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. Author manuscript; available in PMC: 2022 Jul 1.
Published in final edited form as: Psychol Sport Exerc. 2021 Mar 13;55:101935. doi: 10.1016/j.psychsport.2021.101935

Momentary associations between stress and physical activity among children using ecological momentary assessment

Bridgette Do a, Tyler B Mason a, Li Yi b, Chih-Hsiang Yang c, Genevieve F Dunton a,d
PMCID: PMC8031470  NIHMSID: NIHMS1683219  PMID: 33841048

Abstract

Chronic and acute stress may have a detrimental effect on children’s physical activity. Research on stress as a predictor of children’s physical activity has mostly focused on stress between children, rather than how children’s within-day variation in stress may predict physical activity. The current study assessed the within- and between-effects of stress on subsequent physical activity in three different time windows using ecological momentary assessment (EMA1) and accelerometry. Children (N = 190; MBaseline Age =10.1±0.9, 53% female, 56% self-identified Hispanic/Latino) completed six semi-annual assessment bursts across three years. During each burst, participants responded to up to seven (weekend) or three (weekday) randomly prompted EMA surveys on smartphones for seven days and wore a waist-worn accelerometer. Multi-level structural equation modeling examined within- and between-subjects effects of stress as a predictor of children’s subsequent moderate-to-vigorous physical activity (MVPA2) in the 15, 30, and 60 minutes following the EMA prompt. Latent variables were created for within- and between-subjects stress were comprised of three EMA stress items. Higher than average levels of stress (within-subjects) significantly predicted lower MVPA in the subsequent 15, 30 and 60 minutes (ps < .05). Between-subjects stress was not significantly associated with subsequent MVPA (ps > .05). Results indicate that elevated momentary stress predicts less subsequent MVPA. These findings suggest that within-day fluctuations in stress may be a barrier for children engaging in physical activity. Childhood physical activity promotion and interventions should consider the role of children’s stress, aim to reduce the stress children experience throughout the day, and incorporate stress coping strategies.

Keywords: stress, children, ecological momentary assessment, physical activity

Introduction

Physical activity has numerous health benefits for children such as improving aerobic fitness, blood pressure, glucose metabolism, and mental health outcomes, and can also reduce the likelihood of risk factors for chronic diseases later in life (Ahn & Fedewa, 2011; Sallis et al., 2000; Janssen & LeBlanc, 2010). However, less than one in four children aged 6–17 years attain the Physical Activity Guidelines for Americans recommendation of 60 daily minutes of moderate-to-vigorous physical activity (MVPA2; The Child & Adolescent Health Measurement Initiative, 2016). Given these statistics, increasing children’s physical activity is an ongoing and critical public health goal that needs to be addressed. Much of the extant research has focused on examining a variety of psychological and individual factors that may promote or inhibit physical activity, such as enjoyment, stress, depression, and self-efficacy (Moore et al., 2009; Trost et al., 2011; Gunnell et al., 2016; Sallis et al., 1999).

Stress may be an important factor that impacts children’s physical activity because stress can influence the type of behaviors that children decide to engage in. The ego depletion model posits that self-control is a limited resource that becomes depleted as individuals are faced with increased demands (Baumeister et al., 1998). For example, demands, such as psychological stress, may lead to decreased self-control and greater fatigue. Consequently, individuals may be less likely to engage in physical activity and more likely to be sedentary. A systematic review of 168 studies, which included cross-sectional, retrospective, and prospective designs, on the influence of stress on indicators of physical activity reported that the majority of studies found that higher stress was associated with less physical activity. However, 29 studies (17%), reported that higher stress was predictive of more physical activity (Stults-Kolehmainen & Sinha, 2014). Overall, the authors concluded that stress may impede physical activity, similar to how it can influence other behaviors such as smoking, alcohol, and drug use. However, most studies included in the review were conducted among college students and adults. Understanding how stress may uniquely influence physical activity in children is important because children typically engage in different types of activities from adults that may vary in degree of pre-planning and volition such as free-play and youth sports. Moreover, establishing healthy habits during childhood and adolescence can track into adulthood (Trudeau et al., 2004; Telama et al., 2009; De vet et al., 2015). One study among children aged 8–12 years reported that greater stress was associated with fewer minutes of physical activity in a laboratory setting (Roemmich et al., 2013). The small number of studies among children, along with the mixed findings among adults, underscores the need for more population-specific and rigorously designed research in this area.

