Use of the Omnipod 5 Automated Insulin Delivery System Activity Feature Reduces Insulin Delivery and Attenuates the Drop in Glycemia Associated With Exercise in a Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To compare the efficacy of enabling Activity feature 60 (AF-60) or 30 min (AF-30) before prolonged exercise versus the automated mode (Auto) in adults and adolescents with type 1 diabetes wearing the Omnipod 5 System. RESEARCH DESIGN AND METHODS: In this three-way crossover study, 38 participants (age 30 ± 15 years; BMI 24.7 ± 4.1 kg/m2; HbA1c 7.5% ± 0.9% [58 ± 11 mmol/mol]) from the extension phase of the pivotal trial of the Omnipod 5 System completed a 70-min treadmill session at 64-76% maximum heart rate in a postabsorptive state under each of the three conditions. Auto was resumed after exercise, and glycemia and insulin delivery metrics were examined in the 4-h postexercise period. RESULTS: The percentage of participants who developed hypoglycemia during exercise did not differ significantly between Auto (42%) and AF-60 (29%; P = 0.34) or AF-30 (24%; P = 0.14). However, AF-60 and AF-30 reduced insulin delivery compared with Auto in the hour before (P < 0.001) and during exercise (P < 0.001). There was also a favorable attenuation in glucose drop during exercise when comparing Auto (-57 ± -35 mg/dL) with AF-60 (-44 ± -33 mg/dL; P = 0.02) and AF-30 (-36 ± -34 mg/dL; P = 0.01). In the postexercise period, glycemia and insulin delivery were comparable. CONCLUSIONS: Enabling the Activity feature either 60 or 30 min before exercise reduced insulin delivery and attenuated glucose drops relative to Auto, but hypoglycemia incidence was not different across the three conditions. These findings support the use of the Omnipod 5 System for exercise but highlight the importance of using additional strategies, such as earlier use of Activity feature and/or carbohydrate intake to further reduce hypoglycemia risk.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".