Prevalence and determinations of physical inactivity among public hospital employees in Shanghai, China: a cross-sectional study

Abstract

This study aims to explore the prevalence and determinations of physical inactivity among hospital employees in Shanghai, China. A cross-sectional study of 4612 employees aged 19 to 68 years was conducted through stratified cluster sampling from different classes of Shanghai hospitals in 2011. The total physical activity was evaluated using the metabolic equivalent according to the Global Physical Activity Questionnaire. Among the participants, 38.5%, 32.3%, and 64.6% of the employees are inactive at work, commuting, and taking leisure time, respectively. Up to 41.8% of the men and 37.8% of the women (P = 0.012) are physically inactive. When the age and educational level are adjusted, male doctors and medical technicians show a higher percentage of physical inactivity than male workers in logistics (P = 0.001). Among females, employees who are working in second- and third-class hospitals show a higher proportion of physical inactivity than those who are working in community health care centers. Logistic regression analyses show that the odds ratios (ORs) of leisure-time physical inactivity associated with the intensity of physical activity at work are 2.259, 2.897, and 4.266 for men (P<0.001) and 2.456, 3.259, and 3.587 for women (P<0.001), respectively. The time during commuting activities is significantly associated with leisure-time physical inactivity in either sex (OR= 2.116 for men and 2.173 for women, P<0.001). Hospital employees, particularly doctors and medical technicians, show a higher proportion of physical inactivity than other inhabitants in Shanghai. The time and intensity of activity at work and commuting are associated with leisure-time activities.

Keywords

physical inactivity / prevalence / determination, employee / public hospital / cross-sectional study

1 Introduction

Numerous studies have shown the important health benefits of physical activities in various socio-demographic populations [1−4]. However, physical inactivity has been determined as the second most important risk factor for poor health after tobacco smoking. Physical inactivity contributes between 12% and 19% of the risks associated with the five major non-communicable diseases (NCDs) in China, namely, coronary heart disease, stroke, hypertension, cancer, and type 2 diabetes mellitus. Physical inactivity imposes a substantial economic burden on the country because it is solely responsible for more than 15% of the yearly medical and non-medical costs of the main NCDs in the country [5].

Despite well-known evidence, the estimated global number of adults (aged 15 years or older) who are insufficiently active was 31% in 2008 (men 28% and women 34%) [6]. Data from a nationwide surveillance (n = 50 717) in 2007 showed that the prevalence of low-level or no physical activity is 31.1% of the 15- to 69-year-old individuals in China [7].

Job strain and stress may be considered risk factors in the health of employees. A meta-analysis suggested that work-related stress is associated with an unhealthy lifestyle, but longitudinal analyses suggested that both show no direct cause-effect relationship [8]. Nurses are at a higher risk of occupational stress than other occupations in China [9]. A study on hospital-based Chinese physicians revealed that both job strain and effort-reward imbalance are associated with health function [10]. A dose-response relationship also exists between work stress and risk of general and central obesity, and this relationship is largely independent of covariates [11]. Workers who experience job strain show high odds ratios (ORs) of low leisure-time physical activity [12].

Literature on the physical activities of hospital employees is rare. This study aims to explore the prevalence and determinations of physical inactivity and identify the relationship between work domain and other domains of hospital employees in Shanghai, China. The results of this study may serve as a basis for developing new strategies and programs of effective health promotion in Shanghai hospitals.

2 Methods

2.1 Survey population and sampling

A cross-sectional study is conducted on a representative sample of employees aged 19 to 68 years from different classes of public hospitals in Shanghai, China. The hospitals are divided into three classes, namely, third-class hospitals, second-class hospitals, and community health care centers (CHCCs). The stratified cluster sampling used in 2011 is employed, and 4631 participants are recruited (response rate= 87.5%) from four hospitals and four CHCCs. The four hospitals are composed of two third-class hospitals (a comprehensive hospital and a special disease hospital) and two second-class hospitals (one from each of the urban and suburban districts), whereas the four CHCCs are composed of two centers from each of the urban and suburban districts.

2.2 Survey questionnaire and physical activity measure

The physical activity measure used in this study is the Global Physical Activity Questionnaire (GPAQ) [13], which comprises 19 questions about physical activities performed in a typical week. With the support of the World Health Organization (WHO) in 2002, the GPAQ was developed as part of the WHO STEPwise approach to chronic disease risk factor surveillance (STEPS). The STEPS approach has been widely introduced as a feasible approach in monitoring the eight key risk factors of NCDs, particularly in developing countries [14].

The GPAQ measure asks about the frequency (d) and time (min/h) spent doing moderately and vigorously intensive physical activities in three domains: (1) work-related physical activity (paid and unpaid, including household chores), (2) active commuting (walking and cycling), and (3) discretionary leisure-time (recreation) physical activities.

The original text of the questionnaire is not changed after its translation from English to Chinese. Bull et al. [15] studied the reliability and validity of GPAQ in nine countries, including China, Shanghai; the results indicate that GPAQ is a suitable and acceptable instrument for monitoring physical activity in the population health surveillance system. All data collection and processing follow the GPAQ analysis protocol guidelines [13]. The questionnaire is self-completed by the participants during the survey.

2.3 Physical activity data treatment, definitions, and analysis

Energy expenditure estimation is based on the duration, intensity, and frequency of physical activities performed in a typical week. Metabolic equivalent (MET), the unit for measuring the energy expended during physical activity, is applied to the physical activity variables derived from GPAQ. MET is the ratio of the metabolic rate of a specific physical activity to the metabolic rate at rest. One MET is equivalent to the energy cost of sitting quietly (1 kcal·kg⁻¹·h⁻¹), and the oxygen uptake (in ml·kg⁻¹·min⁻¹) with one MET is equal to the oxygen cost in sitting quietly, which is approximately 3.5 ml·kg⁻¹·min⁻¹. MET values and formulas for the computation of MET minutes are based on the intensity of specific physical activities. A moderately intensive activity during work, commuting, and recreation is assigned a value of 4 METs, whereas vigorously intensive activities are assigned a value of 8 METs. The total physical activity score is computed as the sum of all MET-min/week from moderately to vigorously intensive physical activities performed during work, commuting, and recreation [13].

Physical activity levels were initially classified as low, moderate, or high (vigorous) intensity from the definition of the GPAQ analysis framework [13]:

(1) High: Any one of the following two criteria: (a) a vigorously intensive activity for at least three days and accumulating at least 1500 MET-min/week or (b) seven or more days of any combination of walking and moderately or vigorously intensive activities accumulating at least 3000 MET-min/week.

(2) Moderate: Any of the three following criteria: (a) three or more days of vigorously intensive activity performed at least 20 min/d, (b) five or more days of moderately intensive activity and/or walking for at least 30 min/d, or (c) five or more days of any combination of walking and moderately or vigorously intensive activities accumulating at least 600 MET-min/week.

(3) Low: No activity is reported, or some activity is reported but not enough to meet high and moderate categories. The intensity is defined as physical inactivity in this study.

2.4 Statistical analysis

Epi Info and SPSS version 13 are used for data entry and data analysis. Descriptive statistics are computed for demographic characteristics. The proportion of physically inactive individuals among study participants is computed. Chi-square test of independence and T- and F-tests are conducted to assess the association of physical activity status with categorical and continuous variables, respectively. The multivariate logistic regression model is used to estimate the ORs and the 95% confidence intervals (95% CI) of inactivity using age, educational level, occupation, and hospital grade as covariates, and of inactivity at leisure time according to the occupational and commuting physical inactivity categories by sex.

3 Results

3.1 Characteristics of participants

Table 1 shows the baseline characteristics of the 4612 participants aged 19 to 68 years who participated in this study. The mean age is older in men than in women (38.6±10.9 years vs. 33.5±8.7 years, t = 14.47, P<0.001). The dominant education and occupation were undergraduate (43.8%) and doctor (58.5%) for men and college (40.8%, because of less class hours and the lack of a bachelor’s degree compared with the undergraduates) and nurse (59.6%) for women from selected hospitals. The participants in this survey are mostly from second- and third-class hospitals (92.1%).

3.2 Inactivity at work, commuting, and leisure time

Of the 4612 studied participants, 1774 (38.5%) do not report a vigorously or moderately intensive activity during work, whereas 1491 (32.3%) do not walk or cycle for at least 10 min continuously. Inactivity at work and commuting are significantly more prevalent among men than among women (42.4% vs. 37.1%, P = 0.001; 35.9% vs. 31.1%, P = 0.002). Approximately 64.6% of the studied population is not engaged in any leisure-time physical activities. As shown in Table 2, the prevalence of leisure-time inactivity is significantly higher in women than in men (68.6% vs. 53.2%; P<0.001).

3.3 Total physical inactivity

Of the total survey population, 38.8% of employees, 41.8% in men and 37.8% in women (P = 0.012), are classified as low level in terms of total physical activity. Up to 49.6% of 30- to 39-year-old males and 40.2% of 19- to 29-year-old females belong to the low-physical activity category. These percentages are significantly higher than those of male employees aged 29 years and younger and 50 years and older (P<0.001) and female employees aged 50 years and older, respectively (P = 0.014). An increasing trend is observed on the percentage of highly educated male participants who belong to the low-physical activity category (P<0.001). Among male employees, the percentage of doctors and medical technicians who are classified as having low levels of total physical activity is higher than that of workers in logistics (giving priority to support staff of non-medicine personnel in hospitals, P = 0.001). As the grade of the hospital increases, the percentage of physical inactivity among female employees significantly increases (P<0 0.001, Table 3).

As shown in Table 4, age and occupation are significantly related to total physical inactivity in both sexes. The risk of total physical inactivity is significantly higher among 30- to 39-year-old male employees (P = 0.001) and lower among 40- to 49-year-old female employees and 50 and older (P = 0.015 and P = 0.027, respectively) than among employees who are less than 30 years old. Among male employees, the risk of total physical inactivity is significantly higher for doctors and medical technicians than for workers in logistics (P = 0.009 and P = 0.043, respectively). Women who are working in second- and third-class hospitals are significantly correlated with a high likelihood of total physical inactivity (P<0.001 and P = 0.010). The likelihood of total physical inactivity does not significantly vary with regard to educational level (Table 4).

