ArticleFrontiers in neurology2023
Gait and turning characteristics from daily life increase ability to predict future falls in people with Parkinson's disease.
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Determining Falls Risk in People with Parkinson's Disease Using Wearable Sensors: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- Knowledge mapping and research trends of accidental falls in patients with Parkinson's disease from 2003 to 2023: a bibliometric analysis.Frontiers in neurology · 2024Pooled it
- Validity of a Wearable Digital Insole for Assessing Gait ON and OFF in Parkinson's Disease.Annals of clinical and translational neurology · 2026Article
- WearGait-PD: An Open-Access Wearables Dataset for Gait in Parkinson's Disease and Age-Matched Controls.Scientific data · 2026Article
- Association Between Turn Impairments and Cognitive Function in Parkinson Disease.Aging medicine (Milton (N.S.W)) · 2025Article
- Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.European journal of neurology · 2025Observational
- Digital gait biomarkers in Parkinson's disease: susceptibility/risk, progression, response to exercise, and prognosis.NPJ Parkinson's disease · 2025Review
- Cognition is associated with daily-life mobility in people with Parkinson's disease.Clinical parkinsonism & related disorders · 2025Article
- Personalized versus fixed tactile cueing in Parkinson's disease: Protocol for a randomized controlled trial on gait automaticity.PloS one · 2025Article
- Predicting future fallers in Parkinson's disease using kinematic data over a period of 5 years.NPJ digital medicine · 2024Article
- Wearable sensor devices can automatically identify the ON-OFF status of patients with Parkinson's disease through an interpretable machine learning model.Frontiers in neurology · 2024Article
- Article
- L-Dopa/Carbidopa intestinal gel infusion in advanced Parkinson's disease: real-life mobility insights from wearable sensors.Frontiers in neurologyArticle
- Assessing fall risk in multiple sclerosis using patient-reported outcomes and wearable gait metrics.Multiple sclerosis journal - experimental, translational and clinicalArticle
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Objectives: To investigate if digital measures of gait (walking and turning) collected passively over a week of daily activities in people with Parkinson's disease (PD) increases the discriminative ability to predict future falls compared to fall history alone. Methods: We recruited 34 individuals with PD (17 with history of falls and 17 non-fallers), age: 68 ± 6 years, MDS-UPDRS III ON: 31 ± 9. Participants were classified as fallers (at least one fall) or non-fallers based on self-reported falls in past 6 months. Eighty digital measures of gait were derived from 3 inertial sensors (Opal Results: Twenty-five subjects reported falls in the follow-up year. Quantity of gait and turning measures (e.g., number of gait bouts and turns per hour) were similar in future fallers and non-fallers. The AUC to discriminate future fallers from non-fallers using fall history alone was 0.77 (95% CI: [0.50-1.00]). In contrast, the highest AUC for gait and turning digital measures with 4 combinations was 0.94 [0.84-1.00]. From the top 10 models (all AUCs>0.90) via the best subsets strategy, the most consistently selected measures were variability of toe-out angle of the foot (9 out of 10), pitch angle of the foot during mid-swing (8 out of 10), and peak turn velocity (7 out of 10). Conclusions: These findings highlight the importance of considering precise digital measures, captured
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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.