Does increasing the number of channels during neuromuscular electrical stimulation reduce fatigability and produce larger contractions with less discomfort?
Bibliographic record
Abstract
Abstract Purpose: Neuromuscular electrical stimulation (NMES) recruits motor units (MUs) at unphysiologically high rates, leading to contraction fatigability. Rotating NMES pulses between multiple electrodes recruits different MUs from each site, reducing MU firing rates and fatigability. This study aimed to determine whether rotating pulses between an increasing number of stimulation channels (cathodes) reduces contraction fatigability and increases the ability to generate torque during NMES. A secondary outcome was perceived discomfort. Methods: Fifteen neurologically-intact volunteers completed 4 sessions. NMES was delivered over the quadriceps through 1 (NMES1), 2 (NMES2), 4 (NMES4) or 8 (NMES8) channels. Fatigability was assessed over 100 contractions (1s on/1s off) at an initial contraction amplitude that was 20% of a maximal voluntary contraction (MVC). Torque-frequency relationships were characterized over 6 frequencies from 20-120Hz. Results: NMES4 and NMES8 resulted in less decline in torque (42% and 41%) and generated more torque over the 100 contractions than NMES1 and NMES2 (53% and 50% decline in torque). Increasing frequency from 20-120Hz increased torque by 7, 13, 21 and 24% MVC, for NMES1, NMES2, NMES4 and NMES8, respectively. Perceived discomfort was highest during NMES8 . Conclusion: NMES4 and NMES8 reduced contraction fatigability and generated larger contractions across a range of frequencies than NMES1 and NMES2 . NMES8 produced the most discomfort, likely due to small electrodes and high current density. During NMES, more is not better and rotating pulses between 4-channels may be optimal to reduce contraction fatigability and produce larger contractions with minimal discomfort compared to conventional NMES configurations.
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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 | 0.002 |
| 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".