The aim of the present study was to show the validity of a mobile based application (“Serenita”), as a tool for measuring stress level quantitatively. In this interactive app, the user places his finger on the mobile`s camera lens, through which information related to the user’s blood flow, heart rate, and heart rate variability (HRV) is extracted. Physiological signals are then being filtered and processed through a certain machine- algorithm, resulting in a quantitative estimation of the user’s stress level. Method: a mixed sex group of 50 volunteers were recruited to participate in a standardized laboratory experiment, where a psychosocial stress protocol (Trier Social Stress Test-TSST) was implemented. Throughout the course of the experiment, physiological stress response was measured using both salivary cortisol level and Serenita app, hence, using a within subject design. Results: Serenita algorithm was able to effectively detect changes in the participant`s estimated stress level, as expected by the different experimental conditions and followed the robust physiological response pattern usually obtained by the TSST protocol. In addition, a cross correlation of .93 was obtained between the estimated stress level, using Serenita`s algorithm, and Cortisol level measures. Conclusions: these results serve a double validation for Serenita as an effective tool to quantitatively measure physiological stress response. This innovative technique bears important implications for the field of stress research and treatment, providing to the best of our knowledge the first clinically validated non-lab based quantitative physiological stress measurement tool.
Stress, Cortisol, Trier Social Stress Test-TSST, Mobile-application, Digital-health, Stress- measurement