Association of overtime work hours with various stress responses in 59,021 Japanese workers: Retrospective cross-sectional study.

This study aims to clarify the relationships between length of overtime work and various stress responses using large-scale cross-sectional data of Japanese workers. This study's participants are 59,021 Japanese workers in 117 companies. Data was collected by self-reporting questionnaire. The Brief Job Stress Questionnaire was used to measure stress responses on six scales (i.e. "lack of vigor", "irritability", "fatigue", "anxiety", "depression", and "somatic responses"). Length of overtime work hours were classified as 0-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, and >80 hours/month. Multiple linear regression analyses were used to examine the association of stress responses with overtime while adjusting all possible confounders. In result, workers with longer overtime showed significantly higher "irritability", "fatigue", "anxiety", "depression", and "somatic responses" for both genders (p-for-trend <0.001), however, length of overtime was negatively associated with "lack of vigor" among men (p-for-trend <0.001). Men with 61-80 hours of overtime showed high fatigue with high vigor at the same time. Length of overtime was linearly associated with various stress responses, except for "lack of vigor". Length of overtime shows linear associations with various psychosomatic stress responses. However, "lack of vigor" was not consistently associated with overtime. Male workers with 61-80 hours of monthly overtime were more likely to feel vigorous than workers with shorter overtime. However, potential longterm effects of such extreme overtime should not be underestimated and must be paid attention to.

Introduction

Long working hours are shown to affect various health outcomes [1,2], especially in cardiovascular diseases [3-6]. In addition, exceedingly long working hours also deteriorate worker’s mental health causing issues such as alcoholism [7], and sleep disturbances [8]. Avoiding long working is needed for maintaining workers health.

Especially in Japan, long working hours have been regarded as a serious social and health issue. Karoshi (sudden death caused by cardiovascular or cerebrovascular disease due to overwork) and Karojisatsu (suicide due to overwork) have gathered public attention from the mid-1970s [9,10]. Efforts to prevent Karoshi and Karojisatsu by limiting working hours have been attempted, however, the number of claimed and compensated cases of occupational mental disorders are substantially increasing [11,12]. In 2017, the Japanese government issued “The Action Plan for the Realization of Work Style Reform”, which introduced an overtime limit (i.e. less than 100 hours/month) by law [13], in addition to upper limit of regular working hours (40 hours/week). However, much controversy has risen as to whether 100 hours/month is appropriate or not.

In this context, it is important to consider the upper overtime-limit to prevent mental illness among workers. So far, several studies have investigated associations between long working hours and mental health [1,14]. However, many studies used tertile [15,16], quartile [17-19] or quintile [20,21] cut-off were used for evaluating effects of amount of overtime work. Studies in which a broader range of overtime is used are needed for the establishment of an upper overtime-limit as a health policy. In addition, there are only few published in which gender-stratified analysis has been performed. This is due to the limited number of women with long working hours [19,22]. Labor force participation among women of working age increased substantially worldwide [23]. Furthermore, occupational class might influence the relation between overtime and health outcome [24]. To nuance the picture of associations between overtime and mental health, stratified analysis by these factors is also needed.

This study aims to clarify the detailed relationships between length of overtime and different stress responses using large size data of Japanese workers.

Materials and methods

Study design

This study is a cross-sectional study using data which was acquired by the Stress Check Program of Japan.

Japan’s stress check program

Detailed procedures of the Stress Check Program are described elsewhere [25,26]. Briefly, the program was started by an amendment of the Industrial Safety and Health Law in 2015. The program aims at primary prevention of mental health disorders, by reminding the worker of his/her own stress condition and facilitating the improvement of workplace environments. The program requires all workplaces with 50 or more employees to conduct a questionnaire survey regarding psychosocial stress at work at least once a year. Feedback is provided to each employee with aims to decrease the risk of mental health problems through enhancing their awareness of their own stress [26]. The law requires employers to provide all regularly hired employees with opportunities to participate in the Stress Check Program. According to the protocol issued by the Japanese government, employers are encouraged to remind non-responders to complete the Stress Check Program at least one time [25].

Participants and data collection

The eligible participants of this study were 95,004 employees in 223 companies which implemented the Stress Check Program from December 2015 to November 2016, based on Industrial Health and Safety Law. The program was provided to the employer by one health-service company through commission of the employers. The health service company has branches located in four major cities (Sapporo, Tokyo, Osaka and Fukuoka). Based on contracts with each company, they provide the Stress Check Program services to 223 companies, which are located in every prefecture of Japan.

The program was carried out via internet or written questionnaire.

This research was based on data collected as part of the Stress Check Program retrospectively. The primary objective of main project is investigating longitudinal effects of long working hours on workers mental and physical health. Data from participants who declined use of their data for research was excluded. In addition, workers who belong to workplaces with less than 50 employees were excluded because such workplaces have different occupational healthcare requirements under Japanese law. For example, assignment of occupational health physicians is not mandatory for such small workplaces. Furthermore, it is also not mandatory for them to conduct Stress Check Program, therefore, we excluded their data to ensure representability. Shift workers and part-time workers were excluded because their work schedule varies considerably different from other workers.

All data were collected in a non-anonymized manner to give feedback to participants in Stress Check Program, however the data was anonymized before being provided to the authors due to ethical issue.

Independent variables: Length of overtime work hours

This study assessed the length of participants’ monthly length of overtime work hours. Self-reported monthly overtime data was collected in increments of every 10 hours from “20 hours or less” to “141 hours or more”. Due to the smaller number of participants who engaged in 81 hours or more overtime we consolidated them into one category, and thus the present study set the categorization of overtime as the following 8 categories, “20 hours or less”, “21–30 hours”, “31–40 hours”, “41–50 hours”, “51–60 hours”, “61–70 hours”, “71–80 hours” and “81 hours or more”.

Dependent variables: Stress responses

Stress responses were assessed using the Brief Job Stress Questionnaire (BJSQ) following the recommended protocol of the Stress Check Program [25-27]. The BJSQ was originally designed to measure both psychological and somatic, both positive and negative stress responses among workers in any workplace with minimum number of items [28]. Six stress-response scales can be measured by 29 questionnaire items in the BJSQ, and each items were developed by referring some already standardized/authorized questionnaires. In detail, “vigor”, “fatigue”, and “irritability”, consisting of 3 items each, were from the Profile Of Mood States (POMS). “Depression”, consisting of 6 items, was from the Center for Epidemiologic Studies for Depression Scale (CES-D). “Anxiety”, consisting of 3 items, was from the State-Trail Anxiety Inventory (STAI). “Somatic stress responses”, consisting of 11 items, was from Screener for the Somatoform Disorders and the Subjective Wellbeing Inventory (SUBI) [28]. Each item in the BJSQ asks the respondent to choose one of four options on the Likert scale. For example, to assess their vigor level, participants were given the phrase “I have been very active”, and then asked to choose one of four options (i.e., “almost never”, “sometimes”, “often” and “almost always”). The total score on each scale was calculated by summing all items according to established protocol [25]. Then, standardized scores were derived for each scale of stress response. With regards to vigor score, we calculated “lack of vigor” score in this study by subtracting vigor score from 15. Higher scores indicate higher stress responses.

Covariates

We collected data of gender, age, type of job (sales/professional/clerk/services/manual/transport/other), job class (regular/manager/director), employment status (regular/contract/temporary/other) and type of schedule (inflexible/flexible/other) by self-report. In addition, company industry (construction/manufacturing/electricity, gas, heat supply and water/information and communications/transport and postal activities/trade/finance/scientific and development research institutes/accommodations, eating and drinking services/education/medical service and social welfare/public sector/other) and size (51-100/101-300/301-999/1,000–2,999/3,000 or more employees) were also obtained through open information. Job control, supervisor’s support and coworker’s support were also collected from questions on the BJSQ.

Statistical procedure

Multiple linear regression analysis was used to examine the associations of stress responses with overtime. In each model, we used a standardized score of each stress response (such as “lack of vigor” or “irritability”, etc.) as a dependent variable, “length of overtime work hours” as an independent variable, other individual characteristics (such as “age” and “job position”, etc.) as covariates. First, we performed linear trend tests by treating “length of overtime work hours” as a continuous variable to check the linear association between each stress response and overtime. Then, to seek possible thresholds of overtime, “length of overtime work hours” were treated as a dummy variable by setting “less than 20hours” group as a reference category.

For sensitivity analysis, a multiple imputations technique was used because approximately 10% (n = 8,560) of subjects included missing data for any one of the analyzed variables [29]. We created 20 multiple imputed data sets which included all measurement variables using a multivariate normal imputation method under a missing at random assumption, and combined the estimated parameters using Rubin’s combination methods [30].

Finally, stratified analyses by gender, age-category (i.e. less than 30, 30–59 and 60 or older) and job-position (regular and manager) were also performed. In these models, interaction terms between age-category/job-position and overtime work hours were used to explore possible effect modification.

All statistical tests were two-tailed and considered to be statistically significant at the 0.05 level. All analyses were done with Stata, version 15.0.

Ethics

We acquired the written consent for the secondary-use of their data from each participant. In detail, participants were asked “I admit utilizing of my data for academic research purposes.” with “Yes/No” options. Furthermore, the protocol of this study was opened to them through the website, and we accepted their refusal for their participation at any time. This protocol is in line with the Japanese Ethical Guidelines for Epidemiological Research, which regulates that informed consent is not necessarily required for observational studies utilizing the existing data [31]. All procedures of this study received approval from the Tokyo Medical University Ethics Committee (No. 2016–166).

Results and discussions

Fig 1 shows the flow of study participants. By Industrial Health and Safety Law, 95,004 regular workers were offered to receive stress check program at their workplace from December 2015 to November 2016. After excluding 6,016 participants who did not receive the program and 5,518 participants who declined secondary use of their data, the data-set of 83,470 participants was initially established (mean [± standard deviation] age; 44.0 [±11.2] years old, age range: 17–89 years old, 62.7% men). Then, we excluded participants at workplaces with less than 50 employees (n = 921), part-time workers (n = 5,039), shift workers (n = 9,929) and those with any missing data (n = 8,560) from the data-set. Some participants had two or more conditions for exclusion. Finally, 59,201 participants in 117 companies remained for this analysis (inclusion rate: 62.1%).

Fig 1

Flow chart of study participants.

Table 1 shows the characteristics of study participants. The mean (±standard deviation) age was 44.3 (±10.7) years. Men accounted for 69.1% of the participants. The core group comprised middle-aged, male clerk with regular employment status and inflexible type of schedule. Average length of overtime was 26.3 (±20.5) hours per month. Regarding industries among participants, 32.9% (n = 19,401) and 25.7% (n = 15,150) of participants were workers in manufactural companies and public sectors, respectively. More than two thirds of participants were working in companies with 3,000 or more employees. Length of overtime differed significantly by gender, age, type of job, job class, employment status, type of schedule, industrial classification and company size.

Table 1

Characteristics of participants.

