Diabetes research and clinical practice · 2025
Human factors in the use and efficacy of decision support technologies for type 1 diabetes: evidence from a randomized controlled trial.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Pavan Jacopo, Nass Ralf, Fabris Chiara, Fathi Anas El, Emory Emma G, Santini Giulio, Batista Annanda M F B, Oliveri Mary C, Koravi Chaitanya K L, Barnett Charlie L, Barnard-Kelly Katharine, Gonder-Frederick Linda, Breton Marc D
Аннотация
AIM: Studies on decision support systems (DSS) for type 1 diabetes show low user engagement and marginal glycemic benefits. This work investigates the interplay between human factors and DSS use and efficacy.
METHODS: Adults using insulin injections or pump and continuous glucose monitoring (CGM) underwent three 2-month interventions, in randomized order: i) no DSS; ii) informative DSS (iDSS), providing summary feedback for decision-making; iii) prescriptive DSS (pDSS), recommending precise treatment actions. DSS advisory modules included tools for smart bolusing and therapy optimization. Primary outcomes were CGM-derived glycemic metrics. Exploratory analyses investigated the association between glycemic outcomes, DSS use, and psychosocial variables.
RESULTS: Fifty-three participants (26 injections, 27 pump) completed the study. Glycemic outcomes did not differ between interventions. However, using iDSS vs no DSS reduced average time >180 mg/dl for participants with lower diabetes-related knowledge (-6 %, p < 0.001) and higher hemoglobin A1c (-6 %, p < 0.01). Emotional distress (p < 0.001) and hypoglycemia worry (p < 0.01) were associated with lower DSS engagement. Participants engaged more with their preferred system (p < 0.01); 40 % of them preferred iDSS.
CONCLUSIONS: Personalized feedback (iDSS) may offer an important learning tool, especially for individuals with lower diabetes-related knowledge. Addressing diabetes-related distress and hypoglycemia worry could unlock the full potential of DSS technologies.
METHODS: Adults using insulin injections or pump and continuous glucose monitoring (CGM) underwent three 2-month interventions, in randomized order: i) no DSS; ii) informative DSS (iDSS), providing summary feedback for decision-making; iii) prescriptive DSS (pDSS), recommending precise treatment actions. DSS advisory modules included tools for smart bolusing and therapy optimization. Primary outcomes were CGM-derived glycemic metrics. Exploratory analyses investigated the association between glycemic outcomes, DSS use, and psychosocial variables.
RESULTS: Fifty-three participants (26 injections, 27 pump) completed the study. Glycemic outcomes did not differ between interventions. However, using iDSS vs no DSS reduced average time >180 mg/dl for participants with lower diabetes-related knowledge (-6 %, p < 0.001) and higher hemoglobin A1c (-6 %, p < 0.01). Emotional distress (p < 0.001) and hypoglycemia worry (p < 0.01) were associated with lower DSS engagement. Participants engaged more with their preferred system (p < 0.01); 40 % of them preferred iDSS.
CONCLUSIONS: Personalized feedback (iDSS) may offer an important learning tool, especially for individuals with lower diabetes-related knowledge. Addressing diabetes-related distress and hypoglycemia worry could unlock the full potential of DSS technologies.