Is there a relationship between working hours and hypertension? Evidence from the United States

Abstract

BACKGROUND:

Previous studies find conflicted results on the relationship between long working hours and hypertension. Establishing a consensus for the direction of the relationship, more research is needed.

OBJECTIVES:

Although the European Union’s Working Time Directive limits weekly working hours, no such similar restriction exists in the United States. This leads to the important question of which is a better policy. This study bridges a gap in the literature by examining the relationship between working hours and having hypertension among older workers in the United States.

METHODS:

We applied the Cox regression and probit methods to panel data taken from the Health and Retirement Survey (HRS.)

RESULTS:

We found that an increase in a person’s working hours reduces the probability of having high blood pressure for male and female workers.

CONCLUSION:

This study’s findings may raise questions about the need for initiatives in the European Union and other countries that regulate the length of work schedules.

Keywords

Health and retirement survey older workers employee health cox regression

1. Introduction

Although there are several working hours directives all around of the world, those for European Union (EU) member countries were established in November 2003 with “DIRECTIVE 2003/88/EC OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 4 November 2003 concerning certain aspects of the organisation of work time” [1]. This regulation includes not only the limits of working hours, but also daily rest: Minimum daily rest period of 11 consecutive hours per 24-hour period; breaks: Where the working day is longer than six hours, every worker is entitled to a rest break; length of nightwork: Must not exceed an average of eight hours in any 24-hour period. The EU’s working time directive must not exceed 48 hours on average per week, including any overtime in EU countries. According to it, EU countries must have a minimum rest period. However, no such similar restriction exists in the United States. This leads to the important question of which is a better policy. Many studies have investigated the effect of varying working hours, with a specific focus on the detrimental impact on aspects of employee health, such as the prevalence of obesity [2, 3]. In addition, several studies that used small samples, found negative impacts of long working hours associated with several different illnesses, such as cardiovascular diseases [4, 5] and fatigue [6]. In the same vein, studies have provided extensive reviews and used meta-analyses to assess health outcomes [7 –9]. The differences among previous studies were associate with two main issues related to the samples. First of all, studies usually do not use whole populations; for instance, they only include college students or college graduates. Another important issue is that studies use data from different countries; for example, Spain or Japan.

Furthermore, a study that examined the National Longitudinal Survey of Youth (NLSY) found that working long hours was significantly associated with elevated risks of heart disease, non-skin cancer, arthritis, and diabetes [10]. One study also used the NLSY dataset and found that working at least 60 hours per week significantly increased the probability of injury [11].

Another health problem associated with long working hours might be hypertension. There are many studies that have investigated the effect of hypertension at work [12 –16]. In addition, there are studies that found a positive relationship between working hours and hypertension [17 –19]. On the other hand, there are studies that found an inverse relationship between working hours and hypertension [4 , 21]. In addition, some studies could not find a statistically significant relation between them [22, 23]. Therefore, we need to find the relationship between long working hours and hypertension.

Working may reduce the probability of having chronic illnesses because not working (unemployment) has a significant negative effect on wellbeing [24]. This study bridges this gap in the literature by examining the relationship between working hours and the probability of having high blood pressure among older workers (>50 years) in the United States. We applied Cox regression and probit methods to panel data taken from the Health and Retirement Survey (HRS) and found that increasing a person’s working hours reduces the probability of hypertension for male and female workers.

2. Literature review

A growing body of literature has sought to estimate the effects of working hours on employee health. Some studies have focused on specific health outcomes such as arm/hand discomfort [25] and concentration of glycosylated hemoglobin in the blood [26].

In terms of obesity, one study focused on the effect of sleeping time on body/mass index (BMI) in Hong Kong because longer working hours may lead to fewer sleeping hours [27]. They found that increased working hours led to an increased BMI. Several studies have estimated the effect of longer working hours on obesity in Australia [28, 29]; they show that female workers who work longer hours are more likely to gain more weight. Meanwhile, relatively few studies have investigated this issue in the United States. For example, a study [2] examined the relationship between US working hours and weight gain and used the NLSY data to determine that increasing an individual’s working hours increased their BMI.