		Number of companies	Number of participants	(%)	Overtime work hours (hr/month)		Proportion of excessive overtime work hours (%)
					Mean	(S.D.)	61–80 hr/month	81-hr/month
Total		117	59,021	(100.0%)	26.3	(±20.5)	(6.4%)	(1.5%)
Gender
	Men		40,764	(69.1%)	30.6	(±21.9)	(8.7%)	(2.0%)
	Women		18,257	(30.9%)	16.6	(±13.8)	(1.2%)	(0.5%)
Age
	<30		7,518	(12.7%)	25.4	(±20.0)	(4.8%)	(1.6%)
	30–39		13,400	(22.7%)	26.1	(±20.1)	(5.4%)	(1.3%)
	40–49		20,238	(34.3%)	27.4	(±21.1)	(7.0%)	(1.5%)
	50–59		14,671	(24.9%)	27.8	(±21.8)	(8.4%)	(1.8%)
	≥60		3,194	(5.4%)	15.6	(±14.3)	(1.5%)	(0.8%)
Types of job
	Sales		8,736	(14.8%)	41.9	(±22.9)	(22.3%)	(1.5%)
	Professionals		15,401	(26.1%)	29.3	(±21.4)	(5.7%)	(2.2%)
	Clerk		24,796	(42.0%)	20.8	(±16.3)	(2.0%)	(0.9%)
	Service		3,143	(5.3%)	21.5	(±18.3)	(4.5%)	(1.0%)
	Manual		2,649	(4.5%)	22.0	(±19.3)	(4.5%)	(1.9%)
	Transport		530	(0.9%)	26.4	(±26.6)	(5.1%)	(4.9%)
	Other		3,766	(6.4%)	21.0	(±20.9)	(4.4%)	(2.5%)
Job class
	Regular		45,352	(76.8%)	23.5	(±18.7)	(4.3%)	(0.9%)
	Manager		8,671	(14.7%)	41.2	(±22.7)	(16.8%)	(3.5%)
	Director		2,294	(3.9%)	38.1	(±26.5)	(14.7%)	(6.4%)
	Other		2,704	(4.6%)	15.3	(±15.4)	(1.8%)	(1.1%)
Employment status
	Regular		45,427	(77.0%)	30.0	(±21.6)	(8.2%)	(1.8%)
	Contract		10,979	(18.6%)	14.0	(±10.6)	(0.3%)	(0.4%)
	Temporary		1,685	(2.9%)	11.6	(±9.0)	(0.2%)	(0.4%)
	Other		930	(1.6%)	16.6	(±15.5)	(0.9%)	(1.2%)
Types of schedule
	Inflexible		53,805	(91.2%)	26.5	(±20.8)	(6.6%)	(1.4%)
	Flexible		4,442	(7.5%)	24.8	(±20.3)	(4.4%)	(2.1%)
	Other		774	(1.3%)	18.4	(±21.9)	(2.8%)	(3.2%)
Industrial Classification
	Construction	5	9,399	(15.9%)	45.1	(±19.7)	(19.7%)	(1.2%)
	Manufacture	40	19,401	(32.9%)	25.4	(±21.2)	(6.6%)	(2.1%)
	Electricity, gas, heat supply and water	3	179	(0.3%)	40.5	(±27.9)	(11.2%)	(8.4%)
	Information and communications	6	2,137	(3.6%)	25.3	(±16.7)	(3.7%)	(0.4%)
	Transport and postal activities	7	387	(0.7%)	30.3	(±29.2)	(8.0%)	(5.7%)
	Trade	6	1,655	(2.8%)	28.1	(±18.5)	(6.8%)	(0.4%)
	Finance	2	491	(0.8%)	28.9	(±17.8)	(5.1%)	(0.6%)
	Scientific and development research institutes	3	183	(0.3%)	19.6	(±16.4)	(2.2%)	(1.6%)
	Accommodations, eating and drinking services	2	252	(0.4%)	30.6	(±19.0)	(2.0%)	(3.2%)
	Education	3	961	(1.6%)	24.6	(±26.3)	(4.2%)	(5.5%)
	Medical service and social welfare	17	2,593	(4.4%)	24.6	(±25.7)	(3.5%)	(5.4%)
	Others	15	6,233	(10.6%)	14.3	(±11.5)	(0.7%)	(0.6%)
	Public sector	8	15,150	(25.7%)	20.7	(±14.7)	(1.3%)	(0.4%)
Company size (number of employees)
	50–99	42	1,848	(3.1%)	24.4	(±23.0)	(4.1%)	(3.8%)
	100–299	44	4,363	(7.4%)	24.5	(±21.5)	(4.9%)	(2.9%)
	300–999	18	5,108	(8.7%)	23.6	(±19.0)	(4.6%)	(1.3%)
	1,000–2,999	8	7,448	(12.6%)	26.2	(±21.5)	(4.9%)	(2.6%)
	≥3,000	5	40,254	(68.2%)	26.9	(±20.7)	(7.2%)	(1.1%)

Fig 2 shows the results of multiple linear regression analysis for total participants. Among those with 21–80 hours/month of overtime, linear dose-response curves were observed for “irritability”, “fatigue”, “anxiety”, “depression” and “somatic responses”, i.e. the more overtime a participant worked, the severer their stress response levels were. Particularly, a linear trend was clearly found between “fatigue” level and overtime work hours. The beta (95% confidence intervals) of fatigue levels for each overtime category were 0.20 (0.17, 0.22), 0.32 (0.29, 0.34), 0.41 (0.38, 0.43), 0.46 (0.43, 0.49), 0.53 (0.49, 0.58), 0.61 (0.57, 0.66), 0.73 (0.67, 0.79), for, 21–30, 31–40, 41–50, 51–60, 61–70, 71–80 and 81 or more hours/month, respectively. Whereas, the dose-response curve for perceived “lack of vigor” was considerably different from those of other stress-responses. The “lack of vigor” levels remained were comparable across those with less than 60 hours of overtime work hours. Meanwhile, “lack of vigor” levels were lower among those with 61–80 hours of overtime (beta [95% CI]: -0.13 [-0.17, -0.08] and -0.11[-0.16, -0.06] for 61–70 and 71–80 hours/month, respectively). In other words, those with 61–80 hours of overtime were reported to be more vigorous at work, compared to those with less than 60 hours of overtime.

Fig 2

Associations between stress response and overtime work hours; Multiple linear regression results.

* Higher score indicates unfavorable stress-response. Adjusted covariates are gender, age, type of job, job class, employment status, type of schedule, company size, company industry, job control, supervisor’s support and coworker’s support. ** Lack of vigor was derived by reversing the score of vigor for harmonization with other stress-response scales, which higher score indicates unfavorable stress-response.

Table 2 shows the multiple linear regression results stratified by gender. For both genders, workers with 21 hours/month or more overtime reported significantly higher “irritability”, “fatigue”, “anxiety”, “depression” and “somatic responses”, compared to those with less than 20 hours/month of overtime (p<0.001). In addition, workers who engaged in 21–30 hours/month of overtime reported a higher level of “lack of vigor”. However, male workers who engaged in 61–80 hours/month of overtime reported a significantly lower level of “lack of vigor” than those with less than 20 hours/month of overtime (51–60 hours/month: beta = -0.04 [95%CI: -0.07, 0.00], 61–70 hours/month: beta = -0.13 [-0.17. -0.09], 71–80 hours/month: beta = -0.13 [-0.18, -0.09]). This association was not observed for women. Both among men and women there is no increase in effect above 80 hours overtime work/month. Indeed the data did not include any clinical data, however speculatively, there might be a healthy worker effect in this extreme group. That would mean that unhealthy workers in this category either left a job or stopped working whereas healthy workers would stay. This may lead to underestimation of effect. Then, linear trend tests showed that length of overtime was positively associated with “irritability”, “fatigue”, “anxiety” “depression” and “somatic responses” (p for trend <0.001). Conversely, length of overtime was negatively associated with “lack of vigor” (p for trend <0.001). None of these results became significantly different after using the dataset with multiple imputation. For gender differences, significant interactions by gender were observed in the association between overtime work hours and several stress responses. Compared to workers with shorter overtime work hours, those with longer hours tend to have higher stress responses for both genders; however, this association was stronger among women than men. This implies that working women may be more vulnerable than men for long working hours.

Table 2

Associations between overtime work hours and stress responses: Gender-stratified multiple linear regression results.

Overtime work hours/month	n	Lack of Vigor			Irritability			Fatigue			Anxiety			Depression			Somatic responses
		beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p
Men
≤20	15,751	Ref			Ref			Ref			Ref			Ref			Ref
21–30	7,260	0.04	(0.02, 0.07)	**	0.13	(0.10, 0.16)	***	0.16	(0.13, 0.18)	***	0.11	(0.08, 0.14)	***	0.04	(0.02, 0.07)	**	0.03	(0.00, 0.05)	*
31–40	5,812	0.01	(-0.02, 0.04)		0.17	(0.14, 0.19)	***	0.26	(0.23, 0.29)	***	0.16	(0.13, 0.19)	***	0.06	(0.03, 0.08)	***	0.07	(0.04, 0.10)	***
41–50	4,097	0.00	(-0.03, 0.03)		0.19	(0.16, 0.22)	***	0.36	(0.33, 0.39)	***	0.23	(0.20, 0.26)	***	0.12	(0.09, 0.15)	***	0.13	(0.10, 0.17)	***
51–60	3,494	-0.04	(-0.07, 0.00)	*	0.22	(0.18, 0.25)	***	0.42	(0.38, 0.45)	***	0.26	(0.23, 0.29)	***	0.10	(0.07, 0.13)	***	0.14	(0.10, 0.17)	***
61–70	1,814	-0.13	(-0.17, -0.09)	***	0.23	(0.19, 0.28)	***	0.48	(0.44, 0.53)	***	0.38	(0.33, 0.42)	***	0.16	(0.12, 0.21)	***	0.16	(0.11, 0.21)	***
71–80	1,740	-0.13	(-0.18, -0.09)	***	0.33	(0.28, 0.38)	***	0.57	(0.52, 0.61)	***	0.44	(0.40, 0.49)	***	0.22	(0.17, 0.26)	***	0.23	(0.18, 0.27)	***
81-	796	-0.01	(-0.07, 0.06)		0.41	(0.35, 0.48)	***	0.73	(0.66, 0.79)	***	0.42	(0.35, 0.48)	***	0.24	(0.17, 0.30)	***	0.23	(0.16, 0.29)	***
p for linear trend		<0.001 (negative)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Women
≤20	13,529	Ref			Ref			Ref			Ref			Ref			Ref
21–30	2,301	0.07	(0.03, 0.11)	***	0.15	(0.10, 0.19)	***	0.26	(0.21, 0.30)	***	0.19	(0.14, 0.23)	***	0.13	(0.09, 0.17)	***	0.11	(0.07, 0.16)	***
31–40	1,219	0.15	(0.10, 0.20)	***	0.23	(0.17, 0.28)	***	0.47	(0.21, 0.31)	***	0.36	(0.31, 0.42)	***	0.20	(0.15, 0.25)	***	0.19	(0.13, 0.25)	***
41–50	567	0.14	(0.06, 0.22)	***	0.20	(0.12, 0.29)	***	0.52	(0.21, 0.32)	***	0.34	(0.26, 0.42)	***	0.22	(0.14, 0.30)	***	0.18	(0.10, 0.26)	***
51–60	326	-0.04	(-0.14, 0.07)		0.21	(0.11, 0.32)	***	0.60	(0.21, 0.33)	***	0.50	(0.39, 0.61)	***	0.33	(0.23, 0.43)	***	0.25	(0.14, 0.36)	***
61–70	127	0.12	(-0.04, 0.28)		0.25	(0.08, 0.42)	**	0.82	(0.21, 0.34)	***	0.71	(0.54, 0.88)	***	0.54	(0.38, 0.70)	***	0.41	(0.24, 0.58)	***
71–80	97	-0.14	(-0.32, 0.05)		0.39	(0.19, 0.58)	***	0.78	(0.21, 0.35)	***	0.71	(0.52, 0.90)	***	0.60	(0.42, 0.79)	***	0.44	(0.24, 0.63)	***
81-	91	0.01	(-0.18, 0.20)		0.32	(0.12, 0.52)	**	0.62	(0.21, 0.36)	***	0.53	(0.33, 0.72)	***	0.34	(0.15, 0.52)	**	0.13	(-0.07, 0.33)
p for linear trend		<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Interaction terms
Men (ref) versus Women
21–30		0.04	(-0.01, 0.08)		0.02	(-0.03, 0.06)		0.09	(0.04, 0.13)	***	0.07	(0.02, 0.11)	**	0.08	(0.03, 0.13)	**	0.09	(0.04, 0.14)	***
31–40		0.15	(0.09, 0.21)	***	0.06	(0.00, 0.12)	+	0.19	(0.13, 0.25)	***	0.18	(0.12, 0.24)	***	0.13	(0.07, 0.19)	***	0.12	(0.06, 0.19)	***
41–50		0.15	(0.06, 0.23)	***	0.01	(-0.08, 0.09)		0.13	(0.05, 0.22)	**	0.08	(0.00, 0.16)	+	0.08	(0.00, 0.17)	*	0.04	(-0.04, 0.13)
51–60		0.04	(-0.06, 0.14)		-0.03	(-0.14, 0.08)		0.13	(0.03, 0.24)	*	0.18	(0.08, 0.29)	**	0.20	(0.09, 0.30)	***	0.09	(-0.02, 0.20)
61–70		0.29	(0.12, 0.45)	**	-0.02	(-0.19, 0.15)		0.30	(0.13, 0.46)	***	0.28	(0.12, 0.45)	**	0.35	(0.18, 0.51)	***	0.21	(0.04, 0.38)	*
71–80		0.03	(-0.15, 0.22)		0.01	(-0.18, 0.21)		0.16	(-0.03, 0.35)	+	0.20	(0.01, 0.39)	*	0.34	(0.16, 0.53)	***	0.17	(-0.03, 0.36)	+
81-		-0.01	(-0.20, 0.19)		-0.09	(-0.29, 0.12)		-0.09	(-0.29, 0.11)		0.10	(-0.10, 0.30)		0.11	(-0.09, 0.31)		-0.08	(-0.29, 0.12)

