The effect of attentional cues on mechanical efficiency and movement smoothness in running gait: An interdisciplinary investigation
Bibliographic record
Abstract
The aim was to examine the effect of focus of attention cues on foot angle for retraining movement purposes.Twenty (females: 8) rearfoot-striking recreational runners (mass: 72.5 ± 11.8 kg; height: 1.73 ± 0.09 m; age: 32.9 ± 11.3 years) were randomly assigned to an internal focus (IF) (n = 10) or external focus (EF) (n = 10) verbal cue group.Participants performed 5 × 6 minute blocks of treadmill running (control run, 3 × cued running, retention run) at a self-selected running velocity (9.4 ± 1.1 km•h -1 ) during a single laboratory visit.Touchdown foot angle, mechanical efficiency, internal and external work were calculated and, centre of mass (COM) and foot movement smoothness was quantified.Linear-mixed effect models showed an interaction for foot angle (p < 0.001, η p 2 = 0.35) and mechanical efficiency (p < 0.001, η p 2 = 0.40) when comparing the control to the cued running.Only the IF group reduced foot angle and mechanical efficiency during cued running, but not during the retention run.The IF group produced less external work during the 1 st cued run than the control run.COM and foot smoothness were unaffected by cueing.Only an IF produced desired technique changes but at the cost of reduced mechanical efficiency.Movement smoothness was unaffected by cue provision.Changes to foot angle can be achieved within 6 minutes of gait retraining.
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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 | 0.003 |
| 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".