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Helping Users Reflect on Their Own Health-Related Behaviors

  • Rafal Kocielnik
  • Gary Hsieh
  • Daniel Avrahami
Chapter
Part of the Human–Computer Interaction Series book series (HCIS)

Abstract

In this chapter we discuss the use of external sources of data in designing conversational dialogues. We focus on applications in behavior change around physical activity involving dialogues that help users better understand their self-tracking data and motivate healthy behaviors. We start by introducing the areas of behavior change and personal informatics and discussing the importance of self-tracking data in these areas. We then introduce the role of reflective dialogue-based counseling systems in this domain, discuss specific value that self-tracking data can bring, and how it can be used in creating the dialogues. The core of the chapter focuses on six practical examples of design of dialogues involving self-tracking data that we either tested in our research or propose as future directions based on our experiences. We end the chapter by discussing how the design principles for involving external data in conversations can be applied to broader domains. Our goal for this chapter is to share our experiences, outline design principles, highlight several design opportunities in external data-driven computer-based conversations, and encourage the reader to explore creative ways of involving external sources of data in shaping dialogues-based interactions.

References

  1. Abraham C, Michie S (2008) A taxonomy of behavior change techniques used in interventions. Health Psychol 27:379CrossRefGoogle Scholar
  2. Agapie E, Avrahami D, Marlow J (2016) Staying the course: system-driven lapse management for supporting behavior change. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, pp 1072–1083Google Scholar
  3. Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50:179–211CrossRefGoogle Scholar
  4. Bentley F, Tollmar K, Stephenson P et al (2013) Health mashups: presenting statistical patterns between wellbeing data and context in natural language to promote behavior changeGoogle Scholar
  5. Bickmore T, Giorgino T (2006) Health dialog systems for patients and consumers. J Biomed Inform 39:556–571CrossRefGoogle Scholar
  6. Bickmore TW, Picard RW (2005) Establishing and maintaining long-term human-computer relationships. ACM Trans Comput-Hum Interact TOCHI 12:293–327CrossRefGoogle Scholar
  7. Bickmore T, Schulman D, Yin L (2010) Maintaining engagement in long-term interventions with relational agents. Appl Artif Intell 24:648–666.  https://doi.org/10.1080/08839514.2010.492259CrossRefGoogle Scholar
  8. Bouton ME (2014) Why behavior change is difficult to sustain. Prev Med 68:29–36CrossRefGoogle Scholar
  9. Bovend’Eerdt TJ, Botell RE, Wade DT (2009) Writing SMART rehabilitation goals and achieving goal attainment scaling: a practical guide. Clin Rehabil 23:352–361CrossRefGoogle Scholar
  10. Chung C-F, Jensen N, Shklovski IA, Munson S (2017) Finding the right fit: understanding health tracking in workplace wellness programs. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 4875–4886Google Scholar
  11. Colusso L, Hsieh G, Munson SA (2016) Designing Closeness to Increase Gamers’ Performance. In: Proceedings of the 2016 CHI conference on human factors in computing systems. ACM, New York, NY, USA, pp 3020–3024Google Scholar
  12. Conroy DE, Yang C-H, Maher JP (2014) Behavior change techniques in top-ranked mobile apps for physical activity. Am J Prev Med 46:649–652CrossRefGoogle Scholar
  13. Consolvo S, Klasnja P, McDonald DW, et al (2008) Flowers or a robot army?: encouraging awareness & activity with personal, mobile displays. In: Proceedings of the 10th international conference on Ubiquitous computing. ACM, pp 54–63Google Scholar
