Precision exercise prescription: a review integrating autonomic nervous system monitoring and machine learning to optimize VO2max adaptation for health and performance

Purpose Individual adaptation to standardized aerobic training exhibits significant variability, with approximately 15% of participants identified as non-responders regarding maximal oxygen uptake (VO 2 max) gains. This paper investigates how monitoring the autonomic nervous system (ANS) through heart rate variability (HRV) can serve as a non-invasive, practical tool to personalize exercise prescriptions, improve training responsiveness, and enhance cardiovascular health. Methods The study synthesizes evidence from over 40 controlled trials and meta-analyses. It evaluates classical HRV metrics, such as LnRMSSD and the LF/HF ratio, alongside emerging methodologies including detrended fluctuation analysis (DFA-α1) and AI-powered multisensory fusion involving electrodermal activity and skin temperature. Results Findings indicate that early HRV shifts during the first week of training are moderate predictors of long-term VO 2 max adaptation. HRV-guided protocols lead to more uniform VO 2 max gains and fewer non-responders compared to fixed-intensity training. Furthermore, advanced metrics and machine learning models offer more transparent, context-aware insights into physiological readiness than traditional linear analysis. Conclusions Transitioning from “one-size-fits-all” models to ANS-guided, population-stratified prescriptions are essential for maximizing the preventive benefits of exercise. Integrating wearable technology and explainable AI into clinical practice provides a scalable framework for improving functional capacity and cardiovascular health across diverse populations.