Author(s):

  • Johnna Blair, B.S
  • Yuhan Luo, M.S
  • Ning F. Ma, B.A
  • Sooyeon Lee, B.S
  • Eun Kyoung Choe, Ph.D

Abstract:

When a self-monitoring tool is used to enhance behavior awareness, the tool should afford reflection by design. This work examines the “valence of meal” (i.e., healthy versus unhealthy meal) as a means to support reflection on a person’s diet in photo-based meal tracking. To study the effect of imposing valence on meal tracking, we designed two conditions—one focusing on capturing healthy meals, the other capturing unhealthy meals—and conducted a between-subjects diary study with 22 college students over four weeks. According to their group assignment, participants tracked only healthy or unhealthy meals by taking photos and rationalizing in texts why their meals were particularly healthy or unhealthy. We found that participants in both groups became more aware of their diet, but the valence of meal influenced them differently regarding their meal assessment, self-reflection, and food choice intention. We discuss ways to leverage valence in designing reflective meal tracking systems.

Documentation: PMCID: PMC6371351

References:

. Bitesnap. https://getbitesnap.com/

2. Consumer Attitudes toward Food Safety. Nutrition and Health. http://www.foodinsight.org/

3. MakeMyPlate. http://www.makemyplate.co/

4. My fitnesspal. https://www.myfitnesspal.com/

5. MyFoodDiary. calorie counting made easy

6. OneNote. https://www.onenote.com/

7. SuperTracker. https://www.supertracker.usda.gov.

8. YouAte Food Diary & Tracker. https://youate.com/

9. Adriaanse M. A, de Ridder D. T, de Wit J B. Finding the critical cue: implementation intentions to change one’s diet work best when tailored to personally relevant reasons for unhealthy eating. Personality and Social Psychology Bulletin. 2009;35(1):60–71. [PubMed] [Google Scholar]

10. Aikman S. N, Crites S. L, Fabrigar L. R. Beyond affect and cognition: identification of the informational bases of food attitudes. Journal of Applied Social Psychology. 2006;36(2):340–382. [Google Scholar]

11. Amft O, Stager M, Lukowicz P, Troster G. Analysis of chewing sounds for dietary monitoring. UbiComp 2005. 5:56–72. [Google Scholar]

12. Baumer E. P. CHI 2015. Reflective informatics: conceptual dimensions for designing technologies of reflection; pp. 585–594. [Google Scholar]

13. Bird G, Elwood P C. The dietary intakes of subjects estimated from photographs compared with a weighed record. Human nutrition. Applied nutrition. 1983;37(6):470–473. [PubMed] [Google Scholar]

14. Brown B., Chetty M., Grimes A., Harmon E. Reflecting on health: a system for students to monitor diet and exercise. CHI 2006 Extended Abstracts. :1807–1812. [Google Scholar]

15. Calitri R, Pothos E. M, Tapper K, Brunstrom J. M, Rogers P. J. Cognitive biases to healthy and unhealthy food words predict change in bmi. Obesity. 2010;18(12):2282–2287. [PubMed] [Google Scholar]

16. Chang K. S.-P, Danis C. M, Farrell R. G. Ubicomp 2014. Lunch line: using public displays and mobile devices to encourage healthy eating in an organization; pp. 823–834. [Google Scholar]

17. Choe E. K, Jung J, Lee B, Fisher K. IFIP. 2013. Nudging people away from privacy-invasive mobile apps through visual framing; pp. 74–91. [Google Scholar]

18. Choe E. K, Lee B, Munson S, Pratt W, Kientz J A. Persuasive performance feedback: the effect of framing on self-efficacy. AMIA Annual Symposium Proceedings. 2013:825–2013. [PMC free article] [PubMed] [Google Scholar]

19. Choe E. K, Lee B, Zhu H, Riche N. H, Baur D. PervasiveHealth 2017. Understanding self-reflection: how people reflect on personal data through visual data exploration; pp. 173–182. [Google Scholar]

20. Chung C. F, Agapie E, Schroeder J, Mishra S, Fogarty J, Munson S A. CHI 2017. When personal tracking becomes social: examining the use of instagram for healthy eating; pp. 1674–1687. [PMC free article] [PubMed] [Google Scholar]

21. Conner M. T. Understanding determinants of food choice: contributions from attitude research. British Food Journal. 1993;95(9):27–31. [Google Scholar]

22. Consolvo S, Klasnja P, McDonald D. W, Landay J A. Designing for healthy lifestyles: Design considerations for mobile technologies to encourage consumer health and wellness. Human-Computer Interaction. 2014;6(3-4):167–315. [Google Scholar]

23. Cordeiro F, Bales E, Cherry E, Fogarty J. CHI2015. Rethinking the mobile food journal: exploring opportunities for lightweight photo-based capture; pp. 3207–3216. [Google Scholar]

24. Elwood P. C, Bird G. A photographic method of diet evaluation. Human Nutrition. Applied Nutrition. 1983;37(6):474–477. [PubMed] [Google Scholar]

25. Epstein D. A, Cordeiro F, Fogarty J, Hsieh G, Munson S. A. CHI 2016. Crumbs: lightweight daily food challenges to promote engagement and mindfulness; pp. 5632–5644. [PMC free article] [PubMed] [Google Scholar]

26. Furst T, Connors M, Bisogni C. A, Sobal J, Falk L. W. Food choice: a conceptual model of the process. Appetite. 1996;26(3):247–266. [PubMed] [Google Scholar]

