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[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%)(χ~2=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χ~2=101.18,P<0.05)。不同地区研究对象身体活动不足率不同(χ~2=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。
[Objective] To understand the status quo and characteristics of physical activities of different non-agricultural occupations in Shanghai. [Methods] Using the data of 2013 Shanghai Monitoring of Chronic Diseases and its Risk Factors, the non-agricultural occupational population aged 18-59 years was selected as the research object to analyze the status quo of its physical activity deficiencies and its relationship with occupational, traffic, Recreational physical activity, and using non-conditional logistic regression model to analyze the influencing factors of physical inactivity. [Results] A total of 7 068 subjects were included in the analysis. In 2013, the rate of physical inactivity among non-agricultural occupations in Shanghai was 28.95%, while that of males (31.90%) was higher than that of females (25.61%) (χ ~ 2 = 33.88, P <0.05). As the age increased, the rate of physical activity decreased gradually (trend χ ~ 2 = 101.18, P <0.05). The rate of physical activity in different regions was different (χ ~ 2 = 69.70, P <0.05), the highest in rural areas (34.33%) and the lowest in urban areas (24.35%). There was no significant difference in the rate of physical activity in different occupations. Different types of physical activity have different effects on physical activity, accounting for only 56.23% of physical activity when occupational physical activity is considered and 35.12% of physical activity when physical activity is increased, while occupational, transportation and leisure The rate of physical activity in sexual physical activity was 28.95%. The types of physical activity in different occupations had different effects on the physical activity. Multivariate logistic regression analysis showed that occupational differences between men and women had different effects on physical under-performance: among men, the risk of physical under-work among managers was 1.81 times (95% CI: 1.37 to 2.40) for professional and technical personnel, and that for service workers The risk of under-performing physical activity was 1.31 times (95% CI: 1.07 to 1.61) for professional and technical personnel, while there was no statistically significant difference in the number of physical activity among different occupations among women (P = 0.89). [Conclusion] The physical activity characteristics of different occupations in Shanghai non-agricultural occupations are different, and different interventions should be taken for different populations.