  14. Cranshaw J, Elwany E, Newman T et al (2017) Calendar. help: designing a workflow-based scheduling agent with humans in the loop. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 2382–2393Google Scholar
  15. Dhillon B, Kocielnik R, Politis I et al (2011) Culture and facial expressions: a case study with a speech interface. IFIP conference on human-computer interaction. Springer, Berlin Heidelberg, pp 392–404Google Scholar
  16. Fleck R, Fitzpatrick G (2010) Reflecting on reflection: framing a design landscape. In: Proceedings of the 22nd conference of the computer-human interaction special interest group of Australia on computer-human interaction. ACM, pp 216–223Google Scholar
  17. Fogg B (2009) A Behavior Model for Persuasive Design. In: Proceedings of the 4th International Conference on Persuasive Technology. ACM, New York, NY, USA, pp 40:1–40:7Google Scholar
  18. Fujita K, Han HA (2009) Moving beyond deliberative control of impulses: The effect of construal levels on evaluative associations in self-control conflicts. Psychol Sci 20:799–804CrossRefGoogle Scholar
  19. Götzmann V (2015) Towards a persuasive dialog system supporting personal health management. National Research CenterGoogle Scholar
  20. Hunter RF, Ball K, Sarmiento OL (2018) Socially awkward: how can we better promote walking as a social behaviour? BMJ Publishing Group Ltd and British Association of Sport and Exercise MedicineGoogle Scholar
  21. Kim Y-S, Hullman J (2015) User-driven expectation visualization: opportunities for personalized feedbackGoogle Scholar
  22. Kinnafick F-E, Thøgersen-Ntoumani C, Duda JL (2014) Physical activity adoption to adherence, lapse, and dropout a self-determination theory perspective. Qual Health Res 24:706–718CrossRefGoogle Scholar
  23. Kocielnik RD (2014) LifelogExplorer: a tool for visual exploration of ambulatory skin conductance measurements in context. In: Proceedings of measuring behaviorGoogle Scholar
  24. Kocielnik R, Hsieh G (2017) Send me a different message: utilizing cognitive space to create engaging message triggers. In: CSCW. pp 2193–2207Google Scholar
  25. Kocielnik R, Sidorova N (2015) Personalized stress management: enabling stress monitoring with lifelogexplorer. KI-Künstl Intell 29:115–122CrossRefGoogle Scholar
  26. Kocielnik R, Pechenizkiy M, Sidorova N (2012) Stress analytics in education. In: Educational data mining 2012Google Scholar
  27. Kocielnik R, Maggi FM, Sidorova N (2013a) Enabling self-reflection with LifelogExplorer: generating simple views from complex data. In: Pervasive computing technologies for healthcare (PervasiveHealth), 2013 7th international conference on. IEEE, pp 184–191Google Scholar
  28. Kocielnik R, Sidorova N, Maggi FM, et al (2013b) Smart technologies for long-term stress monitoring at work. In: Computer-based medical systems (CBMS), 2013 IEEE 26th international symposium on. IEEE, pp 53–58Google Scholar
  29. Kocielnik R, Avrahami D, Marlow J et al (2018a) Designing for workplace reflection: a chat and voice-based conversational agent. In: Proceedings of the 2018 designing interactive systems conference.  https://doi.org/10.1145/3196709.3196784
  30. Kocielnik R, Xiao Lillian, Avrahami D, Hsieh G (2018b) Reflection companion: a conversational system for engaging users in reflection on physical activity. IMWUT 2:26Google Scholar
  31. Lee MK, Kim J, Forlizzi J, Kiesler S (2015) Personalization revisited: a reflective approach helps people better personalize health services and motivates them to increase physical activity. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing. ACM, pp 743–754Google Scholar
  32. Lewis MA, Uhrig JD, Bann CM et al (2013) Tailored text messaging intervention for HIV adherence: a proof-of-concept study. Health Psychol 32:248CrossRefGoogle Scholar
  33. Li I, Dey A, Forlizzi J (2010) A stage-based model of personal informatics systems. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 557–566Google Scholar
