research paper about stress

Recent developments in stress and anxiety research

• Published: 01 September 2021
• Volume 128 , pages 1265–1267, ( 2021 )

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• Urs M. Nater 1 , 2  

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Stress and anxiety are virtually omnipresent in today´s society, pervading almost all aspects of our daily lives. While each and every one of us experiences “stress” and/or “anxiety” at least to some extent at times, the phenomena themselves are far from being completely understood. In stress research, scientists are particularly grappling with the conceptual issue of how to define stress, also with regard to delimiting stress from anxiety or negative affectivity in general. Interestingly, there is no unified theory of stress, despite many attempts at defining stress and its characteristics. Consequently, the available literature relies on a variety of different theoretical approaches, though the theories of Lazarus and Folkman ( 1984 ) or McEwen ( 1998 ) are relatively pervasive in the literature. One key issue in conceptualizing stress is that research has not always differentiated between the perception of a stimulus or a situation as a stressor and the subsequent biobehavioral response (often called the “stress response”). This is important, since, for example, psychological factors such as uncontrollability and social evaluation, i.e. factors that may influence how an individual perceives a potentially stressful stimulus or situation, have been identified as characteristics that elicit particularly powerful physiological stressful responses (Dickerson and Kemeny 2004 ). At the core of the physiological stress response is a complex physiological system, which is located in both the central nervous system (CNS) and the body´s periphery. The complexity of this system necessitates a multi-dimensional assessment approach involving variables that adequately reflect all relevant components. It is also important to consider that the experience of stress and its psychobiological correlates do not occur in a vacuum, but are being shaped by numerous contextual factors (e.g. societal and cultural context, work and leisure time, family and dyadic systems, environmental variables, physical fitness, nutritional status, etc.) and dispositional factors (e.g. genetics, personality, resilience, regulatory capacities, self-efficacy, etc.). Thus, a theoretical framework needs to incorporate these factors. In sum, as stress is considered a multi-faceted and inherently multi-dimensional construct, its conceptualization and operationalization needs to reflect this (Nater 2018 ).

The goal of the World Association for Stress Related and Anxiety Disorders (WASAD) is to promote and make available basic and clinical research on stress-related and anxiety disorders. Coinciding with WASAD’s 3rd International Congress held in September 2021 in Vienna, Austria, this journal publishes a Special Issue encompassing state-of-the art research in the field of stress and anxiety. This special issue collects answers to a number of important questions that need to be addressed in current and future research. Among the most relevant issues are (1) the multi-dimensional assessment that arises as a consequence of a multi-faceted consideration of stress and anxiety, with a particular focus on doing so under ecologically valid conditions. Skoluda et al. 2021 (in this issue) argue that hair as an important source of the stress hormone cortisol should not only be taken as a complementary stress biomarker by research staff, but that lay persons could be also trained to collect hair at the study participants’ homes, thus increasing the ecological validity of studies incorporating this important measure; (2) the incongruence between psychological and biological facets of stress and anxiety that has been observed both in laboratory and field research (Campbell and Ehlert 2012 ). Interestingly, there are behavioral constructs that do show relatively high congruence. As shown in the paper of Vatheuer et al. ( 2021 ), gaze behavior while exposed to an acute social stressor correlates with salivary cortisol, thus indicating common underlying mechanisms; (3) the complex dynamics of stress-related measures that may extend over shorter (seconds to minutes), medium (hours and diurnal/circadian fluctuations), and longer (months, seasonal) time periods. In particular, momentary assessment studies are highly qualified to examine short to medium term fluctuations and interactions. In their study employing such a design, Stoffel and colleagues (Stoffel et al. 2021 ) show ecologically valid evidence for direct attenuating effects of social interactions on psychobiological stress. Using an experimental approach, on the other hand, Denk et al. ( 2021 ) examined the phenomenon of physiological synchrony between study participants; they found both cortisol and alpha-amylase physiological synchrony in participants who were in the same group while being exposed to a stressor. Importantly, these processes also unfold over time in relation to other biological systems; al’Absi and colleagues showed in their study (al’Absi et al. 2021 ) the critical role of the endogenous opioid system and its relation to stress-related analgesia; (4) the influence of contextual and dispositional factors on the biological stress response in various target samples (e.g., humans, animals, minorities, children, employees, etc.) both under controlled laboratory conditions and in everyday life environments. In this issue, Sattler and colleagues show evidence that contextual information may only matter to a certain extent, as in their study (Sattler et al. 2021 ), the biological response to a gay-specific social stressor was equally pronounced as the one to a general social stressor in gay men. Genetic information is probably the most widely researched dispositional factor; Kuhn et al. show in their paper (Kuhn et al. 2021 ) that the low expression variant of the serotonin transporter gene serves as a risk factor for increased stress reactivity, thus clearly indicating the important role of dispositional factors in stress processing. An interesting factor combining both aspects of dispositional and contextual information is maternal care; Bentele et al. ( 2021 ) in their study are able to show that there was an effect of maternal care on the amylase stress response, while no such effect was observed for cortisol. In a similar vein, Keijser et al. ( 2021 ) showed in their gene-environment interaction study that the effects of FKBP5, a gene very closely related to HPA axis regulation, and early life stress on depressive symptoms among young adults was moderated by a positive parenting style; and (5) the role of stress and anxiety as transdiagnostic factors in mental disorders, be it as an etiological factor, a variable contributing to symptom maintenance, or as a consequence of the condition itself. Stress, e.g., as a common denominator for a broad variety of psychiatric diagnoses has been extensively discussed, and stress as an etiological factor holds specific significance in the context of transdiagnostic approaches to the conceptualization and treatment of mental disorders (Wilamowska et al. 2010 ). The HPA axis, specifically, is widely known to be dysregulated in various conditions. Fischer et al. ( 2021 ) discuss in their comprehensive review the role of this important stress system in the context of patients with post-traumatic disorder. Specifically focusing on the cortisol awakening response, Rausch and colleagues provide evidence for HPA axis dysregulation in patients diagnosed with borderline personality disorder (Rausch et al. 2021 ). As part of a longitudinal project on ADHD, Szep et al. ( 2021 ) investigated the possible impact of child and maternal ADHD symptoms on mothers’ perceived chronic stress and hair cortisol concentration; although there was no direct association, the findings underline the importance of taking stress-related assessments into consideration in ADHD studies. As the HPA axis is closely interacting with the immune system, Rhein et al. ( 2021 ) examined in their study the predicting role of the cytokine IL-6 on psychotherapy outcome in patients with PTSD, indicating that high reactivity of IL-6 to a stressor at the beginning of the therapy was associated with a negative therapy outcome. The review of Kyunghee Kim et al. ( 2021 ) also demonstrated the critical role of immune pathways in the molecular changes due to antidepressant treatment. As for the therapy, the important role of cognitive-behavioral therapy with its key elements to address both stress and anxiety reduction have been shown in two studies in this special issue, evidencing its successful application in obsessive–compulsive disorder (Ivarsson et al. 2021 ; Hollmann et al. 2021 ). Thus, both stress and anxiety are crucial transdiagnostic factors in various mental disorders, and future research needs elaborate further on their role in etiology, maintenance, and treatment.

In conclusion, a number of important questions are being asked in stress and anxiety research, as has become evident above. The Special Issue on “Recent developments in stress and anxiety research” attempts to answer at least some of the raised questions, and I want to invite you to inspect the individual papers briefly introduced above in more detail.

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Urs M. Nater

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Nater, U.M. Recent developments in stress and anxiety research. J Neural Transm 128 , 1265–1267 (2021). https://doi.org/10.1007/s00702-021-02410-3

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Accepted : 13 August 2021

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DOI : https://doi.org/10.1007/s00702-021-02410-3

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Stress and Health: A Review of Psychobiological Processes

Affiliations.

• 1 School of Psychology, University of Leeds, Leeds LS2 9JT, United Kingdom; email: [email protected].
• 2 Department of Psychological Science, School of Social Ecology, University of California, Irvine, California 92697, USA; email: [email protected].
• 3 Division of Primary Care, School of Medicine, University of Nottingham, Nottingham NG7 2UH, United Kingdom; email: [email protected].
• PMID: 32886587
• DOI: 10.1146/annurev-psych-062520-122331

The cumulative science linking stress to negative health outcomes is vast. Stress can affect health directly, through autonomic and neuroendocrine responses, but also indirectly, through changes in health behaviors. In this review, we present a brief overview of ( a ) why we should be interested in stress in the context of health; ( b ) the stress response and allostatic load; ( c ) some of the key biological mechanisms through which stress impacts health, such as by influencing hypothalamic-pituitary-adrenal axis regulation and cortisol dynamics, the autonomic nervous system, and gene expression; and ( d ) evidence of the clinical relevance of stress, exemplified through the risk of infectious diseases. The studies reviewed in this article confirm that stress has an impact on multiple biological systems. Future work ought to consider further the importance of early-life adversity and continue to explore how different biological systems interact in the context of stress and health processes.

Keywords: HPA axis; allostatic load; autonomic nervous system; cortisol; genomics.

Publication types

• Autonomic Nervous System / metabolism
• Hydrocortisone / metabolism
• Hypothalamo-Hypophyseal System / metabolism
• Pituitary-Adrenal System / metabolism
• Stress, Psychological / metabolism*
• Hydrocortisone

• 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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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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Correspondence to László Róbert Kolozsvári .

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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: 27 November 2021

Psychological and biological resilience modulates the effects of stress on epigenetic aging

• Zachary M. Harvanek   ORCID: orcid.org/0000-0003-3702-1051 1 ,
• Nia Fogelman 2 ,
• Ke Xu   ORCID: orcid.org/0000-0002-6472-7052 1 , 3 &
• Rajita Sinha   ORCID: orcid.org/0000-0003-3012-4349 1 , 2 , 4 , 5  

Translational Psychiatry volume  11 , Article number:  601 ( 2021 ) Cite this article

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Our society is experiencing more stress than ever before, leading to both negative psychiatric and physical outcomes. Chronic stress is linked to negative long-term health consequences, raising the possibility that stress is related to accelerated aging. In this study, we examine whether resilience factors affect stress-associated biological age acceleration. Recently developed “epigenetic clocks” such as GrimAge have shown utility in predicting biological age and mortality. Here, we assessed the impact of cumulative stress, stress physiology, and resilience on accelerated aging in a community sample ( N  = 444). Cumulative stress was associated with accelerated GrimAge ( P  = 0.0388) and stress-related physiologic measures of adrenal sensitivity (Cortisol/ACTH ratio) and insulin resistance (HOMA). After controlling for demographic and behavioral factors, HOMA correlated with accelerated GrimAge ( P  = 0.0186). Remarkably, psychological resilience factors of emotion regulation and self-control moderated these relationships. Emotion regulation moderated the association between stress and aging ( P  = 8.82e−4) such that with worse emotion regulation, there was greater stress-related age acceleration, while stronger emotion regulation prevented any significant effect of stress on GrimAge. Self-control moderated the relationship between stress and insulin resistance ( P  = 0.00732), with high self-control blunting this relationship. In the final model, in those with poor emotion regulation, cumulative stress continued to predict additional GrimAge Acceleration even while accounting for demographic, physiologic, and behavioral covariates. These results demonstrate that cumulative stress is associated with epigenetic aging in a healthy population, and these associations are modified by biobehavioral resilience factors.

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Introduction

Cumulative stress can have adverse psychiatric and physical effects, increasing risk for cardiometabolic diseases, mood disorders, post-traumatic stress disorder and addiction [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ]. There are several potential psychological and biological mechanisms through which these effects may occur. For example, stress may reduce psychological resilience measures such as emotion regulation and self-control that are known to protect against psychiatric and physical health outcomes [ 1 , 12 , 13 , 14 ]. Notably, emotional stress exposure decreases cognitive and emotion regulation abilities [ 15 , 16 , 17 , 18 ], and this effect may be modulated by cortisol [ 15 ]. Furthermore, stress decreases self-control abilities [ 19 , 20 , 21 ] and impacts the likelihood of individuals engaging in healthy behaviors such as exercise or maintaining a healthy diet, and is associated with unhealthy behaviors such as smoking, alcohol, and drug use [ 22 , 23 , 24 , 25 ]. Recent evidence also suggests that stress effects on metabolic health may be affected by BMI-related changes in insulin resistance and other gut hormones [ 26 , 27 ]. Indeed, stress’s effects on physiology resulting in alterations in neuro-hormonal signaling pathways as well as increased inflammation are well documented [ 26 , 28 , 29 , 30 ]. Both stress and these physiologic changes may increase the risk of multiple physical and psychiatric illnesses, which in turn increase morbidity and mortality risk. This has often been described as an increased allostatic load, and notably many of these processes, such as metabolic and cardiovascular dysfunction, have been associated with human aging [ 31 ]. For example, insulin signaling might be linked to aging and aging-related diseases in humans [ 32 ], with recent data on metformin (a treatment for insulin resistance) suggesting it may be useful as an anti-aging drug [ 33 ].

There is growing evidence that cumulative stress may adversely impact health via accelerating the cellular aging process. For example, stress shortens telomere length and alters telomerase activity, and this interaction is modified by behavioral and psychological resilience factors [ 34 , 35 , 36 , 37 ]. However, recent studies have demonstrated mixed results on whether characteristics that contribute to resilience improve or worsen the impact of stress on health [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]. These data suggest that resiliency factors may modulate the relationship between chronic stress and aging, but to our knowledge this has not been tested in a healthy community sample. While there are many aspects of resilience, including cultural/societal, environmental, and personal which can decrease the negative consequences of stressors on individuals, herein we will focus on personal-level, psychological skills, including self-control and emotion regulation.

Recently developed DNA methylation-based epigenetic “clocks” appear to provide a more accurate measure of biological age than telomere length [ 48 , 49 , 50 , 51 ]. These clocks are built from a set of DNA methylation markers that correlate with chronologic age and serve as molecular estimators of biological age in cells, tissues, and individuals [ 52 ]. Epigenetic clocks have a significantly higher predictive value than previously used measures such as telomere length for frailty, [ 53 ] mortality risk [ 54 , 55 ], hazard ratios [ 56 ], and chronologic age [ 57 ]. The development of these biological aging markers promises to not only aid in identifying individuals at higher risk for aging-related illnesses, but potentially also developing interventions to prevent accelerated aging.

Previous studies (reviewed by Palma-Gudiel et al [ 58 ]) have utilized epigenetic clocks to demonstrate associations between trauma, early life adversity, or low socioeconomic status and accelerated epigenetic aging. Studies have often been focused upon selected populations, such military veterans [ 45 ], individuals with significant trauma histories [ 59 ], or specific cohorts at higher risk [ 60 , 61 , 62 ]. Notably, these studies did not exclude, and often explicitly included, individuals with significant mental and physical illnesses, including PTSD, MDD, and other disabilities [ 59 , 63 ]. These studies also primarily utilized epigenetic clocks trained upon chronologic age. However, a recently developed epigenetic clock, GrimAge, was trained using biomarkers of mortality and indicators of health, and has superior performance in predicting health outcomes when compared with other epigenetic clocks [ 51 , 64 ].

We utilized GrimAge Acceleration (“GAA”, defined as the residual of the regression of GrimAge to chronologic age, with a positive number indicating biological age greater than chronologic age) to conduct a cross-sectional study to answer three questions. First, is cumulative stress related to epigenetic markers of biological aging in a healthy young-to-middle-aged community population? Second, if stress is associated with epigenetic aging, does stress-related physiology contribute to stress-associated biological aging? And finally, how do psychological factors that contribute to resilience modulate these relationships? Based on previous research, we hypothesized that cumulative stress will be positively associated with GrimAge Acceleration (GAA), that stress effects on GrimAge will be related to changes in the hypothalamic-pituitary-adrenal axis (HPA) and insulin sensitivity, and that strong emotion regulation as measured by the Difficulties in Emotion Regulation Scale (DERS, [ 65 ]) and high self-control as measured by the Self Control Scale-Brief (SCS-B, [ 66 ]) will moderate the relationships between stress, physiology, and accelerated aging (See Fig. 1 for a model summarizing our hypotheses).

We hypothesize that stress is positively associated with accelerated biological aging, which we measure via GrimAge Acceleration (GAA), and that this relationship will be mediated by stress-related physiologic changes such as insulin and HPA signaling. We also hypothesize that strong psychological resilience factors will be protective against the negative consequences of stress on aging. Note that these relationships are predictive, not causative, as this study is cross-sectional and thus directionality of relationships cannot be conclusively examined.

Materials and methods

Cohort recruitment.

The participant cohort included 444 community adults between the ages of 18–50 in the greater New Haven, CT area who volunteered to participate in a study examining the role of stress and self-control at the Yale Stress Center as previously described [ 67 ]. Briefly, participants were recruited via advertisements online, in local newspapers, and at a community center between 2008 and 2012. Participants were excluded if they had a substance use disorder (not including nicotine) as assessed via the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (SCID-I for DSM-IVTR), were pregnant, had a chronic medical condition (e.g, hypertension, diabetes, hypothyroidism), or were unable to read English at or above the 6th grade level. Participants were also excluded if they had a concussion with loss of consciousness greater than 30 minutes, another head injury such as documented traumatic brain injury or another injury with documented lasting deficits, or were using any prescribed medications for any psychiatric or medical disorders. Breathalyzer and urine toxicology screens were conducted at each appointment to ensure the participants were drug-free. Of a total of 1000 potential participants who underwent initial screening for eligibility, epigenetic data combined with physiologic and behavioral data were available on 444, who comprised the current sample. All participants provided written and verbal informed consent to participate, and the research protocol was reviewed and approved by the Yale IRB.

Initial assessment and measurement of physiologic parameters

All eligible subjects met with a research assistant for two intake sessions to complete a physical health review with the Cornell Medical Index (CMI, [ 68 ]), structured clinical interview for diagnoses (SCID) of DSM-IVTR psychiatric illnesses, cumulative stress interview, self-report assessments and a separate morning biochemical evaluation after fasting overnight. The structured clinical interview was performed by masters’ or doctoral level clinical research staff. Fasting insulin and glucose were obtained and Cortisol was assessed at four time-points, spaced 15 min apart beginning at 7:30 AM after overnight fasting and collected while participants were in a quiet and comfortable laboratory setting at the Yale Stress Center. Participants were financially compensated for participating in the study.

Psychological measures

Cumulative stress was assessed using the Cumulative Adversity Inventory (CAI, [ 69 ]), a 140-item multifaceted interview-based assessment of life events and subjective stress through which trained interviewers asked participants about specific stressful events that occurred during their lifetime, which comprised the subscales of major life events, life trauma events and recent life events. For purposes of scoring, a “yes” to the specific stressful event occurring led to a “1” and a sum of all the “yes” endorsements comprised the subscale score for these events subscale. The final subscale of chronic stress was the participant’s own sense of feeling overwhelmed and unable to manage the events for the other subscales listed. This was rated on a “not true”, “somewhat true”, or “very true” scale, with assigned scores of 0, 1, and 2, respectively. The final score is a sum of these values for the chronic stress subscale. The CAI-total score was a sum of each of the subscale score with a higher score indicating a higher overall level of lifetime cumulative stress. The CAI has been demonstrated to have excellent overall reliability as reported in previous research [ 12 , 26 , 70 , 71 , 72 ]. In our population for this study, the alpha reliability is 0.86. It has been previously shown to predict cumulative stress related brain volume reductions and sensitized stress functional responses as well as prediction of physical, metabolic and behavioral responses [ 26 , 70 , 71 , 72 ].

Emotion regulation was assessed using the Difficulties with Emotion Regulation Scale (DERS, [ 65 ]), which is a 41-item trait-level measure that assesses across domains of lack of emotional awareness, goals, clarity, strategies, acceptance, and impulse control in managing emotions. Higher scores on the DERS correspond to lower ability to regulate emotion. Alpha reliability has been reported to be >0.90 for the total score, and ≥0.80 for the sub-scores [ 65 ]. In this population, the alpha reliability is 0.92.

Self-control was assessed using the Self-Control Survey-Brief (SCS-B, [ 66 ]), which is a 13-item scale that assesses overall self-control. A higher score on the SCS-B suggests a stronger level of self-control. There are no sub-scores provided by the SCS-B, and the overall SCS-B has been reported to have an alpha reliability >0.80 [ 66 ]. The alpha reliability in this study is 0.85.

