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Physical Activity Patterns and Their Correlates among Chinese Men in Shanghai


Medicine & Science in Sports & Exercise: October 2007 - Volume 39 - Issue 10 - pp 1700-1707
doi: 10.1249/mss.0b013e3181238a52
BASIC SCIENCES: Epidemiology

Introduction: Physical activity (PA) is inversely related to the risk of many chronic diseases. Understanding PA patterns and their correlates thus has significant public health implications.

Methods: We evaluated PA patterns and their association with socioeconomic status and lifestyle factors in the Shanghai Men's Health Study (SMHS), a cohort of 61,582 Chinese men (participation rate: 74.1%) ages 40-74 living in eight communities of urban Shanghai, China. Information on PA from exercise, household chores, and walking and bicycling for transportation and daily living activities was collected by in-person interviews using a validated questionnaire. Logistic regression analyses were conducted.

Results: Participation in exercise was reported by 35.6% of study participants, walking and cycling for transportation by 22.6% and 23.5%, and walking and cycling for daily living activities by 99.9% and 24.5%. Nine percent had high-PA jobs. All kinds of PA, except household chores, were more common in older men. Education and income levels were positively associated with exercise and housework but inversely associated with transportation and daily living activities. Men with higher BMI participated in more exercise, whereas those with higher waist-to-hip ratio (WHR) were less active in all kinds of PA. Current smokers, particularly heavy smokers, were less active in all kinds of PA compared with former smokers and nonsmokers. Current alcohol drinkers, tea drinkers, and ginseng users were more likely to participate in exercise but less likely to participate in nonexercise PA. Total energy intake was positively associated with PA, except for household chores.

Conclusions: Despite low participation in exercise/sports, most middle-aged and elderly Chinese men in Shanghai participate in a high level of nonexercise PA. Their PA patterns are closely associated with socioeconomic/lifestyle factors.

1Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center and Vanderbilt-Ingram Cancer Center, Nashville, TN; and 2Department of Epidemiology, Shanghai Cancer Institute, Shanghai, CHINA

Address for correspondence: Xiao Ou Shu, M.D., Ph.D., Professor, Department of Medicine, Vanderbilt Epidemiology Center, Sixth Floor, Suite 600, 2525 West End Avenue, Nashville, TN 37203-1738; E-mail:

Submitted for publication November 2006.

Accepted for publication May 2007.

Physical activity (PA) has been shown to reduce the incidence of and mortality from chronic diseases such as type 2 diabetes mellitus, hypertension, cardiovascular disease, obesity, bone disorders, stroke, and cancer (2,3,8,16,18,26). The U.S. Centers for Disease Control and Prevention (CDC) and the American College of Sports Medicine (ACSM) have recommended that adults should engage in at least 30 min of moderate activity 5 d·wk−1 or 20 min of vigorous activity at least 3 d·wk−1, either by participating in exercise/sports or via transportation, daily living activities, and household chores (25).

Patterns of PA differ across countries and ethnic groups. In a study of 15 member states of the European Union, 73.1% adults reported participating in some kind of leisure-time exercise/sports, but few ever commuted by walking or bicycling (21). In the Inter-Tribal Heart Project conducted in the United States, about 80% of Native Americans reported never walking or cycling to and from work (16). Because less PA is required by occupations and transportation in industrialized societies such as those in North America and Europe, PA tends to come more from leisure-time activities (25) in those countries. In developing countries, on the other hand, the PA required for transportation and daily living activities tends to be more important than PA coming from leisure-time exercise/sports (14).

