@inproceedings{mitrovic-etal-2025-preliminary,
    title = "A Preliminary Study on {NLP}-Based Personalized Support for Type 1 Diabetes Management",
    author = "Mitrovi{\'c}, Sandra  and
      Fontana, Federico  and
      Zignoli, Andrea  and
      Mattioni Maturana, Felipe  and
      Berchtold, Christian  and
      Malpetti, Daniele  and
      Scott, Sam  and
      Azzimonti, Laura",
    editor = "Ananiadou, Sophia  and
      Demner-Fushman, Dina  and
      Gupta, Deepak  and
      Thompson, Paul",
    booktitle = "Proceedings of the Second Workshop on Patient-Oriented Language Processing (CL4Health)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/2025.cl4health-1.25/",
    pages = "298--302",
    ISBN = "979-8-89176-238-1",
    abstract = "The proliferation of wearable devices and sports monitoring apps has made tracking physical activity more accessible than ever. For individuals with Type 1 diabetes, regular exercise is essential for managing the condition, making personalized feedback particularly valuable. By leveraging data from physical activity sessions, NLP-generated messages can offer tailored guidance to help users optimize their workouts and make informed decisions. In this study, we assess several open-source pre-trained NLP models for this purpose. Contrary to expectations, our findings reveal that models fine-tuned on medical data or excelling in medical benchmarks do not necessarily produce high-quality messages."
}