Evaluation of mHealth Apps for Diverse, Low-Income Patient Populations: Framework Development and Application Study
Bibliographic record
Abstract
BACKGROUND: The use of mobile technology or smartphones has grown exponentially in the United States, allowing more individuals than ever internet access. This access has been especially critical to households earning less than US $30,000, the majority of whom indicate that smartphones are their main source of internet access. The increasing ubiquity of smartphones and virtual care promises to offset some of the health disparities that cut through the United States. However, disparities cannot be addressed if the medical information offered though smartphones is not accessible or reliable. OBJECTIVE: This study seeks to create a framework to review the strengths and weaknesses of mobile Health (mHealth) apps for diverse, low-income populations. METHODS: Focusing on smoking cessation, diabetes management, and medication adherence as models of disease management, we describe the process for selecting, evaluating, and obtaining patient feedback on mHealth apps. RESULTS: The top 2 scoring apps in each category were QuitNow! and Smoke Free-Quit Smoking Now for smoking cessation, Glucosio and MyNetDiary for diabetes management, and Medisafe and MyMeds for medication adherence. CONCLUSIONS: We believe that this framework will prove useful for future mHealth app development, and clinicians and patient advisory groups in connecting culturally, educationally, and socioeconomically appropriate mHealth apps with low-income, diverse communities and thus work to bridge health disparities.
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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 | 0.068 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".