GPS and Smartphone Technology for Real-World Measurement of Community Mobility in Healthcare.

Abstract

Introduction: A primary goal of physical medicine and rehabilitation is restoring community mobility after injury or illness. However, there is no clinically accepted real-world method to measure community mobility, which fundamentally limits our ability to evaluate treatment effectiveness. This study aimed to develop and validate a digital framework using GPS-enabled smartphones and inertial sensors to monitor community mobility and estimate clinical function in individuals with chronic stroke or lower limb amputation (LLA).

Methods: Ninety individuals with chronic stroke or LLA underwent remote monitoring for 3-9 months. Participants completed standard clinical assessments, and daily mobility data were extracted from GPS and step count features. We conducted four analyses: (1) characterization of group- and individual-level community mobility, (2) evaluation of mobility changes following a mobility-targeted intervention in a single case participant, (3) development of machine-learned models to predict clinical gait outcomes using community data, and (4) estimation of the minimum number of days needed to reliably predict functional outcomes.

Results: Community mobility measures revealed substantial variability both across and within individuals, reflecting diverse functional profiles. In a case study, a participant with LLA demonstrated increased activity and movement diversity following a personalized intervention. Machine-learned models estimated 6-Minute Walk Test and 10-Meter Walk Test scores with clinically acceptable error margins (7-10%) using as few as 14 days of community data. Reliable predictions were achievable with just 3-6 days of monitoring.

Conclusions: GPS- and smartphone-based monitoring offer a feasible and scalable approach to assess real-world mobility. This approach could close a critical gap in the care continuum and enable us to fully evaluate the real-world impact of treatment interventions while also reducing reliance on frequent in-person evaluations.