Lived experiences of a family-based lifestyle intervention for children with overweight and obesity: On sustainability, self-regulation, and program optimization
Bibliographic record
Abstract
Abstract Background: Family-based lifestyle interventions (FBLIs) are an important method for treating childhood weight problems. Despite being recognized as an effective intervention method, the optimal structure of these interventions for children’s overweight and obesity has yet to be determined. Our aim was to better understand participants’ (a) capacity to self-regulate and achieve long-term outcomes, (b) perceptions regarding the optimal structure of FBLIs, and (c) insights into psychological concepts that may explain success of these programs.Methods: To understand participant experiences, we conducted focus group as well as one-to-one interviews with over 100 parents and children three months following their involvement in a 10-week, multi-component, FBLI involving resources, education, and experiential activities relating to healthy nutrition, physical activity, and behavior modification. Results: Thematic analysis identified challenges that can impair lasting behavior change (e.g., physical activity participation) following involvement in a FBLI. On optimizing these programs, participants emphasized fun, interactive content, interpersonal support, appropriate educational content, and behavior change techniques.Conclusions: Concepts rooted in motivational theory could help address calls for greater theoretical and mechanistic insight in FBLIs. Findings may support research advancement and assist health professionals to more consistently realize the potential of these interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".