Weight Loss Strategies Utilized in a Men’s Weight Loss Intervention
Bibliographic record
Abstract
Men are underrepresented in weight loss programs and little is currently known about the weight loss strategies men prefer. This study describes the weight loss strategies used by men during a men-only weight loss program. At baseline, 3 months, and 6 months, participants reported how frequently they used 45 weight loss strategies including strategies frequently recommended by the program (i.e., mentioned during every intervention contact; e.g., daily self-weighing), strategies occasionally recommended by the program (i.e., mentioned at least once during the program; e.g., reduce calories from beverages), and strategies not included in the program (e.g., increase daily steps). At baseline participants ( N = 107, 44.2 years, body mass index = 31.4 kg/m 2 , 76.6% White) reported regularly using 7.3 ± 6.6 ( M ± SD) strategies. The intervention group increased the number of strategies used to 19.1 ± 8.3 at 3 months and 17.1 ± 8.4 at 6 months with no changes in the waitlist group. The intervention group reported increased use of most of the strategies frequently recommended by the program (4 of 5), nearly half of the strategies occasionally recommended by the program (10 of 24), and one strategy not included in the program (of 16) at 6 months. The intervention effect at 6 months was significantly mediated by the number of strategies used at 3 months. This study adds to what is known about men’s use of weight loss strategies prior to and during a formal weight loss program and will help future program developers create programs that are tailored to men.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".