+ p<0.1

*p<0.05, ** p<0.01, ***p<0.001, Higher score indicates unfavorable stress-response. All betas were adjusted by age, type of job, job class, employment status, type of schedule, company size, company industry, job control, supervisors support and coworker’s support. Lack of vigor was derived by reversing the score of vigor for harmonization with other stress-response scales, which higher score indicates unfavorable stress-response

Tables 3 and 4 showed stratified analysis results by age-category and job-class. Compared to middle-aged male workers, older workers showed relatively higher stress responses, especially “fatigue”, “anxiety” and “depression”. This may suggest that older workers constitute a more vulnerable population than younger ones for overtime working than middle-aged workers. Due to the small number of working women in higher job-positions, the interaction by job-class was not clear. It is interesting that the highest beta coefficient in point was observed for “fatigue” among women in administrative positions (beta = 1.16 [0.68. 1.63]).

Table 3

Association between overtime work hours and stress responses: Results of multiple linear regression stratified by gender and age-category.

Overtime work hours/month	n	Lack of Vigor			Irritability			Fatigue			Anxiety			Depression			Somatic responses
		beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p
Men
Younger (aged less than 30 years)
≤20	1,485	Ref			Ref			Ref			Ref			Ref			Ref
21–30	796	0.12	(0.04, 0.20)	**	0.15	(0.07, 0.24)	***	0.22	(0.14, 0.30)	***	0.12	(0.03, 0.20)	**	0.12	(0.04, 0.21)	**	0.03	(-0.06, 0.12)
31–40	546	0.08	(-0.01, 0.17)	+	0.17	(0.07, 0.27)	**	0.28	(0.19, 0.37)	***	0.15	(0.05, 0.24)	**	0.06	(-0.03, 0.16)		0.10	(0.00, 0.20)
41–50	345	0.08	(-0.03, 0.18)		0.21	(0.09, 0.33)	**	0.45	(0.35, 0.56)	***	0.30	(0.19, 0.42)	***	0.28	(0.17, 0.40)	***	0.32	(0.20, 0.44)	***
51–60	291	-0.05	(-0.17, 0.07)		0.29	(0.16, 0.42)	***	0.48	(0.36, 0.59)	***	0.28	(0.15, 0.41)	***	0.14	(0.01, 0.26)	*	0.15	(0.01, 0.28)	*
61–70	146	-0.01	(-0.16, 0.14)		0.35	(0.18, 0.52)	***	0.57	(0.42, 0.72)	***	0.49	(0.33, 0.66)	***	0.37	(0.20, 0.54)	**	0.30	(0.13, 0.48)	***
71–80	135	-0.12	(-0.28, 0.04)		0.40	(0.22, 0.58)	***	0.42	(0.26, 0.58)	***	0.51	(0.34, 0.69)	***	0.41	(0.23, 0.59)	***	0.38	(0.20, 0.56)	***
81-	77	0.05	(-0.16, 0.25)		0.48	(0.26, 0.71)	***	0.89	(0.69, 1.09)	***	0.44	(0.22, 0.66)	***	0.44	(0.22, 0.66)	**	0.27	(0.04, 0.50)	*
p for linear trend		<0.001 (negative)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Middle (aged 30–59 years)
≤20	12,097	Ref			Ref			Ref			Ref			Ref			Ref
21–30	6,132	0.01	(-0.02, 0.03)		0.11	(0.08, 0.14)	***	0.13	(0.10, 0.16)	***	0.08	(0.05, 0.11)	***	0.01	(-0.02, 0.04)		0.01	(-0.02, 0.04)
31–40	5,038	-0.03	(-0.06, 0.00)	*	0.14	(0.11, 0.17)	***	0.24	(0.21, 0.27)	***	0.13	(0.10, 0.16)	***	0.03	(0.00, 0.06)	+	0.04	(0.01, 0.07)	**
41–50	3,624	-0.04	(-0.07, 0.00)	*	0.16	(0.13, 0.20)	***	0.33	(0.30, 0.36)	***	0.19	(0.15, 0.22)	***	0.08	(0.04, 0.11)	***	0.09	(0.06, 0.13)	***
51–60	3,129	-0.07	(-0.10, -0.03)	***	0.19	(0.15, 0.22)	***	0.39	(0.35, 0.42)	***	0.22	(0.19, 0.26)	***	0.07	(0.04, 0.11)	***	0.11	(0.08, 0.15)	***
61–70	1,617	-0.17	(-0.21, -0.12)	***	0.20	(0.16, 0.25)	***	0.45	(0.41, 0.50)	***	0.33	(0.28, 0.37)	***	0.12	(0.07, 0.17)	***	0.13	(0.08, 0.18)	***
71–80	1,577	-0.17	(-0.22, -0.12)	***	0.30	(0.25, 0.35)	***	0.56	(0.51, 0.61)	***	0.40	(0.35, 0.45)	***	0.18	(0.13, 0.22)	***	0.19	(0.14, 0.24)	***
81-	683	-0.04	(-0.10, 0.03)		0.40	(0.33, 0.47)	***	0.70	(0.63, 0.77)	***	0.40	(0.33, 0.47)	***	0.20	(0.13, 0.27)	***	0.20	(0.13, 0.27)	***
p for linear trend		<0.001 (negative)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Older (aged 60 or older)
≤20	2,169	Ref			Ref			Ref			Ref			Ref			Ref
21–30	332	0.01	(-0.09, 0.12)		0.08	(-0.02, 0.18)		0.24	(0.15, 0.33)	***	0.23	(0.13, 0.32)	***	0.13	(0.03, 0.22)	**	0.07	(-0.04, 0.17)
31–40	228	0.09	(-0.03, 0.22)		0.29	(0.17, 0.41)	***	0.34	(0.23, 0.45)	***	0.31	(0.20, 0.42)	***	0.23	(0.12, 0.34)	***	0.22	(0.09, 0.34)	**
41–50	128	-0.06	(-0.22, 0.10)		0.26	(0.10, 0.41)	**	0.52	(0.37, 0.66)	***	0.43	(0.28, 0.57)	***	0.30	(0.16, 0.45)	***	0.30	(0.13, 0.46)	***
51–60	74	-0.04	(-0.25, 0.17)		0.27	(0.06, 0.46)	*	0.57	(0.38, 0.75)	***	0.48	(0.29, 0.66)	***	0.30	(0.11, 0.48)	**	0.37	(0.16, 0.58)	**
61–70	51	-0.36	(-0.61, -0.11)	**	0.15	(-0.09, 0.39)		0.50	(0.28, 0.72)	***	0.51	(0.29, 0.73)	***	0.20	(-0.02, 0.42)	+	0.20	(-0.05, 0.45)
71–80	28	0.14	(-0.20, 0.47)		0.40	(0.08, 0.72)	*	0.68	(0.38, 0.97)	***	0.73	(0.44, 1.03)	***	0.31	(0.02, 0.61)	*	0.47	(0.14, 0.81)	**
81-	36	0.05	(-0.25, 0.35)		0.10	(-0.18, 0.38)		0.59	(0.33, 0.85)	***	0.32	(0.06, 0.58)	**	0.16	(-0.11, 0.42)		0.29	(-0.01, 0.58)	+
p for linear trend		n.s.			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Interaction between overtime and age-category
Middle (ref) versus Young
21–30		0.14	(0.05, 0.21)	**	0.04	(-0.04, 0.13)		0.06	(-0.02, 0.15)		0.03	(-0.05, 0.11)		0.11	(0.03, 0.19)	*	0.04	(-0.05, 0.13)
31–40		0.16	(0.07, 0.25)	**	0.04	(-0.06, 0.13)		0.03	(-0.07, 0.12)		0.01	(-0.08, 0.10)		0.04	(-0.06, 0.13)		0.08	(-0.02, 0.17)
41–50		0.16	(0.05, 0.27)	**	0.06	(-0.05, 0.17)		0.10	(-0.01, 0.21)		0.10	(-0.02, 0.21)		0.21	(0.10, 0.32)	***	0.24	(0.13, 0.36)	***
51–60		0.09	(-0.02, 0.21)		0.12	(0.00, 0.24)	*	0.07	(-0.05, 0.18)		0.04	(-0.08, 0.15)		0.08	(-0.04, 0.20)		0.06	(-0.06, 0.18)
61–70		0.24	(0.08, 0.39)	**	0.18	(0.01, 0.34)	*	0.13	(-0.03, 0.29)		0.17	(0.01, 0.33)	*	0.30	(0.14, 0.46)	***	0.20	(0.03, 0.37)	*
71–80		0.13	(-0.03, 0.29)		0.12	(-0.05, 0.28)		-0.14	(-0.30, 0.02)	+	0.11	(-0.06, 0.27)		0.27	(0.10, 0.44)	**	0.22	(0.05, 0.39)	*
81-		0.08	(-0.13, 0.29)		0.08	(-0.14, 0.30)		0.14	(-0.07, 0.36)		0.01	(-0.21, 0.22)		0.19	(-0.03, 0.41)	+	0.06	(-0.16, 0.29)
Middle (ref) versus Older
21–30		0.06	(-0.04, 0.17)		-0.01	(-0.12, 0.10)		0.12	(0.02, 0.23)	*	0.16	(0.05, 0.27)	**	0.14	(0.03, 0.24)	*	0.10	(-0.01, 0.21)	+
31–40		0.21	(0.09, 0.33)	**	0.20	(0.07, 0.33)	*	0.14	(0.01, 0.26)	*	0.19	(0.07, 0.32)	**	0.22	(0.10, 0.35)	**	0.23	(0.10, 0.36)	***
41–50		0.09	(-0.07, 0.25)		0.17	(0.00, 0.33)	+	0.25	(0.09, 0.41)	*	0.25	(0.09, 0.42)	**	0.27	(0.10, 0.43)	**	0.27	(0.10, 0.44)	**
