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Psychosomatic Medicine 67:577-583 (2005)
© 2005 American Psychosomatic Society

ORIGINAL ARTICLES

Relationship Between Work Stress and Body Mass Index Among 45,810 Female and Male Employees

Anne Kouvonen, PhD, Mika Kivimäki, PhD, Sara J. Cox, PhD, Tom Cox, PhD and Jussi Vahtera, MD, PhD

From the Department of Psychology, University of Helsinki, Finland (A.K., M.K.); the Finnish Institute of Occupational Health, Helsinki, Finland (M.K.); the Institute of Work, Health and Organisations, University of Nottingham, U.K. (S.J.C., T.C.); and the Finnish Institute of Occupational Health, Turku, Finland (J.V.).

Address correspondence and reprint requests to Anne Kouvonen, PhD, Department of Psychology, P.O. Box 9, FI-00014 University of Helsinki, Finland. E-mail: anne.kouvonen{at}helsinki.fi


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 
Objective: The proportion of overweight and obese people has grown rapidly, and obesity has now been widely recognized as an important public health problem. At the same time, stress has increased in working life. The 2 problems could be connected if work stress promotes unhealthy eating habits and sedentary behavior and thereby contributes to weight gain. This study explored the association between work stress and body mass index (BMI; kg/m2).

Methods: We used cross-sectional questionnaire data obtained from 45,810 female and male employees participating in the ongoing Finnish Public Sector Cohort Study. We constructed individual-level scores, as well as occupational- and organizational-level aggregated scores for work stress, as indicated by the demand/control model and the effort–reward imbalance model. Linear regression analyses were stratified by sex and socioeconomic status (SES) and adjusted for age, marital status, job contract, smoking, alcohol consumption, physical activity, and negative affectivity.

Results: The results with the aggregated scores showed that lower job control, higher job strain, and higher effort–reward imbalance were associated with a higher BMI. In men, lower job demands were also associated with a higher BMI. These associations were not accounted for by SES, although an additional adjustment for SES attenuated the associations. The results obtained with the individual-level scores were in the same direction, but the relationships were weaker than those obtained with the aggregated scores.

Conclusions: This study shows a weak association between work stress and BMI.

Key Words: body mass index • demand–control • effort–reward imbalance • job strain

Abbreviations: BMI = body mass index; CHD = coronary heart disease; CRF = corticotropin-releasing factor; MET = metabolic equivalent task; SES = socioeconomic status.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 
Obesity, as indicated by a high body mass index (BMI, kg/m2), is a well-established risk factor for major causes of morbidity and mortality such as cardiovascular disease, hypertension, stroke, type 2 diabetes, and certain forms of cancer (1–4). The etiology of obesity is not well understood (5,6), but it is related to an interaction between biological and environmental factors (7). Genes are important in determining a person’s susceptibility to gain weight. However, energy balance is determined by calorie intake and physical activity. Key behavioral factors leading to obesity are an increased consumption of energy-dense foods high in saturated fats and sugars and reduced physical activity (2).

During the past few decades, obesity has grown to epidemic proportions, and its prevalence is still increasing rapidly (1,2). Globally, already more than 1 billion adults are overweight and at least 300 million of them are clinically obese (2). In parallel with the increasing prevalence of obesity, figures from several countries indicate an increase in work stress (8), a risk factor for cardiovascular diseases (9–15). Exposure to work stress may increase cardiovascular risk directly through pathophysiological changes and indirectly through changes in behavioral risk factors (11,16). Work stress could be connected with a high BMI if the stress-related pathophysiological changes contribute to obesity or if employees with high stress do not find enough time to exercise or prepare healthy meals.

The most widely tested work stress models are the demand/control model (17–19) and the effort–reward imbalance model (20). The demand/control model proposes that employees who do not have enough job control to meet their job demands experience job strain. Employees with jobs characterized by high job strain are supposed to be at increased risk of disease. In the effort–reward imbalance model, chronic work stress is identified as nonreciprocity or an imbalance between high efforts spent and low rewards received at work (21). An imbalance between work effort and the rewards (financial, self-esteem, and social) gained from work results in an increased risk of ill health. Both of these models assume that high work stress also promotes health-risk behaviors such as unhealthy eating habits and physical inactivity, which, in turn, can increase body weight.

