ArticleSensors (Basel, Switzerland)2023
Combination of Clinical and Gait Measures to Classify Fallers and Non-Fallers in Parkinson's Disease.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Determining Falls Risk in People with Parkinson's Disease Using Wearable Sensors: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Resting State Functional Connectivity Among Cortical and Subcortical Grey Matter Structures Can Distinguish Fallers in Parkinson's Disease.The European journal of neuroscience · 2026Article
- Parkinsonian gait improvement through vibratory stride parameter feedback.Journal of neuroengineering and rehabilitation · 2026Article
- Classification of fallers in Parkinson's disease through machine learning based feature analysis.NPJ Parkinson's disease · 2026Article
- Identification of Parkinson's disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach.Frontiers in aging neuroscience · 2025Article
- Predicting future fallers in Parkinson's disease using kinematic data over a period of 5 years.NPJ digital medicine · 2024Article
- A Review of Recent Advances in Cognitive-Motor Dual-Tasking for Parkinson's Disease Rehabilitation.Sensors (Basel, Switzerland) · 2024Review
- The validation of a Japanese version of the New Freezing of Gait Questionnaire (NFOG-Q).Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
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.
methodsIndividuals 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).
resultsSingle 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).
conclusionMultiple clinical and gait aspects must be considered for the classification of fallers and non-fallers in PD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.