{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T13:32:12Z","timestamp":1783431132742,"version":"3.54.6"},"reference-count":43,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T00:00:00Z","timestamp":1666569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland under Research Professorship","doi-asserted-by":"publisher","award":["15\/RP\/2765"],"award-info":[{"award-number":["15\/RP\/2765"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland under Research Professorship","doi-asserted-by":"publisher","award":["19\/FFP\/7002"],"award-info":[{"award-number":["19\/FFP\/7002"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University of Birmingham Dynamic Investment Fund","award":["15\/RP\/2765"],"award-info":[{"award-number":["15\/RP\/2765"]}]},{"name":"University of Birmingham Dynamic Investment Fund","award":["19\/FFP\/7002"],"award-info":[{"award-number":["19\/FFP\/7002"]}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["15\/RP\/2765"],"award-info":[{"award-number":["15\/RP\/2765"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001602","name":"Science Foundation Ireland","doi-asserted-by":"publisher","award":["19\/FFP\/7002"],"award-info":[{"award-number":["19\/FFP\/7002"]}],"id":[{"id":"10.13039\/501100001602","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the \u201cStress-Predict Dataset\u201d, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring.<\/jats:p>","DOI":"10.3390\/s22218135","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"8135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":100,"title":["Stress Monitoring Using Wearable Sensors: A Pilot Study and Stress-Predict Dataset"],"prefix":"10.3390","volume":"22","author":[{"given":"Talha","family":"Iqbal","sequence":"first","affiliation":[{"name":"Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew J.","family":"Simpkin","sequence":"additional","affiliation":[{"name":"School of Mathematical and Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6553-640X","authenticated-orcid":false,"given":"Davood","family":"Roshan","sequence":"additional","affiliation":[{"name":"School of Mathematical and Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland"},{"name":"C\u00daRAM Center for Research in Medical Devices, University of Galway, H91 W2TY Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicola","family":"Glynn","sequence":"additional","affiliation":[{"name":"Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5009-8403","authenticated-orcid":false,"given":"John","family":"Killilea","sequence":"additional","affiliation":[{"name":"Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jane","family":"Walsh","sequence":"additional","affiliation":[{"name":"School of Psychology, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gerard","family":"Molloy","sequence":"additional","affiliation":[{"name":"School of Psychology, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Ganly","sequence":"additional","affiliation":[{"name":"Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Ryman","sequence":"additional","affiliation":[{"name":"Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eileen","family":"Coen","sequence":"additional","affiliation":[{"name":"Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0988-5776","authenticated-orcid":false,"given":"Adnan","family":"Elahi","sequence":"additional","affiliation":[{"name":"Electrical and Electronic Engineering, University of Galway, H91 TK33 Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Wijns","sequence":"additional","affiliation":[{"name":"Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland"},{"name":"C\u00daRAM Center for Research in Medical Devices, University of Galway, H91 W2TY Galway, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atif","family":"Shahzad","sequence":"additional","affiliation":[{"name":"Smart Sensor Laboratory, Lambe Institute of Translational Research, College of Medicine, Nursing Health Sciences, University of Galway, H91 TK33 Galway, Ireland"},{"name":"Centre for Systems Modelling and Quantitative Biomedicine (SMQB), University of Birmingham, Birmingham B15 2TT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,24]]},"reference":[{"key":"ref_1","unstructured":"Executive, H.S. (2022, July 20). Work-Related Ill Health and Occupational Disease in Great Britain, Available online: https:\/\/www.hse.gov.uk\/statistics\/causdis\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.yfrne.2018.03.001","article-title":"More than a feeling: A unified view of stress measurement for population science","volume":"49","author":"Epel","year":"2018","journal-title":"Front. Neuroendocrinol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1007\/s11517-018-1879-z","article-title":"Measuring acute stress response through physiological signals: Towards a quantitative assessment of stress","volume":"57","author":"Arza","year":"2019","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1016\/S0140-6736(16)31714-7","article-title":"Relation between resting amygdalar activity and cardiovascular events: A longitudinal and cohort study","volume":"389","author":"Tawakol","year":"2017","journal-title":"Lancet"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1177\/1359105309346343","article-title":"Perceived stress scale","volume":"15","author":"Reis","year":"2010","journal-title":"J. Health