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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. [ABSTRACT FROM AUTHOR]

Unfavorable work characteristics, such as low job control and too high or too low job demands, have been suggested to increase the likelihood of physical inactivity during leisure time, but this has not been verified in large-scale studies. The authors combined individual-level data from 14 European cohort studies (baseline years from 1985-1988 to 2006-2008) to examine the association between unfavorable work characteristics and leisure-time physical inactivity in a total of 170,162 employees (50% women; mean age, 43.5 years). Of these employees, 56,735 were reexamined after 2-9 years. In cross-sectional analyses, the odds for physical inactivity were 26% higher (odds ratio = 1.26, 95% confidence interval: 1.15, 1.38) for employees with high-strain jobs (low control/high demands) and 21% higher (odds ratio = 1.21, 95% confidence interval: 1.11, 1.31) for those with passive jobs (low control/low demands) compared with employees in low-strain jobs (high control/low demands). In prospective analyses restricted to physically active participants, the odds of becoming physically inactive during follow-up were 21% and 20% higher for those with high-strain (odds ratio = 1.21, 95% confidence interval: 1.11, 1.32) and passive (odds ratio= 1.20, 95% confidence interval: 1.11, 1.30) jobs at baseline. These data suggest that unfavorable work characteristics may have a spillover effect on leisure-time physical activity. [ABSTRACT FROM AUTHOR]

Background: The World Health Organization (WHO) and the International Labour Organization (ILO) are developing Joint Estimates of the work-related burden of disease and injury (WHO/ILO Joint Estimates), with contributions from a large network of experts. Evidence from mechanistic data suggests that exposure to long working hours may increase alcohol consumption and cause alcohol use disorder. In this paper, we present a systematic review and meta-analysis of parameters for estimating the number of deaths and disability-adjusted life years from alcohol consumption and alcohol use disorder that are attributable to exposure to long working hours, for the development of the WHO/ILO Joint Estimates., Objectives: We aimed to systematically review and meta-analyse estimates of the effect of exposure to long working hours (three categories: 41-48, 49-54 and ≥55 h/week), compared with exposure to standard working hours (35-40 h/week), on alcohol consumption, risky drinking (three outcomes: prevalence, incidence and mortality) and alcohol use disorder (three outcomes: prevalence, incidence and mortality)., Data Sources: We developed and published a protocol, applying the Navigation Guide as an organizing systematic review framework where feasible. We searched electronic bibliographic databases for potentially relevant records from published and unpublished studies, including the WHO International Clinical Trials Register, Ovid MEDLINE, PubMed, Embase, and CISDOC on 30 June 2018. Searches on PubMed were updated on 18 April 2020. We also searched electronic grey literature databases, Internet search engines and organizational websites; hand-searched reference list of previous systematic reviews and included study records; and consulted additional experts., Study Eligibility and Criteria: We included working-age (≥15 years) workers in the formal and informal economy in any WHO and/or ILO Member State but excluded children (<15 years) and unpaid domestic workers. We considered for inclusion randomized controlled trials, cohort studies, case-control studies and other non-randomized intervention studies with an estimate of the effect of exposure to long working hours (41-48, 49-54 and ≥55 h/week), compared with exposure to standard working hours (35-40 h/week), on alcohol consumption (in g/week), risky drinking, and alcohol use disorder (prevalence, incidence or mortality)., Study Appraisal and Synthesis Methods: At least two review authors independently screened titles and abstracts against the eligibility criteria at a first stage and full texts of potentially eligible records at a second stage, followed by extraction of data from publications related to qualifying studies. Two or more review authors assessed the risk of bias, quality of evidence and strength of evidence, using Navigation Guide and GRADE tools and approaches adapted to this project., Results: Fourteen cohort studies met the inclusion criteria, comprising a total of 104,599 participants (52,107 females) in six countries of three WHO regions (Americas, South-East Asia, and Europe). The exposure and outcome were assessed with self-reported measures in most studies. Across included studies, risk of bias was generally probably high, with risk judged high or probably high for detection bias and missing data for alcohol consumption and risky drinking. Compared to working 35-40 h/week, exposure to working 41-48 h/week increased alcohol consumption by 10.4 g/week (95% confidence interval (CI) 5.59-15.20; seven studies; 25,904 participants, I 2 71%, low quality evidence). Exposure to working 49-54 h/week increased alcohol consumption by 17.69 g/week (95% confidence interval (CI) 9.16-26.22; seven studies, 19,158 participants, I 2 82%, low quality evidence). Exposure to working ≥55 h/week increased alcohol consumption by 16.29 g/week (95% confidence interval (CI) 7.93-24.65; seven studies; 19,692 participants; I 2 82%, low quality evidence). We are uncertain about the effect of exposure to working 41-48 h/week, compared with working 35-40 h/week on developing risky drinking (relative risk 1.08; 95% CI 0.86-1.36; 12 studies; I 2 52%, low certainty evidence). Working 49-54 h/week did not increase the risk of developing risky drinking (relative risk 1.12; 95% CI 0.90-1.39; 12 studies; 3832 participants; I 2 24%, moderate certainty evidence), nor working ≥55 h/week (relative risk 1.11; 95% CI 0.95-1.30; 12 studies; 4525 participants; I 2 0%, moderate certainty evidence). Subgroup analyses indicated that age may influence the association between long working hours and both alcohol consumption and risky drinking. We did not identify studies for which we had access to results on alcohol use disorder., Conclusions: Overall, for alcohol consumption in g/week and for risky drinking, we judged this body of evidence to be of low certainty. Exposure to long working hours may have increased alcohol consumption, but we are uncertain about the effect on risky drinking. We found no eligible studies on the effect on alcohol use disorder. Producing estimates for the burden of alcohol use disorder attributable to exposure to long working hours appears to not be evidence-based at this time. PROTOCOL IDENTIFIER: https://doi.org/10.1016/j.envint.2018.07.025., Prospero Registration Number: CRD42018084077., (Copyright © 2020 World Health Organization and International Labour Organization. Published by Elsevier Ltd.. All rights reserved.)