Several epidemiological studies have shown that leisure-time PA is associated with higher socioeconomic status (4,6,13,16,22,27). In a review of 45 newly published studies, adults' participation in physical activity in adulthood was found to be inversely related to age, blue-collar occupation, high-risk for heart disease, and nonwhite race, but it was positively related to childlessness, education, male gender, income, and injury history (30). Several studies have shown a correlation between leisure-time exercise/sports and demographic and socioeconomic factors such as age, marital status, educational attainment, gender, and ethnicity (18), as well as lifestyle factors such as smoking (4,22,27,29), alcohol consumption (7,23), and obesity (21). However, determinants for other kinds of activity, such as walking or cycling for transportation or daily living activity, occupational activity, and incidental activity, are less well characterized (30). Understanding patterns of PA and their correlates is very important for the evaluation of the health benefits related to PA and for promoting the benefits of an active lifestyle.

In this report, we describe the patterns of PA and their interrelations and associations with individual socioeconomic status and lifestyle factors in middle-aged and elderly Chinese men living in Shanghai.

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The Shanghai Men's Health Study (SMHS) is a population-based cohort study. All men who resided in the eight study communities and were between the ages of 40 and 74 yr were eligible for the study. Trained interviewers visited the homes of 83,107 eligible men, and 74.1% (N = 61,582) were recruited into the study. Reasons for nonparticipation were refusals (21.1%), absence during the study period (3.1%), and other miscellaneous reasons, including poor health or hearing problems (1.7%). The study protocols were approved by the institutional review boards of all participating institutes, and all participants provided written, informed consent.

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Measurement of physical activity.

At baseline recruitment, PA data were collected using an interviewer-administered and validated physical activity questionnaire (PAQ) (17). The reproducibility of the SMHS PAQ was evaluated using two PAQs administered 1 yr apart, and validity was determined by comparing the PAQs with 1-yr averages of two criterion measures: 12 × 7-d physical activity recalls (PAR) and four quarterly 1-wk physical activity logs (PAL) among 196 men (17). The PAQ had moderate to high reproducibility for measuring adult exercise participation and energy expenditure (κ = 0.60 and rs = 0.68, respectively), nonexercise activities (0.42-0.68), and total daily energy expenditure (rs = 0.68, κquartiles = 0.47). Correlations between the PAQ and criterion measures of adult exercise were for the first PAQ: 0.45 (7-day PAR) and 0.51 (PAL), and second PAQ administration: 0.62 (7-day PAR) and 0.71 (PAL). Correlations between PAQ nonexercise activities and the PAL and 7-d PAR ranged from 0.31 to 0.86. Correlations for total energy expenditure were high (0.62-0.77). Participants were classified as exercisers if they had engaged in regular exercise/sports (at least once a week for at least 3 months) during the preceding 5 yr. Exercisers were further asked to report up to three exercise/sports activities, including type, duration (h·wk−1), and years of participation for each one. Study participants were also asked to report their regular exercise during adolescence (13-15 yr of age) using average duration (h·wk−1), length of participation (yr), and participation in athletic teams. In terms of nonexercise activities, men were asked to report activity related to transportation (i.e., walking and cycling to/from work, for shopping, etc.), stair climbing (flights of stairs/day), and housework (h·d−1). Finally, men were asked to report what proportion of housework was performed by them during the preceding year.

Energy expenditure in standard metabolic equivalent (MET) values was used to estimate the intensity of 22 types of exercise (1). Adult exercise/sports energy expenditure was estimated by the weighted average of energy expended in all activities reported during the 5 yr preceding the interview (MET·h·wk−1·yr−1). Exercises were classified into three groups according to METs: light (3.0 METs), moderate (> 3.0 but ≤ 6.0 METs), vigorous (> 6.0 METs). For nonexercise activities, the energy expenditure index (MET·h·d−1) was estimated using reported duration data and estimated MET values (walking: 3.3; cycling: 4; stair climbing: 9; and housework: 2). We also estimated the proportion of men who reported 150 min·wk−1 of moderate activity or 60 min of vigorous activity per week. Housework was categorized as a light activity (1) and was not included in this estimation.

Occupational activity in the most recent job held by participants was classified into high, medium, or low levels using job codes previously developed for the study population (32). Clerks and accountants are examples of low-activity jobs, and structural workers, metal workers, and machine operators were classified as high-activity jobs.

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Assessment of potential correlates of physical activity.

Baseline information on demographics (age, family size, education, income per capita, and employment history), lifestyle behaviors (cigarette smoking, alcohol consumption, tea consumption, and ginseng use), medical history, and dietary habits was collected during the in-person interview, and associations with PA were evaluated. Smoking was defined as having at least one cigarette per day for at least 6 months, and alcohol consumption was defined as drinking alcoholic beverages at least three times a week for 6 months or more. All participants were measured for their current weight, circumferences of the waist and hips, and sitting and standing height by trained interviewers according to a standard protocol. We calculated body mass index (BMI) as weight in kilograms divided by the square of height in meters. All subjects were categorized as underweight (BMI < 18.5 kg·m−2), normal (BMI: 18.5-24.9 kg·m−2), overweight (BMI: 25.0-29.9 kg·m−2), or obese (BMI > 30 kg·m−2) according to the WHO standard.

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Statistical analysis.

The prevalence of various types of physical activities was calculated as the percentage of men involved in each activity. We have presented the median with the 25th and 75th percentiles of continuous variables because of their skewed distributions. For practical reasons, we dichotomized energy expenditure (MET·h·wk−1) from nonexercise PA at the median and leisure time PA as "participating" or "not participating" to create binary variables to be used in logistic regression. Because exercisers may differ substantially from nonexercisers in lifestyle and other characteristics, we conducted analyses on these two subgroups separately. Associations of physical activity with socioeconomic/lifestyle factors were evaluated by odds ratios (OR) and 95% confidence intervals (CI) derived from logistic regression modeling. Statistical analyses were performed using SAS version 9.1 for Windows (SAS Institute, Cary, NC), and all tests were two tailed.

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Demographic characteristics of the study population are presented in Table 1. The mean age of study participants was 54.9 ± 9.7 yr. Approximately 60% of the study participants had attended high school or above, and 9.7% had per capita incomes of more than 2000 yuan per month. About 17% were employed as professionals, 23.0% as clerks, and 20.7% as manual workers. The mean BMI was 23.7 ± 3.1 kg·m−2; the prevalence rates of overweight (BMI: 25-29.9 kg·m−2) and obesity (BMI: ≥ 30 kg·m−2) were 30.4 and 2.6%, respectively. About 59% of subjects were smokers, 29.3% were alcohol drinkers, and 64.1% were tea drinkers. The average total energy intake was 1909 ± 485 kcal·d−1.

Presented in Table 2 are the physical activity patterns of study subjects during adolescence and adulthood. More than 95% of men participated in exercise/sports during adolescence, with a median duration of 6.5 h·wk−1, whereas the prevalence of regular exercise during the 5 yr preceding the interview was 35.6%. Among regular exercisers in adulthood, 50% participated in regular exercise/sports more than 5 h·wk−1. Most (76.9%) reported participation in only one activity, 19.6% in two activities, and 3.5% in three activities (data not shown in table). Fifty-four percent of participants reported moderate activity and 18.6% reported vigorous activity. Walking or cycling for transportation was reported in 22.6% (median: 2.3 h·wk−1) and 23.5% (median: 4.7 h·wk−1), respectively, of participants who were employed at the time of interview. For daily living activity, 99.9% and 24.5% of men reported walking and cycling, respectively. The median time spent doing housework was 3.5 h·wk−1, and 46.9% reported being responsible for more than half of the household chores. Only 9.0% of participants reported that their most recent job required high levels of activity.

Regular exercisers had higher total energy expenditure (59.1 vs 39.6 MET·h·wk−1) and higher levels of non-leisure time physical activity (43.0 vs 39.6 MET·h·wk−1) than nonexercisers. Regular exercisers reported expending less energy in transportation but more in stair climbing and household chores than nonexercisers (data not shown in tables). Energy expenditure from exercise/sports was positively but weakly correlated with that from transportation (r = 0.12); daily activity was shown to have a positive correlation with housework (r = 0.12) and stair climbing (r = 0.11) (data not shown in tables). Approximately 59.3% of participants walked or bicycled for at least 30 min, 5 d·wk−1, for transportation or daily living activity. In addition, 26.1 and 3.9% of participants had engaged moderate and vigorous exercise/sports activity during leisure time, respectively.

