Toward Closed-Loop Movement–State Coupling: An Integrative Framework for Multisystem Response, Recovery, and Adaptive Movement Exposure

Movement is commonly prescribed and studied through external dose, task category, or predefined progression, although the response to a given movement exposure depends on the measured state in which it is received. Existing allostatic, internal-load, network-physiology, recovery, digital-mobility, and multi-omics literatures address individual components of this problem, while recent adaptive and just-in-time intervention frameworks already formalize prospective tailoring and feedback-based decision processes. Adaptive Movement Research (AMR) is proposed here as a falsifiable integrative research framework rather than a new physiological theory or validated clinical system. AMR examines repeated transitions linking measured state representation, movement exposure, movement-derived output, multisystem response, recovery/resolution, and subsequent state. Its distinctive hypothesis is that explicit integration of these components may provide incremental short-horizon prediction of the response to the next movement exposure beyond conventional dose-, readiness-, physiological-, contextual-, or clinician-guided information. The framework separates established evidence from AMR-specific unknowns and defines a staged research programme progressing from technical validity and within-state reproducibility to state sensitivity, incremental information, next-response prediction, prospective adaptive intervention, and only subsequently to long-term health outcomes. Movement-derived output, multidomain integration, and recovery kinetics must each demonstrate incremental value or be removed from the framework. Negative findings are therefore treated as explicit falsification gates rather than reinterpreted as support for a more complex latent state. AMR’s proposed contribution is not movement individualization, multisystem physiology, state dependence, recovery monitoring, gait phenotyping, or adaptive feedback in isolation. Its testable contribution is whether their explicit integration into a repeated state-transition loop improves prediction and, after separate prospective validation, supports superior next-exposure selection relative to an appropriate conventional comparator.