Ai-Enabled Mhealth Integration For Home Exercise Monitoring: A Novel Assessment Of Cardiometabolic Improvements In Borderline Obese Adults - Balakrishna, Bathala; Alagesan, Jagatheesan (Journal of Neonatal Surgery, 2025)
Background: Borderline obesity significantly elevates long-term cardiometabolic risk. Home-based exercise is a recommended strategy, but poor adherence undermines effectiveness. Artificial intelligence (AI)-enabled mobile health (mHealth) systems can improve monitoring, motivation, and personalization. Objectives: To evaluate the comparative effectiveness of AI-enabled mHealth-guided home exercise versus traditional home exercise in improving anthropometric, cardiovascular, and lipid markers in borderline obese adults. Methods: A 12-week randomized controlled trial was conducted among adults aged 25–45 years with BMI 25–29.9 kg/m². Participants were assigned to either (1) Traditional Home Exercise Group (THEG) or (2) AI-enabled mHealth Home Exercise Group (AI-mHEG). Both groups performed the same standardized exercise regimen (aerobic + strengthening), but AI-mHEG received real-time AI feedback, adherence tracking, automated progressions and exercise-form monitoring. Outcomes measured pre and post-intervention included BMI, WHR, blood pressure, fasting blood glucose, and lipid profile. Data were analyzed using paired and independent t-tests at p < 0.05. Results: AI-mHEG demonstrated significantly greater improvements in BMI, waist circumference, systolic BP and triglycerides. Exercise adherence was significantly higher in AI-mHEG (89%) compared to THEG (63%) (p < 0.01). Conclusion: AI-enabled mHealth systems markedly enhanced exercise adherence and produced superior cardiometabolic benefits compared to traditional unguided home-based exercise. AI-integrated platforms represent a promising, scalable therapeutic strategy for borderline obese populations