Measuring acute stress response through physiological signals: towards a quantitative assessment of stress
- 507 Downloads
Social and medical problems associated with stress are increasing globally and seriously affect mental health and well-being. However, an effective stress-level monitoring method is still not available. This paper presents a quantitative method for monitoring acute stress levels in healthy young people using biomarkers from physiological signals that can be unobtrusively monitored. Two states were induced to 40 volunteers, a basal state generated with a relaxation task and an acute stress state generated by applying a standard stress test that includes five different tasks. Standard psychological questionnaires and biochemical markers were utilized as ground truth of stress levels. A multivariable approach to comprehensively measure the physiological stress response is proposed using stress biomarkers derived from skin temperature, heart rate, and pulse wave signals. Acute physiological stress levels (total-range 0–100 au) were continuously estimated every 1 min showing medians of 29.06 au in the relaxation tasks, while rising from 34.58 to 47.55 au in the stress tasks. Moreover, using the proposed method, five statistically different stress levels induced by the performed tasks were also measured. Results obtained show that, in these experimental conditions, stress can be monitored from unobtrusive biomarkers. Thus, a more general stress monitoring method could be derived based on this approach.
KeywordsStress measurement Stress biomarker Multimodal analysis Multivariable biomarker Acute stress TSST Unobtrusive physiological signals
This project has received funding from the European Union's Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie Grant Agreement No. 745755.
This research was supported by MINECO (FIS-PI12/00514 and TIN2014-53567-R) and by the Centro de Investigación Biomédica en Red sobre Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN) at the Instituto de Salud Carlos III de España.
Compliance with ethical standards
The UAB Ethics Committee approved the study protocol. Participants gave their written informed consent.
All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
- 2.American Psychological Association. (2013) By the numbers: a psychologically healthy workplace fact sheet. Retrieved from http://www.apaexcellence.org/resources/goodcompany/newsletter/article/487
- 7.World Health Organization (2015) WHO | Mental health atlas 2014. WHO, Geneva, 69 pGoogle Scholar
- 10.Lazarus RS (1993) From psychological stress to the emotions: a history of changing outlooks. Annu Rev Psychol 44:1–21. https://doi.org/10.1146/annurev.ps.44.020193.000245 CrossRefPubMedGoogle Scholar
- 11.Hellhammer, D. H., Stone, A. A., Hellhammer, J., & Broderick, J. (2010). Measuring stress. In Encyclopedia of Behavioral Neuroscience (Vol. 2, pp. 186–191). Elsevier Ltd.Google Scholar
- 13.Khoulji S, García E, Aguiló S, Arza A, Garzón-Rey JM, Aguilóa J (2017) Psychological and physiological profiles in oncology caregivers: a multivariable cross-sectional study. Trans Mach Learn Artif Intell 5(4). https://doi.org/10.14738/tmlai.54.3291
- 14.Urwyler SA, Schuetz P, Sailer C, & Christ-Crain M (2015) Copeptin as a stress marker prior and after a written examination—the CoEXAM study. Stress (Amsterdam, Netherlands), 1–4. https://doi.org/10.3109/10253890.2014.993966
- 17.Armario A, Marti O, Molina T, de Pablo J, Valdes M (1996) Acute stress markers in humans: response of plasma glucose, cortisol and prolactin to two examinations differing in the anxiety they provoke. Psychoneuroendocrinology 21(1):17–24 https://www.ncbi.nlm.nih.gov/pubmed/8778900 CrossRefGoogle Scholar
- 26.Cvetković B, Gjoreski M, Šorn J, Maslov P, Kosiedowski M, Bogdański M et al (2017) Real-time physical activity and mental stress management with a wristband and a smartphone. In: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers on—UbiComp ‘17. ACM Press, New York, pp 225–228. https://doi.org/10.1145/3123024.3123184 CrossRefGoogle Scholar
