Abstract
INTRODUCTION: Obesity has reached epidemic proportions globally, posing significant challenges to public health and economic stability. In China, the prevalence of obesity is increasing rapidly, particularly among pregnant women, who face unique risks due to the complex interplay between obesity and pregnancy outcomes. This study aims to evaluate the effectiveness of an Artificial Intelligence-driven Therapeutic Lifestyle Change (AI-TLC) intervention in improving health behaviors and outcomes among obese pregnant women in China. METHODS AND ANALYSIS: This randomized controlled trial will recruit pregnant women aged 18 years or older with a singleton pregnancy between 8 and 12 weeks of gestation and a pre-pregnancy BMI of ≥30.0 kg/m(2). Participants will be randomly assigned to one of three groups: a manual intervention group, an AI intervention group, and a combined AI and manual intervention group. The intervention will focus on therapeutic lifestyle changes, including behavioral modifications, dietary adjustments, and physical activity promotion, supported by a multidisciplinary team. Primary outcomes will include maternal BMI, weight, and adverse pregnancy outcomes, while secondary outcomes will assess physiological indicators, quality of life, mental health, and lifestyle factors. RESULTS: The study will evaluate the effects of health interventions on obese pregnant women through primary outcomes (e.g., BMI, weight, adverse pregnancy outcomes) and secondary outcomes (e.g., physiological indicators, quality of life, mental health) using various statistical methods. The results will provide insights into the intervention's effectiveness and cost-effectiveness across different socioeconomic groups. DISCUSSION: The anticipated findings are expected to demonstrate the efficacy of AI-TLC interventions in managing obesity during pregnancy. This study will contribute valuable evidence to the limited research on AI-based interventions for obese pregnant women, offering potential implications for the development of personalized, efficient, and innovative health strategies. The findings may also inform public health initiatives aimed at improving maternal and child health outcomes in the context of obesity.