Machine learning applied to wearable fitness tracker data and the risk of hospitalizations and cardiovascular events
ML models applied to Fitbit data demonstrate promise in predicting clinical outcomes with strong performance for predicting all-cause hospitalizations and modest performance for predicting incident CVD. Wearable technology could play a role in risk assessment and patient management.
Authors
- Andrew Voigt (ORCID: https://orcid.org/0009-0002-1577-6541)
- Aditi Naniwadekar (ORCID: https://orcid.org/0000-0002-0880-405X)
- N.A. Mark Estes (ORCID: https://orcid.org/0000-0002-5461-7247)
- Samir Saba (ORCID: https://orcid.org/0000-0003-4669-803X)
- Krishna Kancharla (ORCID: https://orcid.org/0000-0003-2550-9359)
- Aditya Bhonsale (ORCID: https://orcid.org/0000-0001-7827-5977)
- Alaa Shalaby (ORCID: https://orcid.org/0000-0002-2000-7678)
- John Kundrick (ORCID: https://orcid.org/0009-0004-8076-279X)
- Sandeep Jain (ORCID: https://orcid.org/0000-0002-5815-370X)
- Virginia Singla
Institutions
- University of Pittsburgh Medical Center (US)
Publication Details
- Journal
- American Journal of Preventive Cardiology
- Published
- 2025-05-17
- DOI
- https://doi.org/10.1016/j.ajpc.2025.101006
- Citations
- 8
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 23.50