Non-gait-specific intervention for the rehabilitation of walking after SCI: role of the arms
Bibliographic record
Abstract
Arm movements modulate leg activity and improve gait efficiency; however, current rehabilitation interventions focus on improving walking through gait-specific training and do not actively involve the arms. The goal of this project was to assess the effect of a rehabilitation strategy involving simultaneous arm and leg cycling on improving walking after incomplete spinal cord injury (iSCI). We investigated the effect of 1) non-gait-specific training and 2) active arm involvement during training on changes in over ground walking capacity. Participants with iSCI were assigned to simultaneous arm-leg cycling (A&L) or legs only cycling (Leg) training paradigms, and cycling movements were assisted with electrical stimulation. Overground walking speed significantly increased by 0.092 ± 0.022 m/s in the Leg group and 0.27 ± 0.072m/s in the A&L group after training. Whereas the increases in the Leg group were similar to those seen after current locomotor training strategies, increases in the A&L group were significantly larger than those in the Leg group. Walking distance also significantly increased by 32.12 ± 8.74 m in the Leg and 91.58 ± 36.24 m in the A&L group. Muscle strength, sensation, and balance improved in both groups; however, the A&L group had significant improvements in most gait measures and had more regulated joint kinematics and muscle activity after training compared with the Leg group. We conclude that electrical stimulation-assisted cycling training can produce significant improvements in walking after SCI. Furthermore, active arm involvement during training can produce greater improvements in walking performance. This strategy may also be effective in people with other neural disorders or diseases. NEW & NOTEWORTHY This work challenges concepts of task-specific training for the rehabilitation of walking and encourages coordinated training of the arms and legs after spinal cord injury. Cycling of the legs produced significant improvements in walking that were similar in magnitude to those reported with gait-specific training. Moreover, active engagement of the arms simultaneously with the legs generated nearly double the improvements obtained by leg training only. The cervico-lumbar networks are critical for the improvement of walking.
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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) | 0.000 | 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) | 0.002 | 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".