ArticleFrontiers in neurology2023
Article in Frontiers in neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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Who cites it
17 citing papers in PubMed.
- Instrumented Walkway Gait Analysis Predicts Fallers in Neurological Disorders: Identifying Digital Biomarkers for Balance Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Instrumented Timed Up and Go Analysis Identifies Biomechanical Markers Across Early Hoehn and Yahr Stages of Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- Exploring the repeatability of curved gait during an instrumented figure of 8 walk test, a new methodology.Scientific reports · 2026Article
- Multidimensional Assessment of Fall Risk Using the Timed Up and Go Test and Complementary Measures in a Charcot-Marie-Tooth Type 1A Neuropathy.Archives of rehabilitation research and clinical translation · 2026Article
- Clinical Outcome Assessments in Parkinson's Disease: A Scoping Review of Current Rating Scales and Future Needs.Movement disorders clinical practice · 2026Article
- Reliability and Discriminant Ability of an Instrumented Timed Up and Go Test in People With Postsurgical Orthopedic Conditions: Quantitative Study.JMIR rehabilitation and assistive technologies · 2026Article
- Validation of the Czech adaptation of Mini-BESTest into clinical practice.European journal of physical and rehabilitation medicine · 2026Article
- IFRA: A Machine Learning-Based Instrumented Fall Risk Assessment Scale Derived from an Instrumented Timed Up and Go Test in Stroke Patients.Healthcare (Basel, Switzerland) · 2026Article
- Impact of dual-tasking and balance confidence on turns and transitions: a cross-sectional study in Parkinson's disease.Scientific reports · 2026Article
- Six-minute walk test instrumented with a single inertial sensor: dataset from healthy adults of different ages.Scientific data · 2026Article
- Digital biomarkers derived from wearable sensors in neurorehabilitation: a scoping review.Frontiers in neurology · 2026Article
- Automated assessment of balance function based on a lightweight end-to-end multimodal deep learning framework: predicting Mini-BESTest scores from center of pressure signals in a single stance condition.Frontiers in bioengineering and biotechnology · 2026Article
- In Parkinson's disease, dual-tasking reduces gait smoothness during the straight-walking and turning-while-walking phases of the Timed Up and Go test.BMC sports science, medicine & rehabilitation · 2025Article
- AI-driven balance evaluation: a comparative study between blind and non-blind individuals using the mini-BESTest.PeerJ. Computer science · 2025Article
- Balance function in critical illness survivors and evaluation of psychometric properties of the Mini-BESTest.Scientific reports · 2024Article
- Minimal detectable change of gait and balance measures in older neurological patients: estimating the standard error of the measurement from before-after rehabilitation data thanks to the linear mixed-effects models.Journal of neuroengineering and rehabilitation · 2024Article
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Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Balance, i.e., the ability not to fall, is often poor in neurological patients and this impairment increases their risk of falling. The Mini-Balance Evaluation System Test (Mini-BESTest), a rating scale, the Timed Up and Go (TUG) test, and gait measures are commonly used to quantify balance. This study assesses the criterion validity of these measures as balance measures. Methods: The probability of being a faller within nine months was used as the balance criterion. The Mini-BESTest, TUG (instrumented with inertial sensors), and walking test were administered before and after inpatient rehabilitation. Multiple and LASSO logistic regressions were used for the analysis. The diagnostic accuracy of the model was assessed with the area under the curve (AUC) of the receiver operating characteristic curve. Mobility measure validity was compared with the Akaike Information Criterion (AIC). Results: Two hundred and fourteen neurological patients (stroke, peripheral neuropathy, or parkinsonism) were recruited. In total, 82 patients fell at least once in the nine-month follow-up. The Mini-BESTest (AUC = 0.69; 95%CI: 0.62-0.76), the duration of the TUG turning phase (AUC = 0.69; 0.62-0.76), and other TUG measures were significant faller predictors in regression models. However, only the turning duration (AIC = 274.0) and Mini-BESTest (AIC = 276.1) substantially improved the prediction of a baseline model, which only included fall risk factors from the medical history (AIC = 281.7). The LASSO procedure selected gender, disease chronicity, urinary incontinence, the Mini-BESTest, and turning duration as optimal faller predictors. Conclusion: The TUG turning duration and the Mini-BESTest predict the chance of being a faller. Their criterion validity as balance measures in neurological patients is substantial.
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