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Journal of Pediatric Psychology logoLink to Journal of Pediatric Psychology
. 2020 Dec 14;46(4):443–453. doi: 10.1093/jpepsy/jsaa121

Bi-Directional Associations Between Real-Time Affect and Physical Activity in Weight-Discordant Siblings

Kathryn E Smith 1,✉, Tyler B Mason 2, Shannon M O’Connor 3, Shirlene Wang 2, Eldin Dzubur 4, Ross D Crosby 5,6, Stephen A Wonderlich 5,6, Sarah-Jeanne Salvy 7, Denise M Feda 8, James N Roemmich 9
PMCID: PMC8056211  PMID: 33313910

Abstract

 

Pediatric obesity confers increased risk for a host of negative psychological and physical health consequences and is reliably linked to low levels of physical activity. Affective antecedents and consequences of physical activity are thought to be important for the development and maintenance of such behavior, though research examining these associations in youth across the weight spectrum remains limited.

Objective 

This study examined bi-directional associations between affect and physical activity (i.e., moderate-to-vigorous physical activity [MVPA] and total activity counts), and the extent to which weight (body mass index z-score [z-BMI]) moderated these associations.

Methods 

Participants were drawn from a prior study of siblings (N = 77; mean age = 15.4 ± 1.4 years) discordant for weight status (39 nonoverweight siblings, 38 siblings with overweight/obesity) who completed ecological momentary assessment (EMA) with accelerometer-assessed physical activity.

Results 

Generalized linear mixed models indicated z-BMI moderated trait-level and momentary associations. When adolescents with higher z-BMI reported momentary negative affect, they evidenced less MVPA within the next hour. Across the sample, greater overall activity was associated with lower negative affect. However, at the momentary level, when adolescents with higher (but not lower) z-BMI evidenced greater activity, they reported decreases in negative affect.

Conclusions 

Findings indicate affective experiences surrounding physical activity differ according to z-BMI. Specifically, momentary negative affect may impede momentary MVPA among youth with higher z-BMI. Further research is warranted to elucidate factors influencing these momentary associations and the extent to which these momentary associations prospectively predict weight change.

Keywords: health behavior, health promotion and prevention, obesity and weight management, psychosocial functioning, stress, weight management

Introduction

Pediatric obesity is a critical global public health problem (Wang and Lim, 2012). Left untreated, childhood obesity often extends into adulthood (Ward et al., 2017) and is associated with chronic disease and early mortality (Cecchini et al., 2010). One of the key behavioral preventions and interventions for pediatric obesity is increasing physical activity and reducing sedentary time (McCambridge et al., 2006). However, most children do not meet recommended guidelines for daily moderate-to-vigorous physical activity (MVPA; e.g., Roman-Viñas et al., 2016). Because physical activity interventions for youth with overweight and obesity have limited success (Nooijen et al., 2017), it is important to discern relevant factors that could be targeted in obesity interventions for youth.

When elucidating targets for physical activity interventions, both momentary antecedents that predict the occurrence of physical activity across the day and the acute consequences of physical activity, which may maintain physical activity, should be considered. To this end, ecological momentary assessment (EMA) and accelerometry are useful methodologies (Dunton, 2017). EMA involves completion of short surveys of mood, behavior, and experiences multiple times across the day for a short period of time, whereas accelerometry continuously collects objective data on all physical activity across the day. EMA and accelerometry have advantages over other relevant methods (e.g., laboratory methods) in that assessment occurs in real-time in the natural environment, which maximizes ecological validity. Moreover, EMA is strong in its ability to examine factors that fluctuate greatly over a short period of time—one such factor is affect.

Recent health behavior theories have highlighted the role of momentary affect in the adoption and maintenance of various health behaviors, including physical activity (Dunton et al., 2020; Williams et al., 2019). Theory and empirical evidence suggest bidirectional associations between affect and health behaviors, such that affective states can precipitate health behaviors and health behaviors cause specific affective responses. For instance, research has provided support for resource depletion models, which suggest that negative affect impedes health-promoting behaviors by causing physical and mental fatigue and reducing self-regulatory capacity (Mill et al., 2016; Tice et al., 2001). In contrast, the Broaden and Build Theory suggests positive affect may bolster health-promoting behaviors by broadening individuals’ attentional span, and can promote engagement in behaviors that increase and maintain positive affect (Fredrickson, 1998). Furthermore, theories posit that physical activity causes meaningful changes in negative and positive affect. Studies suggest that activity can serve to decrease negative affect and increase positive affect by distracting, relaxing, and building self-confidence (Ha et al., 2016; Petruzzello et al., 1991; Russell et al., 2003).

