Effects of Combining Core Muscle Activation with Treadmill Walk on Endurance of Trunk Muscles
Bibliographic record
Abstract
Background: Abdominal bracing is one of the most effective techniques for core muscle training, which if combined with treadmill walk (TW) could provide trunk muscle endurance Trunk muscle endurance, despite being observed as an important factor and a huge component of core spinal stability, especially in holding up the spine during prolonged functional activity, prevention and rehabilitation of lumbar mechanical problems and performance enhancer in sports, the impacts of combining core muscle activation with TW exercises on trunk muscle endurance has not be succinctly investigated. Aims: The objective of this study was to determine the effect of combining abdominal bracing with TW on trunk muscles endurance. Materials and Methods: Eighteen apparently healthy young adults were randomized into three groups (TW without abdominal bracing, TW combined with abdominal bracing and control). McGill endurance test measures were carried out at baseline and after 6 weeks of intervention. All participants followed the assigned intervention protocols. Results: One way analysis of variance did not show a significant between-group difference in the postintervention endurance of trunk muscle among the three groups ( P > 0.05). In the TW combined with the abdominal bracing group, paired-t test showed significant within-group difference in the form of an increase in the holding times (endurance) for the right lateral flexors ( t = −3.758, P = 0.013), left lateral flexors ( t = −4.096, P = 0.005), and extensors ( t = −2.441, P = 0.050). Conclusion: Combining abdominal bracing with TW can be used to improve trunk muscle function through facilitation of trunk muscle endurance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".