The Effects of If-Then Plans on Weight Loss: Results of the 24-Month Follow-up of the McGill CHIP Healthy Weight Program Randomized Controlled Trial
Bibliographic record
Abstract
Abstract Background: The NIH-developed Diabetes Prevention Program (DPP) is successful in achieving clinically significant weight loss in individuals with overweight/obesity when delivered one-on-one. However, due to high cost of implementation, the long-term effectiveness remains limited. In response, a group-based version of the program, called the National DPP, was developed. The average weight loss following participation in this program was only about 3.5% with low long-term weight loss maintenance. Purpose: We aimed to optimize weight loss outcomes of the National DPP by integrating the habit formation tool of if-then plans into the program. Results at 3 and 12 months of participation showed no between-group differences between standard and enhanced DPP but higher weight loss in both groups compared to the National DPP. This paper reports the long-term weight loss maintenance data following participation in the program. Methods: Of the 172 participants enrolled at the beginning of the study, data from 110 participants was available and analyzed at 24 months, i.e. 12 months following the end of the 12-month intervention. Results: No between-group difference in weight loss maintenance was seen. Pooled results showed a significant weight regain from 12 to 24 months, i.e. an average of 7.85lbs of the 20.36lbs lost. However, participants from both groups were still 12.51lbs or 6.13% lighter at 24 months than at baseline. Conclusion: If-then plans did not result in a higher percentage of weight loss at 24-month follow-up. However, at 24 months, both groups maintained a significant portion of the weight lost at the end of intervention.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".