Keadle Sarah Kozey, Shiroma Eric J, Kamada Masamitsu, Matthews Charles E, Harris Tamara B, Lee I-Min
Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland; Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland.
Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute of Aging, Bethesda, Maryland.
Am J Prev Med. 2017 Apr;52(4):541-548. doi: 10.1016/j.amepre.2016.11.010. Epub 2017 Jan 3.
Accelerometers are used increasingly in large epidemiologic studies, but, given logistic and cost constraints, most studies are restricted to a single, 7-day accelerometer monitoring period. It is unknown how well a 7-day accelerometer monitoring period estimates longer-term patterns of behavior, which is critical for interpreting, and potentially improving, disease risk estimates in etiologic studies.
A subset of participants from the Women's Health Study (N=209; mean age, 70.6 [SD=5.7] years) completed at least two 7-day accelerometer administrations (ActiGraph GT3X+) within a period of 2-3 years. Monitor output was translated into total counts, steps, and time spent in sedentary, light-intensity, and moderate to vigorous-intensity activity (MVPA) and bouted-MVPA (i.e., 10-minute bouts). For each metric, intraclass correlations (ICCs) and 95% CIs were calculated using linear-mixed models and adjusted for wear time, age, BMI, and season. The data were collected in 2011-2015 and analyzed in 2015-2016.
The ICCs ranged from 0.67 (95% CI=0.60, 0.73) for bouted-MVPA to 0.82 (95% CI=0.77, 0.85) for total daily counts and were similar across age, BMI, and for less and more active women. For all metrics, classification accuracy within 1 quartile was >90%.
These data provide reassurance that a 7-day accelerometer-assessment protocol provides a reproducible (and practical) measure of physical activity and sedentary time. However, ICCs varied by metric; therefore, future prospective studies of chronic diseases might benefit from existing methods to adjust risk estimates for within-person variability in activity to get a better estimate of the true strength of association.
加速度计在大型流行病学研究中的使用越来越广泛，但由于后勤和成本限制，大多数研究仅限于单次为期7天的加速度计监测期。目前尚不清楚7天的加速度计监测期对长期行为模式的估计效果如何，而这对于病因学研究中解释疾病风险估计以及潜在地改善疾病风险估计至关重要。
来自女性健康研究的一部分参与者（N = 209；平均年龄70.6 [标准差 = 5.7]岁）在2至3年的时间内完成了至少两次为期7天的加速度计测量（ActiGraph GT3X+）。监测输出被转换为总计数、步数以及久坐、轻度、中度至剧烈强度活动（MVPA）和分段MVPA（即10分钟的时间段）所花费的时间。对于每个指标，使用线性混合模型计算组内相关系数（ICC）和95%置信区间，并对佩戴时间、年龄、体重指数和季节进行了调整。数据于2011 - 2015年收集，并于2015 - 2016年进行分析。
ICC范围从分段MVPA的0.67（95%置信区间 = 0.60, 0.73）到每日总计数的0.82（95%置信区间 = 0.77, 0.85），在年龄、体重指数以及活动较少和较多的女性中相似。对于所有指标，在1个四分位数范围内的分类准确率>90%。
这些数据表明，为期7天的加速度计评估方案能够提供可重复（且实用）的身体活动和久坐时间测量方法，令人安心。然而，ICC因指标而异；因此，未来慢性病的前瞻性研究可能会受益于现有方法，以调整活动中个体内部变异性的风险估计，从而更好地估计关联的真实强度。
