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Link to original content: https://doi.org/10.1007/978-3-031-13832-4_16
Comparison of Subjective and Physiological Stress Levels in Home and Office Work Environments | SpringerLink
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Comparison of Subjective and Physiological Stress Levels in Home and Office Work Environments

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Intelligent Computing Methodologies (ICIC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13395))

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Abstract

Work stress can have serious deleterious effects for individuals and society and therefore its management is of great importance. Work environment has been demonstrated as one of the significant factors effecting work stress. Recently, COVID-19 has led to an increased frequency of individuals working in hybrid work environments mainly comprising of home and office environments. The effects these work environments have on individuals’ mental stress is important to understand for both employers and employees so they can mitigate and effectively manage the mental stress. In this paper, we present an intelligent approach to predict the stress occurrences using the physiological data acquired from individuals working in both remote and office locations. Multiple factors are collected related to physiological indicators of stress and subjective performance level. We developed a boosted tree ensemble model which produced binary stress classification accuracy of 99.9%. The statistical outcomes indicate that there is no overall correlation between mental stress and productivity, however there is some indication of mental stress being is influenced by the work environment, the time of day and the day of the week.

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Data Availability Statement.

An anonymized version of the data supporting the conclusions of this article will be made available by the authors upon request to the corresponding author.

References

  1. Ma, J., Peng, Y.: The performance costs of illegitimate tasks: the role of job identity and flexible role orientation. J. Vocat. Behav. 110, 144–154 (2019)

    Article  Google Scholar 

  2. Lupien, S.J., Juster, R.-P., Raymond, C., Marin, M.-F.: The effects of chronic stress on the human brain: from neurotoxicity, to vulnerability, to opportunity. Front. Neuroendocrinol. 49, 91–105 (2018)

    Article  Google Scholar 

  3. Gawlik, K.S., Melnyk, B.M., Tan, A.: Associations between stress and cardiovascular disease risk factors among million hearts priority populations. Am. J. Health Promot. 33(7), 1063–1066 (2019)

    Article  Google Scholar 

  4. Kivimäki, M., Steptoe, A.: Effects of stress on the development and progression of cardiovascular disease. Nat. Rev. Cardiol. 15(4), 215–229 (2018)

    Article  Google Scholar 

  5. van der Valk, E.S., Savas, M., van Rossum, E.F.: Stress and obesity: are there more susceptible individuals? Curr. Obes. Rep. 7(2), 193–203 (2018)

    Article  Google Scholar 

  6. Yazdanpanahi, Z., Nikkholgh, M., Akbarzadeh, M., Pourahmad, S.: Stress, anxiety, depression, and sexual dysfunction among postmenopausal women in Shiraz, Iran, 2015. J. Fam. Community Med. 25(2), 82 (2018)

    Article  Google Scholar 

  7. Quist, S.R., Quist, J.: Keep quiet—how stress regulates hair follicle stem cells. Signal Transduct. Target. Ther. 6(1), 1–2 (2021)

    MathSciNet  Google Scholar 

  8. Hassard, J., Teoh, K.R., Visockaite, G., Dewe, P., Cox, T.: The cost of work-related stress to society: a systematic review. J. Occup. Health Psychol. 23(1), 1 (2018)

    Article  Google Scholar 

  9. Siirtola, P.: Continuous stress detection using the sensors of commercial smartwatch. In: Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers, pp. 1198–1201 (2019)

    Google Scholar 

  10. Can, Y.S., Chalabianloo, N., Ekiz, D., Ersoy, C.: Continuous stress detection using wearable sensors in real life: algorithmic programming contest case study. Sensors 19(8), 1849 (2019)

    Article  Google Scholar 

  11. Ahmadi, N., et al.: Quantifying occupational stress in intensive care unit nurses: an applied naturalistic study of correlations among stress, heart rate, electrodermal activity, and skin temperature. Hum. Factors 64, 00187208211040889 (2021)

    Google Scholar 

  12. Han, L., Zhang, Q., Chen, X., Zhan, Q., Yang, T., Zhao, Z.: Detecting work-related stress with a wearable device. Comput. Ind. 90, 42–49 (2017)

    Article  Google Scholar 

  13. Akbar, F., Mark, G., Pavlidis, I., Gutierrez-Osuna, R.: An empirical study comparing unobtrusive physiological sensors for stress detection in computer work. Sensors 19(17), 3766 (2019)

    Article  Google Scholar 

  14. Harper, M., Ghali, F.: A systematic review of wearable devices for tracking physiological indicators of Dementia-related difficulties. Presented at the Developments in E-Systems (2020)

