Skip to main content

Using Health Chatbots for Behavior Change: A Mapping Study

Abstract

This study conducts a mapping study to survey the landscape of health chatbots along three research questions: What illnesses are chatbots tackling? What patient competences are chatbots aimed at? Which chatbot technical enablers are of most interest in the health domain? We identify 30 articles related to health chatbots from 2014 to 2018. We analyze the selected articles qualitatively and extract a triplet <technicalEnablers, competence, illness> for each of them. This data serves to provide a first overview of chatbot-mediated behavior change on the health domain. Main insights include: nutritional disorders and neurological disorders as the main illness areas being tackled; “affect” as the human competence most pursued by chatbots to attain change behavior; and “personalization” and “consumability” as the most appreciated technical enablers. On the other hand, main limitations include lack of adherence to good practices to case-study reporting, and a deeper look at the broader sociological implications brought by this technology.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

References

  1. 1.

    Abashev, A., Grigoryev, R., Grigorian, K., and Boyko, V., Programming tools for messenger-based chatbot system organization: Implication for outpatient and translational medicines. BioNanoScience. 7(2):403–407, 2017. https://doi.org/10.1007/s12668-016-0376-9.

    Article  Google Scholar 

  2. 2.

    Andersson, G., and Cuijpers, P., Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cognit. Behav. Ther. 38(4):196–205, 2009. https://doi.org/10.1080/16506070903318960.

    Article  Google Scholar 

  3. 3.

    Atay, C., Ireland, D., Liddle, J., Wiles, J., Vogel, A., Angus, D., Bradford, D., Campbell, A., Rushin, O., and Chenery, H. J., Can a smartphone-based chatbot engage older community group members? The impact of specialised content. Alzheimer’s Dement.: J. Alzheimer’s Assoc. 12(7):P1005–P1006, 2016. https://doi.org/10.1016/j.jalz.2016.06.2070.

    Article  Google Scholar 

  4. 4.

    Beun, R.J., Brinkman, W.-P., Fitrianie, S., Griffioen-Both, F., Horsch, C., Lancee, J., and Spruit, S.: Improving adherence in automated e-coaching. In: International conference on persuasive technology. pp. 276–287. Springer, 2016).

  5. 5.

    Bickmore, T. W., Puskar, K., Schlenk, E. A., Pfeifer, L. M., and Sereika, S. M., Maintaining reality: Relational agents for antipsychotic medication adherence. Interact. Comput. 22(4):276–288, 2010. https://doi.org/10.1016/j.intcom.2010.02.001.

    Article  Google Scholar 

  6. 6.

    Brinkman, P., Virtual health agents for behavior change: Research perspectives and directions. Proceedings of the workshop on graphical and robotic embodied agents for therapeutic systems (GREATS16) held during the international conference on intelligent virtual agents (IVA16), 2016.

  7. 7.

    Brixey, J., Hoegen, R., Lan, W., Rusow, J., Singla, K., Yin, X., Artstein, R., and Leuski, A., SHIHbot: A Facebook chatbot for sexual health information on HIV/AIDS. Proceedings of the 18th annual SIGdial meeting on discourse and dialogue. 370–373, 2017.

  8. 8.

    Callejas, Z., Griol, D., McTear, M.F., López-Cózar, R.: A virtual coach for active ageing based on sentient computing and m-health. International workshop on ambient assisted living: 59–66. Springer, 2014.

  9. 9.

    Cameron, G., Cameron, D., Megaw, G., Bond, R., Mulvenna, M., O’Neill, S., Armour, C., and McTear, M., Towards a chatbot for digital counselling. J. Med. Internet Res. 4(1):e3, 2017.

    Google Scholar 

  10. 10.

    Cheng, A., Raghavaraju, V., Kanugo, J., Handrianto, Y.P., and Shang, Y., Development and evaluation of a healthy coping voice interface application using the Google home for elderly patients with type 2 diabetes. Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE annual. Pp. 1–5. IEEE, 2018.

  11. 11.