However, EMA and accelerometry research examining associations between affect and MVPA among children has not consistently mirrored findings in adults. A pilot study of 26 adolescents (ages 13–18) showed that higher momentary negative affect (particularly depression and anger) and lower momentary positive affect measured via EMA predicted decreases MVPA in the next 30 min; in turn, greater MVPA within the 30 min before EMA prompts was associated with higher positive affect (Cushing et al., 2017, 2018). In a larger study of 119 children (ages 9–13) across the weight spectrum, feeling energetic and less tired predicted greater engagement in MVPA in the 30 min following EMA prompts, but momentary positive or negative affect scores did not predict subsequent MVPA (Dunton et al., 2014). In addition, MVPA in the 30 min before the EMA prompt predicted higher subsequent positive affect, feeling energetic, and lower negative affect. A separate study of 8- to 12-year-old children across the weight spectrum also found that momentary positive and negative affect did not predict subsequent MVPA in the 30 or 60 min following EMA prompts, but higher MVPA was followed by increased positive affect (Wen et al., 2018). Last, in a recent study of 8- to 14-year-old youth with overweight or obesity, higher positive affect and lower negative affect predicted greater subsequent physical activity in the 30, 60, and 120 min following EMA prompts; in addition, higher positive affect predicted more subsequent physical activity (Smith et al., 2020).

In sum, while there has been a more consistent trend for physical activity to be followed by positive affect in youth, differences have emerged with regard to affect as a precipitant of physical activity, with only one study finding positive and negative affect to predict MVPA specifically among children with overweight or obesity (Smith et al., 2020). Thus, relationships between affective states and activity may differ as a function of weight status, which is in line with research in adults showing variable relationships between affective states and physical activity ( Unick et al., 2012, 2015). Physical activity levels and physical activity self-efficacy (i.e., the belief that one has the ability to successfully engage in physical activity behaviors) are also lower among children and adolescents who have overweight and obesity, and therefore physical activity may be less normative in this population (Hills et al., 2011; Trost et al., 2001). Consequently, engaging in physical activity may require more deliberate effort and/or may be more susceptible to the influences of aversive emotional states among youth with higher weight. In turn, it is possible that compared with youth with normal weight, the affect regulating effects of physical activity are attenuated in this population given potential physical discomfort and/or appearance concerns.

Given the mixed findings and limited research in this area, the current study utilized EMA and accelerometry in a sample of adolescent siblings discordant for overweight/obesity to examine weight as moderator of bi-directional associations between affect and physical activity. Discordant sibling designs account for shared environmental influences of family. Given that evidence suggests emotional functioning and physical activity are influenced by environmental factors (e.g., McRae et al., 2017; Nelson et al., 2006), this design is particularly useful to examine non-shared environmental effects among discordant siblings raised in the same family. If associations between affect and physical activity are important factors in the development and/or maintenance of obesity, independent of other familial influences, we may expect differing strengths of associations between siblings with lower versus higher weight.

Drawing on prior research in youth (Dunton et al., 2014; Smith et al., 2020; Wen et al., 2018), it was hypothesized that higher negative and lower positive affect would predict less subsequent physical activity among siblings with higher weight (body mass index z-score [z-BMI]), whose physical activity levels may be more susceptible to effects of mood; conversely, affect would not be strongly associated with subsequent activity among siblings with lower weight. In turn, it was expected that siblings with lower weight would report higher positive affect and lower negative affect following physical activity compared with siblings of higher weight, who may experience attenuated affective benefits of physical activity. Although this study focused primarily on momentary affect–activity relations, both within-person (momentary) and between-person (trait-level) effects were included in models.

Materials and Methods

Participants

Participants were adolescents who were drawn from larger study of 40 same-sex biological sibling pairs (N = 80; age 13–17, no more than 4 years apart). Siblings were discordant on weight status (i.e., one sibling was above the 85th BMI percentile and the other sibling was below the 70th BMI percentile). Recruitment information and details regarding the full study protocol have been described previously (see Salvy et al., 2017). Parents and participants provided informed consent and assent, respectively. The Institutional Review Board at the University of Buffalo approved all study procedures.

