Technology-Based Interventions to Reduce Sugar-Sweetened Beverages among Adolescents: A Scoping Review
Bibliographic record
Abstract
This scoping review investigates the effectiveness of technology-based interventions in reducing sugar-sweetened beverage (SSB) consumption among adolescents. The rise in SSB consumption among young individuals has become a global public health concern due to its association with obesity, diabetes, and various other health problems. The purpose of this scoping review is to map out and examine the various technology-based interventions used in reducing sugar-sweetened beverages among children and adolescents. A systematic search of three databases using the PRISMA guideline was followed, and 474 articles were retrieved. Seven articles met the inclusion criteria and the critical appraisal using the critical appraisal skill program (CASP). The seven articles underwent both descriptive and thematic analysis. Four technology-based interventions were identified from the selected articles, which include smartphone apps, online or web-based tools, text messages, and social marketing strategies. Our findings suggest that these interventions hold promise in improving adolescents' eating patterns and health outcomes associated with SSB intake, highlighting their potential as useful strategies in resolving this urgent public health concern.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".