{
  "abstract": "Wearable accelerometers enable continuous monitoring of physical activity (PA), yet conventional summary measures and population-wide clustering approaches may overlook disease-associated behavioral heterogeneity. Here we introduce LocPA, a computational framework that identifies disease-enriched PA patterns through confounder-adjusted activity profiling, similarity network construction, local disease-enrichment testing and community detection. Applying LocPA to 24-hour accelerometry profiles from the UK Biobank, we identified two recurring PA phenotypes across six diseases spanning cardiometabolic, neurological, psychiatric, renal and cardiovascular conditions: a low-activity phenotype characterized by reduced daytime activity and a sleep-disrupted phenotype characterized by reduced daytime and elevated nocturnal activity. In hypertension and diabetes, the sleep-disrupted phenotype was associated with greater disease burden and less favorable clinical and metabolic profiles, differences not consistently captured by conventional PA summary measures. LocPA also identified disease-specific temporal patterns, particularly in Parkinsons disease. Analyses in an independent National Health and Nutrition Examination Survey cohort supported the recurrence of hypertension- and diabetes-enriched patterns across populations and accelerometry measurements. LocPA provides a flexible, disease-driven approach for uncovering clinically relevant temporal PA patterns, with potential applications in disease risk characterization and personalized behavioral interventions.",
  "abstract_shown": true,
  "affiliations": [
    "University of Michigan"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "h. qian"
    },
    {
      "name": "c. wu"
    },
    {
      "name": "B. Li"
    },
    {
      "name": "A. Rosenzweig"
    },
    {
      "affs": [
        0
      ],
      "name": "M. Wang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/4jkj7m6wzay4w.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.22.26366260",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26366260v1.full",
  "id": "4jkj7m6wzay4w",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26366260v1",
  "language": "en",
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Article"
    ],
    [
      "medrxiv",
      "sports medicine"
    ],
    [
      "openalex",
      "2737|Physical Activity and Health"
    ]
  ],
  "openalex": "W7223687340",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26366260v1.full.pdf",
  "pmcid": "PMC13734036",
  "pmid": "42990532",
  "published": "2026-10-22",
  "publisher": "Cold Spring Harbor Laboratory Preprints",
  "ris": "https://research.draconitic.com/paper/4jkj7m6wzay4w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:46Z",
      "native_id": "10.64898/2026.10.22.26366260",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.10.22.26366260v2"
    },
    {
      "first_seen": "2026-11-08T12:23:41Z",
      "native_id": "PMC13734036",
      "source": "pmc",
      "url": "https://doi.org/10.64898/2026.10.22.26366260"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2737|Physical Activity and Health",
      "subfield": "physiology"
    }
  ],
  "title": "A Local Enrichment Computational Framework to Identify Disease-Enriched Physical Activity Patterns",
  "title_text": "A Local Enrichment Computational Framework to Identify Disease-Enriched Physical Activity Patterns",
  "type": "preprint",
  "updated_at": "2026-11-10T00:22:06Z",
  "url": "https://research.draconitic.com/paper/4jkj7m6wzay4w",
  "venue": "medRxiv",
  "versions": []
}