The Cornell Medical Index (CMI) was used to assess for participants’ current health. It is a 195-question interview that captures both physical and psychological health symptoms, and has been validated as an indicator for current general health in many studies [ 68 , 73 , 74 ]. A higher score on the CMI suggests more symptoms and worse overall health. The alpha reliability of the total CMI is 0.94. The psychological subscore has an alpha reliability of 0.92, and the biological subscore has a reliability of 0.90.

Cronbach alpha reliabilities for each of the scales described above were obtained using the alpha function in the R psych package [ 75 ].

DNA methylation and epigenetic clock analysis

DNA for epigenetic analysis was collected from whole blood samples as previously described [ 67 ]. Briefly, all samples were profiled using Illumina Infinium HumanMethylation450 Beadchips, which covers 96% of CpG islands and 99% of RefSeq genes. Quality control on these data are as previously published [ 67 ]. They are described in brief below:

Probe QC : To ensure high-quality data, we set a more stringent threshold of P  < 10 –12 . Intensity values showing P  > 10 −12 were set as zero. Additionally, we removed 11,648 probes on sex chromosomes and 36,535 probes within 10 base pairs of single-nucleotide polymorphisms. Finally, a total of 47,791 probes were removed and the remaining 437,722 probes were used for further analysis.

Sample QC : Using a detection P value < 10 –12 , one sample showing a call rate < 98% was excluded from analysis. Five samples showing sex discrepancy between the methylation predicted sex and self-reported sex were also excluded from analysis.

Data processing and normalization : Data processing and normalization were performed using the recently published protocol (Lehne et al., 2015). We first perform background correction and within-array normalization to the original green/red channel intensity data using the preprocessIllumina function in the minfi R package. The processed data were transformed to M/U methylation categories. Next, we separately performed between-array-normalization with the quantile method using the normalizeBetweenArrays function in the limma R package (version 3.26.2) after dividing the data matrix into 6 independent parts: Type I M Green, Type I M Red, Type I U Red, Type I U Green, Type II Red, Type II Green. The normalized data were merged and the beta value at each CpG site was determined.

After obtaining beta values, epigenetic clock analysis was performed as described in Lu et al. using the New Methylation Age Calculator at https://dnamage.genetics.ucla.edu/new [ 51 ]. Data were normalized as per their protocol, and the advanced analysis option was used. We focus on GrimAge acceleration (GAA), which is defined as the residuals of a linear correlation of GrimAge to chronologic age. No effects of array batch on GAA were observed (Supplementary Fig. 1 ).

The analyses herein were performed without accounting for individual variations in cell types. The Houseman method was used to determine cell type proportion [ 76 ], and the inclusion of cell fractions as covariates in a linear model does not impact the primary conclusions of this paper (see Supplementary material).

Statistical analysis

Data organization and analysis were conducted using R 3.6.3 [ 77 ] and RStudio. Linear regressions were first implemented to examine univariate associations between independent and dependent variables. Multivariable linear regressions adjust for demographic (sex, race, years of education, marital status, income) and behavioral (smoking, alcohol use, and BMI) covariates unless otherwise stated. These covariates were selected due to prior work demonstrating a relationship to epigenetic aging. Chronologic age is incorporated into the model as part of the calculation of GAA (the residual of GrimAge regressed upon chronologic age). There was no significant correlation between chronologic age and GAA. Analyses of the relationship between CAI, GAA, psychological and physiologic variables were performed in both the univariate unadjusted model and the multivariate adjusted model accounting for demographic and behavioral measures, but except when the conclusions differ, statistical values in the text represent the multivariate models for simplicity. CAI, DERS, and SCS were mean-centered to address issues of collinearity (particularly regarding individual regression coefficients) when assessing for moderation.

All tests were two-tailed with alpha set at 0.05. Statistical significance in both standard linear regressions and moderation analyses were assessed from t values. R 2 reported on plots represent the simple relationship between the stated variables, while adjusted R 2 values in the text represent the model. Partial η 2 values represent the effect size for the specific variable, with a value >= 0.01 typically indicating a small effect, >= 0.06 a medium effect, and >= 0.14 a large effect [ 78 ]. Wilcoxon signed-rank test was used to compare data between sexes. Mediation analysis was performed to determine if stress impacts GAA via behavioral and physiologic factors. Simple mediation effects were calculated via R using 10,000 simulations without bootstrapping using the mediation package [ 79 ]. Mediation was considered significant if the proportion mediated was greater than 0 with an alpha of 0.05. Serial mediation was calculated via R using the Lavaan package [ 71 ], with an indirect effect defined as the product of the coefficients of the effect of stress on BMI, of BMI on HOMA, and of HOMA on GAA. Assessment of the individual variables’ attributable GrimAge acceleration as well as confidence intervals were calculated using the Emmeans package using unadjusted pairwise comparisons.

Demographics and clinical characteristics

As shown in Table 1 , study participants were healthy and without evidence of medical or psychiatric diseases. The majority were non-smokers (79.6%), social drinkers with low risky alcohol intake screening scores (72.7% of participants have Alcohol Use Disorders Identification Test (AUDIT) < 8, and 91.7% < 15), and were not obese (74.5% of participants have a BMI < 30, 89.2% < 35). Both physical and psychological symptoms assessed on the Cornell Medical Index (CMI, [ 68 ]) were low, with 86% of participants scoring below the typical screening threshold of 30.

Cumulative stress predicts accelerated biological aging as measured by GrimAge

As expected, there was a high association between individuals’ chronologic age and GrimAge (Age: t  = 51.4, P  < 2e−16, adjusted R 2  = 0.856, Fig. 2A ). This relationship is not altered by inclusion of the covariates of smoking, alcohol use, BMI, race, sex, income, and years of education (Age: t  = 49.1, P  < 2e−16, partial η 2  = 0.848; model (GrimAge ~ Age + covariates) adjusted R 2  = 0.912), and this relationship remained significant accounting for cellular fractions (Supplementary Table 1 ). Also, using a univariate linear regression, greater cumulative stress as measured by the total Cumulative Adversity Index (CAI) score significantly predicted higher GAA (CAI: t  = 4.82  P  = 2.00e−6, η 2  = 0.050, adjusted R 2  = 0.0478, Fig. 2B ). While there were significant differences in GAA based on sex ( P  = 1.33e−7), both males (CAI: P  = 3.35e−4, adjusted R 2  = 0.0586) and females (CAI: P  = 3.12e−5, adjusted R 2  = 0.0652) demonstrated similar correlations between stress and GAA. Further analysis showed these results are consistent across CAI subscales, as well as with the Childhood Trauma Questionnaire and several of its subscales (Supplementary Table 2 ).

A Chronologic age significantly predicts GrimAge ( P  < 2e−16). B Cumulative stress total as measured by the CAI (CAI-Total) significantly predicts GAA before ( P  = 2.00e−6) and after accounting for covariates. C Higher insulin resistance (as measured by HOMA) shows a significant positive correlation with GAA before ( P  = 1.11e−8) and after accounting for covariates. D The Cortisol/ACTH ratio is negatively correlated with GAA before accounting for covariates ( P  = 2.39e−6), but not afterward. P and R 2 values in the figure represent simple univariate models (Y ~ X). In the main text, models are adjusted for covariates as stated.

After accounting for the covariates of smoking, alcohol use, BMI, race, sex, income, and years of education, the relationship between GAA and CAI remains significant (CAI: t  = 2.073, P  = 0.0388, partial η 2  = 0.010; model (GAA ~ CAI-total + covariates): adjusted R 2  = 0.3869); individual covariate effects shown in Supplementary Table 3 ). When considered as potential mediators of the relationship between stress and GAA, BMI (proportion mediated = 0.288, P  = 0.0042) and smoking (proportion mediated = 0.443, P  = 0.0030), but not alcohol use (proportion mediated = 0.001, P  = 0.931), show partial mediating effects (Supplementary Table 4 ).

Consistent with the underlying assumption that GAA is related to measures of health, GAA also predicted psychological and physical health symptoms as measured by the CMI (Supplementary Fig. 2A ; total CMI: t  = 3.449, P  = 6.18e−4, adjusted R 2  = 0.024).

Stress-related physiology is associated with GrimAge acceleration

Given the known relationship between cumulative stress and physiology, we assessed the relationship between the stress-related physiologic factors of insulin resistance and HPA-axis signaling and GAA. We found that higher HOMA (a measure of insulin resistance) significantly predicted GAA (Fig. 2C , HOMA: t  = 2.362, P  = 0.0186, partial η 2  = 0.013; model (GAA ~ HOMA + Covariates): adjusted R 2  = 0.389).

We then assessed whether cortisol/ACTH ratio changes impacted GAA. Indeed, low cortisol/ACTH ratio, a measure of adrenal sensitivity, was associated with GAA in a simple univariate model, (Fig. 2D , Cort/ACTH ratio: t  = −4.78, P  = 2.39e−6, η 2  = 0.049, adjusted R 2  = 0.0470), though this becomes non-significant when accounting for covariates (Cort/ACTH ratio: t  = −0.721, P  = 0.471, partial η 2  = 0.001; model (GAA ~ Cort/ACTH + Covariates): adjusted R 2  = 0.3816). We also find a significant association between stress and Cortisol/ACTH ratio (Supplementary Fig. 2B , CAI: t  = −2.146  P  = 0.0324; model (Cort/ACTH ratio ~ CAI + covariates): adjusted R 2  = 0.2197).

Emotion regulation moderates the relationship between stress and GrimAge acceleration directly

We then asked whether the relationship between cumulative stress and epigenetic aging was modulated by characteristics that contribute to an individual’s psychological resilience. We hypothesized that strong emotion regulation abilities would be protective against stress-related accelerated aging. We found that emotion regulation as assessed by the Difficulties in Emotion Regulation Scale (DERS, [ 65 ]) significantly moderated the relationship between GAA and CAI (Fig. 3A , CAI:DERS: F  = 11.22, P  = 8.82e−4, partial η 2  = 0.025; model (GAA ~ CAI X DERS + covariates): adjusted R 2  = 0.4004), such that poor emotion regulation significantly increased the effects of CAI on GAA. There was not a significant difference between males and females in emotion regulation ( P  = 0.0949).

A Individuals with stronger emotion regulation (as measured by lower DERS scores) suffer less GAA at high stress than individuals with poor emotion regulation before (GAA ~ CAI X DERS P  = 9.51e−5; GAA ~ CAI X DERS + Covariates: P  = 8.82e−4) and after accounting for covariates. For panel A, “good” represents the slope at the 25th percentile of DERS, “fair” at the 50th percentile, and “poor” the 75th percentile. B Better self-control (as measured by higher B-SCS scores) is protective against the effects of stress on GAA before accounting for covariates (GAA ~ CAI X SCS P  = 0.00226; GAA ~ CAI X SCS + Covariates: P  = 0.130), but not after including them in the model. C Stronger self-control moderates the relationship between stress and insulin resistance before (HOMA ~ CAI X SCS P  = 0.0115; HOMA ~ CAI X SCS + Covariates P  = 0.00732) and after accounting for covariates. For panels (B) and (C), “good” represents the slope at the 75th percentile of B-SCS, “fair” at the 50th percentile, and “poor” the 25th percentile.

Self-control moderates the association between stress and insulin resistance, which is associated with GrimAge acceleration

We next assessed whether psychological resilience in the form of self-control (as measured via the SCS-B, [ 66 ]) alters the association between cumulative stress and GAA. We found higher self-control is protective against the effects of stress on GAA before accounting for covariates, but the interaction became non-significant when covariates were accounted for (Fig. 3B , CAI:SCS: F  = 2.303, P  = 0.130, partial η 2  = 0.005; model (GAA ~ CAI X SCS + Covariates: adjusted R 2  = 0.3874).

Given the potential interplay between self-control, insulin resistance, and stress, we next asked whether self-control moderated the relationship between stress and HOMA. We observed that, even when covariates are accounted for, self-control moderates the positive relationship between stress and HOMA, with stronger self-control blunting their relationship (Fig. 3C , CAI:SCS: F = 7.263, P  = 0.00732, partial η 2  = 0.017; model (HOMA ~ CAI X SCS + Covariates: adjusted R 2  = 0.2871). Notably, self-control does not moderate the relationship between CAI and BMI (CAI:SCS: F  = 0.679, P  = 0.41). Self-control did not significantly differ between males and females ( P  = 0.0550).

Exploratory mediation analyses suggest stress influences GrimAge via BMI and HOMA

While our ability to draw causative inferences are limited by the cross-sectional nature of our data, we used mediation analyses to explore potential relationships between weight, insulin resistance, and GAA. We hypothesized that the effects of BMI on GAA may be mediated through insulin resistance. Indeed, mediation analysis suggested that a significant portion of the effect of BMI on GAA may be mediated through HOMA (Supplementary Fig. 3A , proportion mediated = 0.247, P  = 0.02). Given these findings, we next asked whether BMI and insulin resistance act sequentially to mediate the effects of stress on GAA. We identified a significant indirect effect, suggesting that stress may affect GAA through increased BMI and elevated insulin resistance (Supplementary Fig. 3B , indirect effect = 0.003; P  = 0.030), though there continues to be a significant direct effect of stress on GAA as well (direct effect = 0.034, P  = 0.009).

Cumulative stress and estimated change in GrimAge

Finally, we sought to identify the comparative contributions of our significant variables to GAA. To do this, we constructed a linear regression model using all demographic covariates (sex, race, marital status, education, income), behavioral covariates (smoking, alcohol, BMI), physiologic factors (HOMA, Cortisol/ACTH ratio), and psychological factors. In this model, we continue to see a significant interaction between stress and emotion regulation in relation to GAA (CAI:DERS t  = 3.424, P  = 0.000677, partial η 2  = 0.027; model (GAA ~ CAI-total X DERS + HOMA + Cort/ACTH ratio + SCS + Covariates): adjusted R 2  = 0.4056). Notably in this model, HOMA ( t  = 2.308, P  = 0.0215, partial η 2  = 0.012), BMI ( t  = 2.641, P  = 0.00857, partial η 2  = 0.016), and smoking ( t  = 10.47, P  < 2e−16, partial η 2  = 0.204) also demonstrate significant effects on GAA. The impact of the cortisol/ACTH ratio on GAA is not significant ( t  = −0.668, P  = 0.504, partial η 2  = 0.001), and its removal from the model does not impact any of the above conclusions.

Using this final linear model, we estimated the changes in GrimAge for each significant variable (Table 2 ) using estimated marginal means [ 80 ]. When comparing the effects of high stress (CAI-total: 75th percentile) versus low stress (CAI-total: 25th percentile) in those with poor emotion regulation (DERS: 75th percentile), stress was associated with half a year of aging independent of all other covariates and physiologic factors. However, when emotion regulation was strong (DERS: 25th percentile), stress did not independently predict GAA. Again comparing 75th versus 25th percentiles, BMI independently was related to an increase of 0.46 years of GrimAge, and HOMA for ¼ of a year. We also identified daily smoking (3.8 years), male sex (1.2 years), self-identifying as Black (1 year), and never having married (0.71 years) as covariates that significantly predicted accelerated GrimAge. When accounting for cellular fractions we see similar results regarding the relationships between stress, emotion regulation, and GAA. However, when accounting for cellular fractions, the associations between GAA and both HOMA and marital status become non-significant (Supplementary Table 5 ). Prior literature [ 51 ] suggests that GrimAge predicts the hazard ratio exponentially (HR = 1.1 GAA ). Thus, each additional year of GAA would be expected to increase the relative risk of death by approximately 10%.

In this study, we report novel findings that cumulative stress is associated with accelerated epigenetic aging in a healthy, young-to-middle-aged community sample, even after adjusting for sex, race, BMI, smoking, alcohol use, income, marital status, and education. Epigenetic aging was measured by GrimAge, a marker which has previously been associated with increased morbidity and mortality and correlates with physical and psychological health symptoms in our study. The relationship between stress and age acceleration is most prominent in those with poor emotion regulation and was related to behavioral factors such as smoking and BMI. Both stress and GAA were associated with changes in insulin resistance, which was moderated via self-control. These results suggest a relationship between stress, physiology, and accelerated aging that is moderated by emotion regulation and self-control. Overall, these findings point to multiple potentially modifiable biobehavioral targets of intervention that may reduce or prevent the deleterious effects of stress on aging and long-term health outcomes.

This study included a generally healthy, young-to-middle-aged community population, yet we still identified a significant relationship between cumulative stress and age acceleration. The population was taking no prescription medications for any medical conditions, nor were they suffering from current mental illnesses, including major depressive disorder or generalized anxiety disorder. The study includes individuals with obesity, as well as a small number of individuals with risky drinking levels as determined by the AUDIT scores. The frequency of these individuals in the sample is generally in line with those in a community population, and thus we included alcohol use and BMI as covariates to account for the impact of these variables on the results. Prior work has demonstrated that GrimAge better predicts mortality than other epigenetic clocks, and GrimAge predicts lifespan more accurately than self-reporting smoking history, demonstrating that GrimAge is a biologically meaningful and potentially clinically useful biomarker for health [ 51 , 64 ]. Our findings are consistent with recent work showing that those with significant trauma histories [ 59 , 81 ] or with diagnoses of mental illnesses, such as Bipolar disorder or MDD, may experience accelerated aging as measured by epigenetic clocks [ 57 , 81 , 82 , 83 , 84 ]. In particular, this study builds on previous findings by Zannas et al that demonstrated a relationship between trauma and epigenetic aging using the Horvath clock. However, to the best of our knowledge this is the first study to investigate the impact of cumulative stress on epigenetic aging in a healthy community sample without significant physical or mental illness. Also it is the first to our knowledge to identify factors that contribute to psychological resilience as potential modulators of such an effect. This opens the possibility that the distinction between the effects of stress on pathologic and non-pathologic samples may be along a continuum. It would be interesting to examine resilience characteristics in the population studied by Zannas et al to determine if there is a limit to the protective effects of psychological resilience. Thus, preventive interventions that decrease stress and improve resilience may be useful for maintaining long-term mental and physical health.

The relationship between stress and epigenetic aging appears to be modulated via specific psychological traits, including emotion regulation and self-control. Those with better emotion regulation and higher levels of self-control were observed to have less age acceleration even at similar levels of stress. Indeed, based on their GAA, our estimates indicate that the relationship between stress and GrimAge is as powerful as BMI, but only for those with poor emotion regulation. As these are skills that may be developed through specific psychological interventions [ 85 ], these results raise the possibility that building emotion regulation skills could result in improvements in epigenetic aging, morbidity, and mortality [ 86 ] for these populations. As this is a cross-sectional study, we are not able to address whether these relationships are causal. These novel cross-sectional findings provide support for potential future research that may assess whether such an intervention could positively impact epigenetic aging and other indices of long-term health outcomes. Other studies could also examine different aspects of resilience, such as cultural or environmental factors that contribute to resilience to determine if they also are protective against the effects of stress on epigenetic age acceleration. Future studies could also explore other physiologic mechanisms through which psychological resilience may influence epigenetic aging. Based on prior work, inflammation could be particularly important for this relationship. In particular, prior studies have found C-reactive protein [ 87 ] and IL-6 [ 88 ] to be related to emotion regulation and measures of health. The work by Gianaros et al suggests that neurologic activity of the dorsal anterior cingulate cortex may be involved as well.

The relationship between cumulative stress, epigenetic aging, and insulin resistance is of particular note given the prominence of insulin signaling in aging-related pathways [ 89 , 90 ], as well as current trials investigating metformin as a potential anti-aging drug [ 33 ]. In association with this body of work, our study suggests insulin resistance as at least one factor through which stress is associated with accelerated aging, even in a healthy population not suffering from diabetes. As this study is limited by its cross-sectional nature, any causal hypotheses regarding interactions between stress, BMI, insulin resistance, and aging will require longitudinal data to draw specific inferences beyond correlative relationships. Longitudinal studies would also enable prospective assessments of stress, which may be less subject to recall bias based on their current context. This study also identifies the cortisol/ACTH ratio as a potential point of connection between stress and epigenetic aging. However, this measure is somewhat limited in that it reflects an acute measure of the HPA axis, and this relationship becomes non-significant with the inclusion of our covariates. Future studies could utilize other, longer-term measures of HPA axis function such as hair cortisol to better characterize the relationship between stress, epigenetic aging, and the HPA axis.

