The Generative AI Meta-Evaluation (GAME) Study Framework: Global, Regional, and Country-Specific Unequal Difficulty of High BMI Intervention

Abstract

Abstract Background High body mass index (BMI) presents a serious and ongoing global health challenge. However, the difficulty of high BMI intervention has not yet been systematically evaluated. Methods We developed a Generative Artificial Intelligence Meta-Evaluation (GAME) framework, which integrated 18 indicators from 4 dimensions, including “Macro-System Level”, “Socio-Cultural Level”, “Community-Family Level”, and “Individual Level” to evaluate the difficulty of high BMI intervention across 226 locations. The GAME framework applies 8 leading AI models to generate intervention difficulty scores (IDS) of each indicator on a scale from 1 to 5, with higher scores indicating greater difficulty. Meta-analysis was conducted to derive combined scores, evaluate the heterogeneity and sensitivity. Final intervention difficulty scores were calculated as the weighted sum of all 18 indicators. Additionally, SHapley Additive exPlanation (SHAP) values were used to evaluate the importance of each indicator in determining the intervention difficulty. Results The global difficulty of high BMI intervention shows significant imbalance. Norway (IDS = 1.48) exhibited the easiest intervention, while Yemen (IDS = 4.56) faced the greatest challenge. Regions such as Western Europe, Australasia, and High-income Asia Pacific showed lower intervention difficulty, reflecting there are mature public health frameworks, supportive social-cultural environments for healthy lifestyles, and high levels of health awareness. On the contrary, countries in North Africa and Middle East, South Asia, Oceania, and Sub-Saharan Africa faced higher intervention challenges, suggesting the need for long-term, collaborative efforts from multiple sectors. Among the 18 indicators, “Cognition and Awareness” has the most significant impact on intervention difficulty, with the SHAP value of 31.03, followed by “Family life and cognitive patterns” (18.08) and “Health Care System” (11.7). Furthermore, the IDS for high BMI was significantly correlated with Socio-Demographic Index (SDI). Higher SDI values were associated with easier interventions. Finally, the independent external empirical verification demonstrated high consistency between intervention difficulty and increase in annual prevalence of obesity, population mean BMI, and national policies. It supported the GAME framework to characterize global heterogeneity in high BMI intervention challenge. Global results were freely available at http://www.deepburden.com/high-bmi . Conclusion The difficulty of high BMI intervention varies widely across countries and regions, highlighting the need for comprehensive strategies and governance to address the growing health issue effectively.

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