Fully automated waist-worn accelerometer algorithm for detecting children's sleep-period time separate from 24-h physical activity or sedentary behaviors.
Fully automated waist-worn accelerometer algorithm for detecting children's sleep-period time separate from 24-h physical activity or sedentary behaviors.
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DOI：
10.1139/apnm-2013-0173
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发表时间：
2014-01-01
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影响因子：
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通讯作者：
Katzmarzyk, Peter T
中科院分区：
文献类型：
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作者：
Tudor-Locke, Catrine;Barreira, Tiago V;Katzmarzyk, Peter T
分析24小时腰戴加速度计的身体活动和久坐行为数据需要首先确定睡眠时间（从入睡到睡眠结束，包括所有睡眠时期和入睡后的觉醒）。在这项研究中，为了确定儿童的睡眠时间，我们评估了一种已发表的自动算法的有效性，该算法需要非加速度计的睡眠时间和清醒时间输入，相对于标准专家对每分钟腰戴加速度计数据的视觉分析，并验证了一种改进的全自动算法。30名4年级学生（50%为女生）提供了24小时腰穿加速度测量数据。将专家目视检查（标准）、已发表的算法（算法1）和2项额外的自动化改进（算法2，利用仪器的倾斜仪功能，算法3，重点关注就寝和清醒时间点）应用于标准化的24小时时间块。配对t检验用于评估平均睡眠时间的差异（专家标准减去算法估计值）。与标准相比，算法1和算法2分别显著高估睡眠时间43 min和90 min。算法3产生最小的平均差异（2 min），与标准无显著差异。相对于专家目视检查，我们的自动算法3产生了一个精确的估计，并且在类似年龄儿童的预期值内。这种用于24小时腰戴式加速度计数据的全自动算法将有助于将睡眠时间与一天中剩余时间内的久坐行为和所有强度的身体活动分开。
Analysis of 24-h waist-worn accelerometer data for physical activity and sedentary behavior requires that sleep-period time (from sleep onset to the end of sleep, including all sleep epochs and wakefulness after onset) is first identified. To identify sleep-period time in children in this study, we evaluated the validity of a published automated algorithm that requires nonaccelerometer bed- and wake-time inputs, relative to a criterion expert visual analysis of minute-by-minute waist-worn accelerometer data, and validated a refined fully automated algorithm. Thirty grade 4 schoolchildren (50% girls) provided 24-h waist-worn accelerometry data. Expert visual inspection (criterion), a published algorithm (Algorithm 1), and 2 additional automated refinements (Algorithm 2, which draws on the instrument's inclinometer function, and Algorithm 3, which focuses on bedtime and wake time points) were applied to a standardized 24-h time block. Paired t tests were used to evaluate differences in mean sleep time (expert criterion minus algorithm estimate). Compared with the criterion, Algorithm 1 and Algorithm 2 significantly overestimated sleep time by 43 min and 90 min, respectively. Algorithm 3 produced the smallest mean difference (2 min), and was not significantly different from the criterion. Relative to expert visual inspection, our automated Algorithm 3 produced an estimate that was precise and within expected values for similarly aged children. This fully automated algorithm for 24-h waist-worn accelerometer data will facilitate the separation of sleep time from sedentary behavior and physical activity of all intensities during the remainder of the day.