The purpose of this study was to evaluate the relationship between workload, exhaustion, and key health behaviors for weight loss—nutrition and physical activity. Structural equation modeling was used to estimate the path coefficients in a sample of 953 employed adults. The results show that workload and exhaustion were positively related to emotional eating, uncontrolled eating, and percent of calories from fat. In addition, exhaustion was negatively related to physical activity levels. Workload and exhaustion are associated with nutrition and physical activity behaviors that promote weight gain and should be considered in weight management interventions for working adults.

Methods Participants and procedure Participants were recruited from Amazon Mechanical Turk (https://www.mturk.com) using two separate Human Intelligence Tasks (HITs)—one for males and one for females to ensure that sufficient responses were obtained from both sexes. Amazon Mechanical Turk (Mturk) is a crowdsourcing online labor market that connects workers with tasks. Mturk workers must be over 18 years of age. Mturk qualifications were selected that required participants to reside in the United States and be employed full-time (35 hours or more per week). Data were collected using a cross-sectional survey from 1000 participants who responded to the Mturk HITs (500 women and 500 men). Participants were paid US$2 for completing the survey. All procedures were reviewed by the University of Georgia’s Institutional Review Board and the study was determined to be exempt (Approval ID: STUDY00004990). All participants were provided with an electronic copy of the informed consent letter. After reading the consent letter, participants indicated consent by advancing to the first page of the survey. A total of 47 participants were removed from the data due to failing the attention-checking item (n = 4, see below) or not working more than 35 hours per week (n = 43). The remaining sample of 953 participants were included in the analysis. Measures Workload Workload was measured using the eight-item scale validated by Van Veldhoven and Meijman (1994). Responses were on a 4-point Likert-type scale of 1 = never to 4 = always. Example items are “Do you have to work fast?” and “Do you have too much work to do?” The Cronbach’s alpha of the scale was previously reported to be .79 (Janssen, 2001). In this study, the Cronbach’s alpha coefficient was .79 and the composite reliability index was .80. Exhaustion Exhaustion was measured by the Oldenburg Burnout Inventory (OLBI) (Halbesleben and Demerouti, 2005). The exhaustion dimension of the OLBI is seven items, three positively worded and four negatively worded. Example items are “After work, I usually feel worn out and weary” and “After work, I usually feel totally fit for my leisure activities.” The response scale is 1–4 where 1 = totally disagree and 4 = totally agree. The OLBI English translation has demonstrated adequate reliability (α = .74–.87). In this study, the Cronbach’s alpha coefficient and the composite reliability index were both .87. Eating behaviors Eating behaviors were measured using the 21-item Three-Factor Eating Questionnaire (TFEQ-R21). The TFEQ-R21 measures cognitive restraint, uncontrolled eating, and emotional eating. The psychometric properties and reliabilities have been demonstrated in obese and normal weight participants in the United States and Canada (Cappelleri et al., 2009). Previously reported alphas were .70–.78, .84–.89, and .92–.94 for cognitive restraint, uncontrolled eating, and emotional eating, respectively. In this study, the Cronbach’s alpha coefficients were .84, .87, and .94 for the cognitive restraint, uncontrolled eating, and emotional eating scales, respectively. Similarly, the composite reliability indices were .84, .87, and .94. Percent calories from fat Percent of calories from fat was estimated using the 17-item Fat Screener developed by the National Cancer Institute Risk Monitoring and Methods Branch (Thompson et al., 2007). Participants were asked about the frequency of intake of foods such as mayonnaise, cheese, bacon that have been shown to be important predictors of percentage of energy from fat (Williams et al., 2008).1 Physical activity Leisure time physical activity was estimated using the 4-item Godin Leisure-Time Exercise Questionnaire (Godin and Shephard, 1997). Participants were asked how many times in a week that they normally engage in strenuous, moderate, and light activity for at least 15 minutes. A modified leisure-time physical activity score was calculated to represent moderate and vigorous physical activity. Sociodemographics Demographic data including sex, race, ethnicity, age, marital status, income, education, and number of children under 18 living in their household were captured on the survey. Additional questions were asked about hours worked outside of Mturk, hours worked on Mturk, job title, years of employment at current job, and whether they supervise other employees. Attention-checking item The following statement was included on all surveys: “The answer to this item should be Neutral so we know to keep your data.” Response options were 1 (strongly disagree) to 5 (strongly agree), with the correct answer being 3 (neutral). Respondents who failed to correctly answer the attention-checking item were removed from the data analysis. Statistical analysis A two-step procedure for structural equation modeling was used to test the underlying factor structure and the hypothesized relationships between variables (Anderson and Gerbing, 1988). In the first step, two separate measurement models were specified. Model 1 included the following variables: workload, cognitive restraint, uncontrolled eating, and emotional eating. Model 2 contained the following variables: exhaustion, cognitive restraint, uncontrolled eating, and emotional eating. To evaluate model fit, the following fit indices were used: χ2 goodness-of-fit, the standardized root mean square residual (SRMR), root mean square error of approximation (RMSEA), and comparative fit index (CFI). The model was considered to have good fit when the χ2 was not significant, SRMR < .09, RMSEA < .06, and CFI > .90 (Hu and Bentler, 1999). The Bayesian information criterion (BIC) and the Akaike information criterion (AIC) are provided for each measurement model for a comparison to the hypothesized models. In the second step, the path models for Model 1 and Model 2 were specified and parameters were estimated using Maximum Likelihood. The physical activity data were skewed right thus a Tobit model was used to censor the physical activity variable to account for the skewness. The use of the Tobit model does not provide traditional fit statistics (χ2 goodness-of-fit, SRMR, RMSEA) but rather provides BIC and AIC. All hypothesized models were run for the full sample. Differences in path coefficients between males and females were tested using the MODEL CONSTRAINT command. All analysis were run in MPlus statistical package (version 8.0). Robustness check Bowling and Kirkendall (2012) suggest that workload and hours worked measure different constructs. Workload as measured in this study is a perceptual measure that assesses the amount and difficulty of work relative to an individual subjective standard; whereas, hours worked is an objective measure of workload in absolute terms. Hours worked fails to account for the subjective personal standard. For instance, two people could both work 50 hours per week, but one of those persons perceive that as a manageable and reasonable workload while the other person perceives the same hours of work as a high workload that is difficult to manage. Two robustness checks were performed to confirm our proposed models. In the first, we substituted hours worked for workload. In this analysis, we found no associations between hours worked and the nutrition and physical activity outcomes. In the second, we included hours worked in the model as a control variable. Including hours worked as a control variable did not change the direction, magnitude, or significance of the hypothesis tests.

