ArticleMedical & biological engineering & computing2022
Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning.
Article in Medical & biological engineering & computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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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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
13 citing papers in PubMed.
- From Metrics to Meaning in Neurological Rehabilitation: Clinicians' Perspectives on Digital Metrics of Upper Limb Functioning-A Focus Group Study.JMIR rehabilitation and assistive technologies · 2026Article
- Digital Platform to Provide Health Data Feedback for Neurorehabilitation Patients: User-Centered Development and Proof-of-Concept Usability Study.JMIR rehabilitation and assistive technologies · 2026Article
- Rethinking Movement: From Capacity to Context.Kinesiology review (Champaign, Ill.) · 2026Article
- Physical Therapy in Neurorehabilitation with an Emphasis on Sports: A Bibliometric Analysis and Narrative Review.Sports (Basel, Switzerland) · 2024Review
- Influence of virtual reality and task complexity on digital health metrics assessing upper limb function.Journal of neuroengineering and rehabilitation · 2024Observational
- Advanced neuroimaging techniques to explore the effects of motor and cognitive rehabilitation in multiple sclerosis.Journal of neurology · 2024Review
- Computational Modeling to Identify Drugs Targeting Metastatic Castration-Resistant Prostate Cancer Characterized by Heightened Glycolysis.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Upper limb intention tremor assessment: opportunities and challenges in wearable technology.Journal of neuroengineering and rehabilitation · 2024Review
- Robotic systems for upper-limb rehabilitation in multiple sclerosis: a SWOT analysis and the synergies with virtual and augmented environments.Frontiers in robotics and AI · 2024Review
- Data-driven characterization of walking after a spinal cord injury using inertial sensors.Journal of neuroengineering and rehabilitation · 2023Article
- Opportunities and challenges in the development of exoskeletons for locomotor assistance.Nature biomedical engineering · 2023Review
- Classification Models of Action Research Arm Test Activities in Post-Stroke Patients Based on Human Hand Motion.Sensors (Basel, Switzerland) · 2022Article
- Technology-aided assessment of functionally relevant sensorimotor impairments in arm and hand of post-stroke individuals.Journal of neuroengineering and rehabilitation · 2020Observational
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Predicting upper limb neurorehabilitation outcomes in persons with multiple sclerosis (pwMS) is essential to optimize therapy allocation. Previous research identified population-level predictors through linear models and clinical data. This work explores the feasibility of predicting individual neurorehabilitation outcomes using machine learning, clinical data, and digital health metrics. Machine learning models were trained on clinical data and digital health metrics recorded pre-intervention in 11 pwMS. The dependent variables indicated whether pwMS considerably improved across the intervention, as defined by the Action Research Arm Test (ARAT), Box and Block Test (BBT), or Nine Hole Peg Test (NHPT). Improvements in ARAT or BBT could be accurately predicted (88% and 83% accuracy) using only patient master data. Improvements in NHPT could be predicted with moderate accuracy (73%) and required knowledge about sensorimotor impairments. Assessing these with digital health metrics over clinical scales increased accuracy by 10%. Non-linear models improved accuracy for the BBT (+ 9%), but not for the ARAT (-1%) and NHPT (-2%). This work demonstrates the feasibility of predicting upper limb neurorehabilitation outcomes in pwMS, which justifies the development of more representative prediction models in the future. Digital health metrics improved the prediction of changes in hand control, thereby underlining their advanced sensitivity. Graphical Abstract This work explores the feasibility of predicting individual neurorehabilitation outcomes using machine learning, clinical data, and digital health metrics.
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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.