136-OR: Nocturnal Hypoglycemia Prevention Strategies Used by People with Type 1 Diabetes According to Their Insulin Delivery and Blood Glucose Monitoring Technology
Bibliographic record
Abstract
People with T1D (PWT1D) use many strategies to avoid nocturnal hypoglycemia (NH), however, the current evidence on these strategies is limited. Technologies such as continuous subcutaneous insulin infusion (CSII) or continuous glucose monitors (CGM) could help reduce NH and possibly alter PWT1D’ management behaviors. Aim: To describe PWT1D’ preferred NH prevention strategies according to their use of technology. Methods: Self-reported data from an online registry of PWT1D were analyzed. To describe which NH prevention strategies are most often used, we ran a χ2 test with a post hoc pairwise comparison of the groups based on their mode of insulin delivery and glucose monitoring (CSII + self-monitoring of blood glucose (SMBG); CSII + CGM; multiple daily injections (MDI) + CGM; MDI + SMBG). Results: Among 767 adults (64% female, mean age 45 ± 15 years, T1D duration 25 ± 14 years) 42% used MDI + CGM, 40% CSII + CGM, 5% CSII + SMBG, and 13% MDI + SMBG. About 67% reported waking up ≥1 time in the past month due to NH symptoms. The most widely used strategies were verifying bedtime glycemia (71%), having an evening/bedtime snack (60%), and/or reducing nocturnal basal insulin (30%). PWT1D using CSII + CGM or CSII + SMBG relied more on basal insulin reduction (36% and 33.3%, respectively) compared to those using MDI + CGM (26%) or MDI + SMBG (19%). Less PWT1D using CSII + CGM reported having a snack to prevent NH (51%), compared to CSII + SBMG (61%), MDI + CGM (66%), and MDI + SBGM (67%) groups (p<0.001). Conclusion: Results suggest that PWT1D using technology choose different strategies to avoid NH than non-users. CSII users relying on basal insulin adjustment was expected as CSII offers more flexibility for insulin delivery. Snacks remain a popular strategy to prevent NH but are less used by PWT1D on CSII + CGM. While our findings provide some real-life insight into the methods PWT1D use to avoid NH, the efficacy of these strategies still needs further investigation. Disclosure M. K. Talbo: None. V. Messier: Other Relationship; Self; Eli Lilly and Company. K. Desjardins: None. R. Rabasa-lhoret: Advisory Panel; Self; Bayer Inc., Covance Inc., Insulet Corporation, Pfizer Inc., Other Relationship; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Dexcom, Inc., Eli Lilly and Company, HLS Therapeutics Inc., Janssen Pharmaceuticals, Inc., Medtronic, Merck & Co., Inc., Novo Nordisk, Sanofi-Aventis, Research Support; Self; Canadian Institutes of Health Research, Cystic Fibrosis Canada, Diabetes Canada, JDRF, National Institutes of Health, Prometic, Société Francophone du Diabète, Speaker’s Bureau; Self; CMS Canadian Medical&Surgical Knowledge Translation Research Group, CPD Network. A. Brazeau: None. T. Peters: None. Funding Canadian Institutes of Health Research (JT1-157204); JDRF (4-SRA-2018-651-Q-R)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.010 | 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".