Recruitment of Abdominal and Neck Muscles During Four Different Exercises in Healthy Adults
Bibliographic record
Abstract
Background and Purpose: Prolonged mechanical ventilation can greatly increase morbidity and mortality. Diaphragm weakness has been identified as a major contributor for 50% of patients; however, other muscles besides the diaphragm contribute to efficient ventilatory function. The purpose of this study was to compare the change in deoxyhemoglobin (ΔHHb) and muscle oxygen saturation (ΔSmO 2 ) (markers of muscle recruitment) of 3 extra-diaphragmatic muscles across 4 different bed exercises. Methods: Using a pretest–posttest design, healthy participants (n = 18) performed 3 minutes of 30 repetitions of 4 exercises: resisted trunk flexion (TF), resisted neck flexion (NF), expiratory threshold loading (ETL), and neuromuscular electrical stimulation (NMES) of the rectus abdominis and external obliques. Near infrared spectroscopy was used to measure ΔHHb, and ΔSmO 2 in the sternocleidomastoid, rectus abdominis, and external obliques during these exercises. Results: Increases of ΔHHb were highest for the sternocleidomastoid during NF and for rectus abdominis and external obliques during TF ( P < .010). The opposite pattern was shown for ΔSmO 2; decreases of ΔSmO 2 were largest for the sternocleidomastoid during NF and for rectus abdominis and external obliques during TF ( P < .005). No significant differences were observed in ΔHHb and ΔSmO 2 for rectus abdominis and external obliques during ETL versus NF or NMES nor were there differences when comparing NF versus NMES for these 2 muscles. Conclusion: TF and NF are most effective for recruiting abdominal muscles and sternocleidomastoid, respectively, whereas ETL showed a variable response. Stimulation parameters of NMES and its tolerance can limit outcomes.
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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 | 0.000 | 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".