Exercise adaptation and proactive health: an information-theoretic framework based on load entropy
TL;DR
Proactive Health is developed as a scientific framework in which exercise is the central means of cultivating adaptive capacity and an information-theoretic model – load entropy – is introduced to explain why individuals respond differently to the same exercise challenge.
Abstract
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.
