Unlocking Potential: Personalized Lifestyle Therapy for Type 2 Diabetes Through a Predictive Algorithm-Driven Digital Therapeutic

Abstract

Background: We present a digital therapeutic (DTx) using continuous glucose monitoring (CGM) and an advanced artificial intelligence (AI) algorithm to digitally personalize lifestyle interventions for people with type 2 diabetes (T2D). Method: A study of 118 participants with non–insulin-treated T2D (HbA1c ≥ 6.5%) who were already receiving standard care and had a mean baseline (BL) HbA1c of 7.46% (0.93) used the DTx for three months to evaluate clinical endpoints, such as HbA1c, body weight, quality of life and app usage, for a pre-post comparison. The study also included an assessment of initial long-term data from a second use of the DTx. Results: After three months of using the DTx, there was an improvement of 0.67% HbA1c in the complete cohort and −1.08% HbA1c in patients with poorly controlled diabetes (BL-HbA1c ≥ 7.0%) compared with standard of care ( P < .001). The number of patients within the therapeutic target range (< 7.0%) increased from 38% to 60%, and 33% were on the way to remission (< 6.5%). Patients who used the DTx a second time experienced a reduction of −0.76% in their HbA1c levels and a mean weight loss of −6.84 kg after six months ( P < .001) compared with BL. Conclusions: These results indicate that the DTx has clinically relevant effects on glycemic control and weight reduction for patients with both well and poorly controlled diabetes, whether through single or repeated usage. It is a noteworthy improvement in T2D management, offering a non-pharmacological, fully digital solution that integrates biofeedback through CGM and an advanced AI algorithm.