Nonetheless, this study is the first to identify a clear relationship between cumulative stress and GrimAge acceleration in a healthy population, which suggests stress may play a role in accelerated aging even prior to the onset of chronic diseases. Notably, this relationship was strongly moderated by resilience factors, including self-control and emotion regulation. We also identified smoking, BMI, insulin signaling, and potentially HPA signaling as mediators of this response. However, even when accounting for all these factors as well as demographic covariates such as race, cumulative stress continues to demonstrate a significant impact on GAA, suggesting other mechanisms relating stress to aging not identified herein are also present.

Code availability

R scripts utilized for data analysis are available by contacting the authors directly.

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The authors would like to acknowledge the Yale Center of Genome Analysis for DNA methylation profiling. Funding for this study is from NIH Common Fund UL1-DE019586 (R.S.), PL1-DA24859 (R.S.), R01-AA013892 (R.S.), NIH R01DA047063 (K.X.), NIH T32MH019961 (Z.M.H.), NIH R25MH071584 (Z.M.H.). These data were presented at the SOBP virtual conference in April 2021 as a poster.

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Z.M.H., K.X., and R.S. conceptualized the project. Z.M.H. and N.F. performed the data analysis, with recommendations from K.X. and R.S. Z.M.H. produced the figures and tables. Z.M.H. wrote the manuscript, and all authors contributed to and edited the manuscript.

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Harvanek, Z.M., Fogelman, N., Xu, K. et al. Psychological and biological resilience modulates the effects of stress on epigenetic aging. Transl Psychiatry 11 , 601 (2021). https://doi.org/10.1038/s41398-021-01735-7

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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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Article contents

Work, stress, coping, and stress management.

• Sharon Glazer Sharon Glazer University of Baltimore
•  and  Cong Liu Cong Liu Hofstra University
• https://doi.org/10.1093/acrefore/9780190236557.013.30
• Published online: 26 April 2017

Work stress refers to the process of job stressors, or stimuli in the workplace, leading to strains, or negative responses or reactions. Organizational development refers to a process in which problems or opportunities in the work environment are identified, plans are made to remediate or capitalize on the stimuli, action is taken, and subsequently the results of the plans and actions are evaluated. When organizational development strategies are used to assess work stress in the workplace, the actions employed are various stress management interventions. Two key factors tying work stress and organizational development are the role of the person and the role of the environment. In order to cope with work-related stressors and manage strains, organizations must be able to identify and differentiate between factors in the environment that are potential sources of stressors and how individuals perceive those factors. Primary stress management interventions focus on preventing stressors from even presenting, such as by clearly articulating workers’ roles and providing necessary resources for employees to perform their job. Secondary stress management interventions focus on a person’s appraisal of job stressors as a threat or challenge, and the person’s ability to cope with the stressors (presuming sufficient internal resources, such as a sense of meaningfulness in life, or external resources, such as social support from a supervisor). When coping is not successful, strains may develop. Tertiary stress management interventions attempt to remediate strains, by addressing the consequence itself (e.g., diabetes management) and/or the source of the strain (e.g., reducing workload). The person and/or the organization may be the targets of the intervention. The ultimate goal of stress management interventions is to minimize problems in the work environment, intensify aspects of the work environment that create a sense of a quality work context, enable people to cope with stressors that might arise, and provide tools for employees and organizations to manage strains that might develop despite all best efforts to create a healthy workplace.

• stress management
• organization development
• organizational interventions
• stress theories and frameworks

Introduction

Work stress is a generic term that refers to work-related stimuli (aka job stressors) that may lead to physical, behavioral, or psychological consequences (i.e., strains) that affect both the health and well-being of the employee and the organization. Not all stressors lead to strains, but all strains are a result of stressors, actual or perceived. Common terms often used interchangeably with work stress are occupational stress, job stress, and work-related stress. Terms used interchangeably with job stressors include work stressors, and as the specificity of the type of stressor might include psychosocial stressor (referring to the psychological experience of work demands that have a social component, e.g., conflict between two people; Hauke, Flintrop, Brun, & Rugulies, 2011 ), hindrance stressor (i.e., a stressor that prevents goal attainment; Cavanaugh, Boswell, Roehling, & Boudreau, 2000 ), and challenge stressor (i.e., a stressor that is difficult, but attainable and possibly rewarding to attain; Cavanaugh et al., 2000 ).

Stress in the workplace continues to be a highly pervasive problem, having both direct negative effects on individuals experiencing it and companies paying for it, and indirect costs vis à vis lost productivity (Dopkeen & DuBois, 2014 ). For example, U.K. public civil servants’ work-related stress rose from 10.8% in 2006 to 22.4% in 2013 and about one-third of the workforce has taken more than 20 days of leave due to stress-related ill-health, while well over 50% are present at work when ill (French, 2015 ). These findings are consistent with a report by the International Labor Organization (ILO, 2012 ), whereby 50% to 60% of all workdays are lost due to absence attributed to factors associated with work stress.

The prevalence of work-related stress is not diminishing despite improvements in technology and employment rates. The sources of stress, such as workload, seem to exacerbate with improvements in technology (Coovert & Thompson, 2003 ). Moreover, accessibility through mobile technology and virtual computer terminals is linking people to their work more than ever before (ILO, 2012 ; Tarafdar, Tu, Ragu-Nathan, & Ragu-Nathan, 2007 ). Evidence of this kind of mobility and flexibility is further reinforced in a June 2007 survey of 4,025 email users (over 13 years of age); AOL reported that four in ten survey respondents reported planning their vacations around email accessibility and 83% checked their emails at least once a day while away (McMahon, 2007 ). Ironically, despite these mounting work-related stressors and clear financial and performance outcomes, some individuals are reporting they are less “stressed,” but only because “stress has become the new normal” (Jayson, 2012 , para. 4).

This new normal is likely the source of psychological and physiological illness. Siegrist ( 2010 ) contends that conditions in the workplace, particularly psychosocial stressors that are perceived as unfavorable relationships with others and self, and an increasingly sedentary lifestyle (reinforced with desk jobs) are increasingly contributing to cardiovascular disease. These factors together justify a need to continue on the path of helping individuals recognize and cope with deleterious stressors in the work environment and, equally important, to find ways to help organizations prevent harmful stressors over which they have control, as well as implement policies or mechanisms to help employees deal with these stressors and subsequent strains. Along with a greater focus on mitigating environmental constraints are interventions that can be used to prevent anxiety, poor attitudes toward the workplace conditions and arrangements, and subsequent cardiovascular illness, absenteeism, and poor job performance (Siegrist, 2010 ).

Even the ILO has presented guidance on how the workplace can help prevent harmful job stressors (aka hindrance stressors) or at least help workers cope with them. Consistent with the view that well-being is not the absence of stressors or strains and with the view that positive psychology offers a lens for proactively preventing stressors, the ILO promotes increasing preventative risk assessments, interventions to prevent and control stressors, transparent organizational communication, worker involvement in decision-making, networks and mechanisms for workplace social support, awareness of how working and living conditions interact, safety, health, and well-being in the organization (ILO, n.d. ). The field of industrial and organizational (IO) psychology supports the ILO’s recommendations.

IO psychology views work stress as the process of a person’s interaction with multiple aspects of the work environment, job design, and work conditions in the organization. Interventions to manage work stress, therefore, focus on the psychosocial factors of the person and his or her relationships with others and the socio-technical factors related to the work environment and work processes. Viewing work stress from the lens of the person and the environment stems from Kurt Lewin’s ( 1936 ) work that stipulates a person’s state of mental health and behaviors are a function of the person within a specific environment or situation. Aspects of the work environment that affect individuals’ mental states and behaviors include organizational hierarchy, organizational climate (including processes, policies, practices, and reward structures), resources to support a person’s ability to fulfill job duties, and management structure (including leadership). Job design refers to each contributor’s tasks and responsibilities for fulfilling goals associated with the work role. Finally, working conditions refers not only to the physical environment, but also the interpersonal relationships with other contributors.

Each of the conditions that are identified in the work environment may be perceived as potentially harmful or a threat to the person or as an opportunity. When a stressor is perceived as a threat to attaining desired goals or outcomes, the stressor may be labeled as a hindrance stressor (e.g., LePine, Podsakoff, & Lepine, 2005 ). When the stressor is perceived as an opportunity to attain a desired goal or end state, it may be labeled as a challenge stressor. According to LePine and colleagues’ ( 2005 ), both challenge (e.g., time urgency, workload) and hindrance (e.g., hassles, role ambiguity, role conflict) stressors could lead to strains (as measured by “anxiety, depersonalization, depression, emotional exhaustion, frustration, health complaints, hostility, illness, physical symptoms, and tension” [p. 767]). However, challenge stressors positively relate with motivation and performance, whereas hindrance stressors negatively relate with motivation and performance. Moreover, motivation and strains partially mediate the relationship between hindrance and challenge stressors with performance.

Figure 1. Organizational development frameworks to guide identification of work stress and interventions.

In order to (1) minimize any potential negative effects from stressors, (2) increase coping skills to deal with stressors, or (3) manage strains, organizational practitioners or consultants will devise organizational interventions geared toward prevention, coping, and/or stress management. Ultimately, toxic factors in the work environment can have deleterious effects on a person’s physical and psychological well-being, as well as on an organization’s total health. It behooves management to take stock of the organization’s health, which includes the health and well-being of its employees, if the organization wishes to thrive and be profitable. According to Page and Vella-Brodrick’s ( 2009 ) model of employee well-being, employee well-being results from subjective well-being (i.e., life satisfaction and general positive or negative affect), workplace well-being (composed of job satisfaction and work-specific positive or negative affect), and psychological well-being (e.g., self-acceptance, positive social relations, mastery, purpose in life). Job stressors that become unbearable are likely to negatively affect workplace well-being and thus overall employee well-being. Because work stress is a major organizational pain point and organizations often employ organizational consultants to help identify and remediate pain points, the focus here is on organizational development (OD) frameworks; several work stress frameworks are presented that together signal areas where organizations might focus efforts for change in employee behaviors, attitudes, and performance, as well as the organization’s performance and climate. Work stress, interventions, and several OD and stress frameworks are depicted in Figure 1 .

The goals are: (1) to conceptually define and clarify terms associated with stress and stress management, particularly focusing on organizational factors that contribute to stress and stress management, and (2) to present research that informs current knowledge and practices on workplace stress management strategies. Stressors and strains will be defined, leading OD and work stress frameworks that are used to organize and help organizations make sense of the work environment and the organization’s responsibility in stress management will be explored, and stress management will be explained as an overarching thematic label; an area of study and practice that focuses on prevention (primary) interventions, coping (secondary) interventions, and managing strains (tertiary) interventions; as well as the label typically used to denote tertiary interventions. Suggestions for future research and implications toward becoming a healthy organization are presented.

Defining Stressors and Strains

Work-related stressors or job stressors can lead to different kinds of strains individuals and organizations might experience. Various types of stress management interventions, guided by OD and work stress frameworks, may be employed to prevent or cope with job stressors and manage strains that develop(ed).

A job stressor is a stimulus external to an employee and a result of an employee’s work conditions. Example job stressors include organizational constraints, workplace mistreatments (such as abusive supervision, workplace ostracism, incivility, bullying), role stressors, workload, work-family conflicts, errors or mistakes, examinations and evaluations, and lack of structure (Jex & Beehr, 1991 ; Liu, Spector, & Shi, 2007 ; Narayanan, Menon, & Spector, 1999 ). Although stressors may be categorized as hindrances and challenges, there is not yet sufficient information to be able to propose which stress management interventions would better serve to reduce those hindrance stressors or to reduce strain-producing challenge stressors while reinforcing engagement-producing challenge stressors.

Organizational Constraints

Organizational constraints may be hindrance stressors as they prevent employees from translating their motivation and ability into high-level job performance (Peters & O’Connor, 1980 ). Peters and O’Connor ( 1988 ) defined 11 categories of organizational constraints: (1) job-related information, (2) budgetary support, (3) required support, (4) materials and supplies, (5) required services and help from others, (6) task preparation, (7) time availability, (8) the work environment, (9) scheduling of activities, (10) transportation, and (11) job-relevant authority. The inhibiting effect of organizational constraints may be due to the lack of, inadequacy of, or poor quality of these categories.

Workplace Mistreatment

Workplace mistreatment presents a cluster of interpersonal variables, such as interpersonal conflict, bullying, incivility, and workplace ostracism (Hershcovis, 2011 ; Tepper & Henle, 2011 ). Typical workplace mistreatment behaviors include gossiping, rude comments, showing favoritism, yelling, lying, and ignoring other people at work (Tepper & Henle, 2011 ). These variables relate to employees’ psychological well-being, physical well-being, work attitudes (e.g., job satisfaction and organizational commitment), and turnover intention (e.g., Hershcovis, 2011 ; Spector & Jex, 1998 ). Some researchers differentiated the source of mistreatment, such as mistreatment from one’s supervisor versus mistreatment from one’s coworker (e.g., Bruk-Lee & Spector, 2006 ; Frone, 2000 ; Liu, Liu, Spector, & Shi, 2011 ).

Role Stressors

Role stressors are demands, constraints, or opportunities a person perceives to be associated, and thus expected, with his or her work role(s) across various situations. Three commonly studied role stressors are role ambiguity, role conflict, and role overload (Glazer & Beehr, 2005 ; Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964 ). Role ambiguity in the workplace occurs when an employee lacks clarity regarding what performance-related behaviors are expected of him or her. Role conflict refers to situations wherein an employee receives incompatible role requests from the same or different supervisors or the employee is asked to engage in work that impedes his or her performance in other work or nonwork roles or clashes with his or her values. Role overload refers to excessive demands and insufficient time (quantitative) or knowledge (qualitative) to complete the work. The construct is often used interchangeably with workload, though role overload focuses more on perceived expectations from others about one’s workload. These role stressors significantly relate to low job satisfaction, low organizational commitment, low job performance, high tension or anxiety, and high turnover intention (Abramis, 1994 ; Glazer & Beehr, 2005 ; Jackson & Schuler, 1985 ).

Excessive workload is one of the most salient stressors at work (e.g., Liu et al., 2007 ). There are two types of workload: quantitative and qualitative workload (LaRocco, Tetrick, & Meder, 1989 ; Parasuraman & Purohit, 2000 ). Quantitative workload refers to the excessive amount of work one has. In a summary of a Chartered Institute of Personnel & Development Report from 2006 , Dewe and Kompier ( 2008 ) noted that quantitative workload was one of the top three stressors workers experienced at work. Qualitative workload refers to the difficulty of work. Workload also differs by the type of the load. There are mental workload and physical workload (Dwyer & Ganster, 1991 ). Excessive physical workload may result in physical discomfort or illness. Excessive mental workload will cause psychological distress such as anxiety or frustration (Bowling & Kirkendall, 2012 ). Another factor affecting quantitative workload is interruptions (during the workday). Lin, Kain, and Fritz ( 2013 ) found that interruptions delay completion of job tasks, thus adding to the perception of workload.

Work-Family Conflict

Work-family conflict is a form of inter-role conflict in which demands from one’s work domain and one’s family domain are incompatible to some extent (Greenhaus & Beutell, 1985 ). Work can interfere with family (WIF) and/or family can interfere with work (FIW) due to time-related commitments to participating in one domain or another, incompatible behavioral expectations, or when strains in one domain carry over to the other (Greenhaus & Beutell, 1985 ). Work-family conflict significantly relates to work-related outcomes (e.g., job satisfaction, organizational commitment, turnover intention, burnout, absenteeism, job performance, job strains, career satisfaction, and organizational citizenship behaviors), family-related outcomes (e.g., marital satisfaction, family satisfaction, family-related performance, family-related strains), and domain-unspecific outcomes (e.g., life satisfaction, psychological strain, somatic or physical symptoms, depression, substance use or abuse, and anxiety; Amstad, Meier, Fasel, Elfering, & Semmer, 2011 ).

Individuals and organizations can experience work-related strains. Sometimes organizations will experience strains through the employee’s negative attitudes or strains, such as that a worker’s absence might yield lower production rates, which would roll up into an organizational metric of organizational performance. In the industrial and organizational (IO) psychology literature, organizational strains are mostly observed as macro-level indicators, such as health insurance costs, accident-free days, and pervasive problems with company morale. In contrast, individual strains, usually referred to as job strains, are internal to an employee. They are responses to work conditions and relate to health and well-being of employees. In other words, “job strains are adverse reactions employees have to job stressors” (Spector, Chen, & O’Connell, 2000 , p. 211). Job strains tend to fall into three categories: behavioral, physical, and psychological (Jex & Beehr, 1991 ).

Behavioral strains consist of actions that employees take in response to job stressors. Examples of behavioral strains include employees drinking alcohol in the workplace or intentionally calling in sick when they are not ill (Spector et al., 2000 ). Physical strains consist of health symptoms that are physiological in nature that employees contract in response to job stressors. Headaches and ulcers are examples of physical strains. Lastly, psychological strains are emotional reactions and attitudes that employees have in response to job stressors. Examples of psychological strains are job dissatisfaction, anxiety, and frustration (Spector et al., 2000 ). Interestingly, research studies that utilize self-report measures find that most job strains experienced by employees tend to be psychological strains (Spector et al., 2000 ).

Leading Frameworks

Organizations that are keen on identifying organizational pain points and remedying them through organizational campaigns or initiatives often discover the pain points are rooted in work-related stressors and strains and the initiatives have to focus on reducing workers’ stress and increasing a company’s profitability. Through organizational climate surveys, for example, companies discover that aspects of the organization’s environment, including its policies, practices, reward structures, procedures, and processes, as well as employees at all levels of the company, are contributing to the individual and organizational stress. Recent studies have even begun to examine team climates for eustress and distress assessed in terms of team members’ homogenous psychological experience of vigor, efficacy, dedication, and cynicism (e.g., Kożusznik, Rodriguez, & Peiro, 2015 ).

Each of the frameworks presented advances different aspects that need to be identified in order to understand the source and potential remedy for stressors and strains. In some models, the focus is on resources, in others on the interaction of the person and environment, and in still others on the role of the person in the workplace. Few frameworks directly examine the role of the organization, but the organization could use these frameworks to plan interventions that would minimize stressors, cope with existing stressors, and prevent and/or manage strains. One of the leading frameworks in work stress research that is used to guide organizational interventions is the person and environment (P-E) fit (French & Caplan, 1972 ). Its precursor is the University of Michigan Institute for Social Research’s (ISR) role stress model (Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964 ) and Lewin’s Field Theory. Several other theories have since evolved from the P-E fit framework, including Karasek and Theorell’s ( 1990 ), Karasek ( 1979 ) Job Demands-Control Model (JD-C), the transactional framework (Lazarus & Folkman, 1984 ), Conservation of Resources (COR) theory (Hobfoll, 1989 ), and Siegrist’s ( 1996 ) Effort-Reward Imbalance (ERI) Model.

Field Theory

The premise of Kahn et al.’s ( 1964 ) role stress theory is Lewin’s ( 1997 ) Field Theory. Lewin purported that behavior and mental events are a dynamic function of the whole person, including a person’s beliefs, values, abilities, needs, thoughts, and feelings, within a given situation (field or environment), as well as the way a person represents his or her understanding of the field and behaves in that space. Lewin explains that work-related strains are a result of individuals’ subjective perceptions of objective factors, such as work roles, relationships with others in the workplace, as well as personality indicators, and can be used to predict people’s reactions, including illness. Thus, to make changes to an organizational system, it is necessary to understand a field and try to move that field from the current state to the desired state. Making this move necessitates identifying mechanisms influencing individuals.