Procedures

Participants completed a study visit during which they completed a series of measures and laboratory assessments, as well as training on the EMA protocol. The EMA protocol took place over the course of 7 consecutive days (5 weekdays and 2 weekend days) and was administered using a study-provided cell phone that delivered text messages to participants. Participants received text messages approximately every 2 h between 3:00 p.m. and 9:00 p.m. and between 10:00 a.m. and 10:00 p.m. on weekend days. A total of 7 texts were sent on weekend days and 4 texts were sent on weekdays, for a total possible 34 prompts over the course of the protocol. Throughout the EMA protocol, participants also wore a waist-worn accelerometer (MTI Actigraph; Pensacola, FL) and received instructions on use, including appropriate care and placement. Participants were instructed to wear the Actigraph at least 10 h each day for the day to be included in analyses. If there were issues with a participant’s responses (e.g., missing or unclear response), study staff contacted them the next working day to clarify.

Measures

BMI z-Score

Participants’ height and weight were measured with an electronic scale (Model BWB-800S, Tanita, Portage, MI) and digital stadiometer (Model PE-AIM-101, Perspective Enterprises). These measurements were used to calculate BMI (kg/m2) and z-scores using the Center for Disease Control and Prevention growth charts (Kuczmarski et al., 2000).

EMA-Measured Affect

At each EMA survey participants were asked to report on their current affective states. Specifically, the responded (yes/no) if they were currently feeling happy, sad, frustrated, angry, and/or stress (coded as 0 [no] or 1 [yes] for each response). Responses for negative affect states (i.e., sad, frustrated, angry, and stress) were averaged at each signal to reflect the overall likelihood of endorsing negative affect. The reliability of the composite negative affect score was assessed using the two-level composite omega reliability coefficient, which is appropriate for clustered multilevel data (Geldhof et al, 2014). Reliability was excellent at the between-person level (ω = .92) and adequate at the within-person level (ω = .65).

Physical Activity

Physical activity assessed by the waist-worn Actigraph was operationalized as total activity counts (i.e., an index of average total physical activity) and minutes of MVPA. The Actigraph is a well-validated measure of objective physical activity in adolescents (Romanzini et al., 2014). The Actigraph was initialized to sample at 10 Hz for 15-s epochs, and thresholds for activity were consistent with national surveillance data (Belcher et al., 2010; Troiano et al., 2008). Age-specific thresholds for children’s activity levels were adjusted by applying the Freedson prediction equation (Freedson et al., 2005; Harrell et al., 2005). Nonwear time (>60 continuous minutes of zero activity counts) and nonvalid days (<10 h of wear time) were identified; all nonwear time and nonvalid days were removed from analyses. The total duration of MVPA/day (MVPA ≥3 metabolic equivalents [METs]) was also assessed for descriptive purposes. Downloaded data were further processed using R to create time windows of activity counts and MVPA minutes surrounding EMA prompts. Accumulated minutes of MVPA and activity counts were derived from 60-min time windows before and after all EMA prompts. The selection of 60-min windows was based on recent EMA research in youth that has found effects of activity on affect and eating behaviors using similar length time windows (Smith et al., 2020; Wen et al., 2018). Only windows with at least two-thirds valid wear time (40 min) were included in analyses, which resulted in 74.7% and 73.8% of the 60-min windows before and after prompts (respectively).

Statistical Analyses

EMA responses were time-matched to Actigraph-measured activity counts and MVPA minutes ±60 min. Resemblance within sibling pairs in the independent and dependent variables was examined using intraclass correlation coefficients (ICCs). Consistent with prior research (Ufholz et al., 2019), ICC values were interpreted as no family similarity for values ranging .00–.10, slight similarity for scores ranging .11–.40, fair amounts of similarity for scores ranging .41–.60, moderate similarity for scores ranging .61–.80, and high similarity for amounts exceeding .81.

To examine affective antecedents of physical activity, separate generalized linear mixed models (GLMMs) examined EMA-measured positive and negative affective states (i.e., likelihood of endorsing happiness [positive affect] or frustrated, angry, and stress [negative affect]), z-BMI (continuous variable), and their interactions as predictors of MVPA minutes and activity counts in the 60 min following EMA recordings. To examine affective consequences of physical activity, separate GLMMs examined physical activity levels (i.e., MVPA minutes and activity counts) in the 60 min prior to EMA recordings, z-BMI, and their interactions as predictors of EMA-measured positive and negative affective states.

