Novel Technologies for Assessing Dietary Intake: Evaluating the Usability of a Mobile Telephone Food Record Among Adults and Adolescents
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BACKGROUND: The development of a mobile telephone food record has the potential to ameliorate much of the burden associated with current methods of dietary assessment. When using the mobile telephone food record, respondents capture an image of their foods and beverages before and after eating. Methods of image analysis and volume estimation allow for automatic identification and volume estimation of foods. To obtain a suitable image, all foods and beverages and a fiducial marker must be included in the image. OBJECTIVE: To evaluate a defined set of skills among adolescents and adults when using the mobile telephone food record to capture images and to compare the perceptions and preferences between adults and adolescents regarding their use of the mobile telephone food record. METHODS: We recruited 135 volunteers (78 adolescents, 57 adults) to use the mobile telephone food record for one or two meals under controlled conditions. Volunteers received instruction for using the mobile telephone food record prior to their first meal, captured images of foods and beverages before and after eating, and participated in a feedback session. We used chi-square for comparisons of the set of skills, preferences, and perceptions between the adults and adolescents, and McNemar test for comparisons within the adolescents and adults. RESULTS: Adults were more likely than adolescents to include all foods and beverages in the before and after images, but both age groups had difficulty including the entire fiducial marker. Compared with adolescents, significantly more adults had to capture more than one image before (38% vs 58%, P = .03) and after (25% vs 50%, P = .008) meal session 1 to obtain a suitable image. Despite being less efficient when using the mobile telephone food record, adults were more likely than adolescents to perceive remembering to capture images as easy (P < .001). CONCLUSIONS: A majority of both age groups were able to follow the defined set of skills; however, adults were less efficient when using the mobile telephone food record. Additional interactive training will likely be necessary for all users to provide extra practice in capturing images before entering a free-living situation. These results will inform age-specific development of the mobile telephone food record that may translate to a more accurate method of dietary assessment.
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Traditional dietary self-monitoring methods suffer from underreporting and recall issues.
“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 [17]
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 [17]
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A genome-wide association study identifies novel risk loci for type 2 diabetes
Sladek, R., Rocheleau, G., Rung, J., Dina, C., Shen, L., Serre, D., Boutin, P., Vincent, D., Belisle, A., Hadjadj, S., et al. (2007) · Nature · doi:10.1038/nature05616 · Cited by 2,993 · see citing works
Rioux, J. D., Xavier, R. J., Taylor, K. D., Silverberg, M. S., Goyette, P., Huett, A., Green, T., Kuballa, P., Barmada, M. M., Datta, L. W., et al. (2007) · Nature Genetics · doi:10.1038/ng2032 · Cited by 1,769 · see citing works
Subar, A. F. (2003) · American Journal of Epidemiology · doi:10.1093/aje/kwg092 · Cited by 1,070 · see citing works
Issues in dietary intake assessment of children and adolescents
Livingstone, M. B. E., Robson, P. J., & Wallace, J. M. W. (2004) · British Journal Of Nutrition · doi:10.1079/bjn20041169 · Cited by 884 · see citing works
Factors Influencing Food Choices of Adolescents
