New technology in dietary assessment: a review of digital methods in improving food record accuracy
Open accessAbstract
Methods for conducting dietary assessment in the United States date back to the early twentieth century. Methods of assessment encompassed dietary records, written and spoken dietary recalls, FFQ using pencil and paper and more recently computer and internet applications. Emerging innovations involve camera and mobile telephone technology to capture food and meal images. This paper describes six projects sponsored by the United States National Institutes of Health that use digital methods to improve food records and two mobile phone applications using crowdsourcing. The techniques under development show promise for improving accuracy of food records.
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Mobile apps can provide automated feedback on the healthiness of the food based on the photo and also leverage other users to provide feedback through crowdsourcing.
“Mobile apps can provide automated feedback on the healthiness of the food based on the photo and also leverage other users to provide feedback through crowdsourcing [6].”
From Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: retrospective cohort study (2014) · cites this paper as [6]
Traditional dietary self-monitoring methods can be burdensome to people.
“These methods can be burdensome to people [6,7,15-17] and suffer from underreporting and recall issues [15-19].”
From Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: retrospective cohort study (2014) · cites this paper as [6]
Smartphones and photography can be used to make self-monitoring easy and convenient.
“Smartphones and photography can be used to make self-monitoring easy and convenient [6-9].”
From Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: retrospective cohort study (2014) · cites this paper as [6]
Although it is still difficult to estimate food ingredients and portion sizes from a photograph, efforts to develop estimation algorithms based on image processing or crowdsourcing are underway.
“Although it is still difficult to estimate food ingredients and portion sizes from a photograph, efforts to develop estimation algorithms based on image processing or crowdsourcing are underway [6,16].”
From Factors related to sustained use of a free mobile app for dietary self-monitoring with photography and peer feedback: retrospective cohort study (2014) · cites this paper as [6]
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Cites
“Clinical versus Actuarial Judgment.”
Dawes, R. M., Faust, D., & Meehl, P. E. (1989) · Science · doi:10.1126/science.2648573 · Cited by 2,298 · see citing works
A DATA-BASED APPROACH TO DIET QUESTIONNAIRE DESIGN AND TESTING
Block, G., Hartman, A. M., DRESSER, C. M., Carroll, M. D., Gannon, J. G., & Gardner, L. B. (1986) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a114416 · Cited by 2,150 · see citing works
Subar, A. F., Thompson, F. E., Kipnis, V., Midthune, D., Hurwitz, P. E., McNutt, S., McIntosh, A., & Rosenfeld, S. (2001) · American Journal of Epidemiology · doi:10.1093/aje/154.12.1089 · Cited by 1,401 · see citing works
Subar, A. F. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwg092 · Cited by 1,070 · see citing works
Statistical Methods for Estimating Usual Intake of Nutrients and Foods: A Review of the Theory
Dodd, K. W., Guenther, P. M., Freedman, L. S., Subar, A. F., Kipnis, V., Midthune, D., Tooze, J. A., & Krebs‐Smith, S. M. (2006) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2006.07.011 · Cited by 701 · see citing works
Basiotis, P., Welsh, S., Cronin, F., Kelsay, J., & Mertz, W. (1987) · Journal of NutriLife · doi:10.1093/jn/117.9.1638 · Cited by 633 · see citing works
Tooze, J. A., Kipnis, V., Buckman, D. W., Carroll, R. J., Freedman, L. S., Guenther, P. M., Krebs‐Smith, S. M., Subar, A. F., & Dodd, K. W. (2010) · Statistics in Medicine · doi:10.1002/sim.4063 · Cited by 502 · see citing works
The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation
Zhu, F., Bosch, M., Woo, I., Kim, S., Boushey, C. J., Ebert, D. S., & Delp, E. J. (2010) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2010.2051471 · Cited by 355 · see citing works
Recognition and volume estimation of food intake using a mobile device
Puri, M., Zhu, Z., Yu, Q., Divakaran, A., & Sawhney, H. (2009) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2009.5403087 · Cited by 192 · see citing works
Nelson, M., Atkinson, M., & Darbyshire, S. (1996) · British Journal Of Nutrition · doi:10.1079/bjn19960007 · Cited by 189 · see citing works
Willett, W. C. (1998) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195122978.003.05 · Cited by 148 · see citing works
Individual dietary intake methodology: A 50-year review of progress
Medlin, C., & Skinner, J. D. (1988) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(21)07993-1 · Cited by 101 · see citing works
An image analysis system for dietary assessment and evaluation
Zhu, F. M., Bosch, M., Boushey, C. J., & Delp, E. J. (2010) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2010.5650848 · Cited by 90 · see citing works
Foster, E., Matthews, J. N. S., Lloyd, J., Marshall, L. F., Mathers, J. C., Nelson, M., Barton, K., Wrieden, W. L., Cornelissen, P. L., Harris, J. M., et al. (2007) · British Journal Of Nutrition · doi:10.1017/s000711450779390x · Cited by 90 · see citing works
Lee, C. D., Chae, J., Schap, T. E., Kerr, D. A., Delp, E. J., Ebert, D. S., & Boushey, C. J. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600231 · Cited by 58 · see citing works
An Overview of the Technology Assisted Dietary Assessment Project at Purdue University
Khanna, N., Boushey, C. J., Kerr, D. A., Okos, M. R., Ebert, D. S., & Delp, E. J. (2010) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2010.50 · Cited by 54 · see citing works
Segmentation assisted food classification for dietary assessment
Zhu, F. M., Bosch, M., Schap, T. E., Khanna, N., Ebert, D. S., Boushey, C. J., & Delp, E. J. (2011) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.877036 · Cited by 41 · see citing works
Multilevel Segmentation for Food Classification in Dietary Assessment.
