Department of Kinesiology, University of Massachusetts Amherst, Amherst, MA.
Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA.
Med Sci Sports Exerc. 2020 Jan;52(1):225-232. doi: 10.1249/MSS.0000000000002099.
This study aimed to determine the validity of existing methods to estimate sedentary behavior (SB) under free-living conditions using ActiGraph GT3X+ accelerometers (AG).
Forty-eight young (18-25 yr) adults wore an AG on the right hip and nondominant wrist and were video recorded during four 1-h sessions in free-living settings (home, community, school, and exercise). Direct observation videos were coded for postural orientation, activity type (e.g., walking), and METs derived from the Compendium of Physical Activities, which served as the criterion measure of SB (sitting or lying posture, <1.5 METs). Thirteen methods using cut points from vertical counts per minute (CPM), counts per 15-s (CP15s), and vector magnitude (VM) counts (e.g., CPM1853VM), raw acceleration and arm angle (sedentary sphere), Euclidean norm minus one (ENMO) corrected for gravity (mg) thresholds, uni- or triaxial sojourn hybrid machine learning models (Soj1x and Soj3x), random forest (RF), and decision tree (TR) models were used to estimate SB minutes from AG data. Method bias, mean absolute percent error, and their 95% confidence intervals were estimated using repeated-measures linear mixed models.
On average, participants spent 34.1 min per session in SB. CPM100, CPM150, Soj1x, and Soj3x were the only methods to accurately estimate SB from the hip. Sedentary sphere and ENMO44.8 overestimated SB by 3.9 and 6.1 min, respectively, whereas the remaining wrist methods underestimated SB (range, 9.5-2.5 min). In general, mean absolute percent error was lower using hip methods compared with wrist methods.
Accurate group-level estimates of SB from a hip-worn AG can be achieved using either simpler count-based approaches (CPM100 and CPM150) or machine learning models (Soj1x and Soj3x). Wrist methods did not provide accurate or precise estimates of SB. The development of large open-source free-living calibration data sets may lead to improvements in SB estimates.
本研究旨在确定使用 ActiGraph GT3X+ 加速度计 (AG) 在自由生活条件下估计久坐行为 (SB) 的现有方法的有效性。
48 名年轻成年人（18-25 岁）在右髋部和非优势手腕上佩戴 AG，并在自由生活环境（家庭、社区、学校和运动）中进行四次 1 小时的视频记录。直接观察视频根据活动类型进行编码（例如，行走）和从活动纲要中得出的代谢当量（METs），该纲要是 SB 的标准测量（坐姿或卧位，<1.5 METs）。使用每分钟垂直计数 (CPM)、每 15 秒计数 (CP15s) 和矢量幅度 (VM) 计数的切点（例如 CPM1853VM）的 13 种方法、原始加速度和手臂角度（久坐球）、基于欧几里得范数减去一 (ENMO) 的重力 (mg) 校正阈值、单轴或三轴逗留混合机器学习模型（Soj1x 和 Soj3x）、随机森林 (RF) 和决策树 (TR) 模型，从 AG 数据中估计 SB 分钟数。使用重复测量线性混合模型估计方法偏差、平均绝对百分比误差及其 95%置信区间。
平均而言，参与者在每次会议中分别有 34.1 分钟处于 SB 状态。CPM100、CPM150、Soj1x 和 Soj3x 是唯一能够从髋部准确估计 SB 的方法。久坐球和 ENMO44.8 分别高估了 3.9 和 6.1 分钟的 SB，而其余手腕方法则低估了 SB（范围，9.5-2.5 分钟）。总体而言，使用髋部方法比手腕方法的平均绝对百分比误差更低。
从髋部佩戴的 AG 可以使用更简单的基于计数的方法（CPM100 和 CPM150）或机器学习模型（Soj1x 和 Soj3x）实现对 SB 的准确组级估计。手腕方法无法准确或精确地估计 SB。大型开源自由生活校准数据集的开发可能会提高 SB 估计的准确性。