  34. Li I, Dey AK, Forlizzi J (2011) Understanding my data, myself: supporting self-reflection with ubicomp technologies. In: Proceedings of the 13th international conference on Ubiquitous computing. ACM, pp 405–414Google Scholar
  35. Lin J, Mamykina L, Lindtner S, et al (2006) Fish’n’Steps: Encouraging physical activity with an interactive computer game. UbiComp 2006 Ubiquitous Comput, 261–278Google Scholar
  36. Locke EA, Latham GP (2006) New directions in goal-setting theory. Curr Dir Psychol Sci 15:265–268.  https://doi.org/10.1111/j.1467-8721.2006.00449.xCrossRefGoogle Scholar
  37. Maher CA, Lewis LK, Ferrar K et al (2014) Are health behavior change interventions that use online social networks effective?. A systematic review, J Med Internet Res, p 16Google Scholar
  38. McDuff D, Karlson A, Kapoor A, et al (2012) AffectAura: an intelligent system for emotional memory. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 849–858Google Scholar
  39. Miller WR, Rollnick S (2009) Ten things that motivational interviewing is not. Behav Cogn Psychother 37:129–140CrossRefGoogle Scholar
  40. Moon JA (2013) Reflection in learning and professional development: theory and practice. Routledge, AbingdonGoogle Scholar
  41. Myers RS, Roth DL (1997) Perceived benefits of and barriers to exercise and stage of exercise adoption in young adults. Health Psychol 16:277CrossRefGoogle Scholar
  42. Novielli N, de Rosis F, Mazzotta I (2010) User attitude towards an embodied conversational agent: effects of the interaction mode. J Pragmat 42:2385–2397CrossRefGoogle Scholar
  43. Rautalinko E, Lisper H-O, Ekehammar B (2007) Reflective listening in counseling: effects of training time and evaluator social skills. Am J Psychother NY 61:191–209CrossRefGoogle Scholar
  44. Rivera-Pelayo V, Zacharias V, Müller L, Braun S (2012) Applying quantified self approaches to support reflective learning. In: Proceedings of the 2nd international conference on learning analytics and knowledge. ACM, pp 111–114Google Scholar
  45. Rollnick S, Miller WR (1995) What is motivational interviewing? Behav Cogn Psychother 23:325–334CrossRefGoogle Scholar
  46. Schueller SM (2010) Preferences for positive psychology exercises. J Posit Psychol 5:192–203CrossRefGoogle Scholar
  47. Skurnik I, Yoon C, Park DC, Schwarz N (2005) How warnings about false claims become recommendations. J Consum Res 31:713–724.  https://doi.org/10.1086/426605CrossRefGoogle Scholar
  48. Tanumihardjo SA, Anderson C, Kaufer-Horwitz M et al (2007) Poverty, obesity, and malnutrition: an international perspective recognizing the paradox. J Am Diet Assoc 107:1966–1972CrossRefGoogle Scholar
  49. Tollmar K, Bentley F, Viedma C (2012) Mobile health mashups: making sense of multiple streams of wellbeing and contextual data for presentation on a mobile device. In: 2012 6th International conference on pervasive computing technologies for healthcare (PervasiveHealth) and workshops. IEEE, pp 65–72Google Scholar
  50. Treatment C for SA (1999) Chapter 3—Motivational interviewing as a counseling style. Substance Abuse and Mental Health Services Administration (US)Google Scholar
  51. Tufte ER (1991) Envisioning information. Optom Vis Sci 68:322–324CrossRefGoogle Scholar
  52. Tur G, De Mori R (2011) Spoken language understanding: Systems for extracting semantic information from speech. Wiley, HobokenGoogle Scholar
  53. Wang Y-Y, Deng L, Acero A (2005) Spoken language understanding. IEEE Signal Process Mag 22:16–31Google Scholar
  54. Williams JD, Niraula NB, Dasigi P et al (2015) Rapidly scaling dialog systems with interactive learning. In: Natural language dialog systems and intelligent assistants. Springer, Berlin, pp 1–13Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of WashingtonSeattleUSA
  2. 2.FXPALPalo AltoUSA

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