Pilloni Giuseppina, Ko Timothy Sung Hyuk, Kreisberg Erica, Geel Josh, Gutman Josef Maxwell, Sammarco Carrie, Oh Cheongeun, Charvet Leigh
Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA.
Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.
Digit Biomark. 2025 Jun 30;9(1):140-154. doi: 10.1159/000547176. eCollection 2025 Jan-Dec.
Gait is a critical indicator of neurological health, with changes often signaling underlying decline. We developed a remote gait monitoring protocol using off-the-shelf shoe-based sensors (RunScribe) to assess gait parameters in real-world home settings. This protocol, known as Gait Assessment with Innovative Technologies - Home-based Use and Benefit (GAIT-HUB), was tested in individuals with multiple sclerosis (MS), a population at high risk for gait impairment due to the disease's variable progression.
Participants with MS completed an in-clinic baseline gait assessment using a validated sensor (G-Sensor®) and three weekly, remotely supervised gait assessments at home using the RunScribe sensors. Gait parameters were compared across devices using intra-class correlation coefficients (ICCs) and Bland-Altman analyses. Longitudinal reliability of remote assessments and system usability score (SUS) were evaluated.
Twenty-nine participants (76% women, ages 19-67, PDDS range 0-5) successfully completed the home-based assessments. High agreement between devices was observed for gait speed, stride length, and cadence (ICCs >0.90), though phases like stance and swing showed more variability. Bland-Altman analyses indicated minimal bias in most parameters. Longitudinal assessments demonstrated strong reliability (ICCs >0.87) for key metrics, and SUS indicated good-to-excellent usability of the remote protocol.
The GAIT-HUB protocol enables reliable and feasible home-based gait monitoring using wearable sensors that patients can easily self-apply. This approach provides valuable insights into daily mobility patterns beyond clinical visits, supporting more precise and timely assessments of functional status between appointments and offering the potential for seamless integration into telemedicine routine care.
步态是神经健康的关键指标，其变化往往预示着潜在的衰退。我们开发了一种远程步态监测方案，使用现成的基于鞋子的传感器（RunScribe）在现实家庭环境中评估步态参数。这个方案被称为“基于家庭使用和效益的创新技术步态评估（GAIT-HUB）”，在多发性硬化症（MS）患者中进行了测试，由于该疾病进展多变，这一群体存在步态受损的高风险。
MS患者使用经过验证的传感器（G-Sensor®）完成一次门诊基线步态评估，并在家中使用RunScribe传感器进行三次每周一次的远程监督步态评估。使用组内相关系数（ICC）和布兰德-奥特曼分析对不同设备的步态参数进行比较。评估远程评估的纵向可靠性和系统可用性评分（SUS）。
29名参与者（76%为女性，年龄19 - 67岁，PDDS范围0 - 5）成功完成了家庭评估。在步态速度、步长和步频方面，不同设备之间观察到高度一致性（ICC>0.90），尽管站立和摆动等阶段显示出更多变异性。布兰德-奥特曼分析表明大多数参数的偏差最小。纵向评估显示关键指标具有很强的可靠性（ICC>0.87），SUS表明远程方案的可用性良好到优秀。
GAIT-HUB方案能够使用患者可轻松自行佩戴的可穿戴传感器进行可靠且可行的家庭步态监测。这种方法提供了超越临床就诊的日常活动模式的宝贵见解，支持在预约之间对功能状态进行更精确和及时的评估，并有可能无缝集成到远程医疗常规护理中。