Role Stress Theory

Role stress theory mostly isolates the perspective a person has about his or her work-related responsibilities and expectations to determine how those perceptions relate with a person’s work-related strains. However, those relationships have been met with somewhat varied results, which Glazer and Beehr ( 2005 ) concluded might be a function of differences in culture, an environmental factor often neglected in research. Kahn et al.’s ( 1964 ) role stress theory, coupled with Lewin’s ( 1936 ) Field Theory, serves as the foundation for the P-E fit theory. Lewin ( 1936 ) wrote, “Every psychological event depends upon the state of the person and at the same time on the environment” (p. 12). Researchers of IO psychology have narrowed the environment to the organization or work team. This narrowed view of the organizational environment is evident in French and Caplan’s ( 1972 ) P-E fit framework.

Person-Environment Fit Theory

The P-E fit framework focuses on the extent to which there is congruence between the person and a given environment, such as the organization (Caplan, 1987 ; Edwards, 2008 ). For example, does the person have the necessary skills and abilities to fulfill an organization’s demands, or does the environment support a person’s desire for autonomy (i.e., do the values align?) or fulfill a person’s needs (i.e., a person’s needs are rewarded). Theoretically and empirically, the greater the person-organization fit, the greater a person’s job satisfaction and organizational commitment, the less a person’s turnover intention and work-related stress (see meta-analyses by Assouline & Meir, 1987 ; Kristof-Brown, Zimmerman, & Johnson, 2005 ; Verquer, Beehr, & Wagner, 2003 ).

Job Demands-Control/Support (JD-C/S) and Job Demands-Resources (JD-R) Model

Focusing more closely on concrete aspects of work demands and the extent to which a person perceives he or she has control or decision latitude over those demands, Karasek ( 1979 ) developed the JD-C model. Karasek and Theorell ( 1990 ) posited that high job demands under conditions of little decision latitude or control yield high strains, which have varied implications on the health of an organization (e.g., in terms of high turnover, employee ill-health, poor organizational performance). This theory was modified slightly to address not only control, but also other resources that could protect a person from unruly job demands, including support (aka JD-C/S, Johnson & Hall, 1988 ; and JD-R, Bakker, van Veldhoven, & Xanthopoulou, 2010 ). Whether focusing on control or resources, both they and job demands are said to reflect workplace characteristics, while control and resources also represent coping strategies or tools (Siegrist, 2010 ).

Despite the glut of research testing the JD-C and JD-R, results are somewhat mixed. Testing the interaction between job demands and control, Beehr, Glaser, Canali, and Wallwey ( 2001 ) did not find empirical support for the JD-C theory. However, Dawson, O’Brien, and Beehr ( 2016 ) found that high control and high support buffered against the independent deleterious effects of interpersonal conflict, role conflict, and organizational politics (demands that were categorized as hindrance stressors) on anxiety, as well as the effects of interpersonal conflict and organizational politics on physiological symptoms, but control and support did not moderate the effects between challenge stressors and strains. Coupled with Bakker, Demerouti, and Sanz-Vergel’s ( 2014 ) note that excessive job demands are a source of strain, but increased job resources are a source of engagement, Dawson et al.’s results suggest that when an organization identifies that demands are hindrances, it can create strategies for primary (preventative) stress management interventions and attempt to remove or reduce such work demands. If the demands are challenging, though manageable, but latitude to control the challenging stressors and support are insufficient, the organization could modify practices and train employees on adopting better strategies for meeting or coping (secondary stress management intervention) with the demands. Finally, if the organization can neither afford to modify the demands or the level of control and support, it will be necessary for the organization to develop stress management (tertiary) interventions to deal with the inevitable strains.

Conservation of Resources Theory

The idea that job resources reinforce engagement in work has been propagated in Hobfoll’s ( 1989 ) Conservation of Resources (COR) theory. COR theory also draws on the foundational premise that people’s mental health is a function of the person and the environment, forwarding that how people interpret their environment (including the societal context) affects their stress levels. Hobfoll focuses on resources such as objects, personal characteristics, conditions, or energies as particularly instrumental to minimizing strains. He asserts that people do whatever they can to protect their valued resources. Thus, strains develop when resources are threatened to be taken away, actually taken away, or when additional resources are not attainable after investing in the possibility of gaining more resources (Hobfoll, 2001 ). By extension, organizations can invest in activities that would minimize resource loss and create opportunities for resource gains and thus have direct implications for devising primary and secondary stress management interventions.

Transactional Framework

Lazarus and Folkman ( 1984 ) developed the widely studied transactional framework of stress. This framework holds as a key component the cognitive appraisal process. When individuals perceive factors in the work environment as a threat (i.e., primary appraisal), they will scan the available resources (external or internal to himself or herself) to cope with the stressors (i.e., secondary appraisal). If the coping resources provide minimal relief, strains develop. Until recently, little attention has been given to the cognitive appraisal associated with different work stressors (Dewe & Kompier, 2008 ; Liu & Li, 2017 ). In a study of Polish and Spanish social care service providers, stressors appraised as a threat related positively to burnout and less engagement, but stressors perceived as challenges yielded greater engagement and less burnout (Kożusznik, Rodriguez, & Peiro, 2012 ). Similarly, Dawson et al. ( 2016 ) found that even with support and control resources, hindrance demands were more strain-producing than challenge demands, suggesting that appraisal of the stressor is important. In fact, “many people respond well to challenging work” (Beehr et al., 2001 , p. 126). Kożusznik et al. ( 2012 ) recommend training employees to change the way they view work demands in order to increase engagement, considering that part of the problem may be about how the person appraises his or her environment and, thus, copes with the stressors.

Effort-Reward Imbalance

Siegrist’s ( 1996 ) Model of Effort-Reward Imbalance (ERI) focuses on the notion of social reciprocity, such that a person fulfills required work tasks in exchange for desired rewards (Siegrist, 2010 ). ERI sheds light on how an imbalance in a person’s expectations of an organization’s rewards (e.g., pay, bonus, sense of advancement and development, job security) in exchange for a person’s efforts, that is a break in one’s work contract, leads to negative responses, including long-term ill-health (Siegrist, 2010 ; Siegrist et al., 2014 ). In fact, prolonged perception of a work contract imbalance leads to adverse health, including immunological problems and inflammation, which contribute to cardiovascular disease (Siegrist, 2010 ). The model resembles the relational and interactional psychological contract theory in that it describes an employee’s perception of the terms of the relationship between the person and the workplace, including expectations of performance, job security, training and development opportunities, career progression, salary, and bonuses (Thomas, Au, & Ravlin, 2003 ). The psychological contract, like the ERI model, focuses on social exchange. Furthermore, the psychological contract, like stress theories, are influenced by cultural factors that shape how people interpret their environments (Glazer, 2008 ; Thomas et al., 2003 ). Violations of the psychological contract will negatively affect a person’s attitudes toward the workplace and subsequent health and well-being (Siegrist, 2010 ). To remediate strain, Siegrist ( 2010 ) focuses on both the person and the environment, recognizing that the organization is particularly responsible for changing unfavorable work conditions and the person is responsible for modifying his or her reactions to such conditions.

Stress Management Interventions: Primary, Secondary, and Tertiary

Remediation of work stress and organizational development interventions are about realigning the employee’s experiences in the workplace with factors in the environment, as well as closing the gap between the current environment and the desired environment. Work stress develops when an employee perceives the work demands to exceed the person’s resources to cope and thus threatens employee well-being (Dewe & Kompier, 2008 ). Likewise, an organization’s need to change arises when forces in the environment are creating a need to change in order to survive (see Figure 1 ). Lewin’s ( 1951 ) Force Field Analysis, the foundations of which are in Field Theory, is one of the first organizational development intervention tools presented in the social science literature. The concept behind Force Field Analysis is that in order to survive, organizations must adapt to environmental forces driving a need for organizational change and remove restraining forces that create obstacles to organizational change. In order to do this, management needs to delineate the current field in which the organization is functioning, understand the driving forces for change, identify and dampen or eliminate the restraining forces against change. Several models for analyses may be applied, but most approaches are variations of organizational climate surveys.

Through organizational surveys, workers provide management with a snapshot view of how they perceive aspects of their work environment. Thus, the view of the health of an organization is a function of several factors, chief among them employees’ views (i.e., the climate) about the workplace (Lewin, 1951 ). Indeed, French and Kahn ( 1962 ) posited that well-being depends on the extent to which properties of the person and properties of the environment align in terms of what a person requires and the resources available in a given environment. Therefore, only when properties of the person and properties of the environment are sufficiently understood can plans for change be developed and implemented targeting the environment (e.g., change reporting structures to relieve, and thus prevent future, communication stressors) and/or the person (e.g., providing more autonomy, vacation days, training on new technology). In short, climate survey findings can guide consultants about the emphasis for organizational interventions: before a problem arises aka stress prevention, e.g., carefully crafting job roles), when a problem is present, but steps are taken to mitigate their consequences (aka coping, e.g., providing social support groups), and/or once strains develop (aka. stress management, e.g., healthcare management policies).

For each of the primary (prevention), secondary (coping), and tertiary (stress management) techniques the target for intervention can be the entire workforce, a subset of the workforce, or a specific person. Interventions that target the entire workforce may be considered organizational interventions, as they have direct implications on the health of all individuals and consequently the health of the organization. Several interventions categorized as primary and secondary interventions may also be implemented after strains have developed and after it has been discerned that a person or the organization did not do enough to mitigate stressors or strains (see Figure 1 ). The designation of many of the interventions as belonging to one category or another may be viewed as merely a suggestion.

Primary Interventions (Preventative Stress Management)

Before individuals begin to perceive work-related stressors, organizations engage in stress prevention strategies, such as providing people with resources (e.g., computers, printers, desk space, information about the job role, organizational reporting structures) to do their jobs. However, sometimes the institutional structures and resources are insufficient or ambiguous. Scholars and practitioners have identified several preventative stress management strategies that may be implemented.

Planning and Time Management

When employees feel quantitatively overloaded, sometimes the remedy is improving the employees’ abilities to plan and manage their time (Quick, Quick, Nelson, & Hurrell, 2003 ). Planning is a future-oriented activity that focuses on conceptual and comprehensive work goals. Time management is a behavior that focuses on organizing, prioritizing, and scheduling work activities to achieve short-term goals. Given the purpose of time management, it is considered a primary intervention, as engaging in time management helps to prevent work tasks from mounting and becoming unmanageable, which would subsequently lead to adverse outcomes. Time management comprises three fundamental components: (1) establishing goals, (2) identifying and prioritizing tasks to fulfill the goals, and (3) scheduling and monitoring progress toward goal achievement (Peeters & Rutte, 2005 ). Workers who employ time management have less role ambiguity (Macan, Shahani, Dipboye, & Philips, 1990 ), psychological stress or strain (Adams & Jex, 1999 ; Jex & Elaqua, 1999 ; Macan et al., 1990 ), and greater job satisfaction (Macan, 1994 ). However, Macan ( 1994 ) did not find a relationship between time management and performance. Still, Claessens, van Eerde, Rutte, and Roe ( 2004 ) found that perceived control of time partially mediated the relationships between planning behavior (an indicator of time management), job autonomy, and workload on one hand, and job strains, job satisfaction, and job performance on the other hand. Moreover, Peeters and Rutte ( 2005 ) observed that teachers with high work demands and low autonomy experienced more burnout when they had poor time management skills.

Person-Organization Fit

Just as it is important for organizations to find the right person for the job and organization, so is it the responsibility of a person to choose to work at the right organization—an organization that fulfills the person’s needs and upholds the values important to the individual, as much as the person fulfills the organization’s needs and adapts to its values. When people fit their employing organizations they are setting themselves up for experiencing less strain-producing stressors (Kristof-Brown et al., 2005 ). In a meta-analysis of 62 person-job fit studies and 110 person-organization fit studies, Kristof-Brown et al. ( 2005 ) found that person-job fit had a negative correlation with indicators of job strain. In fact, a primary intervention of career counseling can help to reduce stress levels (Firth-Cozens, 2003 ).

Job Redesign

The Job Demands-Control/Support (JD-C/S), Job Demands-Resources (JD-R), and transactional models all suggest that factors in the work context require modifications in order to reduce potential ill-health and poor organizational performance. Drawing on Hackman and Oldham’s ( 1980 ) Job Characteristics Model, it is possible to assess with the Job Diagnostics Survey (JDS) the current state of work characteristics related to skill variety, task identity, task significance, autonomy, and feedback. Modifying those aspects would help create a sense of meaningfulness, sense of responsibility, and feeling of knowing how one is performing, which subsequently affects a person’s well-being as identified in assessments of motivation, satisfaction, improved performance, and reduced withdrawal intentions and behaviors. Extending this argument to the stress models, it can be deduced that reducing uncertainty or perceived unfairness that may be associated with a person’s perception of these work characteristics, as well as making changes to physical characteristics of the environment (e.g., lighting, seating, desk, air quality), nature of work (e.g., job responsibilities, roles, decision-making latitude), and organizational arrangements (e.g., reporting structure and feedback mechanisms), can help mitigate against numerous ill-health consequences and reduced organizational performance. In fact, Fried et al. ( 2013 ) showed that healthy patients of a medical clinic whose jobs were excessively low (i.e., monotonous) or excessively high (i.e., overstimulating) on job enrichment (as measured by the JDS) had greater abdominal obesity than those whose jobs were optimally enriched. By taking stock of employees’ perceptions of the current work situation, managers might think about ways to enhance employees’ coping toolkit, such as training on how to deal with difficult clients or creating stimulating opportunities when jobs have low levels of enrichment.

Participatory Action Research Interventions

Participatory action research (PAR) is an intervention wherein, through group discussions, employees help to identify and define problems in organizational structure, processes, policies, practices, and reward structures, as well as help to design, implement, and evaluate success of solutions. PAR is in itself an intervention, but its goal is to design interventions to eliminate or reduce work-related factors that are impeding performance and causing people to be unwell. An example of a successful primary intervention, utilizing principles of PAR and driven by the JD-C and JD-C/S stress frameworks is Health Circles (HCs; Aust & Ducki, 2004 ).

HCs, developed in Germany in the 1980s, were popular practices in industries, such as metal, steel, and chemical, and service. Similar to other problem-solving practices, such as quality circles, HCs were based on the assumptions that employees are the experts of their jobs. For this reason, to promote employee well-being, management and administrators solicited suggestions and ideas from the employees to improve occupational health, thereby increasing employees’ job control. HCs also promoted communication between managers and employees, which had a potential to increase social support. With more control and support, employees would experience less strains and better occupational well-being.

Employing the three-steps of (1) problem analysis (i.e., diagnosis or discovery through data generated from organizational records of absenteeism length, frequency, rate, and reason and employee survey), (2) HC meetings (6 to 10 meetings held over several months to brainstorm ideas to improve occupational safety and health concerns identified in the discovery phase), and (3) HC evaluation (to determine if desired changes were accomplished and if employees’ reports of stressors and strains changed after the course of 15 months), improvements were to be expected (Aust & Ducki, 2004 ). Aust and Ducki ( 2004 ) reviewed 11 studies presenting 81 health circles in 30 different organizations. Overall study participants had high satisfaction with the HCs practices. Most companies acted upon employees’ suggestions (e.g., improving driver’s seat and cab, reducing ticket sale during drive, team restructuring and job rotation to facilitate communication, hiring more employees during summer time, and supervisor training program to improve leadership and communication skills) to improve work conditions. Thus, HCs represent a successful theory-grounded intervention to routinely improve employees’ occupational health.

Physical Setting

The physical environment or physical workspace has an enormous impact on individuals’ well-being, attitudes, and interactions with others, as well as on the implications on innovation and well-being (Oksanen & Ståhle, 2013 ; Vischer, 2007 ). In a study of 74 new product development teams (total of 437 study respondents) in Western Europe, Chong, van Eerde, Rutte, and Chai ( 2012 ) found that when teams were faced with challenge time pressures, meaning the teams had a strong interest and desire in tackling complex, but engaging tasks, when they were working proximally close with one another, team communication improved. Chong et al. assert that their finding aligns with prior studies that have shown that physical proximity promotes increased awareness of other team members, greater tendency to initiate conversations, and greater team identification. However, they also found that when faced with hindrance time pressures, physical proximity related to low levels of team communication, but when hindrance time pressure was low, team proximity had an increasingly greater positive relationship with team communication.

In addition to considering the type of work demand teams must address, other physical workspace considerations include whether people need to work collaboratively and synchronously or independently and remotely (or a combination thereof). Consideration needs to be given to how company contributors would satisfy client needs through various modes of communication, such as email vs. telephone, and whether individuals who work by a window might need shading to block bright sunlight from glaring on their computer screens. Finally, people who have to use the telephone for extensive periods of time would benefit from earphones to prevent neck strains. Most physical stressors are rather simple to rectify. However, companies are often not aware of a problem until after a problem arises, such as when a person’s back is strained from trying to move heavy equipment. Companies then implement strategies to remediate the environmental stressor. With the help of human factors, and organizational and office design consultants, many of the physical barriers to optimal performance can be prevented (Rousseau & Aubé, 2010 ). In a study of 215 French-speaking Canadian healthcare employees, Rousseau and Aubé ( 2010 ) found that although supervisor instrumental support positively related with affective commitment to the organization, the relationship was even stronger for those who reported satisfaction with the ambient environment (i.e., temperature, lighting, sound, ventilation, and cleanliness).

Secondary Interventions (Coping)

Secondary interventions, also referred to as coping, focus on resources people can use to mitigate the risk of work-related illness or workplace injury. Resources may include properties related to social resources, behaviors, and cognitive structures. Each of these resource domains may be employed to cope with stressors. Monat and Lazarus ( 1991 ) summarize the definition of coping as “an individual’s efforts to master demands (or conditions of harm, threat, or challenge) that are appraised (or perceived) as exceeding or taxing his or her resources” (p. 5). To master demands requires use of the aforementioned resources. Secondary interventions help employees become aware of the psychological, physical, and behavioral responses that may occur from the stressors presented in their working environment. Secondary interventions help a person detect and attend to stressors and identify resources for and ways of mitigating job strains. Often, coping strategies are learned skills that have a cognitive foundation and serve important functions in improving people’s management of stressors (Lazarus & Folkman, 1991 ). Coping is effortful, but with practice it becomes easier to employ. This idea is the foundation for understanding the role of resilience in coping with stressors. However, “not all adaptive processes are coping. Coping is a subset of adaptational activities that involves effort and does not include everything that we do in relating to the environment” (Lazarus & Folkman, 1991 , p. 198). Furthermore, sometimes to cope with a stressor, a person may call upon social support sources to help with tangible materials or emotional comfort. People call upon support resources because they help to restructure how a person approaches or thinks about the stressor.

Most secondary interventions are aimed at helping the individual, though companies, as a policy, might require all employees to partake in training aimed at increasing employees’ awareness of and skills aimed at handling difficult situations vis à vis company channels (e.g., reporting on sexual harassment or discrimination). Furthermore, organizations might institute mentoring programs or work groups to address various work-related matters. These programs employ awareness-raising activities, stress-education, or skills training (cf., Bhagat, Segovis, & Nelson, 2012 ), which include development of skills in problem-solving, understanding emotion-focused coping, identifying and using social support, and enhancing capacity for resilience. The aim of these programs, therefore, is to help employees proactively review their perceptions of psychological, physical, and behavioral job-related strains, thereby extending their resilience, enabling them to form a personal plan to control stressors and practice coping skills (Cooper, Dewe, & O’Driscoll, 2011 ).

Often these stress management programs are instituted after an organization has observed excessive absenteeism and work-related performance problems and, therefore, are sometimes categorized as a tertiary stress management intervention or even a primary (prevention) intervention. However, the skills developed for coping with stressors also place the programs in secondary stress management interventions. Example programs that are categorized as tertiary or primary stress management interventions may also be secondary stress management interventions (see Figure 1 ), and these include lifestyle advice and planning, stress inoculation training, simple relaxation techniques, meditation, basic trainings in time management, anger management, problem-solving skills, and cognitive-behavioral therapy. Corporate wellness programs also fall under this category. In other words, some programs could be categorized as primary, secondary, or tertiary interventions depending upon when the employee (or organization) identifies the need to implement the program. For example, time management practices could be implemented as a means of preventing some stressors, as a way to cope with mounting stressors, or as a strategy to mitigate symptoms of excessive of stressors. Furthermore, these programs can be administered at the individual level or group level. As related to secondary interventions, these programs provide participants with opportunities to develop and practice skills to cognitively reappraise the stressor(s); to modify their perspectives about stressors; to take time out to breathe, stretch, meditate, relax, and/or exercise in an attempt to support better decision-making; to articulate concerns and call upon support resources; and to know how to say “no” to onslaughts of requests to complete tasks. Participants also learn how to proactively identify coping resources and solve problems.

