Core Muscle Activation With Foam Rolling and Static Planks
Bibliographic record
Abstract
The objective of this study was to compare the activation of the core (trunk) musculature during quadriceps and hamstrings foam rolling (FR) vs. prone and supine/reverse static planks to determine if FR is a viable means of training the core musculature. Using a randomized allocation, nine recreationally trained, young adults (18–26 years) performed two sets each of quadriceps and hamstrings FR as well as supine/reverse and prone static planks for 30-s each with 1-min rest between sets and 5-min rest between exercises. Electromyographic (EMG) activity of the lower abdominals (LA), external obliques (EO), lumbosacral erector spinae (LSES), upper lumbar erector spinae (ULES) muscle groups were normalized to a maximum voluntary contraction and analyzed. Quadriceps FR exhibited a very large magnitude greater LA activity compared to reverse plank (p = 0.033, d = 4.42) and hamstrings FR (p = 0.020, d = 3.49), respectively. The prone plank demonstrated very large magnitude higher EO EMG activity compared to reverse plank (p = 0.001, d = 9.17), hamstrings FR (p = 0.002, d = 8.14), and quadriceps FR (p = 0.011, d = 5.97). Reverse plank (p = 0.003, d = 12.06), and quadriceps FR (p = 0.002, d = 7.84) induced greater ULES activity compared to the prone plank and hamstrings FR, respectively. Reverse plank also exhibited very large magnitude higher LSES activity compared to the prone plank (p < 0.001, d = 7.68), hamstrings FR (p = 0.002, d = 4.11), and quadriceps FR (p = 0.005, d = 2.34), respectively. In conclusion, whereas reverse plank was the most effective activator of dorsal core muscles, quadriceps FR may also be a time efficient alternative exercise to activate back (ventral core) muscles. The prone plank is effective for ventral core muscles activation.
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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) | 0.003 | 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".