Assessment of Artificial Intelligence-Driven Fitness and Health Management Programs for Adolescents Using the SuperHyperSoft Set Framework

Abstract

The rising use of artificial intelligence in adolescent fitness and health applications has 
created a need for more sophisticated evaluation frameworks. These platforms often operate in 
complex, dynamic environments where outcomes depend on behavioral, emotional, and 
contextual factors. Traditional evaluation models fail to fully capture this complexity. In this 
study, we apply the SuperHyperSoft Set (SHSS) framework to assess five AI-based health 
platforms targeted at adolescents. SHSS provides a multi-layered structure for organizing and 
analyzing evaluation criteria, while also allowing experts to express uncertainty and 
disagreement in a mathematically consistent way. Through a real-world case study, we 
demonstrate how the model supports a more nuanced and interpretable evaluation. The results 
show a high alignment between the model’s rankings and expert judgments, validating its 
effectiveness. The study also includes sensitivity analysis to confirm the robustness of the 
approach. The findings offer valuable guidance for developers, public health managers, and 
educators working at the intersection of AI and adolescent wellness. 

DOI: 10.5281/zenodo.15335816