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/m 2 ) or (2) overweight (BMI 25-29.9 kg/m 2 ) among participants without overweight/obesity at baseline; (3) obesity (BMI ≥ 30 kg/m 2 ) 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.

Background Job strain is implicated in many atherosclerotic diseases, but its role in peripheral artery disease (PAD) is unclear. We investigated the association of job strain with hospital records of PAD, using individual-level data from 11 prospective cohort studies from Finland, Sweden, Denmark, and the United Kingdom. Methods and Results Job strain (high demands and low control at work) was self-reported at baseline (1985-2008). PAD records were ascertained from national hospitalization data. We used Cox regression to examine the associations of job strain with PAD in each study, and combined the study-specific estimates in random effects meta-analyses. We used τ 2 , I 2 , and subgroup analyses to examine heterogeneity. Of the 139 132 participants with no previous hospitalization with PAD, 32 489 (23.4%) reported job strain at baseline. During 1 718 132 person-years at risk (mean follow-up 12.8 years), 667 individuals had a hospital record of PAD (3.88 per 10 000 person-years). Job strain was associated with a 1.41-fold (95% CI, 1.11-1.80) increased average risk of hospitalization with PAD. The study-specific estimates were moderately heterogeneous (τ 2 =0.0427, I 2 : 26.9%). Despite variation in their magnitude, the estimates were consistent in both sexes, across the socioeconomic hierarchy and by baseline smoking status. Additional adjustment for baseline diabetes mellitus did not change the direction or magnitude of the observed associations. Conclusions Job strain was associated with small but consistent increase in the risk of hospitalization with PAD, with the relative risks on par with those for coronary heart disease and ischemic stroke.