Neumark‐Sztainer, D., Story, M. T., Perry, C. L., & Casey, M. A. (1999) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(99)00222-9 · Cited by 826 · see citing works
The USDA Automated Multiple-Pass Method Accurately Estimates Group Total Energy and Nutrient Intake
Blanton, C. A., Moshfegh, A. J., Baer, D. J., & Kretsch, M. J. (2006) · Journal of Nutrition · doi:10.1093/jn/136.10.2594 · Cited by 767 · see citing works
Chomutare, T., Fernandez-Luque, L., Årsand, E., & Hartvigsen, G. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1874 · Cited by 451 · see citing works
Need for Technological Innovation in Dietary Assessment
Thompson, F. E., Subar, A. F., Loria, C. M., Reedy, J. L., & Baranowski, T. (2010) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2009.10.008 · Cited by 349 · see citing works
Martin, C. K., Han, H., Coulon, S. M., Allen, H. R., Champagne, C. M., & Anton, S. D. (2008) · British Journal Of Nutrition · doi:10.1017/s0007114508027438 · Cited by 330 · see citing works
Use of technology in children’s dietary assessment
Boushey, C. J., Kerr, D. A., Wright, J., Lutes, K. D., Ebert, D. S., & Delp, E. J. (2009) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2008.65 · Cited by 231 · see citing works
Self-citationEvidence-Based Development of a Mobile Telephone Food Record
Six, B. L., Schap, T. E., Zhu, F. M., Mariappan, A., Bosch, M., Delp, E. J., Ebert, D. S., Kerr, D. A., & Boushey, C. J. (2009) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2009.10.010 · Cited by 200 · see citing works
Self-citationChampagne, C. M., BAKER, N. B., DeLany, J. P., Harsha, D. W., & Bray, G. A. (1998) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(98)00097-2 · Cited by 185 · see citing works
Whittaker, R., Maddison, R., McRobbie, H., Bullen, C., Denny, S., Dorey, E., Ellis-Pegler, M., van Rooyen, J., & Rodgers, A. (2008) · Journal of Medical Internet Research · doi:10.2196/jmir.1007 · Cited by 171 · see citing works
A Wearable Electronic System for Objective Dietary Assessment
Sun, M., Fernstrom, J. D., Jia, W., Hackworth, S. A., Yao, N., Li, Y., Li, C., Fernstrom, M. H., & Sclabassi, R. J. (2009) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2009.10.013 · Cited by 159 · see citing works
O’Donnell, C. J., Cupples, L. A., D’Agostino, R. B., Fox, C. S., Hoffmann, U., Hwang, S., Ingellson, E., Liu, C., Murabito, J. M., Polak, J. F., et al. (2007) · BMC Medical Genetics · doi:10.1186/1471-2350-8-s1-s4 · Cited by 138 · see citing works
Assessment of usual dietary intake in population studies of gene–diet interaction
Tucker, K. L. (2007) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2006.07.010 · Cited by 137 · see citing works
Wang, D., Kogashiwa, M., Ohta, S., & Kira, S. (2002) · Journal of Nutritional Science and Vitaminology · doi:10.3177/jnsv.48.498 · Cited by 136 · see citing works
Longitudinal changes in the accuracy of reported energy intake in girls 10–15 y of age
Bandini, L. G., Must, A., Cyr, H., Anderson, S. E., Spadano-Gasbarro, J. L., & Dietz, W. H. (2003) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/78.3.480 · Cited by 121 · see citing works
Yon, B. A., Johnson, R. K., Harvey‐Berino, J., & Gold, B. C. (2006) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2006.05.004 · Cited by 89 · see citing works
Technology-assisted dietary assessment
Zhu, F. M., Mariappan, A., Boushey, C. J., Kerr, D. A., Lutes, K. D., Ebert, D. S., & Delp, E. J. (2008) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.778616 · Cited by 88 · see citing works
Self-citationEnergy Intake and Energy Expenditure
Champagne, C. M., Bray, G. A., KURTZ, A. A., Monteiro, J. B. R., TUCKER, E., Vólaufová, J., & DeLany, J. P. (2002) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(02)90316-0 · Cited by 81 · see citing works
Measurement of dietary exposure: a challenging problem which may be overcome thanks to metabolomics?
