سامانه کنگره های دانشگاه علوم پزشکی و خدمات بهداشتی و درمانی آذربایجان غربی

صفحه اصلی / بیستمین همایش سالیانه بیماری های شایع گوارش و کبد کودکان ایران و دومین همایش بین المللی چاقی کودکان

Artificial Intelligence-Powered Wearables and Lifestyle Interventions: Multidimensional Monitoring as a Cutting-Edge Strategy for Childhood Obesity Prevention

نویسندگان :

Ladan Soltanzadeh¹ Ali Mirzaee AghGonbad² Sulmaz Soltanzadeh³

1- دانشگاه علوم علوم پزشکی ارومیه 2- دانشگاه تبریز 3- دانشگاه علوم پزشکی تهران

کلمات کلیدی :

BMI،childhood obesity،family-based intervention،genetic algorithm،machine learning،multi component،technology،self-monitoring،wearables

چکیده :

Background and Aim: Childhood obesity is a global health crisis associated with type 2 diabetes, cardiovascular diseases, and psychosocial disorders. Despite extensive prevention efforts, its prevalence continues to rise, particularly in underserved communities and high-risk groups. Traditional methods such as nutritional counseling and exercise programs show limited efficacy due to cost, time constraints, and lack of personalization. AI-powered wearables and multidimensional monitoring have emerged as innovative tools for tracking physical activity, sleep, nutrition, and metabolic indicators. This review aims to comprehensively analyze AI-driven wearables and targeted behavioral interventions for childhood obesity prevention, proposing an integrated framework to combine wearable data with machine learning algorithms. Methods: This narrative systematic review examined studies published from 2020 to April 2025. Comprehensive searches were conducted in PubMed, EMBASE, Scopus, and Cochrane using keywords related to AI, wearables, childhood obesity, and multidimensional monitoring. Inclusion criteria encompassed original studies, systematic reviews, and meta-analyses focused on children and adolescents (0–18 years) utilizing AI-powered wearable interventions. Results: The review included 34 studies. AI wearables yielded an average BMI reduction of 0.8–1.2 kg/m² over 6–12 months and increased moderate-to-vigorous physical activity by 15–25 minutes/day in 60% of studies. Machine learning models integrating wearable data achieved superior obesity risk prediction accuracy (AUC: 0.82–0.89) compared to traditional criteria. AI-tailored recommendations improved lifestyle intervention adherence by 35%. However, only 18% of studies included underrepresented populations, and fewer than 25% assessed outcomes beyond 12 months. Conclusion: AI-powered wearables demonstrate significant potential for personalized obesity prevention and improved health metrics. Multidimensional data integration enhances predictive accuracy and intervention efficacy. However, scalability, equity, and long-term data gaps remain critical challenges. Future research must prioritize inclusive study designs and longitudinal trials to enable equitable clinical implementation.