Given that feelings of stress can fluctuate throughout the day, employing momentary data capture methods are needed to elucidate the relationship between variances in feelings of stress and the behavioral changes in response to stress (Jones et al., 2017). However, studies on stress and physical activity among children have largely tested between-subject associations, which distinguishes how children differ from one another. In addition, these studies do not allow for adequate testing of the ego depletion model, which theorizes momentary effects. Ecological momentary assessment (EMA1) is well-suited to examine variations in stress and activity by gathering data on participant’s momentary perceived stress and experiences in their natural environments. EMA on smartphones provides benefits such as reduction of biases related to recall and increased ecological validity (Shiffman et al., 2008). Additionally, repeated measurements collected for each participant can elucidate intraindividual differences, or how individuals vary from day to day or across the course of the day (i.e., within-subject effects). EMA has been successfully utilized in research among children and adolescents (Russell & Gajos, 2020; Mason et al., 2020). Furthermore, objectively-measured physical activity assessed with accelerometry can be used simultaneously with EMA on smartphones to untangle temporal relationships. Employing EMA to examine relationships between acute and chronic stress and physical activity in ecologically valid settings are in line with recent calls for future research (Biddle et al., 2019). To our knowledge, there are no published studies that use EMA to consider both the affective (e.g., emotion, coping/regulation) and cognitive (e.g., perceptions and appraisals) components of stress in the stress-physical activity relationship among children. Therefore, EMA studies among children are needed to elucidate the stress-physical activity relationship in free-living situations. To address these gaps, the current study examined the momentary associations of self-reported stress on subsequent objectively-measured MVPA (i.e., subsequent 15, 30, and 60 minutes) in children. These three subsequent time frames were examined in order to capture the potential acute prospective associations between stress and decision making regarding physical activity (e.g., next 15 minutes) and the longevity of stress effects on physical activity (e.g., 60 minutes). These time frames have been utilized in previous EMA research to capture the associations between stress or affect and subsequent physical activity (Wen et al., 2018; Hevel et al., 2020). It was hypothesized that there would be intraindividual (within-subjects) effects of stress on subsequent MVPA, such that on occasions when children reported greater momentary stress, they would engage in less MVPA in the next 15–60 minutes. It was also hypothesized that there would be interindividual (between-subjects) effects of stress on MVPA, such that children reporting greater stress, on average compared to other children, would engage in less MVPA. Understanding the relationship between momentary stress and physical activity in naturalistic settings may have practical implications for physical activity promotion efforts among children.

Method

Participants

Participants included children enrolled in the Mothers and Their Children’s Health (MATCH) study. The MATCH study was a longitudinal investigation among mothers and children that utilized real-time data capture methodologies to examine children’s obesity risk (Dunton et al., 2015). Participants were recruited through informational flyers and in-person staff visits at elementary schools and community centers throughout Los Angeles, California, USA. The inclusion criteria for children in the MATCH study were: (1) 8–12 years old at baseline, (2) living with the participating mother at least 50% of the time, and (3) able to read and speak English. Study exclusion criteria were: (1) report a physical health condition that may prevent physical activity, (2) currently taking medications for thyroid functions or psychological conditions such as depression, anxiety, mood disorders, and ADHD, (3) currently enrolled in special education programs, (4) currently using oral or inhalant corticosteroids of asthma, and (5) considered underweight by a body mass index (BMI) percentile < 5%. At baseline, 202 children were enrolled in the study. This study was conducted in accordance with the Declaration of Helsinki and all aspects of the study were approved by the Institutional Review Boards at the University of Southern California and Northeastern University (Reference: HS-12-00446).

Study Design

Children participated in an intensive longitudinal study, consisting of six semi-annual measurement bursts across three years. During each burst, children completed seven days of EMA surveys and accelerometry. Eligible children attended two in-person data collection sessions per measurement burst with their mother. The study took place between 2014 and 2018.

