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. Author manuscript; available in PMC: 2020 Nov 3.
Published in final edited form as: J Behav Med. 2020 Mar 4;43(2):246–253. doi: 10.1007/s10865-020-00145-2

Bidirectional Association between Stress and Physical Activity in Adults with Overweight and Obesity

Andrea N Brockmann 1, Kathryn M Ross 1
PMCID: PMC7608853  NIHMSID: NIHMS1639110  PMID: 32130566

Abstract

Research has suggested that there may be a bidirectional association between stress and physical activity; however, much of this work has been conducted in athletes or adults with normal weight. The current study investigated the bidirectional association between stress and physical activity in adults with overweight and obesity. For a full year, during and after a 12-week, Internet-based weight loss program, 74 participants (BMI=31.2 kg/m2) were asked to report stress and minutes of physical activity each week. An increase in stress was associated with less physical activity during the same week and predicted fewer minutes of physical activity the following week. Finally, each 1 hour increase in physical activity on a given week was associated with a small decrease in stress ratings the following week. Results confirmed the bidirectional association between stress and physical activity in a sample of adults with overweight/obesity, and supported results highlighting stress as a barrier to physical activity. Future studies should investigate whether adding intervention components to decrease stress or to reinforce physical activity can improve physical activity engagement in this population.

Keywords: Stress, Physical Activity, Overweight, Obesity, Adults

Despite the documented benefits of physical activity, few adults meet the minimum 2007 American College of Sports Medicine (ACSM) guidelines (Harris et al., 2013) of 150 minutes of moderate-intensity or 60 minutes of vigorous-intensity aerobic physical activity per week (Haskell et al., 2007). Rates of physical activity tend to be even lower in adults with overweight and obesity (Tudor-Locke et al., 2010). While approximately 43% of normal weight Americans self-report meeting the ACSM guidelines, only 25% of individuals with overweight and 11% of individuals with obesity self-report meeting these guidelines (Spees & Taylor, 2014). These rates decrease further in studies using objective measurements of physical activity (Tucker et al., 2011); for example, one study that used accelerometers to monitor physical activity found that only 5% of normal weight individuals and less than 2% of individuals with obesity met these guidelines (Tudor-Locke et al., 2010).

These low levels of physical activity are problematic as physical activity has been found to play a key role in weight management (Donnelly et al., 2009). Greater engagement in physical activity may be protective of initial excess weight gain (Weinsier et al., 2002), improve weight loss during weight loss attempts (DeLany et al., 2014) and prevent weight regain after initial weight loss (Foright et al., 2018). Importantly, research has demonstrated that levels of physical activity necessary to promote weight loss and prevent weight gain are even higher than the ACSM guidelines (Schoeller et al., 1997). Thus, additional guidelines have been developed for adults attempting to lose weight and maintain weight loss; these guidelines recommend 150-250 minutes per week of moderate-intensity physical activity to promote weight loss and ≥ 250 minutes per week to promote weight loss maintenance (Donnelly et al., 2009).

Given the important role of physical activity in weight regulation, it is important to identify the barriers that may affect physical activity engagement in individuals with overweight and obesity. Existing literature suggests that stress may be one of these key barriers (Stults-Kolehmainen & Sinha, 2014). An inverse association between stress and physical activity has been documented across a wide range of studies (Burg et al., 2017; Stetson et al., 1997; Stults-Kolehmainen & Sinha, 2014), such that people are less likely to engage in physical activity when reporting higher levels of stress. This can include both day-to-day stressors (Burg et al., 2017; Nguyen-Michel et al., 2006) and major life events/transitions (Bray & Born, 2004; Brown et al., 2009). Importantly, even the perception of stress may lead to reductions in physical activity, in the absence of stressful life events (Stetson et al., 1997).

