Towards an understanding of real-world mobility in Parkinson's: insights from enhanced contextualisation using GPS-derived location and data-driven modelling of walking speed

Cameron John Kirk¹Rana Zia UR Rehman²Brook Galna³Saverio Ranciati⁴Emma Packer¹Neil Ireson⁵Vitaveska Lanfranchi⁵Claudia Mazzà^6,7Lynn Rochester^1,8,9Alison J. Yarnall^1,10,11Silvia Del Din^1,12*

• ¹Newcastle University, Newcastle upon Tyne, United Kingdom
• ²Janssen Research & Development, High Wycombe, United Kingdom
• ³Murdoch University, Murdoch, Australia
• ⁴Universita degli Studi di Bologna Dipartimento di Scienze Statistiche Paolo Fortunati, Bologna, Italy
• ⁵The University of Sheffield Department of Computer Science, Sheffield, United Kingdom
• ⁶Indivi AG, Basel, Switzerland
• ⁷School of Mechanical, Aerospace and Civil Engineering, The University of Sheffield, Sheffield, United Kingdom
• ⁸Newcastle upon Tyne Hospitals NHS Foundations Trust, Newcastle upon Tyne, United Kingdom
• ⁹National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC, Newcastle upon Tyne, United Kingdom
• ¹⁰National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC),, Newcastle upon Tyne, United Kingdom
• ¹¹Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
• ¹²National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre, Newcastle upon Tyne, United Kingdom

Conventional clinical assessments do not fully capture how Parkinson's 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 modelling 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–outdoor context enhances interpretation of real-world mobility, and (ii) applied Gaussian Mixture Modelling (GMM) to characterise data-driven patterns within walking speed distributions in people with PD. 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 synchronised with GPS data from a smartphone. GMM was applied to estimate the optimal number of walking speed modes. Generalised linear models compared DMOs between indoor and outdoor contexts and between cohorts, adjusting for age and sex. 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. 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.