Socioeconomic characteristics associated with participation in PA were age, education, per capita monthly income, BMI, marital status, and diagnosis of one and more chronic diseases (Tables 3 and 4). All types of nonoccupational PA, except for housework, were more common among older men. High income and education were significantly related to participation in exercise/sports and housework, but were inversely associated with transportation and daily living activity. Men with a high BMI participated more in exercise/sports (lowest vs highest, OR = 1.55), but not in other types of PA, whereas men with high waist-to-hip ratio (WHR) were less active in all kinds of PA. Family size was inversely associated with exercise/sports (OR = 1.27) and household chores (OR = 1.91). Morbidity from chronic diseases was positively associated with participation in exercise/sports (OR = 1.14) but was inversely related to transportation activities (OR = 0.93), daily living activities (OR = 0.95), and housework (OR = 0.94) (Tables 3 and 4).

Current smokers (OR = 0.51, 95% CI: 0.49-0.53) were less active in all types of PA compared with former- and nonsmokers, and this difference was more evident for heavy smokers (OR = 0.36, 95% CI: 0.33-0.39) (Table 3). Current alcohol drinkers (OR = 1.05, 95% CI: 1.01-1.10), tea drinkers (OR = 1.08, 95% CI: 1.04-1.13), and ginseng users (OR = 1.28, 95% CI: 1.23-1.33) were more likely to participate in exercise and sports, but not in other types of PA. Total energy intake was positively associated with participation in all types of PA (OR = 1.27 for exercise/sports, OR = 1.37 for transportation, and OR = 1.54 for daily living activity as increased every 1000 kcal·d−1), but not related to housework activities (Tables 3 and 4).

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Unlike in the United States, where people rarely walk or ride a bicycle to and from work (< 20%) (16) and usually depend on private motorized vehicles (24), more than 56% of Chinese men in the present study walk or cycle for transportation and daily living activities. On the other hand, Chinese men participated relatively less frequently in regular exercise and sports than did their counterparts in the United States (16). As a result, although the percentage of U.S. men who meet current CDC/ACSM PA recommendations during leisure time (32.0% in 1990) (16) was higher than in our population (30.0%), more Chinese men (89.3% in the current study) had engaged in at least 30 min of moderate or vigorous nonoccupational activity (59.3% from transportation and daily living activities, 26.1% from moderate exercise, and 3.9% from vigorous exercise) for 5 d·wk−1. Because of a lack of information on the intensity and length of each PA episode, an accurate assessment of compliance with CDC/ACSM PA recommendations cannot be made by this study. Nevertheless, both our study and an earlier report from China (14) suggest a more important role for transportation and daily living activities as a component of total physical activity in China.

The percentage of Chinese men (89.3%) who had engaged at least 30 min of moderate or vigorous nonoccupational activity 5 d·wk−1 was much higher compared with Chinese women (63.8%) in same area (unpublished data). We found that men spent more time each day in organized sports and leisure activities (12.6 MET·h·wk−1) than Chinese women (9.2 MET·h·wk−1), which is consistent with other reports (6,25). However, the total PA level in men (59.1 MET·h·wk−1) was lower than in women (105.5 MET·h·wk−1) because of their relatively lower transportation and household activity levels. Chinese men in our study seemed more likely to use motor vehicles for transportation (33.0%) than women (2.7%) who were residents of the same area (unpublished data).

Higher education and economic status were positively associated with leisure-time exercise/sports and housework activity but inversely associated with transportation and daily living activities in this study, which is consistent with several previous studies (9,10,13,14,16), suggesting that education is a more important predictor of specific types of PA than of the overall level of PA. PA from exercise/sports was more common among high-income people, but other types of PA were more common among low-income people, comparable to reports from other developing countries (10,11,14). Similar to our study, another report from China also found that highly educated people and people with high income were significantly more likely to participate in leisure-time PA, whereas low income was significantly associated with commuting-related PA that lasted 30 min or more (14). It has been shown that higher-income people tend to adopt new public health messages earlier than do low-income people (11). Education was positively associated with income (r = 0.4, P < 0.001) in the study population, which might explain why high-income and well-educated individuals present higher levels of compliance with PA guidelines, particularly when it comes to leisure-time PA (10). Our findings agree with an earlier observation that blue-collar and other workers with comparatively low skill levels are less likely to participate in exercise/sports than are white-collar or professional workers (14,28).

Obesity usually emerges as having a negative influence on PA (27,30). Martinez-Gonzalez and colleagues (21) report, in a representative sample from 15 member states of the European Union, that individuals in the highest quintile for leisure-time exercise/sports participation were approximately 50% less likely to be obese than those in the lowest quintile. In the present study, however, men with a high BMI were more likely to participate in exercise/sports and less likely to walk or cycle for transportation, whereas men with a high WHR were less active in all areas of PA. The association between BMI and PA may be a result of obese subjects seeking social approval or of reverse causation. The health risks associated with centralized obesity are less well known to the public in China, and thus the association between WHR and PA may be more of a true causal association.

Studies examining the association between marital status and PA behaviors have produced mixed findings. Some studies have reported a positive association between marital status and PA participation (14,20,28), whereas others have reported a null association (5,18,19). In our population, single men were more likely to participate in exercise/sports as compared to those with more than three members in their family. In addition, there was a clear relationship between PA and chronic health problems in our population, which is in line with the results from a previous study (12) and consistent with our previous findings in Chinese women that those with chronic diseases were more likely to participate in exercise/sports (unpublished data). One possible explanation is that people are more likely to seek healthy behaviors after being diagnosed with a chronic disease.

Smoking is a lifestyle factor that is inversely related to PA in this study. It has been suggested that increased PA may conceivably be a motivator for smoking cessation (31). One epidemiological study observed an association between healthy behaviors and PA and found that cigarette smoking was associated with reduced levels of PA, which is supported by our result and other studies (4,14,22,28). Cigarette smoking and impaired physical health was observed in the current study (data not shown) and has been reported in several previous studies (15,29). Impaired health may restrict people from participating in PA. We found that heavy alcohol drinkers had a higher rate of participation in exercise/sports in this population, consistent with several previous reports (7,23). Tea consumption and ginseng use were more common in exercisers, reflecting the behavioral characteristics of healthy people. Total energy intake was positively associated with participation in all types of PA, except household activity, reflecting the higher energy needs of physically active individuals.

There are several limitations in our study. First, as a cross-sectional study, our study cannot draw any causal relationship between PA and the correlates under study. Misreporting of PA status attributable to the nature of self-reports may, at least in part, have biased the observed relationships towards or away from the null. Second, we did not calculate METs from work-related physical activity because of a lack of relevant information, and we cannot estimate its contribution to total PA. Third, no distinction was made during data collection for types of walking, such as slow and brisk walking, causal walking (shopping), or walking to and from work. Thus, the MET values for walking (3.3 METs) applied in the study may slightly overestimate daily PA. On the other hand, this study is the largest and most comprehensive population-based survey conducted in urban Chinese men. The information on PA was collected using a validated PAQ, which alleviates concern about the validity of self-reported PA to a great degree. A notable strength of this instrument was that it examined a broad range of PA, particularly transportation activities, an important source of energy expenditure among Chinese men. Our study provides a comprehensive picture of participants' PA patterns using the type, duration, and length of various types of PA. However, it may not be possible to extrapolate the results of this study directly to other populations, particularly Chinese men in rural areas.