Favé, G., Beckmann, M., Draper, J., & Mathers, J. C. (2009) · Genes & Nutrition · doi:10.1007/s12263-009-0120-y · Cited by 76 · see citing works
Weiss, R., Stumbo, P. J., & Divakaran, A. (2009) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2009.10.011 · Cited by 75 · see citing works
Mahabir, S., Baer, D. J., Giffen, C. A., Subar, A. F., Campbell, W. W., Hartman, T. J., Clevidence, B., Albanes, D., & Taylor, P. R. (2005) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602359 · Cited by 69 · see citing works
Measures of Physical Activity Using Cell Phones: Validation Using Criterion Methods
Bexelius, C., Löf, M., Sandin, S., Lagerros, Y. T., Forsum, E., & Litton, J. (2010) · Journal of Medical Internet Research · doi:10.2196/jmir.1298 · Cited by 67 · see citing works
Personal dietary assessment using mobile devices
Mariappan, A., Bosch, M., Zhu, F. M., Boushey, C. J., Kerr, D. A., Ebert, D. S., & Delp, E. J. (2009) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.813556 · Cited by 57 · see citing works
Self-citationLaska, M. N., Graham, D., Moe, S. G., Lytle, L., & Fulkerson, J. (2011) · Public Health Nutrition · doi:10.1017/s1368980010003186 · Cited by 46 · see citing works
GOODWIN, R. A., Brulé, D., Junkins, E., Dubois, S., & Beer‐Borst, S. (2001) · Journal of the American Dietetic Association · doi:10.1016/s0002-8223(01)00229-2 · Cited by 35 · see citing works
Schap, T. E., Six, B. L., Delp, E. J., Ebert, D. S., Kerr, D. A., & Boushey, C. J. (2011) · Public Health Nutrition · doi:10.1017/s1368980010003794 · Cited by 34 · see citing works
Self-citationMeasuring energy expenditure with simple low cost tools
Kretsch, M. J., Blanton, C. A., Baer, D. J., Staples, R., Horn, W. F., & Keim, N. L. (2004) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2004.05.023 · Cited by 9 · see citing works
Hertzum, M., & Simonsen, J. (2004) · Nordic Conference on Human-Computer Interaction · doi:10.1145/1028014.1028076 · Cited by 6 · see citing works
Seedorf, U., Schulte, H., & Assmann, G. (2007) · Genes & Nutrition · doi:10.1007/s12263-007-0001-1 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Sugar consumption, metabolic disease and obesity: The state of the controversy
Stanhope, K. L. (2016) · Critical Reviews in Clinical Laboratory Sciences · doi:10.3109/10408363.2015.1084990 · Cited by 739 · see citing works
Boushey, C. J., Spoden, M., Zhu, F. M., Delp, E. J., & Kerr, D. A. (2016) · Proceedings of The Nutrition Society · doi:10.1017/s0029665116002913 · Cited by 338 · see citing works
Self-citationLiu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V., Yunsheng, M., Chen, S., & Hou, P. (2018) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2017.2662008 · Cited by 333 · see citing works
Rabbi, M., Pfammatter, A. F., Zhang, M., Spring, B. J., & Choudhury, T. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4160 · Cited by 300 · see citing works
DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment
Liu, C., Cao, Y., Luo, Y., Chen, G., Vokkarane, V. M., & Ma, Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-39601-9_4 · Cited by 297 · see citing works
An umbrella review of the evidence associating diet and cancer risk at 11 anatomical sites
Papadimitriou, N., Markozannes, G., Kanellopoulou, A., Critselis, E., Alhardan, S., Karafousia, V., Kasimis, J. C., Katsaraki, C., Papadopoulou, A., Zografou, M. T., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24861-8 · Cited by 240 · see citing works
Image-Assisted Dietary Assessment: A Systematic Review of the Evidence
Gemming, L., Utter, J., & Mhurchú, C. N. (2014) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2014.09.015 · Cited by 235 · see citing works
Eldridge, A. L., Piernas, C., Illner, A., Gibney, M. J., Gurinović, M. A., de Vries, J. H. M., & Cade, J. E. (2018) · Nutrients · doi:10.3390/nu11010055 · Cited by 212 · see citing works
Turner, T., Spruijt‐Metz, D., Wen, C. K. F., & Hingle, M. D. (2015) · Pediatric Obesity · doi:10.1111/ijpo.12002 · Cited by 172 · see citing works
Feasibility and validity of mobile phones to assess dietary intake
Sharp, D. B., & Allman-Farinelli, M. A. (2014) · Nutrition · doi:10.1016/j.nut.2014.02.020 · Cited by 156 · see citing works
Su, J. J., & Yu, D. S. F. (2021) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2021.104040 · Cited by 154 · see citing works
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
Tahir, G. A., & Loo, C. K. (2021) · Healthcare · doi:10.3390/healthcare9121676 · Cited by 118 · see citing works
The future direction of personalised nutrition: my diet, my phenotype, my genes
Gibney, M. J., & Walsh, M. C. (2013) · Proceedings of The Nutrition Society · doi:10.1017/s0029665112003436 · Cited by 112 · see citing works
Spook, J. E., Paulussen, T. G., Kok, G., & van Empelen, P. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2617 · Cited by 104 · see citing works
Ji, Y.-W., Plourde, H., Bouzo, V., Kilgour, R. D., & Cohen, T. R. (2020) · JMIR mhealth and uhealth · doi:10.2196/16953 · Cited by 106 · see citing works
Nutritional epidemiology: New perspectives for understanding the diet-disease relationship?