The association between work stress and BMI has been studied in relation to the demand/control model (6,22–33), but little empirical research is available regarding the association between effort–reward imbalance and BMI (13). In some studies, high job demands (22,23,33), low job control (23,24), and high job strain (25,33) have been associated with a higher BMI. In contrast, other studies have demonstrated no associations between body weight and job strain (26–28), job control (26,28), or job demands (26,28), and in 2 samples, high job strain was associated with a lower BMI (29,30). The associations between the elements of the job strain model and BMI have also varied by gender (22,24,32,33), but not in a consistent manner. Furthermore, Netterstrom et al. (31) observed a relationship between objective—but not subjective—job strain and BMI.

The discrepancies between research findings can reflect differences in the design, measurement, and the characteristics of study populations. Socioeconomic status (SES) is a potential confounding or modifying factor. In industrialized countries, people with a lower SES tend to be heavier (1,34), and the increase in obesity has been the most marked among them (35,36). Besides, employees in lower SES groups are likely to experience work stress more frequently, and the size of the effects of work stress on health may be higher in these groups (37). It has also been repeatedly shown that socioeconomic factors shape health-related behaviors such as eating habits and physical activity (38). These associations raise the possibility that the association between work stress and BMI may vary according to SES. However, empirical studies of this issue are lacking.

Another limitation of previous studies is that they have typically relied on individuals’ self-reports to determine work stress. Such an assessment strategy is open to bias as a result of individual differences in perceiving work-related factors. An alternative approach would be to examine work stress using aggregated scores (e.g., by assigning a work unit mean score of stress to each member of each work unit). Compared with individual-level measurement, such scores are likely to allow a more accurate examination of genuine contextual factors.

The Present Study
We examined the relationship between work stress, as defined by the demand/control model of job strain and the effort–reward imbalance model, and BMI. The present study adds to prior research in several ways. First, it is the first to examine both leading work stress models in relation to BMI. Second, in addition to individual-level scores, we used occupational- and organizational-level aggregated scores to model the effect of work stress. These scores were based on the responses of all the workers in the same occupation and organizational unit. To ensure statistical power, we focused on a large population covering over 45,000 employees in more than 1200 combinations of organizations and occupations.

Third, we controlled for several confounders or possible predictors of BMI such as SES (1,35,36), age (35,39), marital status (33,39,40), smoking (35,39), alcohol consumption (41), physical activity (40), and negative affectivity. Insufficient control for these factors has left the findings in some earlier studies open to confounding (24,27,29). We also stratified analyses by SES to examine further its role in the association between work stress and BMI. Finally, unlike some previous studies (6,22), we used a continuous measure of body weight. A categorized measure of being overweight/obese may have led to a loss of information and thereby reduced the opportunities to detect weak associations in previous studies.


   METHODS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 
Study Participants
The present study was part of a larger research project. Cross-sectional data were obtained from the Finnish Public Sector Cohort Study, an ongoing prospective study to explore the relationships between behavioral and psychosocial factors and health. It is focused on the entire personnel of 10 towns and 21 hospitals providing specialized health care in the districts where the towns are located. Participation was voluntary. A more detailed description of the study has been given elsewhere (42,43).

In 2000 to 2002, 48,592 employees (32,293 from the towns and 16,299 from the hospitals) responded to a self-administered questionnaire inquiring about weight and height, work stress, health-related behaviors, demographics, and other factors with a response rate of 67%. The majority (81%; N = 39,255) of the respondents were women. Common occupations included registered nurse (16%, N = 7741), teacher (10%, N = 4962), office worker (6%, N = 2824), and technician (2%, N = 1107). Any differences with the eligible population were small. In the towns, the figures for the participants compared with the eligible population (N = 47,351) were as follows: mean age 44.9 compared with 44.5 years; proportion of women 77% compared with 72%; and proportions of higher-grade nonmanual, lower-grade nonmanual, and manual employees 34%, 46%, and 20% compared with 35%, 42%, and 22%, respectively. The corresponding figures for the hospital personnel (N for eligible population = 23,610) were: mean age 43.1 compared with 43.1 years; proportion of women 87% compared with 84%; and proportions of higher-grade nonmanual, lower-grade nonmanual, and manual employees 16%, 77%; and 8% compared with 13%, 81%; and 7%, respectively.

Because 5.7% of the respondents (N = 2782) did not provide information about their height and/or weight, or they had a missing value for aggregated work stress, they were excluded from the analyses, and the final sample consisted of 37,161 women and 8649 men. The mean age for the women was 44.5 years (range, 18–63 years; standard deviation [SD] = 9.38) and 45.1 years (range, 17–65 years, SD = 9.44) for the men.

