AUTHOR=Izumi Keisuke , Minato Kazumichi , Shiga Kiko , Sugio Tatsuki , Hanashiro Sayaka , Cortright Kelley , Kudo Shun , Fujita Takanori , Sado Mitsuhiro , Maeno Takashi , Takebayashi Toru , Mimura Masaru , Kishimoto Taishiro 

TITLE=Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design

JOURNAL=Frontiers in Psychiatry

VOLUME=12

YEAR=2021

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2021.611243

DOI=10.3389/fpsyt.2021.611243

ISSN=1664-0640

ABSTRACT=<p><bold>Introduction:</bold> Mental disorders are a leading cause of disability worldwide. Depression has a significant impact in the field of occupational health because it is particularly prevalent during working age. On the other hand, there are a growing number of studies on the relationship between “well-being” and employee productivity. To promote healthy and productive workplaces, this study aims to develop a technique to quantify stress and well-being in a way that does not disturb the workplace.</p><p><bold>Methods and analysis:</bold> This is a single-arm prospective observational study. The target population is adult (&gt;20 years old) workers at companies that often engage in desk work; specifically, a person who sits in front of a computer for at least half their work hours. The following data will be collected: (a) participants' background characteristics; (b) participants' biological data during the 4-week observation period using sensing devices such as a camera built into the computer (pulse wave data extracted from the facial video images), a microphone built into their work computer (voice data), and a wristband-type wearable device (electrodermal activity data, body motion data, and body temperature); (c) stress, well-being, and depression rating scale assessment data. The analysis workflow is as follows: (1) primary analysis, comprised of using software to digitalize participants' vital information; (2) secondary analysis, comprised of examining the relationship between the quantified vital data from (1), stress, well-being, and depression; (3) tertiary analysis, comprised of generating machine learning algorithms to estimate stress, well-being, and degree of depression in relation to each set of vital data as well as multimodal vital data.</p><p><bold>Discussion:</bold> This study will evaluate digital phenotype regarding stress and well-being of white-collar workers over a 4-week period using persistently obtainable biomarkers such as heart rate, acoustic characteristics, body motion, and electrodermal activity. Eventually, this study will lead to the development of a machine learning algorithm to determine people's optimal levels of stress and well-being.</p><p><bold>Ethics and dissemination:</bold> Collected data and study results will be disseminated widely through conference presentations, journal publications, and/or mass media. The summarized results of our overall analysis will be supplied to participants.</p><p><bold>Registration:</bold> UMIN000036814</p>