ArticleJournal of neuroengineering and rehabilitation2024
Instrumented timed up and go test and machine learning-based levodopa response evaluation: a pilot study.
Article in Journal of neuroengineering and rehabilitation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- 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
- Video-based machine learning models for predicting deep brain stimulation outcomes in Parkinson's disease patients.NPJ Parkinson's disease · 2026Article
- Smart Nanomedicines for Neurodegenerative Diseases: Empowering New Therapies with Molecular Imaging and Artificial Intelligence.Molecular diagnosis & therapy · 2026Review
- Gait dynamics and brain function abnormalities in Parkinson's disease with freezing of gait: a clinical study using resting-state fMRI and wearable devices.Frontiers in neuroscience · 2025Article
- Objective assessment of gait and posture symptoms in Parkinson's disease using wearable sensors and machine learning.Frontiers in aging neuroscience · 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
10 authors.
Funding
Abstract
backgroundThe acute levodopa challenge test (ALCT) is a universal method for evaluating levodopa response (LR). Assessment of Movement Disorder Society's Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) is a key step in ALCT, which is some extent subjective and inconvenience.
methodsThis study developed a machine learning method based on instrumented Timed Up and Go (iTUG) test to evaluate the patients' response to levodopa and compared it with classic ALCT. Forty-two patients with parkinsonism were recruited and administered with levodopa. MDS-UPDRS III and the iTUG were conducted in both OFF-and ON-medication state. Kinematic parameters, signal time and frequency domain features were extracted from sensor data. Two XGBoost models, levodopa response regression (LRR) model and motor symptom evaluation (MSE) model, were trained to predict the levodopa response (LR) of the patients using leave-one-subject-out cross-validation.
resultsThe LR predicted by the LRR model agreed with that calculated by the classic ALCT (ICC = 0.95). When the LRR model was used to detect patients with a positive LR, the positive predictive value was 0.94.
conclusionsMachine learning based on wearable sensor data and the iTUG test may be effective and comprehensive for evaluating LR and predicting the benefit of dopaminergic therapy.
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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.