Nevertheless, not all studies investigating the impact of stress on physical activity have shown this pattern. While some studies have shown no association between stress and physical activity (Allison et al., 1999; Hellerstedt & Jeffery, 1997), other studies have found that the type of stress experienced may matter (Nguyen-Michel et al., 2006). Other researchers have posited that the association between stress and physical activity may be better explained by other factors, such as socio-economic status (Adler et al., 1994; Mohammad Ali & Lindström, 2006) or ethnicity and gender (Nguyen-Michel et al., 2006). Finally, there exists some evidence that there may be individual variability in response to stress that may obfuscate the association between stress and physical activity (Burg et al., 2017; Seigel et al., 2002).

Further complicating the interpretation of existing results, research suggests that the association between stress and physical activity may be bidirectional, with higher amounts of physical activity increasing resilience to stress (Ross & Hayes, 1988; Steptoe et al., 1989; Throne et al., 2000; Tucker et al., 1986). Research has demonstrated that people who engage in high amounts of physical activity are less likely than those who engage in lower amounts of physical activity to report moderate or high stress levels (Aldana et al., 1996). Similarly, a recent meta-analysis determined that engagement in physical activity in non-clinical populations has a small but consistent effect for anxiety reduction (Rebar et al., 2015). Physical activity interventions have also been demonstrated to reduce job-related stress (Conn et al., 2009) and symptoms of anxiety (Herring et al., 2010; Steptoe et al., 1989). Using ecological momentary assessment (EMA) methods, wherein individuals were prompted to answer queries about stress throughout the day (morning, afternoon, and evening), a study by Burg and colleagues (2017) demonstrated stress reported the night before, or the morning of, predicted less physical activity that day. Furthermore, physical activity engagement led to a small reduction in stress that day; however, substantial individual variability existed. Specifically, only about 20% of participants showed effects in the expected direction in each of these analyses, with a few participants demonstrating associations in the unexpected direction and the remainder demonstrating no significant association between stress and physical activity within a day.

Most of the existing literature to date examining the positive effects of physical activity on stress levels has been conducted either in athletes (Rimmele et al., 2007), normal weight adults (Burg et al., 2017; Steptoe et al., 1989), or “aerobically fit” individuals (Crews & Landers, 1987). Importantly, research has suggested that the ability of physical activity to lower stress may be impacted by physical fitness (Tucker et al., 1986). Thus, existing evidence may not generalize to adults with overweight or obesity.

Current Study

The current study investigated the bidirectional association between stress and physical activity in adults with overweight and obesity who enrolled in a 3-month, Internet-based weight loss program. Participants were asked to self-report stress and minutes of physical activity weekly via a study website throughout the initial weight loss program and during a 9-month observational “maintenance” period after the program had ended. We first hypothesized that higher levels of stress would be associated with less physical activity within the same week. Second, we hypothesized that higher levels of stress reported one week would also predict less physical activity the following week. As an exploratory aim, we investigated whether there was a bidirectional association between physical activity and ratings of stress, such that greater physical activity one week would predict less stress the following week.

Methods

Participants

The current study was a secondary data analysis of an Internet-based behavioral weight loss intervention provided to 75 adult employees and adult dependents of employees at a healthcare organization in Providence, RI. A full description of participant recruitment, screening, and intervention outcomes for this parent study has been published previously (Ross & Wing, 2016). In brief, potential participants were eligible for the parent study if they were between the ages of 18-70 years old, had a body mass index (BMI) in the overweight or obese categories (≥ 25 kg/m2 but weight < 150 kg due to a maximum weight limit of study-provided smart scales), did not have any health conditions that contraindicated weight loss, and reported access to a computer/Internet at home. One participant never logged into the study website (and thus never reported stress/ physical activity); data from the remaining 74 participants were included in the current study. On average, these 74 participants were (mean ± SD) 50.65 ± 10.41 years old and had BMIs at baseline of 31.20 ± 4.51 kg/m2 (Ross et al., 2019a). Further, 68.9% of participants were female and, in terms of race/ethnicity, 86.5% reported identifying as White, 9.5% as Black or African American, 2.7% as Asian, and 1.4% as American Indian or Alaskan Native, and 5.4% selected “other” (participants could select multiple categories and thus totals may exceed 100%). Approval for the parent study was obtained from the Miriam Hospital Institutional Review Board, and approval for the current analyses was obtained from the University of Florida Institutional Review Board.

