350
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Assessing Stress with Mobile Systems: A Design Science Approach

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Received 08 Oct 2021, Accepted 08 Dec 2023, Published online: 29 Dec 2023

References

  • Adam, M. T. P., Gimpel, H., Maedche, A., & Riedl, R. (2017). Design blueprint for stress-sensitive adaptive enterprise systems. Business & Information Systems Engineering, 59(4), 277–291. https://doi.org/10.1007/s12599-016-0451-3
  • Aigrain, J. (2016). Multimodal Detection of Stress: Evaluation of the Impact of Several Assessment Strategies [ Doctoral dissertation]. Université Pierre et Marie Curie - France. https://tel.archives-ouvertes.fr/tel-01593313
  • Anusha, S. A., Sukumaran, P., Sarveswaran, V., Surees, S. K., Shyam, A., Tony, A. J., Preejith, P. S., & Mohanasankar, S. (2020). Electrodermal activity based pre-surgery stress detection using a wrist wearable. IEEE Journal of Biomedical and Health Informatics, 24(1), 92–100. https://doi.org/10.1109/JBHI.2019.2893222
  • Arpaia, P., Moccaldi, N., Prevete, R., Sannino, I., & Tedesco, A. (2020). A wearable EEG instrument for real-time frontal asymmetry monitoring in worker stress analysis. IEEE Transactions on Instrumentation and Measurement, 69(10), 8335–8343. https://doi.org/10.1109/TIM.2020.2988744
  • Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications. MIS Quarterly, 35(4), 831–858. https://doi.org/10.2307/41409963
  • Ayzenberg, Y., Rivera, J. H., & Picard, R. (2012). FEEL: Frequent EDA and event logging - a mobile social interaction stress monitoring system. In CHI '12 Extended Abstracts, Austin, TX, USA.
  • Bauer, G., & Lukowicz, P. (2012). Can smartphones detect stress-related changes in the behaviour of individuals? In 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, Lugano, Switzerland.
  • Beckmann, S., Lahmer, S., Markgraf, M., Meindl, O., Rauscher, J., Regal, C., Gimpel, H., Bauer, B. 2017 In 2017 IEEE Life Sciences Conference, Sydney, NSW, Australia.
  • Benlian, A. (2020). A daily field investigation of technology-driven spillovers from work to home. MIS Quarterly, 44(3), 1259–1300. https://doi.org/10.25300/MISQ/2020/14911/
  • Betti, S., Molino Lova, R., Rovini, E., Acerbi, G., Santarelli, L., Cabiati, M., Del Ry, S., & Cavallo, F. (2018). Evaluation of an integrated system of wearable physiological sensors for stress monitoring in working environments by using biological markers. IEEE Transactions on Biomedical Engineering, 65(8), 1748–1758. https://doi.org/10.1109/TBME.2017.2764507
  • Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
  • Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., & Pentland, A. (2014). Daily stress recognition from mobile phone data, weather conditions and individual traits. In Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.
  • Califf, C. B., Sarker, S., & Sarker, S. (2020). The bright and dark sides of technostress: A mixed-methods study involving healthcare IT. MIS Quarterly, 44(2), 809–856. https://doi.org/10.25300/MISQ/2020/14818
  • Can, Y. S., Chalabianloo, N., Ekiz, D., Fernandez-Alvarez, J., Riva, G., & Ersoy, C. (2020). Personal stress-level clustering and decision-level smoothing to enhance the performance of ambulatory stress detection with smartwatches. Institute of Electrical and Electronics Engineers Access, 8, 38146–38163. https://doi.org/10.1109/ACCESS.2020.2975351
  • Carver, C. S., Scheier, M. F., & Weintraub, J. K. (1989). Assessing coping strategies: A theoretically based approach. Journal of Personality and Social Psychology, 56(2), 267–283. https://doi.org/10.1037/0022-3514.56.2.267
  • Chen, T., Yuen, P., Richardson, M., Liu, G., & She, Z. (2014). Detection of psychological stress using a hyperspectral imaging technique. IEEE Transactions on Affective Computing, 5(4), 391–405. https://doi.org/10.1109/TAFFC.2014.2362513
  • Cho, Y. (2017). Automated mental stress recognition through mobile thermal imaging. In 2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, TX, USA.
