Dataset Information

Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults.

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.

SUBMITTER: von Coelln R

PROVIDER: S-EPMC6774889 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults.

von Coelln Rainer R Dawe Robert J RJ Leurgans Sue E SE Curran Thomas A TA Truty Timothy T Yu Lei L Barnes Lisa L LL Shulman Joshua M JM Shulman Lisa M LM Bennett David A DA Hausdorff Jeffrey M JM Buchman Aron S AS

Parkinsonism & related disorders 20190622

<h4>Introduction</h4>Mobility metrics derived from wearable sensor recordings are associated with parkinsonism in older adults. We examined if these metrics predict incident parkinsonism.<h4>Methods</h4>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 wo ...[more]

PMID: 31272924

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Project description:Introduction: Understanding determinants of community mobility disability is critical for developing interventions aimed at preventing or delaying disability in older adults. In an effort to understand these determinants, capturing and measuring community mobility has become a key factor. The objectives of this paper are to present and illustrate the signal processing workflow and outcomes that can be extracted from an activity and community mobility measurement approach based on GPS and accelerometer sensor data and 2) to explore the construct validity of the proposed measurement approach using data collected from healthy older adults in free-living conditions. Methods: Personal, functional impairment and environmental variables were obtained by self-report questionnaires in 75 healthy community-living older adults (mean age = 66 ± 7 years old) living on the island of Montreal, QC, Canada. Participants wore, for 14 days during waking hours on the hip, a data logger incorporating a GPS receiver with a 3-axis accelerometer. Time at home ratio (THR), Trips out (TO), Destinations (D), Maximal distance of destinations (MDD), Active time ratio (ATR), Steps (S), Distance in a vehicle (DV), Time in a vehicle (TV), Distance on foot (DF), Time on foot (TF), Ellipse area (EA), and Ellipse maximum distance (EMD) were extracted from the recordings. Results: After applying quality control criteria, the original data set was reduced from 75 to 54 participants (28% attrition). Results from the remaining sample show that under free-living conditions in healthy older adults, location, activity and community mobility outcomes vary across individuals and certain personal variables (age, income, living situation, professional status, vehicle access) have potential mitigating effects on these outcomes. There was a significant (yet small) relationship (rho < 0.40) between self-reported life space and MDD, DV, EA, and EMD. Conclusion: Wearability and usability of the devices used to capture free-living community mobility impact participant compliance and the quality of the data. The construct validity of the proposed approach appears promising but requires further studies directed at populations with mobility impairments.

Project description:ObjectivesTo determine the relationship between neighborhood-level socioeconomic characteristics, life-space mobility, and incident falls in community-dwelling older adults.DesignProspective, observational cohort study with a baseline in-home assessment and 6-month telephone follow-up.SettingCentral Alabama.ParticipantsCommunity-dwelling adults aged 65 and older recruited from a random sample of Medicare beneficiaries (N = 1,000).MeasurementsNeighborhood disadvantage was measured using a composite index derived from baseline neighborhood-level residential census tract socioeconomic variables. Data on individual-level socioeconomic characteristics, clinical variables, and life-space collected at baseline were included as covariates in a multivariate model using generalized estimating equations to assess the association with incident falls in the 6 months after baseline.ResultsOf the 940 participants who completed baseline and follow-up assessments, 126 (13%) reported one or more new falls in the 6 months after baseline. There was an independent nonlinear association between neighborhood disadvantage (according to increasing quartiles of disadvantage) and incident falls after adjusting for confounders: The lowest quartile served as reference; 2nd quartile odds ratio (OR) = 2.4, 95% confidence interval (CI) = 1.2-4.6; 3rd quartile OR = 1.9, 95% CI = 1.0-3.7; 4th quartile OR = 3.2, 95% CI = 1.7-6.0. Each 10-point decrement in life-space (OR = 1.2, 95% CI = 1.0-1.3) was associated with a higher risk of falls.ConclusionGreater neighborhood disadvantage was associated with greater risk of falls. Life-space also contributes separately to fall risk. Community-dwelling older adults in disadvantaged neighborhoods, particularly those with limited mobility, may benefit from a more-rigorous assessment of their fall risk by healthcare providers. Neighborhood level socioeconomic characteristics should also be an important consideration when identifying vulnerable populations that may benefit the most from fall prevention programs.

Dataset Information

Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults.

Publications

Quantitative mobility metrics from a wearable sensor predict incident parkinsonism in older adults.

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