Boeing, H. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2013.47 · Cited by 103 · see citing works
Smartphone Medical Applications for Women’s Health: What Is the Evidence-Base and Feedback?
Derbyshire, E. J., & Dancey, D. (2013) · International Journal of Telemedicine and Applications · doi:10.1155/2013/782074 · Cited by 99 · see citing works
Rangan, A., O'Connor, S., Giannelli, V., Yap, M. L., Tang, L. M., Roy, R., Louie, J. C. Y., Hebden, L., Kay, J., & Allman-Farinelli, M. A. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4613 · Cited by 94 · see citing works
Influences on eating: a qualitative study of adolescents in a periurban area in Lima, Peru
Banna, J. C., Buchthal, O. V., Delormier, T., Creed‐Kanashiro, H., & Penny, M. E. (2015) · BMC Public Health · doi:10.1186/s12889-016-2724-7 · Cited by 92 · see citing works
Feasibility of a SenseCam-assisted 24-h recall to reduce under-reporting of energy intake
Gemming, L., Doherty, A., Kelly, P., Utter, J., & Mhurchú, C. N. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2013.156 · Cited by 91 · see citing works
Mobile Ecological Momentary Diet Assessment Methods for Behavioral Research: Systematic Review
Schembre, S. M., Liao, Y., O’Connor, S. G., Hingle, M. D., Shen, S., Hamoy, K. G., Huh, J., Dunton, G. F., Weiss, R., Thomson, C. A., et al. (2018) · JMIR mhealth and uhealth · doi:10.2196/11170 · Cited by 85 · see citing works
Self-citationEvaluation of a Mobile Phone Image-Based Dietary Assessment Method in Adults with Type 2 Diabetes
Rollo, M. E., Ash, S., Lyons‐Wall, P., & Russell, A. W. (2015) · Nutrients · doi:10.3390/nu7064897 · Cited by 88 · see citing works
DINO (Diet In Nutrients Out) – an integrated dietary assessment system
Fitt, E., Cole, D., Ziauddeen, N., Pell, D., Stickley, E., Harvey, A., & Stephen, A. M. (2014) · Public Health Nutrition · doi:10.1017/s1368980014000342 · Cited by 86 · see citing works
Analysis of food images: Features and classification
He, Y., Xu, C., Khanna, N., Boushey, C. J., & Delp, E. J. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025555 · Cited by 80 · see citing works
Self-citationCasperson, S. L., Sieling, J., Moon, J., Johnson, L. K., Roemmich, J. N., & Whigham, L. D. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.3324 · Cited by 78 · see citing works
Sanghvi, A., Redman, L. M., Martin, C. K., Ravussin, É., & Hall, K. D. (2015) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.111070 · Cited by 72 · see citing works
What Are They Really Eating? A Review on New Approaches to Dietary Intake Assessment and Validation
Rollo, M. E., Williams, R. L., Burrows, T. L., Kirkpatrick, S. I., Bucher, T., & Collins, C. E. (2016) · Current Nutrition Reports · doi:10.1007/s13668-016-0182-6 · Cited by 71 · see citing works
Single-View Food Portion Estimation Based on Geometric Models
Fang, S., Liu, C., Zhu, F. M., Delp, E. J., & Boushey, C. J. (2015) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2015.67 · Cited by 70 · see citing works
Self-citationKerr, D. A., Pollard, C. M., Howat, P. A., Delp, E. J., Pickering, M. R., Kerr, K. R., Dhaliwal, S. S., Pratt, I., Wright, J., & Boushey, C. J. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-477 · Cited by 69 · see citing works
Self-citationBruening, M. M., van Woerden, I., Todd, M. W., Brennhofer, S. A., Laska, M. N., & Dunton, G. F. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5969 · Cited by 65 · see citing works
Boushey, C. J., Spoden, M., Delp, E. J., Zhu, F. M., Bosch, M., Ahmad, Z., Shvetsov, Y. B., DeLany, J. P., & Kerr, D. A. (2017) · Nutrients · doi:10.3390/nu9030312 · Cited by 67 · see citing works
Self-citationHow Willing Are Adolescents to Record Their Dietary Intake? The Mobile Food Record
Boushey, C. J., Harray, A. J., Kerr, D. A., Schap, T. E., Paterson, S., Aflague, T., Bosch Ruiz, M., Ahmad, Z., & Delp, E. J. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.4087 · Cited by 67 · see citing works
