Human Potential Centre, School of Sport and Recreation, Auckland University of Technology, Auckland, New Zealand.
Institute for Movement Therapy and Movement-Oriented Prevention and Rehabilitation, German Sport University Cologne, Cologne, Germany.
J Phys Act Health. 2024 Aug 19;21(10):1092-1099. doi: 10.1123/jpah.2024-0259. Print 2024 Oct 1.
The ActiPASS software was developed from the open-source Acti4 activity classification algorithm for thigh-worn accelerometry. However, the original algorithm has not been validated in children or compared with a child-specific set of algorithm thresholds. This study aims to evaluate the accuracy of ActiPASS in classifying activity types in children using 2 published sets of Acti4 thresholds.
Laboratory and free-living data from 2 previous studies were used. The laboratory condition included 41 school-aged children (11.0 [4.8] y; 46.5% male), and the free-living condition included 15 children (10.0 [2.6] y; 66.6% male). Participants wore a single accelerometer on the dominant thigh, and annotated video recordings were used as a reference. Postures and activity types were classified with ActiPASS using the original adult thresholds and a child-specific set of thresholds.
Using the original adult thresholds, the mean balanced accuracy (95% CI) for the laboratory condition ranged from 0.62 (0.56-0.67) for lying to 0.97 (0.94-0.99) for running. For the free-living condition, accuracy ranged from 0.61 (0.48-0.75) for lying to 0.96 (0.92-0.99) for cycling. Mean balanced accuracy for overall sedentary behavior (sitting and lying) was ≥0.97 (0.95-0.99) across all thresholds and conditions. No meaningful differences were found between the 2 sets of thresholds, except for superior balanced accuracy of the adult thresholds for walking under laboratory conditions.
The results indicate that ActiPASS can accurately classify different basic types of physical activity and sedentary behavior in children using thigh-worn accelerometer data.
ActiPASS 软件是从 thigh-worn 加速度计的开源 Acti4 活动分类算法开发而来的。然而，原始算法尚未在儿童中进行验证，也未与特定于儿童的算法阈值集进行比较。本研究旨在使用 2 套已发表的 Acti4 阈值来评估 ActiPASS 在分类儿童活动类型方面的准确性。
使用了两项先前研究的实验室和自由生活数据。实验室条件包括 41 名学龄儿童（11.0[4.8]岁；46.5%为男性），自由生活条件包括 15 名儿童（10.0[2.6]岁；66.6%为男性）。参与者在优势大腿上佩戴单个加速度计，并使用视频记录进行注释作为参考。使用 ActiPASS 和原始成人阈值以及特定于儿童的阈值集对姿势和活动类型进行分类。
使用原始成人阈值，实验室条件下的平均平衡准确率（95%CI）从卧位的 0.62（0.56-0.67）到跑步的 0.97（0.94-0.99）。对于自由生活条件，准确性范围从卧位的 0.61（0.48-0.75）到骑自行车的 0.96（0.92-0.99）。所有阈值和条件下，整体静坐行为（坐姿和卧位）的平均平衡准确率均≥0.97（0.95-0.99）。两种阈值之间没有发现有意义的差异，除了实验室条件下成人阈值对步行的平衡准确率更高。
结果表明，ActiPASS 可以使用 thigh-worn 加速度计数据准确地分类儿童的不同基本类型的体力活动和静坐行为。
