stress management research paper

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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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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• Published: 15 April 2020

Practice of stress management behaviors and associated factors among undergraduate students of Mekelle University, Ethiopia: a cross-sectional study

• Gebrezabher Niguse Hailu 1  

BMC Psychiatry volume  20 , Article number:  162 ( 2020 ) Cite this article

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Stress is one of the top five threats to academic performance among college students globally. Consequently, students decrease in academic performance, learning ability and retention. However, no study has assessed the practice of stress management behaviors and associated factors among college students in Ethiopia. So the purpose of this study was to assess the practice of stress management behaviors and associated factors among undergraduate university students at Mekelle University, Tigray, Ethiopia, 2019.

A cross-sectional study was conducted on 633 study participants at Mekelle University from November 2018 to July 2019. Bivariate analysis was used to determine the association between the independent variable and the outcome variable at p  < 0.25 significance level. Significant variables were selected for multivariate analysis.

The study found that the practice of stress management behaviors among undergraduate Mekelle university students was found as 367(58%) poor and 266(42%) good. The study also indicated that sex, year of education, monthly income, self-efficacy status, and social support status were significant predictors of stress management behaviors of college students.

This study found that the majority of the students had poor practice of stress management behaviors.

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Stress is the physical and emotional adaptive response to an external situation that results in physical, psychological and behavioral deviations [ 1 ]. Stress can be roughly subdivided into the effects and mechanisms of chronic and acute stress [ 2 ]. Chronic psychological stress in early life and adulthood has been demonstrated to result in maladaptive changes in both the HPA-axis and the sympathetic nervous system. Acute and time-limited stressors seem to result in adaptive redistribution of all major leukocyte subpopulations [ 2 ].

Stress management behaviors are defined as behaviors people often use in the face of stress /or trauma to help manage painful or difficult emotions [ 3 ]. Stress management behaviors include sleeping 6–8 h each night, Make an effort to monitor emotional changes, Use adequate responses to unreasonable issues, Make schedules and set priorities, Make an effort to determine the source of each stress that occurs, Make an effort to spend time daily for muscle relaxation, Concentrate on pleasant thoughts at bedtime, Feel content and peace with yourself [ 4 ]. Practicing those behaviors are very important in helping people adjust to stressful events while helping them maintain their emotional wellbeing [ 3 ].

University students are a special group of people that are enduring a critical transitory period in which they are going from adolescence to adulthood and can be one of the most stressful times in a person’s life [ 5 ]. According to the American College Health Association’s National College Health Assessment, stress is one of the top five threats to academic performance among college students [ 6 ]. For instance, stress is a serious problem in college student populations across the United States [ 7 ].

I have searched literatures regarding stress among college students worldwide. For instance, among Malaysian university students, stress was observed among 36% of the respondents [ 8 ]. Another study reported that 43% of Hong Kong students were suffered from academic stress [ 9 ]. In western countries and other Middle Eastern countries, including 70% in Jordan [ 10 ], 83.9% in Australia [ 11 ]. Furthermore, based on a large nationally representative study the prevalence of stress among college students in Ethiopia was 40.9% [ 12 ].

Several studies have shown that socio-demographic characteristics and psychosocial factors like social support, health value and perceived self-efficacy were known to predict stress management behaviors [ 13 , 14 , 15 , 16 , 17 ].

Although the prevalence of stress among college students is studied in many countries including Ethiopia, the practice of stress management behaviors which is very important in promoting the health of college students is not studied in Ethiopia. Therefore this study aimed to assess the practice of stress management behaviors and associated factors among undergraduate students at Mekelle University.

The study was conducted at Mekelle university colleges from November 2018 to July 2019 in Mekelle city, Tigray, Ethiopia. Mekelle University is a higher education and training public institution located in Mekelle city, Tigray at a distance of 783 Kilometers from the Ethiopian capital ( http://www.mu.edu.et/ ).

A cross-sectional study was conducted on 633 study participants. Students who were ill (unable to attend class due to illness), infield work and withdrawal were not included in the study.

The actual sample size (n) was computed by single population proportion formula [n = [(Za/2)2*P (1 − P)]/d2] by assuming 95% confidence level of Za/2 = 1.96, margin of error 5%, proportion (p) of 50% and the final sample size was estimated to be 633. A 1.5 design effect was used by considering the multistage sampling technique and assuming that there was no as such big variations among the students included in the study.

Multi-stage random sampling was used. Three colleges (College of health science, college of business and Economics and College of Natural and Computational Science) were selected from a total of the seven Colleges from Mekelle University using a simple random sampling technique in which proportional sample allocation was considered from each college.

Data were collected using a self-administered questionnaire by trained research assistants at the classes.

The questionnaire has three sections. The first section contained questions on demographic characteristics of the study participants. The second section contained questions to assess the practice of stress management of the students. The tool to assess the practice of stress management behaviors for college students was developed by Walker, Sechrist, and Pender [ 4 ]. The third section consisted of questions for factors associated with stress management of the students divided into four sub-domains, including health value used to assess the value participants place on their health [ 18 ]. The second subdomain is self-efficacy designed to assess optimistic self-beliefs to cope with a variety of difficult demands in life [ 19 ] and was adapted by Yesilay et al. [ 20 ]. The third subdomain is perceived social support measures three sources of support: family, friends, and significant others [ 21 ] and was adapted by Eker et al. [ 22 ]. The fourth subscale is perceived stress measures respondents’ evaluation of the stressfulness of situations in the past month of their lives [ 23 ] and was adapted by Örücü and Demir [ 24 ].

The entered data were edited, checked visually for its completeness and the response was coded and entered by Epi-data manager version 4.2 for windows and exported to SPSS version 21.0 for statistical analysis.

Bivariate analysis was used to determine the association between the independent variable and the outcome variable. Variables that were significant at p  < 0.25 with the outcome variable were selected for multivariable analysis. And odds ratio with 95% confidence level was computed and p -value <= 0.05 was described as a significant association.

Operational definition

Good stress management behavior:.

Students score above or equal to the mean score.

Poor stress management behavior:

Students score below the mean score [ 4 ].

Seciodemographic characteristics

Among the total 633 study participants, 389(61.5%) were males, of those 204(32.2%) had poor stress management behavior. The Median age of the respondents was 20.00 (IQR = ±3). More ever, this result showed that 320(50.6%) of the students came from rural areas, 215(34%) of them had poor stress management behavior.

The result revealed that 363(57.35%) of the study participants were 2nd and 3rd year students, of them 195 (30.8%) had poor stress management.

This result indicated that 502 (79.3%) of the participants were in the monthly support category of > = 300 ETB with a median income of 300.00 ETB (IQR = ±500), from those, 273(43.1%) students had poor stress management behavior (Table  1 ).

Status of practice of stress management behaviors of under graduate students at Mekelle University, Ethiopia

Psychosocial factors

This result indicated that 352 (55.6%) of the students had a high health value status of them 215 (34%) had good stress management behavior. It also showed that 162 (25.6%) of the students had poor perceived self-efficacy, from those 31(4.9%) had a good practice of stress management behavior. Moreover, the result showed that 432(68.2%) of the study participants had poor social support status of them 116(18.3%) had a good practice of stress management behavior (Table  1 ).

Practice of stress management behaviors

The result showed that the majority (49.8%) of the students were sometimes made an effort to spend time daily for muscle relaxation. Whereas only 28(4.4%) students were routinely concentrated on pleasant thoughts at bedtime.

According to this result, only 169(26.7%) of the students were often made an effort to determine the source of stress that occurs. It also revealed that the majority (40.1%) of the students were never made an effort to monitor their emotional changes. Similarly, the result indicated that the majority (42.5%) of the students were never made schedules and set priorities.

The result revealed that only 68(10.7%) of the students routinely slept 6–8 h each night. More ever, the result showed that the majority (34.4%) of the students were sometimes used adequate responses to unreasonable issues (Table  2 ).

Status of the practice of stress management behaviors

The result revealed that the practice of stress management behaviors among regular undergraduate Mekelle university students was found as 367(58%) poor and 266(42%) good. (Fig  1 )

Factors associated with stress management behaviors

In the bivariate analysis sex, college, year of education, student’s monthly income’, perceived-self efficacy, perceived social support and perceived stress were significantly associated with stress management behavior at p < =0.25. Whereas in the multivariate analysis sex, year of education, student’s monthly income’, perceived-self efficacy and perceived social support were significantly associated with stress management behavior at p < =0.05.

Male students were 3.244 times more likely to have good practice stress management behaviors than female students (AOR: 3.244, CI: [1.934–5.439]). Students who were in the age category of less than 20 years were 70% less to have a good practice of stress management behaviors than students with the age of greater or equal to 20 year (AOR: 0.300, CI:[0.146–0.618]).

Students who had monthly income less than300 ETB were 64.4% less to have a good practice of stress management behaviors than students with monthly income greater or equal to 300 ETB (AOR: 0.356, CI:[0.187–0.678]).

Students who had poor self- efficacy status were 70.3% less to have a good practice of stress management behaviors than students with good self-efficacy status (AOR: 0.297, CI:[0.159–0.554]). Students who had poor social support were 70.5% less to have a good practice of stress management behaviors than students with good social support status (AOR: 0.295[0.155–0.560]) (Table  3 ).

The present study showed that the practice of stress management behaviors among regular undergraduate students was 367(58%) poor and 266(42%) good. The study indicated that sex, year of education, student’s monthly income, social support status, and perceived-self efficacy status were significant predictors of stress management behaviors of students.

The current study revealed that male students were more likely to have good practice of stress management behaviors than female students. This finding is contradictory with previous studies conducted in the USA [ 13 , 25 ], where female students were showed better practice of stress management behaviors than male students. This difference might be due to socioeconomic and measurement tool differences.

The current study indicated that students with monthly income less than 300 ETB were less likely to have good practice of stress management behaviors than students with monthly income greater than or equal to 300 ETB. This is congruent with the recently published book which argues a better understanding of our relationship with money (income). The book said “the people with more money are, on average, happier than the people with less money. They have less to worry about because they are not worried about where they are going to get food or money for their accommodation or whatever the following week, and this has a positive effect on their health” [ 26 ].

The present study found that first-year students were less likely to have good practice of stress management behaviors than senior students. This finding is similar to previous findings from Japan [ 27 ], China [ 28 ] and Ghana [ 29 ]. This might be because freshman students may encounter a multitude of stressors, some of which they may have dealt with in high school and others that may be a new experience for them. With so many new experiences, responsibilities, social settings, and demands on their time. As a first-time, incoming college freshman, experiencing life as an adult and acclimating to the numerous and varied types of demands placed on them can be a truly overwhelming experience. It can also lead to unhealthy amounts of stress. A report by the Anxiety and Depression Association of America found that 80% of freshman students frequently or sometimes experience daily stress [ 30 ].

The current study showed that students with poor self-efficacy status were less likely to have good practice of stress management behaviors. This is congruent with the previous study that has demonstrated quite convincingly that possessing high levels of self-efficacy acts to decrease people’s potential for experiencing negative stress feelings by increasing their sense of being in control of the situations they encounter [ 14 ]. More ever this study found that students with poor social support were less likely to have a good practice of stress management behaviors. This finding is similar to previous studies that found good social support, whether from a trusted group or valued individual, has shown to reduce the psychological and physiological consequences of stress, and may enhance immune function [ 15 , 16 , 17 ].

Ethics approval and consent to participate

Ethical clearance and approval obtained from the institutional review board of Mekelle University. Moreover, before conducting the study, the purpose and objective of the study were described to the study participants and written informed consent was obtained. The study participants were informed as they have full right to discontinue during the interview. Subject confidentiality and any special data security requirements were maintained and assured by not exposing the patient’s name and information.

Limitation of the study

There is limited literature regarding stress management behaviors and associated factors. There is no similar study done in Ethiopia previously. More ever, using a self-administered questionnaire, the respondents might not pay full attention to it/read it properly.

This study found that the majority of the students had poor practice of stress management behaviors. The study also found that sex, year of education, student’s monthly income, social support status, and perceived-self efficacy status were significant predictors of stress management behaviors of the students.

