Technology Use and Diabetes Management Across Elder Age Groups in Type 1 Diabetes and Latent Auto-Immune Diabetes in Adults, a BETTER Registry Cross-Sectional Analysis
Bibliographic record
Abstract
OBJECTIVES: Real-world data on diabetes management among a heterogenous aging population remain limited. This study aims to provide an overview of technology use and factors associated to its use, diabetes management, and psychosocial aspects experienced by adults aged 50 and over living with type 1 diabetes or latent autoimmune diabetes in adults. METHODS: This cross-sectional study analyzed data from the Canadian BETTER registry, mostly based on self-reported outcomes from individuals living with type 1 diabetes or latent autoimmune diabetes in adults. Comparative analyses were conducted across 3 age groups: 50-59, 60-69, and ≥ 70. RESULTS: Participants (n = 674) were predominantly Caucasian (97% to 98% across groups) and residing in Quebec, Canada (71% to 79%). Insulin pump use was similar across age groups (36% to 39%, P = .822), while continuous glucose monitoring was lower among those aged ≥ 70 years (85% for both 50-59 and 60-69 vs 73% for ≥ 70 years, P = .020). Among other factors, having private insurance and living outside of Quebec were positively associated with both insulin pump and continuous glucose monitoring use. A high proportion (80% to 86%) of participants achieved an HbA1c ≤ 8% across all groups. Level 2 hypoglycemia events in the last month were more frequent among participants aged 50-59 years compared to those aged ≥70 years (6.9 vs 3.4, P = .001). Level 3 hypoglycemia, social and professional support were similar between groups. Interestingly, diabetes-related distress was lower in older age groups. CONCLUSIONS: Most individuals in this cohort adopted technology use but in lower proportion among the group aged ≥70. Overall, diabetes management was good and similar between age groups.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".