Procedures

During the baseline session, mothers provided written informed consent and parental permission for their child, and children provided written and verbal assent. At each measurement burst, anthropometric measurements were taken by study staff; height was measured to the nearest 0.1 cm with a portable statiometer (PE-AIM-101) and weight was measured to the nearest 0.1 kg with an electronically calibrated digital scale (Tanita WB-11a). Participants also completed paper-and-pencil questionnaires. Children were loaned a waist-worn accelerometer (Actigraph GT3X; Actigraph Corp., Pensacola, FL, USA) for the duration of the burst. Participants were instructed to wear the accelerometer on their right hip during all hours, except for when sleeping or in water (i.e., bathing, swimming). For the duration of the measurement burst, children were also loaned an Android smartphone (Motorola G or Motorola X) without a data plan (Motorola Mobility, Chicago, IL, USA). EMA data were collected through a custom software phone application for smartphones through the Android operating system (Google Inc., Mountainview, CA, USA). The application was available in English. During the in-person data collection sessions, participants were trained on how to use the study application through both verbal and written instructions. Children received 100 USD for each completed measurement burst.

Children completed signal-contingent (i.e., randomly-prompted) EMA surveys on their study phone via the phone application through a time-stratified sampling scheme. Participants received EMA prompts on the evening of the first data collection session, across the next six days, and until 5:00 P.M. on the day of the second data collection session when the phone was returned to study staff. Children were prompted up to three times per day on weekdays and up to seven times per day on weekend days. To ensure that children were not prompted during school hours, EMA began at 3:00 P.M. on weekdays. On weekdays, participants were randomly prompted once during each of the following time windows: 3:00–4:00 P.M., 5:00–6:00 P.M., 7:00–8:00 P.M. On weekend days, participants were randomly prompted once during each of the following time windows: 7:00–8:00 A.M., 9:00–10:00 A.M., 11:00 A.M.–12:00 P.M., 1:00–2:00 P.M. 3:00–4:00 P.M., 5:00–6:00 P.M., 7:00–8:00 P.M.

Children were asked to carry the study smartphone with them at all times when they were awake, aside from non-compatible activities (i.e., sleeping, swimming). Participants were instructed to connect the study phone to their home wireless Internet (Wi-Fi) if available. If wireless connection was not available, EMA data were downloaded directly from the study phone by study staff at the end of each measurement burst. Children provided their sleep and wake-up times in the smartphones to ensure that surveys were not prompted while they were sleeping. Participants were notified to complete a survey on the phone application through an audible prompt and/or vibration. Participants were asked to stop their current activity and complete an EMA survey through the study application. After the final reminder, the EMA survey was no longer accessible to the participant. Participants were instructed to ignore prompts during any inconvenient activities (i.e., sleeping, bathing). On average, children completed each survey in 81.5 seconds (Dzubur et al., 2018). If no entry was made, the study application sent up to two reminder signals at 3-minute intervals. (i.e., at 3 and 6 minutes after the initial prompt). The EMA survey closed 12 minutes after the initial prompt and was considered “missed” thereafter.

Measures

Stress.

Children’s stress was measured using three separate EMA items to capture both affective and cognitive (i.e., perceived stress) dimensions of stress. Perceived stress is notably different from affect, despite the important role of negative affect in the conceptualization of perceived stress (DeSteno et al., 2015; Epel et al., 2018). Perceived stress captures cognitions in response to situations or the appraisal of situations, rather than the specific emotional experiences elicited (Cohen et al., 1983; Epel et al., 2018). In order to examine both components of psychological stress, the current study modeled a latent variable from the following three EMA items. Children were asked “How stressed are you feeling right now?” with response options ranging from 1= Not at all to 4 = Extremely (Ebesutani et al., 2012). In addition, children were asked two items adapted from the Stress in Children Scale to assess perceived stress: “I can manage with all the things I have to do right now” and “Things are working out as I have planned right now” (Osika et al., 2007). Both items had answer options of 1 = Yes and 0 = No. The two perceived stress items were reversed scored for the analysis, with a higher score indicating more perceived stress.

Physical Activity.

