Objective: To evaluate the feasibility and discriminative utility of a standardised home-based walk test using wearable sensors in people with Parkinson’s disease (PwP).

Background: Wearable sensors are increasingly used in PD research, and standardised motor tasks such as the two-minute walk (2MW) show associations with clinical severity when conducted in a clinical setting[1]. Extending such assessments to the home setting can increase measurement frequency, capture performance in more representative environments, and reduce participant burden. Establishing whether standardised walking tasks can be reliably performed at home is therefore important toward remote longitudinal monitoring of PD.

Method: As part of the OxQUIP study, 96 PwP and 35 healthy controls (HC) performed a monthly home-based 2MW over up to 17 sessions, wearing two sensors: a smartphone in a pouch on the lower abdomen and a wrist-worn smartwatch. Feasibility was assessed by quantifying session completion rates, sensor wear compliance, and walking data availability (number of straight walks and steps detected[1]). To evaluate discriminative utility, we compared gait features between HC and PwP at session 2.

Results: Participants completed an average of 15.4 sessions each, with a total of 2,022 sessions completed during the study. Of those, 2.7% were marked as invalid due to the participant not wearing the phone in the pouch. PwP demonstrated greater stride duration variability and slower turns compared to HC (p(adj.) <0.05). The number of straight walking periods detected varied between 2 and 33 (IQR=8), indicating differences across home environments.

Conclusion: Home-based walk tests using wearable sensors are feasible across monthly sessions and can detect PD-related gait differences. Despite the standardised nature of the task, differences in participants’ home environment pose challenges in walking quantification and comparability. Remote standardised motor assessments may complement longitudinal monitoring of PD. Further work is needed to harmonise quantification of walking across different home settings.

References: 1Sotirakis, C., et al. Identification of motor progression in Parkinson’s disease using wearable sensors and machine learning. npj Parkinsons Dis. 9, 142 (2023). https://doi.org/10.1038/s41531-023-00581-2
2Küderle, A., et al. MobGap [Computer software]. https://doi.org/10.5281/zenodo.14035833 URL: https://github.com/mobilise-d/mobgap/

« Back to 2026 International Congress

MDS Abstracts - https://www.mdsabstracts.org/abstract/standardised-home-based-walk-tests-can-capture-gait-differences-in-parkinsons-disease-using-wearable-sensors/