    Chung, K., and Park, R.C., Chatbot-based healthcare service with a knowledge base for cloud computing. Cluster Computing. 1–13,2018. doi:https://doi.org/10.1007/s10586-018-2334-5.

  12. 12.

    Crawford, E., Bots are awesome! Humans? Not so much, https://chatbotsmagazine.com/bots-are-awesome-humans-not-so-much-7b2d62630668 .

  13. 13.

    Cruz-Sandoval, D., and Favela, J., Semi-autonomous conversational robot to Deal with problematic behaviors from people with dementia. International conference on ubiquitous computing and ambient intelligence. 677–688. Springer, 2017.

  14. 14.

    Cruzes, D.S., and Dyba, T., Recommended steps for thematic synthesis in software engineering. In: 2011 international symposium on empirical software engineering and measurement. pp. 275–284. IEEE, 2011.

  15. 15.

    Dale, R., The return of the chatbots. Nat. Lang. Eng. 22(5):811–817, 2016. https://doi.org/10.1017/S1351324916000243.

    Article  Google Scholar 

  16. 16.

    Dubosson, F., Schaer, R., Savioz, R., and Schumacher, M., Going beyond the relapse peak on social network smoking cessation programmes: ChatBot opportunities. Swiss Med. Inform. 33, 2017.

  17. 17.

    Elmasri, D., and Maeder, A., A conversational agent for an online mental health intervention. International conference on brain and health informatics. 243–251. Springer, 2016.

  18. 18.

    Eysenbach, G., and Group, C.-E., CONSORT-EHEALTH: Improving and standardizing evaluation reports of web-based and mobile health interventions. Journal of medical Internet research. 13(4), 2011. doi:https://doi.org/10.2196/jmir.1923 .

    Article  Google Scholar 

  19. 19.

    Fadhil, A., A conversational Interface to improve medication adherence: Towards AI support in Patient’s treatment. arXiv preprint arXiv:1803.09844. 2018.

  20. 20.

    Fadhil, A., and Gabrielli, S., Addressing challenges in promoting healthy lifestyles: The al-chatbot approach. Proceedings of the 11th EAI international conference on pervasive computing Technologies for Healthcare. 261–265. ACM, 2017.

  21. 21.

    Fadhil, A., Villafiorita, A.: An adaptive learning with gamification & conversational UIs: The rise of CiboPoliBot. Adjunct publication of the 25th conference on user modeling, adaptation and personalization: 408–412. ACM, 2017.

  22. 22.

    Fernandez-Luque, L., Lattab, A., Hors, S., and Ahmedna, M., Implementation and feasibility study of a tailored health education bot in telegram for mothers of children with obesity and overweight. Qatar Foundation annual research conference proceedings. p. HBPD506. HBKU press Qatar, 2018.

  23. 23.

    Fogg, B. J., Persuasive technology: Using computers to change what we think and do. Ubiquity. 2002(December):5, 2002. https://doi.org/10.1145/764008.763957.

    Article  Google Scholar 

  24. 24.

    Gabrielli, S., Marie, K., and Corte, C.D., SLOWBot (Chatbot) lifestyle assistant. In: Proceedings of the 12th EAI international conference on pervasive computing Technologies for Healthcare. 367–370. ACM, New York, NY, USA, 2018.

  25. 25.

    Griol, D., and Molina, J.M., An ambient assisted living Mobile application for helping people with Alzheimer. International conference on practical applications of agents and multi-agent systems: 3–14. Springer, 2015.

  26. 26.

    Hsu, P., Zhao, J., Liao, K., Liu, T., Wang, C.: AllergyBot: A Chatbot technology intervention for young adults with food allergies dining out. In: Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems. pp. 74–79. ACM, 2017.

  27. 27.

    Huang, C., Yang, M., Huang, C., Chen, Y., Wu, M., Chen, K.: A Chatbot-supported smart wireless interactive healthcare system for weight control and health promotion. 2018 IEEE international conference on industrial engineering and engineering management (IEEM): 1791–1795, 2018.

  28. 28.

