Do changes in working hours increase stress in Japanese white-collar workers?

Introduction: High 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.

Methods: We 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.

Results: We 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.

Discussion: These results show that reducing stress requires decreasing working hours as well as identifying factors that lead to high stress.

1. Introduction

High stress is generally viewed as harmful to an individual's physical and mental state (1). While there are several types of stressors, recent studies have shown that high stress at work is associated with increased risk of negative health outcomes, including impaired cognitive function, depression, cardiovascular diseases, and poor sleep quality (2–4). Hence, the prevention of work-related stress is widely recognized as a critical challenge for occupational health and safety (5).

Overtime work is generally thought to be one of the most influential risk factors for high stress (6). Typically defined as 55 working hours or more per week, overtime work is also a risk factor for health-related problems such as coronary heart disease (7). High job demands can result in burnout and other risks as a consequence of overwork (8). It is believed that stress increase as a result of these factors. Overtime work of more than 40 h per week was associated with higher levels of stress in a representative sample of the United States population (9). In a study analyzing the impact of long working hours on psychosocial stress response among white-collar workers in one Korean company, working 60 or more hours per week was significantly associated with higher stress compared to working 40–44 h per week (10). Among 59,021 Japanese workers in 117 companies, the length of working hours was positively associated with various higher stress responses, including “irritability,” “fatigue,” “anxiety,” “depression,” and “somatic responses” for both genders (11). In fact, in Japan, where overtime work has been prevalent, the government has considered stress management to be of paramount importance. Japan is the first country in the world to make annual stress check tests legally mandatory in a workplace with 50 or more employees, since 2014 (12, 13). The stress check is performed using the Brief Job Stress Questionnaire (BJSQ), developed based on the demand-control-support model (14, 15).

In addition, the Japanese government is trying to reduce long working hours as one measure to address the shortage of workers (16). The outbreak of the novel coronavirus infection may have increased the working hours and various other burdens on some workers. Even previously stress-free individuals may have been exposed to new stress risks. However, previous research has shown that only those currently working long hours tend to have high stress. It remains to be seen which people will likely become highly stressed in the future, depending on changes in working style. Understanding who is prone to high stress among those who are not under high stress may help employers implement measures to prevent high levels of stress.

Therefore, we longitudinally examined how many participants were identified as having high stress after 1 year based on a survey conducted at two time points among a representative sample of Japanese workers. To focus on the increase in stress between the two points, we targeted those identified as having low stress at the first point. Additionally, we examined differences in how stress increased depending on changes in working hours by gender, considering that women and men in Japan have significantly different ways of working (17). As in the previous study mentioned earlier, we targeted white-collar workers aged 20–64 years, which is also validated by the fact that the most common type of work in Japan is white-collar.

2. Materials and methods

2.1. Data and materials

We used the same data as in a previous study (18). A web-based cohort survey regarding the occupational safety of 30,000 workers was conducted twice in October 2018 (T1) and October 2019 (T2) in collaboration with a research company in Tokyo, Japan. This company has one of the largest online research panels in Japan with over 1.8 million voluntarily registered panelists. To minimize selection bias, we selected a sample of Japanese workers aged 20–64 years based on the composition ratio of workers by industry, gender, and age in the Labor Force Survey by the Japanese Ministry of Internal Affairs and Communications. The research company randomly sent an e-mail invitation to registered workers to participate in the study. Workers who provided web-based informed consent were selected to participate in the survey. They completed a self-reported online questionnaire consisting of questions regarding demographic, job-related, and life-related variables, as well as health-related outcomes. The study protocol was approved by the Institutional Review Board of the Jikei University School of Medicine, Tokyo, Japan, in 2018 [No. 30-153(9174)].

2.2. Measures of stress and working hours

We selected full-time employees (over 35 h per week as the average working hours during the last 6 months) who reported being professional or general clerks, based on the Japan Standard Industrial Classification established by the Japanese Ministry of Internal Affairs and Communications.

We targeted those employees identified as having low stress at T1 using the BJSQ, a tool widely used to evaluate job-related stress in Japan (19). In this study, low-stress population was identified as the population not determined to have high stress according to the following criteria. The BJSQ comprises 57 items that assess (A) job stressors, (B) psychological and physical stress responses, and (C) buffering factors. (A) Job stressors include quantitative job overload, qualitative job overload, physical demands, job control, skill utilization, interpersonal conflict, poor physical environment, suitable jobs, and intrinsic rewards (17 items). (B) Psychological and physical stress responses include lassitude, irritation, fatigue, anxiety, depression, and physical stress responses (29 items). (C) Buffering factors include supervisor support, co-worker support, and support from family and friends (9 items). Those whose scores in section (B) were over 76, or whose total scores in sections (A) and (C) were over 75 and scores in section (B) were over 62 were defined as having high stress (20).

