Tracy J Dustin, Donnelly Thomas, Sommer Evan C, Heerman William J, Barkin Shari L, Buchowski Maciej S
Economic Science Institute, Chapman University, Orange, California, United States of America.
Division of Gastroenterology, Hepatology and Nutrition, Department of Medicine, Energy Balance Laboratory, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
PLoS One. 2021 Jan 28;16(1):e0246055. doi: 10.1371/journal.pone.0246055. eCollection 2021.
To adapt and validate a previously developed decision tree for youth to identify bedrest for use in preschool children.
Parents of healthy preschool (3-6-year-old) children (n = 610; 294 males) were asked to help them to wear an accelerometer for 7 to 10 days and 24 hours/day on their waist. Children with ≥3 nights of valid recordings were randomly allocated to the development (n = 200) and validation (n = 200) groups. Wear periods from accelerometer recordings were identified minute-by-minute as bedrest or wake using visual identification by two independent raters. To automate visual identification, chosen decision tree (DT) parameters (block length, threshold, bedrest-start trigger, and bedrest-end trigger) were optimized in the development group using a Nelder-Mead simplex optimization method, which maximized the accuracy of DT-identified bedrest in 1-min epochs against synchronized visually identified bedrest (n = 4,730,734). DT's performance with optimized parameters was compared with the visual identification, commonly used Sadeh's sleep detection algorithm, DT for youth (10-18-years-old), and parental survey of sleep duration in the validation group.
On average, children wore an accelerometer for 8.3 days and 20.8 hours/day. Comparing the DT-identified bedrest with visual identification in the validation group yielded sensitivity = 0.941, specificity = 0.974, and accuracy = 0.956. The optimal block length was 36 min, the threshold 230 counts/min, the bedrest-start trigger 305 counts/min, and the bedrest-end trigger 1,129 counts/min. In the validation group, DT identified bedrest with greater accuracy than Sadeh's algorithm (0.956 and 0.902) and DT for youth (0.956 and 0.861) (both P<0.001). Both DT (564±77 min/day) and Sadeh's algorithm (604±80 min/day) identified significantly less bedrest/sleep than parental survey (650±81 min/day) (both P<0.001).
The DT-based algorithm initially developed for youth was adapted for preschool children to identify time spent in bedrest with high accuracy. The DT is available as a package for the R open-source software environment ("PhysActBedRest").
对先前为青少年开发的决策树进行调整和验证，以确定适用于学龄前儿童的卧床休息情况。
健康学龄前（3 - 6岁）儿童的家长（n = 610；294名男性）被要求帮助孩子在腰部佩戴加速度计7至10天，每天佩戴24小时。有≥3个有效记录夜晚的儿童被随机分配到开发组（n = 200）和验证组（n = 200）。通过两名独立评估人员的视觉识别，将加速度计记录中的佩戴时间段逐分钟确定为卧床休息或清醒状态。为了实现视觉识别自动化，在开发组中使用Nelder - Mead单纯形优化方法对选定的决策树（DT）参数（块长度、阈值、卧床休息开始触发值和卧床休息结束触发值）进行优化，该方法使DT在1分钟时间段内识别的卧床休息与同步视觉识别的卧床休息（n = 4,730,734）的准确性最大化。在验证组中，将具有优化参数的DT性能与视觉识别、常用的萨德睡眠检测算法、青少年（10 - 18岁）的DT以及家长对睡眠时间的调查进行比较。
平均而言，儿童佩戴加速度计8.3天，每天佩戴20.8小时。在验证组中，将DT识别的卧床休息与视觉识别进行比较，得出灵敏度 = 0.941，特异性 = 0.974，准确性 = 0.956。最佳块长度为36分钟，阈值为230次/分钟，卧床休息开始触发值为305次/分钟，卧床休息结束触发值为1,129次/分钟。在验证组中，DT识别卧床休息的准确性高于萨德算法（0.956和0.902）以及青少年的DT（0.956和0.861）（均P<0.001）。DT（564±77分钟/天）和萨德算法（604±80分钟/天）识别的卧床休息/睡眠时间均明显少于家长调查结果（650±81分钟/天）（均P<0.001）。
最初为青少年开发的基于DT的算法适用于学龄前儿童，能够高精度地识别卧床休息时间。该DT可作为R开源软件环境的一个包获取（“PhysActBedRest”）。