MVPA was captured through a waist-worn accelerometer (Actigraph GT3X) with a sampling frequency of 30 Hz and collected as continuous count data that were aggregated into 30 second epochs. Age-adjusted Freedson cut points for youth were used for determining MVPA minutes (Freedson et al., 2005), consistent with studies of national surveillance data (Belcher et al., 2010; Troiano et al., 2008). Non-wear time ( > 60 continuous minutes of zero activity counts) and non-valid days ( < 10 hours of wear time) were removed from the analyses. Accelerometer data and EMA data were linked using time-stamps and the number of MVPA minutes were summed across the 15, 30, and 60 minutes after each EMA prompt. Consistent with current recommendations of 10 hours of wear time across waking hours (i.e., two-thirds of 16 waking hours) and prior studies examining accelerometer windows after EMA prompts (Yang et al., 2020; Maher et al., 2017), each time window of accelerometer data needed to have at least two-thirds of valid accelerometer wear (e.g., minimum of 40 minutes of valid wear during the 60-minute time window) to be included in the analyses (Troiano et al., 2008).

Demographics and Covariates.

Participants completed paper-and-pencil questionnaires during each measurement burst to assess demographics and personal characteristics. Children reported their sex (male vs. female) and date of birth. Mothers self-reported their child’s ethnicity (Hispanic/Latino vs. not Hispanic/Latino) and annual household income (later categorized as less than $35,000, $35,000–$64,999, $65,000–$104,999, $105,000 or more). Children’s ages were calculated using date of birth and the date of anthropometric measurements at each burst; children’s self-report ages were used when dates of birth were not available. Children’s height and weight measurements were used to calculate BMI (kg/m2).

Statistical Analyses

The analytic plan was specified prior to analyses. The number of level-1 data points (i.e., EMA and accelerometer assessments) is considered to be the unit of analyses for testing the within-day effects, assuming non-randomly varying slopes. The estimated slopes range from .57 (r=.26, σx=.40 and σy=.88) to 1.04 (r=.19, σx=.40 and σy=.96). The sample sizes required to achieve statistical power of .8 for this range of slopes were determined using G*Power (V3.0) software (Faul et al., 2007). Linear bivariate regressions were applied with 5% of Type I error rate and two-sided tests. A sample of 144 level-1 units will provide sufficient power to detect a slope of 0.5. With randomly-varying slopes, the required level-1 sample size may be upwardly adjusted based on intraclass correlation coefficients (ICCs). However, the actual analytic level-1 sample size of 10,771 (60-minute model) is expected to be sufficient to handle these adjustments. Multi-level structural equation modeling (MSEM) was conducted in Mplus (Version 8; Muthén & Muthén, 2017). Structural equation modeling was used to estimate a latent construct for momentary stress using the three EMA items. Latent variable modeling allows for the estimation of an unobserved, underlying construct (i.e., stress) through observed variables (Rabe-Hesketh et al., 2007; Bartholomew, 2001). Each of the three EMA items measuring stress were disaggregated into between-subject (Level-2, person) and within-subject (Level-1, prompt) versions to partition the variance (Hedeker et al., 2012). The between-subject version represents the individual mean deviation from the grand mean (mean of all observations across all participants), and the within-subject version represents the deviation from one’s own mean at any given EMA prompt (Curran & Bauer, 2011). Two latent variables were modeled for children’s within-subject stress and children’s between-subject stress, respectively. One loading on each latent variable was set to 1 to provide a metric for the latent variable. A negative binomial function was used for MVPA given the heavily skewed nature of MVPA data with many occasions with zero minutes. The hypothesized model is displayed in Figure 1.

Figure 1.

Figure 1.

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Hypothesized multi-level structural equation model for predicting MVPA minutes in the subsequent 15/30/60 minutes.

Structural equation models controlled for the following demographic factors that were correlated with the outcome variable of interest: age at burst, BMI at burst, sex, Hispanic/Latino ethnicity, and average annual household income quartile across all bursts. All models also controlled for time of day (morning [before 12pm], afternoon [12pm – 5pm], evening [after 5pm]), weekend day vs. weekday, measurement burst, and for the number of MVPA minutes in the 15, 30, or 60 minutes before the EMA prompt.