For blood pressure, even though there are several studies for Japan and Europe [17 , 30], there are few studies for the United States [10, 18]. Therefore, the present study contributes to the literature because it investigates the effect of working hours on the probability of having hypertension for older people and is the first to address this topic for the older US population.

3. Methodology

This study relies on the RAND user-friendly version of the HRS, which was conducted every two years by the University of Michigan. The HRS’s original cohort was comprised of more than 26,000 Americans older than 50 years old. The HRS includes information about respondents such as their sociodemographic characteristics and detailed work histories and began in 1992; the most recent available survey was conducted in 2014.

Our statistical analysis depends on survival analysis, which is suitable for explaining the factors that contribute to the risk of mortality. The Cox proportional hazards regression model states that the hazard rate for the jth subject in the data is:

$\begin{matrix} h (t | x_{1 j}, x_{2 j}, . . . x_{kj}) \\ = h_0 (t) exp (β_{1} x_{1 j} + β_{2} x_{2 j} + . . . + β_{k} x_{k j}) \end{matrix}$ (1)

The Cox model has the important advantage that it does not make potentially untenable distributional assumptions about the hazard rate. In addition, a positive Cox regression coefficient for an independent variable increases the hazard probability.

The dependent variable in the survival analysis used in this study was the risk that a subject will have high blood pressure (over 140 mmHg for diastolic and over 90 mmHg for systolic). Our main independent variable was the person’s working hours. Regarding the effect of retirement on estimates, we added a restriction. The sample includes zero work hours if the individual was in the labor force and reported zero work hours. The covariates included their age, history of smoking, alcohol use, level of education, gender, race, obesity, self-reported health (1 = excellent and 5 = poor), and 16 occupations. From 1992 to 2014, after excluding observations with missing data, our Cox regression sample included 56,696 person-years. Table 1 shows the summary statistics of the dataset. According to (Table 1), 82% of the sample were white. According to the 2010 US census, 77.9% of the people who reported membership of only one racial group were white. The average age of the sample in this study was approximately 60 years old, and the average weekly working hours in our sample was about 38 hours. According to the OECD, the average American worker worked 34.4 hours per week in 2012 (1,790 hours/52 weeks). As presented in Table 1: 40% of the sample had high blood pressure, 72% were married with an average age of 60.31 years, 28% were obese, 82% of them white, and 51% were male. The industrial classification of the sample is also presented in Table 1: sales 11%, health service 2%, personal service 7% and machine operators 5% are some of occupations of the sample.

Table 1

Summary statistics

	Mean	S.D.	Min	Max
Work Hours	37.71	15.02	0	168
High Blood Pressure Dummy 1	0.40	0.49	0	1
Married 2	0.72	0.45	0	1
Age 3	60.31	6.63	50	100
Obese 4	0.28	0.45	0	1
White 5	0.82	0.39	0	1
Male 6	0.51	0.50	0	1
Education 7	13.01	2.97	0	17
Drink 8	0.60	0.49	0	1
Smoke 9	0.18	0.38	0	1
Self-reported Health 10	2.43	1.00	1	5
Occupations
Professional specialty operators/technical support	0.18	0.38	0	1
Sales	0.11	0.31	0	1
Clerical/administrative support	0.16	0.36	0	1
Service:private house hold/cleaning/building service	0.01	0.12	0	1
Service:protection	0.02	0.13	0	1
Service:food preparation	0.03	0.17	0	1
Health service	0.02	0.15	0	1
Personal service	0.07	0.25	0	1
Farming/forestry/fishing	0.03	0.18	0	1
Mechanics/repair	0.03	0.18	0	1
Construction trade/extractors	0.03	0.18	0	1
Precision production	0.03	0.17	0	1
Operators: machine	0.05	0.22	0	1
Operators: transport	0.05	0.22	0	1
Operators: handlers	0.03	0.16	0	1
n	56,696

¹1 is given for all high blood pressure values (over 140 for diastolic and over 90 for systolic) and means yes. ²1 means married. ³Years old. ⁴Boddy Mass Index BMI≥30 1 means yes. ⁵1 means white. ⁶1 means male. ⁷Years of education. ⁸Have you ever drunk alcohol? 1 means yes. ⁹Have you ever smoked cigarettes? 1 means yes. ¹⁰1 means excellent and 5 means poor.

