Exercise Physiology, La Trobe Rural Health School, La Trobe University, Australia.
Research Centre for Data Analytics and Cognition, School of Business, La Trobe University, Australia.
J Sci Med Sport. 2019 Jun;22(6):677-683. doi: 10.1016/j.jsams.2018.12.003. Epub 2018 Dec 11.
To compare accelerometry-derived estimates of physical activity from 9 wrist-specific predictive models and a reference hip-specific method.
Prospective cohort repeated measures study.
110 participants wore an accelerometer at wrist and hip locations for 1 week of free-living. Accelerometer data from three axes were used to calculate physical activity estimates using existing wrist-specific models (3 linear and 6 artificial neural network models) and a reference hip-specific method. Estimates of physical activity were compared to reference values at both epoch (≤60-s) and weekly levels.
9044h were analysed. Physical activity ranged from 7 to 96min per day of moderate-to-vigorous physical activity (MVPA). Method of analysis influenced determination of sedentary behaviour (<1.5 METs), light physical activity (1.5 to <3 METs) and MVPA (>3 METs) (p<0.001, respectively). All wrist-specific models produced total weekly MVPA values that were different to the reference method. At the epoch level, Hildebrand et al. (2014) produced the strongest correlation (r=0.69, 95%CI: 0.67-0.71) with tightest ratio limits of agreement (95%CI: 0.53-1.30) for MVPA, and highest agreement to predict MVPA (94.1%, 95%CI: 94.0-94.1%) with sensitivity of 63.1% (95%CI: 62.6-63.7%) and specificity of 96.0% (95%CI: 95.9-96.0%).
Caution is required when comparing results from studies that use inconsistent analysis methods. Although a wrist-specific linear model produced results that were most similar to the hip-specific reference method when estimating total weekly MVPA, modest absolute and relative agreement at the epoch level suggest that additional analysis methods are required to improve estimates of physical activity derived from wrist-worn accelerometers.
比较 9 种腕部特异性预测模型和参考髋部特异性方法得出的加速度计衍生活动估计值。
前瞻性队列重复测量研究。
110 名参与者在腕部和髋部位置佩戴加速度计 1 周进行自由生活。使用三轴加速度计数据,使用现有的腕部特异性模型(3 个线性模型和 6 个人工神经网络模型)和参考髋部特异性方法计算活动估计值。在时程(≤60 秒)和每周水平上,将活动估计值与参考值进行比较。
分析了 9044 小时的数据。活动范围从每天 7 到 96 分钟的中等到剧烈体力活动(MVPA)。分析方法影响到久坐行为(<1.5 METs)、低强度体力活动(1.5 至<3 METs)和 MVPA(>3 METs)的确定(分别为 p<0.001)。所有腕部特异性模型产生的总每周 MVPA 值均与参考方法不同。在时程水平上,Hildebrand 等人(2014 年)的模型与 MVPA 的相关性最强(r=0.69,95%CI:0.67-0.71),一致性限较紧(95%CI:0.53-1.30),对 MVPA 的预测最高(94.1%,95%CI:94.0-94.1%),灵敏度为 63.1%(95%CI:62.6-63.7%),特异性为 96.0%(95%CI:95.9-96.0%)。
在比较使用不一致分析方法的研究结果时,需要谨慎。虽然腕部特异性线性模型在估计总每周 MVPA 时产生的结果与髋部特异性参考方法最相似,但在时程水平上的适度绝对和相对一致性表明,需要额外的分析方法来提高腕部佩戴加速度计得出的活动估计值。