In each GLMM, siblings were nested within family, and the effects of independent variables (i.e., physical activity variables or affective states) were separated into within-person (i.e., person-mean centered) and between-person (i.e., grand-mean centered) components. That is, within-person associations indicate the degree to which changes in the independent variable (e.g., total activity counts or MVPA minutes prior to EMA recordings), relative to an individual’s own mean, are related to the dependent variable (e.g., likelihood of reporting happiness), whereas between-person associations reflect the degree to which an individual’s average level of physical activity (e.g., average MVPA minutes before or after EMA prompts) across the EMA protocol, relative to other individuals, is associated with overall affect (e.g., total instances of feeling happy). Age and gender were included as covariates in all models. Preliminary analyses also examined the extent to which dependent variables changed during the study; to do so, GLMMs examined time in hours (measured continuously since the first EMA signal) as a univariate predictor of each dependent variable. Time did not emerge as a significant predictor of any dependent variable (ps =.257–.932), indicating that there were no systematic increases or decreases in these variables over the course of EMA; thus, time was not included as a covariate.

Given that the sample involved multiple observations from siblings nested within families, each GLMM specified Level 1 (observation), Level 2 (person), and Level 3 (family) variables, and included a random effect of family to account for differences in dependent variables across families. GLMMs also specified an AR1 serial autocorrelation to account for dependence within the nested data. For models examining MVPA and activity counts as dependent variables, gamma link functions were used to account for skewed distributions of these variables. When gamma link functions were used for continuous variables with potential zero values (i.e., composite negative affect, activity, and MVPA) a constant value (.001) was added to ensure that only nonzero values were available. For models examining happiness as the dependent variable, binary logistic functions were used given the dichotomous nature of this variable; for models assessing negative affect (i.e., mean of stress, sadness, anger, and frustrated endorsements) gamma link functions were specified. The conditional R2 statistic was calculated, which reflects variance explained by random and fixed effects for GLMMs (Nakagawa & Schielzeth, 2013; Nakagawa et al., 2017). GLMMs were conducted using SPSS version 25, and conditional R2 values were calculated using R version 4.0.2.

Results

Descriptive Data

This study excluded three participants due to insufficient accelerometer data to calculate activity windows surrounding EMA prompts, resulting in a final analytic sample of 77 (mean age = 15.4 ± 1.4 years; 41.6% female; 92.5% White). Descriptive information (e.g., age, BMI) for the final analytic sample is shown in Table I. Each participant reported an average of 32.3 ± 4.1 (range: 7–34) EMA recordings during the study, with an average compliance rate of 95.0% across participants. Spearman correlations showed that participants’ compliance rates were not strongly associated with age or z-BMI (age: ρ = 0.03; BMI: ρ = −0.02), and an independent t-test showed compliance rates did not differ significantly by gender (t [74] = .041, p = .967). Across the sample, 134 days were excluded due to nonwear time. The average Actigraph daily wear time was 13.8 ± 1.3 h; each participant included in analyses had an average of 5.1 ± 1.9 (range: 1–7) valid days of accelerometer data (out of a possible 7 days). Independent samples t-tests indicated participants with and without overweight/obesity did not differ significantly with respect to the number of EMA signals completed, average daily Actigraph wear time, total daily MVPA minutes, total activity counts and MVPA minutes within 60-min windows surrounding EMA prompts (i.e., averaged across EMA), or the total number of endorsements of each type of affective state (ps = .130–.916). ICC values revealed no to slight familial resemblance for positive affect (ICC = .14), negative affect (ICC = .14), mean MVPA minutes prior to EMA prompts (ICC = .04), and mean activity counts prior to EMA prompts (ICC = .11).

Table I.

Descriptive Statistics

Total (N = 77)
 Without overweight/obesity (N = 39)
 With overweight/obesity (N = 38)
M SD M SD M SD
Completed EMA recordings 32.29 4.10 32.47 3.36 32.11 4.761
Age 15.36 1.43 15.64 1.42 15.06 1.39
z-BMI 0.78 0.89 0.03 0.52 1.56 0.35
Daily MVPA time (minutes) 23.38 18.34 24.59 20.49 22.07 15.88
MVPA minutes before EMA 1.42 1.33 1.28 1.30 1.58 1.37
MVPA minutes after EMA 1.15 1.33 1.11 1.42 1.19 1.24
Activity counts before EMA 2,0231.65 8,504.27 19,305.28 9,378.46 21,208.11 7,478.01
Activity counts after EMA 17,829.70 8,553.89 17,424.76 9,540.44 18,256.52 7,482.90
Happy EMA instances 28.65 8.01 29.85 7.89 27.42 8.05
Sad EMA instances 0.92 1.79 0.62 1.04 1.24 2.29
Angry EMA instances 1.38 2.06 1.21 1.94 1.55 2.19
Frustrated EMA instances 3.04 4.26 2.31 3.23 3.79 5.04
Stressed EMA instances 3.90 5.26 3.67 4.49 4.13 6.00
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Note. MVPA = moderate-to-vigorous physical activity (minutes); EMA = ecological momentary assessment; z-BMI = body mass index z-score; before = measured within 60 min prior to EMA prompts; after = measured within 60 min after EMA prompts. EMA variables were aggregated within persons.

