7-OR: The Libre Enabled Reduction of A1C through Effective Eating and Exercise Study—LIBERATE CANADA
Bibliographic record
Abstract
Introduction and Objective: Initiating and maintaining new eating and exercise behaviours with meaningful glycemic improvement is often difficult for those living with T2D. The LIBERATE study aimed to evaluate the efficacy of a real world, standardized, six-month, virtual group diabetes self-management education (DSME) program, which incorporated wearable technology including isCGM to support behaviour change. Methods: Participants with T2D and HbA1c ≥ 8% were recruited for this open label, prospective cohort study. The LIBERATE intervention included virtual small group sessions every two weeks for the first 12 weeks and monthly for the last 12 weeks. Participants used the FreeStyle® Libre 2 for glucose monitoring continuously during the first three months and had the option to use three sensors in the last three months. The FitBit Inspire 2 was used to track physical activity. Virtual group sessions were facilitated by a CDE with content created based on current DSME principles emphasizing the use of isCGM data to personalize lifestyle choices. The primary outcome was percent mean change in HbA1c. Paired T-tests were utilized for analysis. Results: In 92 participants (mean age, 55 years; 44% female; diabetes duration, 8 years; 78% not using insulin. HbA1c significantly improved from 9.8% (± 1.5%) to 7.6% (± 1.2%), p<0.01 at endpoint. Percentage mean time in range (TIR) (3.9mmol to 10.0mmol) also increased significantly; mean TIR at baseline = 66.8% (± 26.2%) vs. TIR at midpoint = 74.1% (± 24.5), p<0.05. Conclusion: Combining six months of virtual group coaching designed to empower individual self-management with isCGM technology rapidly improved HbA1c and TIR, with sustained effects seen at six months despite less intensive coaching. These findings support broader implementation of combining wearable technology and virtually supported DSME to enhance diabetes care and patient outcomes, including for those not yet on insulin therapy in T2D. Disclosure S.M. Reichert: Research Support; Abbott Diagnostics. Speaker's Bureau; Abbott. Advisory Panel; Novo Nordisk. Speaker's Bureau; Novo Nordisk. Other Relationship; Novartis Pharmaceuticals Corporation. Advisory Panel; Bausch Health, Sanofi, embecta, Eisai, Eli Lilly and Company. Consultant; Center for Effective Practice (CEP),. Research Support; Western University. Other Relationship; Diabetes Canada. Advisory Panel; Bayer Pharmaceuticals, Inc. Speaker's Bureau; Humber River Health, Federation of Canadian Medical Women, Medscape, Peer Voice. H.C. Gerstein: Advisory Panel; Abbott, Bayer Pharmaceuticals, Inc, Eli Lilly and Company, Novo Nordisk. Consultant; Pfizer Inc, Sanofi, Hanmi Pharm. Co., Ltd. Research Support; Eli Lilly and Company, Novo Nordisk, Hanmi Pharm. Co., Ltd. Other Relationship; Eli Lilly and Company, Novo Nordisk, Sanofi, Boehringer-Ingelheim, Abbott, Jiangsu Hansen, Zuellig Pharma, AstraZeneca. B. Harvey: None. A.G. Mikalachki: None. D. Sherifali: None. M. Mitchell: None. P. Brauer: None. D. Henke: None. L. Vancer: Speaker's Bureau; Abbott. S.B. Harris: Advisory Panel; Abbott. Consultant; Abbott. Research Support; Boehringer-Ingelheim. Advisory Panel; Dexcom, Inc. Consultant; Dexcom, Inc. Research Support; Canadian Institutes of Health Research. Advisory Panel; Eli Lilly and Company. Research Support; Eli Lilly and Company. Consultant; Medscape. Advisory Panel; Novo Nordisk. Research Support; Novo Nordisk, Novartis Pharmaceuticals Corporation. Advisory Panel; Sanofi. Consultant; Sanofi. Funding Abbott
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".