WMRE2030: integrating wearable devices, multi-omics, and artificial intelligence-driven real-time feedback into a...

Precision exercise is increasingly supported by wearable technologies, multi-omics profiling, and artificial intelligence; however, these components are often applied in isolation, limiting their capacity to support continuous and interpretable decision-making in real-world settings. This review proposes WMRE2030, a day-scale closed-loop methodological framework integrating Wearables (W), Multi-omics (M), AI-driven Real-Time Feedback (R), and Exercise (E). Within this architecture, wearable devices continuously capture physiological, behavioural, and contextual states; multi-omics provides relatively stable or periodically updated biological background, response potential, and safety constraints; artificial intelligence organizes heterogeneous information into evidence-constrained and traceable decision support; and exercise functions as the executable intervention whose outcomes are returned to the system for iterative updating. The framework emphasizes "the same architecture, different parameters, " allowing sensors, omics inputs, decision thresholds, and levels of professional oversight to be adapted across clinical populations, the general population, and high-performance athletes. WMRE2030 should currently be regarded as a testable methodological roadmap rather than a validated autonomous prescription system. Future research should evaluate its incremental value through longitudinal, micro-randomized, and multicentre studies, while addressing interoperability, privacy, algorithmic transparency, safety brakes, cost-effectiveness, scalability, and equitable access. By connecting biological interpretation with continuous sensing and adaptive decision support, WMRE2030 may provide a practical pathway toward more reliable and sustainable precision exercise.