27. Graf B, Kruger M, Muller F, Ruhland A, Zech A. Nombot: simplify food tracking. International Conference on Mobile and Ubiquitous Multimedia; 2015. pp. 360–363. [Google Scholar]

28. Grimes A, Harper R. CHI 2008. Celebratory technology: new directions for food research in HCI; pp. 467–476. [Google Scholar]

29. Hales S, Dunn C, Wilcox S, Turner-McGrievy G M. Is a picture worth a thousand words? few evidence-based features of dietary interventions included in photo diet tracking mobile apps for weight loss. Journal of diabetes science and technology. 2016;10(6):1399–1405. [PMC free article] [PubMed] [Google Scholar]

30. Hu F. B, Rimm E, Smith-Warner S. A, Feskanich D, Stampfer M. J, Ascherio A, Sampson L, Wil-lett W. C. Reproducibility and validity of dietary patterns assessed with a food-frequency questionnaire. The American journal of clinical nutrition. 1999;69(2):243–249. [PubMed] [Google Scholar]

31. Hwang M. L, Mamykina L. CHI 2017. Monster appetite: effects of subversive framing on nutritional choices in a digital game environment; pp. 4082–4096. [Google Scholar]

32. Isaacs E, Konrad A, Walendowski A, Lennig T, Hollis V, Whittaker S. CHI 2013. Echoes from the past: how technology mediated reflection improves well-being; pp. 1071–1080. [Google Scholar]

33. Kim Y. H, Jeon J. H, Choe E. K, Lee B, Kim K, Seo J. CHI2016. Timeaware: leveraging framing effects to enhance personal productivity; pp. 272–283. [Google Scholar]

34. Krishnamurthy P, Carter P, Blair E. Attribute framing and goal framing effects in health decisions. Organizational Behavior and Human Decision Processes. 2001;85(2):382–399. [PubMed] [Google Scholar]

35. Li I, Dey A, Forlizzi J. CHI 2010. A stage-based model of personal informatics systems; pp. 557–566. [Google Scholar]

36. Li I, Dey A. K, Forlizzi J. Ubicomp 2011. Understanding my data myself: supporting self-reflection with ubicomp technologies; 405 pp.414 pp. [Google Scholar]

37. Mamykina L, Mynatt E, Davidson P, Greenblatt D. CHI 2008. Mahi: investigation of social scaffolding for reflective thinking in diabetes management; pp. 477–486. [Google Scholar]

38. Masson L, McNeill G, Tomany J, Simpson J, Peace H, Wei L, Grubb D, Bolton-Smith C. Statistical approaches for assessing the relative validity of a food-frequency questionnaire: use of correlation coefficients and the kappa statistic. Public Health Nutrition. 2003;6(03):313–321. [PubMed] [Google Scholar]

39. Moon J. A. A handbook of reflective and experiential learning: theory and practice; Psychology Press, 2004. [Google Scholar]

40. Nelson M, Atkinson M, Darbyshire S. Food photography ii: use of food photographs for estimating portion size and the nutrient content of meals. British journal of nutrition. 1996;76(1):31–49. [PubMed] [Google Scholar]

41. Nelson R. O, Hayes S. C. Theoretical explanations for reactivity in self-monitoring. Behavior Modification. 1981;5(1):3–14. [Google Scholar]

42. Orji R, Vassileva J, Mandryk R. L. Lunchtime: a slow-casual game for long-term dietary behavior change. Personal and Ubiquitous Computing. 2013;17(6):1211–1221. [Google Scholar]

43. Ploderer B, Reitberger W, Oinas-Kukkonen H, van Gemert-Pijnen J. Social interaction and reflection for behaviour change 2014 [Google Scholar]

44. Rooksby J, Rost M, Morrison A, Chalmers M. C. CHI 2014. Personal tracking as lived informatics; pp. 1163–1172. [Google Scholar]

45. Schaefbauer C. L, Khan D. U, Le A, Sczechowski G, Siek K. A. CSCW 2015. Snack buddy: supporting healthy snacking in low socioeconomic status families; pp. 1045–1057. [Google Scholar]

46. Schön D. A. The reflective practitioner: how professionals think in action, volume 5126. Basic books; 1984. [Google Scholar]

47. Thomaz E, Parnami A, Essa I, Abowd G. D. Feasibility of identifying eating moments from first-person images leveraging human computation. In Proceedings of the 4th International SenseCam & Pervasive Imaging Conference; ACM; 2013. pp. 26–33. [Google Scholar]

48. Thomson C. A, Giuliano A, Rock C. L, Ritenbaugh C. K, Flatt S. W, Faerber S, Newman V, Caan B, Graver E, Hartz V, et al. Measuring dietary change in a diet intervention trial: comparing food frequency questionnaire and dietary recalls. American journal of epidemiology. 2003;157(8):754–762. [PubMed] [Google Scholar]

49. Tversky A, Kahneman D. 1985. The framing of decisions and the psychology of choice; pp. 107–129. [PubMed] [Google Scholar]

50. Willett W. C, Sampson L, Stampfer M. J, Rosner B, Bain C, Witschi J, Hennekens C. H, Speizer F. E. Re-producibility and validity of a semiquantitative food frequency questionnaire. American journal of epidemiology. 1985;122(1):51–65. [PubMed] [Google Scholar]

51. Zepeda L, Deal D. Think before you eat: photographic food diaries as intervention tools to change dietary decision making and attitudes. International Journal of Consumer Studies. 2008;32(6):692–698. [Google Scholar]