According to Cooper, Dewe, and O’Driscoll ( 2001 ), secondary interventions are successful in helping employees modify or strengthen their ability to cope with the experience of stressors with the goal of mitigating the potential harm the job stressors may create. Secondary interventions focus on individuals’ transactions with the work environment and emphasize the fit between a person and his or her environment. However, researchers have pointed out that the underlying assumption of secondary interventions is that the responsibility for coping with the stressors of the environment lies within individuals (Quillian-Wolever & Wolever, 2003 ). If companies cannot prevent the stressors in the first place, then they are, in part, responsible for helping individuals develop coping strategies and informing employees about programs that would help them better cope with job stressors so that they are able to fulfill work assignments.

Stress management interventions that help people learn to cope with stressors focus mainly on the goals of enabling problem-resolution or expressing one’s emotions in a healthy manner. These goals are referred to as problem-focused coping and emotion-focused coping (Folkman & Lazarus, 1980 ; Pearlin & Schooler, 1978 ), and the person experiencing the stressors as potential threat is the agent for change and the recipient of the benefits of successful coping (Hobfoll, 1998 ). In addition to problem-focused and emotion-focused coping approaches, social support and resilience may be coping resources. There are many other sources for coping than there is room to present here (see e.g., Cartwright & Cooper, 2005 ); however, the current literature has primarily focused on these resources.

Problem-Focused Coping

Problem-focused or direct coping helps employees remove or reduce stressors in order to reduce their strain experiences (Bhagat et al., 2012 ). In problem-focused coping employees are responsible for working out a strategic plan in order to remove job stressors, such as setting up a set of goals and engaging in behaviors to meet these goals. Problem-focused coping is viewed as an adaptive response, though it can also be maladaptive if it creates more problems down the road, such as procrastinating getting work done or feigning illness to take time off from work. Adaptive problem-focused coping negatively relates to long-term job strains (Higgins & Endler, 1995 ). Discussion on problem-solving coping is framed from an adaptive perspective.

Problem-focused coping is featured as an extension of control, because engaging in problem-focused coping strategies requires a series of acts to keep job stressors under control (Bhagat et al., 2012 ). In the stress literature, there are generally two ways to categorize control: internal versus external locus of control, and primary versus secondary control. Locus of control refers to the extent to which people believe they have control over their own life (Rotter, 1966 ). People high in internal locus of control believe that they can control their own fate whereas people high in external locus of control believe that outside factors determine their life experience (Rotter, 1966 ). Generally, those with an external locus of control are less inclined to engage in problem-focused coping (Strentz & Auerbach, 1988 ). Primary control is the belief that people can directly influence their environment (Alloy & Abramson, 1979 ), and thus they are more likely to engage in problem-focused coping. However, when it is not feasible to exercise primary control, people search for secondary control, with which people try to adapt themselves into the objective environment (Rothbaum, Weisz, & Snyder, 1982 ).

Emotion-Focused Coping

Emotion-focused coping, sometimes referred to as palliative coping, helps employees reduce strains without the removal of job stressors. It involves cognitive or emotional efforts, such as talking about the stressor or distracting oneself from the stressor, in order to lessen emotional distress resulting from job stressors (Bhagat et al., 2012 ). Emotion-focused coping aims to reappraise and modify the perceptions of a situation or seek emotional support from friends or family. These methods do not include efforts to change the work situation or to remove the job stressors (Lazarus & Folkman, 1991 ). People tend to adopt emotion-focused coping strategies when they believe that little or nothing can be done to remove the threatening, harmful, and challenging stressors (Bhagat et al., 2012 ), such as when they are the only individuals to have the skills to get a project done or they are given increased responsibilities because of the unexpected departure of a colleague. Emotion-focused coping strategies include (1) reappraisal of the stressful situation, (2) talking to friends and receiving reassurance from them, (3) focusing on one’s strength rather than weakness, (4) optimistic comparison—comparing one’s situation to others’ or one’s past situation, (5) selective ignoring—paying less attention to the unpleasant aspects of one’s job and being more focused on the positive aspects of the job, (6) restrictive expectations—restricting one’s expectations on job satisfaction but paying more attention to monetary rewards, (7) avoidance coping—not thinking about the problem, leaving the situation, distracting oneself, or using alcohol or drugs (e.g., Billings & Moos, 1981 ).

Some emotion-focused coping strategies are maladaptive. For example, avoidance coping may lead to increased level of job strains in the long run (e.g., Parasuraman & Cleek, 1984 ). Furthermore, a person’s ability to cope with the imbalance of performing work to meet organizational expectations can take a toll on the person’s health, leading to physiological consequences such as cardiovascular disease, sleep disorders, gastrointestinal disorders, and diabetes (Fried et al., 2013 ; Siegrist, 2010 ; Toker, Shirom, Melamed, & Armon, 2012 ; Willert, Thulstrup, Hertz, & Bonde, 2010 ).

Comparing Coping Strategies across Cultures

Most coping research is conducted in individualistic, Western cultures wherein emotional control is emphasized and both problem-solving focused coping and primary control are preferred (Bhagat et al., 2010 ). However, in collectivistic cultures, emotion-focused coping and use of secondary control may be preferred and may not necessarily carry a negative evaluation (Bhagat et al., 2010 ). For example, African Americans are more likely to use emotion-focused coping than non–African Americans (Knight, Silverstein, McCallum, & Fox, 2000 ), and among women who experienced sexual harassment, Anglo American women were less likely to employ emotion focused coping (i.e., avoidance coping) than Turkish women and Hispanic American women, while Hispanic women used more denial than the other two groups (Wasti & Cortina, 2002 ).

Thus, whereas problem-focused coping is venerated in Western societies, emotion-focused coping may be more effective in reducing strains in collectivistic cultures, such as China, Japan, and India (Bhagat et al., 2010 ; Narayanan, Menon, & Spector, 1999 ; Selmer, 2002 ). Indeed, Swedish participants reported more problem-focused coping than did Chinese participants (Xiao, Ottosson, & Carlsson, 2013 ), American college students engaged in more problem-focused coping behaviors than did their Japanese counterparts (Ogawa, 2009 ), and Indian (vs. Canadian) students reported more emotion-focused coping, such as seeking social support and positive reappraisal (Sinha, Willson, & Watson, 2000 ). Moreover, Glazer, Stetz, and Izso ( 2004 ) found that internal locus of control was more predominant in individualistic cultures (United Kingdom and United States), whereas external locus of control was more predominant in communal cultures (Italy and Hungary). Also, internal locus of control was associated with less job stress, but more so for nurses in the United Kingdom and United States than Italy and Hungary. Taken together, adoption of coping strategies and their effectiveness differ significantly across cultures. The extent to which a coping strategy is perceived favorably and thus selected or not selected is not only a function of culture, but also a person’s sociocultural beliefs toward the coping strategy (Morimoto, Shimada, & Ozaki, 2013 ).

Social Support

Social support refers to the aid an entity gives to a person. The source of the support can be a single person, such as a supervisor, coworker, subordinate, family member, friend, or stranger, or an organization as represented by upper-level management representing organizational practices. The type of support can be instrumental or emotional. Instrumental support, including informational support, refers to that which is tangible, such as data to help someone make a decision or colleagues’ sick days so one does not lose vital pay while recovering from illness. Emotional support, including esteem support, refers to the psychological boost given to a person who needs to express emotions and feel empathy from others or to have his or her perspective validated. Beehr and Glazer ( 2001 ) present an overview of the role of social support on the stressor-strain relationship and arguments regarding the role of culture in shaping the utility of different sources and types of support.

Meaningfulness and Resilience

Meaningfulness reflects the extent to which people believe their lives are significant, purposeful, goal-directed, and fulfilling (Glazer, Kożusznik, Meyers, & Ganai, 2014 ). When faced with stressors, people who have a strong sense of meaning in life will also try to make sense of the stressors. Maintaining a positive outlook on life stressors helps to manage emotions, which is helpful in reducing strains, particularly when some stressors cannot be problem-solved (Lazarus & Folkman, 1991 ). Lazarus and Folkman ( 1991 ) emphasize that being able to reframe threatening situations can be just as important in an adaptation as efforts to control the stressors. Having a sense of meaningfulness motivates people to behave in ways that help them overcome stressors. Thus, meaningfulness is often used in the same breath as resilience, because people who are resilient are often protecting that which is meaningful.

Resilience is a personality state that can be fortified and enhanced through varied experiences. People who perceive their lives are meaningful are more likely to find ways to face adversity and are therefore more prone to intensifying their resiliency. When people demonstrate resilience to cope with noxious stressors, their ability to be resilient against other stressors strengthens because through the experience, they develop more competencies (Glazer et al., 2014 ). Thus, fitting with Hobfoll’s ( 1989 , 2001 ) COR theory, meaningfulness and resilience are psychological resources people attempt to conserve and protect, and employ when necessary for making sense of or coping with stressors.

Tertiary Interventions (Stress Management)

Stress management refers to interventions employed to treat and repair harmful repercussions of stressors that were not coped with sufficiently. As Lazarus and Folkman ( 1991 ) noted, not all stressors “are amenable to mastery” (p. 205). Stressors that are unmanageable and lead to strains require interventions to reverse or slow down those effects. Workplace interventions might focus on the person, the organization, or both. Unfortunately, instead of looking at the whole system to include the person and the workplace, most companies focus on the person. Such a focus should not be a surprise given the results of van der Klink, Blonk, Schene, and van Dijk’s ( 2001 ) meta-analysis of 48 experimental studies conducted between 1977 and 1996 . They found that of four types of tertiary interventions, the effect size for cognitive-behavioral interventions and multimodal programs (e.g., the combination of assertive training and time management) was moderate and the effect size for relaxation techniques was small in reducing psychological complaints, but not turnover intention related to work stress. However, the effects of (the five studies that used) organization-focused interventions were not significant. Similarly, Richardson and Rothstein’s ( 2008 ) meta-analytic study, including 36 experimental studies with 55 interventions, showed a larger effect size for cognitive-behavioral interventions than relaxation, organizational, multimodal, or alternative. However, like with van der Klink et al. ( 2001 ), Richardson and Rothstein ( 2008 ) cautioned that there were few organizational intervention studies included and the impact of interventions were determined on the basis of psychological outcomes and not physiological or organizational outcomes. Van der Klink et al. ( 2001 ) further expressed concern that organizational interventions target the workplace and that changes in the individual may take longer to observe than individual interventions aimed directly at the individual.

The long-term benefits of individual focused interventions are not yet clear either. Per Giga, Cooper, and Faragher ( 2003 ), the benefits of person-directed stress management programs will be short-lived if organizational factors to reduce stressors are not addressed too. Indeed, LaMontagne, Keegel, Louie, Ostry, and Landsbergis ( 2007 ), in their meta-analysis of 90 studies on stress management interventions published between 1990 and 2005 , revealed that in relation to interventions targeting organizations only, and interventions targeting individuals only, interventions targeting both organizations and individuals (i.e. the systems approach) had the most favorable positive effects on both the organizations and the individuals. Furthermore, the organization-level interventions were effective at both the individual and organization levels, but the individual-level interventions were effective only at the individual level.

Individual-Focused Stress Management

Individual-focused interventions concentrate on improving conditions for the individual, though counseling programs emphasize that the worker is in charge of reducing “stress,” whereas role-focused interventions emphasize activities that organizations can guide to actually reduce unnecessary noxious environmental factors.

Individual-Focused Stress Management: Employee Assistance Programs

When stress become sufficiently problematic (which is individually gauged or attended to by supportive others) in a worker’s life, employees may utilize the short-term counseling services or referral services Employee Assistance Programs (EAPs) provide. People who utilize the counseling services may engage in cognitive behavioral therapy aimed at changing the way people think about the stressors (e.g., as challenge opportunity over threat) and manage strains. Example topics that may be covered in these therapy sessions include time management and goal setting (prioritization), career planning and development, cognitive restructuring and mindfulness, relaxation, and anger management. In a study of healthcare workers and teachers who participated in a 2-day to 2.5-day comprehensive stress management training program (including 26 topics on identifying, coping with, and managing stressors and strains), Siu, Cooper, and Phillips ( 2013 ) found psychological and physical improvements were self-reported among the healthcare workers (for which there was no control group). However, comparing an intervention group of teachers to a control group of teachers, the extent of change was not as visible, though teachers in the intervention group engaged in more mastery recovery experiences (i.e., they purposefully chose to engage in challenging activities after work).

Individual-Focused Stress Management: Mindfulness

A popular therapy today is to train people to be more mindful, which involves helping people live in the present, reduce negative judgement of current and past experiences, and practicing patience (Birnie, Speca, & Carlson, 2010 ). Mindfulness programs usually include training on relaxation exercises, gentle yoga, and awareness of the body’s senses. In one study offered through the continuing education program at a Canadian university, 104 study participants took part in an 8-week, 90 minute per group (15–20 participants per) session mindfulness program (Birnie et al., 2010 ). In addition to body scanning, they also listened to lectures on incorporating mindfulness into one’s daily life and received a take-home booklet and compact discs that guided participants through the exercises studied in person. Two weeks after completing the program, participants’ mindfulness attendance and general positive moods increased, while physical, psychological, and behavioral strains decreased. In another study on a sample of U.K. government employees, study participants receiving three sessions of 2.5 to 3 hours each training on mindfulness, with the first two sessions occurring in consecutive weeks and the third occurring about three months later, Flaxman and Bond ( 2010 ) found that compared to the control group, the intervention group showed a decrease in distress levels from Time 1 (baseline) to Time 2 (three months after first two training sessions) and Time 1 to Time 3 (after final training session). Moreover, of the mindfulness intervention study participants who were clinically distressed, 69% experienced clinical improvement in their psychological health.

Individual-Focused Stress Management: Biofeedback/Imagery/Meditation/Deep Breathing

Biofeedback uses electronic equipment to inform users about how their body is responding to tension. With guidance from a therapist, individuals then learn to change their physiological responses so that their pulse normalizes and muscles relax (Norris, Fahrion, & Oikawa, 2007 ). The therapist’s guidance might include reminders for imagery, meditation, body scan relaxation, and deep breathing. Saunders, Driskell, Johnston, and Salas’s ( 1996 ) meta-analysis of 37 studies found that imagery helped reduce state and performance anxiety. Once people have been trained to relax, reminder triggers may be sent through smartphone push notifications (Villani et al., 2013 ).

Smartphone technology can also be used to support weight loss programs, smoking cessation programs, and medication or disease (e.g., diabetes) management compliance (Heron & Smyth, 2010 ; Kannampallil, Waicekauskas, Morrow, Kopren, & Fu, 2013 ). For example, smartphones could remind a person to take medications or test blood sugar levels or send messages about healthy behaviors and positive affirmations.

Individual-Focused Stress Management: Sleep/Rest/Respite

Workers today sleep less per night than adults did nearly 30 years ago (Luckhaupt, Tak, & Calvert, 2010 ; National Sleep Foundation, 2005 , 2013 ). In order to combat problems, such as increased anxiety and cardiovascular artery disease, associated with sleep deprivation and insufficient rest, it is imperative that people disconnect from their work at least one day per week or preferably for several weeks so that they are able to restore psychological health (Etzion, Eden, & Lapidot, 1998 ; Ragsdale, Beehr, Grebner, & Han, 2011 ). When college students engaged in relaxation-type activities, such as reading or watching television, over the weekend, they experienced less emotional exhaustion and greater general well-being than students who engaged in resources-consuming activities, such as house cleaning (Ragsdale et al., 2011 ). Additional research and future directions for research are reviewed and identified in the work of Sonnentag ( 2012 ). For example, she asks whether lack of ability to detach from work is problematic for people who find their work meaningful. In other words, are negative health consequences only among those who do not take pleasure in their work? Sonnetag also asks how teleworkers detach from their work when engaging in work from the home. Ironically, one of the ways that companies are trying to help with the challenges of high workload or increased need to be available to colleagues, clients, or vendors around the globe is by offering flexible work arrangements, whereby employees who can work from home are given the opportunity to do so. Companies that require global interactions 24-hours per day often employ this strategy, but is the solution also a source of strain (Glazer, Kożusznik, & Shargo, 2012 )?

Individual-Focused Stress Management: Role Analysis

Role analysis or role clarification aims to redefine, expressly identify, and align employees’ roles and responsibilities with their work goals. Through role negotiation, involved parties begin to develop a new formal or informal contract about expectations and define resources needed to fulfill those expectations. Glazer has used this approach in organizational consulting and, with one memorable client engagement, found that not only were the individuals whose roles required deeper re-evaluation happier at work (six months later), but so were their subordinates. Subordinates who once characterized the two partners as hostile and akin to a couple going through a bad divorce, later referred to them as a blissful pair. Schaubroeck, Ganster, Sime, and Ditman ( 1993 ) also found in a three-wave study over a two-year period that university employees’ reports of role clarity and greater satisfaction with their supervisor increased after a role clarification exercise of top managers’ roles and subordinates’ roles. However, the intervention did not have any impact on reported physical symptoms, absenteeism, or psychological well-being. Role analysis is categorized under individual-focused stress management intervention because it is usually implemented after individuals or teams begin to demonstrate poor performance and because the intervention typically focuses on a few individuals rather than an entire organization or group. In other words, the intervention treats the person’s symptoms by redefining the role so as to eliminate the stimulant causing the problem.

Organization-Focused Stress Management

At the organizational level, companies that face major declines in productivity and profitability or increased costs related to healthcare and disability might be motivated to reassess organizational factors that might be impinging on employees’ health and well-being. After all, without healthy workers, it is not possible to have a healthy organization. Companies may choose to implement practices and policies that are expected to help not only the employees, but also the organization with reduced costs associated with employee ill-health, such as medical insurance, disability payments, and unused office space. Example practices and policies that may be implemented include flexible work arrangements to ensure that employees are not on the streets in the middle of the night for work that can be done from anywhere (such as the home), diversity programs to reduce stress-induced animosity and prejudice toward others, providing only healthy food choices in cafeterias, mandating that all employees have physicals in order to receive reduced prices for insurance, company-wide closures or mandatory paid time off, and changes in organizational visioning.

Organization-Focused Stress Management: Organizational-Level Occupational Health Interventions

As with job design interventions that are implemented to remediate work characteristics that were a source of unnecessary or excessive stressors, so are organizational-level occupational health (OLOH) interventions. As with many of the interventions, its placement as a primary or tertiary stress management intervention may seem arbitrary, but when considering the goal and target of change, it is clear that the intervention is implemented in response to some ailing organizational issues that need to be reversed or stopped, and because it brings in the entire organization’s workforce to address the problems, it has been placed in this category. There are several more case studies than empirical studies on the topic of whole system organizational change efforts (see example case studies presented by the United Kingdom’s Health and Safety Executive). It is possible that lack of published empirical work is not so much due to lack of attempting to gather and evaluate the data for publication, but rather because the OLOH interventions themselves never made it to the intervention stage, the interventions failed (Biron, Gatrell, & Cooper, 2010 ), or the level of evaluation was not rigorous enough to get into empirical peer-review journals. Fortunately, case studies provide some indication of the opportunities and problems associated with OLOH interventions.

One case study regarding Cardiff and Value University Health Board revealed that through focus group meetings with members of a steering group (including high-level managers and supported by top management) and facilitated by a neutral, non-judgemental organizational health consultant, ideas for change were posted on newsprint, discussed, and areas in the organization needing change were identified. The intervention for giving voice to people who initially had little already had a positive effect on the organization, as absence decreased by 2.09% and 6.9% merely 12 and 18 months, respectively, after the intervention. Translated in financial terms, the 6.9% change was equivalent to a quarterly savings of £80,000 (Health & Safety Executive, n.d. ). Thus, focusing on the context of change and how people will be involved in the change process probably helped the organization realize improvements (Biron et al., 2010 ). In a recent and rare empirical study, employing both qualitative and quantitative data collection methods, Sørensen and Holman ( 2014 ) utilized PAR in order to plan and implement an OLOH intervention over the course of 14 months. Their study aimed to examine the effectiveness of the PAR process in reducing workers’ work-related and social or interpersonal-related stressors that derive from the workplace and improving psychological, behavioral, and physiological well-being across six Danish organizations. Based on group dialogue, 30 proposals for change were proposed, all of which could be categorized as either interventions to focus on relational factors (e.g., management feedback improvement, engagement) or work processes (e.g., reduced interruptions, workload, reinforcing creativity). Of the interventions that were implemented, results showed improvements on manager relationship quality and reduced burnout, but no changes with respect to work processes (i.e., workload and work pace) perhaps because the employees already had sufficient task control and variety. These findings support Dewe and Kompier’s ( 2008 ) position that occupational health can be reinforced through organizational policies that reinforce quality jobs and work experiences.