Results

Participant Characteristics and Data Availability

Participants were included in the analyses if they had EMA data from at least one burst (200 children out of 202 children enrolled at baseline). A total of 23,157 EMA prompts were delivered across the six bursts. Of these, 17,799 EMA prompts were answered (M = 76.86%). A total of 10 children were further excluded from the analyses for having missing data on the three stress items, missing accelerometer data (e.g., zero valid days), or missing sex or ethnicity data. Therefore, the final analytic sample included 190 children. Occasions were excluded from the final analyses if the time window for the outcome activity variable or the time window for prior activity variable did not contain at least two-thirds minutes of valid wear time (e.g., minimum 40 minutes of valid wear in the 60 minutes time window), such that 5,508 observations were excluded in the 15-minute model, 5,827 for the 30-minute model, and 6,431 for the 60-minute model. The final 15-minute model included 11,676 EMA observations, the 30-minute model included 11,365 EMA observations, and the 60-minute model included 10,771 EMA observations. Children were more likely to comply to EMA prompts on weekend days compared to weekdays and in the morning compared to afternoon or evening (ps < .05).

Participant characteristics are shown in Table 1. At baseline, children’s age ranged from 8 to 12 years old (M = 10.13, SD = 0.91). The average BMI at baseline was 19.06 kg/m2 (SD = 3.90). Participant demographics regarding self-report ethnicity and family income are similar to the population where the sample was recruited (Los Angeles County), based on the population estimates from 2019 (United States Census Bureau, 2019).

Table I.

Participant characteristics (Level-2=190 children, Level-1=11,676 occasionsa)

Level-1 Variables Burst 1 Burst 2 Burst 3 Burst 4 Burst 5 Burst 6
Feeling stressed (M±SD)b 1.37±0.72 1.32±0.65 1.34±0.68 1.35±0.69 1.38±0.75 1.39±0.73
Manage Things, n (%)c
 Yes 2293 (90.24) 1765 (90.28) 1484 (89.99) 1593 (92.76) 1600 (89.09) 1784 (89.65)
 No 248 (9.76) 190 (9.72) 165 (10.01) 143 (8.24) 196 (10.91) 206 (10.35)
Things Working, n (%)d
 Yes 2096 (82.48) 1615 (82.61) 1368 (82.96) 1405 (80.93) 1457 (81.12) 1581 (79.45)
 No 445 (17.52) 340 (17.39) 281 (17.04) 331 (19.07) 339 (18.88) 409 (20.55)
MVPA minutes (M±SD)
 Subsequent 15 minutes 1.02±1.93 0.84±1.70 0.82±1.75 0.74±1.68 0.68±1.61 0.54±1.45
 Subsequent 30 minutese 2.09±3.55 1.81±3.23 1.66±3.10 1.50±2.99 1.37±2.89 1.08±2.55
 Subsequent 60 minutesf 4.16±6.08 3.67±5.65 3.42±5.73 3.21±5.81 2.62±4.83 2.16±4.65
Level-2 Variables
Child sex, n (%)
 Male 90 (47.37)
 Female 100 (52.63)
Child ethnicity, n (%)
 Hispanic/Latino 107 (56.32)
 Not Hispanic/Latino 82 (43.68)
Average annual household income, n (%)
 Less than $35,000 55 (28.95)
 $35,000–$64,999 40 (21.05)
 $65,000–$104,999 49 (25.79)
 $105,000 or more 46 (24.21)
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Note. MVPA=moderate-to-vigorous physical activity.

a

EMA observations in the 15-minute model.

b

Full item: “How stressed are you feeling right now?”

c

Full item: “I can manage with all the things I have to do right now.”

d

Full item: “Things are working out as I have planned right now.”

e

EMA observations in the 30-minute model.

f

EMA observations in the 60-minute model

ICCs indicated that variances for each of the stress items were predominately due to within-subject differences, with a relatively small proportion of variance was due to between-subject differences (ICCs: feeling stressed = 0.17, manage things = 0.20, things working = 0.18). Two latent variables were created for children’s stress (i.e., within-subject and between-subject). Each latent variable consisted of the within-subject or between-subject versions of the three EMA items, respectively. Observed indicators significantly loaded onto the respective latent variable (ps < .05). Unstandardized loadings are shown in Figure 2.

Figure 2.

Figure 2.

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Multi-level structural equation model for predicting MVPA minutes in the subsequent 15/30/60 minutes with unstandardized loadings.