Affect Predicting Subsequent Physical Activity

Tables II and III show results of GLMMs examining affective states, z-BMI, and their interactions as predictors of MVPA minutes and total activity counts within the 60 min after EMA prompts. Age and gender were associated with physical activity levels, such that younger participants reported greater total activity counts and MVPA minutes (ps: <.001–.005) compared with older participants, and girls reported greater total activity counts (ps = .021–.050) but less MVPA minutes (p = .021) relative to boys. There was a significant interaction of z-BMI and within-person negative affect predicting MVPA minutes in the hour after EMA prompts (B = −0.66, SE = 0.25, p = .007).When participants with higher z-BMI endorsed more negative affective states than their usual level, they evidenced decreased subsequent MVPA minutes in the next hour (Figure 1). In other words, at moments when participants with z-BMI one SD above the sample mean experienced high negative affect (1 SD above their own mean), they accumulated 0.59 min less MVPA than when the same person had low negative affect (1 SD below their own mean). No other significant predictors of total activity counts or MVPA minutes emerged. Conditional R2 values indicated GLMMs accounted for 11.25% to 13.52% of variance in MVPA and activity counts.

Table II.

Positive Affect (Happy) and Interactions With z-BMI Predicting Activity Counts and MVPA Minutes 60 Min After EMA Recordings

Total activity counts
 MVPA minutes
95% CI
 95% CI
B SE t p Lower Upper B SE t p Lower Upper
Intercept 2.98 0.05 65.44 <.001 2.89 3.07 0.75 0.08 9.13 <.001 0.59 0.92
Gender 0.15 0.07 2.15 .032 0.01 0.29 −0.24 0.13 −1.87 .061 −0.49 0.01
Age −0.06 0.02 −2.80 .005 −0.10 −0.02 −0.13 0.03 −3.80 <.001 −0.20 −0.06
z-BMI 0.02 0.03 0.62 .535 −0.05 0.09 −0.01 0.05 −0.27 .784 −0.12 0.09
Happy (between) −0.09 0.24 −0.38 .703 −0.57 0.39 −0.57 0.41 −1.40 .163 −1.38 0.23
Happy (within) −0.01 0.05 −0.21 .833 −0.10 0.08 0.04 0.11 0.38 .706 −0.18 0.27
Happy (between) × z-BMI −0.09 0.30 −0.31 .759 −0.67 0.49 0.59 0.47 1.26 .209 −0.33 1.52
Happy (within) × z-BMI 0.07 0.05 1.41 .160 −0.03 0.18 0.14 0.13 1.02 .309 −0.13 0.40
B SE Z p Lower Upper B SE Z p Lower Upper
Random intercept (family) 0.02 0.01 1.15 .249 0.00 0.08 0.08 0.04 1.97 .049 0.03 0.22
Random intercept (participant) 0.04 0.01 2.59 .010 0.02 0.08 0.05 0.04 1.28 .202 0.01 0.22
Conditional R2 0.1352 0.1125
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Note. MVPA = moderate-to-vigorous physical activity (minutes); EMA = ecological momentary assessment; z-BMI = body mass index z-score; between=grand-mean centered variable; within = person-mean centered variable. Gender was coded such that boys were the reference category. Bolded values indicate significant effects at p < .05.

Table III.

Negative Affect and Interactions With z-BMI Predicting Activity Counts and MVPA Minutes 60 Min After EMA Recordings

