ArticleJournal of rehabilitation and assistive technologies engineering
Smallest real differences for robotic measures of upper extremity function after stroke: Implications for tracking recovery.
Article in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review.Journal of neuroengineering and rehabilitation · 2022Pooled it
- Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach.JMIR biomedical engineering · 2024Article
- Literature review of stroke assessment for upper-extremity physical function via EEG, EMG, kinematic, and kinetic measurements and their reliability.Journal of neuroengineering and rehabilitation · 2023Review
- 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMeasurements from upper limb rehabilitation robots could guide therapy progression, if a robotic assessment's measurement error was small enough to detect changes occurring on a time scale of a few days. To guide this determination, this study evaluated the smallest real differences of robotic measures, and of clinical outcome assessments predicted from these measures.
methodsA total of nine older chronic stroke survivors took part in 12-week study with an upper-limb end-effector robot. Fourteen robotic measures were extracted, and used to predict Fugl-Meyer Assessment-Upper Extremity (FMA-UE) and Action Research Arm Test (ARAT) scores using multilinear regression. Smallest real differences and intraclass correlation coefficients were computed for the robotic measures and predicted clinical outcomes, using data from seven baseline sessions.
resultsSmallest real differences of robotic measures ranged from 8.8% to 26.9% of the available range. Smallest real differences of predicted clinical assessments varied widely depending on the regression model (1.3 to 36.2 for FMA-UE, 1.8 to 59.7 for ARAT), and were not strongly related to a model's predictive performance or to the smallest real differences of the model inputs. Models with acceptable predictive performance as well as low smallest real differences were identified.
conclusionsSmallest real difference evaluations suggest that using robotic assessments to guide therapy progression is feasible.
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Registered trials
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