1095-P: Barriers and Enablers to Diabetes Technologies Use among Individuals with Type 1 Diabetes (T1D)—A Scoping Review
Bibliographic record
Abstract
Introduction and Objective: Advanced technologies such as insulin pumps, continuous glucose monitors (CGM), and automated insulin delivery (AID) are the standard of care in T1D. However, disparities in uptake contribute to inequitable diabetes outcomes. We conducted a scoping review to comprehensively describe barriers and enablers to uptake of technologies in T1D. Methods: A systematic search was conducted in EMBASE, Cochrane, PubMed, and MEDLINE from January 1, 2000 to May 22, 2024. Inclusion criteria were studies of any design which considered barriers and enablers to insulin pumps, CGM, or AID among individuals with T1D. Titles and abstracts and full-text for selected manuscripts were reviewed independently by two reviewers for inclusion and data extraction. Characteristics of included studies were described. Studies were categorized as interventional, qualitative, descriptive, or other. Frequencies of identified barriers and enablers were tabulated. Results: Of 2398 articles identified, 226 (9.4%) were included, studying a total of 1,588,458 patients. The most common study design was cross-sectional (42.5%). Most studies were conducted in the US (61.5%) and published in the past five years (73.9%). Studies focused on CGM (30.1%), insulin pump (30.1%), multiple devices (33.6%), or other (6.2%). The most frequent barriers included racial or ethnic minority status (n = 69), insurance concerns (e.g., coverage) (n = 68), and clinic- and provider-related factors (e.g., gatekeeping of information and/or prescriptions) (n = 37). The most frequent enablers included patient education (n = 31), patient support (n = 31), and provider education (n = 21). Conclusion: This is the first scoping review summarizing barriers and enablers to diabetes technologies use. These results can inform future implementation strategies for promoting equitable use of diabetes technologies. Disclosure L. Palermo: None. J. Senga: None. P. Fakembe: None. X. Zhu: None. R. Shulman: Advisory Panel; Dexcom, Inc. L. Lipscombe: None. H. Witteman: None. A. Banerjee: None. J. Presseau: None. M. Nakhla: None. L. Lovblom: None. R.P. Rabasa-Lhoret: Advisory Panel; Abbott, Eli Lilly and Company, Novo Nordisk, Sanofi, Insulet Corporation. Other Relationship; Medtronic. Advisory Panel; Bayer Pharmaceuticals, Inc. A. Brazeau: Speaker's Bureau; Dexcom, Inc. Research Support; Canadian Institutes of Health Research. Speaker's Bureau; Juvenile Diabetes Research Foundation (JDRF). Research Support; Juvenile Diabetes Research Foundation (JDRF), Diabète Québec, Fonds de recherches du Québec-Santé, Mitacs. A. Weisman: None. Funding Diabetes Canada; Breakthrough T1D; CIHR
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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 | 0.022 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.029 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".