mHealth App to Promote Healthy Lifestyles for Diverse Families Living in Rural Areas: Usability Study
Bibliographic record
Abstract
BACKGROUND: Mobile Integrated Care for Childhood Obesity is a multicomponent intervention for caregivers of young children with obesity from rural communities that was developed in collaboration with community, parent, and health care partners. It includes community programming to promote healthy lifestyles and address social needs and health care visits with an interdisciplinary team. A digital mobile health platform-the Healthy Lifestyle (Nemours Children's Health) dashboard-was designed as a self-management tool for caregivers to use as part of Mobile Integrated Care for Childhood Obesity. OBJECTIVE: This study aimed to improve the usability of the English and Spanish language versions of the Healthy Lifestyle dashboard. METHODS: During a 3-phased approach, usability testing was conducted with a diverse group of parents. In total, 7 mothers of children with obesity from rural communities (average age 39, SD 4.9 years; 4 Spanish-speaking and 3 English-speaking) provided feedback on a prototype of the dashboard. Participants verbalized their thoughts while using the prototype to complete 4 tasks. Preferences on the dashboard icon and resource page layout were also collected. Testing was done until feedback reached saturation and no additional substantive changes were suggested. Qualitative and quantitative data regarding usability, acceptability, and understandability were analyzed. RESULTS: The dashboard was noted to be acceptable by 100% (N=7) of the participants. Overall, participants found the dashboard easy to navigate and found the resources, notifications, and ability to communicate with the health care team to be especially helpful. However, all (N=4) of the Spanish-speaking participants identified challenges related to numeracy (eg, difficulty interpreting the growth chart) and literacy (eg, features not fully available in Spanish), which informed iterative refinements to make the dashboard clearer and more literacy-sensitive. All 7 participants (100%) selected the same dashboard icon and 71% (5/7) preferred the final resource page layout. CONCLUSIONS: Conducting usability testing with key demographic populations, especially Spanish-speaking populations, was important to developing a mobile health intervention that is user-friendly, culturally relevant, and literacy-sensitive.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".