Use of consumer wearables to monitor and predict pain in patients with sickle cell disease.

ABSTRACT:

Background

In sickle cell disease (SCD), unpredictable episodes of acute severe pain, known as vaso-occlusive crises (VOC), disrupt school, work activities and family life and ultimately lead to multiple hospitalizations. The ability to predict VOCs would allow a timely and adequate intervention. The first step towards this ultimate goal is to use patient-friendly and accessible technology to collect relevant data that helps infer a patient's pain experience during VOC. This study aims to: (1) determine the feasibility of remotely monitoring with a consumer wearable during hospitalization for VOC and up to 30 days after discharge, and (2) evaluate the accuracy of pain prediction using machine learning models based on physiological parameters measured by a consumer wearable.

Meth

SUBMITTER: Vuong C 

PROVIDER: S-EPMC10634543 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature