24小时休息和活动行为(即,身体活动,久坐的行为和睡眠)是对健康和福祉至关重要的基本人类行为。功能主成分分析(fPCA)是一种用于表征休息活动节奏的灵活方法,并且不依赖于对活动形状的先验假设。我们研究的目的是将fPCA应用于具有全国代表性的美国成年人样本,以表征24小时休息活动模式的变化,确定模式根据人口统计的不同,社会经济和工作特点,并检查其与一般健康状况的关系。
当前的分析使用了国家健康和营养调查中25岁或以上成年人的数据(NHANES,2011-2014)。使用7天24小时活动记录,我们应用fPCA来得出总体的轮廓,工作日和周末休息活动模式。我们检查了每个休息-活动概况与年龄之间的关系,性别,种族/民族,教育,收入和工作状态使用多元线性回归。我们还使用多元逻辑回归来确定每个休息活动概况与报告不良或健康的可能性之间的关系。
我们确定了四个不同的配置文件(即,高振幅,早起,延长的活动窗口,双相模式),合计占研究样本总变异的86.8%。我们确定了每个休息活动概况与多个社会人口统计学特征之间的许多关联。我们还发现有证据表明,工作日和周末之间的关联不同。最后,我们报道了休息-活动情况与自我评估的健康状况相关.
我们的研究提供的证据表明,人类群体的休息活动模式是由多种人口统计学决定的,社会经济和工作因素,并与健康状况相关。
The 24-h rest and activity behaviors (i.e., physical activity, sedentary behaviors and sleep) are fundamental human behaviors essential to health and well-being. Functional principal component analysis (fPCA) is a flexible approach for characterizing rest-activity rhythms and does not rely on a priori assumptions about the activity shape. The objective of our study is to apply fPCA to a nationally representative sample of American adults to characterize variations in the 24-h rest-activity pattern, determine how the pattern differs according to demographic, socioeconomic and work characteristics, and examine its associations with general health status.
The current analysis used data from adults 25 or older in the National Health and Nutrition Examination Survey (NHANES, 2011-2014). Using 7-day 24-h actigraphy recordings, we applied fPCA to derive profiles for overall, weekday and weekend rest-activity patterns. We examined the association between each rest-activity profile in relation to age, gender, race/ethnicity, education, income and working status using multiple linear regression. We also used multiple logistic regression to determine the relationship between each rest-activity profile and the likelihood of reporting poor or fair health.
We identified four distinct profiles (i.e., high amplitude, early rise, prolonged activity window, biphasic pattern) that together accounted for 86.8% of total variation in the study sample. We identified numerous associations between each rest-activity profile and multiple sociodemographic characteristics. We also found evidence suggesting the associations differed between weekdays and weekends. Finally, we reported that the rest-activity profiles were associated with self-rated health.
Our study provided evidence suggesting that rest-activity patterns in human populations are shaped by multiple demographic, socioeconomic and work factors, and are correlated with health status.