Long working hours and change in body weight: analysis of individual-participant data from 19 cohort studies.

OBJECTIVE: To examine the relation between long working hours and change in body mass index (BMI). METHODS: We performed random effects meta-analyses using individual-participant data from 19 cohort studies from Europe, US and Australia (n = 122,078), with a mean of 4.4-year follow-up. Working hours were measured at baseline and categorised as part time (<35 h/week), standard weekly hours (35-40 h, reference), 41-48 h, 49-54 h and ≥55 h/week (long working hours). There were four outcomes at follow-up: (1) overweight/obesity (BMI ≥ 25 kg/m2) or (2) overweight (BMI 25-29.9 kg/m2) among participants without overweight/obesity at baseline; (3) obesity (BMI ≥ 30 kg/m2) among participants with overweight at baseline, and (4) weight loss among participants with obesity at baseline. RESULTS: Of the 61,143 participants without overweight/obesity at baseline, 20.2% had overweight/obesity at follow-up. Compared with standard weekly working hours, the age-, sex- and socioeconomic status-adjusted relative risk (RR) of overweight/obesity was 0.95 (95% CI 0.90-1.00) for part-time work, 1.07 (1.02-1.12) for 41-48 weekly working hours, 1.09 (1.03-1.16) for 49-54 h and 1.17 (1.08-1.27) for long working hours (P for trend <0.0001). The findings were similar after multivariable adjustment and in subgroup analyses. Long working hours were associated with an excess risk of shift from normal weight to overweight rather than from overweight to obesity. Long working hours were not associated with weight loss among participants with obesity. CONCLUSIONS: This analysis of large individual-participant data suggests a small excess risk of overweight among the healthy-weight people who work long hours.

Job strain and health-related lifestyle: findings from an individual-participant meta-analysis of 118,000 working adults.

OBJECTIVES: We examined the associations of job strain, an indicator of work-related stress, with overall unhealthy and healthy lifestyles. METHODS: We conducted a meta-analysis of individual-level data from 11 European studies (cross-sectional data: n = 118,701; longitudinal data: n = 43,971). We analyzed job strain as a set of binary (job strain vs no job strain) and categorical (high job strain, active job, passive job, and low job strain) variables. Factors used to define healthy and unhealthy lifestyles were body mass index, smoking, alcohol intake, and leisure-time physical activity. RESULTS: Individuals with job strain were more likely than those with no job strain to have 4 unhealthy lifestyle factors (odds ratio [OR] = 1.25; 95% confidence interval [CI] = 1.12, 1.39) and less likely to have 4 healthy lifestyle factors (OR = 0.89; 95% CI = 0.80, 0.99). The odds of adopting a healthy lifestyle during study follow-up were lower among individuals with high job strain than among those with low job strain (OR = 0.88; 95% CI = 0.81, 0.96). CONCLUSIONS: Work-related stress is associated with unhealthy lifestyles and the absence of stress is associated with healthy lifestyles, but longitudinal analyses suggest no straightforward cause-effect relationship between work-related stress and lifestyle.