Top 10 practical lessons learned from physical activity interventions in overweight and obese children and adolescents
Bibliographic record
Abstract
Physical activity (PA) interventions targeting overweight and obese children and adolescents have shown only modest success, and dropout is an area of concern. Proper design and implementation of a PA intervention is critical for maximizing adherence and thus increasing the overall health benefits from PA participation. We propose practical advice based on our collective clinical trial experience with support from the literature on best practices related to PA interventions in overweight and obese children and adolescents. The top 10 lessons learned are (i) PA setting-context is important; (ii) choice of fitness trainer matters; (iii) physical activities should be varied and fun; (iv) the role of the parent-guardian should be considered; (v) individual physical and psychosocial characteristics should be accounted for; (vi) realistic goals should be set; (vii) regular reminders should be offered; (viii) a multidisciplinary approach should be taken; (ix) barriers should be identified early and a plan to overcome them developed; and (x) the right message should be communicated: specifically, what's in it for them? The recommendations in this paper can be used in other pediatric PA programs, physical education settings, and public health programs, with the hope of decreasing attrition and increasing the benefits of PA participation to promote health in children and adolescents.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".