Importance: It is well established that selected lifestyle factors are individually associated with lower risk of chronic diseases, but how combinations of these factors are associated with disease-free life-years is unknown., Objective: To estimate the association between healthy lifestyle and the number of disease-free life-years., Design, Setting, and Participants: A prospective multicohort study, including 12 European studies as part of the Individual-Participant-Data Meta-analysis in Working Populations Consortium, was performed. Participants included 116 043 people free of major noncommunicable disease at baseline from August 7, 1991, to May 31, 2006. Data analysis was conducted from May 22, 2018, to January 21, 2020., Exposures: Four baseline lifestyle factors (smoking, body mass index, physical activity, and alcohol consumption) were each allocated a score based on risk status: optimal (2 points), intermediate (1 point), or poor (0 points) resulting in an aggregated lifestyle score ranging from 0 (worst) to 8 (best). Sixteen lifestyle profiles were constructed from combinations of these risk factors., Main Outcomes and Measures: The number of years between ages 40 and 75 years without chronic disease, including type 2 diabetes, coronary heart disease, stroke, cancer, asthma, and chronic obstructive pulmonary disease., Results: Of the 116 043 people included in the analysis, the mean (SD) age was 43.7 (10.1) years and 70 911 were women (61.1%). During 1.45 million person-years at risk (mean follow-up, 12.5 years; range, 4.9-18.6 years), 17 383 participants developed at least 1 chronic disease. There was a linear association between overall healthy lifestyle score and the number of disease-free years, such that a 1-point improvement in the score was associated with an increase of 0.96 (95% CI, 0.83-1.08) disease-free years in men and 0.89 (95% CI, 0.75-1.02) years in women. Comparing the best lifestyle score with the worst lifestyle score was associated with 9.9 (95% CI 6.7-13.1) additional years without chronic diseases in men and 9.4 (95% CI 5.4-13.3) additional years in women (P < .001 for dose-response). All of the 4 lifestyle profiles that were associated with the highest number of disease-free years included a body-mass index less than 25 (calculated as weight in kilograms divided by height in meters squared) and at least 2 of the following factors: never smoking, physical activity, and moderate alcohol consumption. Participants with 1 of these lifestyle profiles reached age 70.3 (95% CI, 69.9-70.8) to 71.4 (95% CI, 70.9-72.0) years disease free depending on the profile and sex., Conclusions and Relevance: In this multicohort analysis, various healthy lifestyle profiles appeared to be associated with gains in life-years without major chronic diseases.

