Mobile Based Quantitative Measure of Stress (Preprint) (2017)

<sec> <title>BACKGROUND</title> 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. </sec> <sec> <title>OBJECTIVE</title> 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. </sec> <sec> <title>METHODS</title> The current study was designed to validate and fine-tune the algorithms supporting the stress estimation function in this mobile application, under a clinical setting. In order to validate Serenita as an adequate stress estimator it was necessary to build a standardized experimental protocol able to i) effectively induce stress on a set of volunteers, ii) and properly quantify the stress variation. To this end, we adopted the Trier Social Stress Test (TSST -Kirschbaum, Pirke, &amp; Hellhammer, 1993) as this well documented laboratory procedure, was shown to reliably induce stress in human research participants and used extensively in the field of stress studies (e.g., Kudielka, Hellhammer, Kirschbaum, Harmon-Jones, &amp; Winkielman, 2007; Allen, Kennedy, Cryan, Dinan, &amp; Clarke, 2014). The efficiency of TSST as a stress inducer, was explored not only through properly calibrated questionnaires but also with the analysis of the Cortisol, as physiological stress indicator, which is extensively used in clinical setting to determine stress levels and the response to stressful events. Similarly, the current study followed the typical experimental protocol. However, its novelty lies in combining traditional as solid stress inducer (TSST) and indicator (saliva cortisol), with an innovative digital-health assessment tool (Serenita application). Utilizing both tools to measure physiological stress in the course of the experiment, will not only comply with our research goal of establishing reliability and validity, but also will neutralize any potential variability in stress response that might be stemming from sex differences (e.g., Kirschbaum, Klauer, Filipp, &amp; Hellhammer, 1995; Kelly, Tyrka, Anderson, Price, &amp; Carpenter, 2008). </sec> <sec> <title>RESULTS</title> 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. </sec> <sec> <title>CONCLUSIONS</title> The present study aimed at investigating if the stress estimation algorithm, used by the Serenita app, was able to accurately estimate variations on stress levels. The TSST widely established as the standard protocol for stress induction was used to induce stress on a controlled clinical environment. The estimated stress levels show a high agreement rate with the expected stress response of the TSST. Furthermore, the analysis of salivary cortisol levels provided an objective measure of the real variation on stress levels, the average cortisol curve has a correlation index of 0.93 with the estimated stress provided by Serenita’s stress algorithm, supporting the stress estimation algorithm as a feasible way to estimate stress. Finally, in many stress monitoring applications it is useful to know, not just the relative change in stress along the time, but also to obtain a quantitative value for the stress level at any given time. Due to the highly subject-dependent nature of the basal level of stress (and cortisol) this task is relatively complex, however using a priori information such as age, gender, health condition, among others, it is possible to project the stress function into a bounded quantitative stress scale e.g. 0 − 100%. As far as we know this is the first time where an application is providing a quantitative and validated method comparable to measuring stress with a lab test. This tool could serve as a research tool in stress studies. </sec>