Supplementary material for machine learning and statistical analyses of sensor data reveal variability between repeated trials in Parkinson’s disease mobility assessments

Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns provide insight into motor control, balance, and cognitive functions affected by Parkinson’s disease (PD). We assess the test-retest reliability of these tasks in 262 PD participants and 50 controls by evaluating machine learning models based on wearable sensor-derived measures and statistical metrics. This evaluation examines total duration, subtask duration, and other quantitative measures across two trials. We show that the diagnostic accuracy for distinguishing PD from controls decreases by a mean of 1.8% between the first and the second trial, suggesting that task repetition may not be necessary for accurate diagnosis. Although the total duration remains relatively consistent between trials (intraclass correlation coefficient (ICC) = 0.62 to 0.95), greater variability is seen in subtask duration and sensor-derived measures, reflected in machine learning performance and statistical differences. Our findings also show that this variability differs not only between controls and PD participants but also among groups with varying levels of PD severity, indicating the need to consider population characteristics. Relying solely on total task duration and conventional statistical metrics to gauge the reliability of mobility tasks may fail to reveal nuanced variations in movement.

本研究旨在探究运动控制、平衡及认知功能，这些功能受帕金森病（PD）影响，通过诸如计时站立起立测试（TUG）、认知TUG（cogTUG）以及转弯行走等运动任务得以体现。我们对262名帕金森病参与者和50名对照组受试者进行测试-重测可靠性评估，通过分析基于可穿戴传感器测量值和统计指标的机器学习模型。该评估涉及两次测试的总时长、子任务时长及其他定量指标。研究发现，区分帕金森病与对照者的诊断准确性在第一次和第二次测试间平均降低了1.8%，提示任务重复可能并非实现准确诊断的必要条件。尽管两次测试的总时长相对稳定（组内相关系数（ICC）为0.62至0.95），但在子任务时长和传感器测量值方面观察到更大的变异性，这反映在机器学习性能和统计差异上。我们的研究还发现，这种变异性不仅在对照组与帕金森病参与者之间存在差异，而且在帕金森病严重程度不同的群体中也存在差异，这表明考虑人群特征是必要的。仅依靠总任务时长和传统统计指标来评估运动任务的可靠性，可能无法揭示运动变化的细微差异。