Frequency Matters: The Influence of Stimulation Frequency on Force Loss for Human Females and Males
Bibliographic record
Abstract
PURPOSE: Most studies applying repeated neuromuscular electrical stimulation (NMES) to assess intrinsic contractile properties employ frequencies considerably greater than the mean motor unit discharge rate (MUDR) for a given force level. It is hypothesized that force loss increases with stimulation frequency, but this has not been evaluated in the same pool of participants when other parameters are unchanged. Furthermore, there is a paucity of research investigating possible sex-based differences for force loss during an NMES protocol, with the presence or absence of a group difference seemingly dependent on stimulation frequency. To address these limitations, we compared force loss of electrically evoked contractions at (10 Hz), slightly above (15 Hz), and well above (30 Hz) the expected mean MUDR of the quadriceps at 25% maximal voluntary force. METHODS: On three separate occasions, 24 participants (12 females) received 3 min of intermittent NMES (10, 15, or 30 Hz) over the quadriceps of the dominant leg. RESULTS: Force impairment increased with NMES frequency (19.8 ± 14.5, 42.6 ± 8.1, and 52.9 ± 4.7 for 10, 15, and 30 Hz, respectively), with no significant differences between sexes. Relative to the start of each task, the rates of force development (RFD) and relaxation (RFR) slowed markedly during the 10-, 15-, and 30-Hz fatiguing protocols (RFD: 42.1 ± 13.5, 61.6 ± 13.2, and 75.9 ± 9.8; RFR: 38.0 ± 13.9, 64.2 ± 9.1, and 80.4 ± 5.0, respectively). RFD impairment was less at 10 compared with 15 and 30 Hz, whereas the slowing of RFR increased with NMES frequency. Post-hoc analysis revealed no sex-based differences at any time point for RFD or RFR. CONCLUSIONS: These findings underscore the impact of stimulus frequency on muscle fatigability and highlight a lack of sex-based differences for electrically evoked force loss, emphasizing the need for appropriate frequency selection in NMES protocols.
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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 | 0.001 | 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) | 0.005 | 0.001 |
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".