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 STRENGTHS AND LIMITATIONS OF THIS STUDY This study evaluates stress and well-being using biomarkers such as heart rate, acoustic characteristics, and electrodermal activity. This study measures biomarkers over a long-term four-week period. This study will lead to the development of a machine learning algorithm to determine people’s optimal levels of stress and well-being. This is a government-funded study in which many different companies and institutions collaborated for a common purpose. There is a possibility that the algorithm’s prediction accuracy level may decrease when it is applied to demographic groups other than those studied here.