Key result
Load entropy model reconceptualizes exercise load as perceptual uncertainty to guide personalized exercise prescription.
Why the study?
Exercise science still prescribes load largely through fixed, population-derived intensity zones that overlook how individuals perceive and adapt to training stimuli.
Proposes a novel information-theoretic framework based on load entropy to personalize exercise prescription and conceptualize health as adaptive capacity.
May inform entropy-guided personalization in exercise prescription; leaves open prospective validation before clinical adoption.
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.
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Li et al. (2026) conducted a review in Exercise adaptation and proactive health. Load entropy model was evaluated. The load entropy model reconceptualizes exercise load as perceptual uncertainty to explain individual adaptation differences, providing a framework for personalized exercise prescription.
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