YAN Qing-hua, YAO Hai-hong, XU Ji-ying, CHENG Min-na, SHI Yan, ZHONG Wei-jian. Influencing factors of physical inactivity among non-agricultural population in Shanghai in 2013[J]. Journal of Environmental and Occupational Medicine, 2017, 34(8): 681-686. DOI: 10.13213/j.cnki.jeom.2017.16808
Citation: YAN Qing-hua, YAO Hai-hong, XU Ji-ying, CHENG Min-na, SHI Yan, ZHONG Wei-jian. Influencing factors of physical inactivity among non-agricultural population in Shanghai in 2013[J]. Journal of Environmental and Occupational Medicine, 2017, 34(8): 681-686. DOI: 10.13213/j.cnki.jeom.2017.16808

Influencing factors of physical inactivity among non-agricultural population in Shanghai in 2013

  • Objective To understand the prevalence and characteristics of physical activity among Shanghai non-agricultural population.

    Methods Data retrieved from "2013 Shanghai Non-communicable Diseases and Risk Factors Surveillance" were used to in vestigate the residents who were 18-59 years old and engaged in non-agricultural occupations in Shanghai. The prevalence of physical inactivity among different occupations was described. The relationship between physical activity types (work, transportation, and recreational) and physical inactivity was assessed. The influencing factors of physical inactivity were also analyzed using nonconditional logistic regression models.

    Results A total of 7 068 residents participated in this study. The prevalence rate of physical inactivity among Shanghai nonagricultural population was 28.95%. The rate was higher among male (31.90%) than female (25.61%) (χ2=33.88, P < 0.05) and decreased with older age (trend χ2=101.18, P < 0.05). Differences were found in physical inactivity prevalence rates among subjects in different areas (χ2=69.70, P < 0.05), which was highest in rural area (34.33%) and lowest in urban area (24.35%). There was no significant difference among different occupations. Different types of physical activity showed different impacts on physical in activity. The prevalence rates of physical inactivity were 56.23%, 35.12%, and 28.95% when considering work alone, work and transportation, and work, transportation, and recreational physical activities, respectively. The impacts of physical activity types on physical inactivity were different among different occupations. According to the results from multiple logistic regression analysis, the relationship between occupations and physical inactivity was different between males and females. For males, administrators (OR=1.18, 95%CI:1.37-2.40) and service workers (OR=1.31, 95%CI:1.07-1.61) had higher risks of physical inactivity than technologists, but there was no difference for females.

    Conclusion Different characteristics of physical inactivity are identified among non-agricultural populations with different occupations in Shanghai. Specific interventions should be developed for different occupational groups.

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