ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2021
Remote smartphone gait monitoring and fall prediction in Parkinson's disease during the COVID-19 lockdown.
Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Continuous Movement Monitoring at Home Through Wearable Devices: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- eHealth tools to assess the neurological function for research, in absence of the neurologist - a systematic review, part I (software).Journal of neurology · 2024Pooled it
- Instrumental assessment of Sit-to-Stand in Parkinson's disease: a scoping review.Journal of neuroengineering and rehabilitation · 2026Article
- Impact of dual-tasking and balance confidence on turns and transitions: a cross-sectional study in Parkinson's disease.Scientific reports · 2026Article
- Real-world feasibility of privacy-preserving, non-wearable AI for real-time fall detection with disease-specific video classification in parkinsonian syndromes: a proof-of-concept clinical study.Frontiers in neurology · 2026Article
- Sympathetic Burden Measured Through a Chest-Worn Sensor Correlates with Spatiotemporal Gait Performances and Global Cognition in Parkinson's Disease.Sensors (Basel, Switzerland) · 2025Article
- Fear of falling impairs spatiotemporal gait parameters, mobility, and quality of life in Parkinson's disease: a cross-sectional study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Assessing the clinical utility of inertial sensors for home monitoring in Parkinson's disease: a comprehensive review.NPJ Parkinson's disease · 2024Article
- Circadian profile, daytime activity, and the Parkinson's phenotype: A motion sensor pilot study with neurobiological underpinnings.Neurobiology of sleep and circadian rhythms · 2023Article
- A smart devices based secondary prevention program for cerebrovascular disease patients.Frontiers in neurology · 2023Article
- Transcutaneous Auricular Vagus Stimulation Improves Gait and Reaction Time in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2022Article
- Vestibular Rehabilitation Telehealth During the SAEA-CoV-2 (COVID-19) Pandemic.Frontiers in neurology · 2021Article
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundFalls could be serious events in Parkinson's disease (PD). Patient remote monitoring strategies are on the raise and may be an additional aid in identifying patients who are at risk of falling. The aim of the study was to evaluate if balance and timed-up-and-go data obtained by a smartphone application during COVID-19 lockdown were able to predict falls in PD patients.
methodsA cohort of PD patients were monitored for 4 weeks during the COVID-19 lockdown with an application measuring static balance and timed-up-and-go test. The main outcome was the occurrence of falls (UPDRS-II item 13) during the observation period.
resultsThirty-three patients completed the study, and 4 (12%) reported falls in the observation period. The rate of falls was reduced with respect to patient previous falls history (24%). The stand-up time and the mediolateral sway, acquired through the application, differed between "fallers" and "non-fallers" and related to the occurrence of new falls (OR 1.7 and 1.6 respectively, p < 0.05), together with previous falling (OR 7.5, p < 0.01). In a multivariate model, the stand-up time and the history of falling independently related to the outcome (p < 0.01).
conclusionsOur study provides new data on falls in Parkinson's disease during the lockdown. The reduction of falling events and the relationship with the stand-up time might suggest that a different quality of falls occurs when patient is forced to stay home - hence, clinicians should point their attention also on monitoring patients' sit-to-stand body transition other than more acknowledged features based on step quality.
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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 Socratic graph.