51–60		0.15	(-0.05, 0.36)		0.16	(-0.05, 0.38)		0.24	(0.03, 0.45)	*	0.25	(0.04, 0.46)	*	0.27	(0.05, 0.48)	*	0.33	(0.11, 0.55)	**
61–70		-0.08	(-0.33, 0.17)		0.04	(-0.22, 0.29)		0.12	(-0.13, 0.38)		0.20	(-0.06, 0.45)		0.14	(-0.12, 0.40)		0.14	(-0.12, 0.41)
71–80		0.40	(0.07, 0.73)	*	0.18	(-0.16, 0.53)		0.18	(-0.15, 0.51)		0.35	(0.01, 0.69)	*	0.21	(-0.13, 0.55)		0.36	(0.01, 0.72)	*
81-		0.09	(-0.20, 0.39)		-0.30	(-0.61, 0.01)	+	-0.15	(-0.45, 0.16)		-0.06	(-0.36, 0.25)		-0.02	(-0.32, 0.29)		0.08	(-0.23, 0.40)
Women
Younger (aged less than 30 years)
≤20	1,665	Ref			Ref			Ref			Ref			Ref			Ref
21–30	473	0.02	(-0.07, 0.11)		0.13	(0.02, 0.23)	*	0.23	(0.14, 0.33)	***	0.09	(-0.02, 0.19)	+	0.10	(0.00, 0.20)	*	0.08	(-0.02, 0.18)
31–40	246	0.14	(0.01, 0.26)	*	0.18	(0.04, 0.32)	*	0.37	(0.24, 0.49)	***	0.25	(0.12, 0.38)	***	0.26	(0.13, 0.39)	***	0.06	(-0.08, 0.20)
41–50	96	0.11	(-0.08, 0.29)		0.10	(-0.11, 0.31)		0.46	(0.26, 0.65)	***	0.34	(0.13, 0.54)	**	0.23	(0.03, 0.43)	*	0.09	(-0.12, 0.30)
51–60	81	0.11	(-0.09, 0.32)		0.11	(-0.12, 0.34)		0.66	(0.44, 0.87)	***	0.47	(0.25, 0.69)	***	0.47	(0.25, 0.69)	***	0.24	(0.01, 0.47)	*
61–70	15	0.45	(-0.01, 0.91)		0.26	(-0.26, 0.79)		0.58	(0.10, 1.06)	*	0.22	(-0.28, 0.72)		0.49	(0.00, 0.98)	+	0.31	(-0.21, 0.83)
71–80	16	-0.24	(-0.67, 0.20)		0.53	(0.02, 1.03)	*	0.78	(0.32, 1.25)	**	0.73	(0.24, 1.21)	**	0.69	(0.22, 1.16)	**	0.35	(-0.15, 0.85)
81-	20	-0.06	(-0.46, 0.33)		0.06	(-0.39, 0.51)		0.54	(0.12, 0.95)	*	0.24	(-0.20, 0.67)		0.37	(-0.05, 0.80)	+	0.06	(-0.38, 0.51)
p for linear trend		<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Middle (aged 30–59 years)
≤20	10,896	Ref			Ref			Ref			Ref			Ref			Ref
21–30	1,735	0.08	(0.03, 0.13)	**	0.15	(0.10, 0.20)	***	0.27	(0.22, 0.32)	***	0.20	(0.15, 0.25)	***	0.13	(0.08, 0.17)	***	0.12	(0.07, 0.17)	***
31–40	942	0.16	(0.10, 0.22)	***	0.24	(0.18, 0.30)	***	0.50	(0.44, 0.57)	***	0.39	(0.32, 0.45)	***	0.19	(0.12, 0.25)	***	0.22	(0.16, 0.29)	***
41–50	462	0.13	(0.05, 0.22)	**	0.22	(0.12, 0.30)	***	0.53	(0.44, 0.62)	***	0.33	(0.24, 0.42)	***	0.21	(0.13, 0.29)	***	0.18	(0.09, 0.27)	***
51–60	240	-0.12	(-0.23, 0.00)	+	0.23	(0.10, 0.35)	***	0.57	(0.44, 0.70)	***	0.49	(0.37, 0.62)	***	0.26	(0.14, 0.38)	***	0.24	(0.12, 0.37)	***
61–70	110	0.05	(-0.13, 0.22)		0.22	(0.04, 0.40)	*	0.84	(0.65, 1.02)	***	0.77	(0.59, 0.95)	***	0.54	(0.37, 0.71)	***	0.41	(0.23, 0.59)	***
71–80	81	-0.13	(-0.33, 0.07)		0.34	(0.13, 0.55)	**	0.77	(0.56, 0.98)	***	0.68	(0.47, 0.89)	***	0.56	(0.36, 0.76)	***	0.44	(0.23, 0.65)	***
81-	69	0.05	(-0.17, 0.26)		0.41	(0.18, 0.64)	***	0.64	(0.41, 0.87)	***	0.61	(0.38, 0.84)	**	0.34	(0.13, 0.56)	**	0.13	(-0.10, 0.36)
p for linear trend		<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Older (aged 60 or older)
≤20	968	Ref			Ref			Ref			Ref			Ref			Ref
21–30	93	0.17	(-0.04, 0.39)		0.22	(0.03, 0.41)	*	0.27	(0.08, 0.46)	**	0.29	(0.11, 0.47)	**	0.23	(0.06, 0.39)	**	0.08	(-0.12, 0.28)
31–40	31	-0.16	(-0.52, 0.20)		0.21	(-0.11, 0.53)		0.23	(-0.09, 0.54)		0.21	(-0.09, 0.51)		0.08	(-0.20, 0.36)		0.09	(-0.24, 0.41)
41–50	9	0.09	(-0.56, 0.75)		0.35	(-0.24, 0.93)		0.31	(-0.27, 0.89)		0.67	(0.12, 1.21)	*	0.45	(-0.06, 0.97)	+	-0.02	(-0.62, 0.58)
51–60	5	0.99	(0.12, 1.87)	*	1.41	(0.63, 2.19)	***	1.02	(0.24, 1.80)	*	0.71	(-0.02, 1.44)	+	1.08	(0.39, 1.77)	**	0.35	(-0.45, 1.16)
61–70	2	1.36	(-0.02, 2.74)	+	1.10	(-0.13, 2.33)	+	1.16	(-0.07, 2.38)		0.56	(-0.60, 1.71)		0.51	(-0.58, 1.60)		0.21	(-1.06, 1.48)
71–80	0	-			-			-			-			-			-
81-	2	-0.23	(-1.61, 1.16)		-0.37	(-1.61, 0.86)		0.82	(-0.41, 2.05)		-0.18	(-1.34, 0.98)		-0.66	(-1.75, 0.44)		0.32	(-0.96, 1.60)
p for linear trend		n.s.			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.01 (positive)			n.s.
Interaction between overtime and age-category
Middle (ref) versus Young
21–30		-0.05	(-0.15, 0.06)		-0.02	(-0.13, 0.09)		-0.05	(-0.16, 0.07)		-0.14	(-0.25, -0.03)	*	-0.03	(-0.13, 0.07)		-0.03	(-0.14, 0.08)	*
31–40		-0.01	(-0.15, 0.12)		-0.08	(-0.23, 0.06)		-0.15	(-0.29, 0.00)	*	-0.16	(-0.30, -0.01)	*	0.06	(-0.08, 0.20)		-0.15	(-0.29, 0.00)
41–50		0.00	(-0.20, 0.21)		-0.14	(-0.36, 0.08)		-0.07	(-0.29, 0.15)		-0.02	(-0.24, 0.20)		0.01	(-0.20, 0.21)		-0.06	(-0.28, 0.16)
51–60		0.27	(0.04, 0.51)	*	-0.15	(-0.40, 0.10)		0.09	(-0.16, 0.34)		-0.06	(-0.30, 0.19)		0.20	(-0.03, 0.44)	+	0.05	(-0.20, 0.29)
61–70		0.39	(-0.11, 0.89)		0.00	(-0.52, 0.53)		-0.24	(-0.76, 0.29)		-0.54	(-1.06, -0.03)	*	-0.11	(-0.60, 0.39)		-0.05	(-0.58, 0.47)
71–80		-0.07	(-0.56, 0.42)		0.18	(-0.34, 0.70)		0.06	(-0.47, 0.58)		0.09	(-0.42, 0.61)		0.18	(-0.31, 0.67)		-0.04	(-0.56, 0.49)
81-		-0.12	(-0.58, 0.34)		-0.35	(-0.84, 0.13)		-0.09	(-0.58, 0.39)		-0.35	(-0.83, 0.12)		0.03	(-0.43, 0.48)		-0.04	(-0.52, 0.45)
Middle (ref) versus Older
21–30		0.12	(-0.08, 0.32)		0.10	(-0.11, 0.31)		0.06	(-0.15, 0.27)		0.13	(-0.08, 0.33)		0.14	(-0.06, 0.33)		0.01	(-0.20, 0.22)
31–40		-0.27	(-0.60, 0.06)		0.01	(-0.35, 0.36)		-0.20	(-0.55, 0.16)		-0.14	(-0.48, 0.21)		-0.08	(-0.41, 0.25)		-0.07	(-0.42, 0.29)
41–50		0.02	(-0.59, 0.63)		0.17	(-0.48, 0.81)		-0.14	(-0.78, 0.50)		0.34	(-0.29, 0.97)		0.26	(-0.35, 0.86)		-0.12	(-0.76, 0.52)
51–60		1.28	(0.47, 2.10)	**	1.32	(0.46, 2.18)	**	0.65	(-0.21, 1.50)		0.31	(-0.53, 1.16)		0.89	(0.08, 1.70)	*	0.28	(-0.58, 1.14)
61–70		1.33	(0.04, 2.61)	*	0.82	(-0.54, 2.18)		0.34	(-1.01, 1.69)		-0.25	(-1.59, 1.08)		-0.01	(-1.28, 1.26)		-0.13	(-1.48, 1.23)
71–80		-			-			-			-			-			-
81-		-0.36	(-1.65, 0.93)		-0.55	(-1.92, 0.82)		0.32	(-1.04, 1.68)		-0.66	(-2.01, 0.68)		-0.81	(-2.09, 0.47)		0.31	(-1.05, 1.67)

+ p<0.1

*p<0.05, ** p<0.01, ***p<0.001, Higher score indicates unfavorable stress-response. All betas were adjusted by age, type of job, job class, employment status, type of schedule, company size, company industry, job control, supervisors support and coworker’s support. Lack of vigor was derived by reversing the score of vigor for harmonization with other stress-response scales, which higher score indicates unfavorable stress-response

Table 4

Association between overtime work hours and stress responses: Results of multiple linear regression stratified by gender and job-class.