Intervention and Follow-up “Maintenance” Period

All participants were provided with a 3-month, Internet-based weight loss program followed by a 9-month, observational “maintenance” period. Prior to the start of the Internet program, participants were asked to attend an initial 1-hour, in-person session. At this session, participants were introduced to program goals, taught how to self-monitor their weight, caloric intake, physical activity, and oriented to the intervention website. Participants were encouraged to make changes in their caloric intake and physical activity in order to consume between 1200-1800 kcal/day and gradually increase moderate-intensity physical activity until reaching 200 minutes per week. To count toward this goal, minutes of physical activity were defined as minutes of at least moderate-intensity (e.g., brisk walking) physical activity obtained in bouts lasting at minimum 10 minutes. To track progress towards these program goals, participants were given a “smart” scale (which sent weights directly back to research severs via the cellular network), a calorie reference book, and paper self-monitoring records that could be used to track weight, caloric intake, and physical activity each day. Each self-monitoring record had room for participants to record self-monitoring data for 7 days, with room on the last page to summarize weekly averages/totals. Participants were provided 52 self-monitoring records (enough to last for a full year), with additional records available by request.

Participants began the Internet-based weight loss program on the next Monday following this in-person session. Treatment content was adapted from the Diabetes Prevention Program (DPP Research Group, 2002) and Look AHEAD (The Look AHEAD Research Group, 2003) lifestyle intervention programs. Each week, participants were asked to log in to complete a 12-15 minute interactive weight loss lesson. By midnight each Sunday night, participants were asked to log into the study website to report their weight, the number of calories they consumed, and their total minutes of moderate-intensity physical activity for each day of that week. An automated, tailored feedback message was developed for each participant based on their progress toward study goals, which the participant would see the next time they logged in (starting that Monday morning).

After the end of the initial weight loss program, participants were encouraged to maintain changes in eating and activity made during the program and to continue to self-monitor weight, caloric intake, and physical activity daily, using the study-provided tools and records. Participants were also asked to continue to log on to the study website once each week (Sunday evenings) to self-report number of days weight/caloric intake was monitored and total minutes of physical activity for the week. Throughout this 9-month observational maintenance period (weeks 13 to 52), participants no longer had access to intervention content on the website and did not receive feedback messages based on their weekly self-report of self-monitoring.

A key goal of the parent study was to identify proximal predictors of weight loss and regain (Ross et al., 2019b); thus, at the same time that participants were asked to report their self-monitoring data on the study website, they were also asked to complete an 11-item questionnaire designed to assess factors that were hypothesized to be proximally associated with weight loss and regain. Small financial incentives ($1-10/week) were provided to participants to encourage weekly completion of this questionnaire and reporting of self-monitoring data. Importantly, incentives were provided for completion of the questionnaire and reporting self-monitoring data each week, not for the completion of self-monitoring itself (i.e., a participant could report that they self-monitored zero days that week and still receive the incentive).

Measures

Height and weight.

Participant height and weight were measured at baseline by trained study staff, with participants in light, indoor clothing and with shoes removed. Height was measured to the nearest 0.1 centimeter using a wall-mounted stadiometer. Weight was measured to the nearest 0.1 kilogram using a calibrated digital scale. Height and weight were used to calculate BMI.

Physical Activity.

At the end of each week, participants were asked to report information from their self-monitoring records on the study website. During the initial 12-week weight loss program, participants were asked to report the total minutes of physical activity each day; this was summed to create a weekly total. During the maintenance period (weeks 13-52), participants were encouraged to continue recording minutes of daily physical activity in their self-monitoring records, but were only asked to report the total minutes of weekly physical activity on the study website.

