Users’ Perceptions of a Digital Stress Self-Monitoring Application: Research Insights to Design a Practical Innovation

by | Jun 16, 2022 | Research Publications

Author(s):

Myriam Sillevis Smitt

Mehdi Montakhabi

Jessica Morton

Cora van Leeuwen

Klaas Bombeke

An Jacobs 

Abstract:

Self-monitoring is considered a promising tool for self-management in clinical mental health, such as for coping with excessive stress. Detecting debilitating stress before the onset of a psychopathology is becoming more of interest both for practitioners and the scientific community. However, the development of mental well-being technology focusing on stress is disrupted by the complexity of accurately measuring stress, as no clear idea exists on the construct and how it should be measured. There is also limited knowledge on the perception of perceived quality of the outcomes from a stress algorithm and the variety in its behavioural consequences. Therefore, the purpose of this study is to explore the impact of such digital self-monitoring technology for stress. It applies a qualitative method, by using semi-structured interviews. The most important resulting themes to users of this application were data-interpretation and a request for transparency. Results indicated that the majority of the predictions of the stress algorithm were not in line with the expectations of the users. The implications of these findings reveal how stress algorithms can make participants doubt their own self judgment on assessing their daily stress levels.

Documentation:

https://doi.org/10.1007/978-3-031-05028-2_22

References:
  1. Marin, M.-F., et al.: Chronic stress, cognitive functioning and mental health. Neurobiol. Learn. Mem. 96(4), 583–595 (2011). https://doi.org/10.1016/j.nlm.2011.02.016CrossRef  Google Scholar 
  2. Richardson, S., Shaffer, J.A., Falzon, L., Krupka, D., Davidson, K.W., Edmondson, D.: Meta-analysis of perceived stress and its association with incident coronary heart disease. Am. J. Cardiol. 110(12), 1711–1716 (2012). https://doi.org/10.1016/j.amjcard.2012.08.004CrossRef  Google Scholar 
  3. Brosschot, J.F.: Markers of chronic stress: prolonged physiological activation and (un)conscious perseverative cognition. Neurosci. Biobehav. Rev. 35(1), 46–50 (2010). https://doi.org/10.1016/j.neubiorev.2010.01.004CrossRef  Google Scholar 
  4. Murnane, E.L., et al.: Self-monitoring practices, attitudes, and needs of individuals with bipolar disorder: implications for the design of technologies to manage mental health. J. Am. Med. Inform. Assoc. 23(3), 477–484 (2016). https://doi.org/10.1093/jamia/ocv165CrossRef  Google Scholar 
  5. Crossley, G.H., Boyle, A., Vitense, H., Chang, Y., Mead, R.H.: The CONNECT (clinical evaluation of remote notification to reduce time to clinical decision) trial. J. Am. Coll. Cardiol. 57(10), 1181–1189 (2011). https://doi.org/10.1016/j.jacc.2010.12.012CrossRef  Google Scholar 
  6. Firth, J., Torous, J., Yung, A.R.: Ecological momentary assessment and beyond: The rising interest in e-mental health research. J. Psychiatr. Res. 80, 3–4 (2016). https://doi.org/10.1016/j.jpsychires.2016.05.002CrossRef  Google Scholar 
  7. Reisinger, M., Röderer, K.: ‘I’m fine, thank you – Contextualizing Wellbeing and Mental Health for Persuasive Technologies, p. 6 Google Scholar 
  8. Müller, J., Fàbregues, S., Guenther, E.A., Romano, M.J.: Using sensors in organizational research—clarifying rationales and validation challenges for mixed methods. Front. Psychol. 10, 1188 (2019). https://doi.org/10.3389/fpsyg.2019.01188CrossRef  Google Scholar 
  9. Smets, E., De Raedt, W., Van Hoof, C.: Into the wild: the challenges of physiological stress detection in laboratory and ambulatory settings. IEEE J. Biomed. Health Inform. 23(2), 463–473 (2019). https://doi.org/10.1109/JBHI.2018.2883751CrossRef  Google Scholar 
  10. Smets, E.: Towards large-scale physiological stress detection in an ambulant environment, p. 198 Google Scholar 
  11. Eikey, E.V., et al.: Beyond self-reflection: introducing the concept of rumination in personal informatics. Pers. Ubiquit. Comput. 25(3), 601–616 (2021). https://doi.org/10.1007/s00779-021-01573-wCrossRef  Google Scholar 
  12. Karter, A.J., et al.: Longitudinal study of new and prevalent use of self-monitoring of blood glucose. Diabetes Care 29(8), 1757–1763 (2006). https://doi.org/10.2337/dc06-2073CrossRef  Google Scholar 
  13. Compernolle, S., et al.: Effectiveness of interventions using self-monitoring to reduce sedentary behavior in adults: a systematic review and meta-analysis. Int. J. Behav. Nutr. Phys. Act. 16(1), 63 (2019). https://doi.org/10.1186/s12966-019-0824-3CrossRef  Google Scholar 
  14. Baumer, E.P.S.: Reflective informatics: conceptual dimensions for designing technologies of reflection. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul Republic of Korea, April 2015, pp. 585–594 (2015). https://doi.org/10.1145/2702123.2702234
