Accessibility settings

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

Ecological Momentary Assessment (EMA) (445) mHealth for Wellness, Behavior Change and Prevention (3485) Behavior Change (925) Wearable Devices and Sensors (1243) Prevention and Health Promotion (1532) Innovations and Technology for Physical Activity Education (986)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/81502, first published .
Alternative text does not exist

Associations Between 24-Hour Physical Behavior, Self-Perceived Stress, and Coping Self-Efficacy in Everyday Life: Ambulatory Assessment Study

Associations Between 24-Hour Physical Behavior, Self-Perceived Stress, and Coping Self-Efficacy in Everyday Life: Ambulatory Assessment Study

Authors of this article:

Katrin Bonn1, 2 Author Orcid Image ;   Doreen Wohlfarth3 Author Orcid Image ;   Irina Timm2 Author Orcid Image ;   Oliver Bender1 Author Orcid Image ;   Ulrich W Ebner-Priemer2 Author Orcid Image ;   Marco Giurgiu4 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Student Health Management, Baden-Wuerttemberg Cooperative State University Karlsruhe, Erzbergerstraße 121, Karlsruhe, Baden-Wuerttemberg, Germany

    2Mental mHealth Lab, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Baden-Wuerttemberg, Germany

    3Division of Sports and Rehabilitation Medicine, University Hospital Ulm, Ulm, Baden-Wuerttemberg, Germany

    4Institute of Movement Therapy and movement-oriented Prevention and Rehabilitation, German Sport University Cologne, Cologne, North Rhine-Westphalia, Germany

    Corresponding Author:

    Katrin Bonn


    Background: Psychological stress poses a risk to mental and physical health and has become a major public health challenge. As physical behaviors (ie, physical activity, sedentary behavior (SB), and sleep) play a key role in mental well-being, their targeted modification could be an approach to coping with stress in everyday life. Previous studies have primarily either analyzed the associations between isolated physical behaviors and stress-related outcomes or employed cross-sectional designs. Accordingly, there is a need for deeper insights into the within- and between-person associations between physical behavior over a 24-hour cycle and psychological stress in naturalistic settings.

    Objective: This study aimed to investigate how 24-hour physical behavior compositions are associated with daily self-perceived stress and stress-related coping self-efficacy and how replacing time in one behavior with another is linked to changes in both stress-related indicators.

    Methods: A total of 198 healthy university employees (mean age 35.87 y, SD 10.76; 109, 54.8% female) participated in a 15-day ambulatory assessment study. Participants reported their momentary stress and coping self-efficacy perceptions up to 6 times a day in electronic diaries. 24–hour physical behavior was measured using a thigh-worn Move 4 accelerometer. The movement data were then classified on a daily basis into the 4 behavior categories of sleep, SB, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA). In order to obtain physical behavior compositions, an isometric log-ratio transformation was applied, resulting in 4 different sets. The associations between physical behavior compositions, self-perceived stress, and coping self-efficacy were analyzed using 2-level mixed multilevel models. Exploratory reallocation models were conducted to simulate the effects of time shifts from one behavior to another on both stress-related outcomes.

    Results: The geometric average day comprised 33.9% (8.1/24 h) sleep, 45.2% (10.8/24 h) SB, 15.8% (3.8/24 h) LPA, and 5.1% (1.2/24 h) in MVPA. More time spent sleeping compared to being sedentary was associated with lower self-perceived stress (standardized β=–.03; t1988=–2.045; P=.04) but not with coping self-efficacy (t1988=–1.333; P=.18) in the 24-hour cycle. The ratio of SB to the other physical behaviors and time spent in LPA or MVPA relative to SB showed no association with either stress-related outcome. Significant random effects indicate high individual variability in the analyzed associations. The exploratory substitution of SB by LPA, MVPA, or sleep showed no significant changes in self-perceived stress or coping self-efficacy within a 60-minute period.

    Conclusions: Adapting 24-hour physical behavior seems to be a promising approach to reduce stress in everyday life, for example, by extending sleep duration instead of being awake in SB. Further research should be conducted on contextual and personal influencing factors in order to develop tailored stress management interventions for the 24-hour cycle.

    JMIR Mhealth Uhealth 2026;14:e81502

    doi:10.2196/81502

    Keywords

    ambulatory assessment; accelerometry; coping; mobile health; mHealth; perceived stress; sleep 


    The feeling of stress is a familiar human experience in everyday life. In particular, high, recurrent, and long-lasting psychological stress is thought to be associated with various physical and mental illnesses, such as coronary artery disease or depression [1-4]. Furthermore, a link is suggested between higher stress levels and an increased risk of mortality [5,6]. Across several studies, reviews indicate that the prevalence of self-reported stress during the COVID-19 pandemic ranged from 32% to 56%, with a focus on both the general population and specific subgroups, such as patients, health care workers, pregnant women, and students [7-9]. Among higher education employees from 16 different countries, 73% reported experiencing moderate to high levels of stress [10]. Psychological stress has become a public health issue with far-reaching implications for society [11], such as an increased risk of substance misuse or addiction [12-14], poorer relationship quality [15-17], or the financial burden resulting from stress-related productivity losses and absenteeism [11,18].

    Studies indicate that coping self-efficacy, which refers to the belief that one is capable of effectively managing one’s own stress [19-21], might contribute to effective coping [22] and reduced perceived stress in different target groups [23-25]. Numerous studies have explored associations between individual physical behaviors, including physical activity (PA), sedentary behavior (SB), or sleep, with psychological stress and coping strategies. PA was found to exhibit a medium beneficial effect on stress in adults [26], although the association is characterized by a high degree of individual variability [27,28]. More inconclusive results are reported in studies on the association between indicators of SB and stress [29-33]. Thus, further studies are required that take different time frames, contexts, and domains into consideration [27,30,33]. An insufficient sleep duration appears to be another indicator of experiencing more stress [34-36], while sleep quality was positively associated with higher coping self-efficacy in adolescents [23]. Since physical behaviors do not occur in isolation, their interrelationships must be taken into account. This is addressed by the growing paradigm in sport and health science, which conceptualizes physical behaviors as compositional elements within a 24-hour cycle [37,38]. Reflecting this perspective, recent practice-oriented activity guidelines—such as those promoted under the motto “Make Your Whole Day Matter”—increasingly emphasize the need for balanced time allocation across PA, SB, and sleep, including the recommendation to limit screen time [39,40].

    A growing body of literature addresses physical behavior over a 24-hour period and its associations with mental health. Current systematic reviews highlight the positive effects of replacing time in SB with PA for mental health in adults and children [41-43]. For example, among adults, spending 15 minutes more in light physical activity (LPA) instead of being sedentary was associated with lower anxiety, depression, and stress scores [44]. Additionally, reallocating 30 minutes in SB to moderate PA in women or to walking in men aged 21 to 35 years both resulted in a decline in self-reported stress levels [45]. Other cross-sectional studies indicate that replacing time spent in SB with sleep in adults is also associated with lower perceived stress and improved mood [46,47]. However, we are not aware of any study that has examined the association between 24-hour physical behavior and stress-related coping. Overall, much of the evidence linking physical behavior to perceived stress is derived from cross-sectional designs [41,45,47] that analyze between-person differences at a single time point and are therefore inherently limited in their ability to elucidate temporal within-person dynamics, including day-to-day variability in 24-hour physical behavior and stress [48,49].

    To overcome these limitations, intensive longitudinal studies (also known as ambulatory assessment or ecological momentary assessment) are currently the state‐of‐the‐art methodology for examining within‐person associations [50,51]. These study designs have several advantages, namely, the assessment in everyday life, in real‐time, with device‐based methods, and repeated measurements with a high sampling frequency, which enables researchers to track dynamic relationships [48,52]. Therefore, they bypass laboratory distortions and minimize recall biases associated with traditional approaches [53]. Accordingly, the implementation of ambulatory assessments is promising for capturing fluctuations in stress-related processes and their associations with physical behavior throughout the day [54]. To the best of our knowledge, no previous study has focused on this within-person interplay. We aimed to investigate the association of device-based 24-hour physical behavior, self-perceived stress, and coping self-efficacy in everyday life. Based on previous empirical findings and theoretical models [42,46], we expected that the composition of 24-hour physical behavior is associated with both daily perceived stress (hypotheses 1‐4) and coping self-efficacy (hypotheses 5‐8) in healthy adults.

