The Sukaribit Smartphone App for Better Self-Management of Type 2 Diabetes: Randomized Controlled Feasibility Study
Bibliographic record
Abstract
Background A new app, Sukaribit, was designed to enable contact between the caregiver and the patient with the intent to improve self-care and glycemic control (hemoglobin A1c [HbA1c]). Objective This study investigated the feasibility of the study methodology and the intervention in preparation for a larger effectiveness study. Methods Adults with type 2 diabetes were recruited in this randomized controlled feasibility study with a mixed methods design. The intervention group (n=28) tried Sukaribit for 2 months. They were encouraged to report blood glucose levels and medications, and they received feedback from a physician. The control group (n=31) received standard care. Both groups were evaluated with pre and postmeasurements of glycemic control (HbA1c), diabetes distress, physical activity, and self-care. Feasibility was evaluated against 5 progression criteria regarding recruitment, study methods, and active participation. Results Of the 5 progression criteria, only 2 were met or partially met. The recruitment process exceeded expectations, and data collection worked well for self-reported data but not for HbA1c measured with a home testing kit. The participants were less active than anticipated, and the effect sizes were small. Only the number of blood glucose tests per day was positively affected by the intervention, with 0.6 more tests per day in the intervention group. Conclusions Recruitment of participants to a future fully powered study may work with minor adjustments. The collection of HbA1c using home testing constituted a major problem, and an alternative strategy is warranted. Finally, the app was not used as intended. In order to proceed with a larger study, the app and study procedures need improvement.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".