Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults. (August 2019)

• Record Type: Journal Article
• Authors: von Coelln, Rainer; Dawe, Robert J.; Leurgans, Sue E.; Curran, Thomas A.; Truty, Timothy; Yu, Lei; Barnes, Lisa L.; Shulman, Joshua M.; Shulman, Lisa M.; Bennett, David A.; Hausdorff, Jeffrey M.; Buchman, Aron S.
• Is Part Of: Parkinsonism & related disorders. Volume 65(2019)
• Page Start: 190
• Page End: 196
• Publication Date: 2019-08
• Subjects: Parkinsonism -- Gait testing -- Biosensor -- Accelerometry -- Longitudinal; 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.2019.06.012
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Introduction: Mobility metrics derived from wearable sensor recordings are associated with parkinsonism in older adults. We examined if these metrics predict incident parkinsonism. Methods: Parkinsonism was assessed annually in 683 ambulatory, community-dwelling older adults without parkinsonism at baseline. Four parkinsonian signs were derived from a modified Unified Parkinson's Disease Rating Scale (UPDRS). Parkinsonism was based on the presence of 2 or more signs. Participants wore a sensor on their back while performing a 32 foot walk, standing posture, and Timed Up and Go (TUG) tasks. 12 mobility scores were extracted. Cox proportional hazards models with backward elimination were used to identify combinations of mobility scores independently associated with incident parkinsonism. Results: During follow-up of 2.5 years (SD = 1.28), 139 individuals developed parkinsonism (20.4%). In separate models, 6 of 12 mobility scores were individually associated with incident parkinsonism, including: Speed and Regularity (from 32 ft walk), Sway (from standing posture), and 3 scores from TUG subtasks (Posterior sit to stand transition, Range stand to sit transition, and Yaw, a measure of turning efficiency). When all mobility scores were analyzed together in a single model, 2 TUG subtask scores, Range from stand to sit transition (HR, 1.42, 95%CI, 1.09, 1.82) and Yaw from turning (HR, 0.56, 95%CI, 0.42, 0.73) were independently associated with incident parkinsonism. These results were unchanged when controlling for chronic health covariates. Conclusion: Mobility metrics derived from a wearable sensor complement conventional gait testing and have potential to enhance risk stratification of older adults who may develop parkinsonism. Highlights: 139 of 683 individuals (20%) developed parkinsonism during 2.5 years of follow up. 6 of 12 sensor-based metrics at baseline are associated with incident parkinsonism. In a combined model, 2 metrics were independently associated with parkinsonism. Those associations were unchanged when controlling for chronic health conditions.