Results The study sample included 473 females (49.7%) and 478 males (50.3%) with a mean age of 36.1 years (range 19 – 71). Participants worked, on average, 41.9 hours per week outside of Mturk and 10.8 hours weekly on Mturk. Participants were predominantly White (84.7%) and non-Hispanic (94.6%). Participants in the study represented a variety of job categories across varying levels of income. Table 1 shows the sociodemographic variables for the overall sample and by sex. Table 2 provides the means, standard deviations of the variables and the estimated latent variable correlation matrix for Model 1. Workload was significantly positively correlated with emotional eating, uncontrolled eating, and percent of calories from fat. Table 3 provides the means, standard deviations of the variables and the estimated latent variable correlation matrix for Model 2. Exhaustion was significantly positively correlated with emotional eating, uncontrolled eating, and percent of calories from fat. In addition, exhaustion was significantly negatively correlated with physical activity. Table 1. Sociodemographic variables for the overall sample and by sex. View larger version Table 2. Means, standard deviations, and estimated correlation matrix for latent variables in Model 1. View larger version Table 3. Means, standard deviations, and estimated correlation matrix for latent variables in Model 2. View larger version Model fit To evaluate the fit of the measurement model, confirmatory factor analysis was conducted for each model. Model 1 contained the four latent constructs—workload, cognitive restraint, uncontrolled eating, and emotional eating. The measurement model had poor absolute fit of the data χ2 = 930.25 (p < .001) due to the large sample size but acceptable fit per relative fit indices of the SRMR (.04), RMSEA (.05), and CFI (.94). Model 2 contained the four latent constructs—exhaustion, cognitive restraint, uncontrolled eating, and emotional eating. Similar to the first measurement model, this model had poor absolute fit of the data χ2 = 1219.53 (p < .001) due to the large sample size but acceptable fit per relative fit indices of the SRMR (.04), RMSEA (.06), and CFI (.93). Test of Hypothesis 1 to 3—workload Table 4 shows the unstandardized and standardized path coefficients. The hypothesized relationships between workload and eating factors (H1) were partially confirmed. Workload was positively related to emotional eating (β = .21, p < .001) and uncontrolled eating (β = .24, p < .001) but was not significantly related to cognitive restraint. Similarly, workload was positively related to percent of calories from fat (β = .10, p = .007) supporting Hypothesis 2. Workload was not significantly related to physical activity, therefore, Hypothesis 3 was not supported. Models were run grouped by sex using MODEL CONSTRAINT to test for significant differences between the path coefficients for males and females. No significant differences between males and females were found. Table 4. Path coefficients for structural equation models testing relationships of Model 1 for the full sample and by sex. View larger version Test of Hypothesis 4 to 6—exhaustion The relationships between exhaustion and eating factors (H4) were partially confirmed (Table 5). Exhaustion was positively related to emotional eating (β = .42, p < .001) and uncontrolled eating (β = .45, p < .001) but was not significantly related to cognitive restraint. Similarly, exhaustion was positively related to percent of calories from fat (β = .12, p = .001) supporting Hypothesis 5. Exhaustion was negatively associated with physical activity (β = .13, p = .001) supporting Hypothesis 6. Models were run grouped by sex using MODEL CONSTRAINT to test for significant differences between the path coefficients for males and females. No significant differences between males and females were found. Table 5. Path coefficients for structural equation models testing relationships of Model 2 for the full sample and by sex. View larger version