    We hypothesized that the ratio of SB to sleep, LPA, and moderate-to-vigorous physical activity (MVPA) would be positively associated with self-perceived daily stress (H1), whereas more time spent in (1) MVPA relative to SB (H2), (2) LPA relative to SB (H3), and (3) sleep relative to SB (H4) would be associated with less self-perceived stress. Furthermore, we expected the proportion of SB relative to the other physical behaviors to be negatively associated with coping self-efficacy (H5), while a higher ratio of (1) MVPA to SB (H6), (2) LPA to SB (H7), and (3) sleep to SB (H8) is associated with a stronger belief in being able to cope with tasks at hand. Additional exploratory analyses were conducted to examine the effects of reallocating time from one physical behavior to another on perceived stress and coping self-efficacy. Furthermore, subjective sleep quality and exercise behavior were exploratively fitted into the existing models in order to obtain indications of the robustness of results.


    Ethical Considerations

    This study was approved by the Ethics Committee of the Karlsruhe Institute of Technology (A2020-001). Prior to the start of the study, participants received comprehensive information about the aim and procedure of the investigation and provided informed consent. Participants had the option of withdrawing from the study at any time. In order to protect participants’ privacy and confidentiality, we deidentified the data after collection. The successful completion of the study was rewarded with a fitness tracker worth €100 (US $114.24).

    Participants

    Potential participants were recruited via flyers, mailings, word of mouth, and an internal newsletter from the Karlsruhe Institute of Technology. The study leaders consulted with interested parties via email or telephone to ensure that all predefined inclusion criteria for the study were met. Eligibility criteria included predominantly sedentary work and the absence of current physical injuries or psychological disorders. In total, 222 university employees participated in the study between October 2022 and December 2023 in Karlsruhe, Germany.

    Study Design

    This study represents an intensive longitudinal method [55] and is a secondary analysis of a within-person encouragement design investigation, the details of which can be found in Giurgiu et al [56]. However, no overlapping analyses were conducted.

    The study started with an in-person session during which participants received detailed information about the study procedures, completed a questionnaire assessing demographics, and were equipped with a study smartphone (Nokia G50 or 6; Nokia Corporation), a Fitbit wearable (Fitbit Inspire 2; Google), and a Move 4 accelerometer (Movisens GmbH). The ambulatory assessment period began the following day and lasted 15 working days. To maintain motivation and ensure compliance, weekend days were not included [57]. During the study period, participants were asked to wear the Fitbit wearable on their wrist and the Move 4 sensor on their thigh as continuously as possible. Moreover, the smartphone prompted participants up to 8 times per day to answer short e-diary questionnaires, with e-diaries occurring based on a wearable-triggered sedentary algorithm, except for the additional morning and evening queries.

    Measures

    24-Hour Physical Behavior

    Triaxial accelerometer data assessed by a Move 4 sensor (Movisens GmbH) with a sampling frequency of 64 Hz and a range of ±16 gravitational acceleration were used to measure 24-hour physical behavior. The raw accelerometer data were preprocessed after the study period with the software ActiPASS (version 2024.05; ActiPASS) [58]. ActiPASS implements algorithms for nonwear, sleep detection, posture, and activity intensity derived from cadence measures that have demonstrated high accuracy for wake-time movement behaviors and sleep in validity studies [59-61]. Based on the preprocessed data, we aggregated the most commonly examined physical behavior categories—sleep, SB (sitting or lying episodes outside of sleep intervals), LPA (walking with a cadence <100 steps/min and standing), and MVPA (walking with a cadence ≥100 steps/min)—as average minutes per day [38]. In line with previous wearing protocols [62], participants and days were only included when (1) participants had at least 3 valid wear-time days (≥20 h of wear time per day), (2) at least 1 period of walking was detected that day, and (3) days comprised more than zero minutes of sleep.

    Momentary Self-Perceived Stress and Coping Self-Efficacy

    Participants received all e-diaries on the study smartphone (Nokia G50 or 6). A morning and an evening survey were conducted between fixed time periods (morning: 7 AM-8:30 AM, evening: 9:45 PM-11 PM), during which participants were permitted to respond at their chosen time. The e-diary assessments during the day occurred up to 6 times per day between 8:40 AM and 9:40 PM. They were based on a sedentary-triggered design [63], appearing after 30 consecutive minutes in a sitting or lying body position. While half of the triggers exclusively prompted questions on momentary constructs such as affective states or perceived stress, the other half included a prompt to interrupt SB for 3 minutes before answering the identical e-diary questions. To reduce participants’ burden, prompts could be delayed up to 15 minutes and did not occur within at least 30 minutes after a completed e-diary rating.

    Momentary perceived stress and coping self-efficacy were each captured by one item in the e-diary assessments completed during the day and then aggregated at the daily level. Previous studies showed single-item measures to be valid and reliable for stress measurement [64]. Both items were based on the Perceived Stress Scale-10 [65]. Following Jones et al [31], participants were asked to rate their subjectively perceived stress level on a visual Likert scale from 1 to 5 (“Do you feel nervous or stressed at the moment?,” 1=“not at all” to 5=“extremely”). Based on the question “Do you currently feel that you can cope with all your upcoming tasks?,” we assessed participants’ current coping self-efficacy on a Likert scale ranging from 1=“not at all” to 5=“extremely.”

    Covariates

    In the initial in-person session, age in years as well as sex (female vs male) were assessed through self-reports, and anthropometric data were objectively measured in order to calculate the BMI. One adapted item from the Pittsburgh Sleep Quality Index [66] was implemented in the morning survey to assess the perceived sleep quality of the previous night on a scale from zero to 100 (“How would you rate your sleep quality last night?”). In the evening survey, participants used a dichotomous response format to indicate whether they had exercised that day (yes vs no). A definition of the term “sports exercise” was not provided.

    Statistical Analysis

    Prior to compositional data analysis procedures, we used an isometric log-ratio (ilr) transformation to account for the multicollinearity of direct composition measures [67]. The compositions of sleep, SB, LPA, and MVPA were expressed as ilr coordinates, containing information about one activity in relation to the remaining physical behaviors in the 24-hour cycle. In accordance with existing literature, 3 zero values were replaced before the transformation using the log-ratio expectation maximization algorithm [68].

    As is the state of the art when analyzing hierarchically structured intensive longitudinal data, we used a multilevel approach [69]. Prior to fitting the multilevel models, we determined intraclass correlations (ICCs) using unconditional models with daily averaged self-perceived stress (hypotheses 1‐4) and coping self-efficacy (hypotheses 5‐8) as outcomes. Afterwards, multilevel models with repeated measurements (level 1) nested within participants (level 2) were estimated using restricted maximum likelihood and an unstructured covariance matrix for the random effects. In the interest of model parsimony, nonsignificant random effects were excluded. Model assumptions, including multicollinearity, homoscedasticity, and normal distribution of residuals, were checked [69,70]. The covariates age (years), sex (female vs male), BMI (kg/m2), and coping self-efficacy (only in hypotheses 1-4) were selected a priori and added as predictors based on data availability as well as known associations [71-79]. The respective compositions represented by ilr1, ilr2, and ilr3 were centered within clusters in advance and integrated as predictors. The following equation model refers to hypothesis 1 (further models are presented in Supplementary File A in Multimedia Appendix 1).

