Prevention Research Collaboration, Sydney School of Public Health, the University of Sydney, Camperdown, NSW, Australia.
Prevention Research Collaboration, Sydney School of Public Health, the University of Sydney, Camperdown, NSW, Australia.
Prev Med. 2014 Mar;60:102-6. doi: 10.1016/j.ypmed.2013.12.021. Epub 2013 Dec 28.
Most studies on multiple health behaviors include physical inactivity, alcohol, diet, and smoking (PADS), with few including emerging lifestyle risks such as sleep or sitting. We examined whether adding sitting and sleep to a conventional lifestyle risk index improves the prediction of cross-sectional health outcomes (self-rated health, quality of life, psychological distress, and physical function). We also explored the demographic characteristics of adults with these multiple risk behaviors.
We used baseline data of an Australian cohort study (n=191,853) conducted in 2006-2008 in New South Wales. Lifestyle risk index was operationalized as 1) PADS, 2) PADS+sitting, 3) PADS+sleep, and 4) PADS+sitting+sleep. We estimated receiver operating characteristic curve for self-reported binary health outcomes and calculated the area under the curve to illustrate how well each index classified the outcome. We used multiple logistic regression to determine the demographic characteristics of adults with multiple lifestyle risks.
Adding sleep duration but not sitting time to the PADS index significantly improved the classification of all health outcomes. Men, those aged 45-54years, those with 10 years of education or less, and those living in regional/remote areas had higher odds of multiple risk behaviors.
Future research on multiple health behaviors might benefit from including sleep as an additional behavior. In Australia, unhealthy lifestyles tend to cluster in adults with certain demographic characteristics.
大多数关于多种健康行为的研究都包括身体活动不足、饮酒、饮食和吸烟(PADS),很少包括新兴的生活方式风险,如睡眠或久坐。我们研究了将久坐和睡眠添加到传统的生活方式风险指数中是否会改善对横断面健康结果(自我评估健康、生活质量、心理困扰和身体功能)的预测。我们还探讨了具有这些多种风险行为的成年人的人口统计学特征。
我们使用了 2006-2008 年在新南威尔士州进行的澳大利亚队列研究的基线数据(n=191,853)。生活方式风险指数的操作化方法如下:1)PADS,2)PADS+久坐,3)PADS+睡眠,4)PADS+久坐+睡眠。我们估计了自我报告的二元健康结果的接收者操作特征曲线,并计算了曲线下面积,以说明每个指数对结果的分类程度。我们使用多变量逻辑回归来确定具有多种生活方式风险的成年人的人口统计学特征。
将睡眠时间而不是久坐时间添加到 PADS 指数中显著提高了所有健康结果的分类能力。男性、45-54 岁、受教育程度在 10 年或以下、以及居住在地区/偏远地区的人具有更高的多种风险行为的可能性。
未来关于多种健康行为的研究可能受益于将睡眠作为额外的行为进行研究。在澳大利亚,不健康的生活方式往往集中在具有某些人口统计学特征的成年人中。