Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson’s Disease

Hayslenne A. G. O. Araújo, Suhaila M. Smaili, Rosie Morris, Lisa Graham, Julia Das, Claire McDonald, Richard Walker, Samuel Stuart, Rodrigo Vitório*

*Corresponding author for this work

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1 Citation (Scopus)
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Abstract

Although the multifactorial nature of falls in Parkinson’s disease (PD) is well described, optimal assessment for the identification of fallers remains unclear. Thus, we aimed to identify clinical and objective gait measures that best discriminate fallers from non-fallers in PD, with suggestions of optimal cutoff scores. METHODS: Individuals with mild-to-moderate PD were classified as fallers (n = 31) or non-fallers (n = 96) based on the previous 12 months’ falls. Clinical measures (demographic, motor, cognitive and patient-reported outcomes) were assessed with standard scales/tests, and gait parameters were derived from wearable inertial sensors (Mobility Lab v2); participants walked overground, at a self-selected speed, for 2 min under single and dual-task walking conditions (maximum forward digit span). Receiver operating characteristic curve analysis identified measures (separately and in combination) that best discriminate fallers from non-fallers; we calculated the area under the curve (AUC) and identified optimal cutoff scores (i.e., point closest-to-(0,1) corner). RESULTS: Single gait and clinical measures that best classified fallers were foot strike angle (AUC = 0.728; cutoff = 14.07°) and the Falls Efficacy Scale International (FES-I; AUC = 0.716, cutoff = 25.5), respectively. Combinations of clinical + gait measures had higher AUCs than combinations of clinical-only or gait-only measures. The best performing combination included the FES-I score, New Freezing of Gait Questionnaire score, foot strike angle and trunk transverse range of motion (AUC = 0.85). CONCLUSION: Multiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.
Original languageEnglish
Article number4651
Pages (from-to)1-10
Number of pages10
JournalSensors
Volume23
Issue number10
DOIs
Publication statusPublished - 11 May 2023

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