Usability Evaluation of a Mobile Ecological Interface Design Application for Diabetes Management
Bibliographic record
Abstract
Healthcare applications for small-screen mobile devices are becoming increasingly common for medical professionals and patients. Even so, usability issues including navigation and screen clutter remain a challenge. Ecological Interface Design (EID) was used to design a patient-oriented diabetes management display for Java-enabled mobile devices, making it one of the first mobile EID (mEID) applications. This paper presents a usability evaluation of the diabetes management application, which compares the mEID display to a modified taskbased display (mEID+Task). The mEID+Task display integrates functional task characteristics such as frequency and necessity; menu structure, item ordering, item labelling, and input scheme were varied. Results showed that normalised trial completion times were moderately faster in the mEID+Task display than in the mEID display, while no differences were observed in trial completion accuracy. Furthermore, the mEID+Task display received higher preference ratings than the mEID display alone. The findings suggest that the usability of mEID displays can be improved by incorporating a task-oriented approach.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".