Use of Mobile Device Accelerometry to Enhance Evaluation of Postural Instability in Parkinson Disease

Abstract

Objective: To determine the accuracy of inertial measurement unit data from a mobile device using the mobile device relative to posturography to quantify postural stability in individuals with Parkinson disease (PD).

Design: Criterion standard.

Setting: Motor control laboratory at a clinic.

Participants: A sample (N=28) of individuals with mild to moderate PD (n=14) and age-matched community-dwelling individuals without PD (n=14) completed the study.

Interventions: Not applicable.

Main outcome measures: Center of mass (COM) acceleration measures were compared between the mobile device and the NeuroCom force platform to determine the accuracy of mobile device measurements during performance of the Sensory Organization Test (SOT). Analyses examined test-retest reliability of both systems and sensitivity of (1) the equilibrium score from the SOT and (2) COM acceleration measures from the force platform and mobile device to quantify postural stability across populations.

Results: Metrics of COM acceleration from inertial measurement unit data and the NeuroCom force platform were significantly correlated across balance conditions and groups (Pearson r range, .35 to .97). The SOT equilibrium scores failed to discriminate individuals with and without PD. However, the multiplanar measures of COM acceleration from the mobile device exhibited good to excellent reliability across SOT conditions and were able to discriminate individuals with and without PD in conditions with the greatest balance demands.

Conclusions: Metrics employing medial-lateral movement produce a more sensitive outcome than the equilibrium score in identifying postural instability associated with PD. Overall, the output from the mobile device provides an accurate and reliable method of rapidly quantifying balance in individuals with PD. The portable and affordable nature of a mobile device with the application makes it ideally suited to use biomechanical data to aid in clinical decision making.

Keywords: Accelerometry; Feedback, sensory; Mobile applications; Parkinson disease; Postural balance; Rehabilitation.