ArticleNPJ digital medicine2024
Predicting future fallers in Parkinson's disease using kinematic data over a period of 5 years.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of wearable devices in predicting falls in older adults: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- Determining Falls Risk in People with Parkinson's Disease Using Wearable Sensors: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- An interpretable data-driven approach to optimizing clinical fall risk assessment.PLOS digital health · 2026Article
- Closing the Gate Before the Horse is out of the Barn: A Model to Effectively Predict the First Fall in Patients with Parkinson's Disease.Movement disorders clinical practice · 2026Article
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- Attentive Prototype Learning with Wearable Sensor Mutual Information for Fall Risk Stratification of Parkinson's Patients.Bioengineering (Basel, Switzerland) · 2026Article
- Classification of fallers in Parkinson's disease through machine learning based feature analysis.NPJ Parkinson's disease · 2026Article
- Classification of fallers and non-fallers in older adults using electrical IMU signal for gait analysis and explainable deep learning.Scientific reports · 2026Article
- AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.Journal of medical Internet research · 2026Review
- The future of clinical trials? Monitoring mobility as an outcome measure in Parkinson's disease: The mobilise-D study.Journal of Parkinson's disease · 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
- Effects of load carriage methods on fall risk and gait variability during stair ascent: a functional data analysis approach.Frontiers in bioengineering and biotechnology · 2026Article
- Distinguishing Gait Patterns in PD Patients Under Different Treatments via Recurrence Plots and Vision Transformer Fusion.IEEE open journal of engineering in medicine and biology · 2026Article
- Cognitive function is associated with the progression of non-tremor motor symptoms in Parkinson's disease.Frontiers in aging neuroscience · 2026Article
- Classification of fall risk in Parkinson's disease using empirical mode decomposition and machine learning.Frontiers in bioengineering and biotechnology · 2026Article
- Foot placement coordination is impaired in people with Parkinson's disease.Journal of neuroengineering and rehabilitation · 2025Article
- Predicting Freezing of Gait in Parkinson's Disease: A Machine-Learning-Based Approach in ON and OFF Medication States.Journal of clinical medicine · 2025Article
- Verbal fluency is associated with Gait impairment in Progressive Supranuclear Palsy.Clinical parkinsonism & related disorders · 2025Article
- Balance and fall-risk assessment in older adults using wearable plantar pressure and semi-supervised learning.Frontiers in bioengineering and biotechnology · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) increases fall risk, leading to injuries and reduced quality of life. Accurate fall risk assessment is crucial for effective care planning. Traditional assessments are subjective and time-consuming, while recent assessment methods based on wearable sensors have been limited to 1-year follow-ups. This study investigated whether a short sensor-based assessment could predict falls over up to 5 years. Data from 104 people with PD without prior falls were collected using six wearable sensors during a 2-min walk and a 30-s postural sway task. Five machine learning classifiers analysed the data. The Random Forest classifier performed best, achieving 78% accuracy (AUC = 0.85) at 60 months. Most models showed excellent performance at 24 months (AUC > 0.90, accuracy 84-92%). Walking and postural variability measures were key predictors. Adding clinicodemographic data, particularly age, improved model performance. Wearable sensors combined with machine learning can effectively predict fall risk, enhancing PD management and prevention strategies.
Identifiers
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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.