The Use of an Adapted Health IT Usability Evaluation Model (Health-ITUEM) for Evaluating Consumer Reported Ratings of Diabetes mHealth Applications: Implications for Diabetes Care and Management
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Bibliographic record
Abstract
BACKGROUND: The aim of this paper is to present a usability analysis of the consumer ratings of key diabetes mHealth applications using an adapted Health IT Usability Evaluation Model (Health-ITUEM). METHODS: A qualitative content analysis method was used to analyze publicly available consumer reported data posted on the Android Market and Google Play for four leading diabetes mHealth applications. Health-ITUEM concepts including information needs, flexibility/customizability, learnability, performance speed, and competency guided the categorization and analysis of the data. Health impact was an additional category that was included in the study. A total of 405 consumers' ratings collected from January 9, 2014 to February 17, 2014 were included in the study. RESULTS: Overall, the consumers' ratings of the leading diabetes mHealth applications for both usability and health impacts were positive. The performance speed of the mHealth application and the information needs of the consumers were the primary usability factors impacting the use of the diabetes mHealth applications. There was also evidence on the positive health impacts of such applications. CONCLUSIONS: Consumers are more likely to use diabetes related mHealth applications that perform well and meet their information needs. Furthermore, there is preliminary evidence that diabetes mHealth applications can have positive impact on the health of patients.
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it