加速度计校准:考虑功能的重要性。

Scott J Strath, Taylor W Rowley, Chi C Cho, Allison Hyngstrom, Ann M Swartz, Kevin G Keenan, Julian Martinez, John W Staudenmayer
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引用次数: 1

摘要

目的:比较穿戴在臀部的加速度计在运动表现和疾病状况下预测结构化活动中能量消耗的准确性和精度。方法:118名自认为健康的成年人(n = 44)和患有关节炎(n = 23)、多发性硬化症(n =18)、帕金森病(n = 17)和中风(n =18)的成年人(n =18)进行了运动表现测量,并分为两组:1组,正常;第二组,中度损伤;第三组为重度损伤。参与者在完成有组织的活动时佩戴加速度计和便携式代谢测量系统。将加速度计预测的能量成本(任务代谢当量[METs])与测量的METs进行比较,并评估功能损伤和疾病状况。采用线性混合效应模型和贝叶斯信息准则评估模型拟合,评估统计学显著性。结果:与健康者相比,疾病患者的所有活动加速度计每分钟计数(CPM)减少29.5-72.6%。不同疾病的MET预测偏差相似,关节炎为-0.49(-0.71,-0.27),健康为-0.38(-0.53,-0.22),多发性硬化症为-0.44(-0.68,-0.20),帕金森为-0.34(-0.58,-0.09),中风为-0.30(-0.54,-0.06)。对于功能损伤,所有活动的CPM都有分级降低:第1组,1,215 CPM (1,129, 1,301);2组,789 CPM (695, 884);第3组,343 CPM(220,466)。预测MET偏倚在第1组显示相似的结果,-0.37 METs (-0.52, -0.23);2组,-0.44 METs (-0.60, -0.28);第3组,-0.33 METs(-0.55, -0.13)。与疾病状况相比,贝叶斯信息准则模型更适合功能损伤。结论:利用功能改进加速度计校准可以减少可变性,值得进一步探索以改进加速度计对身体活动的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerometer Calibration: The Importance of Considering Functionality.

Purpose: To compare the accuracy and precision of a hip-worn accelerometer to predict energy cost during structured activities across motor performance and disease conditions.

Methods: 118 adults self-identifying as healthy (n = 44) and those with arthritis (n = 23), multiple sclerosis (n = 18), Parkinson's disease (n = 17), and stroke (n =18) underwent measures of motor performance and were categorized into groups: Group 1, usual; Group 2, moderate impairment; and Group 3, severe impairment. The participants completed structured activities while wearing an accelerometer and a portable metabolic measurement system. Accelerometer-predicted energy cost (metabolic equivalent of tasks [METs]) were compared with measured METs and evaluated across functional impairment and disease conditions. Statistical significance was assessed using linear mixed effect models and Bayesian information criteria to assess model fit.

Results: All activities' accelerometer counts per minute (CPM) were 29.5-72.6% less for those with disease compared with those who were healthy. The predicted MET bias was similar across disease, -0.49 (-0.71, -0.27) for arthritis, -0.38 (-0.53, -0.22) for healthy, -0.44 (-0.68, -0.20) for MS, -0.34 (-0.58, -0.09) for Parkinson's, and -0.30 (-0.54, -0.06) for stroke. For functional impairment, there was a graded reduction in CPM for all activities: Group 1, 1,215 CPM (1,129, 1,301); Group 2, 789 CPM (695, 884); and Group 3, 343 CPM (220, 466). The predicted MET bias revealed similar results across the Group 1, -0.37 METs (-0.52, -0.23); Group 2, -0.44 METs (-0.60, -0.28); and Group 3, -0.33 METs (-0.55, -0.13). The Bayesian information criteria showed a better model fit for functional impairment compared with disease condition.

Conclusion: Using functionality to improve accelerometer calibration could decrease variability and warrants further exploration to improve accelerometer prediction of physical activity.

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