Organization-Focused Stress Management: Flexible Work Arrangements

Dewe and Kompier ( 2008 ), citing the work of Isles ( 2005 ), noted that concern over losing one’s job is a reason for why 40% of survey respondents indicated they work more hours than formally required. In an attempt to create balance and perceived fairness in one’s compensation for putting in extra work hours, employees will sometimes be legitimately or illegitimately absent. As companies become increasingly global, many people with desk jobs are finding themselves communicating with colleagues who are halfway around the globe and at all hours of the day or night (Glazer et al., 2012 ). To help minimize the strains associated with these stressors, companies might devise flexible work arrangements (FWA), though the type of FWA needs to be tailored to the cultural environment (Masuda et al., 2012 ). FWAs give employees some leverage to decide what would be the optimal work arrangement for them (e.g., part-time, flexible work hours, compressed work week, telecommuting). In other words, FWA provides employees with the choice of when to work, where to work (on-site or off-site), and how many hours to work in a day, week, or pay period (Kossek, Thompson, & Lautsch, 2015 ). However, not all employees of an organization have equal access to or equitable use of FWAs; workers in low-wage, hourly jobs are often beholden to being physically present during specific hours (Swanberg McKechnie, Ojha, & James, 2011 ). In a study of over 1,300 full-time hourly retail employees in the United States, Swanberg et al. ( 2011 ) showed that employees who have control over their work schedules and over their work hours were satisfied with their work schedules, perceived support from the supervisor, and work engagement.

Unfortunately, not all FWAs yield successful results for the individual or the organization. Being able to work from home or part-time can have problems too, as a person finds himself or herself working more hours from home than required. Sometimes telecommuting creates work-family conflict too as a person struggles to balance work and family obligations while working from home. Other drawbacks include reduced face-to-face contact between work colleagues and stakeholders, challenges shaping one’s career growth due to limited contact, perceived inequity if some have more flexibility than others, and ambiguity about work role processes for interacting with employees utilizing the FWA (Kossek et al., 2015 ). Organizations that institute FWAs must carefully weigh the benefits and drawbacks the flexibility may have on the employees using it or the employees affected by others using it, as well as the implications on the organization, including the vendors who are serving and clients served by the organization.

Organization-Focused Stress Management: Diversity Programs

Employees in the workplace might experience strain due to feelings of discrimination or prejudice. Organizational climates that do not promote diversity (in terms of age, religion, physical abilities, ethnicity, nationality, sex, and other characteristics) are breeding grounds for undesirable attitudes toward the workplace, lower performance, and greater turnover intention (Bergman, Palmieri, Drasgow, & Ormerod, 2012 ; Velez, Moradi, & Brewster, 2013 ). Management is thus advised to implement programs that reinforce the value and importance of diversity, as well as manage diversity to reduce conflict and feelings of prejudice. In fact, managers who attended a leadership training program reported higher multicultural competence in dealing with stressful situations (Chrobot-Mason & Leslie, 2012 ), and managers who persevered through challenges were more dedicated to coping with difficult diversity issues (Cilliers, 2011 ). Thus, diversity programs can help to reduce strains by directly reducing stressors associated with conflict linked to diversity in the workplace and by building managers’ resilience.

Organization-Focused Stress Management: Healthcare Management Policies

Over the past few years, organizations have adopted insurance plans that implement wellness programs for the sake of managing the increasing cost of healthcare that is believed to be a result of individuals’ not managing their own health, with regular check-ups and treatment. The wellness programs require all insured employees to visit a primary care provider, complete a health risk assessment, and engage in disease management activities as specified by a physician (e.g., see frequently asked questions regarding the State of Maryland’s Wellness Program). Companies believe that requiring compliance will reduce health problems, although there is no proof that such programs save money or that people would comply. One study that does, however, boast success, was a 12-week workplace health promotion program aimed at reducing Houston airport workers’ weight (Ebunlomo, Hare-Everline, Weber, & Rich, 2015 ). The program, which included 235 volunteer participants, was deemed a success, as there was a total weight loss of 345 pounds (or 1.5 lbs per person). Given such results in Houston, it is clear why some people are also skeptical over the likely success of wellness programs, particularly as there is no clear method for evaluating their efficacy (Sinnott & Vatz, 2015 ).

Moreover, for some, such a program is too paternalistic and intrusive, as well as punishes anyone who chooses not to actively participate in disease management programs (Sinnott & Vatz, 2015 ). The programs put the onus of change on the person, though it is a response to the high costs of ill-health. The programs neglect to consider the role of the organization in reducing the barriers to healthy lifestyle, such as cloaking exempt employment as simply needing to get the work done, when it usually means working significantly more hours than a standard workweek. In fact, workplace health promotion programs did not reduce presenteeism (i.e., people going to work while unwell thereby reducing their job performance) among those who suffered from physical pain (Cancelliere, Cassidy, Ammendolia, & Côte, 2011 ). However, supervisor education, worksite exercise, lifestyle intervention through email, midday respite from repetitive work, a global stress management program, changes in lighting, and telephone interventions helped to reduce presenteeism. Thus, emphasis needs to be placed on psychosocial aspects of the organization’s structure, including managers and overall organizational climate for on-site presence, that reinforces such behavior (Cancelliere et al., 2011 ). Moreover, wellness programs are only as good as the interventions to reduce work-related stressors and improve organizational resources to enable workers to improve their overall psychological and physical health.

Concluding Remarks

Future research.

One of the areas requiring more theoretical and practical attention is that of the utility of stress frameworks to guide organizational development change interventions. Although it has been proposed that the foundation for work stress management interventions is in organizational development, and even though scholars and practitioners of organization development were also founders of research programs that focused on employee health and well-being or work stress, there are few studies or other theoretical works that link the two bodies of literature.

A second area that requires additional attention is the efficacy of stress management interventions across cultures. In examining secondary stress management interventions (i.e., coping), some cross-cultural differences in findings were described; however, there is still a dearth of literature from different countries on the utility of different prevention, coping, and stress management strategies.

A third area that has been blossoming since the start of the 21st century is the topic of hindrance and challenge stressors and the implications of both on workers’ well-being and performance. More research is needed on this topic in several areas. First, there is little consistency by which researchers label a stressor as a hindrance or a challenge. Researchers sometimes take liberties with labels, but it is not the researchers who should label a stressor but the study participants themselves who should indicate if a stressor is a source of strain. Rodríguez, Kozusznik, and Peiró ( 2013 ) developed a measure in which respondents indicate whether a stressor is a challenge or a hindrance. Just as some people may perceive demands to be challenges that they savor and that result in a psychological state of eustress (Nelson & Simmons, 2003 ), others find them to be constraints that impede goal fulfillment and thus might experience distress. Likewise, some people might perceive ambiguity as a challenge that can be overcome and others as a constraint over which he or she has little control and few or no resources with which to cope. More research on validating the measurement of challenge vs. hindrance stressors, as well as eustress vs. distress, and savoring vs. coping, is warranted. Second, at what point are challenge stressors harmful? Just because people experiencing challenge stressors continue to perform well, it does not necessarily mean that they are healthy people. A great deal of stressors are intellectually stimulating, but excessive stimulation can also take a toll on one’s physiological well-being, as evident by the droves of professionals experiencing different kinds of diseases not experienced as much a few decades ago, such as obesity (Fried et al., 2013 ). Third, which stress management interventions would better serve to reduce hindrance stressors or to reduce strain that may result from challenge stressors while reinforcing engagement-producing challenge stressors?

A fourth area that requires additional attention is that of the flexible work arrangements (FWAs). One of the reasons companies have been willing to permit employees to work from home is not so much out of concern for the employee, but out of the company’s need for the focal person to be able to communicate with a colleague working from a geographic region when it is night or early morning for the focal person. Glazer, Kożusznik, and Shargo ( 2012 ) presented several areas for future research on this topic, noting that by participating on global virtual teams, workers face additional stressors, even while given flexibility of workplace and work time. As noted earlier, more research needs to be done on the extent to which people who take advantage of FWAs are advantaged in terms of detachment from work. Can people working from home detach? Are those who find their work invigorating also likely to experience ill-health by not detaching from work?

A fifth area worthy of further research attention is workplace wellness programing. According to Page and Vella-Brodrick ( 2009 ), “subjective and psychological well-being [are] key criteria for employee mental health” (p. 442), whereby mental health focuses on wellness, rather than the absence of illness. They assert that by fostering employee mental health, organizations are supporting performance and retention. Employee well-being can be supported by ensuring that jobs are interesting and meaningful, goals are achievable, employees have control over their work, and skills are used to support organizational and individual goals (Dewe & Kompier, 2008 ). However, just as mental health is not the absence of illness, work stress is not indicative of an absence of psychological well-being. Given the perspective that employee well-being is a state of mind (Page & Vella-Brodrick, 2009 ), we suggest that employee well-being can be negatively affected by noxious job stressors that cannot be remediated, but when job stressors are preventable, employee well-being can serve to protect an employee who faces job stressors. Thus, wellness programs ought to focus on providing positive experiences by enhancing and promoting health, as well as building individual resources. These programs are termed “green cape” interventions (Pawelski, 2016 ). For example, with the growing interests in positive psychology, researchers and practitioners have suggested employing several positive psychology interventions, such as expressing gratitude, savoring experiences, and identifying one’s strengths (Tetrick & Winslow, 2015 ). Another stream of positive psychology is psychological capital, which includes four malleable functions of self-efficacy, optimism, hope, and resilience (Luthans, Youssef, & Avolio, 2007 ). Workplace interventions should include both “red cape” interventions (i.e., interventions to reduce negative experiences) and “green cape” interventions (i.e., workplace wellness programs; Polly, 2014 ).

A Healthy Organization’s Pledge

A healthy workplace requires healthy workers. Period. Among all organizations’ missions should be the focus on a healthy workforce. To maintain a healthy workforce, the company must routinely examine its own contributions in terms of how it structures itself; reinforces communications among employees, vendors, and clients; how it rewards and cares for its people (e.g., ensuring they get sufficient rest and can detach from work); and the extent to which people at the upper levels are truly connected with the people at the lower levels. As a matter of practice, management must recognize when employees are overworked, unwell, and poorly engaged. Management must also take stock of when it is doing well and right by its contributors’ and maintain and reinforce the good practices, norms, and procedures. People in the workplace make the rules; people in the workplace can change the rules. How management sees its employees and values their contribution will have a huge role in how a company takes stock of its own pain points. Providing employees with tools to manage their own reactions to work-related stressors and consequent strains is fine, but wouldn’t it be grand if organizations took better notice about what they could do to mitigate the strain-producing stressors in the first place and take ownership over how employees are treated?

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Transforming stress through awareness, education and collaboration.

Stress Research

“The difficulty in science is often not so much how to make the discovery but rather to know that one has made it.” – J.D. Bernal

2022 Stress Statistics

Two years after the World Health Organization declared COVID-19 a global pandemic, inflation, money issues and the war in Ukraine have pushed U.S. stress to alarming levels, according to polls conducted for the American Psychological Association .

A late-breaking poll, fielded March 1-3 by The Harris Poll on behalf of APA, revealed striking findings, with more adults rating inflation and issues related to the invasion of Ukraine as stressors than any other issue asked about in the 15-year history of the Stress in America TM  poll. This comes on top of money stress at the highest recorded level since 2015, according to a broader Stress in America poll fielded last month.

Top sources of stress were the rise in prices of everyday items due to inflation (e.g., gas prices, energy bills, grocery costs, etc.) (cited by 87%), followed by supply chain issues (81%), global uncertainty (81%), Russia’s invasion of Ukraine (80%) and potential retaliation from Russia (e.g., in the form of cyberattacks or nuclear threats) (80%).

Adults also reported separation and conflict as causes for straining and/or ending of relationships. Half of adults (51%, particularly essential workers at 61%) said they have loved ones they have not been able to see in person in the past two years as a result of the COVID-19 pandemic. Strikingly, more than half of all U.S. adults (58%) reported experiencing a relationship strain or end as a result of conflicts related to the COVID-19 pandemic, including canceling events or gatherings due to COVID-19 concerns (29%); difference of opinion over some aspect of vaccines (25%); different views of the pandemic overall (25%); and difference of opinion over mask-wearing (24%).

Key Stress Statistics

Americans are one of the most stressed out in the world. The current stress level experienced by Americans is 20 percentage points higher than the global average. The country’s rate is similar to Louisiana’s, the most stressed state. Globally, Greece has the highest reported stress level at 59%.

• 55% of Americans are stressed during the day.
• The global average of the number of stressed people out of 143 countries is 35%.
• Paraguay is the country with the highest positive experience index.
• Afghanistan is the least positive country in the world with a positive experience index of 43% lower than its score in the previous year.
• Stress causes 57% of US respondents to feel paralyzed.
• 63% of US workers are ready to quit their job to avoid work-related stress.
• Chronic stress is commonplace at work with 94% of workers reporting feeling stress at work.
• 59% of Greeks have reported experiencing stress in the previous day.
• Montana is the least stressed US state with a total stress score of 26.81 while Louisiana the most stressed with 59.94.

Causes and Sources of Stress

Living conditions, the political climate, financial insecurity, and work issues are some stressors US adults cite as the cause of their stress. Ineffective communications increase work stress to the point of frustration that workers want to quit.  These stressors, unfortunately, are not something people can just ignore. Quitting a job would result in debt and financial instability which, in turn, would be added stressors.

• 35% of workers say their boss is a cause of their workplace stress.
• 80% of US workers experience work stress because of ineffective company communications.
• 39% of North American employees report their workload the main source of the work stress.
• 49% of 18 – 24 year olds who report high levels of stress felt comparing themselves to others is a stressor.
• 71% of US adults with private health insurance say the cost of healthcare causes them stress while 53% with public insurance say the same.
• 54% of Americans want to stay informed about the news but following the news causes them stress.
• 42% of US adults cite personal debt as a source of significant stress.
• 1 in 4 American adults say discrimination is a significant source of stress.
• Mass shootings are a significant source of stress across all races; 84% of Hispanic report this, the highest among the races.

Stress and Relationships

People under stress admit to taking out their frustration on other people. Targets for venting out include strangers and those they have personal relationships with. Men and women report different levels of how work stress affects their relationships with their spouses.

• 76% of US workers say their workplace stress has had a negative impact on their personal relationships.
• Seven in 10 adults report work stress affects their personal relationships.
• 79% of men report work stress affects their personal relationship with their spouse compared to 61% for women.
• 36% of adults reported experiencing stress caused by a friend or loved one’s long-term health condition.

Stress Management Statistics

A look at the stress management techniques employed by US adults to deal with their stress, an overwhelming majority are self-care practices. Though very helpful, it does not address the stressor at the root of the problem. Stress management programs would be beneficial not only for employees but for the company in the long run.

• 30% of Us adults eat comfort food “more than the usual” when faced with a challenging or stressful event.
• 51% of US adults engage in prayer—a routine activity—when faced with a challenge or stressful situation.
• Coping mechanisms of Gen Z and Millenials experiencing stress in the US 44% of Gen Z and 40% of Millenials sleep in while exercising counts for 14% and 20% respectively.
• 49% of US adults report enduring stressful situations as a coping behavior to handle stress.
• Less than 25% of those with depression worldwide have access to mental health treatments.

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American Psychological Association

Cardiac Coherence and Post-traumatic Stress Disorder in Combat Veterans

Jay P. Ginsberg, Ph.D.; Melanie E. Berry, M.S.; Donald A Powell, Ph.D.

Alternative Therapies in Health and Medicine, A Peer-Reviewed Journal, 2010;16 (4):52-60. PDF version of the complete paper: Cardiac Coherence and PTSD in Combat Veterans

Abstract-PTSD

Background: The need for treatment of posttraumatic stress disorder (PTSD) among combat veterans returning from Afghanistan and Iraq is a growing concern. PTSD has been associated with reduced cardiac coherence (an indicator of heart rate variability [HRV]) and deficits in early-stage information processing (attention and immediate memory) in different studies. However, the co-occurrence of reduced coherence and cognition in combat veterans with PTSD has not been studied before.

Primary Study Objective: A pilot study was undertaken to assess the covariance of coherence and information processing in combat veterans. An additional study goal was an assessment of the effects of HRV biofeedback (HRVB) on coherence and information processing in these veterans.

Methods/Design: A two-group (combat veterans with and without PTSD), a pre-post study of coherence and information processing was employed with baseline psychometric covariates.

Setting: The study was conducted at a VA Medical Center outpatient mental health clinic.

Participants: Five combat veterans from Iraq or Afghanistan with PTSD and five active-duty soldiers with comparable combat exposure who were without PTSD.

Intervention: Participants met with an HRVB professional once weekly for 4 weeks and received visual feedback in HRV patterns while receiving training in resonance frequency breathing and positive emotion induction.

Primary Outcome Measures: Cardiac coherence, word list learning, commissions (false alarms) in go—no go reaction time, digits backward.

Results: Cardiac coherence was achieved in all participants, and the increase in coherence ratio was significant post-HRVB training. Significant improvements in the information processing indicators were achieved. Degree of increase in coherence was the likely mediator of cognitive improvement.

Conclusion: Cardiac coherence is an index of the strength of control of parasympathetic cardiac deceleration in an individual that has cardinal importance for the individual’s attention and affect regulation.

The Effect of a Biofeedback-based Stress Management Tool on Physician Stress: A Randomized Controlled Clinical Trial

Jane B. Lemaire, Jean E. Wallace, Adriane M. Lewin, Jill de Grood, Jeffrey P. Schaefer

Open Medicine 2011; 5(4)E154. PDF version of the complete paper: physician-stress-randomized-controlled-clinical-trial

Abstract- Biofeedback-based Stress Management

Background: Physicians often experience work-related stress that may lead to personal harm and impaired professional performance. Biofeedback has been used to manage stress in various populations.

Objective: To determine whether a biofeedback-based stress management tool, consisting of rhythmic breathing, actively self-generated positive emotions and a portable biofeedback device, reduces physician stress.

Design: Randomized controlled trial measuring the efficacy of a stress-reduction intervention over 28 days, with a 28-day open-label trial extension to assess effectiveness.

Setting: Urban tertiary care hospital.

Participants: Forty staff physicians (23 men and 17 women) from various medical practices (1 from primary care, 30 from a medical specialty and 9 from a surgical specialty) were recruited by means of electronic mail, regular mail and posters placed in the physicians’ lounge and throughout the hospital.

Intervention: Physicians in the intervention group were instructed to use a biofeedback-based stress management tool three times daily. Participants in both the control and intervention groups received twice-weekly support visits from the research team over 28 days, with the intervention group also receiving re-inforcement in the use of the stress management tool during these support visits. During the 28-day extension period, both the control and the intervention groups received the intervention, but without intensive support from the research team.

Main outcome measure: Stress was measured with a scale developed to capture short-term changes in global perceptions of stress for physicians (maximum score 200).

Results: During the randomized controlled trial (days 0 to 28), the mean stress score declined significantly for the intervention group (change -14.7, standard deviation [SD] 23.8; p = 0.013) but not for the control group (change -2.2, SD 8.4; p = 0.30). The difference in mean score change between the groups was 12.5 (p = 0.048). The lower mean stress scores in the intervention group were maintained during the trial extension to day 56. The mean stress score for the control group changed significantly during the 28-day extension period (change -8.5, SD 7.6; p < 0.001).

Conclusion: A biofeedback-based stress management tool may be a simple and effective stress-reduction strategy for physicians.

