A Digital Health Intervention for Weight Management for Latino Families Living in Rural Communities: Perspectives and Lessons Learned During Development
Bibliographic record
Abstract
Rural residents face numerous challenges in accessing quality health care for management of chronic diseases (eg, obesity, diabetes), including scarcity of health care services and insufficient public transport. Digital health interventions, which include modalities such as internet, smartphones, and monitoring sensors, may help increase rural residents' access to health care. While digital health interventions have become an increasingly popular intervention strategy to address obesity, research examining the use of technological tools for obesity management among rural Latino populations is limited. In this paper, we share our experience developing a culturally tailored, interactive health intervention using digital technologies for a family-oriented, weight management program in a rural, primarily Latino community. We describe the formative research that guided the development of the intervention, discuss the process of developing the intervention technologies including issues of privacy and data security, examine the results of a pilot study, and share lessons learned. Our experience can help others design user-centered digital health interventions to engage underserved populations in the uptake of healthy lifestyle and disease management skills.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".