The outcome measure of the present study was to check whether each participant was defined as having high stress at T2 in the following two ways: when the score criterion in section (B) only, representing physical reaction, was met (criterion A); when the score criterion in sections (A) and (C) were met even if the score criterion in section (B) was not met (criterion B).

We grouped self-reported average working hours per week in the last 6 months into seven categories based on how the hours changed between T1 and T2: 35–40 h/week in T1 remained the same or increased in T2, 41–50 h/week in T1 decreased, remained the same, or increased in T2, and 51 h/week or more in T1 decreased or remained the same in T2. Since many Japanese workers are required to work the full 40 legal working hours, we considered working 40 h plus a few hours of overtime per week as the most standard way of working. Therefore, we set the reference group as those who worked 41–50 h per week in both T1 and T2.

2.3. Other variables

We categorized years of experience in the job into four categories: 5 years or less, 6–10 years, 11–20 years, 21 years or more. We split years of education into two categories based on whether each participant graduated from college. We assessed medical background according to whether each participant was using medications. We divided income into four categories: those whose income at T2 increased, decreased, or remained the same compared to T1 and those who did not want to answer. We grouped roommate status into two categories: those whose number of roommates remained the same or increased/decreased.

2.4. Statistical analysis

First, we summarized the demographic and baseline characteristics of those who met the eligibility criteria for each category of working hours. Second, we conducted univariate logistic regression to determine whether participants had high or low stress at the second point based on criteria A and B, as well as only criterion A as the objective variable, and how working hours changed between the first and second points according to the classification mentioned above as the explanatory variable. We also conducted multivariate logistic regression with years of experience, years of education, medical background, income, and roommates as other variables. In these model analyses, crude/adjusted odds ratios (ORs) with 95% confidence intervals (CIs) and p-values were calculated. The robustness of the results was confirmed by a sensitivity analysis in which the cut-off for working hours was changed from 50 to 60 h. Statistical significance was set at P < 0.05. All analyses were conducted using STATA version 17.0 (STATA Corp., College Station, TX, USA).

3. Results

Of 7,012 voluntary registered panelists who answered that they were general clerks or professionals, 5,165 worked an average of over 35 h per week during the last 6 months. Among them, 4,051 were identified as having low stress at T1. After excluding those who did not answer at either T1 or T2 or changed occupations between T1 and T2, data from 3,874 participants (2,167 female and 1,707 male) were analyzed.

The characteristics of the study participants, according to the category of working hours, are shown in Table 1. The mean age was approximately 43 years for both genders. About 45% of female participants and 72.5% of male participants reported having graduated from college; 27.7% of female participants and 58.4% of male participants reported being professional. Among female and male participants, the proportion of those identified as highly stressed based on criteria A and B was 14.6 and 14.3%, respectively. When identifying the highly stressed state using only criterion A, the rates were 13.1 and 12.7% for female and male participants, respectively.

TABLE 1

Table 1. Summary of backgrounds and characteristics of female workers.

Table 2 shows the results of univariate and multivariate logistic regression for both male and female when identifying high stress at T2 based on criteria A and B. The percentages of female and male who were identified as highly stressed at T2 and who worked 41–50 h per week at both T1 and T2 (reference) were 11.2 and 13.6 %, respectively. In Table 3, crude ORs for high stress among female were higher for those who worked 41–50 h per week at T1 and more than 50 h at T2, for those who worked more than 50 h per week at T1 and 35–40 h/41–50 h per week at T2, and for those who worked more than 50 h per week at both T1 and T2 [OR = 2.20 [95% CI, 1.25–3.88]: OR = 2.07 [95% CI, 1.28–3.34]: OR = 1.78 [95% CI, 1.06–3.00], respectively]. However, among male, none of the categories of working hours showed statistically significant results.

TABLE 2

Table 2. Summary of backgrounds and characteristics of male workers.

TABLE 3

Table 3. Estimated odds ratios for high stress based on the Brief Job Stress Questionnaire by gender.

