Intelligent risk prediction in public health using wearable device data

Marium M Raza; Kaushik P Venkatesh; Joseph C Kvedar

doi:10.1038/s41746-022-00701-x

Intelligent risk prediction in public health using wearable device data

NPJ Digit Med. 2022 Oct 13;5(1):153. doi: 10.1038/s41746-022-00701-x.

Authors

Marium M Raza¹, Kaushik P Venkatesh², Joseph C Kvedar²

Affiliations

¹ Harvard Medical School, Boston, MA, USA. mraza@hms.harvard.edu.
² Harvard Medical School, Boston, MA, USA.

Abstract

The importance of infection risk prediction as a key public health measure has only been underscored by the COVID-19 pandemic. In a recent study, researchers use machine learning to develop an algorithm that predicts the risk of COVID-19 infection, by combining biometric data from wearable devices like Fitbit, with electronic symptom surveys. In doing so, they aim to increase the efficiency of test allocation when tracking disease spread in resource-limited settings. But the implications of technology that applies data from wearables stretch far beyond infection monitoring into healthcare delivery and research. The adoption and implementation of this type of technology will depend on regulation, impact on patient outcomes, and cost savings.

Publication types

Editorial