Effect of inspiratory resistive training on diaphragm shear modulus and accessory inspiratory muscle activation
Bibliographic record
Abstract
This study aimed to elucidate changes in diaphragm and accessory inspiratory muscle (sternocleidomastoid (SCM) muscle and intercostal muscle (IC)) function after a 6-week training program. Nineteen male elite collegiate swimmers were assigned to either a control group (n = 9) or training group (n = 10). The subjects in the training group performed 30 maximum inspirations at a load resistance of 50% of maximum inspiratory mouth pressure (PImax) using an inspiratory muscle training device. These were conducted twice per day and 6 days per week. At baseline and after 6 weeks, PImax, shear modulus of the diaphragm, and electromyograms (EMG) of the SCM and IC during a maximal inspiratory maneuver were evaluated. Relative change in PImax was greater in the training group than in controls. The shear modulus during a PImax maneuver had increased significantly in both groups after 6 weeks. EMG amplitudes of the SCM increased in the training group after 6 weeks, but not in the control group. EMG amplitudes of the IC did not change after 6 weeks in either group. These results suggest that 6-week inspiratory resistive training significantly improves the activation of the SCM, which could be one of the major mechanisms behind increases in inspiratory muscle strength after resistive training. Novelty Six-week inspiratory resistive training increased diaphragm stiffness during maximal inspiration maneuver. Six-week inspiratory resistive training increased electromyogram amplitudes of the sternocleidomastoid during maximal inspiration maneuver.
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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".