Table_1_Do changes in working hours increase stress in Japanese white-collar workers?.DOCX

IntroductionHigh stress at work is associated with negative health outcomes for workers, making stress prevention a critical challenge. Overtime work is an influential stress factor. This study, therefore, aimed to longitudinally evaluate how stress increased depending on changes in working hours among Japanese white-collar workers.MethodsWe targeted 3,874 participants who were full-time workers and were recognized as having low stress in a web-based cohort in 2018 (T1) and 2019 (T2). We performed univariate and multivariate logistic regression with the following variables: years of experience, years of education, medical background, income, and roommates.ResultsWe observed a greater increase in stress among female who worked 41–50 h per week at T1 and more than 50 hours per week at T2, and those who worked more than 50 h per week at T1 and 35–40/41–50 h per week at T2, compared to those who worked 41–50 h per week both at T1 and T2, with odds ratios (ORs) and 95% confidence intervals (95% CI) of OR = 2.09, 95% CI (1.18, 3,70); OR =1.86, 95% CI (1.14, 3.03), respectively. However, no association between change in working hours and stress was found among male.DiscussionThese results show that reducing stress requires decreasing working hours as well as identifying factors that lead to high stress.</p

Prevalences of disease labels in patients with the top 10% cost and low to medium cost.

Prevalences of disease labels in patients with the top 10% cost and low to medium cost.</p

Forty-six chronic disease labels classified by the ICD10 codes.

Forty-six chronic disease labels classified by the ICD10 codes.</p

Strengthening the reporting of observational studies in epidemiology statement—A checklist of items that should be included in reports of observational studies.

Strengthening the reporting of observational studies in epidemiology statement—A checklist of items that should be included in reports of observational studies.</p

Heatmap for item-response probabilities of disease labels in the 30 latent class models.

Heatmap for item-response probabilities of disease labels in the 30 latent class models.</p

Distribution of annual medical costs in the insured population who were subscribers in 2015 (n = 16,989,029).

The top 1%, 5%, 10%, 20%, 30% of patients accounted for 26.1%, 46.9%, 59.0%, 73.9% and 83.3% of the total annual medical costs. 1 USD = 120 JPY. (DOCX)</p

Demographic characteristics of the patients with the top 10% cost and low to medium cost.

Demographic characteristics of the patients with the top 10% cost and low to medium cost.</p

Multivariate predictions of the annual total medical cost and 5-year mortality in MetS classes by a generalized linear model (n = 540,615).

Multivariate predictions of the annual total medical cost and 5-year mortality in MetS classes by a generalized linear model (n = 540,615).</p

Model fit for the latent class analysis (BIC and AIC).

BIC = Bayesian Information Criterion; AIC = Akaike Information Criterion. (DOCX)</p

A heatmap for the item-response probabilities of diseases categorized in the metabolic syndrome classes.

A heatmap for the item-response probabilities of diseases categorized in the metabolic syndrome classes.</p