Literature DB >> 21989633

Toward automated, at-home assessment of mobility among patients with Parkinson disease, using a body-worn accelerometer.

Aner Weiss1, Sarvi Sharifi, Meir Plotnik, Jeroen P P van Vugt, Nir Giladi, Jeffrey M Hausdorff.   

Abstract

OBJECTIVE: To develop an automated and objective method to assess mobility in Parkinson disease (PD) patients in daily-life settings and to investigate whether accelerometer-derived measures discriminate between PD and healthy controls as they walk and simulate activities of daily living (ADL).
METHODS: Healthy older adults (17) and patients with PD (22) wore a triaxial accelerometer on their lower back during short walks (validation study) and during a walk around the medical center to simulate daily activities (ADL simulation). The variability (consistency and rhythmicity) of stepping was assessed. The patients completed the walks before and after taking their anti-Parkinsonian medications. Frequency-based acceleration measures included dominant frequency, amplitude (strength of signal frequency), width (frequency dispersion), and slope (a combination reflecting amplitude and width) of the main frequency of the power spectral density in the 0.5- to 3.0-Hz band. A subset of the Unified Parkinson-Disease Rating Scale provided a clinical measure of gait impairment (UPDRS-Gait5). A PD patient and control wore the sensors for 3 days at home.
RESULTS: The width was larger, and the amplitude and slope were smaller in the PD patients compared to the controls in the validation study and ADL simulation (P < .02). The width decreased, and the amplitude and slope increased when patients took anti-Parkinsonian medications (P < .007). Significant correlations were observed between acceleration-derived measures and UPDRS-Gait5. The data obtained at home was similar to the clinic data.
CONCLUSIONS: Frequency-derived measures are valid and sensitive estimates of stride-to-stride variability that can be used to assess the quality and consistency of walking in patients with PD in real-life settings.

Entities:  

Mesh:

Year:  2011        PMID: 21989633     DOI: 10.1177/1545968311424869

Source DB:  PubMed          Journal:  Neurorehabil Neural Repair        ISSN: 1545-9683            Impact factor:   3.919


  63 in total

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6.  Should body weight-supported treadmill training and robotic-assistive steppers for locomotor training trot back to the starting gate?

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8.  Development of a new approach to quantifying stepping stability using ensemble empirical mode decomposition.

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9.  Actigraphy features for predicting mobility disability in older adults.

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10.  Continuous monitoring of turning in Parkinson's disease: Rehabilitation potential.

Authors:  Martina Mancini; Mahmoud El-Gohary; Sean Pearson; James McNames; Heather Schlueter; John G Nutt; Laurie A King; Fay B Horak
Journal:  NeuroRehabilitation       Date:  2015       Impact factor: 2.138

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