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Assessment of a Decision Support System for Adults with Type 1 Diabetes on Multiple Daily Insulin Injections.
Castle, Jessica R; Wilson, Leah M; Tyler, Nichole S; Espinoza, Alejandro Z; Mosquera-Lopez, Clara M; Kushner, Taisa; Young, Gavin M; Pinsonault, Joseph; Dodier, Robert H; Hilts, Wade W; Oganessian, Sos M; Branigan, Deborah L; Gabo, Virginia B; Eom, Jae H; Ramsey, Katrina; Youssef, Joseph El; Cafazzo, Joseph A; Winters-Stone, Kerri; Jacobs, Peter G.
Afiliação
  • Castle JR; Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA.
  • Wilson LM; Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA.
  • Tyler NS; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Espinoza AZ; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Mosquera-Lopez CM; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Kushner T; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Young GM; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Pinsonault J; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Dodier RH; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Hilts WW; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Oganessian SM; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Branigan DL; Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA.
  • Gabo VB; Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA.
  • Eom JH; Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA.
  • Ramsey K; Biostatistics & Design Program, Oregon Health & Science University, Portland, Oregon, USA.
  • Youssef JE; Department of Medicine, Division of Endocrinology, Harold Schnitzer Diabetes Health Center, Oregon Health & Science University, Portland, Oregon, USA.
  • Cafazzo JA; Department of Biomedical Engineering, Artificial Intelligence for Medical Systems Lab, Oregon Health & Science University, Portland, Oregon, USA.
  • Winters-Stone K; Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, Canada.
  • Jacobs PG; Dalla Lana School of Public Health, Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada.
Diabetes Technol Ther ; 24(12): 892-897, 2022 12.
Article em En | MEDLINE | ID: mdl-35920839
ABSTRACT

Introduction:

DailyDose is a decision support system designed to provide real-time dosing advice and weekly insulin dose adjustments for adults living with type 1 diabetes using multiple daily insulin injections. Materials and

Methods:

Twenty-five adults were enrolled in this single-arm study. All participants used Dexcom G6 for continuous glucose monitoring, InPen for short-acting insulin doses, and Clipsulin to track long-acting insulin doses. Participants used DailyDose on an iPhone for 8 weeks. The primary endpoint was % time in range (TIR) comparing the 2-week baseline to the final 2-week period of DailyDose use.

Results:

There were no significant differences between TIR or other glycemic metrics between the baseline period compared to final 2-week period of DailyDose use. TIR significantly improved by 6.3% when more than half of recommendations were accepted and followed compared with 50% or fewer recommendations (95% CI 2.5%-10.1%, P = 0.001).

Conclusions:

Use of DailyDose did not improve glycemic outcomes compared to the baseline period. In a post hoc analysis, accepting and following recommendations from DailyDose was associated with improved TIR. Clinical Trial Registration Number NCT04428645.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diabetes Mellitus Tipo 1 / Insulina Tipo de estudo: Guideline / Prognostic_studies Limite: Adult / Humans Idioma: En Revista: Diabetes Technol Ther Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Diabetes Mellitus Tipo 1 / Insulina Tipo de estudo: Guideline / Prognostic_studies Limite: Adult / Humans Idioma: En Revista: Diabetes Technol Ther Ano de publicação: 2022 Tipo de documento: Article