In conclusion, despite low participation in exercise/sports, 89.3% of middle-aged and elderly Chinese men in Shanghai had engaged in a relatively high level of PA (atleast 30 min of moderate to vigorous nonoccupational activity, 5 d·wk−1). Their PA patterns are closely associated with socioeconomic/lifestyle factors. Follow-up of this cohort will help us to examine secular trends in PA and to prospectively investigate the effect of PA on various health outcomes.

This study was supported by Public Health Service grant number RO1 CA82729 from the National Institutes of Health. We thank the research staff and the study participants of the Shanghai Men's Health Study for their dedicated efforts and Ms Bethanie Hull for technical assistance in the preparation of this manuscript.

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1. Ainsworth, B. E., W. L. Haskell, M. C. Whitt, et al. Compendium of physical activities: an update of activity codes and MET intensities. Med. Sci. Sports Exerc. 32:S498-S516, 2000.
2. Blair, S. N., E. Horton, A. S. Leon, et al. Physical activity, nutrition, and chronic disease. Med. Sci. Sports Exerc. 28:335-349, 1996.
3. Borrell, C., M. Rue, L. Pasarin, I. Rohlfs, J. Ferrando, and E. Ferandez. Trends in social class inequalities in health status, health-related behaviors, and health services utilization in a southern European urban area (1983-1994). Prev. Med. 31:691-701, 2000.
4. Boutelle, K. N., D. M. Murray, R. W. Jeffery, D. J. Hennrikus, and H. A. Lando. Association between exercise and health behaviors in a community sample of working adults. Prev. Med. 30:217-222, 2000.
5. Brownson, R. C., A. A. Eyler, A. C. King, D. R. Brown, Y. L. Shyu, and J. F. Sallis. Patterns and correlates of physical activity among US women 40 years and older. Am. J. Public Health 90:264-270, 2000.
6. Crespo, C. J., S. J. Keteyian, G. W. Heath, and C. T. Sempos. Leisure-time physical activity among US adults: results from the Third National Health and Nutrition Survey. Arch. Intern. Med. 156:93-98, 1996.
7. Dowda, M., B. E. Ainsworth, C. L. Addy, R. Saunders, and W. Riner. Correlates of physical activity among US young adults, 18to 30 years of age from NHANES III. Ann. Behav. Med. 26:15-23, 2003.
8. Gal, D. L., A.-S. Santos, and H. Barros. Leisure-time verse full-day energy expenditure: a cross-sectional study of sedentarism in a Portuguese urban population. BMC Public Health 15:16-22, 2005.
9. Gordon-Larsen, P., M. C. Nelson, and K. Beam. Associations among active transportation, physical activity, and weight status in young adults. Obes. Res. 13:868-875, 2005.
10. Hallal, P. C., M. R. Azevedo, F. F. Reichert, F. V. Siqueira, C. L. P. Araujo, and C. G. Victora. Who, when, and how much? Epidemiology of walking in middle-income country. Am. J. Prev. Med. 28:156-161, 2005.
11. Hallal, P. C., C. D. Victoria, J. C. Wells, and R. C. Lima. Physical inactivity: prevalence and associated variables in Brazilian adults. Med. Sci. Sports Exerc. 35:1894-1900, 2003.
12. Harrison, R. A., P. McElduff, and R. Edwards. Planning to win: health and lifestyles associated with physical activity amongst 15,423 adults. Public Health 120:206-212, 2006.
13. He, X. Z., and D. W. Baker. Difference in leisure-time, household, and work-related physical activity by race, ethnicity, and education. J. Gen. Intern. Med. 20:259-266, 2005.
14. Hu, G., H. Pekkarinen, O. Hanninen, et al. Physical activity during leisure and commuting in Tianjin, China. Bull. World Health Organ. 80:933-938, 2002.
15. Hyland, A., C. Vena, J. Bauer, et al. Cigarette smoking-attributable mortality-United States, 2000. MMWR Morb. Mortal Wkly. Rep. 52:842-844, 2003.
