A smartphone sensor-based digital outcome assessment of multiple sclerosis

Area of Science:

• Neurology
• Digital Health
• Biomedical Engineering

Background:

• Sensor-based monitoring tools are crucial for advancing multiple sclerosis (MS) research and clinical care.
• Existing tools often fail to capture the full spectrum of functional impairment in MS.
• Digital health solutions offer a promising avenue for comprehensive MS monitoring.

Purpose of the Study:

• To evaluate the performance characteristics of the Floodlight Proof-of-Concept (PoC) app for monitoring MS.
• To assess the reliability and clinical relevance of sensor-based measurements in people with MS (PwMS).
• To determine the app's correlation with established clinical and imaging markers of MS disability.

Main Methods:

• A 24-week study enrolled 76 people with MS and 25 healthy controls.
• Smartphone-based active and passive tests assessed cognition, upper extremity function, and gait/balance.
• Intraclass correlation coefficients (ICCs) and Spearman's rank correlation were used to determine reliability and clinical correlations.

Main Results:

• Moderate-to-good test-retest reliability (ICC(2,1) = 0.61-0.85) was observed across tests in PwMS.
• Significant correlations were found between app measures and standard clinical disability scales for all assessed domains.
• Most tests also correlated with Expanded Disability Status Scale, MS Impact Scale, and/or brain volume.

Conclusions:

• The Floodlight PoC app provides reliable and clinically relevant measures of functional impairment in MS.
• These findings support the app's potential utility in both MS clinical research and patient care.
• Sensor-based mobile health tools can effectively capture functional status in individuals with MS.