Trunk muscle activation during moderate- and high-intensity running
Bibliographic record
Abstract
Time constraints are cited as a barrier to regular exercise. If particular exercises can achieve multiple training functions, the number of exercises and the time needed to achieve a training goal may be decreased. It was the objective of this study to compare the extent of trunk muscle electromyographic (EMG) activity during running and callisthenic activities. EMG activity of the external obliques, lower abdominals (LA), upper lumbar erector spinae (ULES), and lumbosacral erector spinae (LSES) was monitored while triathletes and active nonrunners ran on a treadmill for 30 min at 60% and 80% of their maximum heart rate (HR) reserve, as well as during 30 repetitions of a partial curl-up and 3 min of a modified Biering-Sørensen back extension exercise. The mean root mean square (RMS) amplitude of the EMG signal was monitored over 10-s periods with measures normalized to a maximum voluntary contraction rotating curl-up (external obliques), hollowing exercise (LA), or back extension (ULES and LSES). A main effect for group was that triathletes had greater overall activation of the external obliques (p < 0.05), LA (p = 0.01), and LSES (p < 0.05) than did nonrunners. Main effects for exercise type showed that the external obliques had less EMG activity during 60% and 80% runs, respectively, than with the curl-ups (p = 0.001). The back extension exercise provided less ULES (p = 0.009) and LSES (p = 0.0001) EMG activity than the 60% and 80% runs, respectively. In conclusion, triathletes had greater trunk activation than nonrunners did while running, which could have contributed to their better performance. Back-stabilizing muscles can be activated more effectively with running than with a prolonged back extension activity. Running can be considered as an efficient, multifunctional exercise combining cardiovascular and trunk endurance benefits.
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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) | 0.001 | 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".