4. Results

We have reported the results for different statistical methods in separate tables. Table 2 shows the results for high blood pressure from the Cox regression. Working more hours statistically reduces the probability of having high blood pressure for both men and women. Ten and eight out of the 16 occupations statistically reduced the probability of having high blood pressure for men and women, respectively. On the other hand, occupations at protection services (Svc: protection) and transportation operators (Operators: transport) statistically increased the probability of men having high blood pressure. Furthermore, occupations at private household services (Service: private house hold/cleaning/building service) and mechanics/repairs statistically increased the probability of women having hypertension.

Table 2
The Cox model results for high blood pressure

Continuous Dummy

Men Women Men Women

Working Hours 0.993* 0.994*

(0.001) (0.001)

Obese 1 1.243* 1,030 1.243* 1,003

(0.029) (0.025) (0.029) (0.025)

married 1,052 1,039 0.892* 0.880*

(0.029) (0.030) (0.022) (0.022)

age 0.968* 0.942* 0.973* 0.951*

(0.002) (0.002) (0.002) (0.002)

Self reported health 2 1.259* 0.990 1.275* 0.990

(0.015) (0.014) (0.016) (0.014)

white 1,032 0.911 0.777* 0.744*

(0.031) (0.027) (0.021) (0.021)

educ 0.959* 0.954* 0.950* 0.941***

(0.004) (0.004) (0.005) (0.005)

Drink 3 0.943* 1.056* 0.827* 0.846*

(0.022) (0.026) (0.019) (0.021)

Smoke 4 0.948 1,028 0.975 0.941

(0.031) (0.033) (0.034) (0.034)

Occupations 5

Prof specialty opr/tech sup 0.878*** 0.924* 0.831* 0.817*

(0.033) (0.035) (0.036) (0.036)

Sales 1,011 1.083* 0.886* 0.864

(0.041) (0.044) (0.044) (0.043)

Clerical/admin supp 0.867 0.818 1,008 0.985

(0.047) (0.053) (0.041) (0.039)

Svc:prvhhld/clean/bldg svc 0.626 0.529* 1.243 1.234**

(0.106) (0.094) (0.091) (0.091)

Svc:protection 1.170* 1,096 0.904 0.871

(0.077) (0.075) (0.102) (0.096)

Svc:food prep 0.697* 0.541* 0.858* 0.886

(0.076) (0.064) (0.061) (0.060)

Health svc 0.682 0.732 0.864* 0.730*

(0.137) (0.138) (0.054) (0.050)

Personal svc 0.932 0.882 0.777* 0.799*

(0.055) (0.062) (0.042) (0.042)

Farming/forestry/fishing 0.806* 0.993 0.717 0.758

(0.048) (0.058) (0.082) (0.079)

Mechanics/repair 0.798* 0.842 1.681** 1.670*

(0.043) (0.047) (0.331) (0.361)

Constr trade/extractors 0.809* 0.842* 0.249 0.276

(0.041) (0.042) (0.114) (0.123)

Precision production 0.805*** 0.856* 0.982 0.905

(0.052) (0.059) (0.096) (0.088)

Operators: machine 0.898 0.973 1,055 1,039

(0.053) (0.056) (0.070) (0.086)

Operators: transport 1,082 1.121* 0.833 0.714

(0.049) (0.053) (0.101) (0.082)

Operators: handlers 0.748* 0.751* 0.774 0.726***

(0.051) (0.052) (0.072) (0.069)

