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
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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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 | 0.104 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".