  • Choi, M., Koo, G., Seo, M., & Kim, S. W. (2018). Wearable device-based system to monitor a driver’s stress, fatigue, and drowsiness. IEEE Transactions on Instrumentation and Measurement, 67(3), 634–645. https://doi.org/10.1109/TIM.2017.2779329
  • Ciabattoni, L., Ferracuti, F., Longhi, S., Pepa, L., Romeo, L., & Verdini, F. (2017). Real-time mental stress detection based on smartwatch. In 2017 IEEE International Conference on Consumer Electronics, Las Vegas, NV, USA.
  • Ciman, M., & Wac, K. (2018). Individuals’ stress assessment using human-smartphone interaction analysis. IEEE Transactions on Affective Computing, 9(1), 51–65. https://doi.org/10.1109/TAFFC.2016.2592504
  • Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24(4), 385–396. https://doi.org/10.2307/2136404
  • Dobbins, C., & Fairclough, S. (2019). Signal processing of multimodal mobile lifelogging data towards detecting stress in real-world driving. IEEE Transactions on Mobile Computing, 18(3), 632–644. https://doi.org/10.1109/TMC.2018.2840153
  • Elgharib, M., Hefeeda, M., Durand, F., & Freeman, W. T. (2015). Video magnification in presence of large motions. In IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.
  • Fehrenbacher, D. (2017). Affect infusion and detection through faces in computer-mediated knowledge-sharing decisions. Journal of the Association for Information Systems, 18(10), 703–726. https://doi.org/10.17705/1jais.00470
  • Ferdous, R., Osmani, V., Beltran Marquez, J., & Mayora, O. (2015). Investigating correlation between verbal interactions and perceived stress. In 37th Annual International Conference on the IEEE Engineering in Medicine and Biology Society, Milan, Italy.
  • Gao, H., Yuce, A., & Thiran, J. (2014). Detecting emotional stress from facial expressions for driving safety. In 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.
  • Garcia-Ceja, E., Osmani, V., & Mayora, O. (2016). Automatic stress detection in working environments from smartphones’ accelerometer data: A first step. IEEE Journal of Biomedical and Health Informatics, 20(4), 1053–1060. https://doi.org/10.1109/JBHI.2015.2446195
  • Gimpel, H., Regal, C., & Schmidt, M. (2015). myStress: Unobtrusive smartphone-based stress detection. In Proceedings of the 23rd European Conference on Information Systems, Münster, Germany.
  • Gimpel, H., Regal, C., & Schmidt, M. (2019a). Design knowledge on mobile stress assessment. In Proceedings of the 40th International Conference on Information Systems, Munich, Germany.
  • Gimpel, H., Regal, C., & Schmidt, M. (2019b). Life-integrated stress assessment. In Proceedings of the 27th European Conference on Information Systems, Stockhom and Uppsala, Sweden.
  • Gjoreski, M., Gjoreski, H., Lutrek, M., & Gams, M. (2015). Automatic detection of perceived stress in campus students using smartphones. In 2015 International Conference on Intelligent Environments, Prague, Czech Republic.