The study was approved by the Ethics Committee of the Finnish Institute of Occupational Health.

Measures of Work Stress
In the demand/control model and the effort–reward imbalance model, the term "stress" indicates a negative factor suggesting that the challenge an individual faces exceeds his or her coping skills, and thereby leads to unhealthy behaviors and negative affect. Thus, the stress term has migrated from a more neutral term of the relationship between challenges and the individual’s coping skills and is equated with distress rather than eustress (positive stress). We measured such negative work stress as follows.

Job control and job demands measures were derived from the Job Content Questionnaire (44,45). Job control was measured by a 9-item indicator consisting of 2 subscales measuring decision authority and skill discretion (Cronbach’s alpha = 0.82; range, 1–5; 3-year test–retest reliability r = 0.70). The job demands scale was the sum of 2 items inquiring about workload and pace of work (Cronbach’s alpha = 0.70; range, 1–5; 3-year test–retest reliability r = 0.55). The total score was computed for each of the 2 constructs. Job strain was assessed as the ratio of job demand and job control (46).

The workers’ effort in their work was measured with a single question ("How much do you feel you invest in your job in terms of skill and energy?"; range, 1–5) and rewards by feelings of getting a return from work in terms of income, job benefits, recognition, prestige, and personal satisfaction (3 items, Cronbach’s alpha = 0.64; range, 1–5) (47). The measure of effort–reward imbalance was obtained by calculating the ratio of effort and rewards (13). Because part of the hospital sample did not contain the measure of effort–reward imbalance, the analyses including this variable involved 37,767 participants.

In regard to the scales calculated for job control, job demands, and rewards, if half or more of the component items were missing, a value of "missing" was recorded in the total.

To reduce bias arising from differences in reactivity or response styles between individuals, we used work stress-aggregated scores in addition to individual-level scores. We identified each occupational title of each respondent from the employers’ records, expressed in terms of the 4-digit codes of Statistics Finland. We calculated the mean job control, job demands, job strain, and effort–reward imbalance scores for each level within each organization (each town and each hospital). The aggregated scores of the 4-digit occupational level (i.e., the most accurate level) were linked to each participant. If the number of respondents was 9 or less, we used values aggregated onto the next level. Thus, in all cases, the aggregated scores for work stress were based on values derived from 10 or more individual respondents. There was a total of 1224 combinations of organizations and occupations.

Body Mass Index
The respondents self-reported their weight and height. The BMI was calculated as weight (kilograms) divided by height (meters) squared (48).

Potential Confounders
The other variables used in the analysis were: 1) demographics: sex, age, SES (manual, lower-grade nonmanual, and higher-grade nonmanual; based on the Statistics Finland classification of the 5-digit occupational titles), marital status (married or cohabiting vs. other), and job contract (permanent vs. temporary). Sex, age, SES, and job contract were obtained from the employers’ registers; 2) health behaviors: current smoking status, average consumption of absolute alcohol per week (in grams) (42,49), and leisure-time physical activity, measured by metabolic equivalent task (MET)-hours per week (50,51), assessing physical activity performed during leisure time and the journeys to and from work, but not physical activity on the job; 3) negative affectivity, which is the disposition to answer negatively to questionnaires, assessed by the Trait Anxiety Inventory (52) (6 items, Cronbach’s alpha = 0.88). The sociodemographic and health-related behavioral variables were selected because they have predicted obesity or a higher BMI in earlier studies (e.g., (35,39–41,53)). Negative affectivity was measured to control for reporting bias.

Statistical Methods
Linear regression analyses were performed to estimate the relationship of BMI with individual and aggregated scores of each work stress measure. Separate analyses were conducted for the women and men for 3 SES categories (manual, lower-grade nonmanual, and higher-grade nonmanual) and for municipal and hospital workers. Additional analyses were carried out separately for 4 common occupations (registered nurse, teacher, office worker, and technician). Adjustments were made for age, marital status, job contract, smoking status, alcohol consumption, leisure-time physical activity, and negative affectivity. In the analyses for the women and men, SES (1 = manual, 2 = nonmanual) was additionally adjusted for.

We also tested whether the associations between work stress indicators and BMI were dependent on the level of specific covariates (sex, age, type of employer [municipal or hospital], alcohol consumption, and negative affectivity) by entering the crossproduct terms "covariate * work stress measure" in the models.

