{"doi":"10.1159/000548017","title":"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.","journal":"Digital Biomarkers","year":2025,"id":534992,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9569,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":278709,"name":"Megan K. O’Brien","orcid":"0000-0001-8069-365X","position":1,"is_corresponding":false},{"id":690366,"name":"Rachel Maronati","orcid":null,"position":2,"is_corresponding":false},{"id":1220361,"name":"Francesco Lanotte","orcid":"0000-0001-8978-1074","position":3,"is_corresponding":false},{"id":1261045,"name":"Shreya Aalla","orcid":"0009-0002-3656-0483","position":4,"is_corresponding":false},{"id":774944,"name":"Christian Poellabauer","orcid":"0000-0002-0599-7941","position":5,"is_corresponding":false},{"id":383352,"name":"Brad D. Hendershot","orcid":"0000-0001-5400-9551","position":6,"is_corresponding":false},{"id":454335,"name":"John M. Looft","orcid":"0000-0001-5616-4133","position":7,"is_corresponding":false},{"id":278713,"name":"Arun Jayaraman","orcid":"0000-0002-9302-6693","position":8,"is_corresponding":false},{"id":1418127,"name":"Sara Nataletti","orcid":"0009-0007-0776-8732","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:51:52.019261Z","pmid":"41064246","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}