1279-P: Technology Use and Diabetes Management across Elder Age Groups in Type 1 Diabetes and Latent Autoimmune Diabetes of the Adult (LADA), a BETTER Registry Cross-Sectional Analysis
Bibliographic record
Abstract
Introduction and Objective: Real-world data on diabetes management in older adults remain limited. This study aims to provide an overview of technology use and factors associated to its use, diabetes management and psychosocial aspects in adults aged 50 and over living with type 1 diabetes (T1D) or latent autoimmune diabetes in adults (LADA). Methods: This cross-sectional study analyzed data from the BETTER registry including individuals living with T1D or LADA. Comparative analyses were conducted across three age groups: 50-59, 60-69, and ≥70. Results: Participants (n=674) were predominantly Caucasian (97-98%) and residing in Quebec, Canada (71-79%). Insulin pump use was similar across age groups (36-39%, p=0.822), while continuous glucose monitoring (CGM) was lower among those aged ≥70 years (85% for 50-59 and 60-69 vs 73% for ≥70 years, p=0.020). Factors associated with technology use are shown in Fig 1. Most (80-86%) of participants achieved an HbA1c ≤8% across all groups. Level 2 hypoglycemia events in the last month and moderate diabetes-related distress were more frequent among participants aged 50-59 years compared to those aged ≥70 years. Conclusion: Most individuals in this cohort adopted technology but in lower proportion among the group aged ≥70. Overall, diabetes management was good and similar between age groups. Disclosure Y. Wang: None. L. Alexandre-Heymann: None. V. Messier: None. V. Boudreau: None. A. Bandini: None. B. Kelly: None. A. Gravel: None. C. Gagnon: Research Support; Ascendis Pharma A/S, Amryt Pharma. 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. R.P.R. Rabasa-Lhoret: Advisory Panel; Abbott, Eli Lilly and Company, Novo Nordisk, Sanofi, Insulet Corporation. Other Relationship; Medtronic. Advisory Panel; Bayer Pharmaceuticals, Inc. Funding The BETTER registry is supported by a Strategy Patient-Oriented Research (JT1-157204) and Breakthrough T1D formerly JDRF (3-SRA-2024-1523-M-N) Partnership on Innovative Clinical Trial Multi-Year Grant. Additionally, the BETTER registry receives nonrestrictive grants from Dexcom Canada?, Eli Lilly Canada Inc., Novo Nordisk Canada, and Sanofi Canada. This co-funding is unrestricted and had no influence on the study design, data collection, analysis, interpretation, writing of the report, or decision to submit the article for publication. Detailed information about funding can be found at https://type1better.com/en/about/partners/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".