MAN v FAT Soccer: Feasibility Study and Preliminary Efficacy of a Sport-Based Weight-Loss Intervention for Overweight and Obese Men in Australia
Bibliographic record
Abstract
MAN v FAT Soccer is a sport-based weight-loss program for overweight and obese men that originated in the United Kingdom (i.e., as MAN v FAT Football) and appears to successfully engage men with weight loss. We sought to explore whether the program would work in an Australian context by (a) establishing a foundation for the implementation of the program on a larger scale and (b) determining how large-scale implementation is most feasible. We conducted a nonrandomized, single intervention group feasibility trial of MAN v FAT Soccer in Australia with 418 male participants with a body mass index greater than 27.50 kg/m2. Results indicate that the program is acceptable, with participants reporting positive perceptions of the various components of the program and a high proportion reporting intentions to recommend the program to others (95.9%). Furthermore, preliminary effectiveness results indicate positive changes in weight (4.6% reduction) and physical activity (88.5% increase) and improvements in psychological outcomes such as depression (17.6% decrease), stress (19.0% decrease), and body appreciation (19.1% increase). Our findings provide general support for the feasibility of MAN v FAT Soccer and the notion that leveraging competition and masculinity may help drive men's health behavior change.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".