  15. Wolf, G.I., De Groot, M.: A conceptual framework for personal science. Front. Comput. Sci. 2, 21 (2020). https://doi.org/10.3389/fcomp.2020.00021CrossRef  Google Scholar 
  16. Li, I., Dey, A., Forlizzi, J.: A stage-based model of personal informatics systems, p. 10 (2010) Google Scholar 
  17. Epstein, D.A., Ping, A., Fogarty, J., Munson, S.A.: A lived informatics model of personal informatics. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing – UbiComp 2015, Osaka, Japan, pp. 731–742 (2015). https://doi.org/10.1145/2750858.2804250
  18. Tadas, S., Coyle, D.: Barriers to and facilitators of technology in cardiac rehabilitation and self-management: systematic qualitative grounded theory review. J. Med. Internet Res. 22(11), e18025 (2020). https://doi.org/10.2196/18025CrossRef  Google Scholar 
  19. Feng, S., Mäntymäki, M., Dhir, A., Salmela, H.: How self-tracking and the quantified self promote health and well-being: systematic review. J. Med. Internet Res. 23(9), e25171 (2021). https://doi.org/10.2196/25171CrossRef  Google Scholar 
  20. Sharma, S., Singh, G., Sharma, M.: A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans. Comput. Biol. Med. 134, 104450 (2021). https://doi.org/10.1016/j.compbiomed.2021.104450CrossRef  Google Scholar 
  21. Manrai, A.K., et al.: Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375(7), 655–665 (2016). https://doi.org/10.1056/NEJMsa1507092CrossRef  Google Scholar 
  22. Cabitza, F., Rasoini, R., Gensini, G.F.: Unintended consequences of machine learning in medicine. JAMA 318(6), 517 (2017). https://doi.org/10.1001/jama.2017.7797CrossRef  Google Scholar 
  23. Lupton, D.: Health promotion in the digital era: a critical commentary. Health Promot. Int. 30(1), 174–183 (2015). https://doi.org/10.1093/heapro/dau091MathSciNet  CrossRef  Google Scholar 
  24. Morley, J., Floridi, L., Kinsey, L., Elhalal, A.: From what to how: an initial review of publicly available ai ethics tools, methods and research to translate principles into practices. Sci. Eng. Ethics 26(4), 2141–2168 (2019). https://doi.org/10.1007/s11948-019-00165-5CrossRef  Google Scholar 
  25. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3(2), 77–101 (2006). https://doi.org/10.1191/1478088706qp063oaCrossRef  Google Scholar 
  26. Vandendriessche, K., Steenberghs, E., Matheve, A., Georges, A., De Marez, L.: imec. digimeter 2020: Digitale Trends in Vlaanderen (2021). https://biblio.ugent.be/publication/8717212/file/8717464
  27. Cohen, S., Kamarck, T., Mermelstein, R.: A global measure of perceived stress. J. Health Soc. Behav. 24(4), 385 (1983). https://doi.org/10.2307/2136404CrossRef  Google Scholar 
  28. Roth, G., Assor, A., Niemiec, C.P., Ryan, R.M., Deci, E.L.: The emotional and academic consequences of parental conditional regard: comparing conditional positive regard, conditional negative regard, and autonomy support as parenting practices. Dev. Psychol. 45(4), 1119–1142 (2009). https://doi.org/10.1037/a0015272
  29. Suh, H., Shahriaree, N., Hekler, E.B., Kientz, J.A.: Developing and validating the user burden scale: a tool for assessing user burden in computing systems. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems – CHI 2016, Santa Clara, California, USA, pp. 3988–3999 (2016). https://doi.org/10.1145/2858036.2858448
  30. Donnelly, T.T., Long, B.C.: Stress discourse and western biomedical ideology: rewriting stress. Issues Ment. Health Nurs. 24(4), 397–408 (2003). https://doi.org/10.1080/01612840305316CrossRef  Google Scholar 
  31. Lupton, D.: The digitally engaged patient: self-monitoring and self-care in the digital health era. Soc. Theory Health 11(3), 256–270 (2013). https://doi.org/10.1057/sth.2013.10CrossRef  Google Scholar 
  32. Alexander, V., Blinder, C., Zak, P.J.: Why trust an algorithm? Performance, cognition, and neurophysiology. Comput. Hum. Behav. 89, 279–288 (2018). https://doi.org/10.1016/j.chb.2018.07.026CrossRef  Google Scholar 
  33. Bray, E.P., Holder, R., Mant, J., McManus, R.J.: Does self-monitoring reduce blood pressure? Meta-analysis with meta-regression of randomized controlled trials. Ann. Med. 42(5), 371–386 (2010). https://doi.org/10.3109/07853890.2010.489567CrossRef  Google Scholar 
  34. Fletcher, B.R., Hartmann-Boyce, J., Hinton, L., McManus, R.J.: The effect of self-monitoring of blood pressure on medication adherence and lifestyle factors: a systematic review and meta-analysis. Am. J. Hypertens. 28(10), 1209–1221 (2015). https://doi.org/10.1093/ajh/hpv008CrossRef  Google Scholar 
  35. Kanejima, Y., Kitamura, M., Izawa, K.P.: Self-monitoring to increase physical activity in patients with cardiovascular disease: a systematic review and meta-analysis. Aging Clin. Exp. Res. 31(2), 163–173 (2018). https://doi.org/10.1007/s40520-018-0960-7