Real-world outcomes of hybrid obesity care using digital coaching and GLP-1 therapy in a multi-ethnic Asian setting.

BACKGROUND: Obesity remains a major health challenge globally and in Asia, driving cardio-metabolic disease risks. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and mobile health (mHealth) coaching each demonstrate weight loss efficacy, but real-world evidence for hybrid models combining these treatments remains limited, especially in multi-ethnic Asian settings. METHODS: We evaluated real-world outcomes among 708 adults enrolled in NOVI Optimum Plus, a physician-led obesity program in Singapore that integrates GLP-1 RA pharmacotherapy with app-based lifestyle coaching. Data on weight, metabolic indicators, and engagement were extracted from clinical records and the mHealth platform. Linear mixed models estimated changes over 6-18 months, stratified by engagement, metabolic status, and ethnicity. RESULTS: Participants (mean age 42.1 years; 64.1% female) were primarily East Asian (45.5%), European (26.8%), South Asian (13.3%), and Southeast Asian (10.3%). Most received semaglutide (86% oral 14 mg). At 12 months, mean weight loss was 12.7% (95% CI: -14.0, -11.3) and BMI dropped by 4.1 points, with further weight loss reaching 14.7% at 18 months. Systolic blood pressure decreased by 11.5 mmHg, body fat percentage by 8.8%, waist-to-hip ratio improved from 0.83 to 0.80, and HbA1c declined by 0.6%. Greater app engagement was linked to 2.0-2.2% additional weight loss, 0.72 kg/m² more BMI reduction, and up to 2.9 mmHg greater systolic BP drop. More frequent health coach contact contributed modest added improvements for weight and BMI. Weight loss was significantly lower among East Asians and those with hyperglycemia. CONCLUSION: In this real-world Asian setting, hybrid obesity care combining GLP-1 RAs with digital coaching produced clinically meaningful, sustained weight and metabolic improvements. Higher engagement consistently enhanced outcomes, supporting scalable integrated models tailored for diverse populations.