ArticleFrontiers in public health2025
Effect of artificial intelligence driven therapeutic lifestyle changes (AI-TLC) intervention on health behavior and health among obesity pregnant women in China: a randomized controlled trial protocol.
Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence-Based Risk Stratification in Obesity Care: From Diagnosis to Personalised Treatment Pathways.Diagnostics (Basel, Switzerland) · 2026Review
- Intervention for physical activity in pregnant women with pre-pregnancy obesity or overweight based on the Fogg Behavior Model.Frontiers in sports and active living · 2025Article
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Authors and funding
10 authors.
Funding
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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 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.
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