    Google Scholar 

  15. Kaczor, E.E., Carreiro, S., Stapp, J., Chapman, B., Indic, P.: Objective measurement of physician stress in the emergency department using a wearable sensor. In: Proceedings of the... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences, vol. 2020, p. 3729. NIH Public Access (2020)

    Google Scholar 

  16. Wolor, C.W., Dalimunthe, S., Febrilia, I., Martono, S.: How to manage stress experienced by employees when working from home due to the Covid-19 virus outbreak. Int. J. Adv. Sci. Technol. 29(5), 8359–8364 (2020)

    Google Scholar 

  17. Wolor, C.W., Nurkhin, A., Citriadin, Y.: Is Working from home good for work-life balance, stress, and productivity, or does it cause problems? Humanit. Soc. Sci. Lett. 9(3), 237–249 (2021)

    Google Scholar 

  18. Harper, M., Ghali, F., Hussain, A., Al-Jumeily, D.: Challenges in data capturing and collection for physiological detection of dementia-related difficulties and proposed solutions. In: Huang, D.-S., Jo, K.-H., Li, J., Gribova, V., Premaratne, P. (eds.) ICIC 2021. LNCS (LNAI), vol. 12838, pp. 162–173. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84532-2_15

    Chapter  Google Scholar 

  19. Selvanathan, M., Hussin, N.A.M., Azazi, N.A.N.: Students learning experiences during COVID-19: work from home period in Malaysian Higher Learning Institutions. Teach. Public Adm., 0144739420977900 (2020)

    Google Scholar 

  20. Finset, A., et al.: Effective health communication–a key factor in fighting the COVID-19 pandemic. Patient Educ. Couns. 103(5), 873 (2020)

    Article  Google Scholar 

  21. Burdorf, A., Porru, F., Rugulies, R.: The COVID-19 (Coronavirus) pandemic: consequences for occupational health. Scand. J. Work Environ. Health 46(3), 229–230 (2020)

    Article  Google Scholar 

  22. Galanti, T., Guidetti, G., Mazzei, E., Zappalà, S., Toscano, F.: Work from home during the COVID-19 outbreak: the impact on employees’ remote work productivity, engagement, and stress. J. Occup. Environ. Med. 63(7), e426 (2021)

    Google Scholar 

  23. Luis-Martínez, J.M., Martínez-Martínez, M.C., García-Montalvo, I.A.: Physical activity: academic stress regulator in time of covid-19 pandemic. Covid-19 and academic stress: COVID-19 AND ACADEMIC STRESS. J. Negat. No Posit. Results 6(6), 872–880 (2021)

    Google Scholar 

  24. Shao, Y., Fang, Y., Wang, M., Chang, C.-H.D., Wang, L.: Making daily decisions to work from home or to work in the office: the impacts of daily work-and COVID-related stressors on next-day work location. J. Appl. Psychol. 106(6), 825 (2021)

    Article  Google Scholar 

  25. Song, Y., Gao, J.: Does telework stress employees out? A study on working at home and subjective well-being for wage/salary workers. J. Happiness Stud. 21(7), 2649–2668 (2020)

    Article  Google Scholar 

  26. Bolliger, L., Lukan, J., Luštrek, M., De Bacquer, D., Clays, E.: Protocol of the STRess at Work (STRAW) project: how to disentangle day-to-day occupational stress among academics based on EMA, physiological data, and smartphone sensor and usage data. Int. J. Environ. Res. Public Health 17(23), 8835 (2020)

    Article  Google Scholar 

  27. Betti, S., et al.: Evaluation of an integrated system of wearable physiological sensors for stress monitoring in working environments by using biological markers. IEEE Trans. Biomed. Eng. 65(8), 1748–1758 (2017)

    Google Scholar 

  28. Wijsman, J., Grundlehner, B., Liu, H., Penders, J., Hermens, H.: Wearable physiological sensors reflect mental stress state in office-like situations. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 600–605. IEEE (2013)

    Google Scholar 

  29. Harper, M., Ghali, F.: Roles of caregivers in physiological data collection experiments with people with dementia and mitigating the impacts of COVID-19. In: 2021 14th International Conference on Developments in eSystems Engineering (DeSE), pp. 149–155. IEEE (2021)

    Google Scholar 

  30. Simons, A., Doyle, T., Musson, D., Reilly, J.: Impact of physiological sensor variance on machine learning algorithms. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 241–247. IEEE (2020)

    Google Scholar 

  31. Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K.: Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 400–408 (2018)