Self-citationInvestigation of physicians' awareness and use of mHealth apps: A mixed method study
Sezgın, E., Özkan, S., & Yıldırım, S. (2017) · Health Policy and Technology · doi:10.1016/j.hlpt.2017.07.007 · Cited by 69 · see citing works
Lee, J.-E., Song, S., Ahn, J. S., Kim, Y., & Lee, J. E. (2017) · Nutrients · doi:10.3390/nu9070748 · Cited by 65 · see citing works
Zapata-Lamana, R., Lalanza, J. F., Losilla, J.-M., Parrado, E., & Capdevila, L. (2020) · PeerJ · doi:10.7717/peerj.8848 · Cited by 61 · see citing works
Eikey, E. V., Reddy, M. C., & Kuziemsky, C. E. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.08.006 · Cited by 63 · see citing works
Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review
Chotwanvirat, P., Prachansuwan, A., Sridonpai, P., & Kriengsinyos, W. (2024) · Journal of Medical Internet Research · doi:10.2196/51432 · Cited by 71 · see citing works
DIETOS: A recommender system for adaptive diet monitoring and personalized food suggestion
Agapito, G., Calabrese, B., Guzzi, P. H., Cannataro, M., Simeoni, M., Carè, I., Lamprinoudi, T., Fuíano, G., & Pujia, A. (2016) · IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob) · doi:10.1109/wimob.2016.7763190 · Cited by 62 · see citing works
Burrows, T. L., Golley, R. K., Khambalia, A., McNaughton, S. A., Magarey, A., Rosenkranz, R. R., Allman-Farinelli, M. A., Rangan, A., Truby, H., & Collins, C. E. (2012) · Obesity Reviews · doi:10.1111/j.1467-789x.2012.01022.x · Cited by 61 · see citing works
da Costa, F. F., Schmoelz, C. P., Davies, V. F., Pietro, P. F. D., Kupek, E. J., & de Assis, M. A. A. (2013) · JMIR Research Protocols · doi:10.2196/resprot.2646 · Cited by 61 · see citing works
Image-based food volume estimation
Xu, C., He, Y., Khanna, N., Parra, A., Boushey, C. J., & Delp, E. J. (2013) · doi:10.1145/2506023.2506037 · Cited by 59 · see citing works
Self-citationValidation of a Smartphone Image-Based Dietary Assessment Method for Pregnant Women
Ashman, A. M., Collins, C. E., Brown, L., Rae, K. M., & Rollo, M. E. (2017) · Nutrients · doi:10.3390/nu9010073 · Cited by 57 · see citing works
A Systematic Review and Meta-Analysis of Validation Studies Performed on Dietary Record Apps
Zhang, L., Misir, A., Boshuizen, H. C., & Ocké, M. C. (2021) · Advances in Nutrition · doi:10.1093/advances/nmab058 · Cited by 58 · see citing works
Fang, S., Shao, Z., Mao, R., Fu, C., Delp, E. J., Zhu, F. M., Kerr, D. A., & Boushey, C. J. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451461 · Cited by 56 · see citing works
Self-citationPanizza, C. E., Lim, U., Yonemori, K., Cassel, K., Wilkens, L. R., Harvie, M., Maskarinec, G., Delp, E. J., Lampe, J. W., Shepherd, J., et al. (2019) · Nutrients · doi:10.3390/nu11061386 · Cited by 56 · see citing works
Self-citationGilmore, L. A., Duhé, A. F., Frost, E. A. M., & Redman, L. M. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814525189 · Cited by 54 · see citing works
Banna, J. C., Panizza, C. E., Boushey, C. J., Delp, E. J., & Lim, E. (2018) · Nutrients · doi:10.3390/nu10091279 · Cited by 54 · see citing works
Self-citationPelle, T., Bevers, K., van der Palen, J. A. M., van den Hoogen, F. H. J., & van den Ende, C. H. M. (2020) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2020.02.831 · Cited by 54 · see citing works
Harray, A. J., Boushey, C. J., Pollard, C. M., Delp, E. J., Ahmad, Z., Dhaliwal, S. S., Mukhtar, S. A., & Kerr, D. A. (2015) · Nutrients · doi:10.3390/nu7075226 · Cited by 50 · see citing works
Self-citationSun, M., Burke, L. E., Baranowski, T., Fernstrom, J. D., Zhang, H., Chen, H.-C., Bai, Y., Li, Y., Li, C., Yue, Y., et al. (2015) · Journal of Healthcare Engineering · doi:10.1260/2040-2295.6.1.1 · Cited by 50 · see citing works
Distal and proximal predictors of snacking at work: A daily-survey study.
