Exercise adaptation and proactive health: an information-theoretic framework based on load entropy
Year: 2026
Type: article
Abstract: Exercise is among the most powerful stimuli for promoting health, yet exercise science still prescribes load largely through fixed, population-derived intensity zones that overlook how individuals perceive and adapt to training stimuli. This review develops Proactive Health as a scientific framework in which exercise is the central means of cultivating adaptive capacity and introduces an information-theoretic model – load entropy – to explain why individuals respond differently to the same exercise challenge. Drawing on principles from complexity science, information theory and non-equilibrium thermodynamics, the framework conceptualises health as adaptive capacity rather than the absence of disease and positions entropy as a central organising principle of biological adaptation. Within this view, health is reframed as adaptive capacity rather than the absence of disease, and entropy becomes a central organising principle of biological adaptation: exercise perturbs the system, transiently raises local entropy and informational uncertainty, and thereby triggers the self-organised reorganisation that underlies training adaptation. Building on psychophysics, the load entropy model reconceptualises exercise load not as a purely mechanical scalar but as the perceptual uncertainty an individual experiences in discriminating a stimulus, which peaks at an entropy-defined “effective stimulus” – a hypothesised, individually and momentarily specific threshold of maximal adaptive challenge. We show how this construct connects to established practice such as heart-rate-variability-guided training and load monitoring, and how it could inform personalised exercise prescription, rehabilitation and digital-twin, AI-enabled care. As a conceptual and hypothesis-driven review, the work generates testable hypotheses for personalised exercise prescription, rehabilitation and exercise monitoring and provides a translational framework for proactive, adaptive healthcare.
Authors: Xiangchen Li, Yang Gao, Lei Shi
Language: English
Subfield: Orthopedics and Sports Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7208824692