16. Jones, D. A., B. E. Ainsworth, J. B. Croft, C. A. Macera, E. E. Lloyd, and H. R. Yusuf. Moderate leisure-time physical activity: who is meeting the public health recommendations? A national cross-sectional study. Arch. Fam. Med. 7:285-289, 1998.
17. Jurj, A. L., W. Q. Wen, Y. B. Xiang, et al. Reproducibility and validity of the Shanghai Men's Health Study Physical Activity Questionnaire. Am. J. Epidemiol. 165:1124-1133, 2007.
18. Kaplan, M. S., J. T. Newson, B. H. McFarland, and L. Lu. Demographic and psychosocial correlates of physical activity in late life. Am. J. Prev. Med. 21:306-312, 2001.
19. King, A. C., C. C. Castro, S. Wilcox, A. A. Eyler, J. F. Sallis, and R. C. Brownson. Personal and environmental factors associated with physical inactivity among different racial-ethnic groups of US middle-aged older aged adults. Health Psychol. 19:354-364, 2000.
20. King, A. C., D. K. Kiernan, D. K. Ahn, and S. Wilcox. The effect of the marital transitions on changes in physical activity: results from a 10-year community study. Ann. Behav. Med. 20:64-69, 1998.
21. Martinez-Gonzalez, M. A., J. A. Martinez, F. B. Hu, M. J. Gibney, and J. Kearney. Physical inactivity, sedentary lifestyle and obesity in the European Union. Int. J. Obes. Relat. Metab. Disord. 23:1192-1201, 1999.
22. Martinez-Gonzalez, M. A., J. J. Varo, J. L. Santo, et al. Prevalence of physical activity during leisure time in the European Union. Med. Sci. Sports Exerc. 33:1142-1146, 2001.
23. Matthews, C. E., J. R. Hebert, I. S. Ockene, G. Saperia, and P. A. Merriam. Relationship between leisure-time physical activity and selected dietary variables in the Worcester Area Trial for Counseling in Hyperlipidemia. Med. Sci. Sports Exerc. 29:1199-1207, 1997.
24. Newman, P., and J. Kenworthy. Sustainability and Cities: Overcoming Automobile Dependence. Washington DC: Island Press, pp. 68-125, 1999.
25. Pate, R. R., M. Pratt, S. N. Blair, et al. Physical activity and public health. A recommendation from the Centers for Disease Control and Prevention and the American College of Sports Medicine. JAMA 273:402-407, 1995.
26. Pitsavos, C., D. B. Panagiotakos, Y. L. Lentzas, and C. Stenfanadis. Epidemiology of leisure-time physical activity in socio-demographic, lifestyle and psychological characteristics of men and women in Greece: the ATTICA study. BMC Public Health 5:37-45, 2005.
27. Salmon, J., A. Bauman, D. Crawford, A. Timperio, and N. Owen.The association between television viewing and overweight among Australian adults participating in varying levels of leisure time physical activity. Int. J. Obes. 24:600-606, 2000.
28. Salmon, J., N. Owen, A. Bauman, M. K. Schmitz, and M. Booth. Leisure-time, occupational, and household physical activity among professional, skilled, and less-skilled workers and homemakers. Prev. Med. 30:191-199, 2000.
29. Strine, T. W., C. A. Okoro, D. P. Chapman, et al. Health-related quality of life and health risk behavior among smokers. Am. J. Prev. Med. 28:182-187, 2005.
30. Trost, S. G., N. Owen, A. E. Bauman, J. F. Sallis, and W. Brown. Correlates of adults' participation in physical activity: review and update. Med. Sci. Sports Exerc. 34:1996-2001, 2002.
31. Ussher, M. H., A. H. Taylor, R. West, and A. McEwen. Does exercise aid smoking cessation? A systematic review. Addiction 95:199-208, 2000.
32. Zheng, W., X. O. Shu, J. K. McLaughlin, W. H. Chow, Y. T. Gao, and W. J. Blot. Occupational physical activity and the incidence of cancer if the breast, corpus uteri, and ovary in Shanghai. Cancer 71:3620-3624, 1993.


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