    Inkster, B., Sarda, S., and Subramanian, V., An empathy-driven, conversational artificial intelligence agent (Wysa) for digital mental well-being: Real-world data evaluation mixed-methods study. JMIR mHealth and uHealth. 6:e12106, 2018. https://doi.org/10.2196/mhealth.12106.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Isern, D., and Moreno, A., A systematic literature review of agents applied in healthcare. J. Med. Syst. 40(2):43, 2016. https://doi.org/10.1007/s10916-015-0376-2.

    Article  PubMed  Google Scholar 

  30. 30.

    Ivarsson, M., and Gorschek, T., A method for evaluating rigor and industrial relevance of technology evaluations. Empiric. Softw. Eng. 16:365–395, 2011. https://doi.org/10.1007/s10664-010-9146-4.

    Article  Google Scholar 

  31. 31.

    Jeong, S., and Breazeal, C., Toward robotic companions that enhance psychological wellbeing with smartphone technology. Proceedings of the companion of the 2017 ACM/IEEE international conference on human-robot interaction. pp. 345–346. ACM, 2017.

  32. 32.

    Kimani, E., Bickmore, T., Trinh, H., Ring, L., Paasche-Orlow, M.K., and Magnani, J.W., A smartphone-based virtual agent for atrial fibrillation education and counseling. International conference on intelligent virtual agents. 120–127. Springer, 2016.

  33. 33.

    Kitchenham, B.A., Budgen, D., and Brereton, P., Evidence-based software engineering and systematic reviews. CRC Press, 2015.

  34. 34.

    Klopfenstein, L.C., Delpriori, S., Malatini, S., and Bogliolo, A., The rise of bots: A survey of conversational interfaces, patterns, and paradigms. Proceedings of the 2017 conference on designing interactive systems. 555–565. ACM, New York, NY, USA, 2017.

  35. 35.

    Kobori, Y., Osaka, A., Soh, S., and Okada, H., Novel application for sexual transmitted infection screening with an ai chatbot. J. Urol. 199(4, Supplement):e189–e190, 2018. https://doi.org/10.1016/j.juro.2018.02.516.

    Article  Google Scholar 

  36. 36.

    Kowatsch, T., Ni’s sen, M., Shih, C.-H.I., Rüegger, D., Volland, D., Filler, A., Künzler, F., Barata, F., Hung, S., and Büchter, D., Text-based healthcare Chatbots supporting patient and health professional teams: Preliminary results of a randomized controlled trial on childhood obesity. Persuasive embodied agents for behavior change (PEACH2017). ETH Zurich, 2017.

  37. 37.

    Kowatsch, T., Volland, D., Shih, I., Rüegger, D., Künzler, F., Barata, F., Filler, A., Büchter, D., Brogle, B., and Heldt, K., Design and evaluation of a Mobile chat app for the open source behavioral health intervention platform MobileCoach. International conference on design science research in information systems: 485–489. Springer, 2017.

  38. 38.

    Kozinakova, B., Analysis of chatbot systems focusing on the elderly as users. Master Thesis, Politecnico de Milano, 2017.

  39. 39.

    Lambert, A.O.C., Montañez, C.H.T., Martinez, M.B., and Funes-Gallanzi, M., A conversational agent for use in the identification of rare diseases. In: Applications for future internet. 128–139. Springer, 2017.

  40. 40.

    Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., and Lau, A., Conversational agents in healthcare: A systematic review. J. Am. Med. Inform. Assoc., 2018. https://doi.org/10.1093/jamia/ocy072.

    Article  Google Scholar 

  41. 41.

    Lisetti, C., Amini, R., and Yasavur, U., Now all together: Overview of virtual health assistants emulating face-to-face health interview experience. KI-Künstliche Intelligenz. 29(2):161–172, 2015. https://doi.org/10.1007/s13218-015-0357-0.

    Article  Google Scholar 

  42. 42.

    Ly, K. H., Ly, A.-M., and Andersson, G., A fully automated conversational agent for promoting mental well-being: A pilot RCT using mixed methods. Internet Interven. 10:39–46, 2017. https://doi.org/10.1016/j.invent.2017.10.002.