    • H1: Y (stress perception)ij = γ00 + γ01 × sexj + γ02 × agej + γ03 × BMIj + γ10 × coping self-efficacyij + γ20 × ilr1 (SB/LPA×MVPA×Sleep)ij + γ30 × ilr2 (LPA/MVPA×Sleep)ij + γ40 × ilr3 (MVPA/Sleep)ij + u0j + u1j × ilr3 (MVPA/Sleep)ij + rij
    • ilr1 = √(3/4) ln (SB / ∛(LPA×MVPA×Sleep))
    • ilr2 = √(2/3) ln (LPA/√(MVPA×Sleep))
    • ilr3 = √(1/2) ln (MVPA/Sleep)

    Exploratively, we used substitution analyses to examine the expected change in the outcome when time was reallocated between physical behaviors in a 60-minute window. Pairwise reallocations were calculated in 20-minute increments, with estimates based on the average composition of the included participants [80].

    All analyses were performed in R (version 4.4.0; The R Project for Statistical Computing), using the packages compositions [81], zCompositions [82], and lme4 [83], with a predetermined α level of .05. To facilitate the comparison of results, standardized β coefficients were calculated based on established procedures [70].


    Descriptive Statistics

    Due to insufficient accelerometer wear time (<20 h/d, n=19), discontinuation of the study (n=3), and technical problems (n=1), 23 participants were excluded from the analysis (Figure 1). In total, 199 participants answered momentary assessment prompts on 2352 days, representing an average compliance rate of 69.9% at the personal level. On average, 4.4 surveys were completed per day. Days without any stress or coping self-efficacy rating (n=165 d) were excluded from the analysis, which led to the exclusion of one further participant due to the lack of valid days (n=1). A total of 198 participants were included in the final sample, of whom 109 (54.8%) were female participants, and the average age was 35.87 (SD 10.76) years (Table 1).

    ‎
    Figure 1. Participant flow chart. EMA: ecological momentary assessment.
    Table 1. Descriptive characteristics of study participants (N=198, female 54.8%, 109/199).
    VariableMean (SD)MinimumMaximum
    Age (y)a35.87 (10.76)20.1864.74
    BMI (kg/m²)a23.99 (3.28)17.8735.25
    Valid daysb11.84 (3.86)322
    Sleep (h/d)c7.95 (1.12)4.2712.70
    SBd (h/d)c10.51 (1.11)6.6814.14
    LPAe (h/d)c3.94 (1.00)1.917.33
    MVPAf (h/d)c1.35 (0.42)0.512.89
    Self-perceived stressc2.34 (0.65)1.004.30
    Coping self-efficacyc3.64 (0.65)1.005.00

    aAggregated within participants.

    bAccelerometer wear time ≥20 h/d, aggregated within participants.

    cAccelerometer wear time ≥20 h/d, aggregated within days and participants.

    dSB: sedentary behavior.

    eLPA: light physical activity.

    fMVPA: moderate-to-vigorous physical activity.

    Over the course of all days, participants wore the accelerometer for an average of 23.79 (SD 0.65) hours per day. The 24-hour geometric mean composition of physical behavior for all analyzed days consisted of 33.9% sleep (8.1/24 h), 45.2% SB (10.8/24 h), 15.8% LPA (3.8/24 h), and 5.1% MVPA (1.2/24 h). Further information on participants’ physical behavior, self-perceived stress, and coping self-efficacy is provided in Table 1.

    Across all days, the average daily perceived stress score was 2.36 (SD 0.87, mean of per-person minimum=1.49, mean of per-person maximum=3.28), with lower values indicating lower stress perception than higher values (scale: 1‐5). ICC calculations (ρI=0.483) revealed that 51.7% of the variance of the stress perception was based on the within-person fluctuations. The average coping self-efficacy score (scale: 1‐5) was 3.62 (SD 0.85, mean of per-person minimum=2.83, mean of per-person maximum=4.33), where 40.8% of the variance can be explained by within-person variances (ρI=0.592).

    Association Between 24-Hour Physical Behavior and Self-Perceived Stress

    Daily self-perceived stress was not significantly associated with time spent in (1) SB relative to the other behaviors (H1), (2) MVPA relative to SB (H2), or (3) LPA relative to SB (H3) (Table 2). In line with hypothesis 4, results indicate a significant negative association between ilr3 (sleep/SB) and self-perceived stress (standardized β=–.03; t1988=–2.045; P=.04). Coping self-efficacy and age of the participants were both significantly negatively related to perceived stress in all models (Table 2). The presence of significant random effects in models 1, 2, and 3 indicates that the effects of the ratio of MVPA to sleep, sleep to MVPA and SB, as well as sleep to MVPA, LPA, and SB, vary between individuals (Supplementary File B in Multimedia Appendix 2).

    Table 2. Multilevel models analyses to predict daily self-perceived stress: random and fixed effects.
    VariableSelf-perceived stress
    H1aH2bH3cH4d
    B (SE)e/SD (VE)f,95% CIP valueB (SE)e/SD (VE)95% CIP valueB (SE)e/SD (VE)95% CIP valueB (SE)e/SD (VE)95% CIP value
    Fixed effectsg
    Intercept, ß002.29 (0.35)1.59 to 2.98<.0012.26 (0.36)1.57 to 2.96<.0012.26 (0.35)1.57 to 2.96<.0012.26 (0.36)1.56 to 2.95<.001
    Sexh, ß01–0.10 (0.09)–0.28 to 0.07.26–0.10 (0.09)–0.28 to 0.08.28–0.10 (0.09)–0.28 to 0.08.28–0.10 (0.09)–0.28 to 0.08.27
    Age (y), ß02–0.01 (0.00)–0.02 to 0.00.03–0.01 (0.00)–0.02 to 0.00.04–0.01 (0.00)–0.02 to 0.00.04–0.01 (0.00)–0.02 to 0.00.04
    BMI (kg/m2), ß030.02 (0.01)–0.01 to 0.05.190.02 (0.01)–0.01 to 0.05.180.02 (0.01)–0.01 to 0.05.180.02 (0.01)–0.01 to 0.05.17
    Coping self-efficacy, ß10–0.57 (0.02)–0.61 to –0.53<.001–0.57 (0.02)–0.61 to –0.53<.001–0.57 (0.02)–0.61 to –0.53<.001–0.57 (0.02)–0.61 to –0.52<.001
    ilr1i, ß200.05 (0.05)–0.04 to 0.14.23–0.02 (0.04)–0.10 to 0.05.53–0.06 (0.06)–0.17 to 0.05.280.03 (0.03)–0.03 to 0.09.33
    ilr2, ß30–0.01 (0.04)–0.09 to 0.07.87–0.08 (0.06)–0.19 to 0.03.140.01 (0.04)–0.06 to 0.08.780.00 (0.03)–0.06 to 0.07.94
    ilr3, ß400.06 (0.04)–0.02 to 0.15.12–0.02 (0.04)–0.09 to 0.06.65–0.05 (0.04)–0.12 to 0.03.22–0.10 (0.05)–0.20 to 0.00.04
    Random effectsg
    Intercept, u00.61 (0.38)0.55 to 0.68<.0010.61 (0.38)0.55 to 0.68<.0010.61 (0.38)0.55 to 0.68<.0010.61 (0.38)0.55 to 0.68<.001
    ilr3, u10.22 (0.05)0.13 to 0.31<.001N/AjN/AN/AN/AN/AN/AN/AN/AN/A
    ilr2, u1N/AN/AN/A0.31 (0.09)0.18 to 0.43<.001N/AN/AN/AN/AN/AN/A
    ilr1, u1N/AN/AN/AN/AN/AN/A0.31 (0.10)0.19 to 0.44<.001N/AN/AN/A
    Int.-Slope corr.k–0.23–0.56 to 0.12N/A0.09–0.25 to 0.42 N/A0.14–0.19 to 0.46 N/AN/AN/AN/A
    Residual, rl0.54 (0.29)0.52 to 0.56 N/A0.54 (0.29)0.52 to 0.55 N/A0.54 (0.29)0.52 to 0.55 N/A0.55 (0.30)0.53 to 0.56 N/A

    aHypothesis 1: ilr1 (sedentary behavior [SB]/(light physical activity [LPA]*moderate-to-vigorous physical activity [MVPA]*Sleep)), ilr2 (LPA/(MVPA*Sleep)), ilr3 (MVPA/Sleep).

    bHypothesis 2: ilr1 (LPA/(Sleep×MVPA×SB)), ilr2 (Sleep/(MVPA×SB)), ilr3 (MVPA/SB).

    cHypothesis 3: ilr1 (Sleep/(MVPA×LPA×SB)), ilr2 (MVPA/(LPA×SB)), ilr3 (LPA/SB).

    dHypothesis 4: ilr1 (MVPA/(LPA×Sleep×SB)), ilr2 (LPA/(Sleep×SB)), ilr3 (Sleep/SB).

    eUnstandardized estimates and SEs.

    fVE: variance estimate.

    gFixed effects are presented as B (SE); random effects are presented as SD (VE).

    hCompared to female.

    iilr: isometric log ratio coordinate.

    jN/A: not applicable.

    kIntercept-slope correlations are presented as correlation coefficients with 95% CIs.

    lResidual values are presented as SD (VE).

