Personal Activity Trackers and Family Engagement in a Pediatric Obesity Intervention: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Pediatric obesity continues to be a national health crisis. Parents play a critical role in obesity interventions. Digital health interventions, such as personal activity trackers, can help better engage parents in pediatric obesity interventions and improve outcomes. Objective: This study aimed to (1) assess the feasibility and acceptability of implementing personal activity trackers as part of a comprehensive family-based lifestyle intervention for pediatric obesity (BodyWorks) in a Federally Qualified Health Center; (2) evaluate the impact of personal activity trackers on parents' engagement, participant anthropometrics, and the overall program; and (3) examine the associations between steps per day and usage (minutes) with body composition outcomes. Methods: A total of 158 families were randomized to the control (BodyWorks) or intervention (BodyWorks + physical activity tracker) arm. Mean levels of weight-by-height outcomes, including BMI, BMI z scores, and BMI percent of the 95th percentile, were compared between the 2 groups. Results: There were no differences between study arms at baseline. After adjustment, there was a significant group difference in children's BMI z scores from baseline to the postintervention time point (P for interaction=.01). Conclusions: Families in the intervention group that completed the program had slightly better weight outcomes than the controls. Engaging parents through digital health interventions may be an effective way to enhance existing pediatric obesity intervention programs.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".