Enhancing Basal Insulin Prescription Management in Type 2 Diabetes Patients Using Voice-Based Conversational AI (VBCAI):
A Clinical Trial
Key Points:
Question: Can VBCAI aid in optimizing basal insulin dosage for rapid glycemic control in type 2 diabetes patients?
Findings: In a randomized trial involving 32 adults requiring basal insulin adjustment, the group using Voice-Based Conversational AI achieved optimal insulin dose faster (median 15 days vs >56 days) with improved insulin adherence (83% vs 50%) compared to standard care.
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Meaning: VBCAI applications facilitate swift optimization of basal insulin dosage for type 2 diabetes patients.
Abstract:
Importance: Optimizing insulin therapy for type 2 diabetes patients poses challenges due to frequent dose adjustments. This trial explores the efficacy of a Voice-Based Conversational AI app in facilitating basal insulin titration for rapid glycemic control.
Objective: Evaluate the effectiveness of a Voice-Based Conversational AI app in aiding type 2 diabetes patients in basal insulin titration for improved glycemic control.
Design, Setting, and Participants:
A randomized clinical trial conducted at 4 primary care clinics enrolled 32 adults requiring basal insulin initiation or adjustment, followed up for 8 weeks from March 1, 2021, to December 31, 2022.
Interventions: Participants were randomly allocated to receive basal insulin management through a Voice-Based Conversational AI app or standard care.
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Main Outcomes and Measures:
Primary outcomes included time to optimal insulin dose, insulin adherence, and changes in diabetes-related emotional distress. Secondary outcomes encompassed glycemic control and improvement.
Results:
The VBCAI group achieved optimal insulin dosing faster (median 15 days vs >56 days for standard care) and exhibited better insulin adherence (82.9% vs 50.2%). Moreover, the AI group showed higher rates of glycemic control (81.3% vs 25.0%) and improvement in fasting blood glucose levels compared to the standard care group.
Conclusions and Relevance:
This trial highlights the efficacy of Voice-Based Conversational AI in managing basal insulin titration among type 2 diabetes patients, leading to improved insulin dose optimization, adherence, glycemic control, and reduced emotional distress.
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Trial Registration:
ClinicalTrials.gov Identifier: NCT05081011
Introduction:
Managing insulin therapy in type 2 diabetes patients is complex. While essential, frequent dose adjustments are challenging due to clinic visits’ infrequency, resulting in suboptimal dosing and poor glycemic control for most patients.
Self-titration offers a potential solution but demands continuous education and monitoring. Digital health tools, including Voice-Based Conversational AI, provide scalable approaches for real-time decision support in insulin titration. However, the use of such tools, especially voice-based interfaces, remains limited in patient-facing applications.
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Methods:
The Managing Insulin with Voice AI (MIVA) trial was conducted remotely, enrolling English-speaking adults with type 2 diabetes requiring basal insulin initiation or adjustment. Participants were allocated to receive basal insulin management via a VBCAI or standard care.
Results:
Voice-based conversational AI users demonstrated faster insulin dose optimization, improved adherence, and better glycemic control compared to standard care. The AI group engaged consistently with the system, achieving higher rates of glycemic control.
Conclusion:
Voice-based conversational AI proves effective in managing basal insulin titration, indicating its potential to improve care delivery and patient outcomes in type 2 diabetes management.