Zhu, F. M., Bosch, M., Khanna, N., Boushey, C. J., & Delp, E. J. (2011) · PubMed · Cited by 38 · see citing works
A SHORT DIETARY METHOD FOR EPIDEMIOLOGIC STUDIES: III. DEVELOPMENT OF QUESTIONNAIRE12
HANKEN, J. H., Stallones, R. A., & Messinger, H. B. (1968) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a120819 · Cited by 34 · see citing works
Food Composition Table for Short Method of Dietary Analysis (2nd Revision)1
Leichsenring, J. M., & Wilson, E. D. (1951) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(21)30756-8 · Cited by 32 · see citing works
Food volume estimation from a single image using virtual reality technology
Zhang, Z., Yang, Y., Yue, Y., Fernstrom, J. D., Jia, W., & Sun, M. (2011) · IEEE Annual Northeast Bioengineering Conference (NEBEC) · doi:10.1109/nebc.2011.5778625 · Cited by 26 · see citing works
A SHORT DIETARY METHOD FOR EPIDEMIOLOGIC STUDIES
Hankin, J. H., Messinger, H. B., & Stallones, R. A. (1970) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a121168 · Cited by 25 · see citing works
The Need for Clinical Accuracy Guidelines for Blood Glucose Monitors
Klonoff, D. C. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600101 · Cited by 24 · see citing works
Food composition table for short method of dietary analysis (2nd revision).
Leichsenring, J. M., & Donelson Wilson, E. (1951) · PubMed · Cited by 17 · see citing works
Subar, A. F., Kirkpatrick, S. I., Mittl, B., Zimmerman, T. P., Thompson, F. E., Bingley, C., Willis, G., Islam, N. G., Baranowski, T., McNutt, S., et al. (2012) · Circulation · doi:10.1161/circ.125.suppl_10.a029 · Cited by 5 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Medlin C & Skinner JD (1988) 50 year review of individual methods. J Am Dietetic Assoc 88, 1250.
- Noronha J, Hysen E, Zhang H et al. (2011) PlateMate: Crowdsourcing nutrition analysis from food photographs. In: UIST Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, pp. 1-12, doi:10.1145/2047196.2047198, available at https:wiki.engr. illinois.edu/download/attachments/181174324/noronha-plate mate-uist11-small.pdf. · DOI
Cited by
Sorted by citations
Helander, E., Kaipainen, K., Korhonen, I., & Wansink, B. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3084 · Cited by 143 · see citing works
Personalized machine learning of depressed mood using wearables
Shah, R. V., Grennan, G., Zafar-Khan, M., Alim, F., Dey, S., Ramanathan, D., & Mishra, J. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01445-0 · Cited by 123 · see citing works
Freedman, L. S., Midthune, D., Arab, L., Prentice, R. L., Subar, A. F., Willett, W., Neuhouser, M. L., Tinker, L. F., & Kipnis, V. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy126 · Cited by 116 · see citing works
Emerging trends of technology-based dietary assessment: a perspective study
Zhao, X., Xu, X., Li, X., He, X., Yang, Y., & Zhu, S. (2021) · European Journal of Clinical Nutrition · doi:10.1038/s41430-020-00779-0 · Cited by 46 · see citing works
Mulvaney, S. A., Vaala, S., Hood, K. K., Lybarger, C., Carroll, R., Williams, L., Schmidt, D. C., Johnson, K., Dietrich, M. S., & Laffel, L. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0064 · Cited by 29 · see citing works
Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals
Nan, J., Herbert, M. S., Purpura, S., Henneken, A. N., Ramanathan, D., & Mishra, J. (2024) · Sensors · doi:10.3390/s24082640 · Cited by 22 · see citing works
- Authors
- Stumbo, P. J.
- Affiliations
- University of Iowa (US)
- Corresponding author
- Stumbo, Phyllis J.
- Venue
- Proceedings of the Nutrition Society
- Publisher
- Cambridge University Press (CUP)
- Published
- 2013-01-21
- Volume
- 72
- Issue
- 1
- Pages
- 70-76
- ISSN
- 0029-6651 (ISSN-L)
- 1475-2719
- OpenAlex
- W2028292653
- Cited by
- 146
- References
- 25
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 12 · 2023: 3 · 2022: 3 · 2021: 6 · 2020: 15 · 2019: 13 · 2018: 26 · 2017: 22 · 2016: 11 · 2015: 19 · 2014: 8 · 2013: 2
- FWCI
- 13.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of The Nutrition Society (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Keywords
- dietary assessment, food diary, dietary recall, food frequency questionnaire, digital technologies, mobile application, crowdsourcing
- MeSH terms
- Diet, Humans, Image Processing, Computer-Assisted, National Institutes of Health (U.S.), Photography, Technology, United States, Nutrition Assessment, Diet Records, Cell Phone, Crowdsourcing
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