The effect of home‐based neuromuscular electrical stimulation‐resistance training and protein supplementation on lean mass in persons with spinal cord injury: A pilot study
Bibliographic record
Abstract
Abstract In persons with a spinal cord injury (SCI), resistance training using neuromuscular electrical stimulation (NMES‐RT) increases lean mass in the lower limbs. However, whether protein supplementation in conjunction with NMES‐RT further enhances this training effect is unknown. In this randomized controlled pilot trial, 15 individuals with chronic SCI engaged in 3 times/week NMES‐RT, with (NMES+PRO, n = 8) or without protein supplementation (NMES, n = 7), for 12 weeks. Before and after the intervention, whole body and regional body composition (DXA) and fasting glucose and insulin concentrations were assessed in plasma. Adherence to the intervention components was ≥96%. Thigh lean mass was increased to a greater extent after NMES+PRO compared to NMES (0.3 (0.2, 0.4) kg; p < 0.001). Furthermore, fasting insulin concentration and Homeostatic Model Assessment for Insulin Resistance (HOMA‐IR) were decreased similarly in both groups (fasting insulin: 1 [−9, 11] pmol∙L −1 ; HOMA‐IR: 0.1 [−0.3, 0.5] AU; both p ≥ 0.617). Twelve weeks of home‐based NMES‐RT increased thigh lean mass, an effect that was potentiated by protein supplementation. In combination with the excellent adherence and apparent improvement in cardiometabolic health outcomes, these findings support further investigation through a full‐scale randomized controlled trial.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".