    Association Between 24-Hour Physical Behavior and Coping Self-Efficacy

    Contrary to our hypotheses, the composition of SB to LPA, MVPA, and sleep was not significantly associated with the daily averaged coping self-efficacy score (H5). Furthermore, none of the other expected associations between the compositions of physical behavior and coping self-efficacy (H6, H7, H8) were observed (Table 3). A positive association between coping self-efficacy and age became evident in all 4 models (H5-H8). Additionally, random effects were found for the ratio of LPA to SB, as well as LPA to all other remaining activities (Table 3).

    Table 3. Multilevel models analyses to predict daily coping self-efficacy: random and fixed effects.
    VariableCoping self-efficacy
    H5aH6bH7cH8d
    B (SE)e/SD (VE)f95% CIP valueB (SE)e/SD (VE)95% CIP valueB (SE)e/SD (VE)95% CIP valueB (SE)e/SD (VE)95% CIP value
    Fixed effectsg
    Intercept, ß003.36 (0.38)2.61 to 4.11<.0013.36 (0.38)2.61 to 4.11<.0013.37 (0.38)2.62 to 4.12<.0013.36 (0.38)2.61 to 4.11<.001
    Sexh, ß010.14 (0.10)–0.05 to 0.33.150.14 (0.10)–0.05 to 0.33.150.14 (0.10)–0.05 to 0.34.150.14 (0.10)–0.05 to 0.33.15
    Age (y), ß020.01 (0.00)0.00 to 0.02.020.01 (0.00)0.00 to 0.02.020.01 (0.00)0.00 to 0.02.030.01 (0.00)0.00 to 0.02.02
    BMI (kg/m2), ß03–0.01 (0.02)–0.04 to 0.02.65–0.01 (0.02)–0.04 to 0.02.64–0.01 (0.02)–0.04 to 0.02.64–0.01 (0.02)–0.04 to 0.02.65
    ilr1i, ß100.01 (0.05)–0.08 to 0.10.850.09 (0.04)0.00 to 0.18.052–0.10 (0.05)–0.20 to –0.01.020.03 (0.03)–0.03 to 0.09.37
    ilr2, ß200.08 (0.04)0.00 to 0.15.06–0.08 (0.05)–0.17 to 0.01.10–0.01 (0.04)–0.09 to 0.06.750.08 (0.03)0.01 to 0.15.02
    ilr3, ß300.08 (0.03)0.01 to 0.15.020.01 (0.04)–0.06 to 0.09.740.05 (0.04)–0.04 to 0.14.26–0.07 (0.05)–0.17 to 0.03.18
    Random effectsg
    Intercept, u00.67 (0.44)0.59 to 0.73<.0010.67 (0.44)0.59 to 0.74<.0010.67 (0.44)0.59 to 0.74<.0010.67 (0.44)0.59 to 0.73<.001
    ilr1, u1N/AjN/AN/A0.28 (0.08)0.15 to 0.39.005N/AN/AN/AN/AN/AN/A
    ilr3, u1N/AN/AN/AN/AN/AN/A0.26 (0.07)0.12 to 0.38.02N/AN/AN/A
    Int.-Slope corr.kN/AN/A N/A–0.03–0.37 to 0.31 N/A–0.11–0.49 to 0.23 N/AN/AN/A N/A
    Residual, rl0.56 (0.31)0.54 to 0.58 N/A0.55 (0.30)0.53 to 0.57 N/A0.55 (0.30)0.53 to 0.57 N/A0.56 (0.31)0.54 to 0.58 N/A

    aHypothesis 5: ilr1 (SB/(light physical activity [LPA]×moderate-to-vigorous physical activity [MVPA]×Sleep)), ilr2 (LPA/(MVPA*Sleep)), ilr3 (MVPA/Sleep).

    bHypothesis 6: ilr1 (LPA/(Sleep×MVPA×SB)), ilr2 (Sleep/(MVPA×SB)), ilr3 (MVPA/SB).

    cHypothesis 7: ilr1 (Sleep/(MVPA×LPA×SB)), ilr2 (MVPA/(LPA×SB)), ilr3 (LPA/SB).

    dHypothesis 8: ilr1 (MVPA/(LPA×Sleep×SB)), ilr2 (LPA/(Sleep×SB)), ilr3 (Sleep/SB).

    eUnstandardized estimates and SEs.

    fVE: variance estimate.

    gFixed effects are presented as B (SE); random effects are presented as SD (VE).

    hCompared to female.

    iilr: isometric log ratio coordinate.

    jN/A: not applicable.

    kIntercept-slope correlations are presented as correlation coefficients with 95% CIs.

    lResidual values are presented as SD (VE).

    Exploratory Analyses

    We exploratively calculated reallocation analyses within a 60-minute frame, simulating the effects of replacing the same amount of time (20-min increments) in one physical behavior with another on self-perceived stress and coping self-efficacy. The starting point represents the empirically observed mean-centered composition of 490 minutes sleep, 649 minutes SB, 227 minutes LPA, and 74 minutes MVPA. The replacement of SB by LPA, MVPA, or sleep was analyzed, with no significant changes of self-perceived stress or coping self-efficacy within a 60-minute period (Figure 2).

    ‎
    Figure 2. Estimated changes in predicted daily averaged self-perceived stress and coping self-efficacy associated with pairwise reallocationsa. a95% CIs were derived by bootstrapping with 1000 replicates. CSE: coping-self-efficacy; LPA: light physical activity; MVPA: moderate-to-vigorous physical activity; SB: sedentary behavior.

    As part of our exploratory analyses, self-reported exercise behavior was added as an additional fixed predictor in all models including PA (models 1‐3 and 5‐7; Supplementary File C in Multimedia Appendix 3, Tables 1 and 2). We found no statistically significant association between exercise behavior and perceived stress or coping self-efficacy. However, adding exercise behavior revealed a significant positive association of time in SB relative to the remaining physical behaviors on perceived stress (standardized β=.04; t1379=2.039; P=.04). Furthermore, adding subjective sleep quality as a fixed predictor in both models investigating the association of the ratio of sleep to SB revealed sleep quality to be associated with coping self-efficacy (model 8: standardized β=.08; t1769=5.279; P<.001) but not with perceived stress (model 4: standardized β=–.01; t1769=–0.724; P=.47) (Supplementary File C in Multimedia Appendix 3 and Table 3). The addition of sleep quality did not alter the effect of the ratio of sleep duration to SB on self-perceived stress (standardized β=–.03; t1776=–2.364; P=.02).