Coherence Training In Children With Attention-Deficit Hyperactivity Disorder: Cognitive Functions and Behavioral Changes

Anthony Lloyd, Ph.D.; Davide Brett, B.Sc.; Ketith Wesnes, Ph.D.

Alternative Therapies in Health and Medicine, A Peer-Reviewed Journal, 2010; 16 (4):34-42

PDF version of the complete paper: coherence-training-in-children-with-adhd

Abstract-ADHD

Attention-deficit hyperactivity disorder (ADHD) is the most prevalent behavioral diagnosis in children, with an estimated 500 000 children affected in the United Kingdom alone. The need for an appropriate and effective intervention for children with ADHD is a growing concern for educators and childcare agencies. This randomized controlled clinical trial evaluated the impact of the HeartMath self-regulation skills and coherence training program (Institute of HeartMath, Boulder Creek, California) on a population of 38 children with ADHD in academic year groups 6, 7, and 8. Learning of the skills was supported with heart rhythm coherence monitoring and feedback technology designed to facilitate self-induced shifts in cardiac coherence. The cognitive drug research system was used to assess cognitive functioning as the primary outcome measure. Secondary outcome measures assessed teacher and student reposted changes in behavior. Participants demonstrated significant improvements in various aspects of cognitive functioning such as delayed word recall, immediate word recall, word recognition, and episodic secondary memory. Significant improvements in behavior were also found. The results suggest that the intervention offers a physiologically based program to improve cognitive functioning in children with ADHD and improve behaviors that is appropriate to implement in a school environment.

Coherence and Health Care Cost – RCA Actuarial Study: A Cost-Effectiveness Cohort Study

Woody Bedell; Mariette Kaszkin-Bettag, Ph.D.

Alternative Therapies in Health and Medicine, A Peer-Reviewed Journal, 2010;16 (4):26-31. PDF version of the complete paper: rca-actuarial-study-coherence-and-health-care

Abstract-Health and Medicine

Chronic stress is among the most costly health problems in terms of direct health costs, absenteeism, disability, and performance standards. The Reformed Church in America (RCA) identified stress among its clergy as a major cause of higher-than-average health claims and implemented HeartMath (HM) to help its participants manage stress and increase physiological resilience. The 6-week HM program Revitalize You! was selected for the intervention including the emWave Personal Stress Reliever technology.

From 2006 to 2007, completion of a health risk assessment (HRA) provided eligible clergy with the opportunity to participate in the HM program or a lifestyle management program (LSM). Outcomes for that year were assessed with the Stress and Well-being Survey. Of 313 participants who completed the survey, 149 completed the Revitalize You! The program and 164 completed the LSM. Well-being, stress management, resilience, and emotional vitality were significantly improved in the HM group as compared to the LSM group.

In an analysis of the claims costs data for 2007 and 2008, 144 pastors who had participated in the HM program were compared to 343 non-participants (control group). Adjusted medical costs were reduced by 3.8% for HM participants in comparison with an increase of 9.0% for the control group. For the adjusted pharmacy costs, an increase of 7.9% was found compared with an increase of 13.3% for the control group. Total 2008 savings as a result of the HM program are estimated at $585 per participant, yielding a return on investment of 1.95:1. These findings show that HM stress-reduction and coherence-building techniques can reduce health care costs.

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How much stress is too much? A psychiatrist explains the links between toxic stress and poor health − and how to get help

Professor of Psychiatry and Family Medicine, University of Cincinnati

Disclosure statement

Lawson R. Wulsin received funding in 2010 from the Veterans Administration support a secondary analysis of data from the Framingham Heart Study, which was published and contributed in part to the substance of this article.

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COVID-19 taught most people that the line between tolerable and toxic stress – defined as persistent demands that lead to disease – varies widely. But some people will age faster and die younger from toxic stressors than others.

So how much stress is too much, and what can you do about it?

I’m a psychiatrist specializing in psychosomatic medicine , which is the study and treatment of people who have physical and mental illnesses. My research is focused on people who have psychological conditions and medical illnesses as well as those whose stress exacerbates their health issues.

I’ve spent my career studying mind-body questions and training physicians to treat mental illness in primary care settings. My forthcoming book is titled “Toxic Stress: How Stress is Killing Us and What We Can Do About It.”

A 2023 study of stress and aging over the life span – one of the first studies to confirm this piece of common wisdom – found that four measures of stress all speed up the pace of biological aging in midlife. It also found that persistent high stress ages people in a comparable way to the effects of smoking and low socioeconomic status , two well-established risk factors for accelerated aging.

The difference between good stress and the toxic kind

Good stress – a demand or challenge you readily cope with – is good for your health. In fact, the rhythm of these daily challenges, including feeding yourself, cleaning up messes, communicating with one another and carrying out your job, helps to regulate your stress response system and keep you fit.

Toxic stress, on the other hand, wears down your stress response system in ways that have lasting effects, as psychiatrist and trauma expert Bessel van der Kolk explains in his bestselling book “ The Body Keeps the Score .”

The earliest effects of toxic stress are often persistent symptoms such as headache, fatigue or abdominal pain that interfere with overall functioning. After months of initial symptoms, a full-blown illness with a life of its own – such as migraine headaches, asthma, diabetes or ulcerative colitis – may surface.

When we are healthy, our stress response systems are like an orchestra of organs that miraculously tune themselves and play in unison without our conscious effort – a process called self-regulation. But when we are sick, some parts of this orchestra struggle to regulate themselves, which causes a cascade of stress-related dysregulation that contributes to other conditions.

For instance, in the case of diabetes, the hormonal system struggles to regulate sugar. With obesity, the metabolic system has a difficult time regulating energy intake and consumption. With depression, the central nervous system develops an imbalance in its circuits and neurotransmitters that makes it difficult to regulate mood, thoughts and behaviors.

‘Treating’ stress

Though stress neuroscience in recent years has given researchers like me new ways to measure and understand stress , you may have noticed that in your doctor’s office, the management of stress isn’t typically part of your treatment plan.

Most doctors don’t assess the contribution of stress to a patient’s common chronic diseases such as diabetes, heart disease and obesity, partly because stress is complicated to measure and partly because it is difficult to treat. In general, doctors don’t treat what they can’t measure.

Stress neuroscience and epidemiology have also taught researchers recently that the chances of developing serious mental and physical illnesses in midlife rise dramatically when people are exposed to trauma or adverse events, especially during vulnerable periods such as childhood .

Over the past 40 years in the U.S., the alarming rise in rates of diabetes , obesity , depression, PTSD, suicide and addictions points to one contributing factor that these different illnesses share: toxic stress.

Toxic stress increases the risk for the onset, progression, complications or early death from these illnesses.

Suffering from toxic stress

Because the definition of toxic stress varies from one person to another, it’s hard to know how many people struggle with it. One starting point is the fact that about 16% of adults report having been exposed to four or more adverse events in childhood . This is the threshold for higher risk for illnesses in adulthood.

Research dating back to before the COVID-19 pandemic also shows that about 19% of adults in the U.S. have four or more chronic illnesses . If you have even one chronic illness, you can imagine how stressful four must be.

And about 12% of the U.S. population lives in poverty , the epitome of a life in which demands exceed resources every day. For instance, if a person doesn’t know how they will get to work each day, or doesn’t have a way to fix a leaking water pipe or resolve a conflict with their partner, their stress response system can never rest. One or any combination of threats may keep them on high alert or shut them down in a way that prevents them from trying to cope at all.

Add to these overlapping groups all those who struggle with harassing relationships, homelessness, captivity, severe loneliness, living in high-crime neighborhoods or working in or around noise or air pollution. It seems conservative to estimate that about 20% of people in the U.S. live with the effects of toxic stress.

Recognizing and managing stress and its associated conditions

The first step to managing stress is to recognize it and talk to your primary care clinician about it. The clinician may do an assessment involving a self-reported measure of stress .

The next step is treatment. Research shows that it is possible to retrain a dysregulated stress response system. This approach, called “lifestyle medicine ,” focuses on improving health outcomes through changing high-risk health behaviors and adopting daily habits that help the stress response system self-regulate.

Adopting these lifestyle changes is not quick or easy, but it works.

The National Diabetes Prevention Program , the Ornish “UnDo” heart disease program and the U.S. Department of Veterans Affairs PTSD program , for example, all achieve a slowing or reversal of stress-related chronic conditions through weekly support groups and guided daily practice over six to nine months. These programs help teach people how to practice personal regimens of stress management, diet and exercise in ways that build and sustain their new habits.

There is now strong evidence that it is possible to treat toxic stress in ways that improve health outcomes for people with stress-related conditions. The next steps include finding ways to expand the recognition of toxic stress and, for those affected, to expand access to these new and effective approaches to treatment.

• Neuroscience
• Chronic stress
• Adverse childhood experiences
• Adverse childhood events

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• Mental Health

Why Zero Stress Shouldn’t Be Your Goal

H ow many times have you heard that squashing stress is crucial for good health? Stress has become such a wellness buzzword that the quest to get rid of it can feel, well, stressful. But stress isn’t always the enemy. In fact, research suggests some is actually good for you , with potential benefits ranging from enhanced brain function to healthier aging .

In recent decades, some people have grown overly fearful of stress, concluding that it's "the most horrible thing that can happen to you," says Daniela Kaufer, a professor of integrative biology at the University of California, Berkeley. But “it’s a much more complex story," she says. "Stress is a vital, required response.”

What is stress, anyway?

For one thing, it’s ubiquitous: research suggests people feel at least some stress on up to 90% of their days. But what’s actually going on in your body when you’re dealing with family drama or work deadlines?

In a high-stakes situation, your brain directs the adrenal glands to release hormones including adrenaline, causing physiological changes throughout the body that lead to the sweaty palms, fast breathing, and racing heart many people experience when they’re under pressure. The body also releases oxytocin , or the "bonding hormone," during times of stress.

When stress festers for a long time, unaddressed, it’s linked to mental and physical health issues, even raising your risk for chronic conditions like heart disease . But in an immediate sense, a stress response is vital. It can help you power through a hard time or even escape physical danger. And, when acute stress is managed well, it can set you up for better health and well-being in the future.

How stress improves health

In toxicology, there’s a phenomenon known as “hormesis,” which describes substances that are beneficial at low doses but dangerous at high doses. Assaf Oshri, an associate professor of human development and family science at the University of Georgia, has applied that concept to his research on adversity , demonstrating that it works in largely the same way.

Chronic stress, as well as stress resulting from highly traumatic experiences, can be damaging—but moderate amounts of stress can benefit the body and mind, improving cognitive function and boosting resilience , according to Oshri's work. In studies on rats , Kaufer has also demonstrated that acute stress may help the brain work better and prime animals for better reactions next time they encounter stressors.

“ Resilience is a process. It’s not a trait,” Oshri says. “It emerges from your interactions with the environment.” If people aren’t exposed to any stress, he says, they may not build up that resilience muscle. If they’re exposed to too much—or to particularly traumatic forms, like abuse or discrimination—their well-being may suffer. But there seems to be a sweet spot in between, where stress fortifies psychological health and helps people bounce back from difficult situations. (Exactly where that sweet spot is may vary from person to person, Oshri says.)

Even physical health can benefit from some level of stress. Exercising is, at its core, a process of putting stress on the body so it can grow stronger. And some studies also show that short-term stress exposure boosts immune function.

How you deal with stress matters

It’s not just the amount of pressure you’re under that influences well-being; it’s also how you respond to it. Studies have shown that people who believe they can learn and grow from hard experiences fare better during challenging times, as opposed to those who view stressors as completely negative.

Still, it’s okay—even healthy—to be a little rattled by life’s curveballs. A 2024 study found that there’s a “Goldilocks zone” when it comes to emotional responses to stress. People who tend to have either extremely strong or extremely weak reactions to challenging situations are at increased risk of poor health and well-being, explains co-author Jonathan Rush, an assistant professor of psychology at the University of Victoria in Canada. People in the middle, who respond a little but not too much, tend to be healthiest, he says.

“One of the main purposes of having emotions is that they alert us to things in our environment” so we can deal with them appropriately, Rush says. Blocking out your emotions entirely is akin to ignoring a leaky faucet in your bathroom: “eventually,” Rush says, “you’re going to have a flood in your home.”

Mindfulness practices like yoga and meditation can help people cultivate a balance between going off and shutting down in the face of stress, Rush says. Mindfulness isn’t about ignoring negative feelings, but rather acknowledging them so you can manage them in healthier ways, he explains.

Leaning on loved ones during tough times is important too, Kaufer adds, since social support can serve as a buffer against the negative effects of stress and trauma. And if you can, she says, remind yourself that stress is a difficult but necessary part of life.

“You can’t choose exactly what happens to you, but you can choose your response in the moment,” Kaufer says. “Having the idea that you can overcome things, you can grow from things, whatever happens you will have a path forward”—that’s what matters most.

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

Published on 25.3.2024 in Vol 13 (2024)

Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Proposal for a Prospective Cohort Study

Authors of this article:

• Ali Kargarandehkordi, MSc   ; 
• Christopher Slade, MSc   ; 
• Peter Washington, PhD  

Department of Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States

Corresponding Author:

Peter Washington, PhD

Department of Information and Computer Sciences

University of Hawaii at Manoa

1680 East-West Road

Honolulu, HI, 96822

United States

Phone: 1 5126800926

Email: [email protected]

Background: Referred to as the “silent killer,” elevated blood pressure (BP) often goes unnoticed due to the absence of apparent symptoms, resulting in cumulative harm over time. Chronic stress has been consistently linked to increased BP. Prior studies have found that elevated BP often arises due to a stressful lifestyle, although the effect of exact stressors varies drastically between individuals. The heterogeneous nature of both the stress and BP response to a multitude of lifestyle decisions can make it difficult if not impossible to pinpoint the most deleterious behaviors using the traditional mechanism of clinical interviews.

Objective: The aim of this study is to leverage machine learning (ML) algorithms for real-time predictions of stress-induced BP spikes using consumer wearable devices such as Fitbit, providing actionable insights to both patients and clinicians to improve diagnostics and enable proactive health monitoring. This study also seeks to address the significant challenges in identifying specific deleterious behaviors associated with stress-induced hypertension through the development of personalized artificial intelligence models for individual patients, departing from the conventional approach of using generalized models.

Methods: The study proposes the development of ML algorithms to analyze biosignals obtained from these wearable devices, aiming to make real-time predictions about BP spikes. Given the longitudinal nature of the data set comprising time-series data from wearables (eg, Fitbit) and corresponding time-stamped labels representing stress levels from Ecological Momentary Assessment reports, the adoption of self-supervised learning for pretraining the network and using transformer models for fine-tuning the model on a personalized prediction task is proposed. Transformer models, with their self-attention mechanisms, dynamically weigh the importance of different time steps, enabling the model to focus on relevant temporal features and dependencies, facilitating accurate prediction.

Results: Supported as a pilot project from the Robert C Perry Fund of the Hawaii Community Foundation, the study team has developed the core study app, CardioMate. CardioMate not only reminds participants to initiate BP readings using an Omron HeartGuide wearable monitor but also prompts them multiple times a day to report stress levels. Additionally, it collects other useful information including medications, environmental conditions, and daily interactions. Through the app’s messaging system, efficient contact and interaction between users and study admins ensure smooth progress.

Conclusions: Personalized ML when applied to biosignals offers the potential for real-time digital health interventions for chronic stress and its symptoms. The project’s clinical use for Hawaiians with stress-induced high BP combined with its methodological innovation of personalized artificial intelligence models highlights its significance in advancing health care interventions. Through iterative refinement and optimization, the aim is to develop a personalized deep-learning framework capable of accurately predicting stress-induced BP spikes, thereby promoting individual well-being and health outcomes.

International Registered Report Identifier (IRRID): DERR1-10.2196/55615

Introduction

How this research benefits the people of hawaii.

According to the Department of Health Chronic Disease Prevention and Health Promotion Division, 1 in every 3 adults in Hawaii has been diagnosed with hypertension [ 1 ]. Mortality rates associated with heart disease are particularly high for Native Hawaiian and Other Pacific Islander populations, leading to 628 deaths per 100,000 residents as opposed to 154 deaths per 100,000 residents among Asian residents and 167 deaths per 100,000 among White residents in Hawaii [ 1 ].

A recent study conducted by researchers at the John A Burns School of Medicine found that Native Hawaiian and Other Pacific Islander individuals under a physician’s care for hypertension experienced an 18.3 point drop in systolic blood pressure (BP) on average when participating in a 12-week hula program [ 2 , 3 ]. This study provides strong evidence that stress-reducing interventions can reduce hypertension in Native Hawaiian individuals. We hope to build upon this foundational research by leveraging consumer devices (ie, Fitbit) to detect moments of high stress and to provide just-in-time interventions that are culturally grounded. The first step of this long-term research plan is to develop the artificial intelligence (AI) that will power the digital intervention, and that first step is the focus of this grant proposal.

Clinical and Unmet Needs

Hypertension is an indirect cause of hundreds of thousands of annual deaths in the United States alone [ 4 ]. Known as the “silent killer”[ 5 ], elevated BP often remains unnoticed by affected individuals due to a lack of perceptible symptoms, resulting in accumulated harm over the years. While several causes of hypertension are related to an underlying health condition such as kidney disease, diabetes, sleep apnea, or hormone problems [ 6 ]; health condition; and medications combined only account for roughly 1 in 20 cases [ 7 ]. Chronic stress has been repeatedly documented to increase BP [ 8 - 10 ].

Prior studies have found that elevated BP often arises due to a stressful lifestyle, although the effect of exact stressors varies drastically between individuals. Due to the heterogeneous nature of both the stress and BP response to a multitude of lifestyle decisions, it can be difficult if not impossible to pinpoint the most deleterious behaviors in a personalized manner using the traditional mechanism of clinical interviews. Passive sensing technologies deployed on consumer devices have the potential to disrupt this status quo in a positive manner. By continuously monitoring a patient’s lifestyle in naturalistic settings, digital technologies can provide clinicians and patients alike with actionable insights into their health trends with fine-grained precision.

We are interested in the use of wearable technologies to sense cardiovascular signals, as they are noninvasive and are already widely adopted. We will develop machine learning (ML) algorithms that analyze these biosignals to make real-time predictions about BP spikes. The resulting predictions could be used to alert, in real time, patients about unintentionally adverse behaviors as well as clinicians about the frequency of such behaviors. There is a critical opportunity and need to improve diagnostics for repeat health events to enable clinicians to monitor their patients and forecast future issues.

There are countless situations in health care and biomedicine where vast amounts of unlabeled data are collected from a single patient [ 11 ]. Annotations for the event of interest are frequently sparsely dispersed. The development of predictive supervised ML models is infeasible in such circumstances, as classical approaches cannot handle the complexity of the data and modern deep learning approaches require vast amounts of data [ 12 ]. For example, continuous readings from continuously worn glucose monitors can provide enough input data to train a model to make a prediction about patient energy based on glucose, but it is impracticable to require users to log their perceived energy at the same sampling frequency as a wearable device. Similar situations arise from data collected by consumer wearable health devices (eg, smart watches), smartphones, and other devices that measure biological signals.

To support AI development in these situations where vast longitudinal data are collected with minimal human-provided annotations, we propose the development of personalized ML models that are trained solely on an individual’s unlabeled data to learn feature representations that are specific to their baseline temporal dynamics. We will train these models with a novel data set of Fitbit biosignals and corresponding BP readings ( Figure 1 ). We are creating a novel method and framework, which has never been explored in health care, consisting of pretraining neural networks to learn the temporal dynamics of a patient’s biosignals. This method will enable powerful, deep networks to be trained using relatively small data sets, which would not be possible without the self-supervised approach proposed here. From a usability standpoint, patients will only be required to provide tens of annotations tens of times to get a personalized predictive model.

While we propose to apply this new technological innovation toward the prediction of cardiac signals, multimodal time-series personalization can be applied to a variety of other biology and health problems where (1) multiple signals are emitted, (2) the baseline signal patterns are specific to each individual or organism, and (3) it is infeasible to acquire the vast amounts of labels required to train a supervised deep learning model from scratch. Examples of future apps of the proposed methodology include predictions stemming from nanopore signal data or multielectrode neuronal recordings. This method has the potential to dramatically advance the field of precision health care by enabling reliable ML predictions from massive but mostly unlabeled data sets which are trained in a self-supervised manner on data from a single user.