Sonnentag, S., Pundt, A., & Venz, L. (2016) · Journal of Applied Psychology · doi:10.1037/apl0000162 · Cited by 50 · see citing works
A Mobile Food Record For Integrated Dietary Assessment
Ahmad, Z., Bosch, M., Khanna, N., Kerr, D. A., Boushey, C. J., Zhu, F. M., & Delp, E. J. (2016) · International Workshop on Multimedia Assisted Dietary Management (MADiMa) · doi:10.1145/2986035.2986038 · Cited by 50 · see citing works
Self-citationConrad, J., & Nöthlings, U. (2017) · Proceedings of The Nutrition Society · doi:10.1017/s0029665116003025 · Cited by 49 · see citing works
Usability of a smartphone food picture app for assisting 24-hour dietary recall: a pilot study
Hongu, N. K., Pope, B. T., Bilgiç, P., Orr, B. J., Suzuki, A., Kim, A. S., Merchant, N. C., & Roe, D. J. (2015) · Nutrition Research and Practice · doi:10.4162/nrp.2015.9.2.207 · Cited by 48 · see citing works
A comparison of food portion size estimation using geometric models and depth images
Fang, S., Zhu, F. M., Jiang, C., Zhang, S., Boushey, C. J., & Delp, E. J. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532312 · Cited by 46 · see citing works
Self-citationEmerging 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
Park, S., Palvanov, A., Lee, C.-H., Jeong, N., Cho, Y. I., & Lee, H. (2019) · Nutrition Research and Practice · doi:10.4162/nrp.2019.13.6.521 · Cited by 45 · see citing works
Rivera, J., McPherson, A. C., Hamilton, J. K., Birken, C. S., Coons, M. J., Peters, M., Iyer, S., George, T., Nguyen, C., & Stinson, J. (2018) · JMIR Formative Research · doi:10.2196/formative.8248 · Cited by 43 · see citing works
Jong, S. T., Stevenson, R., Winpenny, E. M., Corder, K. L., & van Sluijs, E. M. F. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01802-7 · Cited by 45 · see citing works
Arens‐Volland, A., Spassova, L., & Bohn, T. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.08.006 · Cited by 42 · see citing works
Taylor, J., & Johnson, R. K. (2013) · Nutrition Bulletin · doi:10.1111/nbu.12009 · Cited by 41 · see citing works
Aflague, T., Boushey, C. J., Guerrero, R. T. L., Ahmad, Z., Kerr, D. A., & Delp, E. J. (2015) · Nutrients · doi:10.3390/nu7064403 · Cited by 41 · see citing works
Self-citationShort and long-term energy intake patterns and their implications for human body weight regulation
Chow, C. C., & Hall, K. D. (2014) · Physiology & Behavior · doi:10.1016/j.physbeh.2014.02.044 · Cited by 36 · see citing works
Ho, T. J. H., Lee, C. C. S., Wong, S. N., & Lau, Y. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.09.019 · Cited by 35 · see citing works
Chen, Y., Wong, J. E., Fatehah, A. A., Othman, N. A. S., & Poh, B. K. (2017) · Nutrients · doi:10.3390/nu9010062 · Cited by 34 · see citing works
Chung, L., Law, Q. P. S., Fong, S., Chung, J., & Yuen, P. P. (2015) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15572200 · Cited by 31 · see citing works
Improving dietary data collection tools for better nutritional assessment – A systematic review
Tanweer, A., Khan, S., Mustafa, F. N., Imran, S., Humayun, A., & Hussain, Z.-U.-N. (2022) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2022.100067 · Cited by 31 · see citing works
Wang, J.-S., Hsieh, R., Tung, Y., Chen, Y., Yang, C., & Chen, Y. (2019) · Nutrients · doi:10.3390/nu11102527 · Cited by 30 · see citing works
Treating eating disorders using the internet
Fairburn, C. G., & Murphy, R. (2015) · Current Opinion in Psychiatry · doi:10.1097/yco.0000000000000195 · Cited by 27 · see citing works
Effect of Prior Health Knowledge on the Usability of Two Home Medical Devices: Usability Study
Chaniaud, N., Métayer, N., Mégalakaki, O., & Loup‐Escande, É. (2020) · JMIR mhealth and uhealth · doi:10.2196/17983 · Cited by 27 · see citing works