    Principal Findings

    The relationship between individual physical behaviors and stress has been studied extensively in recent years. Our 15-day ambulatory assessment study with 198 participants expands existing evidence with real-time assessments in a naturalistic setting, providing within- and between-subject insights into participants’ everyday lives. We aimed to investigate the associations between device-based 24-hour physical behavior compositions with perceived stress and stress-related coping self-efficacy in an intensive longitudinal data design. Our results indicate that time spent sleeping relative to being sedentary over the 24-hour cycle is negatively associated with perceived daily stress level (H4) but not with coping self-efficacy (H8). The ratios of SB to the remaining behaviors (H1/H5), MVPA to SB (H2/H6), and LPA to SB (H3/H7) were not significantly related to either self-perceived stress or coping self-efficacy. In the exploratory analyses, replacing time spent in SB by LPA, MVPA, or sleep within a 60-minute period did not result in any significant changes in perceived stress or coping self-efficacy.

    One possible explanation for why the assumed associations between the composition of SB and the other physical behaviors with perceived stress or coping self-efficacy were not evident could be that the level of mental workload during SB was not taken into account. Research has shown that mentally passive SB, for example, watching television, can increase the risk of depression, while mentally active SB, such as reading, is considered a protective factor [84,85]. As the relationship between SB and stress-related parameters is particularly important for developing targeted health-promoting measures in working environments with high levels of sedentariness, it may be advisable to consider the aggregation of data not on a daily basis but rather within the context of working and nonworking hours [74]. When adding exercise behavior as an additional fixed predictor to our main models (H1/H5), more time in SB compared to the remaining behaviors was associated with higher stress levels. Accordingly, the association between more time in SB compared to the remaining behaviors and stress levels might not be independent of other constructs and influencing factors such as mental load or time of the day. Therefore, future research endeavors might be interested in analyzing the associations between the composition of physical behaviors and momentary stress levels at different aggregation levels (eg, 1 or 2 h) [86].

    We found a negative association between the time spent sleeping relative to SB and self-perceived stress, which is in line with previous compositional analysis studies suggesting that replacing sedentary time with sleep is associated with lower psychological stress in young individuals [46] and in working adults [47]. As Bassett et al [87] identified sleep quality, but not sleep duration, to influence cortisol responses to acute psychosocial stress, we exploratively included sleep quality as a further fixed predictor in our model. Sleep quality did not emerge as an additional influencing factor in our model, whereas the association between the ratio of sleep to SB and perceived stress remained significant, underscoring its robustness. Contrary to our hypotheses, a higher ratio of sleep to SB was not related to coping self-efficacy, even after adding sleep quality as a further exploratory fixed effect. The exploratively expanded model revealed a positive association between sleep quality and coping self-efficacy, which is consistent with the findings of Ten Brink et al [23]. This study investigated the importance of coping self-efficacy in breaking the bidirectional cycle between sleep and stress in adolescents. The findings showed that sleep quality not only leads to a higher coping self-efficacy but also a lower level of reported stress the following day [23]. Based on our results, we can only vaguely expect an indirect effect of sleep quality on stress perception, as coping self-efficacy appears to be significantly negatively associated with self-perceived stress in all models (H1-H4). However, these findings emphasize the potential of promoting one’s belief in being able to cope with stress for interventions to reduce stress in everyday life, especially as some areas of work as well as personal obligations do not allow for an increase in sleep duration.

    The compositions of LPA to SB and MVPA to SB in the 24-hour cycle were not significantly associated with either perceived stress or coping self-efficacy. In the limited existing literature, Rong et al [45], for example, found that replacing self-reported SB with walking or moderate PA had a positive effect on perceived stress. One reason for the differing results might be that subjective measures, possibly related to common method variance [88], tend to be more predictive of affect-related outcomes than objective measures [89]. Additionally, our study design (ie, half of the assessments were captured after a sedentary bout of ≥30 min) could have resulted in lower daily stress ratings. Our analyses revealed various significant random effects, for example, for the ratio of LPA to SB and LPA to remaining behaviors on coping self-efficacy, thus underlining the variability across participants regarding the association of 24-hour physical behavior and both stress-related outcomes. The occurrence of these within-subject associations should be taken into account in future investigations. Momentary assessments could provide further insights into the temporal sequencing within a day and help assess potential contextual influences, such as the environment of PA, since it has been found that outdoor PA is associated with lower negative affect than indoor PA [90].

    Overall, based on our results, we suggest examining the relationship between compositional predictors, psychological stress, and further moderating and mediating factors in more detail. Studies and replications should be conducted with diverse and stress-prone samples, such as students [91], to gain application-oriented insights into the interplay of the 24-hour physical behavior and mental health outcomes. A more comprehensive understanding of the complex association between the mechanisms underlying SB, PA, and sleep and the biological, psychosocial, and behavioral facets of stress could advance a greater focus of the 24-hour approach not only in stress management but also in public health [92,93] and health promotion recommendations [34].

    Limitations

    This study has a number of strengths, but a few limitations must be taken into account. The fact that the sample included only university employees with predominantly sedentary working conditions limits the generalizability of the results. Additionally, the observational study design does not allow the elucidation of causalities. Due to its multidimensionality, the assessment of the stress is challenging [94]. In line with previous studies [31,64], we measured both stress-related outcomes with subjective, self-report-based single-item measures that capture only the stress that participants are aware of [95,96]. However, using an ambulatory assessment design promoted the reduction of retrospective biases [50]. Half of our assessments occurred after a sedentary bout, which may have resulted in reduced variance, so that further random sampling studies are required. However, ICC calculations show that both stress-related outcomes can be adequately explained by within-person variance. As we aggregated device-based activity data within days from 12 AM to 11:59 PM, the actual sleep time per day might be distorted, because the time in bed before midnight is not represented as sleep time on the following day. Finally, the implemented sleep algorithm used may incorrectly classify daytime sleep as being sedentary (ie, if not a primary time-in-bed period was detected) [59]. However, this appears to play a subordinate role in the analyses, as daytime sleep was only reported in the evening e-diaries on 5.6% (122/2187) of all analyzed days.

    Conclusions

    This study investigated the associations between device-based 24-hour physical behavior, self-perceived stress, and coping self-efficacy in daily life. Our findings suggest that spending more time asleep relative to being sedentary in the 24-hour period is associated with lower levels of perceived stress on the same day. However, the ratio of sleep to SB was not associated with the perceived ability to cope with daily demands. The composition of SB to the other physical behaviors and time in LPA or MVPA relative to SB showed no association with either stress-related outcome. Nonetheless, within-subject associations and descriptive trends of behavioral reallocation hint at the potential relevance of daily activity patterns for handling stressful situations. These findings underscore the need for future ecologically valid studies that incorporate contextual and individual variables, as well as momentary assessments, in order to better understand the role of physical behavior in stress regulation. Such insights are necessary in order to provide target group–appropriate health-promoting recommendations for coping with stress in everyday life.

    Acknowledgments

    The authors would like to sincerely thank the participants who contributed to the study. They further thank Selina Schneider, Til Becker, Ellen Rulf, Rebecca Nissen, Jonas Behlke, and Lennart Buday for their research support.

    The authors declare the use of generative artificial intelligence in the research and writing process. According to the GAIDeT taxonomy (2025), the following tasks were delegated to generative artificial intelligence tools under full human supervision: code optimization. ChatGPT (GPT-4) by OpenAI (free version) was used to support code optimization, which was further reviewed and revised by the study group.

    Funding

    This research was funded by the German Research Foundation (grant 496846758).

    Data Availability

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

    Authors' Contributions

    MG and UWE-P conceptualized the study and developed the methodology or design. MG and IT collected the data. KB and DW analyzed the data and prepared the original draft. MG supervised the preparation of the original draft. All authors reviewed and edited the manuscript, approved the final version, and agreed with the order of presentation of the authors.

    Conflicts of Interest

    UWE-P receives personal fees for consulting from Boehringer-Ingelheim, which is not related to the submitted work. The other author has declared no conflicts of interest.

    Multimedia Appendix 1

    Structural equation models.

    DOCX File, 44 KB

    Multimedia Appendix 2

    Individual associations of self-perceived stress and the ratio of sleep to moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), and sedentary behavior (SB) at the participant level.

