The effects of different sit- and curl-up positions on activation of abdominal and hip flexor musculature
Bibliographic record
Abstract
The purpose of this study was to evaluate abdominal muscle activation with variations in trunk flexion (sit or curl up) positions, including the protocol currently used by the Canadian Society of Exercise Physiology (CSEP) Health and Fitness Program. Electromyographic (EMG) data were collected during isometric contractions from the upper rectus abdominis (URA), lower rectus abdominis (LRA), external obliques (EO), lower abdominal stabilizers (LAS), rectus femoris (RF), and the biceps femoris (BF) in 14 subjects. Sit-up positions were varied and randomized through 3 variables: the distance the hand traveled along the floor (5, 10, or 15 cm), bent knee or extended knee, and fixed or non-fixed feet. In regard to the distance the hand traveled along the floor, the 10 cm position produced the highest activation of the LRA (p = 0.02), the 5 cm distance produced the lowest RF activation (p = 0.001), and the 15 cm distance produced the lowest activation of the URA (p = 0.001). There was no significant difference between bent-knee and extended-leg sit-up positions; however, there was a trend (p = 0.1) showing that the bent-knee sit-up position produced higher levels of LAS activation and lower levels of RF activation. Foot fixation resulted in significantly lower activation levels of all abdominal sites and higher levels for the RF (p < 0.0001). The technique used for the CSEP Health and Fitness program partial curl- or sit-up test produced the highest or equal activation levels for all abdominal muscle sites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".