    Article  Google Scholar 

  43. 43.

    Miner, A., Chow, A., Adler, S., Zaitsev, I., Tero, P., Darcy, A., and Paepcke, A., Conversational agents and mental health: Theory-informed assessment of language and affect. Proceedings of the fourth international conference on human agent interaction. 123–130. ACM, 2016.

  44. 44.

    Miner, A. S., Milstein, A., Schueller, S., Hegde, R., Mangurian, C., and Linos, E., Smartphone-based conversational agents and responses to questions about mental health, interpersonal violence, and physical health. JAMA Intern. Med. 176(5):619–625, 2016. https://doi.org/10.1001/jamainternmed.2016.0400.

    Article  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Oh, K.-J., Lee, D., Ko, B., and Choi, H.-J., A Chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. Mobile data management (MDM), 2017 18th IEEE international conference on. 371–375. IEEE, 2017.

  46. 46.

    Pereira, J., Díaz, Ó.: Chatbot dimensions that matter: Lessons from the trenches. In: Web engineering, lecture notes in computer science, pp. 129–135. Springer, Cham (2018). doi:https://doi.org/10.1007/978-3-319-91662-0_9

    Chapter  Google Scholar 

  47. 47.

    Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M., Systematic mapping studies in software engineering. Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering. pp. 68–77. BCS Learning & Development Ltd., Swindon, UK, 2008.

  48. 48.

    Richards, D., and Caldwell, P., Improving health outcomes sooner rather than later via an interactive website virtual specialist. IEEE Journal of Biomedical and Health Informatics. 1–1, 2017. doi:https://doi.org/10.1109/JBHI.2017.2782210 .

    Article  Google Scholar 

  49. 49.

    Richards, D., Caldwell, P.H.: Gamification to improve adherence to clinical treatment advice. Health literacy: Breakthroughs in research and practice: Breakthroughs in research and practice. 80, 2017. doi:https://doi.org/10.4018/978-1-5225-1928-7.ch005 .

  50. 50.

    Roniotis, A., and Tsiknakis, M., Detecting depression using voice signal extracted by Chatbots: A feasibility study. In: Interactivity, game creation, design, learning, and innovation. Springer, 2017, 386–392.

  51. 51.

    Schueller, S. M., Tomasino, K. N., and Mohr, D. C., Integrating human support into behavioral intervention technologies: The efficiency model of support. Clin. Psychol.: Sci. Pract. 24(1):27–45, 2017. https://doi.org/10.1111/cpsp.12173.

    Article  Google Scholar 

  52. 52.

    Stratou, G., Morency, L.P., DeVault, D., Hartholt, A., Fast, E., Lhommet, M., Lucas, G., Morbini, F., Georgila, K., Scherer, S., Gratch, J., Marsella, S., Traum, D., and Rizzo, A., A demonstration of the perception system in SimSensei, a virtual human application for healthcare interviews. 2015 international conference on affective computing and intelligent interaction (ACII). pp. 787–789, 2015.

  53. 53.

    van Heerden, A., Ntinga, X., and Vilakazi, K., The potential of conversational agents to provide a rapid HIV counseling and testing services. The Frontiers and advances in data science (FADS), 2017 international conference on. 80–85. IEEE, 2017.

  54. 54.

    Van Vuuren, S., and Cherney, L.R., A virtual therapist for speech and language therapy. International conference on intelligent virtual agents. 438–448. Springer, 2014.

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Óscar Díaz.

Ethics declarations

Conflict of Interest

Juanan Pereira declares that he has no conflict of interest. Óscar Díaz declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Mobile & Wireless Health

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pereira, J., Díaz, Ó. Using Health Chatbots for Behavior Change: A Mapping Study. J Med Syst 43, 135 (2019). https://doi.org/10.1007/s10916-019-1237-1

Download citation

Keywords

  • Chatbots
  • Mobile healthcare
  • Instant messaging
  • Software agents