Cardiorespiratory demand of acute voluntary cycling with functional electrical stimulation in individuals with multiple sclerosis with severe mobility impairment
Bibliographic record
Abstract
Exercise training is one strategy for improving cardiorespiratory fitness (CRF) in multiple sclerosis (MS); however, few modalities are accessible for those with severe mobility impairment. Functional electrical stimulation (FES) cycling is an adapted exercise modality with the potential for improving CRF in people with severe MS. The objective of this study was to characterize the cardiorespiratory response of acute voluntary cycling with FES in people with MS with severe mobility impairment, and to compare this response to passive leg cycling. Eleven participants with MS that required assistance for ambulation completed a single bout of voluntary cycling with FES or passive leg cycling. Oxygen consumption, heart rate (HR), work rate (WR), and ratings of perceived exertion (RPE) were recorded throughout the session. For the FES group, mean exercising oxygen consumption was 8.7 ± 1.8 mL/(kg·min)−1, or 63.5% of peak oxygen consumption. Mean HR was 102 ± 9.7 bpm, approximately 76.4% of peak HR. Mean WR was 27.0 ± 9.2 W, or 57.3% of peak WR, and median RPE was 13.5 (interquartile range = 5.5). Active cycling with FES was significantly (p < 0.05) more intense than passive leg cycling based on oxygen consumption, HR, WR, and RPE during exercise. In conclusion, voluntary cycling with FES elicited an acute response that corresponded with moderate-to vigorous-intensity activity, suggesting that active cycling with FES can elicit a sufficient stimulus for improving CRF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".