Availability of data and materials

The datasets used during the current study is available from the corresponding author on reasonable request.

Abbreviations

Adjusted Odd Ratio

College of Business& Economics

College of health sciences

Confidence interval

College of natural and computational sciences

Crud odds ratio

Ethiopian birr

Master of Sciences

United States of America

United kingdom

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Hailu, G.N. Practice of stress management behaviors and associated factors among undergraduate students of Mekelle University, Ethiopia: a cross-sectional study. BMC Psychiatry 20 , 162 (2020). https://doi.org/10.1186/s12888-020-02574-4

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Individual stress response patterns: Preliminary findings and possible implications

Roles Conceptualization, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

¶ ‡ These authors are joint senior authors on this work.

Affiliation Stress, Hope and Cope Lab., School of Behavioral Sciences, Tel-Aviv Yaffo Academic College, Tel-Aviv Yaffo, Israel

Roles Investigation, Writing – original draft

Roles Conceptualization, Investigation, Resources, Writing – original draft

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Roles Conceptualization, Formal analysis, Methodology, Project administration, Writing – review & editing

• Rebecca Jacoby, 
• Keren Greenfeld Barsky, 
• Tal Porat, 
• Stav Harel, 
• Tsipi Hanalis Miller, 
• Gil Goldzweig

• Published: August 13, 2021
• https://doi.org/10.1371/journal.pone.0255889
• Reader Comments

Research on stress occupied a central position during the 20 th century. As it became evident that stress responses affect a wide range of negative outcomes, various stress management techniques were developed in attempt to reduce the damages. However, the existing interventions are applied for a range of different stress responses, sometimes unsuccessfully.

The aim of this study was to examine whether there are specific clusters of stress responses representing interpersonal variation. In other words, do people have dominant clusters reflecting the different aspects of the known stress responses (physiological, emotional, behavioral, and cognitive)?

The researchers derived a measure of stress responses based on previous scales and used it in two studies in order to examine the hypothesis that stress responses can be grouped into dominant patterns according to the type of response.

The results of Study 1 revealed four distinctive response categories: psychological (emotional and cognitive), physiological gastro, physiological muscular, and behavioral. The results of Study 2 revealed five distinctive response categories: emotional, cognitive, physiological gastro, physiological muscular, and behavioral.

By taking into consideration each person’s stress response profile while planning stress management interventions and then offering them a tailored intervention that reduces the intensity of these responses, it might be possible to prevent further complications resulting in a disease (physical or mental).

Citation: Jacoby R, Greenfeld Barsky K, Porat T, Harel S, Hanalis Miller T, Goldzweig G (2021) Individual stress response patterns: Preliminary findings and possible implications. PLoS ONE 16(8): e0255889. https://doi.org/10.1371/journal.pone.0255889

Editor: Georgia Panayiotou, University of Cyprus, CYPRUS

Received: February 13, 2021; Accepted: July 26, 2021; Published: August 13, 2021

Copyright: © 2021 Jacoby et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data is registered and available through the Open Science Framework (OSF): Goldzweig, G., Jacoby, R., Barsky, K. G., Porat, T., Harel, S., & Miller, T. H. (2021, July 10). Individual stress response patterns: Preliminary findings and possible implications. https://doi.org/10.17605/OSF.IO/XQ2TA .

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

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

Introduction

The concept of stress in its various forms (such as anxiety and fear) has been known since the 18 th century but became central during the 20 th century. Stress models were, consequently, developed in order to explain the processes undertaken in response to stressors. Early stress research underlined the adaptive nature of organisms’ physiological response to acute stress and the potentially deleterious effects of prolonged stressors [ 1 , 2 ]. Later on, the research expanded to humans emphasizing the cognitive system as mediating between stressors and stress responses [ 3 , 4 ]. As it became evident that continuing or repeated stress responses affect a wide range of negative outcomes, both physical and psychological [ 5 , 6 ], various stress management techniques were developed in an attempt to reduce the harms [ 7 – 10 ]. These include psychophysiological techniques (such as relaxation, biofeedback and more) aiming to reduce stress responses and regaining control, as well as cognitive behavioral techniques aiming to challenge misleading perceptions. A substantial body of research has explored the efficacy of these techniques (e.g. [ 10 ]). However, in most cases, interventions were not tailored to specific patterns of responses, and thus, the same interventions were used, sometimes unsuccessfully, for various stress responses. Since stress responses influence organisms’ coping and health outcomes, their study is important. We believe that by better understanding the complexity of stress responses, appropriate interventions can be developed and implemented.

Theories on stress responses

In the past, stress responses were studied and described mainly by physiologists,. Darwin (1809–1882), who studied the manifested responses of animals and humans to emotional situations, was mainly interested in the observable responses and not in the biochemical changes that occur in response to stress [ 11 ]. Immense strides toward understanding stress responses and their physiological basis were made by Cannon (1871–1945) and Selye (1907–1982). Cannon [ 2 ] studied the homeostatic mechanisms underlying the “fight or flight” response to stressful situations, while Selye [ 1 ] later developed the general adaptation syndrome (GAS) theory, which describes the process of responding to an ongoing stress.

According to these theories, exposure to a stressful event activates a series of autonomic system reactions that cause changes within organs [ 12 , 13 ]. The reactions found were the activation of the hypothalamic-pituitary-adrenal axis (HPA) and the sympathetic nervous system (SNS). Activation of the HPA axis and the SNS causes hormonal secretions of adrenaline and cortisol and the behavioral “fight or flight” reaction [ 2 ]. According to Berger et al. [ 14 ], the “fight or flight” response is triggered by osteocalcin, a protein released by the skeleton as a hormone, which, they claimed, is a messenger, sent by bone to regulate crucial processes all over the body, including how we respond to danger.

Porges [ 15 ] asserted that although these arousal theories have empirical support when measuring the effects of acute stress, they neglect other aspects of the physiological stress response such as parasympathetic nervous system (PNS) influences and interaction between sympathetic and parasympathetic processes. This neglect limits the theories’ ability to explain a wide variety of stress responses such as freezing, tonic immobility, fainting, and syncope. Porges’ polyvagal theory looks to explain the mechanism underlying the interpersonal differences of physiological and psychological stress responses. Other theories on the role of oxytocin for moderating the autonomic nervous system (e.g., [ 16 ]) and on gender differences, such as “tend and befriend” [ 17 ], have focused on responses directed toward safety behaviors.

Although these researchers have tried to include a psychological dimension in their models, this was mainly cast in terms of stimulus-response relationships, consistent with the dominant physiological and behavioral approaches of the period, and therefore could not explain why different people who are exposed to the same stimulus respond differently. These models, which were derived from animal behavior, were criticized for their universal approach of focusing solely on biological mechanisms and disregarding humans’ subjective perception of the stress experience [ 18 ].

From the middle of the 20 th century, the concept of stress came to occupy a central position in the psychological literature, and new stress models were developed emphasizing the interactions between individuals and their environment. The leading model in psychological stress research is the Transactional Model developed by Lazarus and Folkman [ 3 ]. This model focuses on the cognitive processes preceding the stress response and promotes the understanding of interpersonal variance in the stress responses to the same events. It emphasizes the importance of the individuals’ appraisal of the meaning of the stressful event and their own resources for coping with this event to help mediate between the stressor and stress responses of the organism. It also established the understanding that different individuals will react to the same event with varying intensity or duration: one will find an event threatening, while the other will find it neutral or realize that they have the required coping resources. The Transactional Model does not, however, explain the interpersonal variance of the stress response patterns and of stress influences on health.

Other theories have proposed a more integrative outlook on the stress-related cascade of events, starting even before the encounter with a potential stressor and resulting in various health outcomes. They have suggested a process that is mediated by cognitive appraisal, behavioral outcomes, and physiological mechanisms [ 19 , 20 ]. For example, Brosschot, Gerin, & Thayer [ 21 ] argue that perseverative cognition as manifested in worry, rumination and anticipatory stress should be considered as they are associated with enhanced cardiovascular, endocrinological, immunological, and neurovisceral activity. Others [ 22 , 23 ] have suggested that personality traits are also likely to influence how people respond to stress. These approaches consider all of the main aspects depicted by prior models and provide a wider perspective for both researchers and clinicians.

The aforementioned theories notwithstanding, individual differences of stress responses as represented by different clusters in a non-pathological population have not, to the best of our knowledge, been studied. The purpose of the current study was, therefore, to address this gap and examine whether reported stress responses do, in fact, reflect clusters of the common stress responses: physiological, emotional, behavioral, and cognitive. We also strived to assess interpersonal variation in stress responses; in other words, do people have dominant clusters of stress responses?

Measuring stress responses

Different scales were developed to measure stress responses. For example, Terluin [ 24 ] developed the Four-Dimensional Symptom Questionnaire (4DSQ) in order to differentiate between general distress and what he considered as psychiatric symptoms, namely, depression, anxiety, and somatization. Schlebusch [ 25 ] developed the Stress Symptom Checklist (SSCL) which consists of three categories: physical, psychological, and behavioral. The checklist was intended to be a diagnostic tool that measures specific stress-related psychopathological conditions or disorders, particularly the intensity (or severity) of stress as reflected by an individual’s physical, psychological, and behavioral reactions.

These scales were primarily intended to measure the total intensity of the stress response in order to identify either pathological or intense stress responses, assuming the existence of a unified stress response for all. They ignored the different patterns people exhibit when confronted by a stressor, thus limiting their ability to characterize an individual’s dominant stress response pattern. Based on these works and others, stress responses were generally classified into four categories: physiological, emotional, behavioral, and cognitive [ 26 , 27 ].

Two studies were conducted in order to examine the hypothesis that stress responses can be grouped into dominant patterns according to the type of response (physiological, emotional, behavioral, and cognitive). Although the existing scales include various items representing the above mentioned categories they are too long and didn’t meet our research purposes. Therefore we have decided to derive a short scale of stress responses, representing the four categories, based on the above mentioned scales (see details under " items selection" in the Study 1 description). Participants in the first study were students while participants in the second study were a sample of people suffering from the stress-related medical syndromes of fibromyalgia (FM), irritable bowel syndrome (IBS), or both. Participants in both studies were asked to rate the extent to which different stress responses characterize their typical responses to stress. The results are presented separately for each study.

The same statistical analysis was used for both studies. Descriptive statistics was calculated for each item (stress response). We conducted an exploratory factor analysis (principle component analyses with Varimax rotation) of all items. For the second study we also calculated sub-scale scores (base and factor analysis) and compared these scores between the study groups.

The two studies were approved by the ethics committee of the Academic College of Tel Aviv-Yaffo, and all participants signed an electronic consent form prior to the study’s initiation.

Step 1: Items selection

In order to create a comprehensive list of stress response measures, the authors screened the two validated stress response questionnaires: the Four-Dimensional Symptom Questionnaire [ 24 ] and the Stress Symptom Checklist [ 25 ]. The responses were pre-classified separately by each of the authors into the four categories: physiological, emotional, behavioral, and cognitive. Discrepancies between the authors were discussed and resolved when at least four of the six authors agreed on the classification. Other items were newly added in order to encompass stress responses in all four categories. Four external experts examined and discussed the content validity of the new items as expressing stress responses and matching the relevant stress response categories. The final list included 66 items.

Step 2: Identifying the partition of stress responses

Participants..

The participants in Study 1 were first-year psychology undergraduate students at the Academic College of Tel Aviv-Yaffo. They participated in the study as part of their undergraduate program requirements and were recruited via the college’s credit database. A total of 100 participants enrolled in the study, with 91 fully completing the questionnaire. All 91 were first-year undergraduate students (84.6% female, 15.4% male) and the mean age was 23.56 years (SD = 1.37, range = 21–29).

The 66 selected items scale.

A short sociodemographic questionnaire including data on: age and gender.

The items were presented to the participants through the Qualtrics XM online platform. Participants were asked to recall a stressful event and to rate each response item on a scale ranging from “not at all” (1) to “always” (5) reflecting the extent to which each item characterize their response to stressful situations. All participants signed an electronic consent form before answering the questionnaires. The data was gathered and stored anonymously.

