Effect of movement speed on trunk and hip exercise performance
Bibliographic record
Abstract
The influence of speed on trunk exercise technique is poorly understood. The aim of this study was to analyse the effect of movement speed on the kinematics and kinetics of curl-up, sit-up and leg raising/lowering exercises. Seventeen healthy, recreationally trained individuals (13 females and 4 males) volunteered to participate in this study. Four different exercise cadences were analysed: 1 repetition/4 s, 1 repetition/2 s, 1 repetition/1.5 s and 1 repetition/1 s. The exercises were executed on a force plate and recorded by three cameras to conduct a 3D photogrammetric analysis. The cephalo-caudal displacement of the centre of pressure and range of motion (ROM) of six joints describing the trunk and hip movements were measured. As sit-up and curl-up speed increased, hip and knee ROM increased. Dorsal-lumbar and upper trunk ROM increased with speed in the curl-up. Faster cadence in the sit-up exercise had minimal effect on trunk ROM: only the upper trunk ROM decreased significantly. In the leg raising/lowering exercise there was a decrease in the pelvic tilt and hip ROM, and increased knee flexion ROM. During higher speed exercises, participants modified their technique to maintain the cadence. Thus, professionals would do well to monitor and control participants' technique during high-speed exercises to maintain performance specificity. Results also suggest division of speed into two cadence categories, to be used as a reference for prescribing exercise speed based on preferred outcome goals.
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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 | 0.001 | 0.004 |
| 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) | 0.002 | 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".