Engaging Children to Support Parental Weight Loss: A Randomized Trial
Bibliographic record
Abstract
Background. Despite evidence that social network members influence the eating behaviors of adults, no study to date has had the primary aim of examining children as support partners for parents in a weight loss intervention. Aim. To evaluate parent adherence with eating/exercise goals and weight loss in a 6-month study engaging children as support partners. Method. Adults with obesity (body mass index ≥ 30 kg/m 2 , n = 102) and at least one child ≥12 years were randomized to a child support or control group. In the child support group, children enrolled with their parent and engaged in a supportive behavior 2 days/week. In the control group, there was no enrolled child support. Parents in both groups selected a healthy eating strategy and daily step goal. Results. There was no difference in weight loss between the child support and control groups (−5.97 vs. −5.42 lbs, p = .81). In the child support group, 30% of children did not engage in the study. The majority of parents whose children did not engage withdrew from the study. In secondary analyses, parent adherence with eating/exercise goals increased with the days of child support ( p < .001). For all participants, low chaos in the home environment ( p < .04) and increased parent adherence with follow-ups ( p < .008) predicted weight loss. Conclusions. We found no treatment effect of child support on weight loss. Active child support of eating/exercise goals appeared to facilitate goal adherence, while anticipated but unrealized child support may have had iatrogenic consequences. Further investigation of family-focused weight loss interventions is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".