Data analysis.

In the first stage of analysis we conducted an exploratory factor analysis (principle component analyses with Varimax rotation) of all 66 items, which revealed four factors (eigenvalue>1.0), accounting for 48% of overall variance. We then screened the items and excluded 36 items based on both content analysis and factor loadings (items with loading< 0.5 were omitted) following two-step analyses. We ended with a final set of 30 items which we found as satisfying for our research purposes (hereinafter the 30 items scale).

In the second stage of analysis, we determined the number of factors according to the Kaiser criterion of eigenvalue> = 1 [ 28 ] and identified 7 factors accounting for 70.38% of the total variance. However, according to the scree plot, 3–4 factors could have been retained. We chose a conservative approach and determined 4 factors. The 4 factors accounted for 58.48% of the total variance.

Table 1 presents the factor loadings and descriptive statistics for each item. It is evident that items loaded on Factor 1 include mostly psychological (emotional and cognitive) responses (introversion, loneliness, confusion, etc.). Factor 2 items include mostly physiological-gastro responses (digestive upset, stomach pains, etc.). Factor 3 items include mostly physiological-muscular responses (neck and shoulder pain, backaches, etc.). Factor 4 items include mostly unregulated behavioral responses (temper flare-ups, nervousness, etc.). The item “physical unrest” was loaded on both Factor 1 and Factor 2.

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https://doi.org/10.1371/journal.pone.0255889.t001

We calculated a mean score for each factor. Factor 1, psychological, had the highest score (mean = 3.16; SD = 0.93; reliability Cronbach’s Alpha = 0.94, McDonalds Omega = 0.94), followed by Factor 4, behavioral, (mean = 3.13; SD = 0.89;reliability Cronbach’s Alpha = 0.84, McDonalds Omega = 0.84), and Factor 2, physiological-gastro, (mean = 2.56; SD = 1.00; reliability Cronbach’s Alpha = 0.65, McDonalds Omega = 0.67), and finally, Factor 3, physiological-muscular, (mean = 2.27; SD = 0.90; reliability: Cronbach’s Alpha = 0.77, McDonalds Omega = 0.79). Differences between all pairs of factors were significant except for Factor 3 vs. Factor 4.

In order to get further insight into the structure of the stress response items we conducted a smallest space analysis (SSA). SSA is a method of non-metric multidimensional scaling (NMDS) in which a set of variables and their inter-correlations are geometrically portrayed in a multidimensional space [ 29 ]. SSA treats each variable (i.e., each questionnaire item) as a point in a Euclidean space—the higher the correlation between two variables, the closer the points in the space. It attempts to find the space with the minimum number of dimensions in which the rank order of relations is preserved. The regional partition of the SSA space can be studied in conjunction with the corresponding content of the mapped variables. All points within a region should be associated with a specific set of variables of the same content [ 30 – 33 ].

As can be seen, the SSA space in Fig 1 is partitioned into four polar (or angular) regions. Each polar region corresponds to one of the four categories—psychological, physiological-gastro, physiological-muscular, and behavioral—with their respective items. Polar regions divide the space into pie-shaped sections, all emanating from a common point. The elements of a polar facet are considered to be unordered but related [ 37 ]; they differ in kind but not necessarily in complexity. It should be noted that each two adjacent categories are close to each other in some respect.

https://doi.org/10.1371/journal.pone.0255889.g001

Participants

Participants were people over 18 years old diagnosed with fibromyalgia (FM), irritable bowel syndrome (IBS) or both. Participants were recruited from four different online forums based in Israel—two of which were dedicated to IBS the rest to FM. All participants volunteered for the study and none were offered any compensation. A total of 217 participants enrolled in the study but only 143 completed the questionnaire. Amongst these, 62 participants reported having FM (43.35%), 45 reported having IBS (31.47%) and 36 reported having both IBS and FM (25.17%). 95.1% of all participants reported being officially diagnosed by a doctor. The reported mean time from diagnosis was 8.43 years (SD = 6.82). 129 were female (90.2%) and 14 were male (9.8%). This gender difference might be partially explained by the fact that both IBS and FM are more common in women worldwide. Mean age was M = 37.67 years SD = 13.2.

The 30 items scale (see Study 1 ).

A short sociodemographic questionnaire including data on: age, gender, diagnosis, time since diagnosis and who gave the diagnosis.

The items were presented to the participants through the Qualtrics XM online platform. Participants were asked to recall a stressful event and to rate each response item on a scale ranging from “not at all” (1) to “always” (5) reflecting the extent to which each item characterize their response to stressful situations. All participants signed an electronic consent form before answering the questionnaires. All data was gathered and stored anonymously.

Data analysis

We determined the number of factors according to the Kaiser criterion of eigenvalue> = 1 [ 36 ] and identified 7 factors accounting for 65.77% of the total variance. We identified 4–5 factors according to the scree plot. The fit to comparison data method (CD) revealed that the 4 factors solutions added significantly to the eigenvalue of 3 factors solution. Nevertheless, we decided on a conservative approach and we set the number of factors at 5. The 5 factors accounted for 58.01% of the total variance.

Table 2 presents the factor loadings and descriptive statistics for each item. Factor 1 included emotional responses identical to those included in factor 1 (psychological) in study 1. Three items that were included in this factor in study 1 (confusion, difficulty concentrating and attention dispersion) were now included in the additional factor 5 that consists of cognitive responses. Factor 2 included physiological- muscular items (identical to factor 3 in study 1) and the items insomnia and fatigue that were included in Factor 1 in study 1. Factor 3 included behavioural items and was identical to factor 4 in study 1. Factor 4 included physiological-gastro items, identical to the items included in Factor 2 in study 1 (except for the physical unrest item that in study 2 was included in Factor 1).

https://doi.org/10.1371/journal.pone.0255889.t002

We calculated a mean score for each factor (see Table 2 ) there were no significant differences between the factors (largest difference was 0.0962). Factors reliability measures: Factor 1—Cronbach’s Alpha = 0.88, McDonalds Omega = 0.88; Factor 2—Cronbach’s Alpha = 0.82, McDonalds Omega = 0.83; Factor 3—Cronbach’s Alpha = 0.85, McDonalds Omega = 0.86; Factor 4—Cronbach’s Alpha = 0.74, McDonalds Omega = 0.75; Factor 5—Cronbach’s Alpha = 0.85, McDonalds Omega = 0.85.

In order to get further insight into the structure of the stress response items we conducted a smallest space analysis (SSA) similar to the SSA conducted in study 1.

As can be seen, the SSA space in Fig 2 is partitioned into five polar (or angular) regions. Each polar region corresponds to one of the five categories—emotional, physiological-gastro, physiological-muscular, behavioral and cognitive—with their respective items. Polar regions divide the space into pie-shaped sections, all emanating from a common point. The elements of a polar facet are considered to be unordered but related; they differ in kind but not necessarily in complexity. It should be noted that each two adjacent categories are close to each other in some respect.

https://doi.org/10.1371/journal.pone.0255889.g002

Table 3 which presents comparisons of the 5 factors between the study groups of study 2 indicate that the IBS group reported on significantly lower levels of physiological-muscular distress in comparison to the other two groups. The IBS group reported significantly higher levels of physiological-gastro distress in comparison to the FM group. The IBS group was also found to be significantly lower in comparison to the other two groups on the cognitive factor.

https://doi.org/10.1371/journal.pone.0255889.t003

Both the emotional and behavioral factors did not differ significantly between the groups.

In this study, we aimed to examine whether there are specific clusters of stress responses, representing interpersonal variation. More specifically, we hypothesized that different stress response clusters will reflect the different aspects of the known physiological, emotional, behavioral, and cognitive stress responses, thus allowing the identification of individual patterns.

The results obtained from Study 1 revealed four distinctive response categories: psychological (emotional and cognitive), physiological-gastro, physiological-muscular and behavioral. The psychological category entails mostly clinical symptoms of depression and anxiety as well as cognitive responses. Two of the categories are physiological responses: one mostly gastro-related symptoms and the other mostly muscle tension symptoms. The fourth category entails unregulated behaviors. The results obtained from Study 2 revealed five distinctive response categories: emotional, cognitive, physiological-gastro, physiological-muscular, and behavioral. As can be seen, the psychological category is divided into emotional and cognitive. These results thus portray an interesting classification that, if understood, may help to shed new light on stress response patterns and to highlight potential psychological and physiological susceptibilities.

It is already well established that psychological stress plays a role in negative physical and mental health conditions [ 20 , 34 – 36 ]. Thus, each of these response patterns may reflect a specific time point or dimension in the cascade of events vulnerability that may have a pathological outcome. For example, it was found that the link between stress and depression and anxiety is underlined by biological mechanisms such as HPA axis activation and inflammatory processes [ 37 ]. It can therefore be postulated that a person characterized by a high score in the emotional category may, in fact, be at risk of not only depression and anxiety disorder but also high cortisol-related illnesses. Such an assumption may be even more pronounced when an individual presents a high score in one or both of the physiological response categories. For instance, irritable bowel syndrome (IBS) was found to be adversely affected by psychological stress via several possible biological pathways including gastrointestinal function [ 38 ]. The digestion-related symptoms characterizing the physiological-gastro category may therefore, indicate susceptibility to such illnesses.

As a result of these findings, we decided to proceed with a study testing whether stress responses in a sample of people suffering from the stress-related medical syndromes of fibromyalgia (FM), irritable bowel syndrome (IBS) or both (FM+IBS) will reflect our assumptions.

Our results (see Table 2 ) indicate that both the IBS and the FM+IBS groups reported experiencing physiological-gastro stress responses during stressful events more often than the FM group. We also found that both the FM and the FM+IBS groups reported experiencing physiological-muscular stress responses during stressful events more often than the IBS group.

These results are compatible with previous research findings regarding pain sensitivity patterns in these groups. Two-thirds of IBS patients have been found to have lower visceral pain thresholds [ 39 ], while their musculoskeletal pain thresholds are normal [ 40 ] or higher than in normal controls [ 41 ]. In contrast, FM patients have decreased musculoskeletal pain thresholds but normal visceral pain thresholds [ 42 , 43 ]. It appears that only the subgroup of patients who have both IBS and FM suffer both from visceral and somatic hypersensitivity [ 42 ]. However, we find that the direction of the correlation needs further study. For example, it is possible that people who have physiological-gastro responses to stress are more likely to develop IBS later, but it is also possible that people who already have IBS are more likely to respond to stress with gastrointestinal symptoms. We suggest that future longitudinal studies inspect the nature of this correlation.

We also found that both the FM and the FM+IBS groups reported experiencing emotional stress during stressful events more often than the IBS group. An earlier finding by Janssens, Zijlema, Joustra, and Rosmalen [ 44 ] that major depressive disorder is more common in FM than in IBS may explain our findings.

Conclusions

Our individual responses to stressful events embody much about who we are and what we have gone through. Our genetics, past experiences, gender, beliefs, and even smoking habits play a key role in how we react to stressors [ 19 , 45 – 47 ]. In mapping these reactions and patterns, we can obtain a clearer image of each person’s stress responses profile. A possible clinical implication of the findings of this study is the understanding that if we take into consideration the individual’s stress responses profile while planning stress management interventions and offer them a tailored intervention that reduces the intensity of these responses, we might prevent further complications resulting in physical or mental disease. Therefore, stress management interventions should be considered seriously and evidence based. An improved validated scale of stress responses may serve in the future as an important tool that will allow for the implementation of such tailored psychological interventions in various settings with minimal resources.

Limitations

Despite these important implications, there are some methodological limitations in the current study. First, the scale we have used for our research purposes is composed of items selected from previous scales and has not been validated. Second, the participants in study 2 (people who suffer from FM or IBS or both) differ from those of study 1 (students). Third, the majority of the participants were female, which might have led to a bias due to gender differences.