Psychol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1650041","DOI":"10.1142\/S0129065716500416","article-title":"Stress Detection Using Wearable Physiological and Sociometric Sensors","volume":"27","author":"Mozos","year":"2017","journal-title":"Int. J. Neural Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.ijpsycho.2018.07.471","article-title":"Loneliness and cardiovascular reactivity to acute stress in younger adults","volume":"135","author":"Brown","year":"2019","journal-title":"Int. J. Psychophysiol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.drugalcdep.2003.10.014","article-title":"Prospective examination of effects of smoking abstinence on cortisol and withdrawal symptoms as predictors of early smoking relapse","volume":"73","author":"Hatsukami","year":"2004","journal-title":"Drug Alcohol Depend."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e13418","DOI":"10.2196\/13418","article-title":"The validity of daily self-assessed perceived stress measured using smartphones in healthy individuals: Cohort study","volume":"7","author":"Ullum","year":"2019","journal-title":"JMIR Mhealth Uhealth"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1007\/s12239-018-0086-0","article-title":"Characterizing driver stress using physiological and operational data from real-world electric vehicle driving experiment","volume":"19","author":"Kim","year":"2018","journal-title":"Int. J. Automot. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/TITB.2011.2169804","article-title":"Development and evaluation of an ambulatory stress monitor based on wearable sensors","volume":"16","author":"Choi","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lamichhane, B., Gro\u00dfekath\u00f6fer, U., Schiavone, G., and Casale, P. (2017). Towards stress detection in real-life scenarios using wearable sensors: Normalization factor to reduce variability in stress physiology. eHealth 360\u00b0, Springer.","DOI":"10.1007\/978-3-319-49655-9_34"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"782756","DOI":"10.3389\/fmedt.2022.782756","article-title":"Exploring Unsupervised Machine Learning Classification Methods for Physiological Stress Detection","volume":"4","author":"Iqbal","year":"2022","journal-title":"Front. Med. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sardo, F.R., Rayegani, A., Nazar, A.M., Balaghiinaloo, M., Saberian, M., Mohsan, S.A.H., Alsharif, M.H., and Cho, H.S. (2022). Recent Progress of Triboelectric Nanogenerators for Biomedical Sensors: From Design to Application. Biosensors, 12.","DOI":"10.3390\/bios12090697"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"93567","DOI":"10.1109\/ACCESS.2021.3082423","article-title":"A Sensitivity Analysis of Biophysiological Responses of Stress for Wearable Sensors in Connected Health","volume":"9","author":"Iqbal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Schmidt, P., Duerichen, R., van Laerhoven, K., Marberger, C., and Reiss, A. (2018, January 16\u201320). Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection. Proceedings of the 20th ACM International Conference on Multimodal Interaction, Boulder, CO, USA.","DOI":"10.1145\/3242969.3242985"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M.A., and Kraaij, W. (2014, January 12\u221216). The swell knowledge work dataset for stress and user modeling research. Proceedings of the 16th International Conference on Multimodal Interaction, Istanbul, Turkey.","DOI":"10.1145\/2663204.2663257"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"el Haouij, N., Poggi, J.-M., Sevestre-Ghalila, S., Ghozi, R., and Ja\u00efdane, M. (2018, January 9\u201313). AffectiveROAD system and database to assess driver\u2019s attention. Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau, France.","DOI":"10.1145\/3167132.3167395"},{"key":"ref_19","unstructured":"Healey, J., and Picard, R. (2002, January 6). SmartCar: Detecting driver stress. Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain."},{"key":"ref_20","unstructured":"Shi, Y., Nguyen, M.H., Blitz, P., French, B., Fisk, S., de la Torre, F., Smailagic, A., Siewiorek, D.P., al\u2019Absi, M., and Ertin, E. (2010). Personalized stress detection from physiological measurements. Int. Symp. Qual. Life Technol., 28\u201329. Available online: http:\/\/www.humansensing.cs.cmu.edu\/sites\/default\/files\/8stress_detect.pdf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1007\/s12668-013-0089-2","article-title":"Towards Measuring Stress with Smartphones and Wearable Devices During Workday and Sleep","volume":"3","author":"Muaremi","year":"2013","journal-title":"Bionanoscience"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1038\/s41597-022-01361-y","article-title":"A multimodal sensor dataset for continuous stress detection of nurses in a hospital","volume":"9","author":"Hosseini","year":"2022","journal-title":"Sci. Data"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Iqbal, T., Elahi, A., Redon, P., Vazquez, P., Wijns, W., and Shahzad, A. (2021). A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare. Diagnostics, 11.","DOI":"10.3390\/diagnostics11030556"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s40846-022-00700-z","article-title":"Photoplethysmography-Based Respiratory Rate Estimation Algorithm for Health Monitoring Applications","volume":"42","author":"Iqbal","year":"2022","journal-title":"J. Med. Biol. Eng."