Because of missing values, the number of subjects varied between the tables and the examined variables. The SPSS 11.0 (SPSS, Inc., Chicago, IL) software package was used for all the analyses.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 
As Table 1 shows, the mean BMI value was higher for the men than for the women. The mean BMI increased significantly with age. Moreover, BMI was significantly related to SES in that manual workers had the highest and higher-grade nonmanual employees the lowest mean BMI. Married or cohabiting women and permanent employees had significantly greater mean BMI values than their counterparts.


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  		TABLE 1. Descriptive Statistics and Mean Value for Body Mass Index (BMI; kg/m2) by Background Variables

 

The SES groups differed in their levels of work stress. Manual workers reported the highest, and higher-grade nonmanual employees the lowest, levels of both individual and aggregated job strain and effort–reward imbalance (p < .001 in all cases) (data not shown).

The results of the linear regression analyses for the women and men are presented in Table 2. There was a significant relationship between higher work stress at the occupational and organizational level and a higher BMI. After adjustment for age, marital status, job contract, smoking, alcohol consumption, leisure-time physical activity, and negative affectivity, lower job control, higher job strain, and higher effort–reward imbalance were significantly related to a higher BMI among the women and men. Moreover, lower job demands were associated with a greater BMI in men. Additional adjustment for SES attenuated the associations, and the associations of BMI with job strain and effort–reward imbalance became insignificant among the men.


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  		TABLE 2. Relation of BMI With Job Control, Job Demands, Job Strain (Ratio of Job Demand and Job Control) and Effort-Reward Imbalance: Standardized Regression Coefficients, Women and Men

 

Table 2 shows that the results for the individual-level work stress scores were in the same direction, but the relationships were weaker than those obtained with the aggregated scores.

We tested whether the relationship between aggregated work stress and BMI varied by the levels of age, alcohol consumption, and negative affectivity by applying interaction terms in the models controlled for sex and all other confounders. However, none of the interaction terms reached statistical significance (data not shown).

We also compared the relation of aggregated work stress with BMI between the municipal and hospital workers. The associations were in the same direction in the 2 groups of employees, but they were slightly stronger among the municipal workers. The following interaction terms with employer type were significant: job control (p < .001) and job strain (p = .013) (data not shown).

As depicted in Table 3, higher job strain (p < .001) and higher effort-reward imbalance (p < .001) were related to a higher BMI in higher-grade nonmanual employees, whereas the corresponding associations were lacking in the other SES groups. In addition, lower job control was significantly associated with a higher BMI among the higher-grade (p < .001) and lower-grade (p = .002) nonmanual employees.


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  		TABLE 3. Relation of BMI With Aggregated Job Control, Job Demands, Job Strain (Ratio of Job Demand and Job Control), and Effort-Reward Imbalance: Standardized Regression Coefficients, SES Groups

 

Table 4 gives results of the aggregated stress scores from the analyses calculated separately for 4 common occupations (registered nurse, teacher, office worker, and technician). Any differences between the occupations were small. Higher job demands and higher strain were significantly associated with a higher BMI among the nurses and teachers. Higher effort–reward imbalance was significantly associated with a greater BMI among the technicians.


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  		TABLE 4. Relation of BMI With Aggregated Job Control, Job Demands, Job Strain (Ratio of Job Demand to Job Control), and Effort-Reward Imbalance: Standardized Regression Coefficients, Four Occupations

 


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 NOTES
 REFERENCES
 
In this large cross-sectional study among Finnish public sector employees, lower job control, higher job strain, and higher effort–reward imbalance at the aggregated occupational and organizational level were significantly associated with a higher BMI. Lower job demands were additionally associated with a higher BMI among the men. All the results remained after controlling for individual-level confounders such as age (35,39), marital status (35,39,40), job contract, smoking (35,39), alcohol consumption (41), physical activity (40), and negative affectivity. Further adjustment for SES attenuated the associations, and the associations of BMI with job strain and effort–reward imbalance became insignificant for the men. The individual-level results were in the same direction as the aggregate-level ones, but the associations were weaker.

Previous studies have typically been based on the individual-level measurement of work stress. Our results are in agreement with the studies that have reported a relationship of higher BMI with low job control (24) and high job strain (33) among women and with high effort–reward imbalance in male and female employees (13).

