Timing of activation of different inspiratory muscles during incremental inspiratory loading in healthy adults: A cross-sectional study
Bibliographic record
Abstract
Purpose To evaluate whether the onset and duration of electromyography (EMG) activity of different inspiratory muscles vary during an incremental inspiratory threshold loading (ITL) in healthy adults and whether it is associated with dyspnea and inspiratory mouth pressure (Pm) at task failure. Methods Twelve healthy adults (30 ± 7 years, six females) performed incremental ITL starting at warm-up (7.6 ± 1.7 cmH 2 O), followed by 50 g increments every two minutes until task failure in this cross-sectional study. EMG onset (relative to inspiratory flow) and activity duration of the costal diaphragm/7th intercostal and extra-diaphragmatic inspiratory muscles (scalene, parasternal intercostal, sternocleidomastoid) were quantified using a validated algorithm. Ventilatory parameters, including Pm, were evaluated. Results With increasing ITL, Pm increased ( p ≤ 0.033), accompanied by increased EMG activity of extra-diaphragmatic muscles ( p ≤ 0.016). Critically, the EMG onset of the sternocleidomastoid ( p < 0.001), parasternal intercostal ( p = 0.002), and scalene ( p = 0.002) occurred earlier relative to inspiratory flow at task failure compared to lower loads. Earlier EMG onsets of these muscles were correlated with higher Pm at task failure (sternocleidomastoid: r = –0.65; parasternal intercostal: r = –0.45; scalene: r = –0.29; p ≤ 0.034). Notably, earlier EMG onsets of scalene at low loads were associated with higher Pm at task failure ( r ≤ –0.75; p ≤ 0.026). Furthermore, an earlier EMG onset of the parasternal intercostal ( r = –0.67; p = 0.023) and sternocleidomastoid ( r = –0.65; p = 0.023) at task failure was associated with greater dyspnea intensity. Conclusion Appreciation of timing of inspiratory muscle EMG may provide further insight into understanding the contributors to ventilatory task failure and dyspnea.
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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 | 0.001 |
| 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 | 0.001 | 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".