While this novel methodology could be applied to myriad domains within health and biology, a natural application is the prediction of cardiac events from wearable biosignals data. We will focus on high BP.

Dissemination Plan

We plan to disseminate our research findings through a combination of (1) research publications in journals, (2) presentations at conferences, (3) as preliminary data for an National Institutes of Health (NIH) R01 application, and (4) as the basis of community-based participatory design sessions where we iteratively develop a culturally informed digital intervention using the AI created in this project. Target journals for submission include Nature Digital Medicine, Science Translational Medicine, Institute of Electrical and Electronics Engineers (IEEE) Transactions on Affective Computing, PLoS Digital Medicine, and Cell patterns. Target conferences include the American Medical Informatics Association (AMIA) Annual Symposium, the Pacific Symposium on Biocomputing (PSB), and the Conference for Computer-Human Interaction (CHI). There are several notices of special interest posted by the NIH that would support a large R01 grant application using the preliminary data from this work.

Specific Aims

We propose the following specific aims: (1) aim 1: create a novel data set of wearable sensor data and corresponding BP measurements, (2) aim 2: develop a personalized self-supervised pretraining procedure for time-series data using both contrastive learning and masked predictions, and (3) aim 3: develop a novel personalized pretraining procedure which exploits the multimodal nature of wearable time series-data.

Recruitment

We will recruit 40 carefully selected participants with diagnosed hypertension and self-reported stressful lifestyles to each participate in a 4-week remote data collection period. Each participant will wear an Omron HeartGuide BP wearable device and a Fitbit Sense 2 wearable watch during all waking hours for at least 15 hours each day. Apart from wearing the devices and periodically syncing the data to the cloud, participants will be asked to follow their normal routine for the duration of the study.

We will recruit adults aged 30 to 70 years in the state of Hawaii who have been diagnosed with hypertension and self-identify as living a high-stress lifestyle. Given the diversity of the population of Hawaii [ 13 ], we aim for the following demographic composition of our participants: 23% White, 37% Asian, 11% Native Hawaiian or Pacific Islander, 7% Black or African American, and 22% of 2 or more races. Approximately 9.5% of the recruited population will have Hispanic or Latino ethnicity.

PW has a network of clinical collaborators at the John A Burns School of Medicine at the University of Hawaii at Mānoa who also practice at local medical centers such as Queen’s Medical Center and Kaiser Permanente’s branch in Hawaii. We will recruit using the following sources: (1) direct recruitment from the Hawaii Pacific Health Center, which the collaborators at the Department of Psychiatry at the University of Hawaii are affiliated with and where they practice clinically; (2) via flyers and emails at the clinics which the Department of Psychiatry at the University of Hawaii regularly provides inpatient and outpatient psychiatric services and consultation at, including The Queen’s Medical Center, Kapiʻolani Medical Center for Women and Children, and Hawaii State Hospital Community mental health centers on Hawaii Island, Molokaʻi, Maui, Kauaʻi, and Lānaʻi; (3) advertisements posted on the University of Hawaii campus and in public settings in Honolulu; and (4) targeted advertisements posted to social media websites. We will work with Anthony Guerrero, the chair of the Department of Psychiatry at the University of Hawaii, to ensure that the recruitment strategies and advertisement of the research program translate across cultures and to ensure effective recruitment as well as diverse and representative data.

We will exclude participants younger than 30 years and older than 70 years. We will require all potential participants to remotely complete the Perceived Stress Scale (PSS), a 10-item scale that is the most widely used psychological instrument for measuring the perception of stress [ 14 ]. We will exclude participants whose PSS score does not exceed 1 SD above the mean for at least one of their demographic brackets (age, gender, or race) as reported by Cohen et al [ 14 ]. We will also ask participants to self-report their BP. We will also exclude participants who do not own a smartphone with continuous network connectivity. During the in-person study intake, we will measure the BP of potential study participants 3 times. We will exclude participants whose BP does not exceed 130/80 mm Hg for at least one of the measurements, as 130/80 mm Hg is the minimum cutoff for stage 1 hypertension.

Data Collection and Storage

We will leverage the existing application programming interface (API) provided by both Omron and Fitbit to record the user’s wearable sensor readings and upload the data to the cloud. Omron’s health care API offers access to time-stamped BP readings as well as activity and sleep approximations. The Fitbit API provides access to sensor readings of heart rate (HR), gyroscope, accelerometer, breathing rate, blood oxygen levels (SpO 2 ), and skin temperature sensor readings. The data will be managed on each participant’s smartphone devices through a mobile app, implemented for both iOS (Apple Inc) and Android, that we will develop. The study team will install the app on the user’s smartphone and configure the Omron and Fitbit devices during study onboarding. The smartphone app will periodically trigger a notification reminding the participant to (1) measure their BP with the Omron wearable, (2) sync the Omron and Fitbit data to the app, and (3) connect to a network while the study app is open to allow the data to be uploaded to a centralized server.

We will store the curated data from each participant on a centralized server hosted on Amazon Web Services (AWS). Because Fitbit is owned by Google, participants' Fitbit data will be uploaded directly to Google's cloud servers, which use the same level of security as other Google products such as Gmail. Access to each participant's Fitbit data on Google's cloud servers is implemented through OAuth, which provides clients with secure delegated access to server resources on behalf of a resource owner (ie, the participants of this study). This mechanism is used by companies such as Amazon (Amazon.com, Inc), Google (Alphabet Inc), Facebook (Meta Platforms, Inc), Microsoft Corporation, and X (X Corp) to permit users to share information about their accounts with third-party applications or websites. In this case, the “third party” is the study team. The Fitbit data and BP readings will be preprocessed on an Elastic Cloud Compute instance on AWS, which is HIPAA (Health Insurance Portability and Accountability Act)-compliant. The Elastic Cloud Compute instance will store the data onto respective database tables using DynamoDB (Amazon.com). Each table will have columns for the child ID and the time-stamp. We will encrypt all server-side data and require secret access keys for data access. DynamoDB tables are automatically encrypted on the server side. To add an additional layer of security, we will implement client-side encryption on the mobile app, ensuring encrypted data transmission over HTTPS connection to move BP data between the devices and AWS. Data access will require a secret access key provided by the AWS administrators to any data analysis team. The data will not be accessible without this key. For further security, we will anonymize all user data on the server side by removing all protected health information from the DynamoDB tables.

We intend to release the curated data ( Figure 2 ) as a publicly available data set for use in the evaluation of multimodal time-series ML models. Such data sets exist for activity and emotion recognition from wearable data, but the prediction of BP from these measurements will be a challenging task that other researchers can attempt with the release of our data set. This will be the first publicly available data set that includes at-home BP measurements alongside wearable sensors such as HR, SpO 2 , and accelerometer readings. This fully anonymized data set will only be released to researchers who sign a Data Use Agreement, which will be approved by the University of Hawaii Data Governance Office.

The app comprises 2 primary screens, account and home. The account screen features user details, a star reward system for active participation in the study, and options to link 2 wearable devices (Fitbit and Omron Heartguide) for data synchronization with our secure and encrypted database. The home screen is divided into 6 sections, including questionnaires, messages, feedback, records, BP readings, and app instructions. Additionally, the CardioMate app includes an administrative area for study managers to view participant statistics and initiate personalized chats, complete with alarm and notification functions.

Feasibility

The most difficult aspect of this aim will be maintaining participant engagement throughout the 4-week study period. The graduate research assistant funded by this project will dedicate some time each day toward running the study and interfacing with participants. We expect participants to open the smartphone app to sync and upload their data on a daily basis, which is a 1-minute time commitment per day.

While we expect no trouble recruiting 40 subjects for participants, we expect some participants to drop off during the study. Since we will have enough devices for 5 concurrent subjects, it will take 8 months to collect all data if no participants drop off. Our study timeline allocates 6 additional months of make-up time to collect data from new participants, accounting for >50% drop-off rate. Given the remote nature of the data collection procedures, we expect some participants to drop off from the study prematurely or to not comply with the study processes. We will therefore remotely monitor the upload progress and send an automated text and email notification to the participant if the data are not uploaded in a timely manner. If 3 consecutive days of participant noncompliance are detected, we will contact the participant for a device return.

Ethical Considerations

Under an expedited review procedure, this research project was approved on April 26, 2023, by the University of Hawaii Institutional Review Board (UHMUIF_2023-00130). The application qualified for expedited review under CFR 46.110 and 21 CFR 56.110, categories 1b, 4a, 4d, and 6. The informed consent process for this human subject research study involves participants completing an interview session where they receive comprehensive information about the research, including its purpose, procedures, and potential risks and benefits. Participants are assured of the voluntary nature of their participation and their right to refuse or withdraw at any time without penalty. For secondary analyses of research data, it is clarified that the original informed consent allows for such analyses without additional consent, as approved by the institutional review board. Privacy and confidentiality protections are emphasized, with participant data anonymized and stored securely on HIPAA-compliant servers. Compensation for participation includes US $135 upon completion along with an additional US $15 for certain eligibility interview tasks, reflecting the time and effort required from participants while respecting the ethical standards. The consent form ensures that no identification of individual participants or users is possible in any images or supplementary materials without explicit consent, with researchers providing relevant consent forms or written communications to uphold participant privacy and consent.

AI Model Training

Self-supervised learning (SSL) is usually used to pretrain an entire data set with no explicit labeling by humans to guide the supervision task. We propose to redesign the SSL paradigm toward the task of model personalization. By pretraining a model only on the vast amounts of data curated from a single individual, the weights of the neural network will learn to make predictions using the inherent structure of each participant’s biosignals. This is essential because the baseline HR, SpO 2 , skin temperature, and movement patterns, regardless of stress, will vary drastically across individuals, limiting the performance of general-purpose ML models.

To train ML models that predict BP based on a user’s wearable biometrics, we will develop and evaluate a series of both long short-term memory and transformer neural networks. The inputs to the models will consist of a separate 1D convolutional backbone for each biometric modality. The convolutional features will be fused upstream into a shared joint dense representation space and finally a dense prediction layer with linear activation for regression prediction. We will implement all models using Tensorflow (Google Brain) [ 15 ].

We will perform a series of self-supervised pretraining tasks to allow the networks to learn the baseline temporal dynamics of each individual’s biosignals. As a pretraining task, we will develop contrastive learning methods to automatically learn embeddings that encode the structure of the signal. For each wearable sensor modality, we will run a sliding window to isolate short-time segments. We will apply signal-based data augmentation techniques to derive a new signal. We will perform contrastive learning to learn neural network embeddings that maximize the similarity between each original segment and its modified version while minimizing the similarity across segments.

We will develop a modified version of the SimCLR (simple framework for contrastive learning of visual representations) algorithm, which will be tuned for the task of personalization to a user’s wearable signal readings. It is often the case that biosignals look highly similar, either due to temporal locality or relative homogeneity of the individual’s activity. To account for this possibility of recurring signal patterns, we will weigh the attract and repel strength of SimCLR based on the temporal distance between two segments of a particular signal. We will run a grid search to tune this repel strength.

The data augmentation techniques that we apply to the signals will be domain-specific, keeping in mind the inherent nature of each sensor. For example, for accelerometer data, rotations simulate different sensor placements and cropping is used to diminish the dependency of event locations [ 16 ]. Across several modalities, sensor noise can be simulated through scaling, magnitude-warping, and jittering [ 16 ]. We will be careful to not apply augmentation strategies that might change the meaning of the underlying signal.

As another pretraining task, we will perform generative pretraining by masking the input signal and predicting the missing portion of the signal using a deep autoencoder architecture. Pretraining in this manner will teach the model to understand the dynamics of each time series signal independent of BP or any other labels.

We will train the model on the first 60% of data (by time), tune hyperparameters on the next 20% of data, and calculate the mean absolute error and mean squared error on the final 20%. This evaluation pattern mimics real-world use, where a model will be calibrated by a user prior to real-world deployment. It is important to emphasize that we will train and test a separate personalized ML model for each individual.

We will evaluate the models by comparing the performance with respect to the number of labeled examples used for supervised fine-tuning. A plot of this comparison will elucidate the number of BP measurements required for model calibration to a single individual. We will plot the mean squared error at 10, 20, 30, 40, 50, 75, 100, 125, and 150 BP annotations, as these are feasible amounts of labels that might be provided by a user in real-world use. To ensure a robust evaluation, we will bootstrap at least 20 random samples of BP annotation subsets for each point on the x-axis and will report the mean and 90% CI. Just as in the plain supervised learning condition, we will create a separate plot for each study participant, as the ML portion of this proposal is testing the personalization of ML models rather than a general-purpose one-size-fits-all ML model which is more typical in ML evaluations.

We will perform a similar style of analysis for other clinical outcomes using publicly available data sets such as the Wearable Stress and Affect Detection (WESAD) [ 17 ] data set, a multimodal sensor data set for stress detection of nurses in a hospital [ 18 ], and K-EmoCon, a multimodal sensor data set for continuous emotion recognition in naturalistic conversations [ 19 ]. Each of these data sets, as well as several other publicly available data sets, contains several hours of multimodal biosignal data that overlap with the signals that we propose to collect, such as skin temperature, accelerometer streams, and HR. These data sets also include time-stamped annotations of end points that are likely to be correlated with BP, including self-perceived stress.

In prior work by other researchers, SSL pretraining approaches have repeatedly demonstrated improved performance over pure supervised learning in a variety of contexts [ 20 - 23 ]. Our preliminary data (see Results section) support that self-supervised pretraining on data solely from each individual results in improved models over purely supervised learning. While unlikely given our preliminary data and prior SSL publications, it is possible that minimal performance gains will be observed when applying the SSL strategies in a personalized manner. In such cases, the negative result would be a noteworthy finding due to prior successes of SSL.

We have developed a smartphone app, CardioMate, that will prompt participants to measure their BP and log their stress ( Figure 2 ). The app comprises 2 primary screens, account and home. The account screen features user details, a star reward system for active participation in the study, and options to link 2 wearable devices (Fitbit and Omron Heartguide) for data synchronization with our secure, encrypted database. The home screen is divided into 6 sections, including questionnaires, messages, feedback, records, BP readings, and app instructions. Additionally, the CardioMate app includes an administrative area for study managers to view participant statistics and initiate personalized chats, complete with alarm and notification functions.

Data collection commenced on February 15, 2024. As of the manuscript submission date of February 24, 2024, a total of 2 participants have been recruited. The data collection period for each participant spans 28 days. Upon completion of the data collection period for each participant, we will proceed with the personalized machine learning model development to predict stress-induced BP spikes in real time. We aim to recruit a total of at least 45 participants and complete the relevant data collection, data analysis, and personalized ML development for each participant by the end of December 2024.

Our initial sets of published experiments have demonstrated promise for personalized SSL of stress but with some caveats. Our experiments on the WESAD data set demonstrated that deep learning model performance improves drastically when using self-supervised personalization when compared to personalization without SSL when there are a small number of labeled data points for supervision [ 24 ]. This effect diminishes with increasing amounts of labeled data [ 25 , 26 ], aligning with prior work that demonstrates that SSL is only beneficial under low-label settings. We have also tried these methods on a particularly challenging data set, a multimodal sensor data set for stress detection of nurses in a hospital [ 18 ]. This data set consists of wearable biosignals measured from nurses who wore Empatica E4 wristbands while conducting their normal shifts. This data set is difficult because (1) the data were collected in the wild rather than in controlled laboratory settings and (2) individual nurses were not consistent about their labeling practices, leading to sparse, irregular, noisy, and otherwise messy labels. Consequently, we found that the difference in area under curve and the receiver operating characteristic curve scores for self-supervised models was only about 2.5% higher on average compared against an equivalent baseline model [ 27 ], and this increase is within the margin of error due to the limited sample size. This lack of improvement in noisy annotation settings highlights the need for HCI innovations to improve data labeling quality for personalized AI within naturalistic settings.

We have also observed improved performance when personalizing affect-related prediction tasks without personalization both using classical ML [ 28 ] and deep learning [ 29 ], as well as when only applying SSL without personalization [ 30 ]. When disentangling and comparing the effects of SSL and personalization separately, we find that SSL yields more benefit than individualization on nonaffective medical data with large time intervals between data points, suggesting that the sampling frequency and other data considerations must be considered [ 30 ]. Collectively, these preliminary results demonstrate promise for the core ML approach that we propose.

The primary objective of this study is to leverage ML algorithms for real-time predictions of stress-induced BP spikes using consumer wearable devices such as Fitbit, providing actionable insights to both patients and clinicians to improve diagnostics and enable proactive health monitoring. Our study is motivated by recent research conducted at the John A Burns School of Medicine, which found that Native Hawaiian and other Pacific Islander individuals under a physician’s care for hypertension experienced an average drop of 18.3 points in systolic BP after participating in a 12-week hula program [ 2 , 3 ]. This study provides strong evidence that stress-reducing interventions can reduce hypertension in Native Hawaiian individuals. We hope to build upon this foundational research by leveraging consumer devices, such as Fitbit, to detect moments of high stress and provide just-in-time interventions that are culturally grounded. The first phase of this long-term research plan involves developing the AI necessary to power the digital intervention, which is the primary focus of this proposal.

The successful development of ML algorithms tailored to individual participants signifies a significant advancement in personalized health care interventions. By using longitudinal data from Fitbit devices and corresponding stress level labels from Ecological Momentary Assessment reports, the study will be able to capture individual-specific patterns effectively, enabling accurate predictions of stress-induced BP spikes. This approach not only enhances the understanding of stress-related hypertension but also provides opportunities for targeted interventions and improved patient outcomes.

Furthermore, the findings of this study contribute to the growing body of literature on the use of wearable devices and ML in health care. The adoption of transformer models for personalized prediction tasks, coupled with SSL techniques for pretraining, represents a novel approach to leveraging advanced computational techniques for real-time health monitoring. By dynamically weighing the importance of different time steps and focusing on relevant temporal features and dependencies, transformer models offer a powerful tool for predicting complex physiological responses such as stress-induced BP spikes. These findings will add to the existing literature by highlighting the potential of ML in improving the accuracy and efficiency of health monitoring systems, particularly in the context of personalized interventions for stress-related hypertension.

It is essential to acknowledge the limitations of this study design. One limitation is the relatively small sample size, which may limit the generalizability of the findings. Additionally, the study focuses primarily on predicting stress-induced BP spikes using wearable device data streams and may not capture other factors contributing to hypertension. Future research should aim to address these limitations by including larger and more diverse samples and exploring additional predictors of hypertension.

The findings of this study will demonstrate the feasibility and potential benefits of leveraging ML algorithms for real-time predictions of stress-induced BP spikes using consumer wearable devices. By developing personalized AI models based on individual biosignals, the study will provide valuable insights into the monitoring and management of stress-related hypertension. These findings will have broader implications for personalized health care interventions and underscore the importance of integrating advanced computational techniques into health care systems to improve patient outcomes. Through iterative refinement and optimization, we aim to develop a personalized deep-learning framework capable of accurately predicting stress-induced BP spikes, thereby promoting individual well-being and health outcomes.

Acknowledgments

This project received a grant (#MedRes_2023_00002689) from the Robert C Perry Fund of the Hawaii Community Foundation.

Data Availability

Upon the completion of the data collection phase, the data sets generated and analyzed during this study will be available in a public repository. The data citation should include a persistent identifier (eg, web URL or DOI) cited per journal style in the reference list. The amassed data will be fully anonymized and made accessible to the academic community. Subsequently, comprehensive guidelines, together with a designated link facilitating data retrieval and use, will be provided.

Conflicts of Interest

None declared.

Peer-review reports from the Medical Research Advisory Committee of the Hawaii Community Foundation (HCF).

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Abbreviations

Edited by A Mavragani; The proposal was externally peer-reviewed by the Medical Research Advisory Committee of the Hawaii Community Foundation (HCF). See the Multimedia Appendix for the peer-review report; submitted 18.12.23; accepted 05.02.24; published 25.03.24.

©Ali Kargarandehkordi, Christopher Slade, Peter Washington. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 25.03.2024.

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