When analyzing other variables together with working hours, ORs for high stress among female were higher for those who worked 41–50 h per week at T1 and more than 50 h at T2, and for those who worked more than 50 h per week at T1 and 35–40 h/41–50 h per week at T2 (OR = 2.09 [95% CI, 1.18–3.70]: OR = 1.86 [95% CI, 1.14–3.03], respectively). In addition, ORs for high stress among female were higher for those whose income had decreased at T2 (OR = 1.54 [95% CI, 1.09–2.17]). ORs for high stress among female were lower for those whose years of experience were more than 10 years and more than 20 years [OR = 0.67 [95% CI, 0.49–0.93]: OR = 0.68 [95% CI, 0.46–1.00], respectively]. On the other hand, among male, none of the categories of working hours showed statistically significant results, except that ORs for high stress were significantly higher among male whose number of roommates had increased or decreased (OR = 2.07 [95% CI, 1.28–3.35]).

The direction of ORs did not change in the sensitivity analyses in which the cut-off for working hours was changed (Supplementary Table S1).

Table 4 shows results of univariate and multivariate logistic regression of both male and female participants when identifying high stress at T2 based only on criterion A. The rate of female and male participants who were identified as highly stressed at T2 and who worked 41–50 h per week both at T1 and T2 (reference) were 9.2 and 12.0%, respectively. In addition to categories of working hours which showed statistically significant data in Table 4, crude ORs for high stress among female were higher for those who worked 35–40 h per week at T1 and T2, and for those who worked 35–40 h per week at T1 and 41–50/51 h per week at T2 as well [OR = 1.52 [95% CI, 1.06–2.18]; OR = 1.69 [95% CI, 1.05–1.72], respectively]. When analyzing other variables, ORs for high stress among male were lower for those whose type of work was professional.

TABLE 4

Table 4. Estimated odds ratios for high stress based on (B) psychological and physical stress responses category of the Brief Job Stress Questionnaire by gender.

4. Discussion

In the present study, we used a nationally representative sample of white-collar workers in Japan to examine the prevalence of those who had low stress at the first time point but had high stress 1 year later. We considered the relationship between changes in working hours and stress levels. The incidence of high stress for male and female in this population based on criteria A and B was 14.6 and 14.3%, respectively, reflecting no gender difference in the number of new cases of stress per year. Our results were consistent with the prevalence of stress studied cross-sectionally (20). We evaluated an association between changing working hours and high stress in a longitudinal design, which had previously been proposed only in a cross-sectional manner (21). Although, as expected, high stress occurred when working hours increased in a longitudinal design, more than 11.5% of those whose working hours either decreased or remained the same were highly stressed 1 year later. Because stress levels can change over time, it is important to monitor these levels in workers (12, 13) to prevent accidents, injuries, and worsening health (5).

Regarding the degree of increasing stress between categories of changes in working hours, an interesting result was that in female, there was a greater increase in stress compared to the reference group in several working categories; contrastingly, none of the working hour categories showed statistically significant data among male. One potential reason for this phenomenon could be that, as the previous study mentioned, long working hours were related to the risk of high stress levels; women were also more likely to respond negatively to high job strain (22) than were men. Higher job strain due to increased working hours can predispose women to higher stress. Additionally, the effect of increased work intensity on worker satisfaction can be buffered by high autonomy (23, 24). In this study, male made up a higher proportion of professional workers than did female, which is common. Professional workers may have higher autonomy (23). Being a professional worker is a buffering factor between long working hours and high stress, given that ORs for high stress among male were lower among those who were professional workers when high stress was evaluated based only on criterion A. Similar results were observed in the previous study: among IT company workers aged 40 years or younger, there were no significant differences in mental and physical status between overtime work (>45 h/month overwork) and non-overtime work groups in both sexes (25). IT professionals often find themselves coerced into accepting high workloads under high pressure to shorten the time to market, as well as to learn new skills because of rapid advances in technology (26, 27). However, elevated job strain can be lessened by providing job autonomy and skill variation to employees (28). Considering professionals have higher job autonomy than do other occupations, high job autonomy could mitigate the negative effects of increased working hours on stress levels.