Overtime work hours/month	n	Lack of Vigor			Irritability			Fatigue			Anxiety			Depression			Somatic responses
		beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p	beta	95% CI	p
Men
Workers in managerial or directorial position
≤20	2,117	Ref			Ref			Ref			Ref			Ref			Ref
21–30	1,602	0.07	(0.01, 0.12)	*	0.11	(0.06, 0.17)	***	0.15	(0.10, 0.21)	***	0.14	(0.08, 0.19)	***	0.08	(0.02, 0.13)	**	0.04	(-0.02, 0.09)
31–40	1,639	0.02	(-0.04, 0.07)		0.12	(0.06, 0.18)	***	0.27	(0.21, 0.32)	***	0.24	(0.18, 0.29)	***	0.13	(0.07, 0.18)	***	0.07	(0.01, 0.12)	*
41–50	1,475	-0.03	(-0.09, 0.03)		0.17	(0.11, 0.23)	***	0.31	(0.25, 0.37)	***	0.28	(0.22, 0.34)	***	0.11	(0.05, 0.17)	***	0.06	(-0.01, 0.12)	+
51–60	1,339	-0.03	(-0.09, 0.04)		0.19	(0.12, 0.25)	***	0.39	(0.33, 0.45)	***	0.32	(0.25, 0.38)	***	0.12	(0.06, 0.18)	***	0.08	(0.02, 0.15)	*
61–70	832	-0.12	(-0.19, -0.05)	**	0.16	(0.09, 0.23)	***	0.43	(0.35, 0.50)	***	0.33	(0.26, 0.40)	***	0.09	(0.02, 0.16)	**	0.02	(-0.06, 0.09)
71–80	915	-0.08	(-0.15, -0.01)	*	0.31	(0.24, 0.38)	***	0.56	(0.49, 0.63)	***	0.47	(0.40, 0.54)	***	0.18	(0.11, 0.25)	***	0.13	(0.06, 0.21)	***
81-	430	-0.02	(-0.11, 0.07)		0.33	(0.24, 0.42)	***	0.72	(0.63, 0.81)	***	0.47	(0.38, 0.56)	***	0.23	(0.14, 0.32)	***	0.11	(0.01, 0.20)	*
p for linear trend		<0.001 (negative)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Workers in regular position
≤20	13,634	Ref			Ref			Ref			Ref			Ref			Ref
21–30	5,658	0.04	(0.01, 0.07)	*	0.12	(0.09, 0.15)	***	0.17	(0.14, 0.20)	***	0.10	(0.07, 0.13)	***	0.04	(0.01, 0.07)	**	0.02	(-0.01, 0.05)
31–40	4,173	0.01	(-0.02, 0.04)		0.16	(0.13, 0.19)	***	0.27	(0.24, 0.30)	***	0.13	(0.10, 0.16)	***	0.04	(0.01, 0.08)	*	0.07	(0.03, 0.10)	***
41–50	2,622	0.02	(-0.02, 0.06)		0.16	(0.12, 0.20)	***	0.41	(0.37, 0.44)	***	0.20	(0.16, 0.24)	***	0.14	(0.10, 0.18)	***	0.17	(0.13, 0.21)	***
51–60	2,155	-0.04	(-0.08, 0.00)	+	0.18	(0.14, 0.23)	***	0.44	(0.40, 0.49)	***	0.22	(0.18, 0.27)	***	0.12	(0.08, 0.16)	***	0.16	(0.11, 0.20)	***
61–70	982	-0.13	(-0.19, -0.07)	***	0.23	(0.16, 0.29)	***	0.55	(0.49, 0.61)	***	0.41	(0.35, 0.47)	***	0.26	(0.19, 0.32)	***	0.26	(0.20, 0.32)	***
71–80	825	-0.18	(-0.24, -0.11)	***	0.25	(0.18, 0.31)	***	0.61	(0.54, 0.67)	***	0.42	(0.36, 0.49)	***	0.31	(0.24, 0.38)	***	0.30	(0.23, 0.37)	***
81-	366	0.01	(-0.08, 0.10)		0.42	(0.33, 0.52)	***	0.76	(0.67, 0.86)	***	0.37	(0.27, 0.47)	***	0.31	(0.21, 0.41)	***	0.34	(0.24, 0.44)	***
p for linear trend		<0.001 (negative)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Interaction between overtime and age-category
Regular (ref) versus Manager
21–30		0.03	(-0.03, 0.09)		0.00	(-0.07, 0.06)		-0.01	(-0.07, 0.06)		0.03	(-0.03, 0.10)		0.02	(-0.04, 0.09)		0.02	(-0.05, 0.08)
31–40		0.00	(-0.06, 0.07)		-0.02	(-0.09, 0.04)		0.01	(-0.06, 0.07)		0.10	(0.04, 0.17)	**	0.07	(0.00, 0.14)	*	0.01	(-0.06, 0.07)
41–50		-0.05	(-0.12, 0.02)		0.02	(-0.05, 0.09)		-0.09	(-0.16, -0.02)	*	0.07	(0.00, 0.14)	*	-0.06	(-0.13, 0.02)		-0.10	(-0.17, -0.02)	**
51–60		0.01	(-0.06, 0.08)		0.04	(-0.04, 0.11)		-0.04	(-0.11, 0.03)		0.08	(0.00, 0.15)	*	-0.04	(-0.11, 0.04)		-0.04	(-0.12, 0.03)
61–70		0.00	(-0.09, 0.09)		-0.03	(-0.12, 0.07)		-0.11	(-0.20, -0.02)	*	-0.10	(-0.19, 0.00)	*	-0.19	(-0.29, -0.10)	***	-0.22	(-0.31, -0.12)	***
71–80		0.09	(0.00, 0.18)	+	0.10	(0.01, 0.20)	*	-0.04	(-0.13, 0.05)		0.02	(-0.08, 0.11)		-0.16	(-0.26, -0.07)	**	-0.13	(-0.23, -0.03)	**
81-		0.01	(-0.12, 0.14)		-0.09	(-0.22, 0.05)		-0.04	(-0.17, 0.10)		0.09	(-0.04, 0.22)		-0.10	(-0.23, 0.03)		-0.23	(-0.37, -0.09)
Women
Workers in managerial or directorial position
≤20	151	Ref			Ref			Ref			Ref			Ref			Ref
21–30	124	0.08	(-0.15, 0.31)		0.04	(-0.18, 0.25)		0.04	(-0.19, 0.27)		-0.01	(-0.23, 0.21)		0.02	(-0.19, 0.23)		0.04	(-0.18, 0.26)
31–40	125	0.08	(-0.16, 0.31)		0.23	(0.01, 0.45)	*	0.43	(0.20, 0.67)	***	0.48	(0.26, 0.71)	***	0.17	(-0.05, 0.38)		0.28	(0.05, 0.50)	*
41–50	81	0.23	(-0.04, 0.49)	+	0.09	(-0.16, 0.34)		0.35	(0.09, 0.62)	**	0.33	(0.07, 0.58)	*	0.03	(-0.21, 0.27)		0.10	(-0.16, 0.35)
51–60	64	0.01	(-0.28, 0.31)		0.10	(-0.17, 0.37)		0.39	(0.09, 0.68)	**	0.28	(0.00, 0.56)	+	0.03	(-0.23, 0.30)		0.14	(-0.14, 0.43)
61–70	33	0.43	(0.06, 0.80)	*	0.46	(0.11, 0.80)	***	0.86	(0.49, 1.23)	***	0.85	(0.50, 1.21)	***	0.39	(0.06, 0.72)	**	0.28	(-0.08, 0.64)
71–80	18	0.10	(-0.38, 0.57)		0.23	(-0.22, 0.67)		1.16	(0.68, 1.63)	***	0.70	(0.25, 1.15)	**	0.77	(0.34, 1.19)	***	0.66	(0.20, 1.12)	**
81-	20	-0.04	(-0.49, 0.40)		0.22	(-0.20, 0.63)		0.52	(0.07, 0.97)	*	0.43	(0.00, 0.86)	*	0.09	(-0.31, 0.50)		0.20	(-0.24, 0.63)
p for linear trend		n.s.			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.05 (positive)			<0.05 (positive)
Workers in regular position
≤20	13,378	Ref			Ref			Ref			Ref			Ref			Ref
21–30	2,177	0.07	(0.03, 0.12)	**	0.15	(0.11, 0.19)	***	0.27	(0.23, 0.32)	***	0.20	(0.15, 0.24)	***	0.14	(0.09, 0.18)	***	0.12	(0.08, 0.17)	***
31–40	1,094	0.16	(0.10, 0.22)	***	0.22	(0.16, 0.28)	***	0.47	(0.41, 0.53)	***	0.35	(0.29, 0.40)	***	0.20	(0.14, 0.26)	***	0.19	(0.13, 0.25)	***
41–50	486	0.13	(0.04, 0.21)	**	0.22	(0.13, 0.30)	***	0.54	(0.45, 0.63)	***	0.34	(0.25, 0.42)	***	0.24	(0.16, 0.33)	***	0.20	(0.12, 0.29)	***
51–60	262	-0.05	(-0.17, 0.06)		0.22	(0.10, 0.34)	***	0.63	(0.51, 0.75)	***	0.55	(0.43, 0.66)	***	0.39	(0.28, 0.51)	***	0.29	(0.17, 0.41)	***
61–70	94	0.03	(-0.16, 0.22)		0.16	(-0.04, 0.36)		0.83	(0.63, 1.02)	***	0.66	(0.46, 0.85)	***	0.57	(0.39, 0.76)	***	0.48	(0.29, 0.68)	***
71–80	79	-0.19	(-0.39, 0.02)	+	0.40	(0.18, 0.61)	***	0.70	(0.48, 0.91)	***	0.70	(0.48, 0.91)	***	0.55	(0.35, 0.75)	***	0.39	(0.18, 0.61)	***
81-	71	0.05	(-0.17, 0.26)		0.33	(0.10, 0.55)	**	0.64	(0.42, 0.87)	***	0.53	(0.31, 0.75)	***	0.38	(0.17, 0.60)	***	0.13	(-0.10, 0.35)
p for linear trend		<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)			<0.001 (positive)
Interaction between overtime and age-category
Regular (ref) versus Manager
21–30		-0.03	(-0.25, 0.19)		-0.11	(-0.34, 0.13)		-0.30	(-0.53, -0.07)	*	-0.23	(-0.46, 0.00)	+	-0.15	(-0.37, 0.07)		-0.09	(-0.33, 0.14)
31–40		-0.14	(-0.37, 0.09)		0.02	(-0.22, 0.26)		-0.13	(-0.36, 0.11)		0.12	(-0.12, 0.35)		-0.06	(-0.28, 0.16)		0.05	(-0.19, 0.28)
41–50		0.06	(-0.20, 0.33)		-0.14	(-0.41, 0.14)		-0.25	(-0.52, 0.03)	+	-0.03	(-0.30, 0.24)		-0.24	(-0.49, 0.02)	+	-0.15	(-0.43, 0.12)
51–60		0.01	(-0.28, 0.31)		-0.07	(-0.37, 0.24)		-0.31	(-0.61, 0.00)	*	-0.28	(-0.58, 0.02)	+	-0.40	(-0.68, -0.11)	**	-0.19	(-0.49, 0.12)
61–70		0.29	(-0.11, 0.68)		0.29	(-0.12, 0.71)		-0.14	(-0.55, 0.28)		0.14	(-0.27, 0.55)		-0.23	(-0.62, 0.16)		-0.29	(-0.71, 0.12)
71–80		0.19	(-0.31, 0.68)		-0.09	(-0.61, 0.43)		0.35	(-0.17, 0.86)		0.02	(-0.49, 0.53)		0.19	(-0.29, 0.68)		0.26	(-0.26, 0.78)
81-		-0.20	(-0.68, 0.28)		-0.09	(-0.60, 0.42)		-0.19	(-0.70, 0.31)		-0.09	(-0.58, 0.41)		-0.30	(-0.77, 0.17)		0.03	(-0.48, 0.53)

+ p<0.1

*p<0.05, ** p<0.01, ***p<0.001, Higher score indicates unfavorable stress-response. All betas were adjusted by age, type of job, employment status, type of schedule, company size, company industry, job control, supervisors support and coworker’s support. Lack of vigor was derived by reversing the score of vigor for harmonization with other stress-response scales, which higher score indicates unfavorable stress-response

This study aimed to clarify the dose-response relationships between length of overtime and stress response using data of 59,021 Japanese workers. As a result, workers with longer overtime showed higher “irritability”, “fatigue”, “anxiety”, “depression” and “somatic responses” than those with shorter overtime, adjusted for possible confounders. In addition, linear dose-response curves were observed between length of overtime and these stress responses. Whereas, only “lack of vigor” was not consistently associated with overtime. Rather, male workers with 61–80 hours of monthly overtime were more likely to feel vigorous than workers with shorter overtime.

