skip to main content
10.1145/2702123.2702154acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Rethinking the Mobile Food Journal: Exploring Opportunities for Lightweight Photo-Based Capture

Published:18 April 2015Publication History

ABSTRACT

Food choices are among the most frequent and important health decisions in everyday life, but remain notoriously difficult to capture. This work examines opportunities for lightweight photo-based capture in mobile food journals. We first report on a survey of 257 people, examining how they define healthy eating, their experiences and challenges with existing food journaling methods, and their ability to interpret nutritional information that can be captured in a food journal. We then report on interviews and a field study with 27 participants using a lightweight, photo-based food journal for between 4 to 8 weeks. We discuss mismatches between motivations and current designs, challenges of current approaches to food journaling, and opportunities for photos as an alternative to the pervasive but often inappropriate emphasis on quantitative tracking in mobile food journals.

References

  1. Amft, O., Stäger, M., Lukowicz, P., and Tröster, G. Analysis of Chewing Sounds for Dietary Monitoring. UbiComp 2005, 56--72. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Andrew, A.H., Borriello, G., and Fogarty, J. Simplifying Mobile Phone Food Diaries: Design and Evaluation of a Food Index-Based Nutrition Diary. PervasiveHealth 2013, 260--263. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Arab, L., Estrin, D., Kim, D.H., Burke, J., and Goldman, J. (2011). Feasibility Testing of an Automated Image-Capture Method to Aid Dietary Recall. Eur J Clin Nutr, 65(10), 1156--1162.Google ScholarGoogle ScholarCross RefCross Ref
  4. Barrett-Connor, E. (1991). Nutrition Epidemiology: How Do We Know What They Ate? Am J Clin Nutr, 54(1 Suppl), 182S--187S.Google ScholarGoogle ScholarCross RefCross Ref
  5. Baumer, E.P.S., Katz, S.J., Freeman, J.E., Adams, P., Gonzales, A.L., Pollak, J., Retelny, D., Niederdeppe, J., Olson, C.M., and Gay, G.K. Prescriptive Persuasion and Open-Ended Social Awareness. CSCW 2012, 475--484. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Bird, G. and Elwood, P.C. (1983). The Dietary Intakes of Subjects Estimated from Photographs Compared with a Weighed Record. Hum Nutr Appl Nutr, 37(6), 470--473.Google ScholarGoogle Scholar
  7. Burke, B.S. (1947). The Dietary History as a Tool in Research. J Am Diet Assoc, 23(12), 1041--1046.Google ScholarGoogle Scholar
  8. Burke, L.E., Wang, J., and Sevick, M.A. (2011). Self-Monitoring in Weight Loss: A Systematic Review of the Literature. J Am Diet Assoc, 111(1), 92--102.Google ScholarGoogle ScholarCross RefCross Ref
  9. Chahoud, G., Aude, Y.W., and Mehta, J.L. (2004). Dietary Recommendations in the Prevention and Treatment of Coronary Heart Disease: Do We Have the Ideal Diet Yet? Am J Cardiol, 94(10), 1260--1267.Google ScholarGoogle ScholarCross RefCross Ref
  10. Choe, E.K., Lee, N.B., Lee, B., Pratt, W., and Kientz, J.A. Understanding Quantified-Selfers? Practices in Collecting and Exploring Personal Data. CHI 2014, 1143--1152. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Cordeiro, F., Epstein, D.,Thomaz, E., Bales, E., Jagannathan, A.K., Abowd, G., and Fogarty, J. Barriers and Negative Nudges: Exploring Challenges in Food Journaling. CHI 2015, To Appear. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Craig, M.R., Kristal, A.R., Cheney, C.L., and Shattuck, A.L. (2000). The Prevalence and Impact of 'Atypical' Days in 4-Day Food Records. J Am Diet Assoc, 100(4), 421--427.Google ScholarGoogle ScholarCross RefCross Ref
  13. Frost, J. and Smith, B.K. Visualizing Health: Imagery in Diabetes Education. DUX 2003, 1--14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Grimes, A. and Harper, R. Celebratory Technology: New Directions for Food Research in HCI. CHI 2008, 467--476. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Heizer, W.D., Southern, S., and McGovern, S. (2009). The Role of Diet in Symptoms of Irritable Bowel Syndrome in Adults: A Narrative Review. J Am Diet Assoc, 109(7), 1204--1214.Google ScholarGoogle ScholarCross RefCross Ref
  16. Hollis, J.F., Gullion, C.M., Stevens, V.J., Brantley, P.J., Appel, L.J., Ard, J.D., Champagne, C.M., Dalcin, A., Erlinger, T.P., Funk, K., Laferriere, D., Lin, P.-H., Loria, C.M., Samuel-Hodge, C., Vollmer, W.M., and Svetkey, L.P. (2008). Weight Loss During the Intensive Intervention Phase of the Weight-Loss Maintenance Trial. Am J Prev Med, 35(2), 118--126.Google ScholarGoogle ScholarCross RefCross Ref
  17. Kanfer, F.H. (1970). Self-Monitoring: Methodological Limitations and Clinical Applications. J Consult Clin Psych, 35(2), 148--152.Google ScholarGoogle ScholarCross RefCross Ref
