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A conceptual schema was developed to represent PA and its linguistic attributes. Five BERT models and three modern LLMs (Llama3-8B, MedAlpaca-13B, and PMC-Llama-13B) were evaluated for classifying PA attributes (binary status, negation, exclusion, and an eleven-class Category) on pre-extracted PA-related sentences.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Clinical notes were a richer source of PA information than structured ICD or SDoH data. On binary tasks, the best BERT model reached F1 0.619 (Exclusion); with Supervised Fine-Tuning (SFT), Llama3-8B reached F1 0.689 (Exclusion). On the 11-class Category task, performance was modest (best macro-F1 0.262, ROC-AUC 0.803, by Llama3-8B).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>In-Context Learning (ICL) was highly variable: while Llama3-8B-ICL achieved the best ROC-AUC on Category, the domain-specific MedAlpaca-13B and PMC-LLaMA-13B essentially failed. These results, together with sparsely represented PA elements (Amount, Frequency, Assessment) and the absence of a downstream evaluation, position this work as an initial proof of feasibility, with supervised domain adaptation still required for reliable clinical PA extraction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>We contribute a PA data model, annotation schema, and a working NLP pipeline with a BERT\/LLM benchmark for sentence-level PA attribute classification. The pipeline supports future end-to-end PA extraction and downstream applications such as phenotyping, risk prediction, and cohort identification.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag101","type":"journal-article","created":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T11:32:29Z","timestamp":1781004749000},"page":"1875-1887","source":"Crossref","is-referenced-by-count":0,"title":["Benchmarking information extraction of physical activity from electronic health record with large language models: an natural language processing pipeline and comparative evaluation"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4322-6753","authenticated-orcid":false,"given":"Han","family":"Yang","sequence":"first","affiliation":[{"name":"Institute for Health Informatics, University of Minnesota , Minneapolis, MN 55455,","place":["United 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