Diaphragm Recruitment Increases during a Bout of Targeted Inspiratory Muscle Training
Bibliographic record
Abstract
PURPOSE: The extent to which the diaphragm is targeted during a bout of inspiratory muscle training (IMT) is unknown. The purpose of this study was to characterize the relative activation patterns of the diaphragm and extradiaphragmatic inspiratory muscles during a bout of IMT and to determine whether diaphragmatic recruitment can be increased by giving subjects specific diaphragmatic breathing instructions (IMTdi). METHODS: Ten healthy men were instrumented with surface EMG electrodes on the sternocleidomastoid (EMGscm), scalenes (EMGsca), parasternal intercostals (EMGpic), and seventh intercostal space (EMG7ic). A multipair esophageal electrode catheter measured crural diaphragmatic EMG (EMGdi) and transdiaphragmatic pressure (Pdi). Trial 1 of IMT involved 25 dynamic inspiratory maneuvers at 40% of maximal inspiratory mouth pressure using a variable flow resistive loading device where subjects were free to choose their own inspiratory muscle recruitment strategy. Trial 2 involved the same procedures, but subjects were given specific instructions to actively recruit their diaphragm. Cervical magnetic stimulation of the phrenic nerves verified the absence of diaphragmatic fatigue before commencing the second trial. RESULTS: Compared with IMT, IMTdi resulted in a significant increase in EMGdi (56 ± 12 vs 73 ± 10%max, P = 0.002) and Pdi swings (39 ± 14 vs 64 ± 17 cm H2O, P < 0.0001) and a decrease in EMGsca (52 ± 21 vs 36 ± 22%max, P = 0.04). There was no difference in EMG7ic (26 ± 19 vs 33 ± 21%max, P = 0.36), EMGpic (31 ± 24 vs 25 ± 15%max, P = 0.22), and EMGscm (58 ± 21 vs 45 ± 24%max, P = 0.08) when comparing IMT versus IMTdi, respectively. CONCLUSIONS: Simple diaphragmatic breathing instructions can significantly increase the recruitment of the diaphragm during IMT compared with a bout of IMT where individuals are free to choose their own inspiratory muscle recruitment strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".