    DOCX File, 251 KB

    Multimedia Appendix 3

    Exploratory analyses: results of adapted multilevel models.

    DOCX File, 63 KB
    1. Cohen S, Janicki-Deverts D, Miller GE. Psychological stress and disease. JAMA. Oct 10, 2007;298(14):1685-1687. [CrossRef] [Medline]
    2. Hammen C. Stress and depression. Annu Rev Clin Psychol. 2005;1(1):293-319. [CrossRef] [Medline]
    3. Keller A, Litzelman K, Wisk LE, et al. Does the perception that stress affects health matter? The association with health and mortality. Health Psychol. Sep 2012;31(5):677-684. [CrossRef] [Medline]
    4. Kivimäki M, Steptoe A. Effects of stress on the development and progression of cardiovascular disease. Nat Rev Cardiol. Apr 2018;15(4):215-229. [CrossRef] [Medline]
    5. Glei DA, Weinstein M. Daily exposure to stressors, daily perceived severity of stress, and mortality risk among US adults. PLOS ONE. 2024;19(5):e0303266. [CrossRef] [Medline]
    6. Russ TC, Stamatakis E, Hamer M, Starr JM, Kivimäki M, Batty GD. Association between psychological distress and mortality: individual participant pooled analysis of 10 prospective cohort studies. BMJ. Jul 31, 2012;345:e4933. [CrossRef] [Medline]
    7. Blasco-Belled A, Tejada-Gallardo C, Fatsini-Prats M, Alsinet C. Mental health among the general population and healthcare workers during the COVID-19 pandemic: a meta-analysis of well-being and psychological distress prevalence. Curr Psychol. Mar 2024;43(9):8435-8446. [CrossRef]
    8. Necho M, Tsehay M, Birkie M, Biset G, Tadesse E. Prevalence of anxiety, depression, and psychological distress among the general population during the COVID-19 pandemic: a systematic review and meta-analysis. Int J Soc Psychiatry. Nov 2021;67(7):892-906. [CrossRef] [Medline]
    9. Amini-Rarani M, Rezaei S, Azami-Aghdash S, Bashzar S, Allahverdi S, Mohseni M. The prevalence of stress during the COVID-19 pandemic: an umbrella review. BMC Public Health. Nov 18, 2025;25(1):4022. [CrossRef] [Medline]
    10. Rahman MA, Das P, Lam L, et al. Health and wellbeing of staff working at higher education institutions globally during the post-COVID-19 pandemic period: evidence from a cross-sectional study. BMC Public Health. Jul 11, 2024;24(1):1848. [CrossRef] [Medline]
    11. Smith MD, Wesselbaum D. Global evidence on the prevalence of and risk factors associated with stress. J Affect Disord. Apr 1, 2025;374:179-183. [CrossRef] [Medline]
    12. Sinha R. Chronic stress, drug use, and vulnerability to addiction. Ann N Y Acad Sci. Oct 2008;1141(1):105-130. [CrossRef] [Medline]
    13. Papp LM, Kouros CD, Armstrong L, Curtin JJ. College students’ momentary stress and prescription drug misuse in daily life: testing direct links and the moderating roles of global stress and coping. Stress Health. Apr 2023;39(2):361-371. [CrossRef] [Medline]
    14. Sinha R. Stress and substance use disorders: risk, relapse, and treatment outcomes. J Clin Invest. Aug 15, 2024;134(16):e172883. [CrossRef] [Medline]
    15. Knox L, Karantzas G, Ferguson E. The role of attachment, insecurity, and stress in partner maltreatment: a meta-analysis. Trauma Violence Abuse. Jan 2024;25(1):721-737. [CrossRef] [Medline]
    16. Neff LA, Nguyen TTT, Williamson HC. Too stressed to help? The effects of stress on noticing partner needs and enacting support. Pers Soc Psychol Bull. Nov 2021;47(11):1565-1579. [CrossRef] [Medline]
    17. Randall AK, Bodenmann G. The role of stress on close relationships and marital satisfaction. Clin Psychol Rev. Mar 2009;29(2):105-115. [CrossRef] [Medline]
    18. Hassard J, Teoh KRH, Visockaite G, Dewe P, Cox T. The cost of work-related stress to society: a systematic review. J Occup Health Psychol. Jan 2018;23(1):1-17. [CrossRef] [Medline]
    19. Bandura A. Self-Efficacy: The Exercise of Control. W H Freeman & Co; 1997. ISBN: 978-0-7167-2626-5
    20. Chesney MA, Neilands TB, Chambers DB, Taylor JM, Folkman S. A validity and reliability study of the coping self-efficacy scale. Br J Health Psychol. Sep 2006;11(Pt 3):421-437. [CrossRef] [Medline]
    21. Schwarzer R, Renner B. Social-cognitive predictors of health behavior: action self-efficacy and coping self-efficacy. Health Psychol. Sep 2000;19(5):487-495. [CrossRef] [Medline]
    22. Drake EC, Sladek MR, Doane LD. Daily cortisol activity, loneliness, and coping efficacy in late adolescence: a longitudinal study of the transition to college. Int J Behav Dev. Jul 1, 2016;40(4):334-345. [CrossRef] [Medline]
    23. Ten Brink M, Lee HY, Manber R, Yeager DS, Gross JJ. Stress, sleep, and coping self-efficacy in adolescents. J Youth Adolesc. Mar 2021;50(3):485-505. [CrossRef] [Medline]
    24. Xu F, Huang L. Impacts of stress and risk perception on mental health of college students during the COVID-19 pandemic: the mediating role of coping efficacy. Front Psychiatry. 2021;12:767189. [CrossRef] [Medline]
    25. Chang MW, Brown R, Wegener DT. Perceived stress linking psychosocial factors and depressive symptoms in low-income mothers. BMC Public Health. Jan 6, 2021;21(1):62. [CrossRef] [Medline]
    26. Singh B, Olds T, Curtis R, et al. Effectiveness of physical activity interventions for improving depression, anxiety and distress: an overview of systematic reviews. Br J Sports Med. Sep 2023;57(18):1203-1209. [CrossRef] [Medline]
    27. Wright LJ, Williams SE, van Zanten JJCSV. Associations between physical activity, sedentary behaviour, and stress using ecological momentary assessment: a scoping review. Ment Health Phys Act. Mar 2023;24:100518. [CrossRef]
    28. Burg MM, Schwartz JE, Kronish IM, et al. Does stress result in you exercising less? Or does exercising result in you being less stressed? Or is it both? Testing the bi-directional stress–exercise association at the group and person (N of 1) level. Ann Behav Med. Dec 2017;51(6):799-809. [CrossRef] [Medline]
    29. Chu AHY, van Dam RM, Biddle SJH, Tan CS, Koh D, Müller-Riemenschneider F. Self-reported domain-specific and accelerometer-based physical activity and sedentary behaviour in relation to psychological distress among an urban Asian population. Int J Behav Nutr Phys Act. Apr 5, 2018;15(1):36. [CrossRef] [Medline]
    30. Felez-Nobrega M, Bort-Roig J, Briones L, et al. Self-reported and activPALTM-monitored physical activity and sedentary behaviour in college students: not all sitting behaviours are linked to perceived stress and anxiety. J Sports Sci. Jul 2020;38(13):1566-1574. [CrossRef] [Medline]
    31. Jones M, Taylor A, Liao Y, Intille SS, Dunton GF. Real-time subjective assessment of psychological stress: associations with objectively-measured physical activity levels. Psychol Sport Exerc. Jul 2017;31:79-87. [CrossRef] [Medline]
    32. Pinto BM, Kindred MD, Dunsiger SI, Williams DM. Sedentary behavior among breast cancer survivors: a longitudinal study using ecological momentary assessments. J Cancer Surviv. Aug 2021;15(4):546-553. [CrossRef] [Medline]
    33. Teychenne M, Stephens LD, Costigan SA, Olstad DL, Stubbs B, Turner AI. The association between sedentary behaviour and indicators of stress: a systematic review. BMC Public Health. Oct 23, 2019;19(1):1357. [CrossRef] [Medline]
