Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults
Introduction
Parkinsonism, including bradykinesia, tremor, rigidity and gait impairment, is common in older adults without Parkinson disease (PD) and as it increases with age it may affect 50% or more of the population by age 85 [1]. Parkinsonism is a heterogeneous syndrome, which is not limited to PD, but can be caused by diverse etiologies, some of which are amenable to treatment [2]. Furthermore, parkinsonism is associated with adverse health outcomes including death, disability, and cognitive impairment [3]. Given the magnitude and consequence of parkinsonism in our aging population, identification of at-risk individuals offers the potential for early interventions that which may prevent the development of parkinsonism [4].
Conventional mobility metrics, such as gait speed, are sensitive but non-specific predictors of parkinsonism [5,6]. Mobility is a multi-dimensional trait derived from dissociable neural control systems within the central nervous system [7,8]. Investigations in gait laboratories have quantified additional facets of mobility necessary for successful locomotion [9,10]. Emerging technologies including wearable sensors show promise for extending these advances beyond the lab or hospital setting to varied venues including outpatient clinics and community studies of aging [11,12].
In a prior cross-sectional study, we found that sensor-derived mobility metrics were related to the severity of parkinsonian signs in older adults [[13], [14], [15]]. However, it is not known if these sensor-derived mobility metrics are associated with incident parkinsonism. To address this question, we used clinical data from older adults, participating in two community-based longitudinal cohort studies, who undergo annual motor testing for parkinsonian signs, as well as mobility testing with a wearable sensor.
Section snippets
Participants
Participants were from two ongoing longitudinal cohort studies, recruited from retirement communities and subsidized housing facilities in the Chicago metropolitan area, the Rush Memory and Aging Project (MAP) and the Minority Aging Research Study (MARS) [16,17].
All procedures in both studies were approved by the Institutional Review Board of Rush University Medical Center and were conducted in compliance with the Declaration of Helsinki. Written informed consent was obtained from all study
Results
We included 683 participants in our longitudinal analysis. Clinical characteristics at baseline are summarized in Table 1 and the individual mobility measures in Table 2.
Discussion
This large longitudinal study shows that sensor-derived mobility metrics recorded outside of the lab setting in the homes of older individuals predict incident parkinsonism. Further analyses of these diverse measures together in a single analytic framework identified a parsimonious combination of metrics that were independently associated with incident parkinsonism. Although gait speed, a sensitive but non-specific predictor of many adverse health outcomes [6,26] was one of the metrics we
Financial disclosure/conflict of interest
The authors declare no conflict of interest concerning the research related to this manuscript.
Funding sources
This work was supported by the NIA (R01AG17917 [DAB, LY], RF1AG22018 [LLB, SL], R01AG56352 [ASB]), the NINDS (R01NS78009 [ASB]), the Illinois Department of Public Health (DAB, SL); and the Robert C. Borwell Endowment Fund (DAB).
Authors’ roles
Rainer von Coelln: Statistical analysis (review and critique), manuscript (first draft, revisions).
Robert J. Dawe: Project (organization, design and execution), software generation (design, execution); statistical analysis (review and critique), manuscript (review and critique).
Sue E. Leurgans: Statistical analysis (design, execution), manuscript (revisions, review and critique).
Thomas A. Curran: Project (execution), software generation (design, execution); Statistical analysis (review and
Financial disclosures
Full Financial Disclosures of all Authors for the Past Year:
Stock Ownership in medically-related fields: none.
Intellectual Property Rights: Jeffrey Hausdorff has submitted a patent application on the use of body fixed sensors for assessing symptoms in Parkinson disease, the intellectual property rights for which are held by the Tel Aviv Medical Center.
Consultancies: Jeffrey Hausdorff serves on the Movement Disorders Society Technology Task Force and on the Michael J Fox Foundation task force on
Acknowledgement
We thank all the participants in the Rush Memory and Aging Project and Minority Aging Research Study. We also thank the staff of the Rush Alzheimer's Disease Center. More information regarding obtaining RADC clinical and postmortem data for research use can be found at the RADC Research Resource Sharing Hub (www.radc.rush.edu).
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