上海市非农业职业人群身体活动不足的影响因素
Influencing factors of physical inactivity among non-agricultural population in Shanghai in 2013
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摘要:
目的 了解上海市不同非农业职业人群身体活动的现状和特点。
方法 使用"2013年上海市慢性病及其危险因素监测"数据，选择其中18~59岁非农业职业人群作为研究对象，分析其身体活动不足的现况及与职业性、交通性、休闲性身体活动的关系，并使用非条件logistic回归模型分析身体活动不足的影响因素。
结果 共有7 068名研究对象纳入分析。2013年上海市非农业职业人群身体活动不足率为28.95%，男性（31.90%）高于女性（25.61%）（χ2=33.88，P<0.05）。随着年龄的增加，身体活动不足率逐渐下降（趋势χ2=101.18，P<0.05）。不同地区研究对象身体活动不足率不同（χ2=69.70，P<0.05），农村地区最高（34.33%），城市地区最低（24.35%）。不同职业中身体活动不足率的差异无统计学意义。不同身体活动类型对身体活动不足的影响不同，仅考虑职业性身体活动时身体活动不足率为56.23%，增加交通性身体活动时身体活动不足率为35.12%，同时考虑职业性、交通性和休闲性身体活动时身体活动不足率为28.95%；不同职业间身体活动类型对身体活动不足的影响不同。多因素logistic回归分析显示，不同性别间职业对身体活动不足的影响不同：在男性中，管理人员身体活动不足的风险是专业技术人员的1.81倍（95%CI：1.37~2.40），服务业人员身体活动不足的风险是专业技术人员的1.31倍（95%CI：1.07~1.61）；而在女性中，不同职业间身体活动不足的风险无统计学差异（P=0.89）。
结论 上海市非农业职业人群中不同职业身体活动特点不同，应针对不同人群采取不同的干预措施。
Abstract: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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