Designing, Implementing, and Evaluating a Home-Based, Multidisciplinary, Family-Centered Pediatric Obesity Intervention: The ProxOb Program
Bibliographic record
Abstract
Although family-based interventions have been suggested as promising approaches for preventing and treating pediatric obesity, available studies failed to include the whole family in its own natural environment and routine. This paper aims to detail the development, implementation, and evaluation phases of the ProxOb home-based, family-centered program and present its feasibility and early results. ProxOb provides families with a 6-month multidisciplinary, home-based, and family-centered intervention followed by an 18-month maintenance phase. A global psychosocial, clinical, and behavior evaluation was conducted at baseline (T0) at the end of the 6-month intervention (T1) and after the 18-month maintenance phase (T2). A total of 130 families with at least one child with obesity completed the ProxOb program so far, and more than 90% of them also presented at least one parent with overweight or obesity. Being part of a single-parent family seemed to increase the chance of completing the intervention (63.0% vs. 33.3% in the drop-outers subgroup, p = 0.03). The BMI z-score for children with obesity (T0 = 4.38 ± 1.05; T1 = 4.06 ± 1.07; T2 = 4.29 ± 1.12) significantly decreased between T0 and T1, followed by weight regain at T2. ProxOb proposes a feasible and replicable real-life approach to address childhood obesity while involving the children’s family.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".