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Association between sleeping hours, working hours and obesity in Hong Kong Chinese: the ‘better health for better Hong Kong’ health promotion campaign

Abstract

Objective:

To study the inter-relationships between sleeping hours, working hours and obesity in subjects from a working population.

Research design:

A cross-sectional observation study under the ‘Better Health for Better Hong Kong’ Campaign, which is a territory-wide health awareness and promotion program.

Subjects:

4793 subjects (2353 (49.1%) men and 2440 (50.9%) women). Their mean age (±s.d.) was 42.4±8.9 years (range 17–83 years, median 43.0 years). Subjects were randomly selected using computer-generated codes in accordance to the distribution of occupational groups in Hong Kong.

Results:

The mean daily sleeping time was 7.06±1.03 h (women vs men: 7.14±1.08 h vs 6.98±0.96 h, P<0.001). Increasing body mass index (BMI) was associated with reducing number of sleeping hours and increasing number of working hours reaching significance in the whole group as well as among male subjects. Those with short sleeping hour (6 h or less) and long working hours (>9 h) had the highest BMI and waist in both men and women. Based on multiple regression analysis with age, smoking, alcohol drinking, systolic and diastolic blood pressure, mean daily sleeping hours and working hours as independent variables, BMI was independently associated with age, systolic and diastolic blood pressure in women, whereas waist was associated with age, smoking and blood pressure. In men, blood pressure, sleeping hours and working hours were independently associated with BMI, whereas waist was independently associated with age, smoking, blood pressure, sleeping hours and working hours in men.

Conclusion:

Obesity is associated with reduced sleeping hours and long working hours in men among Hong Kong Chinese working population. Further studies are needed to investigate the underlying mechanisms of this relationship and its potential implication on prevention and management of obesity.

Introduction

According to the latest WHO report, there is now a global epidemic of obesity that is associated with increased mortality and multiple morbidities.1, 2, 3, 4 In the ongoing search for the underlying etiology and risk factors for obesity, inadequate physical activity and unhealthy dietary pattern are found to be important causes.5, 6 Recently, there are increasing number of reports on the association between short daily sleeping time and obesity.7, 8, 9 These observations have led to the hypothesis that metabolic and hormonal changes associated with stressful lifestyle such as inadequate sleep might affect the body weight.7, 10 In support of this notion, stress-induced hypercortisolemia can lead to obesity and decreased insulin sensitivity.11, 12, 13

Hong Kong is a cosmopolitan city with most of her citizens leading an urbanized and stressful lifestyle. As in the west, obesity is a major public health problem in Hong Kong.14 To test the hypothesis whether short sleeping and long working hours are associated with obesity, we examined the inter-relationships of these parameters in 4793 people from working class participating in a community-based health awareness and promotion program.

Methods and subjects

BHBHK Campaign

The ‘Better Health for Better Hong Kong’ (BHBHK) Campaign commenced in 2000. This is a territory-wide health awareness and promotion program jointly organized by the Health InfoWorld of Hong Kong Hospital Authority (HA) and the Li Ka Shing Foundation. It specifically targets at the low-income working population (i.e. income close to or below the median of the overall working populations in Hong Kong) to raise their awareness regarding the importance of a healthy lifestyle using a wide range of education and health screening strategies. The ultimate goal of the Campaign is to empower the citizens of Hong Kong to take responsibility of their own health which eventually should benefit the community and society by reducing the occurrence of severe illness and loss of societal productivity.

Between July 2000 and March 2002, two leading labor associations in Hong Kong, the Hong Kong Confederation of Trade Union and the Hong Kong Federation of Trade Union, each with a total of 236 sub-unions and 450 000 members, respectively, were invited by the BHBHK Project Team to assist in the recruitment of subjects from the general working population to participate in a health awareness and screening program. These two unions are the biggest labor associations in Hong Kong with members covering more than 50% of the ‘grass-root’ working population.

Subjects were randomly selected by stratified random sampling with computer-generated codes by the project team in accordance to the distribution of occupational groups as recorded in the 1996 Hong Kong Population By-Census Report.15 The recruitment strategies and corresponding percentages among various occupational groups is summarised in Table 1. A total of 11 965 invitations were sent and 4,841 subjects (40.5%) responded. Of the 4841 completed questionairres, 48 had incomplete information on sleeping hours and were excluded from the analysis. Details on working hours were available in 3953 subjects with most of the ‘missing’ data due to the unemployed status of the participants at the time.