    34. Kastelic K, Pedišić Ž, Lipovac D, Kastelic N, Chen ST, Šarabon N. Associations of meeting 24-h movement guidelines with stress and self-rated health among adults: is meeting more guidelines associated with greater benefits? BMC Public Health. May 17, 2021;21(1):929. [CrossRef] [Medline]
    35. Kim HJ, Oh SY, Joo JH, Choi DW, Park EC. The relationship between sleep duration and perceived stress: findings from the 2017 Community Health Survey in Korea. Int J Environ Res Public Health. Sep 3, 2019;16(17):3208. [CrossRef] [Medline]
    36. Yap Y, Slavish DC, Taylor DJ, Bei B, Wiley JF. Bi-directional relations between stress and self-reported and actigraphy-assessed sleep: a daily intensive longitudinal study. Sleep. Mar 12, 2020;43(3):zsz250. [CrossRef] [Medline]
    37. Rosenberger ME, Fulton JE, Buman MP, et al. The 24-hour activity cycle: a new paradigm for physical activity. Med Sci Sports Exerc. Mar 2019;51(3):454-464. [CrossRef] [Medline]
    38. Pedišić Z, Dumuid D, Olds TS. Integrating sleep, sedentary behaviour, and physical activity research in the emerging field of time-use epidemiology: definitions, concepts, statistical methods, theoretical framework, and future directions. Kinesiology (Zagreb). 2017;49(2):252-269. URL: https://ojs.srce.hr/kinesiology/article/view/5401 [Accessed 2026-05-02]
    39. Canadian Society for Exercise Physiology. URL: https://csepguidelines.ca/ [Accessed 2025-01-15]
    40. 24-hour movement guidelines for all australians. Australian Government Department of Health, Disability and Ageing. URL: https:/​/www.​health.gov.au/​topics/​physical-activity-and-exercise/​physical-activity-and-exercise-guidelines-for-all-australians/​ [Accessed 2025-01-15]
    41. Groves CI, Huong C, Porter CD, et al. Associations between 24-h movement behaviors and indicators of mental health and well-being across the lifespan: a systematic review. J Act Sedentary Sleep Behav. Mar 14, 2024;3(1):9. [CrossRef] [Medline]
    42. Kracht CL, Burkart S, Groves CI, et al. 24-hour movement behavior adherence and associations with health outcomes: an umbrella review. J Act Sedentary Sleep Behav. 2024;3(1):25. [CrossRef] [Medline]
    43. Zhou Y, Huang Z, Liu Y, Liu D. The effect of replacing sedentary behavior with different intensities of physical activity on depression and anxiety in Chinese university students: an isotemporal substitution model. BMC Public Health. May 23, 2024;24(1):1388. [CrossRef] [Medline]
    44. Curtis RG, Dumuid D, McCabe H, Singh B, Ferguson T, Maher C. The association between 24-hour activity, sedentary and sleep compositions and mental health in Australian adults: a cross-sectional study. J Act Sedentary Sleep Behav. Aug 1, 2023;2(1):15. [CrossRef] [Medline]
    45. Rong F, Li X, Jia L, et al. Substitutions of physical activity and sedentary behavior with negative emotions and sex difference among college students. Psychol Sport Exerc. May 2024;72:102605. [CrossRef] [Medline]
    46. Meyer JD, Ellingson LD, Buman MP, Shook RP, Hand GA, Blair SN. Current and 1-year psychological and physical effects of replacing sedentary time with time in other behaviors. Am J Prev Med. Jul 2020;59(1):12-20. [CrossRef] [Medline]
    47. Kitano N, Kai Y, Jindo T, Tsunoda K, Arao T. Compositional data analysis of 24-hour movement behaviors and mental health in workers. Prev Med Rep. Dec 2020;20:101213. [CrossRef] [Medline]
    48. Kanning MK, Ebner-Priemer UW, Schlicht WM. How to investigate within-subject associations between physical activity and momentary affective states in everyday life: a position statement based on a literature overview. Front Psychol. 2013;4:187. [CrossRef] [Medline]
    49. Wang X, Cheng Z. Cross-sectional studies: strengths, weaknesses, and recommendations. Chest. Jul 2020;158(1S):S65-S71. [CrossRef] [Medline]
    50. Reichert M, Giurgiu M, Koch E, et al. Ambulatory assessment for physical activity research: state of the science, best practices and future directions. Psychol Sport Exerc. Sep 2020;50:101742. [CrossRef] [Medline]
    51. Trull TJ, Ebner-Priemer U. Ambulatory assessment. Annu Rev Clin Psychol. 2013;9:151-176. [CrossRef] [Medline]
    52. Ebner-Priemer UW, Koudela S, Mutz G, Kanning M. Interactive multimodal ambulatory monitoring to investigate the association between physical activity and affect. Front Psychol. 2012;3:596. [CrossRef] [Medline]
    53. Bussmann JBJ, Ebner-Priemer UW, Fahrenberg J. Ambulatory activity monitoring. Eur Psychol. Jan 2009;14(2):142-152. [CrossRef]
    54. Brüßler S, von Haaren-Mack B, Vogelsang A, Reichert M. Ambulatory assessment for recovery and stress monitoring in the general population. In: Fostering Recovery and Well-Being in a Healthy Lifestyle. Routledge; 2024:38-55. URL: https:/​/www.​taylorfrancis.com/​chapters/​edit/​10.4324/​9781003250654-4/​ambulatory-assessment-recovery-stress-monitoring-general-population-sarah-br%C3%BC%C3%9Fler-birte-von-haaren-mack-anna-vogelsang-markus-reichert [Accessed 2026-05-02]
    55. Bolger N, Laurenceau JP. Intensive Longitudinal Methods: An Introduction to Diary and Experience Sampling Research. Guilford Publications; 2013. ISBN: 978-1-4625-0678-1
    56. Giurgiu M, Timm I, Ebner-Priemer UW, Schmiedek F, Neubauer AB. Causal effects of sedentary breaks on affective and cognitive parameters in daily life: a within-person encouragement design. npj Ment Health Res. Dec 21, 2024;3(1):64. [CrossRef] [Medline]
    57. Wrzus C, Neubauer AB. Ecological momentary assessment: a meta-analysis on designs, samples, and compliance across research fields. Assessment. Apr 2023;30(3):825-846. [CrossRef] [Medline]
    58. Hettiarachchi P, Johansson P. ActiPASS. Zenodo. URL: https://zenodo.org/records/11119064 [Accessed 2026-05-02]
    59. Johansson PJ, Crowley P, Axelsson J, et al. Development and performance of a sleep estimation algorithm using a single accelerometer placed on the thigh: an evaluation against polysomnography. J Sleep Res. Apr 2023;32(2):e13725. [CrossRef] [Medline]
    60. Crowley P, Skotte J, Stamatakis E, et al. Comparison of physical behavior estimates from three different thigh-worn accelerometers brands: a proof-of-concept for the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS). Int J Behav Nutr Phys Act. Aug 16, 2019;16(1):65. [CrossRef] [Medline]
    61. Hettiarachchi P, Aili K, Holtermann A, Stamatakis E, Svartengren M, Palm P. Validity of a non-proprietary algorithm for identifying lying down using raw data from thigh-worn triaxial accelerometers. Sensors (Basel). Jan 29, 2021;21(3):904. [CrossRef] [Medline]
    62. Blodgett JM, Ahmadi MN, Atkin AJ, et al. Device-measured physical activity and cardiometabolic health: the Prospective Physical Activity, Sitting, and Sleep (ProPASS) consortium. Eur Heart J. Feb 7, 2024;45(6):458-471. [CrossRef] [Medline]
    63. Giurgiu M, Niermann C, Ebner-Priemer U, Kanning M. Accuracy of sedentary behavior-triggered ecological momentary assessment for collecting contextual information: development and feasibility study. JMIR Mhealth Uhealth. Sep 15, 2020;8(9):e17852. [CrossRef] [Medline]
