Multiple Sclerosis Performance Test: Technical Development and Usability.

INTRODUCTION: In the clinic, the assessment of patients with multiple sclerosis (MS) is typically qualitative and non-standardized. OBJECTIVES: To describe the MS Performance Test (MSPT), an iPad Air® 2 (Apple, Cupertino, CA, USA)-based neurological assessment platform allowing patients to input relevant information without the aid of a medical technician, creating a longitudinal, clinically meaningful, digital medical record. To report results from human factor (HF) and usability studies, and the initial large-scale implementation in a practice setting. METHODS: The HF study examined use-error patterns in small groups of MS patients and healthy controls (n = 14), the usability study assessed the effectiveness of patient interaction with the tool by patients with a range of MS disability (n = 60) in a clinical setting, and the implementation study deployed the MSPT across a diverse population of patients (n = 1000) in a large MS center for routine clinical care. RESULTS: MSPT assessments were completed by all users in the HF study; minor changes to design were recommended. In the usability study, 73% of patients with MS completed the MSPT, with an average administration time of 32 min; 85% described their experience with the tool as satisfactory. In the initial implementation for routine care, 84% of patients with MS completed the MSPT, with an average administration time of 28 min. CONCLUSION: Patients with MS with varying disability levels completed the MSPT with minimal or no supervision, resulting in comprehensive, efficient, standardized, quantitative, clinically meaningful data collection as part of routine medical care, thus allowing for large-scale, real-world evidence generation. FUNDING: Biogen. TRIAL REGISTRATION: NCT02664324.

Movement measurements at home for multiple sclerosis: walking speed measured by a novel ambient measurement system.

BACKGROUND: Gait disturbance is a major contributor to clinical disability in multiple sclerosis (MS). A sensor was developed to assess walking speed at home for people with MS using infrared technology in real-time without the use of wearables. OBJECTIVE: To develop continuous in-home outcome measures to assess gait in adults with MS. METHODS: Movement measurements were collected continuously for 8 months from six people with MS. Average walking speed and peak walking speed were calculated from movement data, then analyzed for variability over time, by room (location), and over the course of the day. In-home continuous gait outcomes and variability were correlated with standard in-clinic gait outcomes. RESULTS: Measured in-home average walking speed of participants ranged from 0.33 m/s to 0.96 m/s and peak walking speed ranged from 0.89 m/s to 1.51 m/s. Mean total within-participant coefficient of variation for daily average walking speed and peak walking speed were 10.75% and 10.93%, respectively. Average walking speed demonstrated a moderately strong correlation with baseline Timed 25-Foot Walk (rs = 0.714, P = 0.111). CONCLUSION: New non-wearable technology provides reliable and continuous in-home assessment of walking speed.

Validation of an ambient measurement system (AMS) for walking speed.

Walking speed is an important indicator of worsening in a variety of neurological and neuromuscular diseases, yet typically is measured only infrequently and in a clinical setting. Passive measurement of walking speed at home could provide valuable information to track the progression of many neuromuscular conditions. The purpose of this study was to validate the measurement of walking speed by a shelf-top ambient measurement system (AMS) that can be placed in a patient's home. Twenty-eight healthy adults (16 male, 12 female) were asked to walk three pre-defined routes two times each (total of 168 traversals). For each traversal, walking speed was measured simultaneously by five sources: two independent AMSs and three human timers with stopwatches. Measurements across the five sources were compared by generalised estimating equations (GEE). Correlation coefficients compared pairwise for walking speeds across the two AMSs, three human timers, and three routes all exceeded 0.86 (p < .0001), and for AMS-to-AMS exceeded 0.92 (p < .0001). Aggregated across all routes, there was no significant difference in measured walking speeds between the two AMSs (p = .596). There was a statistically significant difference between the AMSs and human timers of 8.5 cm/s (p < .0001), which is comparable to differences reported for other non-worn sensors. The tested AMS demonstrated the ability to automatically measure walking speeds comparable to manual observation and recording, which is the current standard for assessing walking speed in a clinical setting. The AMS may be used to detect changes in walking speed in community settings.