Usability Testing of A Carbohydrate Counting Application designed For and With Young Adults with Type 1 Diabetes (Preprint)
Bibliographic record
Abstract
BACKGROUND Carbohydrate counting assists people with type 1 diabetes (T1D) adjust mealtime insulin doses, however, it is often burdensome. Mobile applications can simplify this process by automating carb estimation and insulin calculations, yet no comprehensive solution currently combines photo-based carb recognition with an integrated bolus calculator. OBJECTIVE This study aimed to optimize a novel app designed to support young adults with T1D in carbohydrate counting and insulin dosing by incorporating feedback from usability testing. METHODS We used a think-aloud protocol and conducted four rounds of interviews, each with 3–5 participants, to assess effectiveness of the app and identify areas for improvement. RESULTS A total of 18 completed the usability testing. Thematic analysis revealed seven key insights; 1) A person-centered design is important for an individualized experience, addressing the individual as a whole, not just their diabetes; 2) An intuitive user interface is essential to maintain engagement, with clear information presentation and easy interaction; 3) The relevance of information, should be presented in familiar language to enhance identification and inclusion; 4) Personalized features for a tailored user experience; 5) Robust data verification mechanisms and override abilities to avoid human and technological errors; 6) A comprehensive application to improve patient-practitioner communication and reduce manual tracking or the use of multiple apps; 7) Linkage of various factors (exercise, diet, time of injection) to glucose levels could improve self-efficacy and promote personalized learning. CONCLUSIONS This will be the first Canadian app to combine carbohydrate counting and insulin dosing while involving end users in its development.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".