    64. Littman AJ, White E, Satia JA, Bowen DJ, Kristal AR. Reliability and validity of 2 single-item measures of psychosocial stress. Epidemiology (Sunnyvale). Jul 2006;17(4):398-403. [CrossRef] [Medline]
    65. Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. Dec 1983;24(4):385-396. [CrossRef] [Medline]
    66. Buysse DJ, Reynolds CF III, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. May 1989;28(2):193-213. [CrossRef] [Medline]
    67. Dumuid D, Pedišić Ž, Palarea-Albaladejo J, Martín-Fernández JA, Hron K, Olds T. Compositional data analysis in time-use epidemiology: what, why, how. Int J Environ Res Public Health. Mar 26, 2020;17(7):2220. [CrossRef] [Medline]
    68. Rasmussen CL, Palarea-Albaladejo J, Johansson MS, et al. Zero problems with compositional data of physical behaviors: a comparison of three zero replacement methods. Int J Behav Nutr Phys Act. Oct 6, 2020;17(1):126. [CrossRef] [Medline]
    69. Snijders TAB, Bosker RJ. Multilevel Analysis: An Introduction to Basic and Advances Multilevel Modeling. SAGE Publications; 2011. ISBN: 978-1-84920-201-5
    70. Hox JJ, Moerbeek M, van de Schoot R. Multilevel Analysis: Techniques and Applications. Taylor & Francis; 2017. ISBN: 978-1-317-30867-6
    71. Hamarat E, Thompson D, Zabrucky KM, Steele D, Matheny KB, Aysan F. Perceived stress and coping resource availability as predictors of life satisfaction in young, middle-aged, and older adults. Exp Aging Res. 2001;27(2):181-196. [CrossRef] [Medline]
    72. Suglia SF, Pamplin JR II, Forde AT, Shelton RC. Sex differences in the association between perceived stress and adiposity in a nationally representative sample. Ann Epidemiol. Oct 2017;27(10):626-631. [CrossRef] [Medline]
    73. Graves BS, Hall ME, Dias-Karch C, Haischer MH, Apter C. Gender differences in perceived stress and coping among college students. PLOS ONE. 2021;16(8):e0255634. [CrossRef] [Medline]
    74. Courtney JB, Turner JA, Puterman E, Almeida DM. Within-person associations between daily stress and physical activity during working and non-working hours. Psychol Sport Exerc. Jan 2025;76:102777. [CrossRef] [Medline]
    75. Stefaniak AR, Blaxton JM, Bergeman CS. Age differences in types and perceptions of daily stress. Int J Aging Hum Dev. Mar 2022;94(2):215-233. [CrossRef] [Medline]
    76. Du C, Adjepong M, Zan MCH, et al. Gender differences in the relationships between perceived stress, eating behaviors, sleep, dietary risk, and body mass index. Nutrients. Feb 28, 2022;14(5):1045. [CrossRef] [Medline]
    77. Tenk J, Mátrai P, Hegyi P, et al. Perceived stress correlates with visceral obesity and lipid parameters of the metabolic syndrome: a systematic review and meta-analysis. Psychoneuroendocrinology. Sep 2018;95:63-73. [CrossRef] [Medline]
    78. Tomiyama AJ. Stress and obesity. Annu Rev Psychol. Jan 4, 2019;70:703-718. [CrossRef] [Medline]
    79. Mouchacca J, Abbott GR, Ball K. Associations between psychological stress, eating, physical activity, sedentary behaviours and body weight among women: a longitudinal study. BMC Public Health. Sep 11, 2013;13(1):828. [CrossRef] [Medline]
    80. Dumuid D, Pedišić Ž, Stanford TE, et al. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Methods Med Res. Mar 2019;28(3):846-857. [CrossRef] [Medline]
    81. van den Boogaart KG, Tolosana-Delgado R. “compositions”: a unified R package to analyze compositional data. Comput Geosci. Apr 2008;34(4):320-338. [CrossRef]
    82. Palarea-Albaladejo J, Martín-Fernández JA. zCompositions — R package for multivariate imputation of left-censored data under a compositional approach. Chemometr Intell Lab Syst. Apr 2015;143:85-96. [CrossRef]
    83. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1-48. [CrossRef]
    84. Hallgren M, Dunstan DW, Owen N. Passive versus mentally active sedentary behaviors and depression. Exerc Sport Sci Rev. Jan 2020;48(1):20-27. [CrossRef] [Medline]
    85. Werneck AO, Hoare E, Stubbs B, van Sluijs EMF, Corder K. Associations between mentally-passive and mentally-active sedentary behaviours during adolescence and psychological distress during adulthood. Prev Med. Apr 2021;145:106436. [CrossRef] [Medline]
    86. Giurgiu M, Ebner-Priemer UW, Dumuid D. Compositional insights on the association between physical activity and sedentary behavior on momentary mood in daily life. Psychol Sport Exerc. Jan 2022;58:102102. [CrossRef]
    87. Bassett SM, Lupis SB, Gianferante D, Rohleder N, Wolf JM. Sleep quality but not sleep quantity effects on cortisol responses to acute psychosocial stress. Stress. 2015;18(6):638-644. [CrossRef] [Medline]
    88. Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. Oct 2003;88(5):879-903. [CrossRef] [Medline]
    89. Konjarski M, Murray G, Lee VV, Jackson ML. Reciprocal relationships between daily sleep and mood: a systematic review of naturalistic prospective studies. Sleep Med Rev. Dec 2018;42:47-58. [CrossRef] [Medline]
    90. Dunton GF, Liao Y, Intille S, Huh J, Leventhal A. Momentary assessment of contextual influences on affective response during physical activity. Health Psychol. Dec 2015;34(12):1145-1153. [CrossRef] [Medline]
    91. Ramón-Arbués E, Gea-Caballero V, Granada-López JM, Juárez-Vela R, Pellicer-García B, Antón-Solanas I. The prevalence of depression, anxiety and stress and their associated factors in college students. Int J Environ Res Public Health. Sep 24, 2020;17(19):7001. [CrossRef] [Medline]
    92. Amnie AG. An investigation of predictors of self-efficacy to cope with stress and implications for health education practice. Am J Health Educ. May 4, 2018;49(3):155-165. [CrossRef]
    93. Rollo S, Antsygina O, Tremblay MS. The whole day matters: understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. J Sport Health Sci. Dec 2020;9(6):493-510. [CrossRef] [Medline]
    94. Epel ES, Crosswell AD, Mayer SE, et al. More than a feeling: a unified view of stress measurement for population science. Front Neuroendocrinol. Apr 2018;49:146-169. [CrossRef] [Medline]
    95. Smets E, Rios Velazquez E, Schiavone G, et al. Large-scale wearable data reveal digital phenotypes for daily-life stress detection. NPJ Digit Med. 2018;1(1):67. [CrossRef] [Medline]
    96. Alberdi A, Aztiria A, Basarab A. Towards an automatic early stress recognition system for office environments based on multimodal measurements: a review. J Biomed Inform. Feb 2016;59:49-75. [CrossRef] [Medline]

    ‎
    ICC: intraclass correlation
    ilr: isometric log-ratio
    LPA: light physical activity
    MVPA: moderate-to-vigorous physical activity
    PA: physical activity
    SB: sedentary behavior

    Edited by Alicia Stone; submitted 04.Aug.2025; peer-reviewed by Amanda Machell, Yen-Yuan Chen; final revised version received 04.Apr.2026; accepted 14.Apr.2026; published 22.May.2026.

    Copyright

    © Katrin Bonn, Doreen Wohlfarth, Irina Timm, Oliver Bender, Ulrich W Ebner-Priemer, Marco Giurgiu. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 22.May.2026.

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