As expected, stress response levels were relatively worse among workers with longer overtime than those with shorter hours. Several longitudinal studies have consistently showed that long working hours could elevate the risk of mental health issues, such as depression [16,18,32,33] or anxiety [16]. Especially, a recent systematic review shows that long working hours are related to higher risk of depression in Asian countries, compared to European countries [34]. Indeed working time regulation differed between European and Asian countries [35]. However, the present study showed results compatible with previous studies and reinforced the evidence that longer working hours could have harmful effects on workers’ mental health.

The present study showed linear dose-response relations between length of overtime and stress responses. This linear, no-threshold association implies that even small reduction of overtime could be beneficial for workers to reduce their stress responses, which have been indicated as major risk factors for sickness absence among workers [36]. Therefore, it is important for health care managers and employers to shorten workers’ overtime to prevent sick leave by alleviating psychological responses such as fatigue.

Here we briefly state possible mechanisms as to why severe stress responses were observed among workers with longer overtime. First, workers with long working hours need a sufficient amount of time for recovery from work, and these workers may not have enough time for relaxing or refreshing themselves after finishing work. This may be one possible reason why older workers showed higher stress responses than workers in middle age among with 21–50 hours/month of overtime. Since older adults may need more recovery time, they may be more vulnerable for overtime working. Furthermore, long working hours also lead to short sleeping hours and deteriorated sleep quality, such as difficulty in falling asleep or waking without feeling refreshed [8,37]. Avoiding overly long working hours is important to prevent workers’ mental health problems by allowing time for good sleeping conditions and refreshment.

Unexpectedly, “lack of vigor” was shown to have a distinct relationship to overtime, which differed from other types of stress responses. Particularly, men with 61–80 hours of overtime showed relatively higher vigor level, together with severe fatigue or anxiety levels at the same time. The detailed mechanisms for this ambivalence were unknown. Referring to the classical stress model introduced by Henry [38], in early to middle stages of the stress response process, stress stimuli may raise vigorous feeling through hormonal mechanisms. A similar phenomenon is also known as “runner’s high” in exercise research [39]. When participants received excessive stress through endurance exercise (i.e. triathlon), almost all participants felt fatigue. Nevertheless, some participants kept their vigorous state and showed increased levels of adrenocorticotropic hormone and beta-endorphin [40,41]. One study of 217 Korean male workers reported a marginally-positive correlation between working hours and adrenaline in urine [42]. Furthermore, association of stress-induced cortisol elevation and low poststress negative affect ratings was also reported [43]. Taken together, it may be speculated that workers with 61–80 hours/month of overtime, who showed high vigor and high fatigue at the same time, may experience a similar state to “runners’ high”. Since the present study did not include any biological data, this state may need to be verified in future studies which use detailed clinical data. In addition, the increase of vigor among male workers with 61–80 hours/month of overtime may reflect their overcommitment to work. Workers with overcommitment often exaggerate their efforts beyond appropriate levels, showing temporal and energetic characteristics of behavior [44]. However, it is reported that such efforts weaken their potential recovery from job demands and increase their frustration when the expected rewards are not fulfilled, which eventually results in poor health such as cardiovascular disease [45] or depression [46]. This unfavorable condition may occur among male workers with 81 or more hours/month of overtime, who showed rather low levels of vigor. Consequently, the total relationship between overtime work hours and “lack of vigor” created an inverse U-shape curve, which is the same as the stress response curve in Henry’s theory, where the last phase is called “overload state” [38]. Due to the cross-sectional study design, the present study could not consider the longitudinal effect of working hours and changes in stress responses. However, it may be possible that a distinguishing increase in vigor may cause a loss of self-control of workload, resulting in excessively long work. Such workers would be in a physically and mentally exhausted state, that is, they would be at a high risk for Karoshi or Karojisatsu.

A positive linear association between length of overtime and vigor was found among men, whereas a negative linear association was found among women. Specific reasons for this gender-gap are unknown. However, self-selection into job with long working hours among men could be speculated. As discussed above, workers with high vigor may aspire to work longer hours. However, that may be difficult for women due to household responsibilities or other familial roles. Hence, in comparison to vigorous men, vigorous women may have relatively shorter length of working hours, which may be one possible cause of this gender-gap. The difference in work-family spillover between working men and women could be suggested as another possible explanator factor. According to the Japanese Survey on Time use and Leisure activities in 2016, among couples with preschool children, the average time for child rearing or household chores were 84 min/week and 370 min/week for men and women, respectively [47]. It is reported that work family negative spillover is more explicit in women, compared with men, and is related with psychological distress [48]. Speculatively, women with long working hours may tend to suffer more from negative work-family spillover, compared to men. Finally, denial and dissimulation may be an increasing problem with increasing number of working hours. This may be a frequent problem in men who want to stand out as invincible. In fact, denial has been pointed out as a possible risk factor for sudden cardiac death [49] although this is hard to examine in epidemiological studies and cannot be studied at all in cross-sectional examinations.

Vigor among workers have attracted research attention in positive occupational health psychology for instance in studies of work engagement [50,51]. Work engagement has been defined as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication and absorption” [52]. Vigor in work-engagement assessments has included concepts of “motivation” or “psychological resilience”, whereas the items for assessment of vigor in BJSQ have been based on POMS. According to Shirom, the conceptualization of vigor in both work-engagement and BJSQ is not exactly the same [53]. In our study “lack of vigor” was measured from the POMS. This negative concept is related on a deeper theoretical level to fatigue. In fact, moderate correlations between “lack of vigor” and “fatigue” were observed (men: r = -0.328, women: r = -0.358). However, the distinction between these interrelated concepts remains unclear theoretically. Exploring the association between overtime work hours and vigor in work engagement is an important research question in future studies.

The possible reason why high vigor were observed among men with longer working hours is a high-level of work-engagement. In addition to dedication and absorption, vigor was conceptually one component of work-engagement [50,51]. In addition, another study showed that excess work engagement was observed in the population with long working hours [54]. The data in the present study did not include any data regarding work-engagement. However, future studies including work engagement may be needed to clarify the distinct association between vigor and overtime work hours among men.

Currently, the Labor Standards Law in Japan technically permits “no limitation of overwork” under article 36 of the law, which allows managers to enforce as much overtime as they think is needed to meet the production target [11]. In response to public criticism of unlimited overtime, the Japanese government recently attempted to amend the law to introduce an overtime limit of both 100 hours/month and 720 hours/year [13]. In the present study, workers with 61–80 hours/month of overtime may be regarded as “full of energy”, but they also have severe fatigue levels. Thus, this regulation criteria may cause a misimpression that they are not a risky population, and consequently, they may engage in additional overwork. Therefore, not only self-management of working hours, but also regulation by law is important to prevent overtime and its related mental illnesses among these workers. Taking this into account, the regulation of 100 hours/month may not be sufficient.

The specific favorable characteristics of the present study are a relatively large sample-size and the fact that participants were selected from different industries and company-sizes. However, some limitations of our study should also be considered. First, a cross-sectional study design limits arguing causality between length of overtime and stress response levels. It is possible that workers with high vigor may have strong motivation to work because of energetic characteristics, that lead to work longer. However, the cross-sectional study design cannot identify the direction of the selection process. To clarify how stress response level and length of overtime are associated or influenced by each other, longitudinal studies are needed. Second, our analysis did not consider individual past/current disease history. It is possible that some workers need to restrict their working hours due to receiving medication at the point of survey. If their stress response levels are higher than other healthy workers even though their overtime work hours is relatively short, the result of the present study may be underestimated. In addition, we did not consider personal characteristics or familial history of mental health. For example, the results of this study may suffer from confounding due to type A behavioral patterns, where workers with such behavioral patterns tend to work long hours with high tension [55]. The results of this study may suffer from confounding due to such unobserved factors. Third, a relatively substantial number of subjects (n = 8,560) were excluded due to missing data. We cannot deny the possibility that workers with higher stress-response are more likely to have missing data or to avoid answering some questions on the BJSQ. If workers with longer overtime and higher stress responses coincide to those with missing data, the results would suffer from underestimation. Fourth, this study did not measure total working hours. Even though this study excluded shift-workers and part-timers, it is possible that it included workers whose regular working hours are relatively short. Their total working hours may not be long even if they reported very long overtime. In addition, overtime work hours were measured only once. Since working hours are not necessarily stable over a long period, our results may be affected by potential misclassification. Whereas, a recent longitudinal study among 18,172 Japanese workers showed that 85% of subjects reported stable overtime work hours for three consecutive years [56], thus misclassification of working hours may not be problematic. Furthermore, overtime work hours were assessed by self-report. Although a previous study showed that measuring overtime through self-report had high validity and that reproducibility was confirmed among Japanese workers [57], self-reported overtime work hours may be less accurate than objectively-measured working hours. In addition, this study did not consider work pace of overtime. Even among workers with the same length of overtime working, a higher stress response may be observed among those who are under severe time-pressure or experience a higher working pace [58]. Fifth, the present study could not perform a nested data analysis due to lack of detailed workplace information. Individual stress responses could be affected not only on the individual level, but also on the group and workplace level [59], thus future studies using multilevel analysis with detailed workplace data would be preferable [60]. Finally, the data in the present study may suffer from anticipation bias. Since participants received the Stress Check Program in non-anonymous manner, they may report their stress levels with anticipated responses, and it may not sufficiently reflect the real situation.

Conclusions

Length of overtime shows linear associations with various psychosomatic stress responses. However, "lack of vigor” was not consistently associated with overtime. Male workers with 61–80 hours of monthly overtime were more likely to feel vigorous than workers with shorter overtime. However, potential longterm effects of such extreme overtime should not be underestimated and must be paid attention to.

A Specific items with internal consistency of the scales of Brief Job Stress Questionnaire used in the study. B Correlation coefficient between stress response scales in BJSQ.

(DOCX)

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Association between overtime work hours and stress responses: Multiple linear regression results with multiple imputation.

(DOCX)

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30 Sep 2019

PONE-D-19-24855

Distinct features in association of overtime work hours with various stress responses: results from 59,021 Japanese workers’ cross-sectional study

PLOS ONE

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Kenji Hashimoto, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: No

Reviewer #2: No

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Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I have read the paper entitled „Distinct features in association of overtime work hours with various stress responses: results from 59,021 Japanese workers’ cross-sectional study” submitted to PLOSone with great interest. The study describes patterns of association between hours worked overtime and several indicators of strain or impaired (mental) health. Below I raise a couple of questions, that need to be clarified. I hope my comments are helpful in improving the paper.

1. Theory

1.1. As far as I have understood the PLOSone does not emphasizes *theoretical* contributions to the literature. However, there are a couple of meta-analyses and large survey studies about overtime. Hence, stating clearly how this study adds to the literature would be beneficial. You argue that your sample is stratified and the others were not, but why is that important?

1.2. On a related note, you seem to provide a more fine-grained perspective on hours of overtime. Are there any inconsistencies that would suggest taking a closer look? In other words, besides the technical aspect: How does your approach advance knowledge? Does it provide a more accurate picture? Does it help to gain a more integrated picture (comparing many different types of strain in one study)? Does it facilitate comparison between strain indicators? Does it add data on Japanese employees? I recommend stating more clearly how this study enriches existing research. I am quite sure it does.

