Dataset Information

Real-life driving outcomes in Parkinson disease.

ABSTRACT: To determine the incidence of and risk factors for driving outcomes in drivers with Parkinson disease (PD).In a prospective cohort study, we ascertained the time until driving cessation, a crash, or a traffic citation using self-report and state Department of Transportation records in 106 licensed, active drivers with PD and 130 controls.Drivers with PD stopped driving earlier than controls, hazard ratio (95% confidence interval) = 7.09 (3.66-13.75), p < 0.001. Cumulative incidence of driving cessation at 2 years after baseline was 17.6% (11.5%-26.5%) for PD and 3.1% (1.2%-8.1%) for controls. No significant differences between groups on times to first crash or citation were detected. However, the number of observed crashes was low. Cox proportional hazards models showed that significant baseline risk factors for driving cessation in PD were older age, preference to be driven by somebody else, positive crash history, use of compensatory strategies, low driving exposure, impairments in visual perception (especially visual processing speed and attention) and cognitive abilities, parkinsonism (especially activities of daily living score and total daily dose of antiparkinsonian medications), and higher error counts on a road test. Within PD, crashes were associated with poorer postural stability and history of driving citations, and citations were associated with younger age and road errors at baseline.Drivers with PD are at a higher risk of driving cessation than elderly control drivers. A battery evaluating motor and nonmotor aspects of PD, driving record, and performance can be useful in assessing future driving outcomes in PD.

SUBMITTER: Uc EY

PROVIDER: S-EPMC3115811 | biostudies-other | 2011 May

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

Real-life driving outcomes in Parkinson disease.

Uc E Y EY Rizzo M M Johnson A M AM Emerson J L JL Liu D D Mills E D ED Anderson S W SW Dawson J D JD

Neurology 20110501 22

<h4>Objective</h4>To determine the incidence of and risk factors for driving outcomes in drivers with Parkinson disease (PD).<h4>Methods</h4>In a prospective cohort study, we ascertained the time until driving cessation, a crash, or a traffic citation using self-report and state Department of Transportation records in 106 licensed, active drivers with PD and 130 controls.<h4>Results</h4>Drivers with PD stopped driving earlier than controls, hazard ratio (95% confidence interval) = 7.09 (3.66-13. ...[more]

PMID: 21624988

Similar Datasets

Project description:BackgroundWearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments.ObjectiveThis paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD.MethodsThe Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch's method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation.ResultsFrom 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor.ConclusionsWe present a new video-referenced data set that includes unscripted activities in and around the participants' homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.

Dataset Information

Real-life driving outcomes in Parkinson disease.

Publications

Real-life driving outcomes in Parkinson disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets