Exercise and Physical Activity Resource Center, University of California, San Diego, La Jolla, California, United States of America.
Center for Wireless and Population Health Systems, University of California, San Diego, La Jolla, California, United States of America.
PLoS One. 2020 Sep 4;15(9):e0237719. doi: 10.1371/journal.pone.0237719. eCollection 2020.
This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children.
59 healthy boys and girls aged 9-11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis.
Fitbit Charge HR underestimated steps (11.8 steps per minute), heart rate (3.58 bpm), and metabolic equivalents (0.55 METs per minute) and overestimated energy expenditure (0.34 kcal per minute) relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) (sensitivity MVPA: 85.4%; specificity SLPA: 83.1%). Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs.
Fitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices.
本研究旨在评估 Fitbit Charge HR（一款消费级多传感器活动追踪器）测量儿童身体活动和睡眠的性能。
59 名 9-11 岁健康男孩和女孩佩戴 Fitbit Charge HR，通过 14 项标准化实验室和现场评估（包括坐姿、固定自行车、跑步机步行或慢跑、楼梯步行、户外步行和敏捷性训练）的组合，评估身体活动测量的准确性。通过 26 名男孩和女孩在家中进行无人监督的 PSG 过夜记录，评估睡眠测量的准确性与多导睡眠图（PSG）的相关性。主要分析包括使用 Bland-Altman 方法评估测量之间的一致性（偏差），以及基于分钟的逐分钟（EBE）分析。
与研究级测量相比，Fitbit Charge HR 低估了步数（每分钟约 11.8 步）、心率（每分钟约 3.58 次）和代谢当量（每分钟约 0.55 METs），高估了能量消耗（每分钟约 0.34 千卡）（p<0.05）。该设备对中度和剧烈体力活动（MVPA）和久坐和轻度体力活动（SLPA）的分类准确率为 84.8%（MVPA 敏感性：85.4%；SLPA 特异性：83.1%）。总睡眠时间、睡眠后觉醒和睡眠中心率的平均偏差估计值分别为 14 分钟、9 分钟和 1.06 次/分钟，在分类睡眠和分类清醒时的敏感性分别为 95.8%和特异性分别为 56.3%。
在分类中度和剧烈强度体力活动和睡眠方面，Fitbit Charge HR 的敏感性足够，但在检测清醒方面存在局限性，在检测睡眠时比在检测运动时更准确地检测心率。需要进一步研究以了解这些消费类设备的潜在挑战和局限性。