Effects of children’s stress on subsequent physical activity

Table 2 shows the results of the multi-level structural equation model examining the within- and between-subject effects of children’s momentary stress on subsequent physical activity. After controlling for covariates, results indicated that at times when children felt more stressed than usual, they engaged in less MVPA in the next 15, 30, and 60 minutes (within-subject effects). There were no significant between-subject effects of stress on subsequent MVPA. All models controlled for minutes of MVPA in the 15, 30, or 60 minutes prior to an EMA prompt; prior MVPA minutes were positively associated with subsequent MVPA in the respective time window. In all three models, results showed that children engaged in more MVPA on weekdays compared to weekend days. In addition, children engaged in more MVPA earlier in the day. Age and sex were also associated with MVPA; younger children and males engaged in more MVPA. Self-reported ethnicity (Hispanic/Latino) or having a higher average annual household income were not associated with MVPA. Children’s BMI at the measurement burst was significantly associated with MVPA in the 15, 30, and 60 minutes following an EMA prompt; children with a higher BMI engaged in less subsequent MVPA.

Table 2.

Results of multilevel models predicting children’s physical activity in the 15, 30, and 60 minutes following an EMA prompt

MVPA in the next 15 minutes MVPA in the next 30 minutes MVPA in the next 60 minutes
N N N
Level-1 (Prompts) 11,676 11,365 10,771
Level-2 (People) 190 190 190
Estimate (SE) 95 % CI P Estimate (SE) 95 % CI P Estimate (SE) 95 % CI P
WS stress −0.17 (0.07) [−0.29, −.04] .03 −0.17 (0.07) [−0.28, −0.05] .02 −0.20 (0.06) [−0.30, −0.09] <.01
BS stress 0.08 (0.19) [−0.24, 0.40] .68 −0.01 (0.16) [−0.27, 0.25] .96 −0.10 (0.16) [−0.36, 0.15] .49
Burst −0.02 (0.02) [−0.05, 0.01] .30 −0.02 (0.02) [−0.05, 0.01] .25 −0.02 (0.02) [−0.05, 0.02] .46
Weekend day −0.11 (0.04) [−0.18, −0.04] .01 −0.12 (0.04) [−0.19, −0.06] .003 −0.15 (0.04) [−0.17, −0.05] .01
Time of day −0.12 (0.03) [−0.16, −0.08] <.001 −0.12 (0.03) [−0.16, −0.08] <.001 −0.11 (0.03) [−0.15, −0.07] <.001
Age −0.13 (0.04) [−0.19, −0.07] <.001 −0.14 (0.04) [−0.20, −0.08] <.001 −0.17 (0.04) [−0.24, −0.11] <.001
BMI (kg/m2) −0.02 (0.01) [−0.03, −0.01] .01 −0.02 (0.01) [−0.03, −0.004] .02 −0.02 (0.01) [−0.03, −0.004] .03
Prior activity 0.26 (0.01) [0.24, 0.27] <.001 0.13 (0.01) [0.12, 0.14] <.001 0.06 (0.003) [0.05, 0.06] <.001
Sex (Male) 0.14 (0.06) [0.04, 0.24] .02 0.19 (0.06) [0.10, 0.29] <.01 0.23 (0.06) [0.13, 0.33] <.01
Hispanic/Latino −0.03 (0.06) [−0.14, 0.07] .59 −0.03 (0.06) [−0.13, 0.07] .64 0.01 (0.07) [−0.10, 0.12] .91
Average Income 0.04 (0.03) [−0.01, 0.08] .17 0.04 (0.03) [−0.01, 0.08] .15 0.03 (0.03) [−0.02, 0.07] .36
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Note. WS=within-subjects; BS=between-subjects; BMI=body mass index

Discussion

The current study utilized EMA and accelerometry to examine the within- and between-subject effects of children’s self-reported stress on subsequent MVPA. Results indicated that as expected, when children reported experiencing greater stress than usual, they subsequently engaged in less MVPA over the next 15, 30, and 60 minutes (within-subject effects). On the other hand, the results did not support our hypotheses for between-subject effects; there were no significant between-subject effects of children’s stress on subsequent MVPA. This study improves knowledge on psychological factors that influence physical activity among children and highlights the value in examining the effects of fluctuations in momentary stress on physical activity outcomes.