Discussion Our findings suggest that both workload and exhaustion are related to percent of calories consumed from fat, emotional eating, and uncontrolled eating. High workload was related to higher intake of calories from fat, uncontrolled eating, and emotional eating. However, workload was not related to cognitive restraint or moderate/vigorous physical activity. Similarly, exhaustion was related to higher intake of calories from fat, uncontrolled eating, and emotional eating. In addition, exhaustion was negatively related to moderate/vigorous physical activity but, similar to the relationships with workload, exhaustion was not related to cognitive restraint. These findings indicate an important relationship between work factors and behaviors critical for weight management. In Model 1 (workload), the model explained 4 percent of the variance in emotional eating, 6 percent of the variance in uncontrolled eating, and 1 percent of the variance in percent of calories from fat. In Model 2 (exhaustion), the model explained 18 percent of the variance in emotional eating, 21 percent of the variance in uncontrolled eating, 2 percent of the variance in percent of calories from fat, and 2 percent of the variance in physical activity. The lack of association for cognitive restraint for both workload and exhaustion may be due to the fact that cognitive restraint is the deliberate and intentional control of food that is associated with efforts to lose weight or “diet.” We did not ask participants if they were trying to actively lose weight or diet and, as such, many participants may not have been practicing cognitive restraint. Our results are consistent with a previous study conducted by Hellerstedt and Jeffery (1997) that found men who had high job demands consumed more calories from fat but no relationship between job demands and physical activity. Hellerstedt and Jeffery (1997) looked at job demands rather than workload specifically, did not investigate the relationship of exhaustion and health behaviors, and only included fat intake and physical activity (not eating behaviors) as outcomes. Nevanperä et al. (2012) previously reported that burnout was associated with high levels of emotional eating and uncontrolled eating in Finnish women, which is consistent with this study. In a separate study, Ahola et al. (2012) reported associations between burnout and low levels of physical activity in Finnish workers. Our study did not find significant differences in path estimates for males and females suggesting that the effects observed for both workload and exhaustion are not moderated by sex. This differs from the reported findings of job demands and health behaviors for males and females reported by Hellerstedt and Jeffery (1997) where men but not women with the highest job demands consumed more calories from fat. These findings are not directly comparable because Hellerstedt and Jeffery used psychological job demands from Karasek’s Job Content Instrument as the independent variable rather than a measure of workload (Karasek et al., 1985). While there is overlap in these constructs, workload measured in this study is specific to the frequency in which respondents’ experienced excessive and challenging work capturing both the amount and difficulty of the work. In comparison, the measure by Hellerstedt and Jeffery is concerned with whether workers agree or disagree that their work is hard or conflicts with other demands. Given the relationship between workload and nutrition behaviors and the relationship of exhaustion and both nutrition and physical activity behaviors, it is important to consider work factors in the context of weight loss interventions. Weight loss interventions offered in worksite settings and, more broadly to employed adults, may have limited effectiveness in the absence of strategies that identify and address high workload and exhaustion. Studies examining the effects of workload and exhaustion on organization and employee performance outcomes provide some guidance for intervention strategies at both the individual and organization level. With regard to workload, Bowling and Kirkendall (2012) point out that excessive workload can often be mitigated by training or job redesign and is not an inherent factor of most occupations. Individual approaches for reducing exhaustion have been relatively limited but a few do show positive effects (Maslach et al., 2001). Management level strategies that increase perceptions of fairness and equity in the workplace have shown promise at reducing exhaustion (Maslach et al., 2001). While it may not always be possible to redesign jobs or to change the job such that workload is reduced, increasing resources that buffer the effects of job demands should be explored as an alternative strategy. In previous research, autonomy, social support from colleagues, having a high-quality relationship with the supervisor, and performance feedback were all found to buffer the effects of job demands on exhaustion (Bakker et al., 2005). Future research should investigate whether these resources also buffer the effect of workload on nutrition and physical activity behaviors. One limitation of our study is that it is cross-sectional and does not allow us to examine the causal paths between workload, exhaustion, and health behaviors. In addition, all our measures were collected at the same time using the same instrument and as such may be subject to common method variance resulting in inflated relationships. In this study, we focus on workload specifically. From this research, we do not know if other job demands such as emotional demands or work-home interference are related to nutrition and physical activity behaviors. Future research should determine whether other job demands demonstrate the same relationship with nutrition and physical activity behaviors as those observed with workload. The use of the Amazon Mturk sample is not representative of all workers. The sample in this study was young, highly educated, and predominantly White, non-Hispanic. It is under representative of older workers, persons without access to the Internet, and Hispanic and minority workers. It will be important to confirm the findings here in more diverse samples of workers. In summary, results from this study provide evidence of a relationship between workload, burnout, and health behaviors critical to weight loss. Worksite weight loss programs and weight management programs offered to working adults should incorporate ways to assess workload and exhaustion and address high workload or exhaustion through behavioral therapy. Additional research is needed to develop strategies that minimize the effects of workload and exhaustion on eating behaviors and physical activity.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iD

Heather M Padilla https://orcid.org/0000-0003-2082-8158

Notes 1.

There is an ongoing debate of the effects of high-fat and low-fat dairy products on weight gain. Readers are encouraged to refer to Kratzet al. (2013) for more information on health effects of high-fat dairy.