Objectives: Several recent large-scale studies have indicated a prospective association between job strain and coronary heart disease, stroke and diabetes. Job strain is also associated with poorer mental health, a risk factor for cardiometabolic disease. This study investigates the prospective relationships between change in job strain, poor mental health and cardiometabolic disease, and whether poor mental health is a potential mediator of the relationship between job strain and cardiometabolic disease., Methods: We used data from five cohort studies from Australia, Finland, Sweden and UK, including 47 757 men and women. Data on job strain across two measurements 1-5 years apart (time 1 (T1)-time 2 (T2)) were used to define increase or decrease in job strain. Poor mental health (symptoms in the top 25% of the distribution of the scales) at T2 was considered a potential mediator in relation to incident cardiometabolic disease, including cardiovascular disease and diabetes, following T2 for a mean of 5-18 years., Results: An increase in job strain was associated with poor mental health (HR 1.56, 95% CI 1.38 to 1.76), and a decrease in job strain was associated with lower risk in women (HR 0.70, 95% CI 0.60-0.84). However, no clear association was observed between poor mental health and incident cardiometabolic disease (HR 1.08, 95% CI 0.96-1.23), nor between increase (HR 1.01, 95% CI 0.90-1.14) and decrease (HR 1.08, 95% CI 0.96-1.22) in job strain and cardiometabolic disease., Conclusions: The results did not support that change in job strain is a risk factor for cardiometabolic disease and yielded no support for poor mental health as a mediator., Competing Interests: Competing interests: None., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Obesity increases the risk of several chronic diseases, but the extent to which the obesity-related loss of disease-free years varies by lifestyle category and across socioeconomic groups is unclear. We estimated the number of years free from major non-communicable diseases in adults who are overweight and obese, compared with those who are normal weight., Methods: We pooled individual-level data on body-mass index (BMI) and non-communicable diseases from men and women with no initial evidence of these diseases in European cohort studies from the Individual-Participant-Data Meta-Analysis in Working Populations consortium. BMI was assessed at baseline (1991-2008) and non-communicable diseases (incident type 2 diabetes, coronary heart disease, stroke, cancer, asthma, and chronic obstructive pulmonary disease) were ascertained via linkage to records from national health registries, repeated medical examinations, or self-report. Disease-free years from age 40 years to 75 years associated with underweight (BMI <18·5 kg/m 2 ), overweight (≥25 kg/m 2 to <30 kg/m 2 ), and obesity (class I [mild] ≥30 kg/m 2 to <35 kg/m 2 ; class II-III [severe] ≥35 kg/m 2 ) compared with normal weight (≥18·5 kg/m 2 to <25 kg/m 2 ) were estimated., Findings: Of 137 503 participants from ten studies, we excluded 6973 owing to missing data and 10 349 with prevalent disease at baseline, resulting in an analytic sample of 120 181 participants. Of 47 127 men, 211 (0·4%) were underweight, 21 468 (45·6%) normal weight, 20 738 (44·0%) overweight, 3982 (8·4%) class I obese, and 728 (1·5%) class II-III obese. The corresponding numbers among the 73 054 women were 1493 (2·0%), 44 760 (61·3%), 19 553 (26·8%), 5670 (7·8%), and 1578 (2·2%), respectively. During 1 328 873 person-years at risk (mean follow-up 11·5 years [range 6·3-18·6]), 8159 men and 8100 women developed at least one non-communicable disease. Between 40 years and 75 years, the estimated number of disease-free years was 29·3 (95% CI 28·8-29·8) in normal-weight men and 29·4 (28·7-30·0) in normal-weight women. Compared with normal weight, the loss of disease-free years in men was 1·8 (95% CI -1·3 to 4·9) for underweight, 1·1 (0·7 to 1·5) for overweight, 3·9 (2·9 to 4·9) for class I obese, and 8·5 (7·1 to 9·8) for class II-III obese. The corresponding estimates for women were 0·0 (-1·4 to 1·4) for underweight, 1·1 (0·6 to 1·5) for overweight, 2·7 (1·5 to 3·9) for class I obese, and 7·3 (6·1 to 8·6) for class II-III obese. The loss of disease-free years associated with class II-III obesity varied between 7·1 and 10·0 years in subgroups of participants of different socioeconomic level, physical activity level, and smoking habit., Interpretation: Mild obesity was associated with the loss of one in ten, and severe obesity the loss of one in four potential disease-free years during middle and later adulthood. This increasing loss of disease-free years as obesity becomes more severe occurred in both sexes, among smokers and non-smokers, the physically active and inactive, and across the socioeconomic hierarchy., Funding: NordForsk, UK Medical Research Council, US National Institute on Aging, Academy of Finland, Helsinki Institute of Life Science, and Cancer Research UK., (Copyright © 2018 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objectives This systematic review and meta-analysis combined published study-level data and unpublished individual-participant data with the aim of quantifying the relation between long working hours and the onset of depressive symptoms. Methods We searched PubMed and Embase for published prospective cohort studies and included available cohorts with unpublished individual-participant data. We used a random-effects meta-analysis to calculate summary estimates across studies. Results We identified ten published cohort studies and included unpublished individual-participant data from 18 studies. In the majority of cohorts, long working hours was defined as working ≥55 hours per week. In multivariable-adjusted meta-analyses of 189 729 participants from 35 countries [96 275 men, 93 454 women, follow-up ranging from 1-5 years, 21 747 new-onset cases), there was an overall association of 1.14 (95% confidence interval (CI) 1.03-1.25] between long working hours and the onset of depressive symptoms, with significant evidence of heterogeneity (I 2 =45.1%, P=0.004). A moderate association between working hours and depressive symptoms was found in Asian countries (1.50, 95% CI 1.13-2.01), a weaker association in Europe (1.11, 95% CI 1.00-1.22), and no association in North America (0.97, 95% CI 0.70-1.34) or Australia (0.95, 95% CI 0.70-1.29). Differences by other characteristics were small. Conclusions This observational evidence suggests a moderate association between long working hours and onset of depressive symptoms in Asia and a small association in Europe.

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.

