Impact of Passive Leg Cycling in Persons With Spinal Cord Injury: A Systematic Review
Bibliographic record
Abstract
Background: Passive leg cycling is an important clinical tool available for rehabilitation after spinal cord injury (SCI). Passive cycling can be used to derive exercise-related benefits in patients with poor motor control. There have been a number of studies examining the effects of passive cycling on a variety of outcomes. There is need for a systematic assessment of the cycling parameters and the associated clinical changes in cardiovascular, neuromuscular, and musculoskeletal outcomes after passive cycling. Objectives: To assess the effectiveness of passive leg cycling interventions on cardiovascular, neuromuscular, and musculoskeletal outcomes post SCI, and to describe intensity, duration, and type of passive leg cycling post SCI. Methods: PRISMA guided systematic review of literature based on searches in the following databases: PubMed/MEDLINE, PEDro, EMBASE, Cochrane Library, and Google Scholar. Peer-reviewed publications that were written in English were included if they described the effects of a single session or multiple sessions of passive leg cycling in persons post SCI. Results: Eleven papers were included: two were randomized controlled trials (RCTs), one was a crossover trial, and the rest were pre-post single-group designs. Three studies (including two RCTs) reported statistically significant benefits of multiple sessions of passive cycling on leg blood flow velocity, spasticity, reflex excitability and joint range of motion, and markers of muscle hypertrophy. About half of the single session studies showed statistically significant improvement in acute responses. Conclusion: Multiple sessions of passive leg cycling showed benefits in three categories – cardiovascular, musculoskeletal, and neurological – with medium to large effect sizes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".