N 29092 29092 27604 27602

	Continuous	Dummy
Working Hours	0.993***		0.994***
	(0.001)		(0.001)
Obese 1	1.243***	1,030	1.243***	1,003
	(0.029)	(0.025)	(0.029)	(0.025)
married	1,052	1,039	0.892***	0.880***
	(0.029)	(0.030)	(0.022)	(0.022)
age	0.968***	0.942***	0.973***	0.951***
	(0.002)	(0.002)	(0.002)	(0.002)
Self reported health 2	1.259***	0.990	1.275***	0.990
	(0.015)	(0.014)	(0.016)	(0.014)
white	1,032	0.911**	0.777***	0.744***
	(0.031)	(0.027)	(0.021)	(0.021)
educ	0.959***	0.954***	0.950***	0.941***
	(0.004)	(0.004)	(0.005)	(0.005)
Drink 3	0.943*	1.056*	0.827***	0.846***
	(0.022)	(0.026)	(0.019)	(0.021)
Smoke 4	0.948	1,028	0.975	0.941
	(0.031)	(0.033)	(0.034)	(0.034)
Occupations 5
Prof specialty opr/tech sup	0.878***	0.924*	0.831***	0.817***
	(0.033)	(0.035)	(0.036)	(0.036)
Sales	1,011	1.083*	0.886*	0.864**
	(0.041)	(0.044)	(0.044)	(0.043)
Clerical/admin supp	0.867**	0.818**	1,008	0.985
	(0.047)	(0.053)	(0.041)	(0.039)
Svc:prvhhld/clean/bldg svc	0.626**	0.529***	1.243**	1.234**
	(0.106)	(0.094)	(0.091)	(0.091)
Svc:protection	1.170*	1,096	0.904	0.871
(0.077)	(0.075)	(0.102)	(0.096)
	Svc:food prep	0.697***	0.541***	0.858*	0.886
	(0.076)	(0.064)	(0.061)	(0.060)
Health svc	0.682	0.732	0.864*	0.730***
	(0.137)	(0.138)	(0.054)	(0.050)
Personal svc	0.932	0.882	0.777***	0.799***
	(0.055)	(0.062)	(0.042)	(0.042)
Farming/forestry/fishing	0.806***	0.993	0.717**	0.758**
	(0.048)	(0.058)	(0.082)	(0.079)
Mechanics/repair	0.798***	0.842**	1.681**	1.670*
	(0.043)	(0.047)	(0.331)	(0.361)
Constr trade/extractors	0.809***	0.842***	0.249**	0.276**
	(0.041)	(0.042)	(0.114)	(0.123)
Precision production	0.805***	0.856*	0.982	0.905
	(0.052)	(0.059)	(0.096)	(0.088)
Operators: machine	0.898	0.973	1,055	1,039
	(0.053)	(0.056)	(0.070)	(0.086)
Operators: transport	1,082	1.121*	0.833	0.714**
	(0.049)	(0.053)	(0.101)	(0.082)
Operators: handlers	0.748***	0.751***	0.774**	0.726***
	(0.051)	(0.052)	(0.072)	(0.069)
N	29092	29092	27604	27602

*p < 0.05, **p < 0.01, ***p < 0.001. ¹BMI≥30. ²1 means excellent and 5 means poor. ³Have you ever drunk alcohol? 1 means yes. ⁴Have you ever smoked cigarettes? 1 means yes. ⁵The reference group is “Managerial specialty oper.”

Although these results strongly suggest that working longer hours reduces the probability of having hypertension, we used another approach for a robustness check. We used two probit regressions for the panel data. Table 3 shows the results for high blood pressure. Probit regression on the panel data shows that the coefficients are as follows: male is –0.003 (standard error [SE]: 0.00), and female is –0.01 (SE: 0.00). These indicate that working more hours statistically reduces the probability of having high blood pressure for both men and women.

