ArticleDigital biomarkers
GPS and Smartphone Technology for Real-World Measurement of Community Mobility in Healthcare.
Article in Digital biomarkers. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Staying Active: Promoting Activity Engagement for Poststroke Pain Management With Insights From Ecological Momentary Assessment.Archives of physical medicine and rehabilitation · 2026Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.