Among female, both increased and decreased working hours caused a greater increase in stress than when working hours were stable. Women's working hours could have been reduced involuntarily, resulting in high stress. Furthermore, employers might have cut their working hours due to poor job performance related to distress associated with work–family conflict (29, 30). Indeed the interaction between conditions at work and at home plays an important role in determining the health of women employees, whereas the health of employed men is determined more selectively by working conditions (31). This difference is expected to be more evident in Japan, where women are still responsible for performing domestic chores according to traditional gender roles (32). In fact, among middle-aged Japanese working women, regular employees have poorer self-reported health than non-regular employees, a difference attributed to experiencing more strain related to work–family conflict (33). Additionally, in various countries, women face an increased risk of health problems due to long working hours and greater family responsibilities (34, 35). Hence, work–family conflict could induce high levels of stress in women employees. To reduce such conflict, employers could willingly adopt flexible work schedules, which could promote a better balance between work and personal life (36).

Incidence of high stress might directly and indirectly be related to economic status. In women, reduction of income could be related to increasing stress, and lead to dissatisfaction at work; this situation can adversely affect employees' mental and physical health and their overall wellbeing (37). This is also true in men; ORs for high stress among men were significantly higher for those whose number of roommates increased or decreased. Studies show that being separated is associated with a high prevalence of psychological distress in both men and women (38). One reason high stress was also observed in male whose roommates had increased could be related to male gender roles at home. In Japan, gender roles are still characterized by the model of a strong man being the breadwinner and by women's low level of participation in the workforce (39). Marriage has been shown to enhance negative effects of unemployment on men's mental health but act as a buffer among women; thus, it is reasonable to suppose that marriage can stimulate men's sense of responsibility, which could predispose them to high stress (40).

When evaluating the high-stress state based only on criterion A, the incidence in female and male was still 13.1 and 12.7%, respectively. A previous study also showed a subtle difference in prevalence between criteria A and B and using only criterion A. In a financial company comprising 7,356 male and 7,362 female employees, the prevalence of high stress based on criteria A and B of the BJSQ was 5.6% for male and 15.0% for female. When evaluated based on criterion A, the prevalence was 4.5% for male and 13.2% for female (19). Combined with the results of previous studies, our results suggest that over the course of 1 year, Japanese employees can progress to high stress levels severe enough to cause physical reactions. Studies have indicated that workers in Japan working more than 50 h per week have an increased risk of occupational accidents (41), and workers in the EU who work more than 55 h per week have an increased risk of stroke, atrial fibrillation, and several other diseases (42–44). Notably, in this study, the increase in stress occurred in all categories for male, although there was no difference among the categories of the changes in working hours. The effect on objective outcome may be different when stress increases in situations with increased working hours and in other situations. Further studies are needed to evaluate long-term outcomes in Japan.

In this study, we investigated working hours to identify groups prone to high stress among homogeneous groups not in a high stress state, especially regarding changes in working hours. In recent years, efforts have been made to reduce the working hours in Japan (16); however, the amount of reduction varies by industry, and in some occupations, the workload increased due to the COVID-19 pandemic (45). From a preventive perspective, it might be important to not only manage working hours according to job demands, but also create a positive workplace by, for example, providing co-worker support and job autonomy, and allowing flexible work styles. It is also crucial to consider how to manage high-stress situations (46). For such a study, it is also important to evaluate the continuous state of high stress and changes in the previous stress state based on a homogeneous group.

Although this study provides new insights into the incidence of high stress over 1 year, and how changes in stress depend on changes in working hours by gender, several limitations should be mentioned. First, recall bias may have occurred because working hours per week were self-reported; those identified as highly stressed in the second survey were more likely to report longer working hours than they actually performed. Second, there is the possibility of selection bias; responses to stress in a population that can respond to two web questionnaires may contain bias. However, because the sampling was based on the distribution of the population by age, gender, and industry, the results were consistent with high stress rates in other studies. Third, this study evaluated average working hours during the last 6 months, which made it impossible to follow in more detail how working hours changed. Fourth, our results regarding the effect of changes in working hours on stress levels may not be applicable to the current working environment where many employees have been obliged to work from home, changed their commute time, and transportation methods due to the COVID-19 pandemic. However, working from home has a negative effect on employees' physical and mental health (47). It is important to identify other variables, besides working hours, that can contribute to high stress. Lastly, we did not show a causal relationship between changes in working hours and stress, because we did not perform any interventions to increase or decrease working hours. Thus, how stress changes after intentional increases in working hours remains to be seen.

Data availability statement

The datasets presented in this article are not readily available because they contain information that could compromise the privacy of research participants, based on the “Ethical Guidelines for Medical and Health Research involving Human Subjects” by the Japanese government. Requests to access the datasets should be directed to corresponding author.

