328-OR: Hypoglycemia Associated with Physical Activity in Automated Insulin Delivery Users—Frequency and Prevention Strategies
Bibliographic record
Abstract
Introduction & Objectives: Even with automated insulin delivery (AID), hypoglycemia during and after physical activity (PA) remains a concern for people living with type 1 diabetes (pwT1D). The aim is to evaluate frequency of and strategies to prevent and treat hypoglycemic events (HE). Methods: Active pwT1D shared glycemic and pump data, PA logbook and food diaries for any HE prevention or correction strategy for 6-weeks. Values are presented as median (interquartile range) unless specified. We looked at HE on CGM defined according to international consensus in AID-treated pwT1D during, 1-h and 4-h post-PA using Chi2 or Anova tests. Results: A total of 25 pwT1D (17 females) aged 50 (23 to 75) years and A1c 6.7 (6.4-7.0) % completed 436 PA sessions: 17 (6-49) per participant lasting 50 (35-60) minutes, with 25.2% low, 63.8% moderate and 11.0% vigorous intensity. Participants’ prevention strategies included: pre-exercise snack (37.4%; 20 (15-25) g of carbohydrates), PA mode (18.8%), meal bolus reduction (15.6%), snacking during/after (11.2%; 24 (15-35) g of carbohydrates), pump disconnection (4.6%), and other strategies (16.5%; e.g. PA session suspended, or basal insulin reduced/suspended). Reported strategies per session were 29.6% for no strategy, 34.6% for one and 35.8% for > 1. HE occurred 5 times during PA (1.1%), 28 (6.4%) 1-h post PA and 61 (14.0%) 4-h post-PA. PA intensity and duration are associated with HE during PA (p = 0.006 and p = 0.001, respectively). Insulin on board at PA start was not associated with HE, but last meal insulin bolus (before PA) was associated to 1-h post-PA HE (p = 0.018). Participants (n = 7) using an open-source AID had significantly less hypoglycemia 4-h post-PA (6.6 vs. 15.4% of sessions, p = 0.021). Conclusion: Carbohydrate intake is the most common prevention strategy. Incidence of hypoglycemia during PA is low but tends to increase after PA. Open-source AID may reduce post-PA hypoglycemia. Disclosure V. Boudreau: None. J.E. Yardley: Speaker's Bureau; Dexcom, Inc. Research Support; LifeScan Diabetes Institute. C.L. Russon: None. T. Chahal: None. R. St-Amand: None. R.P.R. Rabasa-Lhoret: Other Relationship; Abbott, AstraZeneca, Bayer Inc., Boehringer-Ingelheim, Dexcom, Inc. Research Support; Diabetes Canada. Other Relationship; Eli Lilly and Company. Research Support; Cystic Fibrosis Canada, Canadian Institutes of Health Research, FFRD - Fondation Francophone pour la Recherche du Diabète. Other Relationship; Janssen Pharmaceuticals, Inc. Research Support; Juvenile Diabetes Research Foundation (JDRF). Other Relationship; Novo Nordisk, GlaxoSmithKline plc. Consultant; HLS Therapeutics Inc., Insulet Corporation. Speaker's Bureau; CPD Networks. Other Relationship; Medtronic. Consultant; Pfizer Inc. Speaker's Bureau; Tandem Diabetes Care, Inc. Other Relationship; Sanofi. Speaker's Bureau; Vertex Pharmaceuticals Incorporated. Research Support; SFD - Société Francophone du Diabète. J. Molveau: None. Funding Diabetes Canada (OG-3-21-5586-RR)
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".