1.3. The choice of strain measures is not justified at all. They seem to be taken for granted. However, it is not clear why exactly those aspects were chosen. I understand that this has been discussed in the other study using these data. For readers who do not want to take the effort of looking up details elsewhere, please add a short description of the strain indicators. What characterizes fatigue in the sense you mean? How does it differ from lack of vigor both conceptually and empirically? Is “lack of vigor” the opposite of vigor? I found the expression quite complicated, as it includes a negation. Please also note that there is a long list of papers on the question whether vigor is the opposite of exhaustion (work engagement vs. burnout) (Demerouti, Mostert, & Bakker, 2010; González-Romá, Schaufeli, Bakker, & Lloret, 2006).

1.4. Please also add a more systematic review of how overtime is related to fatigue, lack of vigor, depression etc. respectively. These details would help judge your results in the light of prior research. Is irritability the same thing as irritation (Mohr, Müller, Rigotti, Aycan, & Tschan, 2006)? I think, it is important to locate all variables in the nomological network and to be clear what these concepts actually mean.

2. Methods

2.1. Please report sample items for each scale. Please also describe the scope of each scale.

2.2. You excluded participants from small organizations and participants who did not receive the program? What does receiving the program mean? It is not trivial to me why these groups were excluded?

2.3. You exclude a portion of 15 per cent because of missing data. Which data were missing? For instance, if only single items or single scales were missing by these persons, wouldn’t inclusion of all available data be better? At a minimum, I would make a case that exclusion of these cases does not affect your results and conclusions. For instance, you could compare the focal (conservative) vs. the larger sample applying a more liberal approach of exclusion.

2.4. Do you have information on nesting of data (which persons belonged to which organization)? If yes, please examine the degree of nesting of data (Luke, 2004). If nesting is an issue you should take nesting into account by virtue of multilevel modeling. IF you lack information, please discuss ignorance of nesting of the data as a limitation of your study.

2.5. I commend the comprehensive description of the sample.

3. Results

3.1. Please provide a correlation table including all focal variables. Include means, standard deviations, and a measure of reliability of multi-item scales, such as Cronbachs Alpha or Omega. This would facilitate inclusion of your study in meta-analyses. This would also help understand the counter-intuitive results for “lack of vigor”. I would expect fatigue and vigor to be highly negatively correlated. However, in the work engagement literature there are also aspects like being very resilient while working intensively (Mills, Culbertson, & Fullagar, 2012).

3.2. What does p for trend mean?

3.3. It was not totally clear to me what you did with the ANOVA. What were the factors? Were the classes of overtime hours one factor? Would an omnibus test really be the best approach? Given your specific expectations, a contrast analysis might be viable, too. As far as I understood Table 2, you compared all classes to the reference group of no or few hours overtime. A contrast analysis would help being more specific an allow comparisons between all groups.

3.4. Given your high number of significance tests, probably a Bonferroni-correction or something would be warranted. Given the probably very high test power, have you considered reporting effect sizes? I mean with a sample of 50,000+ almost any small effect likely will be significant.

3.5. I think, you mention regression analysis in the methods section. What did the regression model look like? What did you analyze? Where are the results reported?

4. Discussion

4.1. I appreciate the comprehensive discussion of results and the acknowledgment of limitations.

Demerouti, E., Mostert, K., & Bakker, A. B. (2010). Burnout and work engagement: A thorough investigation of the independency of both constructs. Journal of Occupational Health Psychology, 15(3), 209–222. https://doi.org/10.1037/a0019408

González-Romá, V., Schaufeli, W. B., Bakker, A. B., & Lloret, S. (2006). Burnout and work engagement: Independent factors or opposite poles? Journal of Vocational Behavior, 68(1), 165–174. https://doi.org/10.1016/j.jvb.2005.01.003

Luke, D. A. (2004). Multilevel modeling. Thousand Oaks: Sage.

Mills, M. J., Culbertson, S. S., & Fullagar, C. J. (2012). Conceptualizing and measuring engagement: An analysis of the Utrecht Work Engagement Scale. Journal of Happiness Studies, 13(3), 519–545. https://doi.org/10.1007/s10902-011-9277-3

Mohr, G., Müller, A., Rigotti, T., Aycan, Z., & Tschan, F. (2006). The assessment of psychological strain in work contexts: Concerning the structural equivalency of nine language adaptations of the irritation scale. European Journal of Psychological Assessment, 22(3), 198–206. https://doi.org/10.1027/1015-5759.22.3.198

Reviewer #2: Manuscript ID: PONE-D-19-24855: Distinct features in association of overtime work hours with various stress responses: results from 59,021 Japanese workers’ cross-sectional study

General comments:

Important issue and large sample size make the attractive setting of this study. However, there are minor revsions to be established.

The authors present a retrospective evaluation of data from the national stress check program. In order to give feedback to participant’s data were collected in a non-anonymized manner. This needs to be specified more clearly in methods section and needs to be discussed in limitations. Subjective assessment in self-reporting measures might reveal anticipated responses and not the real situation. Most likely anticipation-bias might have influenced the responses of workers in a relationship of professional dependency.

The BJSQ in its original form comprises 17 items on job stress, 29 items on psychological and physical stress reactions and 11 items on social support at work. (See: Tsutsumi A, Inoue A, Eguchi H. J Occup Health. 2017; 59(4): 356–360.)

Please specify whether you focused on the psychological and physical stress reactions with 29 items.

Specific comments:

Page 1, title

Specify in the title that this is a retrospective cross-sectional study.

Suggested wording: “Association of overtime work hours with various stress responses in 59,021 Japanese workers: retrospective cross-sectional study”

Page 3, abstract

Lines 35, 42…: Suggested phrasing: “overtime work” e.g. length of overtime work

Page 3, abstract

Line 37: Suggested phrasing: “self-reporting”

Page 5, introduction

Line 58: Delete: “too much”

Page 6, study design

Line 81: To my understanding, this is a retrospective data evaluation of non-anonymized data acquired by the Stress Check Program.

Please specify primary and secondary study objectives

Page 7, participants and data collection

Line 95: Consider phrasing: “eligible participants” …

Line 96: December 2015 to January 2016, …

Provide further information on how eligible participants were contacted, e.g. did the health service company support you with epidemiologic data and mail addresses?

Were non-respondents repeatedly contacted?

Page 8, stress responses

Line 119: Please specify whether you focused on the psychological and physical stress reactions with 29 items.

It would be helpful to the reader to know the original wording of the 29 items. Please enclose the questionnaire as appendix or as supplementary information to this manuscript.

Page 9, covariates

Line 131: Does history of diseases mean past medical history? This would mean a detailed register of previous diseases and provide the base for correlations between general health and number of overtime hours per month.

Page 9, statistical procedure

Line 140: The authors used multiple linear regression fitting a linear equation to independent variables and associated dependent variables. Multiple imputation was used for missing data management.

As there is known confounding by gender, qualification, leading position and age group I suggest to evaluate subgroups male/female in leading/non-leading position and age groups, in particular <30, 31-59, and >60 years (the range was 17 to 89 years).

Ordinal variables could be analyzed with the Mann-Whitney U test (n=2) and the Kruskal-Wallis test (n>2) in subgroup analysis.

Page 11, results and discussions

Line 164: Suggested wording: “did not receive” …

Line 170: Please distinguish between response rate and inclusion rate.

Out of 95,004 eligible workers, 88.988 were contacted of whom 83,470 responded (response rate: 87.9%). After exclusion of 24,449 workers, a total of 59,021 participants were included (inclusion rate: 62.1%)

Page 12, Table 1

It should be mentioned in results that the core group comprised middle-aged, male clerk with regular employment status and inflexible type of schedule. Consider that majority of workers reported overtime work of less than 20 hours a month.

Page 13, Table 1

Most commonly observed was manufacture with company size exceeding 3,000 employees.

Page 14

Results of questionnaires are based on subjective assessments. Better write “somebody reported” instead of “somebody was”.

A subgroup evaluation of age groups, in particular <30, 31-59, and >60 years might reveal striking differences in perceived fatigue and lack of vigor.

Line 188: suggested wording: “perceived” in perceived lack of vigor” …

Line 193: suggested wording: “reported” in reported to be more vigorous at work …

Line 197: suggested wording: “reported” in reported significantly higher …

Page 15

Line 204: When discussing differences between man and women perform a subgroup evaluation focusing on gender distribution in leading/non-leading positions.

Line 206: When taking about selection-bias arising from different health conditions consider that you lack profound variables on physical and mental health.

Page 16/17, table 2

In Characteristics: suggested wording: Overtime work hours/month

Page 18

Line 223: Take into account that overtime work not necessarily implies being under time pressure and working at high speed.

Line 230: Health perceptions differ according to age and cultural background and cannot be considered independently of a personal sense of vigor.

Consider that European Working Time Directives limit daily working time to 11 hours and weekly working time to 48 hours. (http://ec.europa.eu/social/main.jsp?catId=706&langId=en&intPageId=205).

Page 19

Line 241: Consider that the lifespan perspective of work design and ageing at work includes modification of job demands in relation to age

Page 21

Line: 280: Consider that effects of work-to-family spillover and from job demands that interfere with the family domain involve men and women.

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6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: Yes: Oliver Weigelt

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

9 Dec 2019

Dear Prof. Heber and Dr.Hashimoto

Thank you for giving us the opportunity to revise and resubmit this manuscript.

We would like to thank the Reviewers for their time and their valuable comments, which help us to improve the quality of our manuscript greatly. Below is our specific response to each comment.

Please note that sentences in italics are the original text; italics with underline denote the revised or added text. Since we have revised the title based on the suggestion from the reviewer #2'. Now the new title is "Association of overtime work hours with various stress responses in 59,021 Japanese workers: retrospective cross-sectional study".

Thank you again for considering our manuscript.

Yours sincerely,

Yuko Odagiri, M.D., Ph.D.

Department of Preventive Medicine and Public Health, Tokyo Medical University, Japan

Mailing address: 6-1-1, Shinjuku, Shinjuku-ku, Tokyo, 160-8402, Japan

Telephone: +81-3-5269-9785, Fax:+ 81-3-3353-0162

E-mail: odagiri@tokyo-med.ac.jp

Submitted filename: [Plos One, R1] Response letter ver5.0 for reviewes.docx

Click here for additional data file.

31 Dec 2019

PONE-D-19-24855R1

Association of overtime work hours with various stress responses in 59,021 Japanese workers: retrospective cross-sectional study

PLOS ONE

Dear Dr. Odagiri,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The reviewer #1 addressed several minor concerns about your revised manuscript. Please revise your manuscript carefully again.

We would appreciate receiving your revised manuscript by Feb 14 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

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Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Kenji Hashimoto, PhD

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I have gone through the revision of he manuscript entitled Association of overtime work hours with various stress responses in 59,021 Japanese workers: retrospective cross-sectional study. I think the authors were very responsive to the reviewer comments. In my view, the manuscript in its current form has the potential to make a meaningful contribution to the literature on overtime. Below I outline a couple of minor issues that ideally should be addressed to make the manuscript more reader-friendly.

1. On page 8 and 9 you state that you combined the categories 70-80 hours and 81+ hours to one category. To me it seems like this decision is not reflected in all the analyses presented. I suppose this statement occurs erroneously. Please align the statements in the methods section with the focal analyses.

2. I suggest you introduce the focal stress responses in the theory section. At a minimum, you should give readers (unfamiliar with the specific literature on overtime) an idea which facets of experienced stress you study why combining these variables makes sense. In response to one of my comments to added a discussion on work engagement, vigor etc. I suggest stating upfront which facets of individual stress responses your study is aimed at.