Objective: To investigate whether work related stress, measured and defined as job strain, is associated with the overall risk of cancer and the risk of colorectal, lung, breast, or prostate cancers., Design: Meta-analysis of pooled prospective individual participant data from 12 European cohort studies including 116,056 men and women aged 17-70 who were free from cancer at study baseline and were followed-up for a median of 12 years. Work stress was measured and defined as job strain, which was self reported at baseline. Incident cancers (all n=5765, colorectal cancer n=522, lung cancer n=374, breast cancer n=1010, prostate cancer n=865) were ascertained from cancer, hospital admission, and death registers. Data were analysed in each study with Cox regression and the study specific estimates pooled in meta-analyses. Models were adjusted for age, sex, socioeconomic position, body mass index (BMI), smoking, and alcohol intake, Results: A harmonised measure of work stress, high job strain, was not associated with overall risk of cancer (hazard ratio 0.97, 95% confidence interval 0.90 to 1.04) in the multivariable adjusted analyses. Similarly, no association was observed between job strain and the risk of colorectal (1.16, 0.90 to 1.48), lung (1.17, 0.88 to 1.54), breast (0.97, 0.82 to 1.14), or prostate (0.86, 0.68 to 1.09) cancers. There was no clear evidence for an association between the categories of job strain and the risk of cancer., Conclusions: These findings suggest that work related stress, measured and defined as job strain, at baseline is unlikely to be an important risk factor for colorectal, lung, breast, or prostate cancers.

Background: Job strain (i.e., high job demands combined with low job control) is a frequently used indicator of harmful work stress, but studies have often used partial versions of the complete multi-item job demands and control scales. Understanding whether the different instruments assess the same underlying concepts has crucial implications for the interpretation of findings across studies, harmonisation of multi-cohort data for pooled analyses, and design of future studies. As part of the 'IPD-Work' (Individual-participant-data meta-analysis in working populations) consortium, we compared different versions of the demands and control scales available in 17 European cohort studies., Methods: Six of the 17 studies had information on the complete scales and 11 on partial scales. Here, we analyse individual level data from 70 751 participants of the studies which had complete scales (5 demand items, 6 job control items)., Results: We found high Pearson correlation coefficients between complete scales of job demands and control relative to scales with at least three items (r > 0.90) and for partial scales with two items only (r = 0.76-0.88). In comparison with scores from the complete scales, the agreement between job strain definitions was very good when only one item was missing in either the demands or the control scale (kappa > 0.80); good for job strain assessed with three demand items and all six control items (kappa > 0.68) and moderate to good when items were missing from both scales (kappa = 0.54-0.76). The sensitivity was > 0.80 when only one item was missing from either scale, decreasing when several items were missing in one or both job strain subscales., Conclusions: Partial job demand and job control scales with at least half of the items of the complete scales, and job strain indices based on one complete and one partial scale, seemed to assess the same underlying concepts as the complete survey instruments.

Background: Tobacco smoking is a major contributor to the public health burden and healthcare costs worldwide, but the determinants of smoking behaviours are poorly understood. We conducted a large individual-participant meta-analysis to examine the extent to which work-related stress, operationalised as job strain, is associated with tobacco smoking in working adults., Methodology and Principal Findings: We analysed cross-sectional data from 15 European studies comprising 166,130 participants. Longitudinal data from six studies were used. Job strain and smoking were self-reported. Smoking was harmonised into three categories never, ex- and current. We modelled the cross-sectional associations using logistic regression and the results pooled in random effects meta-analyses. Mixed effects logistic regression was used to examine longitudinal associations. Of the 166,130 participants, 17% reported job strain, 42% were never smokers, 33% ex-smokers and 25% current smokers. In the analyses of the cross-sectional data, current smokers had higher odds of job strain than never-smokers (age, sex and socioeconomic position-adjusted odds ratio: 1.11, 95% confidence interval: 1.03, 1.18). Current smokers with job strain smoked, on average, three cigarettes per week more than current smokers without job strain. In the analyses of longitudinal data (1 to 9 years of follow-up), there was no clear evidence for longitudinal associations between job strain and taking up or quitting smoking., Conclusions: Our findings show that smokers are slightly more likely than non-smokers to report work-related stress. In addition, smokers who reported work stress smoked, on average, slightly more cigarettes than stress-free smokers.