The DNA of Fit-Tech: optimizing physical performance through genetic analysis and AI-driven exercise planning

Atonte, Brandon Fangmbeng (2025) The DNA of Fit-Tech: optimizing physical performance through genetic analysis and AI-driven exercise planning. International Journal of Science and Research Archive, 15 (1). pp. 1552-1556. ISSN 2582-8185

This study explores the integration of genetic analysis, body composition assessment, and artificial intelligence (AI) to develop personalized fitness and nutritional programs. By analyzing genetic variations (e.g., ACTN3, ACE, BDNF) and leveraging AI-driven models, we propose a framework that optimizes training regimens, nutritional strategies, and injury prevention with 87% predictive accuracy for training responses. While genetics provide critical insights, athletic success remains a multifactorial outcome influenced by environment, psychology, and epigenetics. Ethical considerations, including data privacy and model bias, are critically addressed. Preliminary validation demonstrates significant improvements over traditional methods, though longitudinal studies are needed to confirm long-term efficacy.