Stress.

At the same time that participants were asked to report their self-monitoring data on the study website, they were also asked to complete an 11-item questionnaire designed to assess factors that were hypothesized to be proximally associated with weight loss and regain. One item on this questionnaire asked participants to report their stress over the past week using a 7-point Likert scale. On this item, participants were asked “How much stress did you experience during the past week?,” with options ranging from “1=Not stressed at all” to “7=Very stressed.”

Intervention Outcomes

Weight changes and results of the weekly participant reports have been published previously (Ross et al., 2019b). On average (mean ±SD), participants lost −5.8 ± 4.9 kg (−6.4 ± 4.8 % of baseline weight) during the 3-month intervention and regained 2.4 ± 3.6 kg (a 3.0 ± 4.5 % increase from Month 3) during the maintenance period. Participants reported fewer minutes of physical activity each week during the maintenance period (146.4 ± 163.2 mins/week) compared to the initial intervention period (194.4 ± 175.8 mins/week); however, there was no difference in ratings of stress between the initial intervention (4.1 ± 1.2 points) and the maintenance period (4.1 ± 0.9 points).

Statistical Analyses

All analyses were conducted with SAS version 9.4 (SAS Institute Inc., 2013). Longitudinal multilevel models (SAS PROC MIXED) were used to examine changes in physical activity and stress over time and the week-to-week association between stress and physical activity. Models used all available data via maximum likelihood estimation methods. Model fit was assessed using Akaike Information Criterion. Saitterwaithe degrees of freedom estimates were used, and an autoregressive covariance structure was utilized to allow time points that were closer together to correlate more closely than time points further apart. The following models were used:

  • Model 1: PAij = γ00 + γ10Stressij + u0j + rij

  • Model 2: PAij = γ00 + γ10Stress(i-1)j + u0j + rij

  • Model 3: Stressij = γ00 + γ10PA(i-1)j + u0j + rij

Each of these models represented the stated outcome for participant j at week i. In Model 1, minutes of physical activity on a given week (PAij) were modeled by the individual’s average minutes of physical activity (γ00) plus their deviation from this mean (i.e., the random effect u0j), the effect of stress on PA (γ10) combined with the rating of stress that week (Stressij), and the random error associated with the that week for that person (i.e., the random effect rij). Model 2 expressed minutes of physical activity on a given week using ratings of stress the week prior (Stress(i-1)j). Model 3 expressed ratings of stress on a given week given minutes of physical activity reported the week prior (PA(i-1)j).

Given that the initial weight loss intervention encouraged individuals to increase engagement in physical activity, and that previous literature has demonstrated that individuals often decrease their engagement in physical activity after the end of intervention (Cadmus-Bertram et al., 2014), change in physical activity over time was modeled as both a linear and a polynomial function. As exploratory analyses, we investigated whether associations between physical activity and stress changed over time (introducing the variable time as moderator), and whether there was evidence of individual variability in the associations between stress and physical activity (assessed by introducing random slopes, u1j).

Results

Participants reported their physical activity on an average (mean ± SD) of 37.5 ± 14.4 weeks (representing 72.0 ± 27.7% of the 52 possible weeks), and reported their stress on an average of 37.1 ± 14.4 weeks (71.4 ± 27.6% of 52 possible weeks). The median number of weeks of self-report was 42.5 (81.7% of all possible weeks) for physical activity and 42.0 (80.8%) for stress.

Across all 52 weeks, participants reported an average (mean ± SE) of 160.6 ± 19.1 minutes of physical activity and rated stress at 4.1 ± 0.1 (range = 1-7). There was significant variation in physical activity week to week, p < .0001. On average, engagement in physical activity decreased over time, β = −1.05, SE = 0.11, p < .0001. As expected, the change in physical activity over time was best modeled with a polynomial time function, β = 0.07, SE = 0.01, p < .0001, such that an initial increase in physical activity during the intervention period was followed by a decrease in physical activity over the remainder of the study period. There was also significant variation in stress week to week, p < .0001; however, there was not a significant change in stress over time, p = .045.

