{
  "abstract": "Wearable devices offer a scalable, objective measurement of physical activity, but most studies reduce these data to a single metric over 7-days. Here, we compare wearable-derived phenotyping approaches to understand their impact on activity-disease associations, analyzing 11 million days of Fitbit data from 29,303 All of Us Research Program participants. We derive four daily activity metrics (step count, peak 1-min cadence, peak 30-min cadence, and heart rate per step) across five time-windows (1-day, 1-week, 1-month, 6-months, 1-year), then run phenome-wide analyses across > 700 incident and >1,400 prevalent disease outcomes. Though all phenotypes are highly correlated, longer measurement windows yield stronger, more stable associations: 1-year step count was associated with 389 prevalent and 61 incident outcomes (versus 271 and 33 for 1-day step count) after Bonferroni-correction, including novel links to chronic pain syndrome and SARS-CoV-2. Differences between prevalent and incident associations suggest that activity metrics may act as both early markers of disease and risk factors. These findings highlight how large-scale, longitudinal wearable data can advance understanding of health and disease.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "name": "Evelynne S. Fulda",
      "orcid": "0000-0001-5903-131X"
    },
    {
      "name": "Bennett J. Waxse",
      "orcid": "0000-0001-5086-3419"
    },
    {
      "name": "Slavina B. Goleva",
      "orcid": "0000-0001-5177-9286"
    },
    {
      "name": "Tam C. Tran"
    },
    {
      "name": "Henry J. Taylor",
      "orcid": "0000-0003-2088-5240"
    },
    {
      "name": "Caitlin P. Bailey",
      "orcid": "0000-0001-6141-6673"
    },
    {
      "name": "Dana L. Wolff-Hughes"
    },
    {
      "name": "Huan Mo"
    },
    {
      "name": "Chenjie Zeng",
      "orcid": "0000-0002-0149-5661"
    },
    {
      "name": "Jacob M. Keaton"
    },
    {
      "name": "Tracey M. Ferrara",
      "orcid": "0000-0001-9599-413X"
    },
    {
      "name": "Anya Topiwala",
      "orcid": "0000-0002-8408-0372"
    },
    {
      "name": "Aiden Doherty",
      "orcid": "0000-0003-1840-0451"
    },
    {
      "name": "Joshua C. Denny",
      "orcid": "0000-0002-3049-7332"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/exas8730wbj80.bib",
  "cited_by": 0,
  "doi": "10.1038/s41467-026-78529-2",
  "fulltext_url": null,
  "id": "exas8730wbj80",
  "issns": [
    "2041-1723"
  ],
  "landing_url": "https://www.nature.com/articles/s41467-026-78529-2",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2737|Physical Activity and Health"
    ]
  ],
  "openalex": "W7222292725",
  "pdf_url": "https://www.nature.com/articles/s41467-026-78529-2_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-14",
  "publisher": "Springer Science and Business Media LLC",
  "ris": "https://research.draconitic.com/paper/exas8730wbj80.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:38Z",
      "native_id": "10.1038/s41467-026-78529-2",
      "source": "crossref",
      "url": "https://www.nature.com/articles/s41467-026-78529-2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2737|Physical Activity and Health",
      "subfield": "physiology"
    }
  ],
  "title": "11 million days of longitudinal Fitbit data reveal novel future health insights",
  "title_text": "11 million days of longitudinal Fitbit data reveal novel future health insights",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:38Z",
  "url": "https://research.draconitic.com/paper/exas8730wbj80",
  "venue": "Nature Communications",
  "versions": []
}