Table · dataset · 2026
Table 1_WMRE2030: integrating wearable devices, multi-omics, and artificial intelligence–driven real-time feedback into a daily-scale closed-loop framework for a new era of precision exercise.docx
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<p>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.
Description
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.
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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.</p>
Links
Where it is published
- DOI doi.org/10.3389/fphys.2026.1883295.s001 ↗
DOI / persistent id · from figshare com
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from figshare com
Topics
- From keywords
- Artificial intelligence · Astronomy & Astrophysics · Chemistry · Computer Science & AI · Digital health · Earth & Environmental Science · Economics & Finance · Engineering · Exercise physiology · Humanities · Life Sciences · Medicine & Health · Ocean & Atmospheric Science · Social Science
- Inferred from text
- Longitudinal study 65% · Tabular 65%
Provenance · 1 source records, 21 field assertions
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| concepts[field].anzsrc:group:4602 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['artificial intelligence'] |
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