Self-Determined Health App Evaluation Questionnaire Development: Mixed Methods Study
Bibliographic record
Abstract
Background: The rapid increase in the number of health apps and their volatility in Austrian pp stores for Android and Apple are signs of a flourishing business sector in the wellness industry. Objective: In this report, a questionnaire for informed decision-making by users was developed and evaluated using health apps in the categories "Nutrition", "Exercise", "Mental Health" and "Symptom Checker". Methods: Evaluation criteria were derived from multiple reference documents and weighted in a survey, as well as a focus group meeting. Further, the selected evaluation criteria were tested against selected apps, which were most popular in the above-mentioned categories in fall 2023. Results: The short questionnaire is to be made publicly available to citizens and covers the categories of the quality of the app provider (regulatory compliance, safety, and quality assurance), the content quality of the app (functionality and evidence-based content), and the user-friendliness of the app. It consists of 6 question items, which appeared to be problematic in the evaluation most often. For ease of use, a short questionnaire with the most critical questions and helpful tips from the evaluation was expanded by the question of importance and with meaningful advice on how to find the right information in an app. Conclusions: The evaluation based on the comprehensive questionnaire using selected apps confirmed the importance of such a questionnaire since most of the apps lacked basic properties in terms of safety and security.
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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 | 0.062 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".