The current study’s findings are consistent with previously published EMA studies among young adults; higher momentary stress was related to lower self-reported physical activity over the next few hours in a college sample (Schultchen et al., 2019) and greater next day anticipated stress was associated with a 22% decrease in the odds of exercising the next day (Burg et al., 2017). Our within-subject results suggest that intraindividual variations in stress may be a barrier for engagement in physical activity among children. Previous EMA studies have yet to examine the relationship between momentary feelings of stress and perceived stress with subsequent physical activity among children; however, several studies that used EMA assessed the effects of negative affect, which included feelings of stress, on MVPA minutes. Findings among older adults indicated that greater negative affect predicted lower levels of upcoming MVPA minutes (Dunton et al., 2009), whereas null within-subject effects of negative affect on physical activity were found among a sample of children aged 9–13 years old (Dunton et al., 2014) and among the same sample of children in the current study (Wen et al., 2018; Yang et al., 2020). The present study’s significant within-subject findings suggest that stress, a multi-faceted construct comprised of affective and cognitive stress, may play an influential role on physical activity among children, whereas negative affect (e.g., feeling sad, stressed, mad or angry, nervous or anxious) may not (Dunton et al., 2014). When children experience more stress than usual, this may lead to ego depletion, or a decreased availability of self-control and increased fatigue (Baumeister et al., 2007). Childhood obesity prevention programs may benefit by targeting children’s self-control, encouraging children to engage in stress-coping strategies during times of stress, and by aiming to reduce major stressors among children (Hagger et al., 2010). Despite the low “average” levels of momentary stress among the sample, these data are from many occasions across children and during 10–19% of these occasions, children reported experiencing stress (e.g., answering “No” to “I can manage with all of the things I have to do right now” or “Things are working out as I have planned right now”, respectively). The results suggest these are times vulnerable to low physical activity engagement and interventions need to provide stress-coping strategies during these moments. Stress management may be an effective intervention strategy that ensues both physical and mental health benefits.

The null between-subject effects of stress are inconsistent with findings from a previous experimental study among children. However, prior studies did not partition out within- and between-subject associations and thus may conflate the two effects. An experimental study reported significant, inverse relationships between children’s stress and subsequent physical activity. The authors reported that children who had greater interpersonal-induced stress chose to ride a cycle ergometer less and chose sedentary activities (e.g., watching cartoons, playing video games) instead (Roemmich et al., 2003). It is possible that the stress children experience in their everyday lives differs from the feelings of stress that were elicited by preparing and delivering a videotaped speech in the aforementioned study. The experimental study also had one exercise option, whereas children in the current study were asked to follow their usual routine with self-selected exercise types during the study monitoring period. On the other hand, the null-between subject results of the current study are consistent with a longitudinal study among adolescents examining stress and future physical activity (Reynolds et al., 1990). The current study lends increased credibility to these previous findings by boosting ecological validity and reducing recall bias.

In addition to the significant within-subject results, results showed that children engaged in more MVPA on weekdays compared to weekend days. In addition, children engaged in more MVPA earlier in the day. Age and sex were also associated with MVPA; younger children and males engaged in more MVPA. A post-hoc analyses examined whether the associations between children’s stress (within-subject) and subsequent physical activity differed by day of week (i.e., weekend vs. weekday). In all three models (i.e., 15 minutes, 30 minutes, and 60 minutes), the day of week and children’s stress interaction term was not significant (ps > .05). These results indicate day of week did not moderate the association between children’s momentary stress and subsequent physical activity. Future research should continue to explore differences by time-varying factors in other samples of children given the previous literature examining day of week as an effect modifier of children’s physical activity (Hart et al., 2011; de Brito et al., 2020). Additionally, future research can examine the potential moderating role of environmental (i.e., physical and social) contexts on the within-day associations of momentary stress and physical activity among children. Past studies have utilized EMA to collect additional information on physical and social contexts in which children experience greater stress than usual, such as with certain types of people (e.g., parents, friends) or in particular physical settings (e.g., home, friends’ homes, after-school program, parks; Dunton, et al., 2014). Moreover, elucidating the situations or triggers (e.g., physical or social context, responsibilities, experiences, interpersonal conflicts, daily hassles) that cause children stress may lead to future intervention targets.