Total activity counts
 MVPA minutes
95% CI
 95% CI
B SE t p Lower Upper B SE t p Lower Upper
Intercept 2.98 0.05 64.60 <.001 2.89 3.07 0.77 0.08 9.41 <.001 0.61 0.93
Gender 0.14 0.07 1.96 .050 0.00 0.29 −0.30 0.13 −2.30 .021 −0.55 −0.04
Age −0.06 0.02 −2.93 .003 −0.11 −0.02 −0.13 0.03 −3.81 <.001 −0.20 −0.06
z-BMI 0.02 0.03 0.45 .653 −0.05 0.08 −0.03 0.05 −0.52 .604 −0.13 0.08
Negative affect (between) 0.20 0.37 0.54 .590 −0.52 0.92 1.06 0.60 1.76 .079 −0.12 2.23
Negative affect (within) −0.03 0.08 −0.36 .722 −0.19 0.13 −0.31 0.20 −1.59 .113 −0.69 0.07
Negative affect (between) × z-BMI −0.36 0.49 −0.72 .471 −1.33 0.61 −0.91 0.78 −1.17 .243 −2.44 0.62
Negative affect (within) × z-BMI −0.07 0.10 −0.73 .466 −0.27 0.12 −0.66 0.25 −2.69 .007 −1.14 −0.18
B SE Z p Lower Upper B SE Z p Lower Upper
Random intercept (family) 0.02 0.01 1.23 .220 0.00 0.08 0.08 0.04 1.92 .054 0.03 0.21
Random intercept (participant) 0.04 0.01 2.57 .010 0.02 0.08 0.05 0.04 1.35 .178 0.01 0.21
Conditional R2 0.1326 0.1136
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Note. MVPA = moderate-to-vigorous physical activity (minutes); EMA = ecological momentary assessment; z-BMI = body mass index z-score; between = grand-mean centered variable; within = person-mean centered variable. Gender was coded such that boys were the reference category. Bolded values indicate significant effects at p < .05.

Figure 1.

Figure 1.

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Interaction of within-person negative affect (relative to one’s own average) and body mass index z-score (relative to other individuals) predicting moderate-to-vigorous physical activity time within 60 min following EMA recordings. High and low values reflect 1 SD above/below individual and sample means.

Physical Activity Predicting Subsequent Affect

Tables IV and V display results of GLMMs assessing MVPA minutes and total activity counts in the 60 min prior to EMA prompts as predictors of affective states. Girls reported more negative affect relative to boys (ps = .001–.003), though there were no other main effects of gender or age on affect. There was a significant between-person effect of activity counts predicting negative affect (B = −0.09, SE = 0.04, p = .013), indicating participants who evidenced less activity counts across the EMA protocol reported greater overall negative affect. There was a significant interaction of z-BMI and within-person activity counts predicting negative affect in the hour after EMA prompts overall happiness (B = −0.02, SE = 0.01, p = .010). As shown in Figure 2, when participants with higher z-BMI (1 SD above the sample mean) evidenced greater activity counts (1 SD above their own mean), they reported lower negative affect (i.e., decrease of 0.06) compared with when they evidenced less activity counts (1 SD below their own mean). Conversely when participants with lower z-BMI (1 SD below the sample mean) evidenced greater activity counts (1 SD above their own mean), they reported greater negative affect (i.e., increase of 0.003) compared with when they evidenced lower activity counts (1 SD below their own mean). No other significant predictors of affective states emerged. Conditional R2 values indicated GLMMs accounted for 35.38% to 51.81% of variance in affective states.

Table IV.

Activity Counts 60 Min Before EMA Recordings and Interactions With z-BMI Predicting Affective States

Happy
 Negative Affect
95% CI
 95% CI
 95% CI
Activity counts B SE t p Lower Upper Exp Lower Upper B SE t p Lower Upper
Intercept 3.07 0.36 8.64 <.001 2.37 3.77 21.57 10.74 43.33 −4.70 0.36 −13.15 <.001 −5.40 −4.00
Gender −0.53 0.54 −0.99 .322 −1.59 0.52 0.59 0.20 1.69 1.83 0.56 3.29 .001 0.74 2.93
Age −0.04 0.16 −0.29 .775 −0.35 0.26 0.96 0.70 1.30 −0.01 0.13 −0.05 .959 −0.27 0.25
z-BMI −0.15 0.23 −0.66 .507 −0.59 0.29 0.86 0.55 1.34 0.05 0.19 0.29 .773 −0.31 0.42
Activity (between) 0.02 0.04 0.42 .673 −0.07 0.10 1.02 0.94 1.11 −0.09 0.04 −2.48 .013 −0.17 −0.02
Activity (within) −0.01 0.01 −1.02 .308 −0.02 0.01 0.99 0.98 1.01 <−0.01 0.01 −0.28 .777 −0.01 0.01
Activity (between) × z-BMI −0.03 0.05 −0.65 .514 −0.13 0.06 0.97 0.88 1.07 −0.02 0.04 −0.47 .637 −0.10 0.06
Activity (within) × z-BMI <0.01 0.01 −0.63 .531 −0.02 0.01 1.00 0.98 1.01 −0.02 0.01 −2.59 .010 −0.03 0.00
B SE Z p Lower Upper B SE Z p Lower Upper
Random intercept (family) 1.28 0.75 1.71 .087 0.41 4.02 2.01 0.71 2.83 .005 1.01 4.03
Random intercept (participant) 1.69 0.59 2.87 .004 0.85 3.34 1.34 0.43 3.08 .002 0.71 2.53
Conditional R2 0.5181 0.3538
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Note. EMA = ecological momentary assessment; z-BMI = body mass index z-score; between = grand-mean centered variable; within = person-mean centered variable; Exp = odds ratio. Gender was coded such that boys were the reference category. Bolded values indicate significant effects at p < .05.