Aydın, G., & Silahtaroğlu, G. (2021) · PLoS ONE · doi:10.1371/journal.pone.0244302 · Cited by 31 · see citing works
Feasibility of Reviewing Digital Food Images for Dietary Assessment among Nutrition Professionals
Fatehah, A. A., Poh, B. K., Safii, N. S., & Wong, J. E. (2018) · Nutrients · doi:10.3390/nu10080984 · Cited by 27 · see citing works
Can mobile augmented reality systems assist in portion estimation? A user study
Stütz, T., Dinic, R., Domhardt, M., & Ginzinger, S. (2014) · IEEE International Symposium on Mixed and Augmented Reality - Arts, Media, and Humanities (ISMAR-AMH) · doi:10.1109/ismar-amh.2014.6935438 · Cited by 26 · see citing works
Ahn, Y., Bae, J., & Kim, H.-S. (2016) · Nutrition Research and Practice · doi:10.4162/nrp.2016.10.3.342 · Cited by 26 · see citing works
Raatz, S. K., Scheett, A. J., Johnson, L. K., & Jahns, L. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.3744 · Cited by 26 · see citing works
Ashman, A. M., Collins, C. E., Brown, L., Rae, K. M., & Rollo, M. E. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6469 · Cited by 25 · see citing works
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Lytle, L. A., Nicastro, H. L., Roberts, S. B., Evans, M. E., Jakicic, J. M., Laposky, A. D., & Loria, C. M. (2018) · Obesity · doi:10.1002/oby.22157 · Cited by 24 · see citing works
Prado, L. M. B., Reyes, Ó. A., & García-Perea, M. D. (2018) · JMIR mhealth and uhealth · doi:10.2196/10409 · Cited by 23 · see citing works
Vasiloglou, M. F., van der Horst, K., Stathopoulou, T., Jaeggi, M. P., Tedde, G. S., Lu, Y. L., & Mougiakakou, S. G. (2021) · JMIR mhealth and uhealth · doi:10.2196/24467 · Cited by 23 · see citing works
PEGASO e-Diary: User Engagement and Dietary Behavior Change of a Mobile Food Record for Adolescents
Caon, M., Prinelli, F., Angelini, L., Carrino, S., Mugellini, E., Orte, S., Serrano, J. C. E., Atkinson, S., Martin, A., & Adorni, F. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.727480 · Cited by 25 · see citing works
A mobile phone user interface for image-based dietary assessment
Ahmad, Z., Khanna, N., Kerr, D. A., Boushey, C. J., & Delp, E. J. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2041334 · Cited by 22 · see citing works
Self-citationPrado, L. M. B., Sharp, B., & García-Perea, M. D. (2016) · JMIR Research Protocols · doi:10.2196/resprot.5782 · Cited by 21 · see citing works
State of the Art in Clinical Informatics: Evidence and Examples
McCoy, A. B., Wright, A. T., Eysenbach, G., Malin, B., Patterson, E. S., Xu, H., & Sittig, D. F. (2013) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1638827 · Cited by 21 · see citing works
Gender and age are associated with healthy food purchases via grocery voucher redemption
Hardin‐Fanning, F., & Gokun, Y. (2014) · Rural and Remote Health · doi:10.22605/rrh2830 · Cited by 21 · see citing works
Comulada, W. S., Swendeman, D. T., Koussa, M., Mindry, D. L., Medich, M., Estrin, D. L., Mercer, N., & Ramanathan, N. (2017) · Public Health Nutrition · doi:10.1017/s1368980017003044 · Cited by 20 · see citing works
An Integrated System for Mobile Image-Based Dietary Assessment
Shao, Z., Han, Y., He, J., Mao, R., Wright, J., Kerr, D. A., Boushey, C. J., & Zhu, F. M. (2021) · doi:10.1145/3475725.3483625 · Cited by 20 · see citing works
Self-citationEfficient superpixel based segmentation for food image analysis
Wang, Y., Liu, C., Zhu, F. M., Boushey, C. J., & Delp, E. J. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532818 · Cited by 20 · see citing works
Self-citationBathgate, K., Sherriff, J. L., Leonard, H., Dhaliwal, S. S., Delp, E. J., Boushey, C. J., & Kerr, D. A. (2017) · Nutrients · doi:10.3390/nu9030273 · Cited by 20 · see citing works
