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Laboratory gait analysis requires costly specialized equipment, while research wearables can impose sustained-use burdens. Smartphone inertial measurement units (IMUs) offer a widely available platform for longitudinal assessment within a patient\u2019s natural environment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following PRISMA-ScR guidelines, PubMed, Embase, and Scopus were searched for peer-reviewed studies using internal smartphone sensors to map reported clinical applications, methodological implementation, validation approaches, and translational barriers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Eighty-four studies met the inclusion criteria. Neurology was the most frequently represented clinical domain (36.9%), with additional applications in geriatrics (14.3%) and orthopedics (13.1%). 10.7% (\n                      <jats:italic>n<\/jats:italic>\n                      =9) were classified as validation-focused; the remainder had diagnostic (36.9%), prognostic (38.1%), or longitudinal monitoring (14.3%) aims. Most assessments remained controlled (54.8%), supervised (67.9%), and dependent on active testing protocol (73.8%), whereas only 23.8% captured passive, free-living mobility. Implementation was heterogeneous: 71.4% of protocols required fixed device placements, 82.1% used custom software, and 7.1% integrated with native platforms. The extracted measures included activity volume, gait-quality metrics, and machine-learning features derived from raw inertial data.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Smartphones provide a widely available platform for multidimensional mobility sensing, but the mapped evidence largely concerns feasibility, technical validation, or associations with clinical status and outcomes. Routine clinical utility has not been established. Translation will require standardized acquisition and reporting, cross-device and cross-platform validation, context-aware analysis, privacy-preserving data governance, and prospective evidence that implementation improves clinical decisions or patient-relevant outcomes. Passive monitoring may complement standardized assessment by providing longitudinal measures of real-world performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00415-026-14131-2","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T15:42:32Z","timestamp":1789486952000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical utility and methodological implementation of smartphone-based mobility analysis: a scoping review"],"prefix":"10.1007","volume":"273","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5266-4253","authenticated-orcid":false,"given":"Edwin H. 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