Facilitating User Participation in Digital Health Research: The mHealth Impact Registry
Bibliographic record
Abstract
Background The proliferation of technology galvanizes providers, researchers, and entrepreneurs to revolutionize health care and care delivery with diverse audiences. Digital health provides promise in improving health outcomes; however, the pace of technology requires rapid research to remain relevant in the marketplace. User experience (UX) research provides critical information about patient/client preferences while rigorous research trials demonstrate digital health efficacy. Despite the need for such research, the recruitment and enrollment process for digital health research remains time consuming and expensive, particularly when engaging underrepresented populations. Developed in the Colorado School of Public Health, the mHealth Impact Registry is a newly launched platform designed for rapid and responsive recruitment of participants for digital health research studies. While the use of registries in research is robust, the application in digital health research is quite limited. Objective This poster illustrates the development and testing of the mHealth Impact Registry’s Web-based platform, health status survey, mobile app, and participant database to reach underrepresented populations in digital health research. Methods Formative methods used a user centered approach to document user preferences for Registry design followed by iterative testing to ensure usability and navigability. Results End-user feedback was captured from multiple stakeholder groups (ie, Patient and Family Research Advisory Panel and mHealth Community Advisory Board) to refine recruitment strategy (ie, letters, video development). A health status survey was developed in both English and Spanish using the online software (ie, Qualtrics) that informs the back-end database. A detailed requirements document outlined technical and functional requirements for the mobile app (ie, iOS and Android) and Web-based platform (ie, Wordpress and Amazon Web Services). Conclusions Due to the need for rapid, rigorous, and inclusive research in digital health, a registry containing a pool of diverse participants would not only accelerate the recruitment and enrollment process but would also help to improve the reach and engagement of digital health solutions for underrepresented populations. The mHealth Impact Registry would house diverse participants, supporting quick enrollment and active participation in studies for which they are eligible. Improving the accessibility of participants and the speed of enrollment has promise in ensuring digital health solutions are relevant upon dissemination and commercialization.
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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 | 0.107 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".