Monaghan Patrick G, VanNostrand Michael, Takla Taylor N, Fritz Nora E
Department of Health Care Sciences, Wayne State University, Detroit, MI 48201, USA.
Neuroimaging and Neurorehabilitation Laboratory, Wayne State University, Detroit, MI 48201, USA.
Sensors (Basel). 2025 Mar 13;25(6):1780. doi: 10.3390/s25061780.
Multiple sclerosis (MS) is a chronic neurodegenerative disease characterized by mobility impairments that limit physical activity and reduce quality of life. While traditional clinical measures and participant-reported outcomes provide valuable insights, they often fall short of fully capturing the complexities of real-world mobility. This study evaluates the predictive value of combining sensor-derived clinical measures and participant-reported outcomes to better forecast prospective physical activity levels in individuals with MS. Forty-six participants with MS completed surveys assessing fatigue, concern about falling, and perceived walking ability (MSWS-12), alongside sensor-based assessments of gait and balance. Over three months, participants wore Fitbit devices to monitor physical activity, including step counts and total activity levels. Forward stepwise regression revealed that a combined model of participant-reported outcomes and sensor-derived measures explained the most variance in future physical activity, with MSWS-12 and backward walking velocity emerging as key predictors. These findings highlight the importance of integrating subjective and objective measures to provide a more comprehensive understanding of physical activity patterns in MS. This approach supports the development of personalized interventions aimed at improving mobility, increasing physical activity, and enhancing overall quality of life for individuals with MS.
多发性硬化症（MS）是一种慢性神经退行性疾病，其特征是行动障碍，限制身体活动并降低生活质量。虽然传统的临床测量方法和参与者报告的结果提供了有价值的见解，但它们往往无法完全捕捉现实世界中行动的复杂性。本研究评估了将传感器衍生的临床测量方法与参与者报告的结果相结合的预测价值，以更好地预测MS患者未来的身体活动水平。46名MS患者完成了评估疲劳、跌倒担忧和自我感知行走能力（MSWS-12）的调查，同时进行了基于传感器的步态和平衡评估。在三个月的时间里，参与者佩戴Fitbit设备来监测身体活动，包括步数和总活动水平。向前逐步回归分析表明，参与者报告的结果和传感器衍生测量方法的组合模型解释了未来身体活动中最大的方差，其中MSWS-12和向后行走速度成为关键预测因素。这些发现强调了整合主观和客观测量方法的重要性，以便更全面地了解MS患者的身体活动模式。这种方法支持开发个性化干预措施，旨在改善MS患者的行动能力、增加身体活动并提高整体生活质量。