In addition, there are some theoretical concerns. Since our results are based on retrospective reports, participants may not accurately remember how they usually act and feel and may appraise how they have always responded to stress according to the salience of events rather than actual frequency. Previous research has suggested that people who suffer from chronic pain have an attention bias that makes pain more salient to them than it would be in normal controls (e.g., [ 48 ]). We therefore propose that future studies use a daily log of stressful events and subsequent stress reactions in order to circumvent possible memory biases.

Another major challenge is differentiating between stress responses and stress coping strategies. Some theorists have even preferred to limit the concept of coping to voluntary responses [ 49 ], while others have included automatic and involuntary responses as well [ 50 , 51 ]. However, it is difficult to distinguish between voluntary and involuntary responses—if “volition” even exists at all. Libet [ 52 ] posited that if volition does indeed exist, it is only expressed when we use a conscious effort to think or behave differently than we are used to. Furthermore, thoughts and behaviors that are intentional and effortful when first used may, he claimed, become automatic and involuntary with repetition.

These limitations notwithstanding, our study calls for a detailed observation of the components of the existing stress models, focusing specifically on the function of stress responses and their impact on health while implementing tailored interventions that take into consideration individuals’ specific response clusters.

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• Open access
• Published: 24 February 2024

Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level

• Monika Teuber 1 ,
• Daniel Leyhr 1 , 2 &
• Gorden Sudeck 1 , 3  

BMC Public Health volume  24 , Article number:  598 ( 2024 ) Cite this article

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Physical activity has been proven to be beneficial for physical and psychological health as well as for academic achievement. However, especially university students are insufficiently physically active because of difficulties in time management regarding study, work, and social demands. As they are at a crucial life stage, it is of interest how physical activity affects university students' stress load and recovery as well as their academic performance.

Student´s behavior during home studying in times of COVID-19 was examined longitudinally on a daily basis during a ten-day study period ( N  = 57, aged M  = 23.5 years, SD  = 2.8, studying between the 1st to 13th semester ( M  = 5.8, SD  = 4.1)). Two-level regression models were conducted to predict daily variations in stress load, recovery and perceived academic performance depending on leisure-time physical activity and short physical activity breaks during studying periods. Parameters of the individual home studying behavior were also taken into account as covariates.

While physical activity breaks only positively affect stress load (functional stress b = 0.032, p  < 0.01) and perceived academic performance (b = 0.121, p  < 0.001), leisure-time physical activity affects parameters of stress load (functional stress: b = 0.003, p  < 0.001, dysfunctional stress: b = -0.002, p  < 0.01), recovery experience (b = -0.003, p  < 0.001) and perceived academic performance (b = 0.012, p  < 0.001). Home study behavior regarding the number of breaks and longest stretch of time also shows associations with recovery experience and perceived academic performance.

Conclusions

Study results confirm the importance of different physical activities for university students` stress load, recovery experience and perceived academic performance in home studying periods. Universities should promote physical activity to keep their students healthy and capable of performing well in academic study: On the one hand, they can offer opportunities to be physically active in leisure time. On the other hand, they can support physical activity breaks during the learning process and in the immediate location of study.

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Introduction

Physical activity (PA) takes a particularly key position in health promotion and prevention. It reduces risks for several diseases, overweight, and all-cause mortality [ 1 ] and is beneficial for physical, psychological and social health [ 2 , 3 , 4 , 5 ] as well as for academic achievement [ 6 , 7 ]. However, PA levels decrease from childhood through adolescence and into adulthood [ 8 , 9 , 10 ]. Especially university students are insufficiently physically active according to health-oriented PA guidelines [ 11 ] because of academic workloads as well as difficulties in time management regarding study, work, and social demands [ 12 ]. Due to their independence and increasing self-responsibility, university students are at a crucial life stage. In this essential and still educational stage of the students´ development, it is important to study their PA behavior. Furthermore, PA as health behavior represents one influencing factor which is considered in the analytical framework of the impact of health and health behaviors on educational outcomes which was developed by the authors Suhrcke and de Paz Nieves [ 13 , 14 ]. In light of this, the present study examines how PA affects university students' academic situations.

Along with the promotion of PA, the reduction of sedentary behavior has also become a crucial part of modern health promotion and prevention strategies. Spending too much time sitting increases many health risks, including the risk of obesity [ 15 ], diabetes [ 16 ] and other chronic diseases [ 15 ], damage to muscular balances, bone metabolism and musculoskeletal system [ 17 ] and even early death [ 15 ]. University students are a population that has shown the greatest increase in sedentary behavior over the last two decades [ 18 ]. In Germany, they show the highest percentage of sitting time among all working professional groups [ 19 ]. Long times sitting in classes, self-study learning, and through smartphone use, all of which are connected to the university setting and its associated behaviors, might be the cause of this [ 20 , 21 ]. This goes along with technological advances which allow students to study in the comfort of their own homes without changing locations [ 22 ].

To counter a sedentary lifestyle, PA is crucial. In addition to its physical health advantages, PA is essential for coping with the intellectual and stress-related demands of academic life. PA shows positive associations with stress load and academic performance. It is positively associated with learning and educational success [ 6 ] and even shows stress-regulatory potential [ 23 ]. In contrast, sedentary behavior is associated with lower cognitive performance [ 24 ]. Moreover, theoretical derivations show that too much sitting could have a negative impact on brain health and diminish the positive effects of PA [ 16 ]. Given the theoretical background of the stressor detachment model [ 25 ] and the cybernetic approach to stress management in the workplace [ 26 ], PA can promote recovery experience, it can enhance academic performance, and it is a way to reduce the impact of study-related stressors on strain. Load-related stress response can be bilateral: On the one hand, it can be functional if it is beneficial to help cope with the study demands. On the other hand, it can be dysfunctional if it puts a strain on personal resources and can lead to load-related states of strain [ 27 ]. Thus, both, the promotion of PA and reduction of sedentary behavior are important for stress load, recovery, and performance in student life, which can be of particular importance for students in an academic context.

A simple but (presumably) effective way to integrate PA and reduce sedentary behavior in student life are short PA breaks. Due to the exercises' simplicity and short duration, students can perform them wherever they are — together in a lecture or alone at home. Short PA breaks could prevent an accumulation of negative stressors during the day and can help with prolonged sitting as well as inactivity. Especially in the university setting, evidence of the positive effects of PA breaks exists for self-perceived physical and psychological well-being of the university students [ 28 ]. PA breaks buffer university students’ perceived stress [ 29 ] and show positive impacts on recovery need [ 30 ] and better mood ratings [ 31 , 32 ]. In addition, there is evidence for reduction in tension [ 30 ], overall muscular discomfort [ 33 ], daytime sleepiness or fatigue [ 33 , 34 ] and increase in vigor [ 34 ] and experienced energy [ 30 ]. This is in line with cognitive, affective, behavioral, and biological effects of PA, all categorized as palliative-regenerative coping strategies, which addresses the consequences of stress-generating appraisal processes aiming to alleviate these consequences (palliative) or restore the baseline of the relevant reaction parameter (regenerative) [ 35 , 36 ]. This is achieved by, for example, reducing stress-induced cortisol release or tension through physical activity (reaction reduction) [ 35 ]. Such mechanisms are also in accordance with the previously mentioned stressor detachment model [ 25 ]. Lastly, there is a health-strengthening effect that impacts the entire stress-coping-health process, relying on the compensatory effects of PA which is in accordance to the stress-buffering effect of exercise [ 37 ]. Health, in turn, effects educational outcomes [ 13 , 14 ]. Therefore, stress regulating effects are also accompanied with the before mentioned analytical framework of the impact of health and health behaviors on educational outcomes [ 13 , 14 ].

Focusing on the effects of PA, this study is guided by an inquiry into how PA affects university students' stress load and recovery as well as their perceived academic performance. For that reason, the student´s behavior during home studying in times of COVID-19 is examined, a time in which reinforced prolonged sitting, inactivity, and a negative stress load response was at a high [ 38 , 39 , 40 , 41 , 42 ]. Looking separately on the relation of PA with different parameters based on the mentioned evidence, we assume that PA has a positive impact on stress load, recovery, and perceived academic performance-related parameters. Furthermore, a side effect of the home study behavior on the mentioned parameters is assumed regarding the accumulation of negative stressors during home studying. These associations are presented in Fig.  1 and summarized in the following hypotheses:

Overview of the assumed effects and investigated hypotheses of physical activity (PA) behavior on variables of stress load and recovery and perceived academic performance-related parameters

Hypothesis 1 (path 1): Given that stress load always occurs as a duality—beneficial if it is functional for coping, or exhausting if it puts a strain on personal resources [ 27 ] – we consider two variables for stress load: functional stress and dysfunctional stress. In order to reduce the length of the daily surveys, we focused the measure of recovery only on the most obvious and accessible component of recovery experience, namely psychological detachment. PA (whether performed in leisure-time or during PA breaks) encourages functional stress and reduce dysfunctional stress (1.A) and has a positive effect on recovery experience through psychological detachment (1.B).

Hypothesis 2 (path 2): The academic performance-related parameters attention difficulties and study ability are positively influenced by PA (whether done in leisure-time or during PA breaks). We have chosen to assess attention difficulties for a cognitive parameter because poor control over the stream of occurring stimuli have been associated with impairment in executive functions or academic failure [ 43 , 44 , 45 , 46 ]. Furthermore, we have assessed the study ability to refer to the self-perceived feeling of functionality regarding the demands of students. PA reduces self-reported attention difficulties (2.A) and improves perceived study ability, indicating that a student feels capable of performing well in academic study (2.B).

Hypothesis 3: We assume that a longer time spent on studying at home (so called home studying) could result in higher accumulation of stressors throughout the day which could elicit immediate stress responses, while breaks in general could reduce the influence of work-related stressors on strain and well-being [ 47 , 48 ]. Therefore, the following covariates are considered for secondary effects:

the daily longest stretch of time without a break spent on home studying

the daily number of breaks during home studying

Study setting

The study was carried out during the COVID-19 pandemic containment phase. It took place in the middle of the lecture period between 25th of November and 4th of December 2020. Student life was characterized by home studying and digital learning. A so called “digital semester” was in effect at the University of Tübingen when the study took place. Hence, courses were mainly taught online (e.g., live or via a recorded lecture). Other events and actions at the university were not permitted. As such, the university sports department closed in-person sports activities. For leisure time in general, there were contact restrictions (social distancing), the performance of sports activities in groups was not permitted, and sports facilities were closed.

Thus, the university sports department of the University of Tübingen launched various online sports courses and the student health management introduced an opportunity for a new digital form of PA breaks. This opportunity provided PA breaks via videos with guided physical exercises and health-promoting explanations for a PA break for everyday home studying: the so called “Bewegungssnack digital” [in English “exercise snack digital” (ESD)] [ 49 ]. The ESD videos took 5–7 min and were categorized into three thematic foci: activation, relaxation, and coordination. Exercises were demonstrated by one or two student exercise leaders, accompanied by textual descriptions of the relevant execution features of each exercise.

Participants

Participants were recruited within the framework of an intervention study, which was conducted to investigate whether a digital nudging intervention has a beneficial effect on taking PA breaks during home study periods [ 49 ]. Students at the University of Tübingen which counts 27,532 enrolled students were approached for participation through a variety of digital means: via an email sent to those who registered for ESD course on the homepage of the university sports department and to all students via the university email distribution list; via advertisement on social media of the university sports department (Facebook, Instagram, YouTube, homepage). Five tablets, two smart watches, and one iPad were raffled off to participants who engaged actively during the full study period in an effort to motivate them to stick with it to the end. In any case, participants knew that the study was voluntary and that they would not suffer any personal disadvantages should they opt out. There was a written informed consent prompt together with a prompt for the approval of the data protection regulations immediately within the first questionnaire (T0) presented in a mandatory selection field. Positive ethical approval for the study was given by the first author´s institution´s ethics committee of the faculty of the University of Tübingen.