Ethics statement

The studies involving human participants were reviewed and approved by the Institutional Review Board of the Jikei University School of Medicine, Tokyo, Japan, in 2018 [No. 30-153(9174)]. The patients/participants provided their written informed consent to participate in this study.

Author contributions

TY and KT: conceptualization. TY: data collection. TA and KT: methodology. MO and TA: statistical analyses and writing–original draft. KT: supervising. All authors critically revised the manuscript, contributed to the interpretation of the data, and approved the final manuscript.

Funding

This work was partially supported by Industrial Disease Clinical Research Grants from the Ministry of Health, Labor and Welfare, Japan (180901-01), the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency of Japan (JPMEERF18S11708), and JSPS KAKENHI (Grant Number 21H03402). The funders had no role in study design, data collection, or the decision to publish this Correspondence.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1076024/full#supplementary-material

References

1. Clough BA, March S, Chan RJ, Casey LM, Phillips R, Ireland MJ. Psychosocial interventions for managing occupational stress and burnout among medical doctors: a systematic review. Syst Rev. (2017) 6:144. doi: 10.1186/s13643-017-0526-3

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Fishta A, Backé EM. Psychosocial stress at work and cardiovascular diseases: an overview of systematic reviews. Int Arch Occup Environ Health. (2015) 88:997–1014. doi: 10.1007/s00420-015-1019-0

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Gavelin HM, Domellöf ME, Åström E, Nelson A, Launder NH, Neely AS, et al. Cognitive function in clinical burnout: a systematic review and meta-analysis. Work Stress. (2022) 36:86–104. doi: 10.1080/02678373.2021.2002972

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Johnson DA, Lisabeth L, Lewis TT, Sims M, Hickson DA, Samdarshi T, et al. The contribution of psychosocial stressors to sleep among African Americans in the Jackson heart study. Sleep. (2016) 39:1411–9. doi: 10.5665/sleep.5974

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Leka S, Jain A, Zwetsloot G, Cox T. Policy-level interventions and work-related psychosocial risk management in the European Union. Work Stress. (2010) 24:298–307. doi: 10.1080/02678373.2010.519918

CrossRef Full Text | Google Scholar

6. Bannai A, Ukawa S, Tamakoshi A. Long working hours and psychological distress among school teachers in Japan. J Occup Health. (2015) 57:20–7. doi: 10.1539/joh.14-0127-OA

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Kivimäki M, Nyberg ST, Batty GD, Fransson EI, Heikkilä K, Alfredsson L, et al. Job strain as a risk factor for coronary heart disease: a collaborative meta-analysis of individual participant data. Lancet Lond Engl. (2012) 380:1491–7. doi: 10.1016/S0140-6736(12)60994-5

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Chirico F. Job stress models for predicting burnout syndrome: a review. Ann Ist Super Sanita. (2016) 52:443–56. doi: 10.4415/ANN_16_03_17

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Grosch JW, Caruso CC, Rosa RR, Sauter SL. Long hours of work in the U. S: associations with demographic and organizational characteristics, psychosocial working conditions, and health. Am J Ind Med. (2006) 49:943–52. doi: 10.1002/ajim.20388

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Lee K, Suh C, Kim JE, Park JO. The impact of long working hours on psychosocial stress response among white-collar workers. Ind Health. (2017) 55:46–53. doi: 10.2486/indhealth.2015-0173

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Kikuchi H, Odagiri Y, Ohya Y, Nakanishi Y, Shimomitsu T, Theorell T, et al. Association of overtime work hours with various stress responses in 59,021 Japanese workers: Retrospective cross-sectional study. PLoS ONE. (2020) 15:e0229506. doi: 10.1371/journal.pone.0229506

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Kawakami N, Tsutsumi A. The Stress Check Program: a new national policy for monitoring and screening psychosocial stress in the workplace in Japan. J Occup Health. (2016) 58:1–6. doi: 10.1539/joh.15-0001-ER

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Mishiba T. The background and current state of implementing a legal system for stress checks in Japan. Ind Health. (2022) 60:183–95. doi: 10.2486/indhealth.2021-0090

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Kawada T, Otsuka T. Relationship between job stress, occupational position and job satisfaction using a brief job stress questionnaire (BJSQ). Work Read Mass. (2011) 40:393–9. doi: 10.3233/WOR-2011-1251

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Karasek RA. Job demands, job decision latitude, and mental strain: implications for job redesign. Adm Sci Q. (1979) 24:285–308. doi: 10.2307/2392498

CrossRef Full Text | Google Scholar

16. Ministry Ministry of Health Labour and Welfare. Report of Monthly Labour Survey. Tokyo: Roumugyousei Co., Ltd. (2022).

