Engagement with an AI- and CGM-Integrated Digital Health Platform Is Associated with Clinically Significant Weight...

Obesity affects millions of U.S. adults and is linked to cardiometabolic disease. This study examined whether user engagement with the Signos app, which combines artificial intelligence (AI) and continuous glucose monitoring (CGM), is associated with weight loss.

This study included adults (≥18 years) with BMI ≥ 30 kg/m² and without diabetes who enrolled in the Signos System, a platform integrating CGM data with AI-driven behavioral recommendations informed by glucose responses, dietary intake, physical activity, and user-entered data. Engagement was quantified as total meaningful actions per day (TMAPD). Longitudinal within-participant analyses compared weight change during engaged (TMAPD > 0) and nonengaged (TMAPD = 0) periods among 3007 participants, expressed as weight loss percentage per week (WLPW%). Cross-sectional analyses evaluated 180-day total body weight loss (TBWL) among 1147 participants.

In this longitudinal cohort, weight loss was greater during periods of engagement than non-engagement (1.17% ± 0.04% vs. 0.44% ± 0.03% per week; P < 0.001). Weight loss during engaged periods did not differ significantly between early and late engagers (P > 0.05). In the 180-day cohort (n = 1,147), mean TBWL was 5.14% ± 0.19%. Participants with higher engagement (TMAPD ≥ 1.02) achieved 5.90% ± 0.25% TBWL compared with 4.38% ± 0.23% in the lower engagement group (P < 0.001).

Greater engagement with the Signos System was associated with greater weight loss in adults with obesity and without diabetes. Weight loss outcomes were similar regardless of whether engagement occurred earlier or later during platform use. These findings suggest that engagement with AI-guided, CGM-informed behavioral interventions may support clinically meaningful weight reduction. Approved by the WCG Institutional Review Board (protocol no. 20212524) and conducted in accordance with the Declaration of Helsinki.