- 27.Picard RRW, Fedor S, & Ayzenberg Y (2014) Multiple arousal theory and daily-life electrodermal activity asymmetry. Emotion Review Google Scholar
- 31.Katsis CD, Katertsidis NS, & Fotiadis DI (2011) An integrated system based on physiological signals for the assessment of affective states in patients with anxiety disorders. In Biomedical Signal Processing and Control (Vol. 6, pp. 261–268). https://doi.org/10.1016/j.bspc.2010.12.001
- 35.Baumeister R, & Vohs K (2007) Encyclopedia of social psychology. (E. Harmon-Jones & P. Winkielman, Eds.) Social Neuroscience. California: SAGE Publications, Inc.Google Scholar
- 36.Jönsson P, Wallergård M, Osterberg K, Hansen AM, Johansson G, Karlson B (2010) Cardiovascular and cortisol reactivity and habituation to a virtual reality version of the Trier Social Stress Test: a pilot study. Psychoneuroendocrinology 35(9):1397–1403. https://doi.org/10.1016/j.psyneuen.2010.04.003 CrossRefPubMedGoogle Scholar
- 37.Kudielka BM, Hellhammer DH, & Kirschbaum C (2007). Ten years of research with the Trier Social Stress Test. In Social Neuroscience (pp. 56–83). https://doi.org/10.4135/9781412956253.n539
- 39.Rabasa C, Gagliano H, Pastor-Ciurana J, Fuentes S, Belda X, Nadal R, Armario A (2015) Adaptation of the hypothalamus–pituitary–adrenal axis to daily repeated stress does not follow the rules of habituation: a new perspective. Neurosci Biobehav Rev 56:35–49. https://doi.org/10.1016/J.NEUBIOREV.2015.06.013 CrossRefPubMedGoogle Scholar
- 40.UAB. (2014) ES3 stress measuring project| Universitat Autònoma de Barcelona. Retrieved June 17, 2014, from http://www.es3-project.es/
- 42.Pujol J, Giménez M, Ortiz H, Soriano-Mas C, López-Solà M, Farré M, Deus J, Merlo-Pich E, Harrison BJ, Cardoner N, Navinés R, Martín-Santos R (2013) Neural response to the observable self in social anxiety disorder. Psychol Med 43(4):721–731. https://doi.org/10.1017/S0033291712001857 CrossRefPubMedGoogle Scholar
- 43.Arza A, Garzón JM, Hemando A, Aguiló J, Bailon R, Garzon JM, … Bailon R (2015) Towards an objective measurement of emotional stress: preliminary analysis based on heart rate variability. In Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 3331–3334). IEEE. https://doi.org/10.1109/EMBC.2015.7319105
- 44.Sörnmo L, Laguna P (2005) Bioelectrical signal processing in cardiac and neurological applications. Academic PressGoogle Scholar
- 45.Gil E, María Vergara J, Laguna P (2008) Detection of decreases in the amplitude fluctuation of pulse photoplethysmography signal as indication of obstructive sleep apnea syndrome in children. Biomed Signal Process Control 3(3):267–277. https://doi.org/10.1016/j.bspc.2007.12.002 CrossRefGoogle Scholar
- 47.Arza A, Lazaro J, Gil E, Laguna P, Aguilo J, & Bailon R (2013) Pulse transit time and pulse width as potential measure for estimating beat-to-beat systolic and diastolic blood pressure. In Computing in Cardiology Conference (CinC) (pp. 887–890). IEEEGoogle Scholar
- 48.Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 28(3):R1–R39. https://doi.org/10.1088/0967-3334/28/3/R01
- 53.Jozami Guldberg LS (2014) Estrés psicosocial agudo: Efectos sobre el cortisol y α-amilasa en saliva. Universidad Autónoma de BarcelonaGoogle Scholar
- 55.Engert V, Vogel S, Efanov SI, Duchesne A, Corbo V, Ali N, Pruessner JC (2011) Investigation into the cross-correlation of salivary cortisol and alpha-amylase responses to psychological stress. Psychoneuroendocrinology 36(9):1294–1302. https://doi.org/10.1016/j.psyneuen.2011.02.018 CrossRefPubMedGoogle Scholar
- 59.Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. (1996) Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation, 93(5), 1043–1065. https://doi.org/10.1161/01.cir.93.5.1043
- 60.Hernando D, Hernando A, Casajús JA, Laguna P, Garatachea N, Bailón R (2018) Methodological framework for heart rate variability analysis during exercise: application to running and cycling stress testing. Med Biol Eng Comput 56(5):781–794. https://doi.org/10.1007/s11517-017-1724-9 CrossRefPubMedGoogle Scholar