Participants ( N  = 57) who completed the daily surveys on at least half of the days of the study period, were included in the sample (male = 6, female = 47, diverse = 1, not stated = 3). As not all subjects provided data on all ten study days, the total number of observations was between 468 and 540, depending on the variable under study (see Table  1 ). The average number of observations per subject was around eight. Their age was between 18 and 32 years ( M  = 23.52, SD  = 2.81) and they were studying between the 1st to 13th semester ( M  = 5.76, SD  = 4.11) within the following major courses of study: mathematical-scientific majors (34.0%), social science majors (22.6%), philosophical majors (18.9%), medicine (13.2%), theology (5.7%), economics (3.8%), or law (1.9%). 20.4% of the students had on-site classroom teaching on university campus for at least one day a week despite the mandated digital semester, as there were exceptions for special forms of teaching.

Design and procedures

To examine these hypothesized associations, a longitudinal study design with daily surveys was chosen following the suggestion of the day-level study of Feuerhahn et al. (2014) and also of Sonnentag (2001) measuring recovery potential of (exercise) activities during leisure time [ 50 , 51 ]. Considering that there are also differences between people at the beginning of the study period, initial base-line value variables respective to the outcomes measured before the study period were considered as independent covariates. Therefore, the well-being at baseline serves as a control for stress load (2.A), the psychological detachment at baseline serves as a control for daily psychological detachment (2.B), the perception of study demands serves as a control for self-reported attention difficulties (1.A), and the perceived study ability at baseline serves as a control for daily study ability (2.B).

Subjects were asked to continue with their normal home study routine and additionally perform ESD at any time in their daily routine. Data were collected one to two days before (T0) as well as daily during the ten-day study period (Wednesday to Friday). The daily surveys (t 1 -t 10 ) were sent by email at 7 p.m. every evening. Each day, subjects were asked to answer questions about their home studying behavior, study related requirements, recovery experience from study tasks, attention, and PA, including ESD participation. The surveys were conducted online using the UNIPARK software and were recorded and analyzed anonymously.

Measures and covariates

In total, five outcome variables, two independent variables, and seven covariates were included in different analyses: three variables were used for stress load and recovery parameters, two variables for academic performance-related parameters, two variables for PA behavior, two variables for study behavior, four variables for outcome specific baseline values and one variable for age.

Outcome variables

Stress load & recovery parameters (hypothesis 1).

Stress load was included in the analysis with two variables: functional stress and dysfunctional stress. Followingly, a questionnaire containing a word list of adjectives for the recording of emotions and stress during work (called “Erfassung von Emotionen und Beanspruchung “ in German, also known as EEB [ 52 ]) was used. It is an instrument which were developed and validated in the context of occupational health promotion. The items are based on mental-workload research and the assessment of the stress potential of work organization [ 52 ]. Within the questionnaire, four mental and motivational stress items were combined to form a functional stress scale (energetic, willing to perform, attentive, focused) (α = 0.89) and four negative emotional and physical stress items were combined to form dysfunctional stress scale (nervous, physically tensioned, excited, physically unwell) (α = 0.71). Participants rated the items according to how they felt about home studying in general on the following scale (adjustment from “work” to “home studying”): hardly, somewhat, to some extent, fairly, strongly, very strongly, exceptionally.

Recovery experience was measured via psychological detachment. Therefore, the dimension “detachment” of the Recovery Experience Questionnaire (RECQ [ 53 ]) was adjusted to home studying. The introductory question was "How did you experience your free time (including short breaks between learning) during home studying today?". Students responded to four statements based on the extent to which they agreed or disagreed (not at all true, somewhat true, moderately true, mostly true, completely true). The statements covered subjects such as forgetting about studying, not thinking about studying, detachment from studying, and keeping a distance from student tasks. The four items were combined into a score for psychological detachment (α = 0.94).

Academic performance-related parameters (hypothesis 2)

Attention was assessed via the subscale “difficulty maintaining focused attention performance” of the “Attention and Performance Self-Assessment” (ASPA, AP-F2 [ 54 ]). It contains nine items with statements about disturbing situations regarding concentration (e.g. “Even a small noise from the environment could disturb me while reading.”). Participants had to answer how often such situations happened to them on a given day on the following scale: never, rarely, sometimes, often, always. The nine items were combined into the AP-F2 score (α = 0.87).

The perceived study ability was assessed using the study ability index (SAI [ 55 ]). The study ability index captures the current state of perceived functioning in studying. It is based on the Work Ability Index by Hasselhorn and Freude ([ 56 ]) and consists of an adjusted short scale of three adapted items in the context of studying. Firstly, (a) the perceived academic performance was asked after in comparison to the best study-related academic performance ever achieved (from 0 = completely unable to function to 10 = currently best functioning). Secondly, the other two items were aimed at assessing current study-related performance in relation to (b) study tasks that have to be mastered cognitively and (c) the psychological demands of studying. Both items were answered on a five-point Likert scale (1 = very poor, 2 = rather poor, 3 = moderate, 4 = rather good, 5 = very good). A sum index, the SAI, was formed which can indicate values between 2 and 20, with higher values corresponding to higher assessed functioning in studies (α = 0.86). In a previous study it already showed satisfying reliability (α = 0.72) [ 55 ].

Independent variables

Pa behavior.

Two indicators for PA behavior were included via self-reports: the time spent on ESD and the time spent on leisure-time PA (LTPA). Participants were asked the following overarching question daily: “How much time did you spend on physical activity today and in what context”. For the independent variable time spent on PA breaks, participants could answer the option “I participated in the Bewegungssnack digital” with the amount of time they spent on it (in minutes). To assess the time spent on LTPA besides PA breaks, participants could report their time for four different contexts of PA which comprised two forms: Firstly, structured supervised exercise was reported via time spent on (a) university sports courses and (b) other organized sports activities. Secondly, self-organized PA was indicated via (c) independent PA at home, such as a workout or other physically demanding activity such as cleaning or tidying up, as well as via (d) independent PA outside, like walking, cycling, jogging, a workout or something similar. Referring to the different domains of health enhancing PA [ 57 ], the reported minutes of these four types of PA were summed up to a total LTPA value. The total LTPA value was included in the analysis as a metric variable in minutes.

Covariates (hypothesis 3)

Regarding hypothesis 3 and home study behavior, the longest daily stretch of time without a break spent on home studying (in hours) and the daily number of breaks during home studying was assessed. Therein, participants had to answer the overarching question “How much time did you spend on your home studying today?” and give responses to the items: (1) longest stretch of time for home studying (without a break), and (2) number of short and long breaks you took during home studying.

In principle, efforts were made to control for potential confounders at the individual level (level 2) either by including the baseline measure (T0) of the respective variable or by including variables assessing related trait-like characteristics for respective outcomes. The reason why related trait-like characteristics were used for the outcomes was because brief assessments were used for daily surveys that were not concurrently employed in the baseline assessment. To enable the continued use of controlling for person-specific baseline characteristics in the analysis of daily associations, trait-like characteristics available from the baseline assessment were utilized as the best possible approximation.To sum up, four outcome specific baseline value variables were measured before the study period (at T0). The psychological detachment with the RECQ (α = 0.87) [ 53 ] was assessed at the beginning to monitor daily psychological detachment. Further, the SAI [ 55 ] was assessed at the beginning of the study period to monitor daily study ability. To monitor daily stress load, which in part measures mental stress aspects and negative emotional stress aspects, the well-being was assessed at the beginning using the WHO-Five Well-being Index (WHO-5 [ 58 ]). It is a one-dimensional self-report measure with five items. The index value is the sum of all items, with higher values indicating better well-being. As the well-being and stress load tolerance may linked with each other, this variable was assumed to be a good fit with the daily stress load indicating mental and emotional stress aspects. With respect to student life, daily academic performance-related attention was monitored with an instrument for the perception of study demands and resources (termed “Berliner Anforderungen Ressourcen-Inventar – Studierende” in German, the so-called BARI-S [ 59 ]). It contains eight items which capture overwork in studies, time pressure during studies, and the incompatibility of studies and private life. All together they form the BARI-S demand scale (α = 0.85) which was included in the analysis. As overwork and time pressure may result in attention difficulties (e.g. Elfering et al., 2013), this variable was assumed to have a good fit with academic performance-related attention [ 60 ]. Additionally, age in years at T0 was considered as a sociodemographic factor.

Statistical analysis

Since the study design provided ten measurement points for various people, the hierarchical structure of the nested data called for two-level analyses. Pre-analyses of Random-Intercept-Only models for each of the outcome variables (hypothesis 1 to 3) revealed an Intra-Class-Correlation ( ICC ) of at least 0.10 (range 0.26 – 0.64) and confirmed the necessity to perform multilevel analyses [ 61 ]. Specifically, the day-level variables belong to Level 1 (ESD time, LTPA time, longest stretch of time without a break spent on home studying, daily number of breaks during home studying). To analyze day-specific effects within the person, these variables were centered on the person mean (cw = centered within) [ 50 , 62 , 63 , 64 ]. This means that the analyses’ findings are based on a person’s deviations from their average values. The variables assessed at T0 belong to Level 2, which describe the person level (psychological detachment baseline, SAI baseline, well-being, study demands scale, age). These covariates on person level were centered around the grand mean [ 50 ] indicating that the analyses’ findings are based how far an individual deviates from the sample's mean values. As a result, the models’ intercept reflects the outcome value of an average student in the sample at his/her daily average behavior in PA and home study when all parameters are zero. For descriptive statistics SPSS 28.0.1.1 (IBM) and for inferential statistics R (version 4.1.2) were used. The hierarchical models were calculated using the package lme4 with the lmer-function in R in the following steps [ 65 ]. The Null Model was analyzed for all models first, with the corresponding intercept as the only predictor. Afterwards, all variables were entered. The regression coefficient estimates (”b”) were considered for statistical significance for the models and the respective BIC was provided.

In total, five regression models with ‘PA break time’ and ‘LTPA time’ as independent variables were computed due to the five measured outcomes of the present study. Three models belonged to hypothesis 1 and two models to hypothesis 2.

Hypothesis 1: To test hypothesis 1.A two outcome variables were chosen for two separate models: ‘functional stress’ and ‘dysfunctional stress’. Besides the PA behavior variables, the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’, and the ‘well-being’ at the beginning of the study as corresponding baseline variable to the output variable were also included as independent variables in both models. The outcome variable ‘psychological detachment’ was utilized in conjunction with the aforementioned independent variables to test hypotheses 1.B, with one exception: psychological detachment at the start of the study was chosen as the corresponding baseline variable.

Hypothesis 2: To investigate hypothesis 2.A the outcome variable ‘attention difficulties’ was selected. Hypothesis 2.B was tested with the outcome variables ‘study ability’. Both models included both PA behavior variables as well as the ‘number of breaks’, the ‘longest stretch of time without a break spent on home studying’, ‘age’ and one corresponding baseline variable each: the ‘study demand scale’ at the start of the study for ‘attention difficulties’ and the ‘SAI’ at the beginning of the study for the daily ‘study ability’.

Hypothesis 3: In addition to both PA behavior variables, age and one baseline variable that matched the outcome variable, the covariates ‘daily longest stretch of time spent on home studying’ and ‘daily number of breaks during home studying’ were included in the models for all five outcome variables.

Handling missing data

The dataset had up to 18% missing values (most exhibit the variables ‘daily longest stretch of time without a break spent on home studying’ with 17.89% followed by ‘daily number of breaks during homes studying’ with 16.67%, and ‘functional / dysfunctional stress’ with 12.45%). Therefore, a sensitivity analysis was performed using the multiple imputation mice-package in the statistical program R [ 66 ], the package howManyImputation based on Von Hippel (2020, [ 67 ]), and the additional broom package [ 68 ]. The results of the models remained the same, with one exception for the Attention Difficulties Model: The daily longest stretch of time without a break spent on home studying showed a significant association (Table  1 in supplement). Due to this almost perfect consistency of results between analyses based on the dataset with missing data and those with imputed data alongside the lack of information provided by the packages for imputed datasets, we decided to stick with the main analysis including the missing data. Thus, in the following the results of the main analysis without imputations are presented.

Table 1 shows the descriptive statistics of the variables used in the analysis. An overview of the analysed models is presented in Table  2 .