Google Scholar

17. Iida A. Gender inequality in Japan: the status of women, and their promotion in the workplace. Corvinus J Int Aff. (2018) 3:43–52. doi: 10.14267/cojourn.2018v3n3a5

CrossRef Full Text | Google Scholar

18. Yamauchi T, Sasaki T, Takahashi K, Umezaki S, Takahashi M, Yoshikawa T, et al. Long working hours, sleep-related problems, and near-misses/injuries in industrial settings using a nationally representative sample of workers in Japan. PLoS ONE. (2019) 14:e0219657. doi: 10.1371/journal.pone.0219657

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Tsutsumi A, Shimazu A, Eguchi H, Inoue A, Kawakami N, A. Japanese stress check program screening tool predicts employee long-term sickness absence: a prospective study. J Occup Health. (2018) 60:55–63. doi: 10.1539/joh.17-0161-OA

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Tsutsumi A, Inoue A, Eguchi H. How accurately does the brief job stress questionnaire identify workers with or without potential psychological distress? J Occup Health. (2017) 59:356–60. doi: 10.1539/joh.17-0011-BR

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Hsu YY, Bai CH, Yang CM, Huang YC, Lin TT, Lin CH. Long hours' effects on work-life balance and satisfaction. BioMed Res Int. (2019) 2019:5046934. doi: 10.1155/2019/5046934

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Padkapayeva K, Gilbert-Ouimet M, Bielecky A, Ibrahim S, Mustard C, Brisson C, et al. Gender/sex differences in the relationship between psychosocial work exposures and work and life stress. Ann Work Expo Health. (2018) 62:416–25. doi: 10.1093/annweh/wxy014

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Lopes H, Lagoa S, Calapez T. Work autonomy, work pressure, and job satisfaction: An analysis of European Union countries. Econ Labour Relat Rev. (2014) 25:306–26. doi: 10.1177/1035304614533868

CrossRef Full Text | Google Scholar

24. Mazzola JJ, Schonfeld IS, Spector PE. What qualitative research has taught us about occupational stress. Stress Health. (2011) 27:93–110. doi: 10.1002/smi.1386

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Nishikitani M, Nakao M, Karita K, Nomura K, Yano E. Influence of overtime work, sleep duration, and perceived job characteristics on the physical and mental status of software engineers. Ind Health. (2005) 43:623–9. doi: 10.2486/indhealth.43.623

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Mano RS, Mesch GS. E-mail characteristics, work performance and distress. Comput Hum Behav. (2010) 26:61–9. doi: 10.1016/j.chb.2009.08.005

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Pawlowski SD, Robey D. Bridging user organizations: knowledge brokering and the work of information technology professionals. MIS Q. (2004) 28:645–72. doi: 10.2307/25148658

CrossRef Full Text | Google Scholar

28. Bakker AB, Demerouti E. Multiple levels in job demands-resources theory: Implications for employee well-being and performance. In: Diener E, Oishi S, Tay L, editors. Handbook Well-Being. Salt Lake City, UT: DEF Publishers (2018).

Google Scholar

29. Bashir U, Ismail Ramay M. Impact of stress on employees job performance: a study on banking sector of Pakistan. Int J Mark Stud. (2010) 2:122–6. doi: 10.5539/ijms.v2n1p122

CrossRef Full Text | Google Scholar

30. Dyrbye LN, Shanafelt TD, Johnson PO, Johnson LA, Satele D, West CP, et al. cross-sectional study exploring the relationship between burnout, absenteeism, and job performance among American nurses. BMC Nurs. (2019) 18:57. doi: 10.1186/s12912-019-0382-7

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Krantz G, Berntsson L, Lundberg U. Total workload, work stress and perceived symptoms in Swedish male and female white-collar employees. Eur J Public Health. (2005) 15:209–14. doi: 10.1093/eurpub/cki079

PubMed Abstract | CrossRef Full Text | Google Scholar

32. Belarmino M, Roberts MR. Japanese gender role expectations and attitudes: a qualitative analysis of gender inequality. J Int Womens Stud. (2019) 20:272–88.