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Roshan, D., Ferguson, J., Pedlar, C.R., Simpkin, A., Wyns, W., Sullivan, F., and Newell, J. (2021). A comparison of methods to gen-erate adaptive reference ranges in longitudinal monitoring. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247338"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.ijpsycho.2019.06.013","article-title":"Vulnerability to stress: Personality facet of vulnerability is associated with cardiovascular adaptation to recurring stress","volume":"144","author":"Hughes","year":"2019","journal-title":"Int. J. Psychophysiol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.neubiorev.2013.11.005","article-title":"Biological and psychological markers of stress in humans: Focus on the Trier Social Stress Test","volume":"38","author":"Allen","year":"2014","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"557","DOI":"10.3389\/fpsyg.2017.00557","article-title":"The stroop color and word test","volume":"8","author":"Scarpina","year":"2017","journal-title":"Front. Psychol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1097\/PSY.0000000000000918","article-title":"Stress reactivity to the trier social stress test in traditional and virtual environments: A meta-analytic comparison","volume":"83","author":"Helminen","year":"2021","journal-title":"Psychosom. Med."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"385","DOI":"10.2307\/2136404","article-title":"A global measure of perceived stress","volume":"24","author":"Cohen","year":"1983","journal-title":"J. Health Soc. Behav."},{"key":"ref_31","first-page":"121","article-title":"Review of the psychometric evidence of the perceived stress scale","volume":"6","author":"Lee","year":"2012","journal-title":"Asian Nurs. Res. (Korean Soc. Nurs. Sci.)"},{"key":"ref_32","unstructured":"Spielberger, C.D., Gorsuch, R., Lushene, R., Vagg, P., and Jacobs, G. (1983). Manual for the Stait-Trait Anxiety Inventory, Consulting Psychologists Press."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, Z., and Fu, S. (2014, January 9\u201314). An analysis of pilot\u2019s physiological reactions in different flight phases. Proceedings of the International Conference on Engineering Psychology and Cognitive Ergonomics, Heraklion, Greece.","DOI":"10.1007\/978-3-319-07515-0_10"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"m441","DOI":"10.1136\/bmj.m441","article-title":"Calculating the sample size required for developing a clinical prediction model","volume":"368","author":"Riley","year":"2020","journal-title":"BMJ"},{"key":"ref_35","unstructured":"(2022, July 28). E4 Wristband Technical Specifications. Available online: https:\/\/support.empatica.com\/hc\/en-us\/articles\/202581999-E4-wristband-technical-specifications."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1080\/24721840.2020.1841564","article-title":"Effects of stress on performance during highly demanding tasks in student pilots","volume":"31","author":"Pedret","year":"2021","journal-title":"Int. J. Aerosp. Psychol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chandra, V., Priyarup, A., and Sethia, D. (2021, January 23\u201324). Comparative Study of Physiological Signals from Empatica E4 Wristband for Stress Classification. Proceedings of the International Conference on Advances in Computing and Data Sciences, Nashik, India.","DOI":"10.1007\/978-3-030-88244-0_21"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kim, M., Kim, J., Park, K., Kim, H., and Yoon, D. (2021, January 14). Comparison of Wristband Type Devices to Measure Heart Rate Variability for Mental Stress Assessment. Proceedings of the 2021 International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea.","DOI":"10.1109\/ICTC52510.2021.9620772"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Giorgi, A., Ronca, V., Vozzi, A., Sciaraffa, N., di Florio, A., Tamborra, L., Simonetti, I., Aric\u00f2, P., di Flumeri, G., and Rossi, D. (2021). Wearable technologies for mental workload, stress, and emotional state assessment during working-like tasks: A comparison with laboratory technologies. Sensors, 21.","DOI":"10.3390\/s21072332"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-020-01648-w","article-title":"Validity of the Empatica E4 Wristband to Measure Heart Rate Variability (HRV) Parameters: A Comparison to Electrocardiography (ECG)","volume":"44","author":"Schuurmans","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_41","unstructured":"(2022, July 28). E4 Data-BVP Expected Signal. Available online: https:\/\/support.empatica.com\/hc\/en-us\/articles\/360029719792-E4-data-BVP-expected-signal."},{"key":"ref_42","unstructured":"(2022, July 28). E4 Data-IBI Expected Signal. Available online: https:\/\/support.empatica.com\/hc\/en-us\/articles\/360030058011-E4-data-IBI-expected-signal."},{"key":"ref_43","unstructured":"(2022, July 28). E4 Wristband Data. Available online: https:\/\/support.empatica.com\/hc\/en-us\/sections\/200582445-E4-wristband-data."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8135\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:01:49Z","timestamp":1760144509000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,24]]},"references-count":43,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218135"],"URL":"https:\/\/doi.org\/10.3390\/s22218135","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,24]]}}}