Arm crank ergometer “spin” training improves seated balance and aerobic capacity in people with spinal cord injury
Bibliographic record
Abstract
BACKGROUND: There is some evidence that upper-body training modalities can improve not only aerobic capacity but also seated balance in people with spinal cord injury (SCI), even in those classified with motor-complete paralysis above T6. Here, we evaluated the effect of arm crank ergometry (ACE) "spin" training on trunk muscle recruitment and its effects on seated balance and aerobic capacity. METHODS: Eight individuals with high-level complete and 6 with either a low-level complete or a motor-incomplete SCI participated in this study. Participants completed 5 weeks of a group ACE "spin" training protocol which featured modulations in cadence and resistance as well as back-supported and unsupported bouts. Surface electromyography was used to confirm trunk muscle recruitment during unsupported ACE. Changes in aerobic capacity (peak oxygen consumption) and seated balance control (center of pressure parameters) were assessed at pre- and post-intervention. RESULTS: Unsupported ACE was effective for eliciting trunk muscle activity (P < .05). Following training, peak oxygen consumption significantly improved by an average of 16% (P = .005). Static sitting balance significantly improved from pre- to post-intervention, but only when tested with eyes closed as measured by a reduction in area (P = .047) and velocity of center of pressure (P = .013). No significant changes were observed in static sitting balance with eyes open or in dynamic sitting balance. CONCLUSION: Group ACE "spin" classes may benefit not only aerobic fitness but also static seated balance control in people with SCI.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".