Effects on stress load and recovery (hypothesis 1)

Hypothesis 1.A: The Model Functional Stress explained 13% of the variance by fixed factors (marginal R 2  = 0.13), and 52% by both fixed and random factors (conditional R 2  = 0.52). The time spent on ESD as well as the time spent on PA in leisure showed a positive significant influence on functional stress (b = 0.032, p  < 0.01). The same applied to LTPA (b = 0.003, p  < 0.001). The Model Dysfunctional Stress (marginal R 2  = 0.027, conditional R 2  = 0.647) showed only one significant result. The dysfunctional stress was only significantly negatively influenced by the time spent on LTPA (b = 0.002, p  < 0.01).

Hypothesis 1.B: With the Model Detachment, fixed factors contributed 18% of the explained variance and fixed and random factors 46% of the explained variance for psychological detachment. Only the amount of time spent on LTPA revealed a positive impact on psychological detachment (b = 0.003, p  < 0.001).

Effects on academic performance-related parameters (hypothesis 2)

Hypothesis 2.A: The Model Attention Difficulties showed 13% of the variance explained by fixed factors, and 51% explained by both fixed and random factors. It showed a significant negative association only for the time spent on LTPA (b = 0.003, p  < 0.001).

Hypothesis 2.B: The Model SAI showed 18% of the variance explained by fixed factors, and 39% explained by both fixed and random factors. There were significant positive associations for time spent on ESD (b = 0.121, p  < 0.001) and time spent on LTPA (b = 0.012, p  < 0.001). The same applied to LTPA (b = 0.012, p  < 0.001).

Effects of home study behavior (hypothesis 3)

Regarding the independent covariates for the outcome variables functional and dysfunctional stress, there were no significant results for the number of breaks during homes studying or the longest stretch of time without a break spent on home studying. Considering the outcome variable ‘psychological detachment’, there were significant results with negative impact for both study behavior variables: breaks during home studying (b = 0.058, p  < 0.01) and daily longest stretch of time without a break (b = 0.120, p  < 0.01). Evaluating the outcome variables ‘attention difficulties’, there were no significant results for the number of breaks during home studying or the longest stretch of time without a break spent on home studying. Testing the independent study behavior variables for the SAI, it increased with increasing number in daily breaks during homes studying relative to the person´s mean (b = 0.183, p  < 0.05). No significant effect was found for the longest stretch of time without a break spent on home studying ( p  = 0.07).

The baseline covariates of the models showed expected associations and thus confirmed their inclusion. The baseline variables well-being showed a significant impact on functional stress (b = 0.089, p  < 0.001), psychological detachment showed a positive effect on the daily output variables psychological detachment (b = 0.471, p  < 0.001), study demand scale showed a positive association on difficulties in attention (b = 0.240, p  < 0.01), and baseline SAI had a positive effect on the daily SAI (b = 0.335, p  < 0.001).

The present study theorized that PA breaks and LTPA positively influence the academic situation of university students. Therefore, impact on stress load (‘functional stress’ and ‘dysfunctional stress’) and ‘psychological detachment’ as well as academic performance-related parameters ‘self-reported attention difficulties’ and ‘perceived study ability’ was taken into account. The first and second hypotheses assumed that both PA breaks and LTPA are positively associated with the aforementioned parameters and were confirmed for LTPA for all parameters and for PA breaks for functional stress and perceived study ability. The third hypothesis assumed that home study behavior regarding the daily number of breaks during home studying and longest stretch of time without a break spent on home studying has side effects. Detected negative effects for both covariates on psychological detachment and positive effects for the daily number of breaks on perceived study ability were partly unexpected in their direction. These results emphasize the key position of PA in the context of modern health promotion especially for students in an academic context.

Regarding hypothesis 1 and the detected positive associations for stress load and recovery parameters with PA, the results are in accordance with the stress-regulatory potential of PA from the state of research [ 23 ]. For hypothesis 1.A, there is a positive influence of PA breaks and LTPA on functional stress and a negative influence of LTPA on dysfunctional stress. Given the bilateral role of stress load, the results indicate that PA breaks and LTPA are beneficial for coping with study demands, and may help to promote feelings of joy, pride, and learning progress [ 27 ]. This is in line with previous evidence that PA breaks in lectures can buffer university students’ perceived stress [ 29 ], lead to better mood ratings [ 29 , 31 ], and increase in motivation [ 28 , 69 ], vigor [ 34 ], energy [ 30 ], and self-perceived physical and psychological well-being [ 28 ]. Looking at dysfunctional stress, the result point that LTPA counteract load-related states of strain such as inner tension, irritability and nervous restlessness or feelings of boredom [ 27 ]. In contrast, short PA breaks during the day could not have enough impact in countering dysfunctional stress at the end of the day regarding the accumulation of negative stressors during home studying which might have occurred after the participant took PA breaks. Other studies have been able to show a reduction in tension [ 30 ] and general muscular discomfort [ 33 ] after PA breaks. However, this was measured as an immediate effect of PA breaks and not with general evening surveys. Blasche and colleagues [ 34 ] measured effects immediately and 20 min after different kind of breaks and found that PA breaks led to an additional short‐ and medium‐term increase in vigor while the relaxation break lead to an additional medium‐term decrease in fatigue compared to an unstructured open break. This is consistent with the results of the present study that an effect of PA breaks is only observed for functional stress and not for dysfunctional stress. Furthermore, there is evidence that long sitting during lectures leads to increased fatigue and lower concentration [ 31 , 70 ], which could be counteracted by PA breaks. For both types of stress loads, functional and dysfunctional stress, there is an influence of students´ well-being in this study. This shows that the stress load is affected by the way students have mentally felt over the last two weeks. The relevance of monitoring this seems important especially in the time of COVID-19 as, for example, 65.3% of the students of a cross-sectional online survey at an Australian university reported low to very low well-being during that time [ 71 ]. However, since PA and well-being can support functional stress load, they should be of the highest priority—not only as regards the pandemic, but also in general.

Looking at hypothesis 1.B; while there is a positive influence of LTPA on experienced psychological detachment, no significant influence for PA breaks was detected. The fact that only LTPA has a positive effect can be explained by the voluntary character of the activity [ 50 ]. The voluntary character ensures that stressors no longer affect the student and, thus, recovery as detachment can take place. Home studying is not present in leisure times, and thus detachment from study is easier. The PA break videos, on the other hand, were shot in a university setting, which would have made it more difficult to detach from study. In order to further understand how PA breaks affect recovery and whether there is a distinction between PA breaks and LTPA, future research should also consider other types of recovery (e.g. relaxation, mastery, and control). Additionally, different types of PA breaks, such as group PA breaks taken on-site versus video-based PA breaks, should be taken into account.

Considering the confirmed positive associations for academic performance-related parameters of hypothesis 2, the results are in accordance with the evidence of positive associations between PA and learning and educational success [ 6 ], as well as between PA breaks and better cognitive functioning [ 28 ]. Looking at the self-reported attention difficulties of hypothesis 2.A, only LTPA can counteract it. PA breaks showed no effects, contrary to the results of a study of Löffler and collegues (2011, [ 31 ]), in which acute effects of PA breaks could be found for higher attention and cognitive performance. Furthermore, the perception of study demands before the study periods has a positive impact on difficulties in attention. That means that overload in studies, time pressure during studies, and incompatibility of studies and private life leads to higher difficulties with attention in home studying. In these conditions, PA breaks might have been seen as interfering, resulting in the expected beneficial effects of exercise on attention and task-related participation behavior [ 72 , 73 ] therefore remaining undetected. With respect to the COVID-19 pandemic, accompanying education changes, and an increase in student´s worries [ 74 , 75 ], the perception of study demands could be affected. This suggests that especially in times of constraint and changes, it is important to promote PA in order to counteract attention difficulties. This also applies to post-pandemic phase.

Regarding the perceived academic performance of hypothesis 2.B, both PA breaks and LTPA have a positive effect on perceived study ability. This result confirms the positive short-term effects on cognition tasks [ 76 ]. It is also in line with the positive function of PA breaks in interrupting sedentary behavior and therefore counteracting the negative association between sitting behavior and lower cognitive performance [ 24 ]. Additionally, this result also fits with the previously mentioned positive relationship between LTPA and functional stress and between PA breaks and functional stress.

According to hypothesis 3, in relation to the mentioned stress load and recovery parameters, there are negative effects of the daily number of breaks during home studying and the longest stretch of time without a break spent on home studying on psychological detachment. As stressors result in negative activation, which impede psychological detachment from study during non-studying time [ 25 ], it was expected and confirmed that the longest stretch of time without a break spent on home studying has a negative effect on detachment. Initially unexpected, the number of breaks has a negative influence on psychological detachment, as breaks could prevent the accumulation of strain reactions. However, if the breaks had no recovery effect through successful detachment, the number might not have any influence on recovery via detachment. This is indicated by the PA breaks, which had no impact on psychological detachment. Since there are other ways to recover from stress besides psychological detachment, such as relaxation, mastery, and control [ 53 ], PA breaks must have had an additional impact in relation to the positive results for functional stress.

In relation to the mentioned academic performance-related parameters, only the number of breaks has a positive influence on the perceived study ability. This indicates that not only PA breaks but also breaks in general lead to better perceived functionality in studying. Paulus and colleagues (2021) found out that an increase in cognitive skills is not only attributed to PA breaks and standing breaks, but also to open breaks with no special instructions [ 28 ]. Either way, they found better improvement in self-perceived physical and psychological well-being of the university students with PA breaks than with open breaks. This is also reflected in the present study with the aforementioned positive effects of PA breaks on functional stress, which does not apply to the number of breaks.

Overall, it must be considered that the there is a more complex network of associations between the examined parameters. The hypothesized separate relation of PA with different parameters do not consider associations between parameters of stress load / recovery and academic performance although there might be a interdependency. Furthermore, moderation aspects were not examined. For example, PA could be a moderator which buffer negative effects of stress on the study ability [ 55 ]. Moreover, perceived study ability might moderate stress levels and academic performance. Further studies should try to approach and understand the different relationships between the parameters in its complexity.

Limitations

Certain limitations must be taken into account. Regarding the imbalanced design toward more female students in the sample (47 female versus 6 male), possible sampling bias cannot be excluded. Gender research on students' emotional states during COVID-19, when this study took place, or students´ acceptance of PA breaks is diverse and only partially supplied with inconsistent findings. For example, during the COVID-19 pandemic, some studies reported that female students were associated with lower well-being [ 71 ] or worse mental health trajectories [ 75 , 77 ]. Another study with a large sample of students from 62 countries reported that male students were more strongly affected by the pandemic because they were significantly less satisfied with their academic life [ 74 ]. However, Keating and colleges (2020) discovered that, despite the COVID-19 pandemic, females rated some aspects of PA breaks during lectures more positively than male students did. However, this was also based on a female slanted sample [ 78 ]. Further studies are needed to get more insights into gender bias.

Furthermore, the small sample size combined with up to 16% missing values comprises a significant short-coming. There were a lot of possibilities which could cause such missing data, like refused, forgotten or missed participation, technical problems, or deviation of the personal code for the questionnaire between survey times. Although the effects could be excluded by sensitive analysis due to missing data, the sample is still small. To generalize the findings, future replication studies are needed.

Additionally, PA breaks were only captured through participation in the ESD, the specially instructed PA break via video. Effects of other short PA breaks were not include in the study. However, participants were called to participate in ESD whenever possible, so the likelihood that they did take part in PA breaks in addition to the ESD could be ignored.

With respect to the baseline variables, it must be considered that two variables (stress load, attention difficulties) were adjusted not with their identical variable in T0, but with other conceptually associated variables (well-being index, BARI-S). Indeed, contrary to the assumption the well-being index does only show an association with functional stress, indicating that it does not control dysfunctional stress. Although the other three assumed associations were confirmed there might be a discrepancy between the daily measured variables and the variables measured in T0. Further studies should either proof the association between these used variables or measure the same variables in T0 for control the daily value of these variables.

Moreover, the measuring instruments comprised the self-assessed perception of the students and thus do not provide an objective information. This must be considered, especially for measuring cognitive and academic-performance-related measures. Here, existing objective tests, such as multiple choice exams after a video-taped lecture [ 72 ] might have also been used. Nevertheless, such methods were mostly used in a lab setting and do not reflect reality. Due to economic reasons and the natural learning environment, such procedures were not applied in this study. However, the circumstances of COVID-19 pandemic allowed a kind of lab setting in real life, as there were a lot of restrictions in daily life which limited the influence of other covariates. The study design provides a real natural home studying environment, producing results that are applicable to the healthy way that students learn in the real world. As this study took place under the conditions of COVID-19, new transformations in studying were also taken into account, as home studying and digital learning are increasingly part of everyday study.

However, the restrictions during the COVID-19 pandemic could result in a greater extent of leisure time per se. As the available leisure time in general was not measured on daily level, it is not possible to distinguish if the examined effects on the outcomes are purely attributable to PA. It is possible that being more physical active is the result of having a greater extent of leisure time and not that PA but the leisure time itself effected the examined outcomes. To address this issue in future studies, it is necessary to measure the proportion of PA in relation to the leisure time available.

Furthermore, due to the retrospective nature of the daily assessments of the variables, there may be overstated associations which must be taken into account. Anyway, the daily level of the study design provides advantages regarding the ability to observe changes in an individual's characteristics over the period of the study. This design made it possible to find out the necessity to analyze the hierarchical structure of the intraindividual data nested within the interindividual data. The performed multilevel analyses made it possible to reflect the outcome of an average student in the sample at his/her daily average behavior in PA and home study.

Conclusion and practical implications

The current findings confirm the importance of PA for university students` stress load, recovery experience, and academic performance-related parameters in home studying. Briefly summarized, it can be concluded that PA breaks positively affect stress load and perceived study ability. LTPA has a positive impact on stress load, recovery experience, and academic performance-related parameters regarding attention difficulties and perceived study ability. Following these results, universities should promote PA in both fashions in order to keep their students healthy and functioning: On the one hand, they should offer opportunities to be physically active in leisure time. This includes time, environment, and structural aspects. The university sport department, which offers sport courses and provides sport facilities on university campuses for students´ leisure time, is one good example. On the other hand, they should support PA breaks during the learning process and in the immediate location of study. This includes, for example, providing instructor videos for PA breaks to use while home studying, and furthermore having instructors to lead in-person PA breaks in on-site learning settings like universities´ libraries or even lectures and seminars. This not only promotes PA, but also reduces sedentary behavior and thereby reduces many other health risks. Further research should focus not only on the effect of PA behavior but also of sedentary behavior as well as the amount of leisure time per se. They should also try to implement objective measures for example on academic performance parameters and investigate different effect directions and possible moderation effects to get a deeper understanding of the complex network of associations in which PA plays a crucial role.

Availability of data and materials

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

Abbreviations

Attention and Performance Self-Assessment

"Berliner Anforderungen Ressourcen-Inventar – Studierende" (instrument for the perception of study demands and resources)

Centered within

Grand centered

“Erfassung von Emotionen und Beanspruchung “ (questionnaire containing a word list of adjectives for the recording of emotions and stress during work)

Exercise snack digital (special physical activity break offer)

Intra-Class-Correlation

Leisure time physical activity

• Physical activity

Recovery Experience Questionnaire

Study ability index

World Health Organization-Five Well-being index

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Acknowledgements

We would like to thank Juliane Moll, research associate of the Student Health Management of University of Tübingen, for the support in the coordination and realization study. We would like to express our thanks also to Ingrid Arzberger, Head of University Sports at the University of Tübingen, for providing the resources and co-applying for the funding. We acknowledge support by Open Access Publishing Fund of University of Tübingen.

Open Access funding enabled and organized by Projekt DEAL. This research regarding the conduction of the study was funded by the Techniker Krankenkasse, health insurance fund.

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Contributions

M.T. and G.S. designed the study. M.T. coordinated and carried out participant recruitment and data collection. M.T. analyzed the data and M.T. and D.L. interpreted the data. M.T. drafted the initial version of the manuscript and prepared the figure and all tables. All authors contributed to reviewing and editing the manuscript and have read and agreed to the final version of the manuscript.

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Correspondence to Monika Teuber .

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Teuber, M., Leyhr, D. & Sudeck, G. Physical activity improves stress load, recovery, and academic performance-related parameters among university students: a longitudinal study on daily level. BMC Public Health 24 , 598 (2024). https://doi.org/10.1186/s12889-024-18082-z

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Measurement of Human Stress: A Multidimensional Approach

Achsah Dorsey , Elissa Scherer , Randy Eckhoff , and Robert Furberg .

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Stress is a multidimensional construct that comprises exposure to events, perceptions of stress, and physiological responses to stress. Research consistently demonstrates a strong association between stress and a myriad of physical and mental health concerns, resulting in a pervasive and interdisciplinary agreement on the importance of investigating the relationship between stress and health. Developing a holistic understanding of stress requires assessment of the three domains vital to the study of stress: (1) the presence of environmental stressors, (2) psychological and biological reactions to stressors, and (3) the length of time over which the stressor or stress response occurs. Research into all three domains requires multiple methods. Self-reports allow for subjective evaluations of stress that illuminate the duration and severity of the psychological response to stressors. Biomarkers, in turn, capture a more-objective measure of stress and create a deeper understanding of the biological response to chronic and acute stress. Finally, the use of digital biomarkers allows for further exploration of the physiological fluctuations caused by stress by measuring the changes occurring at the same time as the stressor. Future research on stress and health should favor a multidimensional approach that creates a triangulated picture of stress, drawing from each of the three aforementioned method groups.

• Introduction

Stress is consistently associated with a myriad of physical and mental morbidities, as well as mortality. 1 Additionally, the financial burden of stress is estimated to be $300 billion per year in the United States alone. 2 As a result, there is pervasive and interdisciplinary agreement on the importance of understanding and alleviating stress to improve quality of life. However, the measurement of stress varies greatly within the large body of work linking stress and well-being. Researchers attempting to quantify stress should first understand the measurement strategies available to them and the strengths and limitations of each form of measurement. The goal of this paper is to define stress and offer an overview of measurement methods available to researchers interested in capturing stress.

Key Research Needs.

• Defining Stress

The definition of stress has evolved over several decades, and a universally adopted definition has yet to be reached. Seminal models of stress proposed by Selye in the 1950s define stress as the “non-specific response of the body to any demand for change” or the “rate of wear and tear on the body.” 3 Proposed refinements to these definitions in the decades since have differentiated between stressors, defined as stimuli that challenge an organism’s biological and psychological equilibrium (i.e., homeostasis), and the stress response, defined as the process through which the organism attempts to restore homeostasis. 4 Although the revised definitions differentiate between the cause and effect of stress, they have frequently received critique for being too vague, as nearly all activities that an organism undertakes constitute a challenge to homeostasis. 5 One model created to further clarify a working definition of stress, the psychophysiological stress concept, has been widely adopted in stress-related research since its dissemination in 2011. 5 Developed by Koolhaas and colleagues, the psychophysiological model of stress defines stress as the perceived or anticipated inability to successfully cope with situations that are not predictable or controllable. 6 This inability to cope with an experience leads to both subjective feelings of stress and objective biological changes in the body. 6

In defining stress, it is also important to recognize that stress can be conceptualized both as an acute reaction and a chronically accumulated state throughout the life course. Taken with the information above, this highlights three domains vital to the study of stress: (1) the presence of environmental stressors, (2) psychological and biological reactions to stressors, and (3) the length of time over which the stressor or stress response occurs. 7 Many subjective and objective measures have been developed and validated to quantify these domains. The following sections will cover not only subjective measures that capture perception of environmental stressors and psychological response but also objective measures that capture biological change.

• Measures of Self-Report

Self-report measures of stress consist of series of questions or prompts that inquire about respondents’ lived experience with various components of stress. Several measures have been developed to quantify both reactions to acute stressors and the accumulation of chronic stress ( Table 1 ).

Inventory of subjective (self-report) measures of acute and chronic stress.

Common measures to assess chronic stress include both the Life Events and Difficulties Schedule (LEDS) 20 and the Trier Inventory for Chronic Stress (TICS). 17 , 21 The LEDS captures exposure to severe acute events (i.e., those lasting less than 1 month) and severe chronic difficulties (i.e., those lasting longer than 1 month) over the previous year through a semi-structured interview that prompts the individual to recall 95 possible life events. The individual provides additional context around each experience; a trained expert then codes the context. LEDS stressors are grouped into 10 domains: education, work, reproduction, housing, money/possessions, crime/legal, health/treatment/accidents, marital/partner relationship, other relationships, and miscellaneous. The TICS attempts to capture chronic stress through a more-structured questionnaire covering nine factors of chronic stress: “work overload, social overload, pressure to perform, work discontent, excessive demands at work, lack of social recognition, social tensions, social isolation, and chronic worrying. Participants in these surveys rate 57 items covering how often situations within the nine domains occur (i.e., never, rarely, sometimes, often, or very often) over a recall period of 3 months.

Several measures have been designed to capture stress over a shorter period of time, including the Cohen Perceived Stress Scale (PSS) and the Stress Overload Scale (SOS), among others ( Table 1 ). The most commonly used measure for assessing global stress perceptions is the 10-item PSS. 15 This survey captures the degree to which a person views their life as uncontrollable, unpredictable, and overloaded in the past month. Scores are calculated using a five-point scale (0 = never, 1 = almost never, 2 = once in a while, 3 = often, and 4 = very often) that is summed for a total score, where higher scores represent a greater level of perceived stress. The PSS has been translated into a variety of languages, 22 , 23 which allows for its use in both English and non-English-speaking populations. As an alternative to the PSS, the SOS was designed to measure when stress overwhelms a person’s coping mechanisms. 13 Twenty-four statements touch on personal vulnerability and event load, and an additional six items are used to discourage inconsistent responses. A shorter version of the SOS (10 items) also exists. 24 Each of the statements is evaluated on a 5-point graded-response scale (1 = not at all, 5 = a lot) to investigate feelings and experiences from the prior week.

Though measures such as the PSS or the SOS capture stress over a shorter time than measures of more-chronic stress, they are still frequently criticized for having great potential for bias because individuals are asked to report retrospectively on cumulative stress experienced. In response to this limitation, the ecological momentary assessment (EMA) method was developed. EMA is the repeated measurement of an individual’s subjective experience of stress in the context of daily life. 25 This method can be used to capture momentary stress at multiple points throughout the day, which can then be aggregated to create a picture of stress levels over time. As technology has evolved, the smartphone has become a popular tool for conducting EMAs and contextualized survey items, including single-item measures. 26 For example, a question prompted by a beep on a responder’s phone, such as “How stressed were you right before the beep went off?” can capture real-time stress with a variety of response options (not at all, a little, moderately, quite a bit, and extremely). 27

One measure used for EMAs is the Photographic Affect Meter (PAM). PAM is a validated, one-item, mobile application–based measure of affect that correlates with the widely used Positive and Negative Affect Schedule (PANAS). 8 As shown in Figure 1 , subjects select an image from a grid of 16 pictures that best represents their current emotional state.

The Photographic Affect Meter (PAM).

PAM is used to construct a set of numbers for the purpose of quantitative analyses. The first is a categorical 1–16 score that maps on to the PANAS scale. 9 Consistent with the conceptualization of the PANAS, this affect scale ranges from low valence (the level of pleasure that an event generates) and arousal (the level of autonomic activation that an event creates) to high pleasure and arousal. Therefore, PAM also produces two separate affect scores for valence and arousal, which are calculated according to the chosen image’s position in the 16-image grid. Valence is computed using a 1 to 4 score (negative, slightly negative, slightly positive, positive) based on the column position of the image. Arousal is computed in a similar fashion using the row position of the image, with 1 representing calm or low arousal and 4 representing excitement or high arousal.