Table V.

MVPA Time 60 min Before EMA Recordings and Interactions With z-BMI Predicting Affective States

Happy
 Negative Affect
95% CI
 95% CI
 95% CI
B SE t p Lower Upper Exp Lower Upper B SE t p Lower Upper
Intercept 2.96 0.37 8.02 <.001 2.24 3.68 19.31 9.36 39.84 −4.62 0.37 −12.61 <.001 −5.34 −3.90
Gender −0.39 0.57 −0.68 .498 −1.50 0.73 0.68 0.22 2.07 1.73 0.57 3.03 .003 0.61 2.85
Age −0.07 0.16 −0.43 .670 −0.37 0.24 0.94 0.69 1.27 0.12 0.14 0.85 .396 −0.15 0.39
z-BMI −0.12 0.21 −0.58 .563 −0.54 0.30 0.88 0.58 1.34 0.01 0.19 0.05 .960 −0.36 0.38
MVPA (between) 0.05 0.19 0.27 .783 −0.32 0.43 1.05 0.72 1.54 0.10 0.16 0.61 .539 −0.22 0.42
MVPA (within) −0.03 0.02 −1.56 .119 −0.07 0.01 0.97 0.94 1.01 −0.01 0.02 −0.33 .741 −0.04 0.03
MVPA (between) × z-BMI 0.24 0.21 1.14 .255 −0.18 0.67 1.28 0.84 1.95 −0.09 0.20 −0.44 .661 −0.47 0.30
MVPA (within) × z-BMI −0.02 0.02 −0.79 .429 −0.06 0.02 0.98 0.95 1.02 −0.04 0.02 −1.81 .071 −0.08 0.00
B SE Z p Lower Upper B SE Z p Lower Upper
Random intercept (family) 1.60 0.81 1.97 .049 0.59 4.33 2.08 0.74 2.81 .005 1.04 4.19
Random intercept (participant) 1.45 0.55 2.65 .008 0.69 3.03 1.38 0.46 3.00 .003 0.72 2.65
Conditional R2 0.5092 0.3604
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Note. MVPA = moderate-to-vigorous physical activity (minutes); EMA = ecological momentary assessment; z-BMI = body mass index z-score; between = grand-mean centered variable; within = person-mean centered variable; Exp = odds ratio. Gender was coded such that boys were the reference category. Bolded values indicate significant effects at p < .05.

Figure 2.

Figure 2.

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Interaction of within-person activity counts (relative to one’s own average) and body mass index z-score (relative to other individuals) predicting negative affect within 60 min following ecological momentary assessment recordings. High and low values reflect 1 SD above/below individual and sample means.

Discussion

This study utilized a discordant sibling design to examine the reciprocal relationships between physical activity and affect among adolescents across the weight spectrum, as well as explored the degree to which weight moderates these relationships. Consistent with prior research in adolescents (Cushing et al., 2017, 2018), there was meaningful heterogeneity in the relationships between affect and physical activity, with present results demonstrating that z-BMI moderated some of these associations. Momentary negative affect (i.e., composite endorsement of feeling sad, stressed, frustrated, and/or angry) had an impeding effect on subsequent MVPA among youth with overweight/obesity. Conversely, when these youth evidenced greater overall lifestyle activity they endorsed less subsequent negative affect. In addition to moderating within-person effects, lower overall activity (i.e., total activity counts) was related to greater negative affect.

Together these findings lend further nuance to the growing literature suggesting that momentary affective correlates of physical activity may differ across the lifespan as well as weight spectrum. Furthermore, the discordant sibling design and analytic approach suggest that associations between affect and physical activity may be important mechanisms underlying the onset and maintenance of pediatric obesity, independent of shared environmental influences of family. The pattern of findings among adolescents with overweight/obesity indicates the experience of momentary negative affect has a detrimental momentary effect on subsequent physical activity in this population. Although this is largely consistent with research in adults indicating negative trait-level associations between stress and physical activity (Stults-Kolehmainen & Sinha, 2014), and negative momentary associations between stress and physical activity (Almeida et al., 2020; Schultchen et al., 2019), it is notable that momentary effects have not been consistently observed in prior research on children and adolescents across the weight spectrum (Dunton et al., 2014). This study identified weight status as a potential source of previously documented variability in relationships between affect and physical activity (Cushing et al., 2017, 2018), such that physical activity engagement in youth with overweight/obesity may be susceptible to worsening affective states and/or emotion regulation problems. Furthermore, results suggested that overall lifestyle activity (rather than MVPA) has a regulating effect on negative affect among youth with higher z-BMI. However, the opposite effect was observed among youth with lower z-BMI. Youth with overweight/obesity may experience light activity more pleasurable than more intense activities. In line with research in adults (e.g., Bond et al., 2014), breaking up periods of sedentary time with light activity could be particularly useful to harness in future obesity prevention and intervention efforts.

Last, it is notable that there were no momentary effects of physical activity on subsequent positive affect (i.e., happy). This was somewhat unexpected given that prior EMA work in children and adolescents has demonstrated such associations (Dunton et al., 2014; Smith et al., 2020; Wen et al., 2018). It may be that the current study did not sufficiently capture all aspects of positive affect that are likely to predict or be followed by physical activity (e.g., calmness, alert, relaxed). In light of the relatively low overall levels of MVPA observed across the sample (i.e., an average of <25 min/day), it is also possible that longer durations of more intensive activity are required to exert regulating effects on affective states.

Though this study has particular strength in the use of naturalistic assessment methodology, limitations are important to note. A relatively limited number of affective states were assessed, with only one positively valenced affective state (i.e., happy). In addition, each affective state was assessed as a dichotomous variable, which may have limited variability and the ability to detect effects. Thus, other validated dimensional measures of momentary affect that capture a broader range of affective experiences and intensities would be useful to consider in future work (e.g., Positive and Negative Affect Schedule; Watson et al., 1988). Despite high overall EMA compliance, lapse time to complete EMA prompts was also not recorded. It is also important to note that 134 days were excluded for nonwear time, which highlights the need to better understand potential reasons for nonwear and enhance compliance in future studies. The magnitude of effects was small, and thus further research and replication are warranted to determine their practical significance. The sample consisted of mostly Caucasian adolescents, all of whom were age 13 or older; consequently, the present findings may not generalize to younger children or other demographic groups. Although obesity is increasing among adolescents of all socio-demographic backgrounds, this is an important issue given that epidemiological studies consistently show the incidence of pediatric obesity is higher among racial and ethnic minorities groups, particularly Black and Hispanic youth (Cheung et al., 2016). As such, further research is necessary to examine whether the observed effects are consistent and/or potentially magnified within these populations.

In conclusion, this study indicates that naturalistically assessed affective experiences related to physical activity meaningfully differ between adolescents with and without overweight/obesity. Further, the pattern of findings has important implications for research and intervention efforts moving forward. In particular, more work is needed to understand the psychological, social, and environmental factors that influence short-term associations between affective states and physical activity engagement in youth with overweight/obesity, such as weight stigma, self-efficacy, and social support surrounding physical activity. Furthermore, living with a weight-discordant sibling may impact these factors, as recent research found children reported the greater negative weight-based talk from siblings compared with other family members (Berge et al., 2016). Prospective research is also warranted to assess the degree to which microtemporal (i.e., momentary) affective responses to physical activity predict macrolevel outcomes, such as the adoption and maintenance of physical activity behaviors during and after adolescence, as well as the extent to which to which momentary affect-activity associations are related to excess weight gain.

Clarifying these associations may ultimately help to tailor approaches to target real-time affective mechanisms that contribute to physical activity among children with overweight and obesity. For example, targeting up-stream contextual factors related to affective antecedents of physical activity (e.g., weight-related stigma, emotion regulation skills, self-efficacy) may help to promote physical activity among youth with overweight/obesity (Williams et al., 2019). In turn, identifying and facilitating factors that are associated with more positive affective responses to and appraisals of physical activity (e.g., social and environmental contextual factors; Dunton et al., 2015 ) may serve to increase uptake and maintenance of physical activity behavior over time.

Funding

This work was supported by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD064958) to J.N.R. and the United States Department of Agriculture, Agricultural Research Service, 3062-51000-51- 00D. S.M.O. was supported by the National Institute of Mental Health (T32MH082761).

Conflicts of interest: None declared.

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