Health App Review Tool: Matching mobile apps to Alzheimer’s populations (HART Match)
Bibliographic record
Abstract
Aim: This brief report provides an overview of the development and structure of the Health App Review Tool. Methods: The Health App Review Tool has been designed to assess smart phone health apps according to their compatibility to individuals within the Alzheimer’s disease community. Specifically, app features and functions are characterized according to their appropriateness to the needs, abilities, and preferences of potential users. The Health App Review Tool is comprised of two components, the App and User Assessment; each component includes four complementary domains. Items in these domains can be compared between App and User assessments using a scoring key that will produce a match score. The score indicates the level of appropriateness in reference to the app’s ability to meet the user’s needs. Discussion: The Health App Review Tool was designed using available evidence and stakeholder preference data to ensure a user-centered design. The result was the development of a tool built on evidence and informed by the perceptions and preferences of those within and working with the Alzheimer’s disease population. App and User domains include usefulness, complexity, accessibility, and external variables. This unique matching approach is anticipated to significantly impact individualized, client-centered care. We anticipate that this study will serve as a model for future development of technology matching tools for other diagnostic populations. Discussion: The Health App Review Tool was designed using available evidence and stakeholder preference data to ensure a user-centered design. The result was the development of a tool built on evidence and informed by the perceptions and preferences of those within and working with the Alzheimer’s disease population. App and User domains include usefulness, complexity, accessibility, and external variables. This unique matching approach is anticipated to significantly impact individualized, client-centered care. We anticipate that this study will serve as a model for future development of technology matching tools for other diagnostic populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".