Technology-Based Obesity Prevention Interventions Among Hispanic Adolescents in the United States: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Given that today's adolescents are digital front-runners, technology-based obesity prevention strategies are age-appropriate for this population. The use of remote and wireless technologies may be suitable for extending the reach and engagement of obesity prevention efforts among high-risk Hispanic youths, as this subgroup is disproportionately affected by barriers that limit participation in traditional, in-person interventions. OBJECTIVE: The purpose of this scoping review was to examine the intervention and sample characteristics of technology-based obesity prevention interventions among Hispanic adolescents. We also examined feasibility criteria to assess the acceptability and appropriateness of technology-based strategies among Hispanic youths. METHODS: A comprehensive search of Embase and PubMed identified 7 studies that met the inclusion criteria. Data were extracted by 2 independent reviewers. RESULTS: Of the 7 included studies, half (n=4, 57%) used a randomized control trial design, with equal implementation in school (n=3, 43%) and clinic (n=4, 57%) settings. Studies commonly targeted improvements in diet (n=4, 57%) and physical activity (n=7, 100%), with only 1 (14%) study focused on sedentary behaviors. Just 2 (29%) studies reported the use of behavioral theories or models. Studies focused primarily on youths in early (n=5, 71%) or middle (n=6, 86%) adolescence, and there was limited information reported on socioeconomic status. Only 3 (43%) study conducted formative work, and few (n=3, 43%) reported on acceptability. Only 1 (14%) study reported that materials were available in Spanish and English, and only 1 (14%) study used culturally tailored content. Additionally, 3 (43%) studies used strategies that considered social determinants of health. CONCLUSIONS: To increase our understanding of the feasibility and effectiveness of technology-based obesity prevention strategies among Hispanic adolescents, there is a need for more feasibility studies that are theoretically grounded and comprehensively report on feasibility-related outcomes. Future studies should also leverage technology to simultaneously address multiple health behaviors beyond diet and physical activity. The result of this review can be used to guide the development of future technology-based obesity prevention strategies among Hispanic adolescents. TRIAL REGISTRATION: CliniclaTrials.gov NCT04953442; https://clinicaltrials.gov/ct2/show/NCT04953442.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".