Inspiratory muscle activity and breathing pattern during different intensity inspiratory muscle training
Bibliographic record
Abstract
Introduction: Inspiratory muscle training at high intensity (high IMT) has been known to increasing maximal inspiratory mouth pressure (MIP), which is an index of inspiratory muscle strength, more than that at low intensity (low IMT). However, it is unclear how the individual inspiratory muscle activates during those IMT. In this study, we aimed to characterize differences between the scalene (SC) and the parasternal intercostal (PARA) muscle activities and breathing pattern during low and high IMT maneuvers. Methods: In 5 healthy male participants, mean age 24.4 ± 3.0 yrs, weight 65.8 ± 10.1 kg, height 171.2 ± 5.5 cm, MIP 104.7 ± 20.4 cmH2O, we inserted fine wire electrodes into SC and PARA under high-resolution ultrasound guidance. Low and high IMT maneuvers were performed at 15% and 60% of MIP intensities respectively, using an inspiratory threshold loading device. We assessed SC and PARA EMG activities, maximal inspiratory flow rate (MIFR), inspiratory time (TI) and tidal volume (VT) during resting breathing, low and high IMT maneuvers, respectively. Results: Compared to low IMT, high IMT resulted in a decrease in MIFR and prolongation of TI and unchanging in VT. Both SC and PARA EMG activities during low and high IMT increased, compared to resting breathing. PARA EMG activity (38.2 ± 20.8 %EMGmax) during high IMT was higher than during low IMT (25.6 ± 19.8 %EMGmax), while there was no difference between low and high IMT in SC EMG activity. Conclusions: We conclude that high IMT may be a training strategy that more activates PARA rather than SC and increases duration of loaded inspiration. This study was approved by the Kitasato University Medical Ethics Organization.
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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".