Table 3

The probit model results for high blood pressure

	Continuous		Dummy
	Men	Women	Men	Women
Working Hours	–0.003		–0.005
	(0.002)		(0.003)
Obese¹	0.777***	0.777***	0.938***	0.848***
	(0.094)	(0.094)	(0.117)	(0.102)
married	0.004	0.008	–0.232	–0.301*
	(0.124)	(0.123)	(0.141)	(0.122)
age	0.365***	0.363***	0.563***	0.452***
	(0.007)	(0.007)	(0.010)	(0.012)
Self reported health²	0.578***	0.578***	0.897***	0.791***
	(0.037)	(0.037)	(0.048)	(0.043)
white	–1.298***	–1.247***	–7.119***	–5.156***
	(0.178)	(0.178)	(0.205)	(0.239)
educ	0.066**	0.066**	–0.102**	–0.005
	(0.023)	(0.023)	(0.032)	(0.029)
Drink³	–0.035	–0.035	–0.157	–0.174*
	(0.081)	(0.081)	(0.098)	(0.086)
Smoke⁴	–0.664***	–0.659***	–0.663***	–0.499***
	(0.116)	(0.116)	(0.150)	(0.130)
Occupations⁵
Prof specialty opr/tech sup	–0.128	–0.129	–0.657**	–0.335
	(0.169)	(0.169)	(0.221)	(0.197)
Sales	0.093	0.091	–0.294	–0.185
	(0.162)	(0.163)	(0.233)	(0.204)
Clerical/admin supp	0.393	0.382	0.131	–0.046
	(0.231)	(0.231)	(0.200)	(0.175)
Svc:prvhhld/clean/bldg svc	–0.612	–0.579	0.828*	0.808*
	(1.311)	(1.299)	(0.404)	(0.341)
Svc:protection	0.568	0.557	–1,418	–0.253
	(0.296)	(0.296)	(0.811)	(0.692)
Svc:food prep	0.290	0.273	–0.400	0.060
	(0.567)	(0.569)	(0.343)	(0.288)
Health svc	0.787	0.772	–0.147	0.234
	(0.955)	(0.959)	(0.330)	(0.283)
Personal svc	–0.166	–0.175	–0.202	0.037
	(0.267)	(0.268)	(0.278)	(0.233)
Farming/forestry/fishing	–0.837***	–0.828**	–1.534**	–1,097
	(0.253)	(0.252)	(0.587)	(0.563)
Mechanics/repair	0.249	0.234	2,353	1,779
	(0.219)	(0.220)	–1,202	(0.940)
Constr trade/extractors	0.108	0.097	–1,994	–0.585
	(0.208)	(0.208)	–1,522	–1,354
Precision production	–0.076	–0.080	–1.446**	–0.930
	(0.248)	(0.249)	(0.542)	(0.479)
Operators: machine	0.227	0.221	–0.793*	–0.295
	(0.237)	(0.237)	(0.340)	(0.310)
Operators: transport	0.112	0.118	–0.528	–0.598
	(0.211)	(0.212)	(0.619)	(0.464)
Operators: handlers	–0.424	–0.425	0.794	0.618
	(0.258)	(0.258)	(0.517)	(0.425)
N	30626	30626	29987	29987

Furthermore, instead of using actual working hours as an independent variable, we used four categories in accordance with Dembe et al. (2016), specifically 35–40, 41–48, 49–54, and 55 + hours per week (the reference group is less than 35 hours per week).According to these results, all four groups show a lower probability of hypertension for men and women and effects become stronger for longer working hours groups. These results are in contrast to EU directives and may raise questions on the necessity of the EU directive 1 .

5. Conclusion

The proportion of older workers in the United States has increased over time in line with global trends. For instance, a study has highlighted that the number of people over 60 years old will reach one billion by 2020 and nearly two billion by 2050 [31]. Therefore, further studies should investigate the relationship between working hours and health in the aging workforce. This study was the first attempt to investigate the relationship between working hours and hypertension among older adults in the United States. This analysis of 12 biennial surveys based on HRS data suggests that longer working hours are associated with a lower probability of high blood pressure for men and women.