Potential explanations for the association between work stress and BMI involve diet and physical activity. First, employees with time pressure may select the most rapidly and easily procured and consumed meals available at the expense of food with higher nutritional value and lower calories (54). Moreover, stressed workers may skip lunch during workdays, which can promote overeating in the evenings. Among higher-grade nonmanual employees, high job strain can also be connected to a higher BMI through more frequent social occasions to gain weight (22) (e.g., business lunches or dinners). Besides, there is evidence that work stress would modestly increase the amount eaten, especially the amount of fatty and sweet foods (54). Dallman et al. (55) proposed that people might eat high-fat and carbohydrate caloric content "comfort food" in an attempt to reduce activity in the corticotropin-releasing factor (CRF)-driven central chronic stress-response network with its attendant anxiety. According to these authors, feeling better could result from a reduction in the central CRF expression and the resulting dysphorias. This is a plausible mechanism linking work stress with a tendency to eat energy-dense foods.

Second, fatigue related to high strain and demanding work could increase sedentary behavior off the job (18). In addition, passive work that includes low job control may encourage a passive lifestyle, including sedentary behavior outside work (26,56), or it may be a surrogate for work that is not physically active (57). In the present data, manual workers had the lowest level of leisure-time physical activity, whereas lower-grade nonmanual employees had the highest.

Third, it is possible that high work stress produces weight loss from diminished appetite and/or increased physical activity in some individuals, whereas in others, it is associated with weight gain resulting from increased eating and decreased physical activity (58). The neutralizing effect of variables going in different directions for different people can lead to the overall finding of no or only a weak relationship between work stress and BMI. Indeed, this neutralization may explain why our findings suggest a weak rather than strong association between work stress and BMI.

The present study has specific strengths. The data included a large sample of women and men employed in a wide range of occupations. Furthermore, the percentage of participation was acceptable (67%), and nonresponse occurred randomly enough to limit the potential for selection bias. Several known and potential confounders were controlled for. In addition, this study adds to the body of data by examining both leading work stress models in relation to BMI and conducting the analyses separately for SES groups and several disparate occupations. Finally, analyses were calculated for aggregated scores of work stress, which may be less biased measures of the true contextual effects than individual-level stress scores are. The use of aggregated scores minimized the problem of common-method variance.

Limitations
However, our results should be interpreted in light of at least 5 limitations. First, our data were cross-sectional; thus, the possibility of reversed causality cannot be ruled out. Body weight may affect the experience of work stress. For example, stigmatization and discrimination of obese employees have been documented (59), and both are important sources of stress. Such reversed causality between stress and BMI is a possible explanation for the observed associations, in particular when the individual-level scores of work stress are used. We used also aggregated scores of stress derived from occupation- and organization-level mean scores. Individuals’ BMI is unlikely to influence such scores. However, our study is open to reversed causality in cases in which heavier respondents have been selected to more stressful positions. Future research should examine prospective data to determine whether work stress leads to increased body weight or whether initial body weight is a predictor of individual differences in perceived work stress and stress-induced eating (32).

Second, we could not assess the effect of work stress exposure duration, because the length of the period in which the worker had been exposed to stressful work conditions was unknown to us (60). Because only long-term stress exposure is assumed to contribute to adverse physiological changes, the inability to determine exposure duration increases the likelihood of an underestimation of the associations between work stress and BMI.

Third, weight and height were self-reported, which can cause bias. Although self-reported and measured values of BMI have been observed to correlate strongly, both women and men overestimate their height and underestimate their weight (22,61). In particular, obese people tend to underestimate their weight and underweight people tend to make an overestimation (62,63). This can lead to an underestimation of the real associations between work stress and BMI.

Fourth, eating behavior is probably an important mediating factor in the relationship between work stress and a higher BMI. Unfortunately, our questionnaire did not include questions about dietary habits, and therefore we were unable to examine their mediating role.

Finally, the present data were female-dominated and from the public sector. Therefore, the findings should be interpreted with caution until they are validated in studies using other samples.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
NOTES
 REFERENCES
 
This study of a large heterogeneous employee population showed significant but substantially weak relationships between BMI and occupational- and organizational-level job control, job strain, and effort–reward imbalance. These results suggest that other factors that have predicted obesity in previous studies such as SES differences in eating habits and leisure-time physical activity may be more important in attempts to prevent additional increases in the prevalence of obesity.


   NOTES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
REFERENCES
 

Dr. Kouvonen was working as a Visiting Research Fellow at the Institute of Work, Health and Organisations, the University of Nottingham when preparing this paper.

DOI:10.1097/01.psy.0000170330.08704.62


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 NOTES
 REFERENCES
 

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