{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T03:21:16Z","timestamp":1793416876697,"version":"4.5.0"},"reference-count":63,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T00:00:00Z","timestamp":1771545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Aging Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Conventional clinical assessments do not fully capture how Parkinson\u2019s disease (PD) affects mobility in daily life. Integrating digital mobility outcomes (DMOs) from wearable devices with GPS-derived contextual data could provide richer insight into real-world mobility, yet this approach remains largely unexplored. Similarly, data-driven modeling of DMO distributions, such as walking speed, may reveal clinically relevant changes in mobility that are obscured by averaged measures. This study (i) examined how indoor\u2013outdoor context enhances interpretation of real-world mobility, and (ii) applied Gaussian Mixture Modeling (GMM) to characterize data-driven patterns within walking speed distributions in people with PD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Fifty-two people with PD (PwP) and 19 older adult controls were recruited from the CiC and Mobilise-D studies. DMOs were estimated from a single wearable device, and indoor-outdoor location was synchronized with GPS data from a smartphone. GMM was applied to estimate the optimal number of walking speed modes. Generalized linear models compared DMOs between indoor and outdoor contexts and between cohorts, adjusting for age and sex.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Thirty-nine PwP and 17 controls had valid contextual data. Both cohorts performed significantly more indoor than outdoor walking bouts, with longer walking durations outdoors. Only controls walked significantly slower and with shorter strides indoors versus outdoors, while both groups showed longer stride duration indoors. Between-cohort differences emerged only outdoors, with PwP exhibiting higher cadence. Most participants across both cohorts displayed three walking speed modes, which were associated with medication dosage and motor severity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study demonstrates the potential of GPS-derived contextual information to enhance interpretation of real-world mobility outcomes in PD. Walking speed modes show promise for capturing novel clinical insight, though further technical and clinical validation is required to establish their robustness and clinical relevance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnagi.2026.1746429","type":"journal-article","created":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T06:42:31Z","timestamp":1771569751000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Toward an understanding of real-world mobility in Parkinson\u2019s: insights from enhanced contextualisation using GPS-derived location and data-driven modeling of walking speed"],"prefix":"10.3389","volume":"18","author":[{"given":"Cameron","family":"Kirk","sequence":"first","affiliation":[{"name":"Faculty of Medical Sciences, Translational and Clinical Research Institute, Newcastle University","place":["Newcastle upon Tyne, United 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