Compliance to prescribed training among recreational swimmers using augmented-reality swim goggles: A randomised controlled trial
Bibliographic record
Abstract
Complying with prescribed training plans is an important challenge for swimmers, as deviations from intended intensity or duration can reduce gains in performance and fitness. This randomised controlled trial investigated whether real-time visual feedback enhances compliance with prescribed training protocols among recreational swimmers. Fifty-seven participants were randomised into feedback (FB) and non-feedback (NFB) groups and completed 35 workouts over 12 weeks across three training volumes (small, medium, large). The FB group used FORM Goggles to receive real-time visual feedback; the NFB group used printed instructions and standard timing tools. Metrics included workout length count, workout effort, incomplete workouts, interval effort, rest time, and stroke type. Compliance was analysed using generalised linear mixed-effects models. The FB group demonstrated significantly better compliance with workout length count than the NFB group in the small and large plans (p < 0.004), with large effect sizes. Interval effort compliance was also higher in the FB group for the large training plan (69% vs. 58%, p = 0.044). Other metrics showed no meaningful group differences. These findings suggest that real-time visual feedback improves adherence to prescribed workout length and, to a lesser extent, interval effort, supporting its potential value in recreational swim training programmes.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".