Macpherson Chelsea E, Bland Marghuretta D, Gordon Christine, Miller Allison E, Newman Caitlin, Holleran Carey L, Dy Christopher J, Peterson Lindsay, Lohse Keith R, Lang Catherine E
Program in Physical Therapy, Washington University School of Medicine, St. Louis, MO 63110, USA.
Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Sensors (Basel). 2025 Jul 25;25(15):4618. doi: 10.3390/s25154618.
Wearable movement sensors can measure upper limb (UL) activity, but single variables may not capture the full picture. This study aimed to replicate prior work identifying five multivariate categories of UL activity performance in people with stroke and controls and expand those findings to other UL conditions.
Demographic, self-report, and wearable sensor-based UL activity performance variables were collected from 324 participants (stroke = 49, multiple sclerosis = 19, distal UL fracture = 40, proximal UL pain = 55, post-breast cancer = 23, control = 138). Principal component (PC) analyses (12, 9, 7, or 5 accelerometry input variables) were followed by cluster analyses and numerous assessments of model fit across multiple subsets of the total sample.
Two PCs explained 70-90% variance: PC1 (overall UL activity performance) and PC2 (preferred-limb use). A five-variable, five-cluster model was optimal across samples. In comparison to clusters, two PCs and individual accelerometry variables showed higher convergent validity with self-report outcomes of UL activity performance and disability.
A five-variable, five-cluster model was replicable and generalizable. Convergent validity data suggest that UL activity performance in daily life may be better conceptualized on a continuum, rather than categorically. These findings highlight a unified, data-driven approach to tracking functional changes across UL conditions and severity of functional deficits.
可穿戴运动传感器能够测量上肢（UL）活动，但单一变量可能无法全面反映情况。本研究旨在重复之前的工作，确定中风患者和对照组中上肢活动表现的五个多变量类别，并将这些发现扩展到其他上肢情况。
收集了324名参与者的人口统计学、自我报告以及基于可穿戴传感器的上肢活动表现变量（中风患者 = 49例，多发性硬化症患者 = 19例，上肢远端骨折患者 = 40例，上肢近端疼痛患者 = 55例，乳腺癌术后患者 = 23例，对照组 = 138例）。进行主成分（PC）分析（12、9、7或5个加速度计输入变量），随后进行聚类分析，并对总样本的多个子集进行多次模型拟合评估。
两个主成分解释了70 - 90%的方差：主成分1（总体上肢活动表现）和主成分2（优势肢体使用情况）。一个五变量、五聚类模型在各个样本中是最优的。与聚类相比，两个主成分和单个加速度计变量在上肢活动表现和残疾的自我报告结果方面显示出更高的收敛效度。
一个五变量、五聚类模型是可重复且可推广的。收敛效度数据表明，日常生活中的上肢活动表现可能在一个连续体上得到更好的概念化，而不是分类。这些发现突出了一种统一的、数据驱动的方法，用于跟踪上肢不同情况及功能缺陷严重程度下的功能变化。
