Racial Differences in Weight Loss Mediated by Engagement and Behavior Change
Bibliographic record
Abstract
<strong></strong><p class="Pa7"><strong>Objective: </strong>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.</p><p class="Pa7"><strong>Design: </strong>Data for this analysis were from a randomized controlled trial, collected at baseline and 4-months.</p><p class="Pa7"><strong>Setting: </strong>The intervention included monthly face-to-face group sessions and an Internet component that participants were recommended to use at least once weekly.</p><p class="Pa7"><strong>Participants: </strong>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.</p><p class="Pa7"><strong>Intervention: </strong>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.</p><p class="Pa7"><strong>Main outcome measure: </strong>Multiple linear regression was used to evaluate race group differences in weight change.</p><p class="Pa7"><strong>Results: </strong>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.</p><p class="Default"><strong>Conclusions: </strong>The weight loss disparity may be addressed through improved website engagement and adoption of weight control behaviors. <em></em></p><p class="Default"><em>Ethn Dis. </em>2018;28(1):43-48; doi:10.18865/ed.28.1.43.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".