It is important to understand the risk of illnesses in an older workforce because of its policy implications for ideas such as restricting weekly working hours. Our results strongly suggest that working longer hours reduces the probability of having hypertension for older workforce. Therefore, this study’s findings may raise questions about the need for such initiatives in the European Union and other countries that regulate the length of work schedules.

Although this is the first study that has examined the older US population in the literature, it has some limitations. First, the sample only examines older people. The results might differ when all age groups are considered. Second, our results depend on self-reported variables, which may cause measurement errors. Another possible problem in this study is a healthy worker effect (HWE). The HWE is a type of sample selection problem and suggests that employees usually have lower overall death rates and better health status than the general population. The main reason for the HWE is that usually ill and disabled people are excluded from employment. It means that they drop from the sample. However, one study uses a Norwegian dataset and finds the effect of HWE is small [32].

In conclusion, we strongly believe that we need further studies into this topic. We think that there are two areas on which studies should focus. First, we need studies for different countries to determine this study’s robustness. Future research must also focus on the mechanism behind the relationship, because we need to understand what specific aspects cause the relationship between hypertension and working hours.

Conflict of interest

Authors declare no conflict of interest.

Funding

There was no funding for this study.

These results are not reported here. Available on request.

References

European Parliament, Council of the European Union. EUR-Lex Access to European Union law. Directive 2003/88/EC concerning certain aspects of the organisation of working time. Official Journal L 299;4 Nov 2003, [updated 2003 Now 18; cited 2018July 12]. Available from: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32003L0088&from=EN

CourtemancheC %. Longer Hours and LargerWaistlines? The Relationship between Work Hours and Obesity. Forum for Health Economics & Policy. 2009;12(2):1–33.

MercanMA %. A research note on the relationship between long working hours on weight gain for older workers in the United States. Research on Aging. 2014;36(5):557–67.

Nakanishi

, Yoshida

, Nagano

, Kawashimo

, Naka-mura

, TataraK %. Long working hours and risk for hypertension in Japanese male white collar workers. Journal of Epidemiology and Community Health. 2001;55(5):316–22.

UehataT %. Long working hours and occupational stress related cardiovascular attacks among middle-aged workers in Japan. Journal of Human Ergology. 1991;20:147–53.

Nagashima

, Suwazono

, Okubo

, Uetani

, Kobayashi

, Kido

, et al. Working hours and mental and physical fatigue in Japanese workers. Occupational Medicine. 2007;57(6):449–52.

Cosgrove

, Sargeant

, Caleyachetty

, GriffinSJ %. Work-related stress and Type 2 diabetes: Systematic review and meta-analysis. Occupational Medicine-Oxford. 2012;62(3):167–73.

Kang

, Park

, Seo

, Kim

, Lim

, et al. Long Working Hours and Cardiovascular Disease A Meta-Analysis of Epidemiologic Studies. Journal of Occupational and Environmental Medicine. 2012;54(5):532–7.

Virtanen

, Heikkila

, Jokela

, Ferrie

, Batty

, Vahtera

, et al. Long Working Hours and Coronary Heart Disease: A Systematic Review and Meta-Analysis. American Journal of Epidemiology. 2012;176(7):586–96.

10.

Dembe

, Yao

. Chronic Disease Risks From Exposure to Long-Hour Work Schedules Over a 32-Year Period. Journal of Occupational and Environmental Medicine. 2016;58(9):861–7.

11.

Dembe

, Erickson

, Delbos

, BanksSM %. The mpact of overtime and long work hours on occupational injuries and illnesses: New evidence from the United States. Occupational and Environmental Medicine. 2005;62(9):588–97.

12.

Joshi

, Kiran

, Singla

, SahAN %. Stress management through regulation of blood pressure among college students. Work. 2016;54(3):745–52

13.

Chandrasiri

, Dissanayake

, De SilvaV %. Health promoion in workplaces as a strategy for modification of risk factors for Non Communicable Diseases (NCDs): A practical example from Sri Lanka. Work. 2016;55(2):281–4.

14.

Kawada

, Kuratomi

, Kanai

, Suto

, Nishime

, Koizumi

, et al. Anthropometric obesity indices and metabolic syndrome in Japanese working men. Work. 2009;34(1):89–94.

15.

Wang

, DelpL %. Health status, job stress and work-related injury among Los Angeles taxi drivers. Work. 2014;49(4):705–12.

16.

Giahi

, Khoubi

, AmiriM %. The association between nsomnia and cardiovascular risk factors in bus drivers in Iran. Work. 2016;55(1):207–14.

17.

Artazcoz

, Cortès

, Escribà-Agüir

, Cascant

, VillegasR %. Understanding the relationship of long working hours with health status and health-related behaviours. Journal of Epidemiology and Community Health. 2009;63(7):521–7

18.

Yang

, Schnall

, Jauregui

, Su

, BakerD %. Work hours and self-reported hypertension among working people in California. Hypertension. 2006;48(4):744–50.

19.

Nakamura

, Sakurai

, Morikawa

, Miura

, Ishizaki

, Kido

, et al. Overtime Work and Blood Pressure in Normotensive Japanese Male Workers. American Journal of Hypertension. 2012;25(9):979–85.

20.

Wada

, Katoh

, Aratake

, Furukawa

, Hayashi

, Satoh

, et al. Effects of overtime work on blood pressure and body mass index in Japanese male workers. Occupational Medicine-Oxford. 2006;56(8):578–80.

21.

Imai

, Kuwahara

, Nishihara

, Nakagawa

, Yamamoto

, Honda

, et al. Association of overtime work and hypertension in a Japanese working population: A cross-sectional study. Chronobiology International. 2014;31(10):1108–14.

22.

Itani

, Kaneita

, Ikeda

, Kondo

, Murata

, OhidaT %. Associations of Work Hours and Actual Availability of Weekly Rest Days with Cardiovascular Risk Factors. Journal of Occupational Health. 2013;55(1):11–20.

23.

Pimenta

, Beunza

, Bes-Rastrollo

, Alonso

, Loàpez

, Velaàsquez-MeleàndezG, etal. Workhours and incidence of hypertension among Spanish University graduates: The Seguimiento Universidad de Navarra prospective cohort. Journal of Hypertension. 2009;27(1):34–40.

24.

Van der Meer

, WielersR %. Happiness, unemployment and self-esteem. in Spring Meeting RC28:” Social Inequality. Cohesion and Solidarity. 2015;1:1–28.

25.

Bergqvist

, Wolgast

, Nilsson

, VossM %. Musculoskeletal Disorders among Visual-Display Terminal Workers -Individual, Ergonomic, and Work Organizational-Factors. Ergonomics. 1995;38(4):763–76.

26.

Cesana

, Panza

, Ferrario

, Zanettini

, Arnoldi

, GriecoA %. Can Glycosylated Hemoglobin Be a Job Stress Parameter. Journal of Occupational and Environmental Medicine. 1985;27(5):357–60.

27.

, Chan

, Wong

, Hui

, Tong

, et al. Association between sleeping hours, working hours and obesity in Hong Kong Chinese: The ‘better health for better Hong Kong’ health promotion campaign. International Journal of Obesity. 2007;31(2):254–60.

28.

, Hauck

, HollingsworthB %. Employment, work hours and weight gain among middle-aged women. International Journal of Obesity. 2013;37(5):718–24.

29.

, HollingsworthB %. Employment patterns and changes in body weight among young women. Preventive Medicine. 2011;52(5):310–6.

30.

Hayashi

, Kobayashi

, Yamaoka

, YanoE %. Effect of overtime work on 24-hour ambulatory blood pressure. Journal of Occupational and Environmental Medicine. 1996;38(10):1007–11.

31.

Bloom

, Canning

, FinkG %. Implications of population ageing for economic growth. Oxford Review of Economic Policy. 2010;26(4):583–612.

32.

Nielsen

, KnardahlS %. The healthy worker effect: Do health problems predict participation rates in, and the results of, a follow-up survey? International Archives of Occupational and Environmental Health. 2016;89(2):231–8.