Self-citationPatient-Generated Health Photos and Videos Across Health and Well-being Contexts: Scoping Review
Ploderer, B., Rezaei Aghdam, A., & Burns, K. (2022) · Journal of Medical Internet Research · doi:10.2196/28867 · Cited by 18 · see citing works
Performance of the Digital Dietary Assessment Tool MyFoodRepo
Zuppinger, C., Taffé, P., Burger, G., Badran-Amstutz, W., Niemi, T., Cornuz, C., Belle, F. N., Chatelan, A., Paclet, M. L., Bochud, M., et al. (2022) · Nutrients · doi:10.3390/nu14030635 · Cited by 18 · see citing works
Segovia‐Siapco, G., & Sabaté, J. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.5418 · Cited by 18 · see citing works
Saliency-Aware Class-Agnostic Food Image Segmentation
Yarlagadda, S. K., Montserrat, D. M., Güera, D., Boushey, C. J., Kerr, D. A., & Zhu, F. M. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3440274 · Cited by 17 · see citing works
Self-citationIndividual differences in the forced swimming test and neurochemical kinetics in the rat brain
Sequeira‐Cordero, A., Mora-Gallegos, A., Cuenca-Berger, P., & Trías, J. F. (2014) · Physiology & Behavior · doi:10.1016/j.physbeh.2014.01.037 · Cited by 16 · see citing works
Kerr, D. A., Dhaliwal, S. S., Pollard, C. M., Norman, R., Wright, J., Harray, A. J., Shoneye, C. L., Solah, V. A., Hunt, W., Zhu, F. M., et al. (2017) · Nutrients · doi:10.3390/nu9030244 · Cited by 16 · see citing works
Self-citationCoarse-to-Fine Nutrition Prediction
Wang, B., Bu, T., Hu, Z., Yang, L., Zhao, Y., & Li, X. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3313638 · Cited by 15 · see citing works
Yildirim, P., Ekmekci, I. O., & Holzinger, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39146-0_18 · Cited by 15 · see citing works
Boushey, C. J., Delp, E. J., Ahmad, Z., Wang, Y., Roberts, S. M., & Grattan, L. M. (2016) · Harmful Algae · doi:10.1016/j.hal.2016.03.013 · Cited by 14 · see citing works
Self-citationEvaluation of a Novel WeChat Applet for Image-Based Dietary Assessment among Pregnant Women in China
Ding, Y., Lu, X., Xie, Z., Jiang, T., Song, C., & Wang, Z. (2021) · Nutrients · doi:10.3390/nu13093158 · Cited by 14 · see citing works
Probst, Y., & Zammit, G. (2015) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2013.816653 · Cited by 14 · see citing works
- Authors
- Daugherty, B. L., Schap, T. E., Ettienne-Gittens, R., Zhu, F. M., Bosch, M., Delp, E. J., Ebert, D. S., Kerr, D. A., & Boushey, C. J.
- Affiliations
- Purdue University West Lafayette (US)
- Curtin University (AU)
- Corresponding author
- Boushey, Carol J
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2012-04-13
- Volume
- 14
- Issue
- 2
- Pages
- e58
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC3376510
- OpenAlex
- W1966584624
- Cited by
- 176
- References
- 32
- Citations by year
- 2026: 4 · 2025: 1 · 2024: 9 · 2023: 5 · 2022: 7 · 2021: 14 · 2020: 14 · 2019: 6 · 2018: 14 · 2017: 19 · 2016: 23 · 2015: 25 · 2014: 15 · 2013: 17 · 2012: 3
- FWCI
- 34.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eSpace (Curtin University) (submittedVersion, cc-by)
- Funders
- National Cancer Institute (R25 CA90956, U01CA130784, R25CA090956)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK073711)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
- Keywords
- dietary assessment, food photography, image acquisition, fiducial markers, food volume estimation, image analysis, adolescents, adults, usability, age differences
- MeSH terms
- Adolescent, Adult, Energy Intake, Humans, Diet Records, Self Efficacy, Cell Phone
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