Passive leg cycling increases activity of the cardiorespiratory system in people with tetraplegia
Bibliographic record
Abstract
Individuals with cervical spinal cord injury (SCI) are at an increased risk for cardiovascular disease. Exercise is well-established for preventing cardiovascular disease; however, there are limited straightforward and safe exercise approaches for increasing the activity of the cardiorespiratory system after cervical SCI. The objective of this study was to investigate the cardiorespiratory response to passive leg cycling in people with cervical SCI. Beat-by-beat blood pressure, heart rate, and cerebral blood flow were measured before and throughout 10 minutes of cycling in 11 people with SCI. Femoral artery flow-mediated dilation was also assessed before and immediately after passive cycling. Safety was monitored throughout all study visits. Passive cycling elevated systolic blood pressure (5 ± 2 mm Hg), mean arterial pressure (5 ± 3 mm Hg), stroke volume (2.4 ± 0.8 mL), heart rate (2 ± 1 beats/min) and cardiac output (0.3 ± 0.07 L/min; all p < 0.05). Minute ventilation (0.67 ± 0.23 L/min), tidal volume (70 ± 30 mL) and end-tidal PO2 (2.6 ± 1.23 mm Hg) also increased (all p < 0.05). Endothelial function was improved immediately after exercise (1.62 ± 0.13%, p < 0.01). Passive cycling resulted in an incidence of autonomic dysreflexia. Therefore, passive leg cycling increased the activity of the cardiorespiratory system and improved endothelial function, indicating it may be a beneficial exercise intervention for the cardiovascular and respiratory systems in people with cervical SCI. Novelty: Passive leg cycling increases the activity of the cardiorespiratory system and improves markers of cardiovascular health in cervical SCI. Passive leg cycling exercise is an effective, low-cost, practical, alternative exercise modality for people with cervical SCI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".