high-income countries (Europe, the US, the UK, Japan and Korea) and on China. All of them (including four cross-sectional and seven longitudinal studies) reported a positive relationship between subjective financial strain and depression, holding after adjustments (see S1 and S3 Tables for details of the covariates used)). The intertemporal association between subjective financial strain and depression was reported in two studies [ 44 , 59 ]. For example, Richardson et al. found that increased subjective stress at baseline was associated with greater depression over time [ 59 ]. Similarly, Chi and Chou also found that higher levels of subjective financial strain measured at baseline were associated with more depressive symptoms after three years among Chinese older people [ 44 ]. The association between changes in subjective financial strain and depression was found in one longitudinal study [ 49 ]. Using data from the annual Belgian Household Panel Survey, Lorant et al. found that the worsening subjective financial strain was significantly associated with the increased risk of depressive symptoms and that of caseness of depression [ 49 ].

The positive and significant association between perceived financial strain in childhood and depression in adults was found in both a cross-sectional study and a longitudinal study [ 42 , 47 ]. Using cross-sectional data from 19 European countries in 2014, Boe et al. found that younger adults (25–40) who had experienced financial difficulties as children had higher depression scores in adulthood, while older adults (over 40) did not [ 42 ]. A similar association between adverse childhood financial situation and adults’ depression was also found in a longitudinal study [ 47 ]. Based on a national representative sample of 9,645 South Korean adults without depressive symptoms at baseline, Kim, et al. found that experiencing financial difficulties in childhood was associated with the increased chance of depression in adulthood [ 47 ]. Furthermore, the effect of experiencing financial difficulties in childhood on depression was weaker than that of current financial difficulties [ 47 ].

The gender difference of the association between perceived financial strain and depression was examined in two studies and no statistical difference between females and males was observed, though women tended to report worse depression [ 36 , 44 ].

Summary and discussion of the findings

This systematic review is the most comprehensive synthesis of observational studies quantifying the association between indicators of financial stress and depression in both high- and low- and middle-income countries to date. Findings regarding the relationship between financial stress and depression vary across different indicators of financial stress. Economic indicators such as material assets, unsecured debt, financial hardship, and subjective measures of financial stress are relatively strong and persistent predictors of depressive symptoms, while absolute income and wealth levels have an inconclusive association with depression. The only longitudinal evidence on relative income and relative wealth suggests a stronger relationship between relative income or relative wealth and depressive symptoms than that between absolute income or wealth and depression. Additionally, this review finds that the association between indicators such as income, material assets or wealth and depression is more pronounced in lower socioeconomic groups (i.e., low income or low wealth group). This review is unable to make a conclusion regarding the association between debt and depression across different socioeconomic subgroups. The only evidence is provided in one study showing that there is no difference in the association between debt and depression by assets level. Additionally, there is insufficient evidence to conclude a common pattern regarding the association between financial stressors and depression by gender or age groups, though differences of this relationship across age or gender groups are observed in some of the reviewed studies.

The income-depression association is inconclusive, although income is one of the most commonly used indicators of the individual or household’s economic situation. The reviewed studies consistently reported a positive association between low income and depressive symptoms in univariable analyses. However, this association was largely reduced or even became insignificant when other social and economic factors (such as educational level, employment status and so on) and health status were controlled for [ 31 , 33 ]. The findings are consistent with the results from the previous reviews and empirical research where different mental disorders were considered including depression [ 18 , 23 , 60 ]. It is likely that income has a close correlation with other dimensions of the socioeconomic condition such as educational levels and employment status that affect an individual’s mental health independently from income per se [ 23 ].

Furthermore, this review finds that compared to absolute income (or wealth), relative income (or wealth) in a reference group is a more important risk factor of depression. There is evidence showing a positive association between low-income ranks and current depression scores as well as follow-up depression scores, while no association is found between absolute low income and depression [ 58 ]. The findings here are in line with the previous review, which mainly focused on the association between income inequality and depression [ 61 ]. Patel et al.’s review concluded that a higher level of income inequality at the neighbourhood level was strongly associated with a higher risk of depression [ 61 ]. This review only identified one study investigating the association between relative income or relative wealth and depression. The insufficient evidence on this topic suggests the need for more research to investigate the mental health effects of relative income (or wealth).

