7-OR: The Libre Enabled Reduction of A1C through Effective Eating and Exercise Study—LIBERATE CANADA
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».