Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes
Authors: Nayak Ashwin , Vakili Sharif , Nayak Kristen , Nikolov Margaret , Chiu Michelle , Sosseinheimer Philip , Talamantes Sarah , Testa Stefano , Palanisamy Srikanth , Giri Vinay , Schulman Kevin 
Published: December 2023

Importance

Optimizing insulin therapy for patients with type 2 diabetes can be challenging given the need for frequent dose adjustments. Most patients receive suboptimal doses and do not achieve glycemic control.

Objective

To examine whether a voice-based conversational artificial intelligence (AI) application can help patients with type 2 diabetes titrate basal insulin at home to achieve rapid glycemic control.

Design, Setting, and Participants

In this randomized clinical trial conducted at 4 primary care clinics at an academic medical center from March 1, 2021, to December 31, 2022, 32 adults with type 2 diabetes requiring initiation or adjustment of once-daily basal insulin were followed up for 8 weeks. Statistical analysis was performed from January to February 2023.

Interventions

Participants were randomized in a 1:1 ratio to receive basal insulin management with a voice-based conversational AI application or standard of care.

Main Outcomes and Measures

Primary outcomes were time to optimal insulin dose (number of days needed to achieve glycemic control), insulin adherence, and change in composite survey scores measuring diabetes-related emotional distress and attitudes toward health technology and medication adherence. Secondary outcomes were glycemic control and glycemic improvement. Analysis was performed on an intent-to-treat basis.

Results

The study population included 32 patients (mean [SD] age, 55.1 [12.7] years; 19 women [59.4%]). Participants in the voice-based conversational AI group more quickly achieved optimal insulin dosing compared with the standard of care group (median, 15 days [IQR, 6-27 days] vs &amp;gt;56 days [IQR, &amp;gt;29.5 to &amp;gt;56 days]; a significant difference in time-to-event curves; P = .006) and had better insulin adherence (mean [SD], 82.9% [20.6%] vs 50.2% [43.0%]; difference, 32.7% [95% CI, 8.0%-57.4%]; P = .01). Participants in the voice-based conversational AI group were also more likely than those in the standard of care group to achieve glycemic control (13 of 16 [81.3%; 95% CI, 53.7%-95.0%] vs 4 of 16 [25.0%; 95% CI, 8.3%-52.6%]; difference, 56.3% [95% CI, 21.4%-91.1%]; P = .005) and glycemic improvement, as measured by change in mean (SD) fasting blood glucose level (−45.9 [45.9] mg/dL [95% CI, −70.4 to −21.5 mg/dL] vs 23.0 [54.7] mg/dL [95% CI, −8.6 to 54.6 mg/dL]; difference, −68.9 mg/dL [95% CI, −107.1 to −30.7 mg/dL]; P = .001). There was a significant difference between the voice-based conversational AI group and the standard of care group in change in composite survey scores measuring diabetes-related emotional distress (−1.9 points vs 1.7 points; difference, −3.6 points [95% CI, −6.8 to −0.4 points]; P = .03).

Conclusions and Relevance

In this randomized clinical trial of a voice-based conversational AI application that provided autonomous basal insulin management for adults with type 2 diabetes, participants in the AI group had significantly improved time to optimal insulin dose, insulin adherence, glycemic control, and diabetes-related emotional distress compared with those in the standard of care group. These findings suggest that voice-based digital health solutions can be useful for medication titration.

Trial Registration

ClinicalTrials.gov Identifier: NCT05081011