Efficacy of Clinic-Based Telehealth vs. Face-to-Face Interventions for Obesity Treatment in Children and Adolescents in the United States and Canada: A Systematic Review
Bibliographic record
Abstract
Background: Childhood obesity is an ever-growing public health concern in the United States and globally. By 2030, it is estimated that 70% of the world's population of children and adolescents will be obese. Therefore, efforts to reduce childhood obesity are of utmost importance, particularly with the current coronavirus disease 2019 pandemic, as rates are expected to soar due to social distancing measures and restrictions. This systematic review aims to examine the literature regarding the effectiveness of clinic-based telehealth vs. face-to-face modalities to reduce obesity among school-aged children. Methods: An electronic database search of articles published in English over the last 10 years was undertaken in PubMed, Medline, and CINAHL. Key terms used to identify studies included school-aged children and adolescents with overweight and obesity in clinic-based weight management interventions conducted face-to-face or via telehealth, and having efficacy determined through changes in measured child BMI as primary outcomes and dietary and physical activity changes, as well as assessing feasibility and satisfaction with telehealth, as secondary outcomes. Results: Out of 1093 articles identified, 10 met the inclusion criteria. While both telehealth and face-to-face weight management interventions are effective in reducing obesity in children and adolescents, the evidence is lacking in which is more effective. Of the 10 studies, 5 showed outcome improvements when both telehealth and face-to-face interventions were combined as adjunct therapies. Conclusions: Findings support using telehealth in conjunction with face-to-face visits for obesity treatment among children and adolescents. However, more research involving telehealth weight management interventions for young children is recommended.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".