ArticleScientific reports2025
Predicting fall risk through step width variability at increased gait speed in community dwelling older adults.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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
18 citing papers in PubMed.
- Development of a Wearable Sensor Platform for Fall Risk Assessment and Fall Detection.Sensors (Basel, Switzerland) · 2026Article
- Beyond gait speed: a multidimensional motor signature of motoric cognitive risk syndrome identified through domain-specific anomaly detection.GeroScience · 2026Article
- Instrumented Walkway Gait Analysis Predicts Fallers in Neurological Disorders: Identifying Digital Biomarkers for Balance Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Association between stride length variability and osteosarcopenia in community-dwelling older women: a cross-sectional study.BMC geriatrics · 2026Article
- How Algorithmic Technologies 'Constitute' the Older Body: A Study of Fall-Detection Wearables.Sociology of health & illness · 2026Article
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- A temporal convolutional network approach for gait-based fall risk prediction to support safe sport participation in older adults.MethodsX · 2026Article
- Classification of fallers and non-fallers in older adults using electrical IMU signal for gait analysis and explainable deep learning.Scientific reports · 2026Article
- VisionMD-Gait: scalable clinical gait assessment from smartphone videos.Scientific reports · 2026Article
- Article
- Relaxed Stiffness of Lower Extremity Muscles and Step Width Variability as Key Differences Between Sarcopenia and Dynapenia in Community-Dwelling Older Adults: A Cross-Sectional Study.Life (Basel, Switzerland) · 2025Article
- One-Year Prospective Study on Smartphone-Based Coefficient of Variation Analysis of Seated Stepping Movements for Fall Risk Prediction in Older Adults.Sensors (Basel, Switzerland) · 2025Article
- Association Between Physical Performance, Gait Variability, and Fall Risk in Community-Dwelling Older Adults: Predictive Validity of Step-Width Variability for Screening of Fall Risk.Life (Basel, Switzerland) · 2025Article
- Biomechanical Effects of a Passive Lower-Limb Exoskeleton Designed for Half-Sitting Work Support on Walking.Sensors (Basel, Switzerland) · 2025Article
- Development and Evaluation of an Immersive Virtual Reality Application for Road Crossing Training in Older Adults.Geriatrics (Basel, Switzerland) · 2025Article
- Associations of Lower Limb Muscle-Tendon Properties with Dual-Task Gait Variability: A Cross-Age Study.Healthcare (Basel, Switzerland) · 2025Article
- Balance and Gait Disorders in the Aged Population. Causes, Assessment and Management: A Literature Review.Clinical interventions in aging · 2025Review
- Robust Human Gait Speed Recognition Under Non-Ideal Conditions for Suspension-Assisted Walking Systems.Healthcare technology lettersArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to investigate the relationship of fall risk with spatiotemporal gait variables and variability in community-dwelling older adults and identify the key predictors of fall risk. A total of 303 older adults were classified into the fall-risk and non-fall-risk groups based on their Short Physical Performance Battery scores. The stance phase time, double stance time, stride length, step width, velocity, cadence, and coefficient of variation (CV) for each variable were measured on a treadmill at the preferred speed and 20% increased walking speed. The results showed that the fall-risk group had a significantly increased stance phase and double stance time, while the stride length, velocity, and cadence were significantly reduced compared to the non-fall-risk group at both speeds. Additionally, the CV for most variables was significantly higher in the fall-risk group. Notably, the step width variability was higher in the fall-risk group under both speed conditions and showed greater discriminative power at increased walking speed. Thus, gait variability, particularly step width variability, is a crucial predictor of fall risk. The increased variability observed in the fall-risk group at higher walking speed suggests that it could be a useful early indicator for predicting fall risk.
Indexed as
Identifiers
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.