Home // International Journal On Advances in Life Sciences, volume 15, numbers 3 and 4, 2023 // View article

Analyzing and Reporting Wearable Sensor Data Quality in Digital Biomarker Research

Authors:
Hui Zhang
Regan Giesting
Guangchen Ruan
Leah Miller
Neel Patel
Chakib Battioui
Ju Ji
Ming Zhong
Andrew Kaczorek
Tianran Zhang
Yi Lin Yang

Keywords: digital health technology, connected clinical trial, sensor data, data quality assessment, data visualization, digital biomarker

Abstract:
Digital Health Technologies (DHT) utilize a combination of computing platforms, connectivity, software, and sensors for healthcare-related uses. Today, these technologies collect complex digital data from participants in clinical investigations, including a large amount of wearable sensor signals. These collected data are used to develop digital biomarkers (dBMs), which can act as indicators for health outcomes for monitoring life quality and measuring drug efficacy. One essential step towards realizing the full potential of these complex digital data is to define the fundamental principles and methods to demonstrate sufficient data quality and fidelity needed for the research. This paper aims to develop a digital data quality assessment framework across the complete data life cycle in dBM research, including data quality metrics and methods to analyze and report digital data quality. Aggregating and reporting digital data quality is often challenging and error-prone. We developed Magnol.Ai, a data platform equipped with data quality assessment and reporting tools that allow us to define data compliance criteria and view data quality reports at different levels in a consumable fashion.

Pages: 72 to 86

Copyright: Copyright (c) to authors, 2023. Used with permission.

Publication date: December 30, 2023

Published in: journal

ISSN: 1942-2660