Perspectives and Behaviors of People with Diabetes toward Time in Range and Glucose Control in Diabetes Management: An Online Survey
Bibliographic record
Abstract
INTRODUCTION: This study assessed experiences, attitudes, and behaviors of people with diabetes (PwD) regarding diabetes self-management and glucose control, and their level of awareness, knowledge, and attitudes toward time in range (TIR). METHODS: This quantitative survey was conducted using an online questionnaire across seven countries. Respondents were PwD classified into three subgroups: type 1 (T1), type 2 insulin (T2 insulin), and type 2 not on insulin (T2 N/insulin). RESULTS: Respondents included 621 people in the T1, 780 people in the T2 insulin, and 735 people in the T2 N/insulin subgroups. Awareness of TIR was low, particularly in the T2 N/insulin subgroup (T1 53%, T2 insulin 29%, T2 N/insulin 9%). Despite a lower current use of continuous glucose monitoring (CGM) among the T2 insulin and T2 N/insulin participants (38% and 9%, respectively), versus T1 participants (64%), most (> 70%) were positive toward utilizing new tools and measures to self-manage blood glucose. Recommendations from their healthcare professionals (HCPs) were cited as a strong motivator to try new measures for analyzing glucose levels. The main barriers cited were limited access to CGM and lack of understanding of TIR benefits. Cost was the main reason given by ≥ 40% of respondents for stopping CGM use. CONCLUSIONS: There is an unmet need in diabetes management, and TIR and CGM offer a potential solution. PwD are motivated to manage their blood glucose levels and are positive toward utilizing new tools and measures to achieve this goal. HCPs play a pivotal role in informing and guiding PwD on new measures for analyzing glucose.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".