Google Scholar

33. Honjo K, Iso H, Ikeda A, Yamagishi K, Saito I, Kato T, et al. Cross-sectional association between employment status and self-rated health among middle-aged Japanese Women: the influence of socioeconomic conditions and work-life conflict. J Epidemiol. (2020) 30:396–403. doi: 10.2188/jea.JE20190005

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Dembe AE, Yao X. chronic disease risks from exposure to long-hour work schedules over a 32-year period. J Occup Environ Med. (2016) 58:861. doi: 10.1097/JOM.0000000000000810

PubMed Abstract | CrossRef Full Text | Google Scholar

35. Gilbert-Ouimet M, Trudel X, Talbot D, Vézina M, Milot A, Brisson C. Long working hours associated with elevated ambulatory blood pressure among female and male white-collar workers over a 25-year follow-up. J Hum Hypertens. (2022) 36:207–17. doi: 10.1038/s41371-021-00499-3

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Kelly EL, Moen P, Tranby E. Changing workplaces to reduce work-family conflict: schedule control in a white-collar organization. Am Sociol Rev. (2011) 76:265–90. doi: 10.1177/0003122411400056

PubMed Abstract | CrossRef Full Text | Google Scholar

37. Faragher EB, Cass M, Cooper CL. The Relationship between Job Satisfaction and Health: A Meta-Analysis. In: Cooper CL, editor. London: Palgrave Macmillan UK. (2013).

PubMed Abstract | Google Scholar

38. Gutiérrez-Lobos K, Wölfl G, Scherer M, Anderer P, Schmidl-Mohl B. The gender gap in depression reconsidered: the influence of marital and employment status on the female/male ratio of treated incidence rates. Soc Psychiatry Psychiatr Epidemiol. (2000) 35:202–10. doi: 10.1007/s001270050229

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Tsutsui J. Female labor participation and the sexual division of labor: a consideration of the persistent male-breadwinner model. Jpn Labor Rev. (2016) 13:80–100.

Google Scholar

40. Artazcoz L, Benach J, Borrell C, Cortès I. Unemployment and mental health: understanding the interactions among gender, family roles, and social class. Am J Public Health. (2004) 94:82–8. doi: 10.2105/AJPH.94.1.82

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Anzai T, Yamauchi T, Ozawa M, Takahashi K. A generalized structural equation model approach to long working hours and near-misses among healthcare professionals in Japan. Int J Environ Res Public Health. (2021) 18:7154. doi: 10.3390/ijerph18137154

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Kivimäki M, Nyberg ST, Batty GD, Kawachi I, Jokela M, Alfredsson L, et al. Long working hours as a risk factor for atrial fibrillation: a multi-cohort study. Eur Heart J. (2017) 38:2621–8. doi: 10.1093/eurheartj/ehx324

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Kivimäki M, Jokela M, Nyberg ST, Singh-Manoux A, Fransson EI, Alfredsson L, et al. Long working hours and risk of coronary heart disease and stroke: a systematic review and meta-analysis of published and unpublished data for 603,838 individuals. Lancet Lond Engl. (2015) 386:1739–46. doi: 10.1016/S0140-6736(15)60295-1

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Ervasti J, Pentti J, Nyberg ST, Shipley MJ, Leineweber C, Sørensen JK, et al. Long working hours and risk of 50 health conditions and mortality outcomes: a multicohort study in four European countries. Lancet Reg Health Eur. (2021) 11:100212. doi: 10.1016/j.lanepe.2021.100212

PubMed Abstract | CrossRef Full Text | Google Scholar

45. Gai R, Tobe M. Managing healthcare delivery system to fight the COVID-19 epidemic: experience in Japan. Glob Health Res Policy. (2020) 5:23. doi: 10.1186/s41256-020-00149-0

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Kivimäki M, Nyberg ST, Fransson EI, Heikkilä K, Alfredsson L, Casini A, et al. Associations of job strain and lifestyle risk factors with risk of coronary artery disease: a meta-analysis of individual participant data. CMAJ Can Med Assoc J. (2013) 185:763–9. doi: 10.1503/cmaj.121735

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Xiao Y, Becerik-Gerber B, Lucas G, Roll SC. Impacts of working from home during COVID-19 pandemic on physical and mental well-being of office workstation users. J Occup Environ Med. (2021) 63:181–90. doi: 10.1097/JOM.0000000000002097

PubMed Abstract | CrossRef Full Text | Google Scholar