ArticleScientific reports2021
Prediction of fall risk among community-dwelling older adults using a wearable system.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.
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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
31 citing papers in PubMed, 4 syntheses or guidelines pooled it, 55 citations in OpenAlex.
- Accuracy of wearable devices in predicting falls in older adults: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- The effect of chair-based exercise on physical functioning in postmenopausal women: a meta-analysis.BMC geriatrics · 2025Pooled it
- The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review.Journal of medical Internet research · 2024Pooled it
- A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.Sensors (Basel, Switzerland) · 2022Pooled it
- Quantitative assessment of the 30-second sit-to-stand test using computer vision technology for physical therapists.Journal of neuroengineering and rehabilitation · 2026Article
- Enhancing Physiological Complexity Through Mindfulness: A Wearable-Based Intervention for First Responders and Their Partners.Healthcare (Basel, Switzerland) · 2026Article
- Comparative analysis of wearable-derived gait features with intrinsic risk indicators for fall risk prediction in older adults.Scientific reports · 2026Article
- Predicting fall risk among older adults with sarcopenia in China using machine learning models: a six-year longitudinal study from CHARLS.BMC geriatrics · 2026Article
- Identifying and predicting gait stability metrics in people with stroke in uneven-surface walking using machine learning.Scientific reports · 2026Article
- Predicting first falls among older adults with chronic conditions and polypharmacy using routinely collected health records.Frontiers in artificial intelligence · 2026Article
- Gait Event Detection and Gait Parameter Estimation from a Single Waist-Worn IMU Sensor.Sensors (Basel, Switzerland) · 2025Article
- Reimagining falls prevention with insights from systems mapping on the use of millimetre-wave radar for remote health monitoring.Scientific reports · 2025Article
- Wearable Technologies for Health Promotion and Disease Prevention in Older Adults: Systematic Scoping Review and Evidence Map.Journal of medical Internet research · 2025Article
- A scoping review of machine learning models to predict risk of falls in elders, without using sensor data.Diagnostic and prognostic research · 2025Review
- Balance and Gait Disorders in the Aged Population. Causes, Assessment and Management: A Literature Review.Clinical interventions in aging · 2025Review
- Phybrata Digital Biomarkers of Age-Related Balance Impairments, Sensory Reweighting, and Intrinsic Fall Risk.Medical devices (Auckland, N.Z.) · 2025Article
- Predicting future fallers in Parkinson's disease using kinematic data over a period of 5 years.NPJ digital medicine · 2024Article
- The Effect of Sensory Reweighting on Postural Control and Cortical Activity in Parkinson's Disease: A Pilot Study.Archives of rehabilitation research and clinical translation · 2024Article
- Identifying determinants for falls among Iranian older adults: insights from the Bushehr Elderly Health Program.BMC geriatrics · 2024Article
- Correlation enhanced distribution adaptation for prediction of fall risk.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
7 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Falls are among the most common cause of decreased mobility and independence in older adults and rank as one of the most severe public health problems with frequent fatal consequences. In the present study, gait characteristics from 171 community-dwelling older adults were evaluated to determine their predictive ability for future falls using a wearable system. Participants wore a wearable sensor (inertial measurement unit, IMU) affixed to the sternum and performed a 10-m walking test. Measures of gait variability, complexity, and smoothness were extracted from each participant, and prospective fall incidence was evaluated over the following 6-months. Gait parameters were refined to better represent features for a random forest classifier for the fall-risk classification utilizing three experiments. The results show that the best-trained model for faller classification used both linear and nonlinear gait parameters and achieved an overall 81.6 ± 0.7% accuracy, 86.7 ± 0.5% sensitivity, 80.3 ± 0.2% specificity in the blind test. These findings augment the wearable sensor's potential as an ambulatory fall risk identification tool in community-dwelling settings. Furthermore, they highlight the importance of gait features that rely less on event detection methods, and more on time series analysis techniques. Fall prevention is a critical component in older individuals' healthcare, and simple models based on gait-related tasks and a wearable IMU sensor can determine the risk of future falls.
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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.