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New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods

  • C. J. Boushey (a1), M. Spoden (a1), F. M. Zhu (a2), E. J. Delp (a2) and D. A. Kerr (a3)...
Abstract

For nutrition practitioners and researchers, assessing dietary intake of children and adults with a high level of accuracy continues to be a challenge. Developments in mobile technologies have created a role for images in the assessment of dietary intake. The objective of this review was to examine peer-reviewed published papers covering development, evaluation and/or validation of image-assisted or image-based dietary assessment methods from December 2013 to January 2016. Images taken with handheld devices or wearable cameras have been used to assist traditional dietary assessment methods for portion size estimations made by dietitians (image-assisted methods). Image-assisted approaches can supplement either dietary records or 24-h dietary recalls. In recent years, image-based approaches integrating application technology for mobile devices have been developed (image-based methods). Image-based approaches aim at capturing all eating occasions by images as the primary record of dietary intake, and therefore follow the methodology of food records. The present paper reviews several image-assisted and image-based methods, their benefits and challenges; followed by details on an image-based mobile food record. Mobile technology offers a wide range of feasible options for dietary assessment, which are easier to incorporate into daily routines. The presented studies illustrate that image-assisted methods can improve the accuracy of conventional dietary assessment methods by adding eating occasion detail via pictures captured by an individual (dynamic images). All of the studies reduced underreporting with the help of images compared with results with traditional assessment methods. Studies with larger sample sizes are needed to better delineate attributes with regards to age of user, degree of error and cost.