Some of the reviewed studies have suggested a positive association between debt and depression despite the substantial heterogeneity in definitions and measurements of debt, study methods, study contexts, and targeted population. The association between debt and depressive symptoms is mainly driven by unsecured debt (e.g., credit card) or late mortgage payments. Secured debt (e.g., mortgage debt) per se is not associated with depressive symptoms. However, depression may still be more likely when individuals or households are no longer able to manage their debt or perform debt obligations. For example, the reviewed evidence shows that mortgage arrears have a significant effect on more severe depression, even when the effect of consumer debt and mortgage debt on depressive symptoms are considered within the same model [ 46 , 48 ]. The findings regarding the relationship between debt and depression are consistent with the findings from the previous reviews on the association between debt and health where depression was one of the outcomes [ 22 , 62 ].

An important consideration regarding the debt-depression relationship is that having personal or household debt does not always lead to depression, as debt is not always a sign of financial problems. Some personal and household loans are taken to finance housing purchases, business, and investments, which are granted based on the borrower’s financial situation and payback abilities. Additionally, except for stress, debt might also bring benefits to mental well-being by generating consumption, feelings of attainment or satisfaction or making investments [ 63 , 64 ]. As a result, the financial stress derived from debt could be partially offset by such positive mental well-being effects. The review suggests that future longitudinal research on the impact of debt on depression should consider mediators to understand the nature of the causal association between debt and mental health.

It should be noted that nearly a half of the reviewed studies are cross-sectional, limiting the ability to draw a conclusion on the directions and the causality of the associations between some indicators of financial stress and depression. A few of the longitudinal studies considered the reverse relationship and/or the unobserved bias using econometric methods. The majority of these longitudinal studies mainly focused on the relationship between debt or subjective measures of financial stress and depression. They provide supportive evidence that, debt and subjective financial stress might lead to subsequent depressive symptoms. Longitudinal evidence remains limited as to the understanding of both directions and causality of the relationships between other indicators of financial stress and depression. For example, only three longitudinal studies provided an exploration of the association between income and depression. The casual relationship between some indicators of financial stress (such as low income, material assets, wealth, financial hardship) and depression should therefore be interpreted with caution.

This review includes a number of studies focusing on the older-aged population. The signs of the relationship between financial stress and depression in different age subgroups do not show a significant difference. Despite this, it should be noted that there might be heterogeneity in the magnitude of the relationship between financial stress and depression across different age subgroups. However, it is difficult to identify if including the studies based on adults aged 50 and over would make the generalisability of the findings towards this population. Because a cross-study comparison is almost impossible as there is a substantial heterogeneity in different studies regarding country contexts, measurements of exposures and outcome variables, study methods and so on.

Based on the reviewed evidence, three possible mechanisms may be behind the relationship between financial stress and depression.

Social causation

Firstly, as highlighted in the introduction, the effects of financial stress on depression can be explained by social causation theory. The reviewed evidence supports the social causation pathway according to which individuals or households who have low income or low wealth are more likely to be exposed to economic uncertainty, unhealthy lifestyle, worse living environment, deprivation, malnutrition, decreased social capital and so on [ 20 , 47 , 65 ]. Those factors might lead to a higher risk of developing depressive symptoms. Individuals or households with limited financial resources are more vulnerable to stressful financial events, which might increase the risk of experiencing depression. This mechanism is applicable to the studies where financial stress is measured by economic indicators related to poverty, such as income poverty, deprivation, and financial hardship.

Psychological stress

The reviewed studies also show that subjective measures of financial stress have adverse effects on depression. Indeed, some studies state that subjective financial stress is more important than objective measures such as the amount of debt [ 13 , 41 , 66 ]. Objective indicators of financial stress might have an indirect effect on depression, which is mediated by the individual’s perception of those objective indicators as resulting in financial stress. Experiencing a similar objective financial situation, people may report different perceptions of the objective financial situation due to the heterogeneity of personal experiences, abilities to manage financial resources, aspirations, and perceived sufficiency of financial resources [ 67 ]. For example, individuals with limited financial resources are more likely to be concerned about the uncertainty of the future financial situation. The expectation of financial stress, not just their occurrence, may also cause depression. Furthermore, people living in poverty face substantial uncertainty and income volatility. The long-run exposure to stress from coping with this volatility may also threaten mental health [ 68 ]. Therefore, it is reasonable to believe that both the respondent’s perception of financial stress and objective measures of financial stress lie at the heart of the relationship between financial stress and depression.

Social selection

Other studies suggest that depression might negatively impact the finances of individuals [ 20 , 69 ]. Social selection theory states that individuals who have mental disorders are more likely to drift into or maintain a worse financial situation [ 20 ]. Evidence shows that mental problems might increase expenditure on healthcare, reduce productivity, and lead to unemployment, as well as be associated with social stigma, all of which are related to lower levels of income [ 18 , 65 , 70 ]. However, some scholars argue that the relative importance of social causation and social selection varies by diagnosis [ 71 ]. Social causation theory is more important to the relationship between financial stress and depression or substance use; while social selection theory is more important in relation to severe mental disorders such as schizophrenia [ 70 , 72 ].

