Understanding how people with Parkinson's disease turn in gait from a real-world in-home dataset. (December 2022)

• Record Type: Journal Article
• Authors: Morgan, Catherine; Jameson, Jack; Craddock, Ian; Tonkin, Emma L.; Oikonomou, George; Isotalus, Hanna Kristiina; Heidarivincheh, Farnoosh; McConville, Ryan; Tourte, Gregory J.L.; Kinnunen, Kirsi M.; Whone, Alan
• Is Part Of: Parkinsonism & related disorders. Volume 105(2022)
• Page Start: 114
• Page End: 122
• Publication Date: 2022-12
• Subjects: Remote sensing technology -- Home environment -- Gait analysis -- Mobility -- Parkinson's disease; Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2022.11.007
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Introduction: Turning in gait digital parameters may be useful in measuring disease progression in Parkinson's disease (PD), however challenges remain over algorithm validation in real-world settings. The influence of clinician observation on turning outcomes is poorly understood. Our objective is to describe a unique in-home video dataset and explore the use of turning parameters as biomarkers in PD. Methods: 11 participants with PD, 11 control participants stayed in a home-like setting living freely for 5 days (with two sessions of clinical assessment), during which high-resolution video was captured. Clinicians watched the videos, identified turns and documented turning parameters. Results: From 85 hours of video 3869 turns were evaluated, averaging at 22.7 turns per hour per person. 6 participants had significantly different numbers of turning steps and/or turn duration between "ON" and "OFF" medication states. Positive Spearman correlations were seen between the Movement Disorders Society-sponsored revision of the Unified Parkinson's Disease Rating Scale III score with a) number of turning steps (rho = 0.893, p < 0.001), and b) duration of turn (rho = 0.744, p = 0.009) "OFF" medications. A positive correlation was seen "ON" medications between number of turning steps and clinical rating scale score (rho = 0.618, p = 0.048). Both cohorts took more steps and shorter durations of turn during observed clinical assessments than when free-living. Conclusion: This study shows proof of concept that real-world free-living turn duration and number of turning steps recorded can distinguish between PD medication states and correlate with gold-standard clinical rating scale scores. It illustrates a methodology for ecological validation of real-world digital outcomes. Highlights: People with PD turn (in gait) on average 22.7 times per hour during waking hours at home. Turning parameters can differentiate between "ON" and "OFF" medication status in PD. Real world turning parameters correlate with gold-standard clinical rating scales. In-home turns look very different when the person is observed by a clinician. Wall-mounted cameras with human annotators can be used to validate sensor data.