SynthesisJMIR mHealth and uHealth2025
Use of Wearable Sensors to Assess Fall Risk in Neurological Disorders: Systematic Review.
Synthesis in JMIR mHealth and uHealth, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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Who cites it
13 citing papers in PubMed.
- An interpretable data-driven approach to optimizing clinical fall risk assessment.PLOS digital health · 2026Article
- Olfactory dysfunction is associated with gait impairment in older adults: evidence for a shared amygdala substrate.GeroScience · 2026Article
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- Phase-Specific Biomechanical Reorganization After Robotic Rehabilitation in Patients with Stroke: A Sensor-Derived Waveform Analysis.Life (Basel, Switzerland) · 2026Article
- Estimating Magnetic Field at Joint Centers Reduces Kinematic Errors in Inertial Motion Capture.Research square · 2026Article
- Classification of fallers and non-fallers in older adults using electrical IMU signal for gait analysis and explainable deep learning.Scientific reports · 2026Article
- Discriminating Between Fallers and Non-Fallers Using Kinematic Data from the Heel2Toe™ Wearable Sensor.Sensors (Basel, Switzerland) · 2026Article
- Assessment of Fall Risk in Neurological Disorders and Technology: Relationship Between Silver Index and Gait Analysis.Sensors (Basel, Switzerland) · 2026Article
- Sensor-Derived Trunk Stability and Gait Recovery: Evidence of Neuromechanical Associations Following Intensive Robotic Rehabilitation.Sensors (Basel, Switzerland) · 2026Article
- IMU-based gait analysis methods: a systematic review of techniques for different body locations.Frontiers in sports and active living · 2026Review
- Muscle and mind: rewiring cognitive-motor recovery through exercise-responsive neurophysiology in neurological populations.Frontiers in psychology · 2026Review
- Artificial intelligence and telemedicine in elderly healthcare: A mixed-methods study.BMC geriatrics · 2025Article
- Gait Event Detection and Gait Parameter Estimation from a Single Waist-Worn IMU Sensor.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAssessing fall risk, especially in individuals with neurological disorders, is essential to prevent hospitalization, hypomobility, and reduced functional independence. Wearable sensors are increasingly used in neurorehabilitation, as they enable unsupervised fall risk assessment by providing continuous monitoring during daily functional tasks, thereby offering a reflection of the individual's real-world fall risk.
objectiveWe systematically reviewed the literature on reliable biomechanical gait parameters detected with wearable sensors to assess fall risk in neurological disorders, focusing on patients with Parkinson disease, multiple sclerosis, stroke, or Alzheimer disease. In addition, we examined the latest advancements in wearable sensor technology, including best practices for device placement as well as data processing and analysis.
methodsWe conducted a comprehensive systematic search for relevant peer-reviewed articles published up to April 18, 2025, using PubMed, Web of Science, Embase, and IEEE Xplore, which are the most used databases in the fields of health and bioengineering.
resultsThe 19 included studies involved 2630 patients with neurological disorders, including 226 (8.59%) with multiple sclerosis (n=7, 37% studies), 2305 (87.64%) with Parkinson disease (n=8, 53% studies), 51 (1.94%) with stroke (n=3, 16% studies), and 48 (1.83%) with Alzheimer disease or cognitive impairment (n=1, 5% study).
conclusionsThis review highlights the role of wearable technologies in assessing fall risk in patients with neurological disorders. Although the included studies showed variation in methods and a focus on technology over clinical context, the lack of standardization reflects ongoing advancements, which may be seen as a strength.
trial registrationPROSPERO CRD42023463944; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023463944.
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What Socratic 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 Socratic graph.