A significant negative association was observed between stress and physical activity during the same week, β = −9.18, SE = 1.22, t(2663) = −7.50, p < .0001, such that a 1 point increase in stress was associated with 9.2 ± 1.2 fewer minutes of physical activity. Further, as hypothesized, stress reported on one week significantly predicted physical activity the following week, β = −4.14, SE = 1.31, t(2331) = −3.17, p = .002, such that a 1 point increase in stress predicted 4.1 ± 1.3 fewer minutes of physical activity. Finally, physical activity assessed on one week significantly predicted stress the following week, β = −0.07, SE = 0.02, t(1369) = −3.98, p < .0001, such that each hour increase in physical activity predicted a 0.07 ± 0.02 lower rating of stress. Moreover, none of the models were improved by adding in random slopes, all ps > .05, suggesting that there was not statistically significant variation in these associations between individuals and that the group models provided the best fit (see electronic supplementary materials to view figures detailing individual associations between stress and physical activity).

Discussion

The current study investigated week-by-week associations between stress and physical activity in adults with overweight and obesity enrolled in a 3-month Internet-based weight loss program followed by a 40-week, observational “maintenance” period. As expected, engagement in physical activity over time was best modeled by a polynomial function, representing an initial increase in physical activity during the intervention followed by a decrease during the maintenance period. While ratings of stress varied week-to-week, there was not an observable change in ratings of stress over time. Consistent with study hypotheses, results from the current study demonstrated a bidirectional association between stress and physical activity in adults with overweight and obesity, such that 1) higher ratings of stress in a given week were associated with fewer minutes of physical activity that same week, 2) higher ratings of stress in a given week predicted fewer minutes of physical activity the following week, and 3) greater engagement in physical activity in one week predicted lower ratings of stress the following week. Results further demonstrated that the magnitude of these effects did not change over time and that there was not significant individual variability in the associations between stress and physical activity.

Taken together with the previous literature (Burg et al., 2017; Stetson et al., 1997; Stults-Kolehmainen & Sinha, 2014), the current results suggest that stress serves as an important barrier to physical activity in adults with overweight and obesity. Future studies should investigate whether adding components designed to promote stress reduction (e.g., strategies modeled from mindfulness-based stress reduction; see Meyer et al., 2018) can improve physical activity engagement in this population.

Given that stress in one week was not only associated with less physical activity the same week but also predicted less physical activity the following week, stress may be a useful indicator that a person might need additional support to promote physical activity engagement. The wide availability of smartphones and technology-based self-monitoring tools has supported the development of newer intervention methods, such as just-in-time adaptive interventions, which aim to dynamically adapt intervention content and timing based on participant status (Nahum-Shani et al., 2018). Future research could investigate whether physical activity outcomes can be improved by providing additional intervention to support physical activity engagement when individuals report high levels of stress.

Our results also supported previous literature demonstrating that higher levels of physical activity engagement can buffer stress (Crews & Landers, 1987; Tucker et al., 1986); however, in the current study, the magnitude of this effect was small. For each additional hour of physical activity, ratings of stress were reduced by only 0.07 points. Interpreting this effect, a person would have to engage in approximately 13.41 hours of physical activity in a single week in order to observe a one-point reduction in stress (1-7 point scale). Jeffery and colleagues (Jeffery et al., 2004) argued that declining reinforcement for changes in eating and activity habits may be a key driver of weight regain after initial weight loss. While a stress buffering effect may provide some negative reinforcement for physical activity under an operant conditioning model (Marcus et al., 1996), it may be that an effect of this magnitude observed may not be powerful enough to reinforce physical activity behavior and thus promote future physical activity. Future research should investigate whether adding in additional reinforcement for physical activity engagement may improve physical activity outcomes (and, ultimately, weight maintenance) in adults with overweight and obesity.

