Contraction fatigability during interleaved neuromuscular electrical stimulation of the ankle dorsiflexors does not depend on contraction amplitude
Bibliographic record
Abstract
Interleaved neuromuscular electrical stimulation (iNMES) involves alternating stimulus pulses between the tibialis anterior muscle and common peroneal nerve. The current investigation aimed to characterize the relationship between contraction amplitude, motor unit (MU) “overlap”, and contraction fatigability during iNMES. It was hypothesized that as iNMES generates progressively larger contractions, more MUs would be recruited from both sites (i.e., more MU overlap), resulting in more fatigability for larger than smaller contractions. Fourteen participants completed 3 sessions. Fatigability was assessed as the decline in torque over 180 contractions (0.3 s “on”, 0.7 s “off”) when iNMES was delivered to produce initial contractions of ∼5%, 15%, or 30% of a maximal voluntary contraction. Although MU overlap increased significantly with contraction amplitude, the relative (percent) decline in torque was not different between the contraction amplitudes and torque declined on average by 23%. Contraction fatigability was not significantly correlated with either MU overlap or initial contraction amplitude. In conclusion, iNMES can produce fatigue-resistant contractions across a functionally-meaningful range of contraction amplitudes for rehabilitation. Novelty Interleaved neuromuscular electrical stimulation progressively recruits MUs as contraction amplitude increases. However, the relative amount of fatigability of recruited MUs was not different as contraction amplitude increased. This suggests iNMES can be used effectively to produce fatigue-resistant and functionally meaningful contractions.
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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) | 0.001 | 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".