The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Self-citationDaugherty, 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. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1967 · Cited by 176
Self-citationOnline dietary intake estimation: the Food4Me food frequency questionnaire
Forster, H., Fallaize, R., Gallagher, C., O’Donovan, C. B., Woolhead, C., Walsh, M. C., Macready, A. L., Lovegrove, J. A., Mathers, J. C., Gibney, M. J., et al. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3105 · Cited by 151
How 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
Self-citationWong, S. S., Boushey, C. J., Novotny, R., & Gustafson, D. R. (2008) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2007.12.006 · Cited by 53
Self-citation
Cited by
Nutrition and prevention of cognitive impairment
Scarmeas, N., Anastasiou, C. A., & Yannakoulia, M. (2018) · The Lancet Neurology · doi:10.1016/s1474-4422(18)30338-7 · Cited by 612
Bailey, R. L. (2021) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2021.02.007 · Cited by 431
Park, Y., Dodd, K. W., Kipnis, V., Thompson, F. E., Potischman, N., Schoeller, D. A., Baer, D. J., Midthune, D., Troiano, R. P., Bowles, H., et al. (2018) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqx002 · Cited by 367
Chronotype: Implications for Epidemiologic Studies on Chrono-Nutrition and Cardiometabolic Health
Almoosawi, S., Vingeliene, S., Gachon, F., Voortman, T., Palla, L., Johnston, J. D., Van Dam, R. M., Darimont, C., & Karagounis, L. G. (2019) · Advances in Nutrition · doi:10.1093/advances/nmy070 · Cited by 247
“In this respect, novel advances in the use of technologybased dietary assessmentmay provide opportunities to better capture the various dimensions of chrono-nutrition (135).”
BackgroundFreedman, 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
Metadata
- Authors
- Boushey, C. J., Spoden, M., Zhu, F. M., Delp, E. J., & Kerr, D. A.
- Venue
- Proceedings of the Nutrition Society
- Publisher
- Cambridge University Press (CUP)
- Published
- 2016-12-12
- Volume
- 76
- Issue
- 3
- Pages
- 283-294
- ISSN
- 0029-6651 (ISSN-L)
- 1475-2719
- OpenAlex
- W2560828904
- Cited by
- 335
- References
- 5
- FWCI
- 20.86
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods. https://app.cochat.ai/papers/10.1017/s0029665116002913
- The paper
- Boushey, C. J., Spoden, M., Zhu, F. M., Delp, E. J., & Kerr, D. A. (2017). New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods. Proceedings of the Nutrition Society. https://doi.org/10.1017/s0029665116002913
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.