PaperarXiv:2611.03983
A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data (FITTER; arXiv 2611.03983)
arXiv 2611.03983v1 (2026-11-02)
TL;DR
Pipeline: role agents + deterministic guideline auditor. Evaluation: computational metrics, component ablation, structured clinician review.
- Form factor: wrist
Abstract
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.
Verbatim from arXiv (opens in a new tab) · arXiv metadata (abstracts are released under CC0)
Results on ChatPPG leaderboards
All leaderboardsNo results from this paper on ChatPPG leaderboards yet. Browse the leaderboards.
Tasks & signals
Signals
Keywords
Models
How it works
Cite this paper
Li, X., Lin, Y., Wu, Y., Wang, F., Wang, M., Gary, R. A., Kariuki, J., Smith, J., Billinger, S. A., Xiao, C., Hu, X., & Yan, R. (2026). A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data (FITTER; arXiv 2611.03983). arXiv 2611.03983v1. https://arxiv.org/abs/2611.03983
Li, Xintong, Yufen Lin, Yuhua Wu, Fengran Wang, Minxiao Wang, Rebecca A. Gary, and Jacob Kariuki, et al. 2026. "A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data (FITTER; arXiv 2611.03983)." arXiv 2611.03983v1. https://arxiv.org/abs/2611.03983.
Li, X., Lin, Y., Wu, Y., Wang, F., Wang, M., Gary, R. A. and Kariuki, J. et al. (2026) 'A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data (FITTER; arXiv 2611.03983)', arXiv 2611.03983v1. Available at: https://arxiv.org/abs/2611.03983
Li, Xintong, et al. "A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data (FITTER; arXiv 2611.03983)." arXiv 2611.03983v1, 2026. https://arxiv.org/abs/2611.03983.
X. Li et al., "A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data (FITTER; arXiv 2611.03983)," arXiv 2611.03983v1, 2026, [Online]. Available: https://arxiv.org/abs/2611.03983.
@misc{li2026guidelinegrounded,
title = {{A Guideline-Grounded Multi-Agent Framework for Personalized Exercise Prescription from Clinical and Wearable Data (FITTER; arXiv 2611.03983)}},
author = {Li, Xintong and Lin, Yufen and Wu, Yuhua and Wang, Fengran and Wang, Minxiao and Gary, Rebecca A. and Kariuki, Jacob and Smith, James and Billinger, Sandra A. and Xiao, Canhua and Hu, Xiao and Yan, Runze},
year = {2026},
howpublished = {arXiv 2611.03983v1},
eprint = {2611.03983},
archivePrefix = {arXiv}
}Added to the ChatPPG research hub