Self-report measures are useful tools in establishing subjective evaluations of stress levels. They are often cost-effective and correlated with clinical endpoints of interest, but they can be lengthy to administer and are subject to several types of bias. 28 – 30 Self-report measures are best used in combination with objective biological or digital markers of stress to understand the comprehensive impact on outcomes of interest. 6

Stressors cause a variety of biological responses. These physiological reactions can be measured using biomarkers that represent the stress response from the hypothalamic-pituitary-adrenal (HPA) axis, the autonomic nervous system (ANS), and the immune system ( Table 2 ). 48

Inventory of objective (chemical and digital) measures of acute and chronic stress.

The HPA axis is particularly responsive to psychosocial stress. One output of this system is cortisol, a hormone secreted from the adrenal gland that plays a vital role in cognition, metabolism, and immune function as well as the stress response. Levels of cortisol in the body fluctuate daily in response to routine events, unexpected stressors, and circadian patterns that render salivary cortisol highest in the morning and lowest at night. 49 , 50 In addition to daily variation, cortisol may be chronically high or low. Both the acute fluctuation and the chronic compounding of cortisol are of interest to researchers. Acute cortisol change can be reliably measured in saliva and blood, cortisol in urine can be used to establish daily cortisol secretion, 42 and cortisol in hair can be used to gather long-term cortisol levels over weeks or months. 51 Salivary and hair cortisol are commonly used in studies investigating stress because collection is relatively quick and easy. Assessment of cortisol in saliva captures acute cortisol production over the past 15–20 minutes and is often used to measure daily stress.

Collecting saliva samples is comparatively easy and painless (a cotton swab is placed in the mouth of a participant and then sealed in a container), but a single saliva cortisol sample tells little about levels of stress over an extended period of time, and collection time is incredibly important, as cortisol levels vary across the day. Therefore, multiple samples of salivary cortisol throughout the day are needed to accurately capture daily stress. Cortisol levels measured in hair are used as a longer-term biomarker of HPA axis activity because cortisol is incorporated into the hair as it grows. 52 Thus, hair cortisol captures a much longer period: a 1-cm hair segment reflects total cortisol secretion in the past month. These cumulative levels of hair cortisol do not provide information about HPA axis activity specific to any particular time within the measurement period.

The ANS modulates organ system activity through autonomic reflexes that respond to internal and external stimuli. Catecholamines, including norepinephrine and epinephrine, are hormones made by the adrenal glands that serve as strong biomarkers of ANS activity, specifically sympathetic-adrenal medullary (SAM) activity. 53 .SAM activity occurs as a rapid response to a stressor to produce alertness and aid in responsive decision making. By contrast, HPA axis function is more representative of exposure to repeated stressors. Catecholamines can be captured acutely through blood concentration and as a short-term biomarker of daily stress through a 24-hour urine collection protocol. 42 , 43 Alpha-amylase, an enzyme that breaks down starch, is another biomarker of ANS activity and can be detected in saliva samples. 40 Like cortisol, epinephrine, and norepinephrine found in the urine, alpha-amylase fluctuates over the day, so multiple measures are needed to properly understand changes in alpha-amylase in response to stressors. 40

Another way to capture stress response is to measure immune activation. 54 Cytokines are a group of proteins that serve as signaling molecules that help mediate and regulate immune function and can be measured from blood samples. 54 Interleukin (IL)-6 is one type of cytokine that plays a vital role in the development of fever and regulates the acute inflammatory response in the liver. 38 Although IL-6 is correlated with the development of cardiovascular disease and other chronic inflammatory disorders, it is very sensitive and easily elevated by exercise. When collecting samples, participants should refrain from exercise for approximately 12 hours to get an accurate reading.

C-reactive protein (CRP) is another substance integral to immune system response that is used to investigate stress. 44 CRP is made in the liver and released in reaction to inflammation, and it has been linked to stress response in humans. One advantage to using CRP as a measure of stress is that it lacks diurnal variation. However, CRP values can never be diagnostic on their own, and information about other pathological markers is vital to interpreting CRP levels.

Other immune measures of stress include the measurement of antibodies for common herpes viruses, including Epstein Barr virus antibodies, herpes simplex virus type 1 (HSV-1) antibodies, and cytomegalovirus antibodies. 45 , 55 , 56 These viruses are persistent infections that require adequate cell-mediated immunity to maintain in their latent states. 57 , 58 Longer-term stress can decrease the immune system’s ability to keep these viruses from reactivating. 57 , 58 As they reactivate, increased antibodies produced in response to circulating viral particles can be used as a measure of stress. 57 , 58

The biomarkers listed here provide quantifiable measures of the various physiological pathways that lead to a biological stress response. The use of these biomarkers in stress research allows for a more-objective measure of stress and a deeper understanding of the alterations in biological systems caused by chronic and acute stress. However, researchers often assume the validity of biomarkers despite the need for continued evaluation. 59 Many stress biomarkers indicate levels of inflammation, but many factors influence inflammation, and they should be considered in the analysis. 60

The methods for obtaining biological samples for analysis may be a limitation for many researchers. Both the investigator collecting the samples and the participant need appropriate training because of the need for valid and consistent collection procedures. 61 Correct processing after sample collection, shipping requirements, and the proper storage of the biological samples are also critical for reliable results but can prove difficult in resource-limited areas.

• Digital Measurement of Biomarkers

The use of sensors, software tools, and signal processing methods to explore complex biological signals related to stress has increased with recent advances in computational methods and concurrent, widespread adoption of consumer electronics. 62

Although a broad range of systems influence cardiac performance and functions, the ANS is the most prominent. 63 Like some of the aforementioned biomarkers, pulse and heart rate variability (HRV) can be used as proxies for fluctuations in the ANS in response to external and internal stressors. Pulse represents the arterial palpitation of the heartbeat and is reported in beats per minute. When individuals are exposed to stressors, their breathing and pulse can temporarily change. The alteration in heartbeat is measured through touch at any place where an artery can be compressed; two of the common areas for measuring pulse are the wrist and neck. HRV, or the fluctuating time between heart beats, can also be used as a proxy for ANS response to regulatory impulses and stress. 31 Typically, when the ANS is stimulated, variation between heart beats is low, and during relaxed states, variability is high. Advantageously, fluctuations in HRV can be measured while other physiological markers of stress remain constant. 64

HRV, as well as pulse, can be captured through different techniques with varying validity and convenience. Electrical impulses cause cardiac muscle to squeeze and pump blood from the heart, which can be captured through electrical leads attached to an electrocardiogram (ECG). The ECG method is the gold standard for measuring electrical activity of the heart. However, it is difficult to reliably collect ECG data in a cost-efficient, ecologically valid manner. In response to these challenges, wearable device manufacturers have turned to photoplethysmography (PPG) sensors. PPG is an optical measurement of blood volume variations as capillaries expand and contract with each heartbeat. 65 Data collected using wearable PPG sensors has shown varied validity when compared with the gold-standard ECG. 66 This is mostly because of differences in what PPG and ECG measure. PPG measures variability in peripheral pulse, whereas ECG directly measures the electrical cycles of heart function. Several factors, including blood pressure and age-related changes to the vascular system, influence peripheral pulse. 67 Differing device quality and sensor placement can also explain differences between ECG and PPG. Placement is most commonly on the wrist, but can also be on the forehead, earlobes, upper arm, torso, fingertips, and ankles. Each of these locations experiences unique amounts of movement, which can limit the sensor’s ability to capture an accurate PPG reading. To counter the effect of movement on PPG sensors, wearable manufacturers are incorporating three-axis accelerometer data into their heart rate algorithms. 65 As ECG and PPG wearable technology improves, large-scale health studies are producing robust, evidence-based results using user-owned devices. 68

Electrodermal activity (EDA) is another physiological measure of stress response that can be captured through sensor-based measurement. 69 , 70 EDA is the variation in electrical characteristics of the skin. These fluctuations in conductivity are caused by the changes in the ANS and are considered the most-useful index of ANS arousal. 64 EDA is measured through continuous monitoring of the involuntary changes in skin conductivity. EDA is particularly useful when assessing changing impacts of stress because of the continuous alterations in skin conductance 71 and because it is the only ANS variable associated with psychosocial stress that is not correlated with other parasympathetic markers. 64 Researchers can capture EDA through wearable devices such as the Empatica. 69

Like biomarkers, sensor-based measures provide an objective method for quantifying the physiological response to psychosocial stress. Gathering complex, multisensory data on pulse, HRV, and EDA is easy and minimally invasive because of advances in technology. The development of small, reliable, and cost-effective biometric devices has created an increase in access and interest among consumers. However, these digital measures can only capture the stress response contemporaneously. In addition, the accuracy of some commercially available biometric devices is questionable, and assessment of the data quality is needed when using this technology.

• Relationships Between Subjective and Objective Measurement of Stress

Subjective and objective measures of stress are related, but not identical, constructs. The psychological experience of stress may not always translate to measurable biological change and vice versa. 5 Limited research exploring associations between subjective and objective measures exists, and that which does suggests notable, but weak, associations between the two types of measures. For example, a recent study by Weckesser and colleagues found that only 16% of the variance in hair cortisol was explained by the subjective Weekly Hassle Scale, and that hair cortisol was not significantly related to the PSS or the TICS. 6 In contrast, other studies have found statistically significant relationships between cortisol and the PSS, 72 and other self-report measures such as the SOS 13 , 73 or aggregated subjective stress measures built from a combination of other measures. 74 The relationship between perceived and objective measures of stress may be further explained by other factors, such as resilience. For example, a recent study by Lehrer and colleagues found that psychological resilience moderates the association between perceived stress (measured by the PSS) and hair cortisol; higher resilience reduces the association between perceived stress and hair cortisol. 75 Further exploration of the relationships between self-report and objective measures of stress is needed to provide greater understanding of the impacts of stress on health.

• Future Research Needs

Stress is a multidimensional construct that comprises exposure to events, perceptions of stress, and physiological responses to stress. A nuanced understanding of the links between stress and health requires assessment of each of these components in both acute and chronic scenarios. Employing self-reports allows for subjective evaluations of stress that illuminate the duration and severity of the psychological response to stressors. This information is vital to understanding the physiological stress response measured by biomarkers. Biomarkers, in turn, capture a more-objective measure of stress and create a deeper understanding of the biological response to chronic and acute stress. Finally, the use of digital biomarkers allows for further exploration of the physiological fluctuations caused by stress by measuring the changes occurring at the same time as the stressor. Future research should therefore favor a multidimensional approach that creates a triangulated picture of stress, drawing from perceived measures of stress as well as chemical and digital biomarkers. This will enable a more-comprehensive and holistic understanding of environmental stress triggers and corresponding psychological and biological responses.

Achsah Dorsey , PhD, is an assistant professor of Anthropology at the University of Massachusetts—Amherst.

Elissa Scherer is a public health analyst at RTI International.

Randy Eckhoff is a research programmer at RTI International.

Robert Furberg , PhD MBA, is a clinical informaticist.

RTI International is an independent, nonprofit research organization dedicated to improving the human condition. The RTI Press mission is to disseminate information about RTI research, analytic tools, and technical expertise to a national and international audience. RTI Press publications are peer-reviewed by at least two independent substantive experts and one or more Press editors.

Suggested Citation

Dorsey, A., Scherer, E., Eckhoff, R., and Furberg, R. (2022). Measurement of Human Stress: A Multidimensional Approach . RTI Press Publication No. OP-0073-2206. Research Triangle Park, NC: RTI Press. https://doi.org/10.3768/rtipress.2022.op.0073.2206

RTI International is an independent, nonprofit research institute dedicated to improving the human condition. We combine scientific rigor and technical expertise in social and laboratory sciences, engineering, and international development to deliver solutions to the critical needs of clients worldwide.

RTI Press Associate Editor

Valerie Williams

This work is distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 license (CC BY-NC-ND), a copy of which is available at https://creativecommons.org/licenses/by-nc-nd/4.0

• Cite this Page Dorsey A, Scherer E, Eckhoff R, et al. Measurement of Human Stress: A Multidimensional Approach [Internet]. Research Triangle Park (NC): RTI Press; 2022 Jun. doi: 10.3768/rtipress.2022.op.0073.2206
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