Smartphone App Use for Diabetes Management: Evaluating Patient Perspectives
Bibliographic record
Abstract
BACKGROUND: Finding novel ways to engage patients in chronic disease management has led to increased interest in the potential of mobile health technologies for the management of diabetes. There is currently a wealth of smartphone apps for diabetes management that are available for free download or purchase. However, the usability and desirability of these apps has not been extensively studied. These are important considerations, as these apps must be accepted by the patient population at a practical level if they are to be utilized. OBJECTIVE: The purpose of this study was to gain insight into patient experiences related to the use of smartphone apps for the management of type 1 diabetes. METHODS: Adults with type 1 diabetes who had previously (or currently) used apps to manage their diabetes were eligible to participate. Participants (n=12) completed a questionnaire in which they were required to list the names of preferred apps and indicate which app functions they had used. Participants were given the opportunity to comment on app functions that they perceived to be missing from the current technology. Participants were also asked whether they had previously paid for an app and whether they would be willing to do so. RESULTS: MyFitnessPal and iBGStar were the apps most commonly listed as the best available on the market. Blood glucose tracking, carbohydrate counting, and activity tracking were the most commonly used features. Ten participants fulfilled all eligibility criteria, and indicated that they had not encountered any one app that included all of the functions that they had used. The ability to synchronize an app with a glucometer or insulin pump was the most common function that participants stated was missing from current app technology. One participant had previously paid for a diabetes-related app and the other 9 participants indicated that they would be willing to pay. CONCLUSIONS: Despite dissatisfaction with the currently available apps, there is interest in using these tools for diabetes management. Adapting existing technology to better meet the needs of this patient population may allow these apps to become more widely utilized.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".