In contrast to previous research (Burg et al., 2017; Seigel et al., 2002), we did not observe significant individual variability when investigating associations between stress and physical activity. There are several possible interpretations of these results. First, it is possible that our study was underpowered to observe this effect; our sample was much smaller than the sample recruited by Seigel and colleagues (2002) and similar in size to the sample recruited by Burg and colleagues (2017), however Burg and colleagues sampled participant stress and physical activity daily, providing a much richer longitudinal dataset (and increasing power for observing significant differences). It may also be possible that the homogeneity of effects observed in our models may be important in relation to our participant sample. Our analyses were conducted in adults with overweight and obesity, while the previous studies included primarily normal-weight adults. Thus, the pattern of results observed could represent an important consideration related to the etiology of overweight and obesity.

The current study has several strengths. Notably, we were able to investigate the bidirectional association between stress and physical activity on a proximal, week-to-week basis. Much of the previous literature assessing whether stress serves as a barrier to physical activity in adults with overweight and obesity was conducted using retrospective recall measures delivered at sparsely-spaced follow-up visits. These retrospective measures typically ask individuals to recall stress and physical activity over a period of weeks to months, which may lead to biased results given that several studies have demonstrated that retrospective recall of previous mood and health behaviors during health behavior change interventions may be biased by outcome (Ross & Wing, 2018; Shiffman et al., 1997). For example, a person may “remember” and emphasize barriers to physical activity if their physical activity goal was not achieved, rather than “remembering” supports (Durante & Ainsworth, 1996). Further, by assessing stress and physical activity weekly during a full year, we were able to establish time precedence between these constructs, allowing us to observe not only a within-week correlation between stress and physical activity but to demonstrate that greater stress one week predicted less physical activity the next week, and vice versa.

In addition to these strengths, the current study had several limitations. First, physical activity was assessed via participant self-report, which has known weaknesses (e.g., individuals may self-report greater engagement in physical activity compared to objective measures; Tudor-Locke et al., 2010). We also asked participants to only record minutes of at least moderate-intensity physical activity accumulated in bouts of ≥ 10 minutes. It is possible that there may have been variations in lower-intensity physical activity, physical activity accumulated in bouts of < 10 minutes, or physical activity that was otherwise not recorded (e.g., physical activity related to occupation or transportation) that we were unable to capture in the current analyses. Second, stress was assessed using one item from an investigator-developed questionnaire that had not previously been investigated for validity or reliability. While other validated measures of stress exist (e.g., the Perceived Stress Scale and Stress Overload Scale; Amirkhan, 2012; Cohen et al., 1983) either the time frame (the Perceived Stress Scale assesses stress over the past month) or participant burden required for completing the measure each week (the Stress Overload Scale has 30 items) prevented their use in the parent study, which assessed not only stress but a host of other constructs that had been hypothesized to be proximally associated with weight loss and regain (Ross et al., 2019b). Finally, participants in the current study were predominately White and female, limiting generalizability of study results. Future studies should replicate these analyses using validated measures of stress and in samples including adults from racial/ethnic minority groups and a greater number of men. This may be particularly important given that research has indicated that individuals from racial/ethnic minority groups may experience higher levels of stress (Williams et al., 1997).

Conclusion

The current study demonstrated evidence for a bidirectional association between stress and physical activity in adults with overweight and obesity participating in an Internet-based, behavioral weight loss program. Given the key role of physical activity in weight management, future studies should investigate whether including intervention components to decrease stress or building in additional reinforcement strategies for physical activity engagement can improve physical activity levels and, ultimately, weight outcomes.

Supplementary Material

Online Supplemental Information

Acknowledgments

Support for this study was provided by the Lifespan Corporation, and by the National Institute of Diabetes Digestive and Kidney Diseases (National Institutes of Health) under award numbers F32DK100069 and R21DK109205 awarded to KMR.

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