ArticleSensors (Basel, Switzerland)2020
Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review.GeroScience · 2024Pooled it
- Investigating mechanisms and monitoring outcomes in functional neurological disorder: the unrealized potential of digital remote monitoring technologies.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2026Review
- Rethinking Movement: From Capacity to Context.Kinesiology review (Champaign, Ill.) · 2026Article
- Neural control meets biomechanics in the motor assessment of neurological disorders: a narrative review.Frontiers in neural circuits · 2025Review
- Walking on a Balance Beam as a New Measure of Dynamic Balance to Predict Falls in Older Adults and Patients with Neurological Conditions.Sports medicine - open · 2024Article
- Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies.Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists · 2024Review
- DUO-GAIT: A gait dataset for walking under dual-task and fatigue conditions with inertial measurement units.Scientific data · 2023Article
- Association between daily gait speed patterns and cognitive impairment in community-dwelling older adults.Scientific reports · 2023Article
- Open-source dataset reveals relationship between walking bout duration and fall risk classification performance in persons with multiple sclerosis.PLOS digital health · 2022Article
- The Effect of Dual-Task Motor-Cognitive Training in Adults with Neurological Diseases Who Are at Risk of Falling.Brain sciences · 2022Article
- Bedtime Monitoring for Fall Detection and Prevention in Older Adults.International journal of environmental research and public health · 2022Article
- Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice.Sensors (Basel, Switzerland) · 2021Article
- An Experimental Study on the Validity and Reliability of a Smartphone Application to Acquire Temporal Variables during the Single Sit-to-Stand Test with Older Adults.Sensors (Basel, Switzerland) · 2021Article
- Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Neurological patients can have severe gait impairments that contribute to fall risks. Predicting falls from gait abnormalities could aid clinicians and patients mitigate fall risk. The aim of this study was to predict fall status from spatial-temporal gait characteristics measured by a wearable device in a heterogeneous population of neurological patients. Participants (
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What OpenQuestion holds
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.