Limitations of the review

This systematic review is the first to comprehensively pool observational studies on the association between individual or household finances and depression or depressive symptoms. However, this review is subject to several limitations. First, since there is substantial heterogeneity in the measurements and definitions of exposure (financial stress) and the outcome variable (depression), targeted populations, and methodologies between studies, a meta-analysis combining the data from the reviewed studies is neither appropriate nor practical. As such, only a narrative approach is used in this review without quantitatively synthesising the data from the studies, which are difficult to compare. Second, the majority of studies reported evidence on high-income countries like the US, the UK, European countries. Therefore, the conclusions of this review are more immediately generalisable to these contexts as opposed to low-and middle-income country contexts. Third, this review undertakes the search on six databases for any related peer-reviewed journal articles without searching for other resources to find grey or unpublished literature and conference abstracts. Excluding the unpublished studies might limit the findings of this review since studies with significant results are more likely to get published [ 73 ]. The published studies may lead this review to overestimate the associations between any financial exposures and depressive symptoms. Fourth, the included exposures in this review are the most direct indicators (i.e., proximal indicators) of financial stress. The findings in this review might not be generalisable to the relationship between a distal factor (e.g., job loss) and depression. Evidence has suggested that a significant life event or experience, for example, job loss, is associated with financial stress and thereby can predict subsequent major depression [ 74 ]. Future review on financial stress and mental health outcomes might benefit from further including the effect of distal factors (such as job loss, changes in working hours, changes in marital status, and so on) on mental health.

Implications

This review has a number of implications for public policy around financial circumstances and depression. Firstly, it highlights the role that measures aimed at alleviating financial poverty and inequality could have in improving public mental health. Secondly, it suggests attention needs to be focused on unsecured debt as a public health measure. For example, providing financial counselling services and financial education to those who have debt stress and depression may help them to effectively deal with individual debt problems and associated depression. Meanwhile, the regulation of unsecured debt markets is crucial to the sustainable development of unsecured lending markets, and thereby supports both financial health and mental health. Thirdly, this review highlights the importance of targeted interventions to break the cycle of financial stress and depression. For example, instead of a one-for-all intervention focusing on the general population, interventions targeted at lower socioeconomic groups might be more effective since the association between financial stress and depression is more pronounced in these groups.

In terms of practical support, the interdisciplinary collaboration of psychologists and financial professionals in the development of interventions aiming to break the vicious cycle of depression and financial stress could be useful. For example, interventions such as poverty alleviation programs, the provision of financial advice or financial education might have a beneficial influence on mental health. The collaboration of policymakers in both mental health areas and financial areas might create win-win situations, having mutual benefits for both areas and saving costs to society in the long run.

Finally, this review highlights the need for further research in certain areas. First, this review suggests that more longitudinal research or randomised control trials (where feasible) are needed to further clarify the directions of the causal relationships and possible mechanisms between different financial stressors and depression or other mental disorders. A better understanding of this can help to design more effective interventions either aimed at alleviating financial stress or at improving mental health. For example, anti-poverty programs such as direct cash transfers might be more helpful for families in poverty or deprivation, where social causation plays the main role in the effect of financial stress on depression. However, if financial stress is due to social comparison, rather than absolute poverty, challenging social attitudes may be more beneficial. This review also calls for more future research to investigate the heterogeneity of the relationship and the difference in the direction of the relationship between financial stress and depression or other mental disorders across different populations. This would provide more precise and solid evidence for developing targeted interventions as noted above.

Second, this review finds that the majority of the existing studies on the financial stress-depression relationship are based on high-income countries. However, low-and middle-income countries have higher levels of poverty and economic inequality, as well as a high economic burden caused by mental disorders and low levels of investment in mental health [ 69 , 75 ]. Therefore, more future research that is based on low-and middle-income country contexts would be important.

In conclusion, this systematic review of the link between financial stress and depression in adults found that financial stress is positively associated with depression, in particular among low socioeconomic groups. The findings suggest directions for policymakers and the need for greater collaboration between psychology and financial professionals, which will be beneficial to developing targeted interventions either to mitigate depression or alleviate financial stress. Further longitudinal research would be useful to investigate the causality and mechanisms of the relationship between different dimensions of financial stress and depression.

Supporting information

S1 checklist, s1 appendix, s2 appendix, s3 appendix, acknowledgments.

We would like to thank two anonymous reviewers for their comments that helped to improve and clarify the manuscript.

Funding Statement

The author(s) received no specific funding for this work.

Data Availability