Understanding what specific stressors or contexts that result in elevated momentary stress, or what may deplete self-control, can provide further insight into the stress-physical activity relationship. Real-time data capture methodologies, such as context-sensitive EMA, can automatically prompt questions or reminders based on an individual’s activities, interactions, location, or previous self-reported stress (Shiffman et al., 2008). Future mobile interventions, such as just-in-time adaptive interventions (JITAIs), could provide children the right amount and type of behavioral support (e.g., mindfulness-based stress reduction skills, encourage positive self-talk, breathing exercises, suggestions for calming activities) during moments of stress and can even be tailored to the type of situation (e.g., location, social context, stressor type) that children are in (Nahum-Shani et al., 2018). Furthermore, the content, timing, and intensity (i.e., dose) of behavioral support can be tailored based on the level of momentary stress or the environment (Hardeman et al., 2019). Mobile interventions could facilitate healthy stress-coping techniques or encourage physical activity immediately after reports of stress or when an individual is in a context vulnerable to increased stress, such as after-school when children have increased demands (e.g., homework, chores, extracurricular activities).

The study had several strengths, including the assessment of multiple components of stress through EMA, objectively-measured physical activity through accelerometry, and longitudinal data across three years. However, there were limitations that should be noted. We are unable to infer causality between stress and physical activity due to the current study’s methodology. In addition, there may be acute bi-directional relationships between stress and physical activity that were not examined (Stults-Kolehmainen & Sinha, 2014). It is also possible that there were instances where only partial activity bouts were captured, such that an EMA prompt occurred in the middle activity and only the activity occurring after the prompt was assessed. To account for physical activity prior to an EMA prompt, MVPA in the 15, 30, or 60 minutes before the EMA prompt was controlled for in each model. Children were asked to remove the accelerometer when in the water, therefore physical activity that occurred in water was not captured (e.g. swimming, water sports); the accelerometers were also not sensitive to cycling. However, data collection occurred during non-summer months when children would be less likely to engage in water-based activities. Future studies may benefit by utilizing wearable sensors that are water-proof and sensitive to activities like cycling. While one of the strengths of the current study was the ability to assess multiple components of stress through EMA, future studies may benefit by including additional items to capture other facets of stress (e.g., stressful life events, perceived helplessness, duration, severity) (Tate et al., 2015; Schneiderman et al., 2008). In order to minimize participant burden, adapting a limited number of brief items is suggested. In addition, the current study did not assess self-control directly; future studies may benefit by further exploring the ego depletion model in children’s physical activity by assessing both momentary stress and self-control. Lastly, the study findings may not be generalizable to children in other age ranges, who experience different developmental changes that influence emotion regulation (Zimmerman & Iwanski, 2014) or those from different racial, ethnic, and socioeconomic backgrounds who may face different stressors or barriers to physical activity (e.g., access to affordable, safe, and inclusive activities, structural racism, discrimination) (McNeill et al., 2006). The findings may also not be generalizable to children in different geographic locations, given that characteristics of the built environment (e.g., parks and recreation access, walkability, greenness, climate) may have an influential role on children’s physical activity levels (Kaczynski & Henderson, 2007). Future research can integrate global position systems (GPS) or Geographic Information Systems (GIS) with accelerometry and EMA to assess physical environment exposures (Yi et al., 2019).

Conclusion

Findings contribute to the literature on the complex relationship between stress and physical activity in children. The results suggest that children’s momentary stress predicted less MVPA in the following 15, 30, and 60 minutes. Addressing children’s momentary stress in free-living environments may be an important component of successful physical activity promotion efforts. Study results propose that effective physical activity promotion and interventions for children should incorporate stress-coping strategies and resources. Future research should examine the effectiveness of utilizing mobile technology to deliver stress-coping strategies and physical activity promoting messages to children in real-time. Findings of this study provide a new understanding on the role of stress on physical among children in naturalistic settings. Childhood physical activity promotion programs should incorporate momentary stress reduction strategies to have beneficial effects on children’s overall physical activity.

Highlights.

  • Momentary variations in stress predicted children’s subsequent physical activity

  • When children reported more stress than usual, they did less physical activity

  • Between-subjects stress was not associated with physical activity

  • Momentary stress may have an immediate and acute impact on physical activity

Acknowledgements

This work was supported by the National Institutes of Health/National Heart Lung and Blood Institute [grant number R01HL119255] American Cancer Society (118283-MRSGT-10-012-01CPPB).

Footnotes

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Conflicts of Interests

Declaration of interests: none.

1

EMA = ecological momentary assessment

2

MVPA = moderate-to-vigorous physical activity

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