  18. Kong, F. and Tan, J. DietCam: Regular Shape Food Recognition with a Camera Phone. BSN 2011, 127--132. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Lansky, D. and Brownell, K.D. (1982). Estimates of Food Quantity and Calories: Errors in Self-Report among Obese Patients. Am J Clin Nutr, 35(4), 727--732.Google ScholarGoogle ScholarCross RefCross Ref
  20. Livingstone, M.B., Prentice, A.M., Strain, J.J., Coward, W.A., Black, A.E., Barker, M.E., McKenna, P.G., and Whitehead, R.G. (1990). Accuracy of Weighed Dietary Records in Studies of Diet and Health. BMJ, 300(6726), 708--712.Google ScholarGoogle ScholarCross RefCross Ref
  21. Mamykina, L., Mynatt, E., Davidson, P., and Greenblatt, D. MAHI: Investigation of Social Scaffolding for Reflective Thinking in Diabetes Management. CHI 2008, 477--486. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Mamykina, L., Mynatt, E.D., and Kaufman, D.R. Investigating Health Management Practices of Individuals with Diabetes. CHI 2006, 927--936. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Mankoff, J., Hsieh, G., Hung, H.C., Lee, S., and Nitao, E. Using Low-Cost Sensing to Support Nutritional Awareness. UbiComp 2002, 371--376. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Marr, J.W. (1971). Individual Dietary Surveys: Purposes and Methods. World Rev Nutr Diet, 13, 105--164.Google ScholarGoogle ScholarCross RefCross Ref
  25. Mattila, E., Pärkkä, J., Hermersdorf, M., Kaasinen, J., Vainio, J., Samposalo, K., Merilahti, J., Kolari, J., Kulju, M., Lappalainen, R., and Korhonen, I. (2008). Mobile Diary for Wellness Management - Results on Usage and Usability in Two User Studies. IEEE Trans Inf Technol Biomed, 12(4), 501--512. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Nelson, M., Atkinson, M., and Darbyshire, S. (1996). Food Photography II: Use of Food Photographs for Estimating Portion Size and the Nutrient Content of Meals. Br J Nutr, 76(1), 31--49.Google ScholarGoogle ScholarCross RefCross Ref
  27. Noronha, J., Hysen, E., Zhang, H., and Gajos, K.Z. Platemate: Crowdsourcing Nutritional Analysis from Food Photographs. UIST 2011, 1--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Purpura, S., Schwanda, V., Williams, K., Stubler, W., and Sengers, P. Fit4Life: The Design of a Persuasive Technology Promoting Healthy Behavior and Ideal. CHI 2011, 423--432. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Rahman, T., Adams, A.T., Zhang, M., Cherry, E., Zhou, B., Peng, H., and Choudhury, T. BodyBeat: A Mobile System for Sensing Non-Speech Body Sounds. MobiSys 2014, 2--13. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Sicherer, S.H. and Sampson, H.A. (2010). Food Allergy. J Allergy Clin Immunol, 125(2 Suppl 2), S116--S125.Google ScholarGoogle ScholarCross RefCross Ref
  31. Stone, A.A., Shiffman, S., Schwartz, J.E., Broderick, J.E., and Hufford, M.R. (2003). Patient Compliance with Paper and Electronic Diaries. Control Clin Trials, 24(2), 182--199.Google ScholarGoogle ScholarCross RefCross Ref
  32. Thomaz, E., Parnami, A., Essa, I., and Abowd, G.D. Feasibility of Identifying Eating Moments from First-Person Images Leveraging Human Computation. SenseCam 2013, 26--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Tsai, C.C., Lee, G., Raab, F., Norman, G.J., Sohn, T., Griswold, W.G., and Patrick, K. (2007). Usability and Feasibility of PmEB: A Mobile Phone Application for Monitoring Real Time Caloric Balance. Mobile Netw Appl, 12(2-3), 173--184. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Urist, J. My Fitness Band Is Making Me Fat: Users Complain of Weight Gain with Trackers. Today.com, July 16, 2014.Google ScholarGoogle Scholar
  35. Wansink, B. and van Ittersum, K. (2007). Portion Size Me: Downsizing Our Consumption Norms. J Am Diet Assoc, 107(7), 1103--1106.Google ScholarGoogle ScholarCross RefCross Ref
  36. Wilde, M.H. and Garvin, S. (2007). A Concept Analysis of Self-Monitoring. J Adv Nurs, 57(3), 339--350.Google ScholarGoogle ScholarCross RefCross Ref
  37. Zepeda, L. andDeal, D.(2008). Think BeforeYouEat: PhotographicFood DiariesasInterventionToolstoChange Dietary DecisionMakingand Attitudes.IntJ ConsumStud, 32(6), 692--698.Google ScholarGoogle Scholar
  38. Zhu, F., Bosch, M., Boushey, C.J., and Delp, E.J. (2010). An Image Analysis System for Dietary Assessment and Evaluation. ICIP 2010, 1853--1856.Google ScholarGoogle Scholar

Index Terms

  1. Rethinking the Mobile Food Journal: Exploring Opportunities for Lightweight Photo-Based Capture

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
      April 2015
      4290 pages
      ISBN:9781450331456
      DOI:10.1145/2702123

      Copyright © 2015 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 18 April 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      CHI '15 Paper Acceptance Rate486of2,120submissions,23%Overall Acceptance Rate6,199of26,314submissions,24%

      Upcoming Conference

      CHI '24
      CHI Conference on Human Factors in Computing Systems
      May 11 - 16, 2024
      Honolulu , HI , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader