The relationship between long working hours and weight gain in older workers in Europe

Abstract

BACKGROUND:

Several studies have been performed on the relationship between working conditions and health. Numerous parameters still require further study, including working hours and obesity among different groups, specifically older workers in national, regional, and international levels.

OBJECTIVE:

Working hours have considerable effects on the socio-cultural, psychological, and economic aspects of people’s lives and health. While long working hours increases income level and raises living standards, it increases the risk of certain health problems. This study investigated whether working hours are associated with obesity in upper-middle-aged workers.

METHODS:

The Survey of Health, Ageing and Retirement in Europe (SHARE) dataset was used for the analyses. Analyses were carried out by means of a Cox regression of the panel dataset created with the data in question, surveyed by European Commission to 12,000 participants.

RESULTS:

The survey was performed in Austria, Belgium, Switzerland, Germany, Denmark, Spain, France, Greece, Italy, the Netherlands, Sweden, the Czech Republic, Poland, and Ireland. We found that in most countries, especially Sweden and the Netherlands, upper-middle-aged employees working > 59 hours per week are more likely to gain weight than their counterparts working < 59 hours.

CONCLUSIONS:

Our findings raise awareness of obesity in older workers, and highlight the need to regulate working conditions and hours in the European Union and other countries.

Keywords

Obesity older workforce public health nutrition overemployment

1 Introduction

For many people, work is a lifelong activity in which they spend the majority of their waking hours. According to the Time Use Institute, 21% of waking hours are spent working [1]. However, many people work more than this to attain higher incomes and subsequently, higher living standards [2]. Nevertheless, longer working hours impose many socio-economic [3] and health problems. For example, working long hours is associated with an increased risk of cardiovascular diseases [4, 5], increased blood concentration of glycosylated hemoglobin (HbA1c) [6], and hypertension [7, 8]. Working longer hours is also associated with arm and hand discomfort [9] and adverse physical and mental health [10]. Many literature reviews and meta-analyses [11 –13] support these negative health outcomes. However, the relationship between working long hours and obesity among the older workers in Europe has not been previously investigated. Ours is the first to investigate this specific topic and compare results between different countries.

Obesity is one health problem associated with working long hours. Obesity is related to metabolic syndromes, particularly type 2 diabetes, cardiovascular diseases, hypertension, hypercholesterolemia, and hypertriglyceridemia, increasing the risk for other diseases, like coronary heart conditions [14]. Lamon-Fava et al. [15] reported a strong relationship between coronary heart disease and high body mass index (BMI), which is used as a unit of measurement to identify obesity. While the diseases caused by obesity are diverse, obesity is, in fact, itself a disease. The prevalence of obesity has more than tripled since the 1980s, reaching 2.1 billion in 2017 [16]. It has been classified as one of the most challenging global health problems.

Although obesity has many different causes and types, the prevailing theory is that dietary habits are the underlying cause. According to The European Food Information Council (EUFIC) dietary habits have different kinds of determinants such as biological, economic, physical, social and psychological. Working hours is one of major economic determinant of dieatry habits [17]. A person’s development of dietary habits goes back to the embryo period. The need for food is the most fundamental human need, and continues until death. The concept of need is subjective between individuals [18, 19]. Therefore, dietary needs have complicated origins that vary between individuals. While past studies [20 –23] focused more on the dietary habits of infants, every individual has distinct needs. Thus, individuals’ needs differ depending on age, sex, profession, genetics, and physiological structure, and individuals can successfully manage nutrition by keeping well-balanced diets. Otherwise, making the stomach full, appeasing hunger, eating and drinking whatever he or she wants spoil the individual’s balanced diet.. This creates an imbalance between the energy (calories) they receive through their diet and the energy (calories) they spend each day. As a consequence, if individuals gain more energy than they spend, the energy that is not spent is stored as fat within the body. This is one reason why some individuals are susceptible to obesity.

In Latin, obesity means eating excessively. It has various definitions beyond the traditional ones, such as the excessive accumulation of fat or being overweight. For example, the percentage of adipose tissue compared to overall bodyweight for adults is a highly utilized indicator of obesity. If this exceeds 30% for women and 25% for men, obesity is likely [24]. Another important indicator of obesity is the measurement of waist circumference. Obesity is likely when the waist circumference exceeds 94 cm in men and 80 cm in women, and it is highly likely when it exceeds 102 cm in men and 88 cm in women [16]. However, most commonly, obesity is diagnosed using body mass index (BMI), defined as weight in kilograms divided by height in meters squared (kg/m²). According to the World Health Organization (WHO), a BMI value ranging between 20 and 25 kg/m² is normal for adults. Individuals with a body mass index of 30 kg/m² and above are classified as obese [25].