  • Glenn, T., & Monteith, S. (2014). New measures of mental state and behavior based on data collected from sensors, smartphones, and the internet. Current Psychiatry Reports, 16(523), 1–10. https://doi.org/10.1007/s11920-014-0523-3
  • Greene, S., Thapliyal, H., & Caban-Holt, A. (2016). A survey of affective computing for stress detection: Evaluating technologies in stress detection for better health. IEEE Consumer Electronics Magazine, 5(4), 44–56. https://doi.org/10.1109/MCE.2016.2590178
  • Gregor, S., & Jones, D. (2007). The anatomy of a design theory. Journal of the Association for Information Systems, 8(5), 312–335. https://doi.org/10.17705/1jais.00129
  • Gregor, S., Kruse, L., & Seidel, S. (2020). Research perspectives: The anatomy of a design principle. Journal of the Association for Information Systems, 21(6), 1622–1652. https://doi.org/10.17705/1jais.00649
  • Habib Ur Rehman, M., Liew, C. S., Wah, T. Y., Shuja, J., & Daghighi, B. (2015). Mining personal data using smartphones and wearable devices: A survey. Sensors, 15(2), 4430–4469. https://doi.org/10.3390/s150204430
  • Hammen, C. (2005). Stress and depression. Annual Review of Clinical Psychology, 1(1), 293–319. https://doi.org/10.1146/annurev.clinpsy.1.102803.143938
  • Hancock, P. A., & Warm, J. S. (1989). A dynamic model of stress and sustained attention. Human Factors, 31(5), 519–537. https://doi.org/10.1177/001872088903100503
  • Hassard, J., Teoh, K. R. H., Visockaite, G., Dewe, P., & Cox, T. (2018). The cost of work-related stress to society: A systematic review. Journal of Occupational Health Psychology, 23(1), 1–17. https://doi.org/10.1037/ocp0000069
  • Hevner, A. R., March, S. T., Park, J. [., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105. https://doi.org/10.2307/25148625
  • Hjortskov, N., Rissén, D., Blangsted, A. K., Fallentin, N., Lundberg, U., & Søgaard, K. (2004). The effect of mental stress on heart rate variability and blood pressure during computer work. European Journal of Applied Physiology, 92(1–2), 84–89. https://doi.org/10.1007/s00421-004-1055-z
  • Iivari, J., Rotvit Perlt Hansen, M., & Haj-Bolouri, A. (2021). A proposal for minimum reusability evaluation of design principles. European Journal of Information Systems, 30(3), 286–303. https://doi.org/10.1080/0960085X.2020.1793697
  • Köffer, S., Anlauf, L., Ortbach, K., & Niehaves, B. (2015). The intensified blurring of boundaries between work and private life through IT consumerisation. In Proceedings of the 23rd European Conference on Information Systems, Münster, Germany.
  • Koldijk, S., Neerincx, M. A., & Kraaij, W. (2018). Detecting work stress in offices by combining unobtrusive sensors. IEEE Transactions on Affective Computing, 9(2), 227–239. https://doi.org/10.1109/TAFFC.2016.2610975
  • Lane, N. D., Mohammod, M., Lin, M., Yang, X., Lu, H., Ali, S., Doryab, A., Berke, E., Choudhury, T., & Campbell, A. (2011). BeWell: A smartphone application to monitor, model and promote wellbeing. In Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, Dublin, Ireland.
  • Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. Springer.
  • Lefter, I., Burghouts, G. J., & Rothkrantz, L. J. (2016). Recognizing stress using semantics and modulation of speech and gestures. IEEE Transactions on Affective Computing, 7(2), 162–175. https://doi.org/10.1109/TAFFC.2015.2451622
  • Levy, Y., & Ellis, T. J. (2006). A systems approach to conduct an effective literature review in support of information systems research. Informing Science: The International Journal of an Emerging Transdiscipline, 9, 181–212. https://doi.org/10.28945/479
  • Liao, W., Zhang, W., Zhu, Z., & Ji, Q. (2005). A real-time human stress monitoring system using dynamic bayesian network. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA.
  • LiKamWa, R., Liu, Y., Lane, N. D., & Zhong, L. (2013). Moodscope: Building a mood sensor from smartphone usage patterns. In 11th Annual International Conference on Mobile Systems, Applications, and Services, Taipeh, Taiwan.
  • Lu, H., Frauendorfer, D., Rabbi, M., Mast, M. S., Chittaranjan, G. T., Campbell, A. T., Gatica-Perez, D., & Choudhury, T. (2012). Stresssense: Detecting stress in unconstrained acoustic environments using smartphones. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, Pittsburgh, PA, USA.
  • March, S. T., & Smith, G. F. (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), 251–266. https://doi.org/10.1016/0167-9236(94)00041-2
  • March, S., & Storey, V. (2008). Design science in the information systems discipline: An introduction to the special issue on design science research. MIS Quarterly, 32(4), 725–730. https://doi.org/10.2307/25148869
  • Maxhuni, A., Hernandez-Leal, P., Morales, E. F., Sucar, L. E., Osmani, V., & Mayora, O. (2021). Unobtrusive stress assessment using smartphones. IEEE Transactions on Mobile Computing, 20(6), 2313–2325. https://doi.org/10.1109/TMC.2020.2974834
  • Mayya, S., Jilla, V., Tiwari, V. N., Nayak, M. M., & Narayanan, R. (2015). Continuous monitoring of stress on smartphone using heart rate variability. In 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, Belgrade, Serbia.
  • Mental Health Foundation. (2018). Stress: Are we coping? London, UK: Mental Health Foundation.
  • Meth, H., Mueller, B., & Maedche, A. (2015). Designing a requirement mining system. Journal of the Association for Information Systems, 16(9), 799–837. https://doi.org/10.17705/1jais.00408
  • Meulendijk, M., Meulendijks, E., Jansen, P., Numans, M., & Spruit, M. (2014). What concerns users of medical apps? Exploring non-functional requirements of medical mobile applications. In Proceedings of the 22nd European Conference on Information Systems, Tel Aviv, Israel.
  • Möller, F., Schoormann, T., Strobel, G., & Hansen, M. R. P. (2022). Unveiling the cloak: Kernel theory use in design science research. In ICIS 2022 Proceedings, Copenhagen, Denmark.
  • Momeni, N., Dell’agnola, F., Arza, A., & Atienza, D. (2019). Real-time cognitive workload monitoring based on machine learning using physiological signals in rescue missions. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany.
  • Müller, L., Rivera-Pelayo, V., Kunzmann, C., & Schmidt, A. (2011). From stress awareness to coping strategies of medical staff: Supporting reflection on physiological data. In Proceedings of the 2nd International Conference on Human Behavior Understanding, Amsterdam, Netherlands.
  • Paré, G., Trudel, M. ‑., Jaana, M., & Kitsiou, S. (2015). Synthesizing information systems knowledge: A typology of literature reviews. Information & Management, 52(2), 183–199. https://doi.org/10.1016/j.im.2014.08.008
  • Park, J. [., Kim, J., & Kim, S. ‑. (2018). Prediction of daily mental stress levels using a wearable photoplethysmography sensor. In TENCON 2018 - 2018 IEEE Region 10 Conference.
  • Peffers, K., Tuunanen, T., Rothenberger, M., & Chatterjee, S. (2007). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77. https://doi.org/10.2753/MIS0742-1222240302
  • Rachakonda, L., Mohanty, S. P., Kougianos, E., & Sundaravadivel, P. (2019). Stress-lysis: A DNN-integrated edge device for stress level detection in the IoMT. IEEE Transactions on Consumer Electronics, 65(4), 474–483. https://doi.org/10.1109/TCE.2019.2940472
  • Riedl, R. (2012). On the biology of technostress: Literature review and research agenda. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 44(1), 18–55. https://doi.org/10.1145/2436239.2436242
  • Riedl, R., & Javor, A. (2012). The biology of trust: Integrating evidence from genetics, endocrinology, and functional brain imaging. Journal of Neuroscience, Psychology, and Economics, 5(2), 63–91. https://doi.org/10.1037/a0026318
  • Rodrigues, J. G. P., Kaiseler, M., Aguiar, A., Silva Cunha, J. P., & Barros, J. (2015). A mobile sensing approach to stress detection and memory activation for public bus drivers. IEEE Transactions on Intelligent Transportation Systems, 16(6), 3294–3303. https://doi.org/10.1109/TITS.2015.2445314
  • Rosemann, M., & Vessey, I. (2008). Toward improving the relevance of information systems research to practice: The role of applicability checks. MIS Quarterly, 32(1), 1–22. https://doi.org/10.2307/25148826
  • Salo, M., Pirkkalainen, Eng Huang Chua, C., Koskelainen, T., & Pirkkalainen, H. (2022). Formation and mitigation of technostress in the personal use of IT. MIS Quarterly, 46(2), 1073–1108. https://doi.org/10.25300/MISQ/2022/14950
  • Sandulescu, V., & Dobrescu, R. (2015). Wearable system for stress monitoring of firefighters in special missions. In 2015 E-Health and Bioengineering Conference.
  • Schubert, C., Lambertz, M., Nelesen, R. A., Bardwell, W., Choi, J. ‑., & Dimsdale, J. E. (2009). Effects of stress on heart rate complexity - a comparison between short-term and chronic stress. Biological Psychology, 80(3), 325–332. https://doi.org/10.1016/j.biopsycho.2008.11.005
  • Sonnenberg, C., & Vom Brocke, J. (2012). Evaluations in the science of the artificial: Reconsidering the build-evaluate pattern in design science research. In Proceedings of the 7th International Conference on Design Science Research in Information Systems: Advances in Theory and Practice.
  • Tams, S., Thatcher, J. B., & Grover, V. (2018). Concentration, competence, confidence, and capture: An experimental study of age, interruption-based technostress, and task performance. Journal of the Association for Information Systems, 19(9), 857–908. https://doi.org/10.17705/1jais.00511
  • Tarafdar, M., Cooper, C. L., & Stich, J. ‑. (2019). The technostress trifecta - techno eustress, techno distress and design: Theoretical directions and an agenda for research. Information Systems Journal, 29(1), 6–42. https://doi.org/10.1111/isj.12169
  • Thoits, P. A. (1995). Stress, coping, and social support processes: Where are we? What next? Journal of Health and Social Behavior, 35, 53–79. https://doi.org/10.2307/2626957
  • Tundis, A., Uzair, M., & Mühlhäuser, M. (2020). Human physical status detection related to danger situations based on smartwatch and smartphone. In 2020 IFIP Networking Conference.
  • Vaishali, B., Amalan, S., Preejith, S. P., Joseph, J., & Sivaprakasam, M. (2020). HRV based stress assessment of individuals in a work environment. In 2020 IEEE International Symposium on Medical Measurements and Applications.
  • Van den Broek, K., Hassard, J., & Flemming, D. (2014). Calculating the costs of work-related stress and psychosocial risks. The Literature Review. https://doi.org/10.2802/20493
  • Venable, J., Pries-Heje, J., & Baskerville, R. (2016). FEDS: A framework for evaluation in design science research. European Journal of Information Systems, 25(1), 77–89. https://doi.org/10.1057/ejis.2014.36
  • Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
  • Vom Brocke, J., Hevner, A., Léger, P. M., Walla, P., & Riedl, R. (2020). Advancing a NeuroIS research agenda with four areas of societal contributions. European Journal of Information Systems, 29(1), 9–24. https://doi.org/10.1080/0960085X.2019.1708218
  • Walls, J. G., Widmeyer, G. R., & El Sawy, O. A. (1992). Building an information system design theory for vigilant EIS. Information Systems Research, 3(1), 36–59. https://doi.org/10.1287/isre.3.1.36
  • Wang, R., Chen, F., Chen, Z., Li, T., Harari, G., Tignor, S., Zhou, X., Ben-Zeev, D., & Campbell, A. T. (2014). Studentlife: Assessing mental health, academic performance and behavioral trends of college students using smartphones. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing.
  • Wang, F., Wang, Y., Wang, J., Xiong, H., Zhao, J., & Zhang, D. (2019). Assessing mental stress based on smartphone sensing data: An empirical study. In 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation.
  • Yu, B., Zhang, B., An, P., Xu, L., Xue, M., & Hu, J. (2019). An unobtrusive stress recognition system for the smart office. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
  • Þórarinsdóttir, H., Kessing, L. V., & Faurholt-Jepsen, M. (2017). Smartphone-based self-assessment of stress in healthy adult individuals: A systematic review. Journal of Medical Internet Research, 19(2), 1–13. https://doi.org/10.2196/jmir.6397

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.