Quantification of stress and well-being using pulse, speech, and electrodermal data: Study concept and design

Abstract

Introduction: Mental disorders are a leading cause of disability worldwide and, among mental disorders, major depressive disorder was highly ranked in years lived with disability. 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. Methods and analysis: This is a single-arm prospective observational study. The target population is adult (>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 or connected to the computer (pulse wave data extracted from the facial video images), a microphone built into or connected to 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 (New Occupational Stress Questionnaire, Perceived Stress Scale, Satisfaction With Life Scale, Japanese version of Positive and Negative Affect Schedule, Japanese Flourishing Scale, Subjective Well-being / Ideal Happiness, and Japanese version of Patient Health Questionnaire-9). 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. Ethics and dissemination: 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. Registration: UMIN000036814

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

“…The authors are grateful to Mr. Yuichiro Suzuki (Panasonic) and Mr. Yoshifumi Onishi (NEC) for technical support in this study. This manuscript has been released as a pre-print at medRxiv ( 30 ).…”