According to the WHO, overweight and obesity cause more deaths worldwide than underweight. For example, 65% of the world’s population live in countries where overweight and obesity kill more people than underweight (this includes all high-income and most middle-income countries). The Monitoring of Trends and Determinants in Cardiovascular Diseases (MONICA) study conducted by the WHO in 1980 revealed quite striking results regarding obesity [26]. This study encompassed Africa, America, Europe, the Eastern Mediterranean, the Western Pacific, and Southeastern Asia and introduced 20-year epidemiological reporting at the global level. According to the results of the study, obesity is dramatically increasing in all age and gender groups in developing countries. In Europe and America, the obesity rate, which is 20% for adults, has increased by 50% between 1965-1980. Furthermore a 2014 study of overweight and obesity in all age and gender groups from 1980-2013 found that worldwide, the proportion of overweight or obese adults rate increased to 50%, and prevalence also increased substantially in children and adolescents in both developed and developing countries [27].

Taking into account a high prevalence of obesity and its associated adverse health outcomes, it is important to investigate the underlying causes. In one attempt to do this, researchers [28] showed that insufficient sleep was linked to obesity. Moreover, the tendency to eat at work, have an unbalanced diet, and feel stressed all lead to the development of obesity, and are all consequences of extended working hours; this was systematically reported by Wanjek [29] to the International Labour Organization (ILO). However, some studies have investigated how long working hours affect these other (physical inactivity, drug usage, number of births, level of income, antidepressant usage, and frequency of going on diet) variables [30 –32].

The human life span has increased secondary to today’s better living standards; in addition, the older population has started to increase while the younger population has started to decrease due to declining birth rates. According to “Ageing in the Twenty-First Century: A Celebration and a Challenge” published by the United Nations Population Fund in 2012, the older population is steadily rising. The report indicates that the older population, which encompasses those aged 60 or over, which was 205 million in 1950, reached 810 million in 2012. It is predicted that this figure will rise to 2 billion by 2050 United Nations Population Fund ((UNFPA), 2012)). According to Bloom et al. [33], the number of people over the age of 60 is projected to reach 1 billion by 2020 and almost 2 billion by 2050 (22% of the world’s population). Therefore, we need to understand the obesity epidemic and investigate the impact of long working hours on the risk of obesity, specifically among older workers in European Countries. In addition, we need to compare the results between countries. Comperative analyses may help to understand whether each country has special characteristics regarding obesity or obesity has a common behaviour among the countries.These comperative findings may help the European Union to regulate a common policy framework for all member countries.

2 Literature review

Although a growing body of literature has sought to estimate the effects of working hours on employee health, the most important hurdles in this research are data limitations. Current datasets are typically created for industrial, regional, or specific purposes only. Because of this, datasets at the national level are extremely limited. National statistics databases do exist, however, by the World Health Organization and the International Labour Organization. The statistics of underdeveloped or developing countries are either unavailable or outdated. In developing countries, there is also a sample size problem; few studies are conducted using nationally representative datasets. For example, Dembe et al. [34] used the National Longitudinal Survey of Youth (NLSY) dataset, a nationally representative dataset for the United States.

Working long hours may lead to weight gain for several reasons. Chou et al. [35] examined the relationship between relative price levels of meals and body mass index. They investigated the effects of the meal price in fast food restaurants, and the food price consumed at home. The study found that fast food and full-service restaurants are related to undesirable weight outcomes in the United States. However, they noted that their results might also be interpreted to mean that the growth of restaurants is a response to the increasing value of the nonmarket activity. Therefore, higher food prices may lead to some individuals to work longer hours. Working long hours leaves less time to exercise. It also leaves less time for food preparation; therefore, these workers may eat more fast food. Finally, long working hours may reduce the time available for sleep. Researchers investigated the effect of sleep time on body mass index in Hong Kong, and showed that increased working hours decrease sleeping time, leading to increased body mass index [36].

An association between gender and obesity has also been shown. Au et al. [37] used data from the Australian Longitudinal Study of Women’s Health, which included women aged 45–50 years. They showed that female workers who work longer hours are more likely to gain more weight. These results are similar to Au and Hollingsworth’s [38] findings on young female workers.

One study conducted by Courtemanche [30] investigated the relationship between working hours and weight gain in the United States. He found that an increase in an individual’s working hours increases body mass index and the probability of being obese. In addition, he showed that changes in labor force participation were responsible for the 1.4% rise in adult obesity.

Although many studies have investigated obesity among workers or just older people [39 –51], few studies have focused on specifically older workers and long working hours at a national level and most of them are for the United States [29], but none specifically for older workers in Europe. Mercan [31] used the Health and Retirement Survey (HRS) with Cox and probit regressions and found that working at least 60 hours a week was related to 10% Body mass index gain. Thus, all the results of previous studies suggest that workers who work more than 59 hours a week are more likely to gain weight.

In this study, we examined the relationship between working hours and changes in body mass index among older workers in Europe. European Countries’ datasets were applied with the aim of comparative and region-wide analyses. These comperative findings may help the European Union to regulate a common policy implementation for all member countries. The Cox regression method was applied to the panel data from the Survey of Health, Ageing and Retirement in Europe (SHARE).

3 Methodology and data

It has been previously suggested that an increase in a person’s working hours increases the probability of him or her being obese. This has been shown by applying the Cox regression method to panel data from SHARE [52]. Stated differently, older workers who work longer are more likely to gain weight than older workers who work less. We added to this study by performing Cox regression analyses on the effects of other variables such as age, self-reported health, gender, marriage, education, drinking, smoking, and eleven other items related to disease history.

This study relies on data sets from SHARE wave 4 release 1.1.1, as of March 28, 2013, SHARE wave 1 and 2 release 2.6.0, as of November 29, 2013, or SHARELIFE release 1, as of November 24, 2010. The SHARE data collection has been primarily funded by the European Commission through the 5th Framework Programme (project QLK6-CT-2001-00360 in the thematic program Quality of Life), 6th Framework Programme (projects SHARE-I3, RII-CT-2006-062193, COMPARE, CIT5- CT-2005-028857, and SHARELIFE, CIT4-CT-2006-028812) and 7th Framework Programme (SHARE-PREP, N° 211909, SHARE-LEAP, N° 227822, and SHARE M4, N° 261982). Additional funding from the U.S. National Institute on Aging (U01 AG09740-13S2, P01 AG005842, P01 AG08291, P30 AG12815, R21 AG025169, Y1-AG-4553-01, IAG BSR06-11, and OGHA 04-064) and the German Ministry of Education and Research, as well as from various national sources; we gratefully acknowledge these organisations [53]. We complete missing values of our dataset from these institutions.

Statistical analyses depended on two approaches: survival analysis and probit model analysis. The survival analysis method was especially suitable, as the goal was to explain the factors that contributed to the risk of high body mass index in older workers (>50 years). The Cox proportional hazards regression model states that the hazard rate for the subject is:

$\begin{matrix} h (t | x_{1 j}, x_{2 j}, \dots x_{kj}) = h_{0} (t) exp (β_{1} x_{1 j} + β_{2} x_{2 j} \\ + \dots + β_{k} x_{kj)} \end{matrix}$ (1)

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

In survival analysis, our dependent variable was the risk that a subject would increase his or her BMI by 10% in a given year. Our main independent variable was the binary work hours variable; one indicated that the individual worked more than 59 hours a week. There is no upper limit, but this can be considered as more than 48 hours per week and less than 24×7 = 148 per week. We chose 60 hours as the cutoff to assess the effect of very long work hours as was chosen by Dembe et al. [34] who used the same number of hours. The covariates included age, history of smoking, alcohol use, obesity (by BMI≥30), number of chronic diseases (including heart attack, hypertension, high blood cholesterol, cerebral vascular disease, diabetes, chronic lung disease, arthritis, cancer, stomach or duodenal ulcer, Parkinson’s disease, cataracts, and hip or femoral fractures), level of education, gender, race, and self-reported health (1-excellent; 5-poor) from SHARE. According to our obesity definition, (30≤BMI), 8% of the sample was obese, as reported in Table 1. The average working hours were 34.43; the average age was 55.56 as predicted; 49% were men; and 6% were single adults.

Table 1

Summary statistics

	Mean	S.D.	Min	Max
Work hours	34.43	14.91	0.3	168
Obese¹	0.08	0.28	0	1
Age	55.56	5.55	34.3	85.8
Self-reported health²	2.40	0.97	1	5
Male	0.49	0.50	0	1
Single	0.06	0.24	0	1
Amount of conditions³	0.72	0.92	0	6
Education	12.81	4.16	0	25
Drink⁴	0.82	0.38	0	1
Smoke⁵	0.23	0.42	0	1
N	11.374

¹BMI≥30.

²1 means excellent and 5 means poor.

³How many out of eleven chronic conditions did the individual have? (A heart attack, High blood pressure or hypertension, High blood cholesterol, A stroke or cerebral vascular disease, Diabetes or high blood sugar, Chronic lung disease, Arthritis, Cancer or malignant tumor, Stomach or duodenal ulcer, peptic ulcer, Parkinson disease, Cataracts, and Hip fracture or femoral fracture.)

⁴Have you ever drunk alcohol? 1 means yes.

⁵ Have you ever smoked cigarettes? 1 means yes.

4 Results and conclusion

Analysis of three surveys from SHARE data examined the relationship between older workers’ work hours and BMI. The results suggest that older workers’ hours were associated with high BMI. Table 2 shows the results from the Cox regression analyses that depended on Equation 1 for a 10% increase in body mass index, as the marginal impact of the probit model. We included all workers older than 49 years, the highest estimate in our sample. The Cox proportional regression analyses show that in most European Countries (Austria, Denmark, France, Germany, Italy, Netherlands, Poland, Spain, and Switzerland), working more than 59 hours per week produced a higher body mass index gain hazard rate compared with those working less than 60 hours a week. In contrast, only two of European Countries (Belgium and the Czech Republic), have lower hazard rates, which are statistically insignificant, as reported in Table 2. Ten countries out of 12 had statistically significant results (^***p < 0.01, ^**p < 0.05, ^*p < 0.1). Standing out was Sweden, in which working more than 59 hours per week produced an 83% higher (significant) body mass index gain hazard rate, compared with working less than 60 hours a week. In contrast, the Czech Republic had a 7% lower hazard rate, which was statistically insignificant.

Table 2

Cox regression results

	Coeff.	S.E.
Austria	1.05^*	0.04
Belgium	0.99	0.04
Czech Republic	0.93	0.09
Denmark	1.07^*	0.16
France	1.32^**	0.14
Germany	1.14^*	0.16
Italy	1.08^*	0.06
Netherlands	1.54^***	0.25
Poland	1.38^**	0.22
Spain	1.01^*	0.03
Sweden	1.83^***	0.37
Switzerland	1.06^*	0.07

***p < 0.01 **p < 0.05 *p < 0.1.

Although this is the first study on older workers in Europe, several limitations are present. First, the sample only examines older people. The results might differ when all age groups are considered. Moreover, the results depend on self-reported variables, which may cause measurement errors. Finally, the effect of the workers without any health problems is unknown. The proportion of healthy workers could have caused sample bias, since employees usually have lower overall death rates and better health statuses than the general population.

According to the International Labour Organization, the number of people≥60 years will have increased 10 times in just 150 years (from 204 million in 1950 to 2.8 billion in 2100). In addition, according to the World Health Organization, overall, more than 10% of the world’s adult population is obese. There have been some attempts to investigate the relationship between long work hours and obesity among older workers before this study [14 , 37]. This study investigated the relationship between weight gain and working hours for European countries and found that older workers who worked at least 60 hours were more likely to gain weight than workers who worked less than 60 hours. Therefore, further studies should investigate the underlying mechanisms of this relationship and its potential implications for the prevention and management of excess weight and obesity, specifically in the older workforce.

According to the World Health Organization, at least 2.8 million adults die each year as a result of being overweight or obese [53]. In addition, almost half of the diabetes burden, between 7% and 41% of certain cancer burdens, and almost a quarter of the ischemic heart disease burden are attributable to overweight and obesity [8]. Obesity is, therefore, one of the most important and prevalent global public health problems with economic and social implications [10 , 51]. Therefore, further surveys and research need to be supported by national, international, and supranational institutions to provide a dynamic worldwide database for studies. This will also increase the global awareness of obesity.

It is important to increase awareness of the obesity problem in older workers. Increased knowledge may affect the implementation of policies, such as restricting weekly working hours. Therefore, this study’s findings may help the European Union and other countries to regulate the length of working hours. We strongly believe that further studies are needed on this topic. Three topics are of utmost importance. Analyses must be done for different countries to determine this study’s robustness at the beginning. Secondly, analyses should apply to a regional and global level. As a final step, research must also focus on the mechanism of action, because we need to understand what drives the relationship between obesity and working hours.

Conflict of interest

The authors declare no conflict of interest.

Funding

There was no funding for this study.

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