Advertisement

Co-creating Requirements and Assessing End-User Acceptability of a Voice-Based Chatbot to Support Mental Health: A Thematic Analysis of a Living Lab Workshop

Chapter
  • 278 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 704)

Abstract

Mental health and mental wellbeing have become an important factor to many citizens navigating their way through their environment and in the work place. New technology solutions such as chatbots are potential channels for supporting and coaching users to maintain a good state of mental wellbeing. Chatbots have the added value of providing social conversations and coaching 24/7 outside from conventional mental health services. However, little is known about the acceptability and user led requirements of this technology. This paper uses a living lab approach to elicit requirements, opinions and attitudes towards the use of chatbots for supporting mental health. The data collected was acquired from people living with anxiety or mild depression in a workshop setting. The audio of the workshop was recorded and a thematic analysis was carried out. The results are the co-created functional requirements and a number of use case scenarios that can be of interest to guide future development of chatbots in the mental health domain.

Notes

Acknowledgements

This research has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 823907 (MENHIR: Mental health monitoring through interactive conversations https://menhir-project.eu).

References

  1. 1.
    Abd-alrazaq AA, Alajlani M, Alalwan AA, Bewick BM, Gardner P, Househ M (2019) An overview of the features of chatbots in mental health: a scoping review. Int J Med Inf 132:103978CrossRefGoogle Scholar
  2. 2.
    Cummins N, Scherer S, Krajewski J, Schnieder S, Epps J, Quatieri TF (2015) A review of depression and suicide risk assessment using speech analysis. Speech Commun 71:10–49CrossRefGoogle Scholar
  3. 3.
    Fitzpatrick KK, Darcy A, Vierhile M (2017) Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (woebot): a randomized controlled trial. JMIR Mental Health 4(2):e19CrossRefGoogle Scholar
  4. 4.
    Greenhalgh T, Jackson C, Shaw S, Janamian T (2016) Achieving research impact through co-creation in community-based health services: literature review and case study. Milbank Q. 94(2):392–429CrossRefGoogle Scholar
  5. 5.
    Guest G, MacQueen KM, Namey EE (2011) Applied Thematic Analysis. SAGE Publications, Thousand OaksGoogle Scholar
  6. 6.
    Hollis C, Morriss R, Martin J, Amani S, Cotton R, Denis M, Lewis S (2015) Technological innovations in mental healthcare: harnessing the digital revolution. Brit J Psychiatry J Mental Sci 206(4):263–265CrossRefGoogle Scholar
  7. 7.
    Inkster B, Sarda S, Subramanian V (2018) An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: real-world data evaluation mixed-methods Study. JMIR mHealth uHealth 6(11):e12106CrossRefGoogle Scholar
  8. 8.
    Jull J, Giles A, Graham ID (2017) Community-based participatory research and integrated knowledge translation: advancing the co-creation of knowledge. Implementation Sci 12(1):150CrossRefGoogle Scholar
  9. 9.
    Lee M, Ackermans S, van As N, Chang H, Lucas E, IJsselsteijn W (2019) Caring for vincent: a chatbot for self-compassion. In: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, CHI ’19, Glasgow, Scotland UK , pp 1–13Google Scholar
  10. 10.
    McTear M, Callejas Z, Barres DG (2016) The Conversational Interface: Talking to Smart Devices. Springer, HeidelbergCrossRefGoogle Scholar
  11. 11.
    Mulvenna MD, Bergvall-Kåreborn B, Galbraith B, Wallace J, Martin S (2011) Living labs are innovation catalysts. In: Howlett RJ (ed) Innovation through Knowledge Transfer 2010. Springer, Heidelberg, pp 253–264CrossRefGoogle Scholar
  12. 12.
    Ringeval F, Schuller B, Valstar M, Cummins N, Cowie R, Tavabi L, Schmitt M, Alisamir S, Amiriparian S, Messner EM, Song S, Liu S, Zhao Z, Mallol-Ragolta A, Ren Z, Soleymani M, Pantic M (2019) AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition. In: Proceedings of AVEC ’19. ACM Press, NiceGoogle Scholar
  13. 13.
    Ta V, Griffith C, Boatfield C, Wang X, Civitello M, Bader H, DeCero E, Loggarakis A (2020) User experiences of social support from companion chatbots in everyday contexts: thematic analysis. J Med Internet Res 22(3):e16235CrossRefGoogle Scholar
  14. 14.
    Vaidyam AN, Wisniewski H, Halamka JD, Kashavan MS, Torous JB (2019) Chatbots and conversational agents in mental health: a review of the psychiatric landscape. Can J Psychiatry 64(7):456–464Google Scholar
  15. 15.
    World Health Organization, Victorian Health Promotion Foundation, University of Melbourne: Promoting mental health: concepts, emerging evidence and practice. World Health Organization (2004). https://apps.who.int/iris/bitstream/handle/10665/42940/9241591595.pdf

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021

Authors and Affiliations

  1. 1.University of GranadaGranadaSpain
  2. 2.Ulster UniversityNewtownabbeyNorthern Ireland
  3. 3.Università degli Studi della Campania Luigi VanvitelliCasertaItaly
  4. 4.University of UlmUlmGermany
  5. 5.Action Mental HealthNewtownardsNorthern Ireland

Personalised recommendations