Developing a Web-Based Weight Management Program for Childhood Cancer Survivors: Rationale and Methods
Bibliographic record
Abstract
BACKGROUND: Due to advances in the field of oncology, survival rates for children with cancer have improved significantly. However, these childhood cancer survivors are at a higher risk for obesity and cardiovascular diseases and for developing these conditions at an earlier age. OBJECTIVE: In this paper, we describe the rationale, conceptual framework, development process, novel components, and delivery plan of a behavioral intervention program for preventing unhealthy weight gain in survivors of childhood acute lymphoblastic leukemia (ALL). METHODS: A Web-based program, the Healthy Eating and Active Living (HEAL) program, was designed by a multidisciplinary team of researchers who first identified behaviors that are appropriate targets for weight management in childhood ALL survivors and subsequently developed the intervention components, following core behavioral change strategies grounded in social cognitive and self-determination theories. RESULTS: The Web-based HEAL curriculum has 12 weekly self-guided sessions to increase parents' awareness of the potential impact of cancer treatment on weight and lifestyle habits and the importance of weight management in survivors' long-term health. It empowers parents with knowledge and skills on parenting, nutrition, and physical activity to help them facilitate healthy eating and active living soon after the child completes intensive cancer treatment. Based on social cognitive theory, the program is designed to increase behavioral skills (goal-setting, self-monitoring, and problem-solving) and self-efficacy and to provide positive reinforcement to sustain behavioral change. CONCLUSIONS: Lifestyle interventions are a priority for preventing the early onset of obesity and cardiovascular risk factors in childhood cancer survivors. Intervention programs need to meet survivors' targeted behavioral needs, address specific barriers, and capture a sensitive window for behavioral change. In addition, they should be convenient, cost-effective and scalable. Future studies are needed to evaluate the feasibility of introducing weight management early in cancer care and the efficacy of early weight management on survivors' health outcomes.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".