Metraipolis › archive › dc_952ee366efce

A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data — arXiv:2611.03983

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03983
License (as archived): CC0-1.0
Archived: 2026-11-04T21:35:34.648Z
SHA-256 of the archived text: cb13d2124f99d9c33dd9422cf8193739ece68582c4d80e13cdc1922ee578a39d
Stable URL of this page: https://metraipolis.com/documento/dc_952ee366efce

How to cite

A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data — arXiv:2611.03983 — arXiv (en), https://arxiv.org/abs/2611.03983 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_952ee366efce (Metraipolis archive dc_952ee366efce, sha256 cb13d2124f99d9c33dd9422cf8193739ece68582c4d80e13cdc1922ee578a39d)

BibTeX

@misc{dc952ee366efce,
  title = {A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data — arXiv:2611.03983},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03983},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_952ee366efce; sha256 cb13d2124f99d9c33dd9422cf8193739ece68582c4d80e13cdc1922ee578a39d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03983. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Translating population-level exercise guidance into individualized prescriptions requires integrating clinical and wearable data, including diagnoses, medications, functional limitations, and baseline activity, with condition-specific recommendations, a process difficult to scale beyond specialist rehabilitation settings. We developed FITTER, a multi-agent framework that integrates these data with condition-specific guidance to generate personalized FITT-VP prescriptions and applies deterministic safety audit and repair to correct rule-checkable violations. We evaluated FITTER using data from 400 All of Us participants (300 cancer, 100 stroke) against five LLM comparators through computational evaluation, ablation, and structured clinician review. FITTER achieved 98.5% safety-constraint compliance versus 75.8-85.8% for comparators and showed greater quantitative dosing precision and guideline alignment. Cancer-cohort ablation identified safety audit and repair as the largest contributor to safety-constraint compliance. Clinicians reviewed 80 prescriptions across safety, personalization, instruction concreteness, anticipated effectiveness, and clinical acceptability. FITTER provides a clinician-facing approach integrating clinical and wearable data, guidance, and explicit safety constraints.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2611.03983 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T21:35:34.326Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).