To estimate the pooled prevalence and explore temporal patterns of disordered eating attitudes and body dissatisfaction among undergraduate Nutrition students in Brazil through a systematic review and meta-analysis. Positive screening for disordered eating attitudes and clinically relevant body dissatisfaction was consistently reported among Nutrition undergraduates in Brazil. However, given the small number of heterogeneous cross-sectional studies, predominantly based on non-probabilistic samples, the pooled prevalence estimates should be interpreted as summary estimates derived from the currently available evidence rather than as definitive national prevalence estimates. Level of Evidence: Level I, systematic review and meta-analysis.
Obesity requires structured and coordinated surveillance, because clinical, anthropometric, behavioural, technological, and epidemiological information is often collected in fragmented systems. In Italy, data on obesity and its comorbidities are distributed across registries, observational studies, and local institutional initiatives. This fragmentation limits longitudinal monitoring, national comparability, and the development of data-driven tools for prevention, diagnosis, and clinical management. This paper presents the conceptual proposal of OBESI–NET (Osservatorio Basato su Ecosistema Sanitario Digitale di Intelligenza Artificiale per l’Obesità–NETwork Nazionale). OBESI–NET is proposed as a national software ecosystem intended to connect Italian Society of Obesity clinical centers, healthcare professionals, and artificial intelligence engineering groups. The proposed infrastructure would support the standardized collection, secure integration, and analysis of heterogeneous data. These data include clinical, behavioural, anthropometric, imaging, wearable sensor, and, in future developments, multi-omics data. The expected contribution of OBESI–NET is not the presentation of an already implemented system, but the definition of a national model for obesity-centerd digital surveillance and AI (Artificial Intelligence)-supported precision medicine. Compared with existing digital obesity projects and registries, the proposal emphasizes integration within routine clinical practice and coordination through SIO (Società italiana dell’Obesità) centers. It also emphasizes longitudinal real-world data collection, multimodal data integration, and privacy-preserving approaches, such as federated learning. If implemented and validated, OBESI–NET could provide a framework for epidemiological monitoring, clinical research, treatment stratification, and public health planning in obesity care. Level of Evidence: Level V, expert opinion and conceptual project proposal.