    Google Scholar 

  32. Nath, R.K., Thapliyal, H., Caban-Holt, A.: Machine learning based stress monitoring in older adults using wearable sensors and cortisol as stress biomarker. J. Signal Process. Syst., 1–13 (2021). https://doi.org/10.1007/s11265-020-01611-5

  33. Dawson, M.E., Schell, A.M., Filion, D.L.: The electrodermal system (2017)

    Google Scholar 

  34. Healey, J.A.: Wearable and automotive systems for affect recognition from physiology. Massachusetts Institute of Technology (2000)

    Google Scholar 

  35. van Woerkom, M., Meyers, M.C.: My strengths count! Effects of a strengths-based psychological climate on positive affect and job performance. Hum. Resour. Manage. 54(1), 81–103 (2015)

    Article  Google Scholar 

  36. Harper, M., Ghali, F., Hussain, A., Al-Jumeily, D.: Review of methods for data collection experiments with people with dementia and the impact of COVID-19. In: Huang, D.-S., Jo, K.-H., Li, J., Gribova, V., Premaratne, P. (eds.) ICIC 2021. LNCS (LNAI), vol. 12838, pp. 132–147. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-84532-2_13

    Chapter  Google Scholar 

  37. Palumbo, R.: Let me go to the office! An investigation into the side effects of working from home on work-life balance. Int. J. Public Sect. Manage. (2020)

    Google Scholar 

  38. Freisthler, B., Gruenewald, P.J., Tebben, E., McCarthy, K.S., Wolf, J.P.: Understanding at-the-moment stress for parents during COVID-19 stay-at-home restrictions. Soc. Sci. Med. 279, 114025 (2021)

    Article  Google Scholar 

  39. Tsai, M.-C.: The good, the bad, and the ordinary: the day-of-the-week effect on mood across the globe. J. Happiness Stud. 20(7), 2101–2124 (2019)

    Article  Google Scholar 

  40. Khullar, V., Tiwari, R.G., Agarwal, A.K., Dutta, S.: Physiological signals based anxiety detection using ensemble machine learning. In: Tavares, J.M.R.S., Dutta, P., Dutta, S., Samanta, D. (eds.) Cyber Intelligence and Information Retrieval. LNNS, vol. 291, pp. 597–608. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-4284-5_53

    Chapter  Google Scholar 

  41. Elzeiny, S., Qaraqe, M.: Machine learning approaches to automatic stress detection: a review. In: 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), pp. 1–6. IEEE (2018)

    Google Scholar 

  42. Khan, W., Crockett, K., O’Shea, J., Hussain, A., Khan, B.M.: Deception in the eyes of deceiver: a computer vision and machine learning based automated deception detection. Expert Syst. Appl. 169, 114341 (2021)

    Article  Google Scholar 

  43. Khan, W., Alusi, S., Tawfik, H., Hussain, A.: The relationship between non-motor features and weight-loss in the premanifest stage of Huntington’s disease. PLoS ONE 16(7), e0253817 (2021)

    Article  Google Scholar 

  44. Shatte, A.B., Hutchinson, D.M., Teague, S.J.: Machine learning in mental health: a scoping review of methods and applications. Psychol. Med. 49(9), 1426–1448 (2019)

    Article  Google Scholar 

  45. Khan, S.A., Khan, W., Hussain, A.: Phishing attacks and websites classification using machine learning and multiple datasets (a comparative analysis). In: Huang, D.S., Premaratne, P. (eds.) ICIC 2020. LNCS, vol. 12465, pp. 301–313. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60796-8_26

    Chapter  Google Scholar 

  46. Mahesh, B.: Machine learning algorithms-a review. Int. J. Sci. Res. (IJSR) 9, 381–386 (2020)

    Google Scholar 

  47. Singh, A., Thakur, N., Sharma, A.: A review of supervised machine learning algorithms. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 1310–1315. IEEE (2016)

    Google Scholar 

  48. Zhang, C., Ma, Y.: Ensemble Machine Learning: Methods and Applications. Springer, New York (2012). https://doi.org/10.1007/978-3-030-60796-8_26

    Book  MATH  Google Scholar 

  49. Khamis, H.: Measures of association: how to choose? J. Diagn. Med. Sonogr. 24(3), 155–162 (2008)

    Article  Google Scholar 

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Harper, M., Ghali, F., Khan, W. (2022). Comparison of Subjective and Physiological Stress Levels in Home and Office Work Environments. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_16

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  • DOI: https://doi.org/10.1007/978-3-031-13832-4_16

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