Table 1 Recruitment strategies and response rates of the 4841 subjects from the Better Health for Better Hong Kong (BHBHK) project

Assessment

On the day of assessment, all subjects were asked to complete a questionnaire. Before this self-complete survey, a group interview involving around 20 subjects each time was conducted with a research nurse explaining individual questions of the questionnaire. Apart from past medical history, smoking and alcohol drinking habits, social background (occupation and education level), the number of sleeping and working hours as well as that of rest days per month were also documented. The amount of average daily sleeping hours and working hours were specifically asked and subjects reported their answers by giving an exact figure (estimated to the nearest 0.1 h).

The master questionnaire consisted of collections of questions that had been previously used in several local smaller scale studies on lifestyle and cardiovascular assessment. The data were collected by a self-completion questionnaire. Most questions including self-report measures of behaviors such as smoking, alcohol use, sleeping and working hours evaluation had been pretested before this project and reviewed for face validity. However, test–retest reliability has not been measured.

All subjects underwent a health test that included measurements of blood pressure (BP), body weight, waist circumference, a random capillary blood sample for glucose and cholesterol measurement using desktop analyzers (Accutrend GC, Roche Diagnostics, Hoffmann-La Roche Ltd, Basel, Switzerland). BP was measured in the right arm after at least 5 min of rest using the Dinamapp machine. The same research personnel, equipments and desktop machines were used in all these field studies.

Body weight, height and waist circumference were measured with subjects wearing light clothings and without shoes. The minimum waist measurement between xiphisternum and umbilicus was taken as the waist circumference. BMI was calculated with body weight (in kg) divided by height square (in m2). Body mass index (BMI) was categorized using cutoff points as suggested by the World Health Organization (WHO) Expert Consultation Report in 2004: BMI 18.5–23.0 kg/m2 implies low risk, 23.0–27.5 kg/m2 moderate risk and 27.5 kg/m2 high to very high risk.16 Using the Asian definition according to the WHO Western Pacific Region Guideline in 2000, obesity was defined as BMI 25 kg/m2 and/or waist 80 cm in women or 90 cm in men.17

The socio-economic status (SES) classification according to occupation is summarized as follows:

  • Occupational group 1: social classes I and II, professional or managerial

  • Occupational group 2: social class III, non-manual

  • Occupational group 3: social class III, manual

  • Occupational group 4: social classes IV and V, unskilled

Statistical analysis

Statistical analysis was performed using the SPSS (version 10.0) software on an IBM compatible computer. All results are expressed as mean±s.d. or n (%) as appropriate. The Student's t-test, χ2 test or one-way analysis of variance (ANOVA) were used for between-group comparisons where appropriate. ANCOVA (ANOVA with covariates) was used to compare groups with adjustment for relevant covariates. Age-adjusted partial correlation coefficient (r) was used to assess the correlation between daily sleeping and working hours and other clinical parameters.

Multiple linear regression analysis on BMI and waist were performed in both gender using age (years), smoking (yes=1, no=0), alcohol drinking (yes=1, no=0), BP (mmHg), daily sleeping time (hours), daily working time (hours) and monthly rest day (days) as independent variables. A P-value <0.05 (two-tailed) was considered to be significant.

Results

Of the 4793 subjects, 2353 (49.1%) were men and 2440 (50.9%) were women. Their mean age (±s.d.) was 42.4±8.9 years (range 17-83 years, median 43.0 years). These demographic characteristics are similar to that of the working age population in Hong Kong. Table 2 summarizes their clinical characteristics. The mean daily sleeping time was 7.06±1.03 h (women vs men: 7.14±1.08 h vs 6.98±0.96 h, P<0.001). Figure 1 shows the daily sleeping and working hours according to categories of BMI. Increasing BMI was associated with reducing number of sleeping hours and increasing number of working hours reaching significance in the whole group as well as among male subjects. Using age-adjusted partial correlation coefficients, BMI and waist were inversely correlated with mean daily sleeping hours (overall: r=−0.037, P=0.022 and r=−0.046, P=0.004, respectively; women: r=0.007, P=0.779 and r=0.002, P=0.950, respectively; men: r=−0.054, P=0.011 and r=−0.060, P=0.005, respectively) as well as positively correlated with mean daily working hours (r=0.084, P<0.001 and r=0.163, P<0.001, respectively; women: r=0.012, P=0.616 and r=0.014, P=0.578, respectively; men: r=0.080, P<0.001 and r=0.128, P<0.001, respectively) and, similarly, reaching significance in the whole group as well as among male subjects. Number of rest days per month was not correlated with BMI or waist.

