从ActiGraph,Apple Watch,Garmin和Fitbit使用机械摇摆台的原始加速度计数据进行比较
James W White1, Olivia L Finnegan1, Nick Tindall1
1Department of Exercise Science, University of South Carolina, Columbia, SC, United States of America.
PloS one
|March 29, 2024
概括
来自Apple,Garmin和Fitbit等可穿戴设备的原始加速度计数据可靠地用于估计运动. 然而,没有任何设备显示出与参考摇表具有统计学意义的等价性,这表明精确测量的潜在限制.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 人类运动分析 人类运动分析
背景情况:
- 可穿戴设备为客观的身体活动监测提供了有希望的途径.
- 评估来自消费者可穿戴设备的原始加速度计数据的准确性对于可靠的健康研究至关重要.
- 研究级加速度计经常与机械设备进行比较以验证.
研究的目的:
- 评估消费者和研究级可穿戴设备原始加速度计输出的可靠性和有效性.
- 为了将设备数据与校准的机械摇摆表进行比较,作为参考.
- 为了确定原始加速度计数据对于运动估计的适用性.
主要方法:
- 将40个设备 (ActiGraph,Apple Watch,Garmin Vivoactive,Fitbit Sense) 的原始加速度计数据与机械摇摆表进行比较.
- 使用类内相关系数 (ICC) 测试了设备间的可靠性.
- 使用皮尔森相关性,林的一致性相关系数 (CCC),绝对误差,平均偏差和等价性测试来评估有效性.
主要成果:
- 所有测试的设备都显示出高可靠性 (ICCs ≥0.88).
- ActiGraph,果和Fitbit与参考数据显示出极好的一致性 (CCC≥0.83),而Garmin显示出中等一致性 (CCC=0.59).
- ActiGraph,果和Fitbit都有类似的绝对误差,而Garmin则显示出更高的误差;没有设备达到与参考数据的统计等价性.
结论:
- 来自ActiGraph,Apple,Garmin和Fitbit设备的原始加速度计数据可靠地用于估计运动.
- 虽然对ActiGraph,Apple和Fitbit的一致性很高,但这些设备都没有达到与参考摇器的统计等价性.
- 未来的研究应该专注于对可穿戴设备的原始加速度计数据的无关设备和协调方法.
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