Racial Differences in Weight Loss Mediated by Engagement and Behavior Change
Bibliographic record
Abstract
Objective: We set out to determine if a primarily Internet-delivered behavioral weight loss intervention produced differential weight loss in African American and non-Hispanic White women, and to identify possible mediators. Design: Data for this analysis were from a randomized controlled trial, collected at baseline and 4-months. Setting: The intervention included monthly face-to-face group sessions and an Internet component that participants were recommended to use at least once weekly. Participants: We included overweight or obese African American and non-Hispanic White women (n=170), with at least weekly Internet access, who were able to attend group sessions. Intervention: Monthly face-to-face group sessions were delivered in large or small groups. The Internet component included automated tailored feedback, self-monitoring tools, written lessons, video resources, problem solving, exercise action planning tools, and social support through message boards. Main Outcome Measure: Multiple linear regression was used to evaluate race group differences in weight change. Results: Non-Hispanic White women lost more weight than African American women (-5.03% vs.-2.39%, P=.0002). Greater website log-ins and higher change in Eating Behavior Inventory score in non-Hispanic White women partially mediated the race-weight loss relationship. Conclusions: The weight loss disparity may be addressed through improved website engagement and adoption of weight control behaviors.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".