Analysis Method of Real-World Digital Biomarkers for Clinical Impact in Cancer Patients
We have developed an effective way to estimate daily steps of consumer wearable data containing unknown data gaps. Daily step counts on days with sufficient sampling are a strong predictor of the timing and occurrence of clinical events, with individuals exhibiting higher daily step counts having reduced hazard of death or clinical events.
Authors
- Kate Lyden (ORCID: https://orcid.org/0000-0002-3343-9007)
- Elad Neeman (ORCID: https://orcid.org/0000-0002-3133-0956)
- Sharon Davis (ORCID: https://orcid.org/0000-0001-6290-5196)
- Reem Yunis (ORCID: https://orcid.org/0000-0001-5439-1607)
- Ingrid Oakley‐Girvan (ORCID: https://orcid.org/0000-0003-0328-921X)
- Raymond Liu (ORCID: https://orcid.org/0000-0002-4667-9632)
- Ai Kubo (ORCID: https://orcid.org/0000-0002-1035-828X)
- Sara Aghaee (ORCID: https://orcid.org/0000-0003-4912-5069)
- Jennifer M. Blankenship (ORCID: https://orcid.org/0000-0002-8052-2516)
- Yaya Zhai
Institutions
- Kaiser Permanente (US)
- Global Strategy Group (US)
- West Coast University (US)
- Public Health Institute (US)
- Kaiser Permanente San Rafael Medical Center (US)
Publication Details
- Journal
- Digital Biomarkers
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1159/000543898
- Citations
- 2
- Primary Topic
- Cancer survivorship and care
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- National Cancer Institute