The association of work-related and commuting activities among the study population with the likelihood of leisure-time physical inactivity is similar in both sexes (Table 5). Multivariate analyses adjusted for age, education, occupation, and hospital grade at work show that the ORs of leisure-time physical inactivity associated with only moderate, only vigorous, and both moderately and vigorously intensive physical activities at work are 2.259, 2.897, and 4.266 for men (P<0.001) and 2.456, 3.259, and 3.587 for women (P<0.001), respectively. As shown in Table 5, walking or cycling to and from workplaces is significantly associated with the likelihood of leisure-time physical inactivity in either sex (OR= 2.116 for men and OR= 2.173 for women, P<0.001).

4 Discussion

Physical inactivity has a major health effect worldwide [16]. Our data from previous surveillance showed that 29.6% of the population (n = 17 174) of Shanghai inhabitants aged 15 to 69 years had low levels of physical activity; 34.2% are men and 24.8% are women. The results of this study suggest that more hospital employees are more physically inactive than inhabitants in Shanghai regardless of sex [17]. The finding is consistent with our hypothesis that hospital employees are more probably physically inactive than inhabitants in Shanghai. Analysis between sexes shows a higher prevalence of physical inactivity in men than in women, similar to the result obtained in the Shanghai population [17]. On the basis of domain stratification, female employees are more active than male employees during work time. This finding may be related to the fact that women undertake more household chores aside from hospital tasks than men. Results also show that age is an independent factor of total physical inactivity and that more young employees perform insufficient physical activity than old ones in both sexes. Similar results were observed among Shanghai inhabitants; that is, 25- to 34-year-old men and 20- to 29-year-old women mostly demonstrate physical inactivity [17]. Therefore, a population intervention on promoting physical activity should focus on adults aged 40 years and younger.

Findings on the association of socio-economic position and occupation with total physical activity levels are inconsistent [18−22]. The study shows that more doctors and medical technicians have physical inactivity than male workers in logistics. This result may be related to the relatively longer sedentary time or lesser working activity of doctors and medical technicians than male workers. Female employees who are working in second- and third-class hospitals have a higher prevalence of physical inactivity than those who are working in a CHCC. This finding may be associated with job strain or working stress, which may be a risk of physical inactivity for a population and is detrimental to the health of hospital employees [12,23]. Prospective data from the Finnish Public Sector Study showed that workplace stress is associated with a slightly increased risk of physical inactivity [24]. Job strain in nurses suggests that its negative effects have led to the recognition of nursing as a stressful occupation [25]. Second- and third-class public hospitals in China have been given priority to save lives in cases of emergency and to diagnose and treat other difficult cases, whereas CHCCs have been responsible for serving basic health care and treating common diseases. In other words, a higher responsibility may be involved in the greater stress or job strain among employees in second- and third-class hospitals than in CHCCs. This finding supports that occupational stress is a risk factor associated with physical inactivity. Further evidence of this relationship is necessary through future research that measures job strain and physical activity.

If the work activity of employees is inconveniently changed for hospital environment reasons, they can pre-arrange their activities during commuting and leisure time. The results of a prospective cohort study showed that survival benefit ranges from 1.5 to 3.6 years for moderate leisure-time physical activity and 2.6 to 4.7 years for high leisure-time physical activity compared with low leisure-time physical activity among the different levels of occupational physical activity [26]. A study with combined individual-level data from 14 European cohort studies (baseline years are from 1985−1988 to 2006−2008) for 170 162 employees (50% women; mean age is 43.5 years) suggested that unfavorable work characteristics may have a spillover effect on leisure-time physical activity [27]. The results of the present study suggest that leisure-time physical activity can be affected by the duration and intensity of physical work activity and commuting in both men and women, although the effects can benefit their health. This finding explains the reason why female employees are more active at work and commuting and less active at leisure time than males. Physical activities and commuting are an effective method for individuals who are inactive at leisure time to “kill two birds with one stone.” Walking or cycling not only mitigates commuter traffic but also compensates for the lack of leisure-time activity. This recommendation should be aimed at employees who spend less time in a recreational physical activity because most of them are inactive during leisure time.

5 Conclusions

Hospital employees have a higher proportion of physical inactivity than inhabitants in Shanghai. No report on physical inactivity for specific hospital employees exists to date. Results show that Shanghai hospitals should be a key intervention place in promoting physical activity. Male employees have a higher inactive percentage at work and commuting than female employees, young employees have a higher percentage of total physical inactivity than old ones, and male doctors and medical technicians have a higher percentage of physical inactivity than workers in logistics. Time and intensity of activity at work and commuting can be associated with leisure-time inactivity. The findings indicate the differences in physical inactivity between different occupations and suggest an intervention on advocating physical activities within different hospitals, particularly among doctors and medical technicians. Commuting actively, such as walking or cycling, should be encouraged because it can be an opportunity to improve physical inactivity.

One limitation of this survey is the slight weakness of the sample size to be representative of CHCCs because of the small number of participants.

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