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Corresponding author
* Corresponding author: C. J. Boushey, fax 808-586-2982, email cjboushey@cc.hawaii.edu
References
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1. Reedy, J, Krebs-Smith, SM, Miller, PE et al. (2014) Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. J Nutr 144, 881889.
2. Harmon, BE, Boushey, CJ, Shvetsov, YB et al. (2015) Associations of key diet-quality indexes with mortality in the Multiethnic Cohort: the dietary patterns methods project. Am J Clin Nutr 101, 587597.
3. Willett, W (2012) Nutritional Epidemiology, 3rd ed. New York: Oxford University Press.
4. DeLany, JP (2013) Energy requirement methodology. In Nutrition in the Prevention and Treatment of Disease, 3rd ed., pp. 8195 [Coulston, AM, Boushey, CJ and Ferruzzi, M, editors]. San Diego: Elsevier.
5. Thompson, FE & Subar, AF (2013) Dietary assessment methodology. In Nutrition in the Prevention and Treatment of Disease, 3rd ed., pp. 546 [Coulston, AM, Boushey, CJ and Ferruzzi, M, editors]. San Diego: Elsevier.
6. Moshfegh, AJ, Rhodes, DG, Baer, DJ et al. (2008) The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am J Clin Nutr 88, 324332.
7. Subar, AF, Kipnis, V, Troiano, R et al. (2003) Using intake biomarkers to evaluate the extent of dietary misreporting in a large sample of adults: the OPEN Study. Am J Epidemiol 158, 113.
8. Hankin, JH & Wilkens, LR (1994) Development and validation of dietary assessment methods for culturally diverse populations. Am J Clin Nutr 59, 198S200S.
9. Nelson, M, Atkinson, M & Darbyshire, S (1996) Food photography II: use of food photographs for estimating portion size and the nutrient content of meals. Br J Nutr 76, 3149.
10. Forster, H, Fallaize, R, Gallagher, C et al. (2014) Online dietary intake estimation: the Food4Me food frequency questionnaire. J Med Internet Res 16, e150.
11. Wong, SS, Boushey, CJ, Novotny, R et al. (2008) Evaluation of a computerized food frequency questionnaire to estimate calcium intake of Asian, Hispanic, and non-Hispanic white youth. J Am Diet Assoc 108, 539543.
12. Kristal, AR, Kolar, AS, Fisher, JL et al. (2014) Evaluation of web-based, self-administered, graphical food frequency questionnaire. J Acad Nutr Diet 114, 613621.
13. Wilken, LR, Novotny, R, Fialkowski, MK et al. (2013) Children's Healthy Living (CHL) Program for remote underserved minority populations in the Pacific region: rationale and design of a community randomized trial to prevent early childhood obesity. BMC Public Health 13, 944.
14. Kirkpatrick, SI, Subar, AF, Douglass, D et al. (2014) Performance of the Automated Self-Administered 24-hour recall relative to a measure of true intakes and to an interviewer-administered 24-h recall. Am J Clin Nutr 100, 233240.
15. Foster, E, O'Keeffe, M, Matthews, JN et al. (2008) Children's estimates of food portion size: the effect of timing of dietary interview on the accuracy of children's portion size estimates. Br J Nutr 99, 185190.
16. Gemming, L, Utter, J & Mhurchu, CN (2014) Image-assisted dietary assessment: a systematic review of the evidence. J Acad Nutr Diet 115, 6477.
17. Ptomey, L, Willis, EA, Goetz, JR et al. (2015) Digital photography improves estimates of dietary intake in adolescents with intellectual and developmental disabilities. Disabil Health J 8, 146150.
18. Pettitt, C, Liu, J, Kwasnicki, RM et al. (2016) A pilot study to determine whether using a lightweight, wearable micro-camera improves dietary assessment accuracy and offers information on macronutrients and eating rate. Br J Nutr 115, 160167.
19. Beltran, A, Dadabhoy, H, Chen, T et al. (2016) Adapting the eButton to the abilities of children for diet assessment. In Proceedings of Measuring Behavior 2016, (Dublin, Ireland, 25-27 May 2016), pp. 72–81. Available at: http://www.measuringbehavior.org/files/2016/MB2016_Proceedings.pdf
20. Microsoft Research (2013) SenseCam overview. http://research.microsoft.com/en-us/um/cambridge/projects/sensecam/ (accessed September 2016).
21. Gemming, L, Rush, E, Maddison, R et al. (2015) Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall. Br J Nutr 113, 284291.
22. Casperson, SL, Sieling, J, Moon, J et al. (2015) A mobile phone food record app to digitally capture dietary intake for adolescents in a free-living environment: usability study. JMIR mHealth uHealth 3, e30.
23. Schap, TE, Six, BL, Delp, EJ et al. (2011) Adolescents in the United States can identify familiar foods at the time of consumption and when prompted with an image 14 h postprandial, but poorly estimate portions. Public Health Nutr 14, 11841191.
24. Six, BL, Schap, TE, Zhu, FM et al. (2010) Evidence-based development of a mobile telephone food record. J Am Diet Assoc 110, 7479.
25. Daugherty, BL, Schap, TE, Ettienne-Gittens, R et al. (2012) Novel technologies for assessing dietary intake: evaluating the usability of a mobile telephone food record among adults and adolescents. JMIR 14, e58.
26. Rollo, ME, Ash, S, Lyons-Wall, P et al. (2015) Evaluation of a mobile phone image-based dietary assessment method in adults with type 2 diabetes. Nutrients 7, 48974910.
27. Pladevall, M, Divine, G, Wells, KE et al. (2015) A randomized controlled trial to provide adherence information and motivational interviewing to improve diabetes and lipd control. Diab Educ 41, 136146.
28. Jia, W, Chen, HC, Yue, Y et al. (2014) Accuracy of food portion size estimation from digital pictures acquired by a chest-worn camera. Public Health Nutr 17, 16711681.
29. Okos, M & Boushey, C (2012) Density standards meeting organized by Purdue University: a report. Int J Food Prop 15, 467469.
30. Kelkar, S, Stella, S, Boushey, C et al. (2011) Developing novel 3D measurement techniques and prediction method for food density determination. Procedia 1, 483491.
31. Kerr, DA, Harray, AJ, Pollard, CM et al. (2016) The connecting health and technology study: a 6-month randomized controlled trial to improve nutrition behaviours using a mobile food record and text meassaging support in young adults. Int J Behav Nutr Phys Act 13, 114.
32. Zhu, F, Bosch, M, Woo, I et al. (2010) The use of mobile devices in aiding dietary assessment and evaluation. IEEE J Sel Top Signal Process 4, 756766.
33. Zhu, F, Bosch, M, Khanna, N et al. (2015) Multiple hypotheses image segmentation and classification with application to dietary assessment. IEEE J Biomed Health Inf 19, 377388.
34. Xu, C, Zhu, F, Khanna, N et al. (2012) Image enhancement and quality measures for dietary assessment using mobile devices. In Proc IS&T/SPIE Conf on Computational Imaging X, vol. 8296, p. 82. Available at: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1283931
35. Xu, C, Khanna, N, Boushey, CJ et al. (2011) Low complexity image quality measures for dietary assessment using mobile devices. In IEEE Int Symp on Multimedia 12460771. Available at: http://ieeexplore.ieee.org/document/6123371/
36. Woo, I, Otsmo, K, Kim, SY et al. (2010) Automatic portion estimation and visual refinement in mobile dietary assessment. Electr Imaging Sci Technol, Comput Image VIII 7533, 110.
37. Chae, J, Woo, I, Kim, S et al. (2011) Volume estimation using food specific shape templates in mobile image-based dietary assessment. In Proc IS&T/SPIE Conf on Computational Imaging IX 7873, pp. 18. PMC3198859.
38. Lee, CD, Chae, J, Schap, TE et al. (2012) Comparison of known food weights with image-based portion-size automated estiamtion and adolescents’ self-reported portion size. J Diab Sci Technol 6, 17.
39. Fang, S, Liu, F, Zhu, FM et al. (2015) Single-view food portion estimation based on geometric models. In ISM 2015, 385390.
40. McGuire, B, Chambers, E IV, Godwin, S et al. (2001) Size categories most effective for estimating portion size of muffins. J Am Diet Assoc 101, 470472.
41. Six, BL, Schap, TE, Kerr, DA et al. (2011) Evaluation of the food and nutrient database for dietary studies for use with a mobile telephone food record. J Food Compost Anal 24, 11601167.
42. Ahmad, Z, Khanna, N, Kerr, DA et al. (2014) A mobile phone user interface for image based dietary assessment. In Proc IS&T/SPIE Conf on Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications, p. 903007-903007-9. Available at: http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=1833067
43. Boushey, CJ, Harray, AJ, Kerr, DA et al. (2015) How willing are adolescents to record their dietary intake? the mobile food record. JMIR mHealth uHealth 3, e47.
44. Aflague, TF, Boushey, CJ, Guerrero, RT et al. (2015) Feasibility and use of the mobile food record for capturing eating occasions among children ages 3–10 years in Guam. Nutrients 15, 44034415.
45. Kerr, DA, Pollard, CM, Howat, P et al. (2012) Connecting Health and Technology (CHAT): protocol of a randomized controlled trial to improve nutrition behaviours using mobile devices and tailored text messaging in young adults. BMC Public Health 12, 477.
46. Zhu, F, Bosch, M, Khanna, N et al. (2011) Multilevel segmentation for food classification in dietary assessment. In Proc Seventh Int Symp on Image and Signal Processing and Analyais (ISPA), pp. 337342. PMC3224861
47. Meyers, A, Johnston, N, Rathod, V et al. (2015) Im2Calories: towards an automated mobile vision food diary. In Proc IEEE Int Conf on Computer Vision, pp. 12331241. Available at: https://www.cs.ubc.ca/~murphyk/Papers/im2calories_iccv15.pdf
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