Table 2 Clinical characteristics, sleeping time and working time in 4793 subjects participating in a health awareness and screening program
Figure 1
figure1

Mean daily sleeping and working hours in subjects categorized according to their BMI. (a) Mean daily sleeping time. (b) Mean daily working time.

Tables 3 and 4 summarize the clinical particulars of subjects categorized according to their daily sleeping or working hours (mean±s.d.). There was a higher percentage of obese subjects in the group with shorter sleeping time and longer working time when compared to the referent group of <6 h for sleeping and >9 h for working hours, respectively.

Table 3 Clinical particulars of the 4793 subjects categorized according to their daily sleeping time (mean±s.d.)
Table 4 Clinical particulars of the 3953 subjects categorized according to their daily working time (mean±s.d.)

Based on multiple regression analysis with age, smoking, alcohol drinking, systolic and diastolic BP, mean daily sleeping hours and working hours as independent variables, BMI was independently associated with age, systolic and diastolic BP in women, whereas waist was associated with age, smoking and BP. In men, BP, sleeping hours and working hours were independently associated with BMI, whereas waist was independently associated with age, smoking, BP, sleeping hours and working hours in men (Table 5).

Table 5 Multiple regression analysis on BMI and waist (in different gender) with age, smoking, drinking, BP, daily sleeping time, daily working time and monthly rest day as independent variables

Based on occupation, of the 4841 subjects, 452 were retired and 69 had missing data on occupation. For the other 4320 subjects, 99 (2.2%) were professionals or managers (group 1), 1594 (36.9%) were skilled non-manual workers (group 2), 1808 (41.9%) were skilled manual workers (group 3) and 819 (19.0%) were unskilled workers (group 4). This is consistent with the SES pattern of the working age population (excluding professionals and managers or occupational group 1) of Hong Kong as well as the coverage of the working population by the labor associations of this study (40% occupational group 2, 40% group 3 and 20% group 4).

Discussion

In this health awareness and screening program, we found independent associations among obesity (BMI and waist), sleeping hours and working hours. Although these findings are of interest, its interpretation is limited by the voluntary nature of the participants and the relatively low response rate of only 40%. Nevertheless, similar response rate is typically seen with other health awareness programs. Generally speaking, volunteers in health-related programs are more ‘health conscientious’ and, hence, may have better ‘health’ than non-responders. Besides, since the program was a cross-sectional study, causality between sleeping and working time and obesity cannot be delineated. Evaluation of instrument validity in this study was not optimal such that criterion-related validity had not been assessed. Our study is also limited by inadequate assessment on the reliability and generalizability of the questionnaire. Despite this measurement bias, we were able to demonstrate these interesting relationships in our working population. Besides, our program was supported by two labor associations in Hong Kong. Our subjects were randomly selected using computer-generated codes in proportions corresponding to the distributions of different occupational groups and socio-economical classes in Hong Kong. Together with the relatively large sample size, our findings are thus of public health interest.

Obesity is now reaching epidemic proportions in many parts of the world.1, 3 The prevalence of obesity in Hong Kong (defined by BMI 25 kg/m2) is approximately 30% based on two local surveys conducted in the mid 1990s.14, 18 This figure is comparable to that reported in the US (obesity defined by BMI 30 kg/m2).14 Westernization or urbanization changes such as inadequate physical activity, unhealthy diet and stressful lifestyle have been identified to be culprits for the surge of obesity prevalence in the past decades.5, 6, 13, 19 Recently, inadequate sleep has been added to his growing list of risk factors for obesity.7, 8, 9 Vorona et al.7 studied 1001 Caucasians from primary care population and found that overweight (BMI25–29 kg/m2) and obese (BMI30-39kg/m2) patients slept less than those with normal BMI (17–24 kg/m2). Similar findings have also been reported in children in the US and Japan indicating an inverse relationship between daily sleeping hours and risk of childhood obesity.8, 9 In our present study, we observed a clear inverse trend between daily sleeping hours and BMI, reaching statistical significance in men.

Physical activity and diet may be important confounding variables in the association between obesity, sleeping and working time. Unfortunately, the information was not available during the present analysis. In particular, low sleeping hours could be related to ‘busyness’ and lack of ‘leisure time’ of the subjects, which lead to low physical activity, poor eating habits and hence, obesity. In accord to this, our data also showed that increased mean daily working hours was associated with higher BMI. However, both the lack of leisure time physical activity and a sedentary occupation were associated with an increased risk of ischemic heart disease death.20 It is unlikely that an occupation is having a long working hour to the degree that leisure time is completely nil. In fact, there was report showing that obesity was inversely associated with social class, education, and distance walked, but positively associated with time spent watching television.21 People should have time left after a long day’s work but they might spend it on watching television or other sedentary activity instead of exercise. In fact, longer working time, especially in sedentary occupation, seems to be a stronger indication for more leisure time physical activity.

Based on both clinical and experimental studies, insufficient sleep has been shown to associate with multiple metabolic and hormonal changes including reduction in glucose tolerance, increase in cortisol levels and sympathovagal response.11, 19, 22 Besides, sleep deprivation is associated with reduced leptin and increased ghrelin levels,23, 24, 25 both of which can increase appetite and, hence, possibly, weight gain.

Similar to sleep deprivation, both aging and psychosocial stress are associated with increased plasma cortisol and reduced growth hormone and sex hormones.19, 22, 26, 27, 28, 29 In this regard, there is now growing epidemiological evidence suggesting the importance of psychosocial, cognitive and behavioral parameters and chronic diseases such as obesity, diabetes and cardiovascular diseases.30, 31, 32 Although formal psychological testing were not performed in our subjects, it is conceivable that subjects who need to work long hours and have poor sleep may be under higher level of stress which might be reflected in their obesity. In support of this notion, long duration in a low socio-economic occupational status has been reported to be associated with increased visceral obesity, hypercortisolemia and perceived stress.33 Besides, in keeping with the reported hormonal abnormalities that regulate appetite, increased food intake has also been reported in subjects who are under stress.34, 35 Against this background, it is noteworthy that the associations between obesity, short sleeping hours and long working hours were observed mainly in men but not women. Such an observation is in keeping with the well-recognized more ‘stressful’ lifestyle, increased cardiovascular risk and shorter lifespan of men than women.36, 37

In conclusion, obesity is associated with reduced sleeping hours and long working hours in men among Hong Kong Chinese working population. Further studies are needed to investigate the underlying mechanisms of this relationship and its potential implication on prevention and management of obesity.

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Acknowledgements

We thank all participating labor unions and their members as well as all advisors and members of the project team in making this study possible. We are particularly grateful to the Li Ka Shing Foundation for providing partial funding to the project (all other financial support comes from Hospital Authority, HA). The HA Health InfoWorld is supported by the HA which is the governing body of all public hospitals in Hong Kong with a mission to promote public awareness of importance of healthy lifestyle and disease prevention. Members of the Research Committee of the BHBHK Campaign include Professor Cecilia LW Chan, Professor Juliana CN Chan, Dr Gary TC Ko, Professor Stanley SC Hui, Professor CY Chiu, Mrs Rosalie SY Kwong, Ms Selina Khor, Ms CY Wong, Mr Spencer DY Tong, Mrs Amy WY Chan, Ms. Ruby LP Kwok and Mr Patrick TS Wong.

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Ko, G., Chan, J., Chan, A. et al. Association between sleeping hours, working hours and obesity in Hong Kong Chinese: the ‘better health for better Hong Kong’ health promotion campaign. Int J Obes 31, 254–260 (2007). https://doi.org/10.1038/sj.ijo.0803389

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Keywords

  • sleeping hours
  • working hours
  • Hong Kong Chinese
  • health promotion

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