Adaptations in Muscular Strength for Individuals With Multiple Sclerosis Following Robotic Rehabilitation: A Scoping Review
Bibliographic record
Abstract
Muscular weakness and loss of motor function are common symptoms of multiple sclerosis. Robotic rehabilitation can improve sensorimotor function and motor control in this population. However, many studies using robotics for rehabilitation have overlooked changes in muscular strength, despite research demonstrating its utility in combating functional impairments. The purpose of this scoping review was to critically examine changes in muscular strength following robotic rehabilitation interventions for individuals with multiple sclerosis. A literature search of five databases was conducted and search terms included a combination of three primary terms: robotic rehabilitation/training, muscular strength, and multiple sclerosis. Thirty one articles were found, and following inclusion criteria, 5 remained for further investigation. Although muscular strength was not the primary targeted outcome of the training for any of the included articles, increases in muscular strength were present in most of the studies suggesting that robotic therapy with a resistive load can be an effective alternative to resistance training for increasing muscular strength. Outcome measures of isometric knee-extensor force (kg) (right: p < 0.05, left: p < 0.05), isometric knee flexion and extension torque (Nm) (p < 0.05), ankle dorsiflexion and plantarflexion torque (Nm) (all p < 0.05) and handgrip force (kg) (p < 0.05) all improved following a robotic training intervention. These adaptations occurred with sustained low resistive loads of hand grip or during gait training. This scoping review concludes that, despite a lack of studies focusing on strength, there is evidence robotics is a useful modality to improve muscular strength in combination with motor control and neuromotor improvements. A call for more studies to document changes in strength during robotic rehabilitation protocols is warranted.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".