3. On a related note, in response to my comment in the last review, you added a section to argue for the differences between vigor as a facet of work engagement and vigor as a facet affect in terms of the POMS. According to this section both conceptualizations are different because they have been conceptualized by different authors or in different domains. In my view, such a statement is unfortunate for two reasons: First, it does not clarify what the actual differences are. Second, it is not a strong argument for distinguishing the two conceptualizations of vigor. I think, Shirom has worked out the conceptual overlap and distinctiveness between energy-related constructs in several chapters and papers. Just one idea: The vigor facet of work engagement is explicitly work-related. The vigor facet of the POMS is more generic. I don’t think it is necessary to call for future research on the work engagement vigor facet. However, I urge you to be precise regarding the definition and the conceptualization of the stress responses. Please state clearly upfront (in the theory section) what defines fatigue, vigor, depressive mood etc. and why specifically these stress responses do make sense as a bundle. I would explain at the beginning what vigor and fatigue have in common and to what extent they are different. Please note that this is a conceptual rather than an empirical question. You might find the literature on human energy as a unifying framework helpful (Quinn, Spreitzer, & Lam, 2012).

4. Please be more specific in the methods sections which stress response has been derived from the POMS and which has been derived from the other scales.

5. In my last review, I encouraged thinking of ways how your study might provide a theoretical contribution (besides the more descriptive contribution). My sense is that you have basically ignored my advice and reiterate over and over again your initial idea of doing an analysis of overtime and stress responses including more women. I have been taught not trying to make others write the paper I would like to read or I would like to write myself. Therefore, my advice for future papers would be: Take constructive advice seriously and consider thinking outside the box. Your sample size is much larger than in any meta-analysis on any subject I know. However, instead of scooping the potential inherent in the data to inform theory building to integrate research on overtime etc., you do descriptive piece on whether different amounts of relate differently to a set of stress-responses. Given that this is exactly what PLOSone embraces, there is no need to respond to this comment of mine.

6. I appreciate the clarification regarding the analyses. I think the analyses treating overtime as a continuous variable is most relevant. Consider making this analysis more focal. My impression is that – in the current version of the manuscript – you emphasize the regression models applying the overtime categories as dummy variables. For instance, in Table 2 you examine over and over again, whether 61 hours of overtime is different from less than 20 hours and whether 41 hours are different from less than 20 hours. I think the linear analysis and the dummy analyses test different hypotheses: Linear change vs. change after crossing a threshold of 21 hours. In my view, there are two ways to deal with this issue: First, make the threshold analyses less focal to the manuscript. Second, provide a rationale for conducting these competing views. Again, this would be very interesting and relevant from a theoretical perspective – although theoretical considerations don’t seem to be your primary aim with this manuscript. At a minimum, I would explain the rationale of the two competing views and the two sets of analyses more clearly in the paper – probably mentioning this aspect in the theory section already.

7. There is something like “lineally” in the manuscript. I am not sure whether this expression actually exists in English. Probably, “linearly” would be a better fit if you mean to refer to linear trajectories.

Reviewer #2: Manuscript Number: PONE-D-19-24855R1

Manuscript Title: Association of overtime work hours with various stress responses in 59,021 Japanese workers: retrospective cross-sectional study

All my comments raised in the first round of review were adequately addressed and I thank the authors for having carefully revised the manuscript. In my judgement this manuscript is now acceptable for publication in PLOS ONE.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Oliver Weigelt

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

30 Jan 2020

Reviewer #1

I have gone through the revision of he manuscript entitled Association of overtime work hours with various stress responses in 59,021 Japanese workers: retrospective cross-sectional study. I think the authors were very responsive to the reviewer comments. In my view, the manuscript in its current form has the potential to make a meaningful contribution to the literature on overtime. Below I outline a couple of minor issues that ideally should be addressed to make the manuscript more reader-friendly.

Comment #1

On page 8 and 9 you state that you combined the categories 70-80 hours and 81+ hours to one category. To me it seems like this decision is not reflected in all the analyses presented. I suppose this statement occurs erroneously. Please align the statements in the methods section with the focal analyses.

Our response for this comment

We apologize this confusing notation. We used eight categories for overtime, i.e.“20 hours or less”, “21-30 hours”, “31-40 hours”, “41-50 hours”, “51-60 hours”, “61-70 hours”, “71-80 hours” and “81 hours or more”. The category of “81+” was generated after consolidating all categories from “81-90 hours“ to “140+ hours”. We revised the manuscript to clarify this point.

(Method section, page 8-9, line 122-127)

This study assessed the length of participants’ monthly length of overtime working hours. Self-reported monthly overtime data was collected in increments of every 10 hours from “20 hours or less” to “141 hours or more”. Due to the smaller number of participants who engaged in 81 hours or more overtime we consolidated them into one category, and thus the present study set the categorization of overtime as the following 8 categories, “20 hours or less”, “21-30 hours”, “31-40 hours”, “41-50 hours”, “51-60 hours”, “61-70 hours”, “71-80 hours” and “81 hours or more”.

Comment #2&3

I suggest you introduce the focal stress responses in the theory section. At a minimum, you should give readers (unfamiliar with the specific literature on overtime) an idea which facets of experienced stress you study why combining these variables makes sense. In response to one of my comments to added a discussion on work engagement, vigor etc. I suggest stating upfront which facets of individual stress responses your study is aimed at.

On a related note, in response to my comment in the last review, you added a section to argue for the differences between vigor as a facet of work engagement and vigor as a facet affect in terms of the POMS. According to this section both conceptualizations are different because they have been conceptualized by different authors or in different domains. In my view, such a statement is unfortunate for two reasons: First, it does not clarify what the actual differences are. Second, it is not a strong argument for distinguishing the two conceptualizations of vigor. I think, Shirom has worked out the conceptual overlap and distinctiveness between energy-related constructs in several chapters and papers. Just one idea: The vigor facet of work engagement is explicitly work-related. The vigor facet of the POMS is more generic. I don’t think it is necessary to call for future research on the work engagement vigor facet. However, I urge you to be precise regarding the definition and the conceptualization of the stress responses. Please state clearly upfront (in the theory section) what defines fatigue, vigor, depressive mood etc. and why specifically these stress responses do make sense as a bundle. I would explain at the beginning what vigor and fatigue have in common and to what extent they are different. Please note that this is a conceptual rather than an empirical question. You might find the literature on human energy as a unifying framework helpful (Quinn, Spreitzer, & Lam, 2012).

Our response for these comments

(Since these comments related with each other, we would like to respond together.)

First of all, we appreciate this thoughtful comment. As you pointed, theoretical consideration would be needed to interpret the findings in this paper. With regards to difference between vigor and fatigue, we thought the correlation between these two factors is informative for readers, so we added that information in results section. Furthermore we added some explanation in discussion section. These are from our thought that we would like this paper to put more focus on epidemiological contribution.

(Discussion part, Page 30-31, Line 341-352)

Vigor among workers have attracted research attention in positive occupational health psychology for instance in studies of work engagement (50,51). Work engagement has been defined as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication and absorption”(52). Vigor in work-engagement assessments has included concepts of “motivation” or “psychological resilience”, whereas the items for assessment of vigor in BJSQ have been based on POMS. According to Shirom, the conceptualization of vigor in both work-engagement and BJSQ is not exactly the same (53). In our study “lack of vigor” was measured from the POMS. This negative concept is related on a deeper theoretical level to fatigue. In fact, moderate correlations between “lack of vigor” and “fatigue” were observed (men: r=-0.328, women: r=-0.358). However, the distinction between these interrelated concepts remains unclear theoretically. Exploring the association between overtime working hours and vigor in work engagement is an important research question in future studies

Comment #4

Please be more specific in the methods sections which stress response has been derived from the POMS and which has been derived from the other scales.

Our response for this comment

We appreciate this comment. We added detailed explanation how stress-response scales in the BJSQ were chosen into six scales.

(Method section, page 9, line 130-140)

Stress responses were assessed using the Brief Job Stress Questionnaire (BJSQ) following the recommended protocol of the Stress Check Program (25–27). The BJSQ was originally designed to measure both psychological and somatic, both positive and negative stress responses among workers in any workplace with minimum number of items (28). Six stress-response scales can be measured by 29 questionnaire items in the BJSQ, and each items were developed by referring some already standardized/authorized questionnaires. In detail, “vigor”, “fatigue”, and “irritability” , consisting of 3 items each, were from the Profile Of Mood States (POMS). “Depression”, consisting of 6 items, was from the Center for epidemiologic Studies for Depression Scale (CES-D). “Anxiety”, consisting of 3 items, was from the State-Trail Anxiety Inventory (STAI). “Somatic stress responses”, consisting of 11 items, was from Screener for the Somatoform Disorders and the Subjective Wellbeing Inventory (SUBI)(28).

Comment #5

I appreciate the clarification regarding the analyses. I think the analyses treating overtime as a continuous variable is most relevant. Consider making this analysis more focal. My impression is that – in the current version of the manuscript – you emphasize the regression models applying the overtime categories as dummy variables. For instance, in Table 2 you examine over and over again, whether 61 hours of overtime is different from less than 20 hours and whether 41 hours are different from less than 20 hours. I think the linear analysis and the dummy analyses test different hypotheses: Linear change vs. change after crossing a threshold of 21 hours. In my view, there are two ways to deal with this issue: First, make the threshold analyses less focal to the manuscript. Second, provide a rationale for conducting these competing views. Again, this would be very interesting and relevant from a theoretical perspective – although theoretical considerations don’t seem to be your primary aim with this manuscript. At a minimum, I would explain the rationale of the two competing views and the two sets of analyses more clearly in the paper – probably mentioning this aspect in the theory section already.

Our response for this comment

We appreciate this valuable comment. We agree with adding explanation why we use two statistical models simultaneously. Thus, we added explanations in the manuscript.

(Method section page 11-12, line 160-167)

Multiple linear regression analysis was used to examine the associations of stress responses with overtime. In each model, we used a standardized score of each stress response (such as “lack of vigor” or “irritability”, etc.) as a dependent variable, “length of overtime work hours” as an independent variable, other individual characteristics (such as “age” and “job position”, etc.) as covariates. First, we performed linear trend tests by treating “length of overtime work hours” as a continuous variable to check the linear association between each stress response and overtime. Then, to seek possible thresholds of overtime, “length of overtime work hours” were treated as a dummy variable by setting “less than 20hours” group as a reference category.

Comment #6

There is something like “lineally” in the manuscript. I am not sure whether this expression actually exists in English. Probably, “linearly” would be a better fit if you mean to refer to linear trajectories.

Our response for this comment

We apologize this mistake, thus we revised the part.

(Abstract section, page 3, line 45-46)

Length of overtime was linearly associated with various stress responses, except for “lack of vigor”.

Submitted filename: [Plos One, R2] Response to reviwers.docx

Click here for additional data file.

10 Feb 2020

Association of overtime work hours with various stress responses in 59,021 Japanese workers: retrospective cross-sectional study

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Reviewer #1: I think you have addressed the most important issues from my last review. Of note, you have done a good job clarifying the aspects that needed clarification. I ask you for one more thing:

Please include a correlation table including all relevant variables (dependent, independent, covariates) for the focal sample (no separate correlations per sex needed here). Inclusion of the correlation table is important for researchers doing meta-ananlyses on one or several of the variables studied. I am sorry, I ask you for this so late in the process. Your mention of the correlations between vigor and fatigue, made me aware that reporting the correlations is mandatory for most survey studies.Please also include a measure of reliability (Cronbachs Alpha or McDonalds Omega). These pieces of information are important for meta-analyses, too.

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Reviewer #1: Yes: Oliver Weigelt

14 Feb 2020

PONE-D-19-24855R2

Association of overtime work hours with various stress responses in 59,021 Japanese workers: retrospective cross-sectional study

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