Emerging trends of technology-based dietary assessment: a perspective study

Zhao, Xueyin; Xu, Xiaochen; Li, Xiuyan; He, Xi; Yang, Yang; Zhu, Shankuan

doi:10.1038/s41430-020-00779-0

Cited by 46Nutritional Studies and DietNutrition, Health and Food BehaviorMobile Health and mHealth Applications

Emerging trends of technology-based dietary assessment: a perspective study

Zhao, Xueyin, Xu, Xiaochen, Li, Xiuyan, He, Xi, Yang, Yang, Zhu, Shankuan·2021·European Journal of Clinical Nutrition

DOI 10.1038/s41430-020-00779-0

Open this paper in CoChat — free account

Integrity signals

Registry

Crossref · 10.1038/s41430-020-00779-0

Cites

46 of 46 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Triangulating dietary data with data from wearable tech, mobile apps, school canteen sales data, ecological momentary assessment, direct observation, and proxy reports can provide insights into behaviour changes.
“Triangulating this data with data from wearable tech, mobile apps, and school canteen sales data, ecological momentary assessment (EMA) for real-time data collection, direct observation of adolescents’ eating behaviours, and proxy reports from family members can provide insights into behaviour changes [70–76].”
From School-based healthy eating interventions for adolescents aged 10–19 years: an umbrella review (2024) · cites this paper as [71]

Dietary assessment methods in epidemiologic studies
Shim, J., Oh, K., & Kim, H. C. (2014) · Epidemiology and Health · doi:10.4178/epih/e2014009 · Cited by 1,556 · see citing works
Addressing Current Criticism Regarding the Value of Self-Report Dietary Data
Subar, A. F., Freedman, L. S., Tooze, J. A., Kirkpatrick, S. I., Boushey, C., Neuhouser, M. L., Thompson, F. E., Potischman, N., Guenther, P. M., Tarasuk, V., et al. (2015) · The Journal of Nutrition · doi:10.3945/jn.115.219634 · Cited by 1,047 · see citing works
Dietary Assessment Methodology
Thompson, F. E., & Subar, A. F. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-802928-2.00001-1 · Cited by 944 · see citing works
Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology
Illner, A., Freisling, H., Boeing, H., Huybrechts, I., Crispim, S. P., & Slimani, N. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dys105 · Cited by 404 · see citing works
New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods
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
A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method
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
Validity of the Remote Food Photography Method (RFPM) for Estimating Energy and Nutrient Intake in Near Real‐Time
Martin, C. K., Correa, J. B., Han, H., Allen, H. R., Rood, J. C., Champagne, C. M., Gunturk, B. K., & Bray, G. A. (2011) · Obesity · doi:10.1038/oby.2011.344 · Cited by 270 · see citing works
Comparison of Interviewer-Administered and Automated Self-Administered 24-Hour Dietary Recalls in 3 Diverse Integrated Health Systems
Thompson, F. E., Dixit‐Joshi, S., Potischman, N. A., Dodd, K. W., Kirkpatrick, S. I., Kushi, L. H., Alexander, G., Coleman, L. A., Zimmerman, T. P., Sundaram, M. E., et al. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu467 · Cited by 236 · 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
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
A New Method for Measuring Meal Intake in Humans via Automated Wrist Motion Tracking
Dong, Y., Hoover, A. W., Scisco, J. L., & Muth, E. R. (2012) · Applied Psychophysiology and Biofeedback · doi:10.1007/s10484-012-9194-1 · Cited by 231 · see citing works
Detecting Periods of Eating During Free-Living by Tracking Wrist Motion
Dong, Y., Scisco, J. L., Wilson, M., Muth, E. R., & Hoover, A. W. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2282471 · Cited by 228 · see citing works
Evaluation of New Technology-Based Tools for Dietary Intake Assessment—An ILSI Europe Dietary Intake and Exposure Task Force Evaluation
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
FoodCam: A real-time food recognition system on a smartphone
Kawano, Y., & Yanai‬, ‪. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2000-8 · Cited by 190 · see citing works
How we eat determines what we become: opportunities and challenges brought by food delivery industry in a changing world in China
Maimaiti, M., Zhao, X., Jia, M., Ru, Y., & Zhu, S. (2018) · European Journal of Clinical Nutrition · doi:10.1038/s41430-018-0191-1 · Cited by 189 · see citing works
Self-citation
Validity and reproducibility of a web-based, self-administered food frequency questionnaire
Labonté, M.-È., Cyr, A., Baril-Gravel, L., Royer, M.-M., & Lamarche, B. (2012) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2011.163 · Cited by 189 · see citing works
Novel Technologies for Assessing Dietary Intake: Evaluating the Usability of a Mobile Telephone Food Record Among Adults and Adolescents
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. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.1967 · Cited by 176 · see citing works
Dietary Assessment Toolkits: An Overview
Dao, M. C., Subar, A. F., Warthon-Medina, M., Cade, J. E., Burrows, T., Golley, R. K., Forouhi, N. G., Pearce, M., & Holmes, B. A. (2019) · Public Health Nutrition · doi:10.1017/s1368980018002951 · Cited by 176 · see citing works
Measuring diet in the 21st century: use of new technologies
Cade, J. E. (2016) · Proceedings of The Nutrition Society · doi:10.1017/s0029665116002883 · Cited by 159 · 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
New technology in dietary assessment: a review of digital methods in improving food record accuracy
Stumbo, P. J. (2013) · Proceedings of the Nutrition Society · doi:10.1017/s0029665112002911 · Cited by 146 · see citing works
Design, Development and Construct Validation of the Children’s
Khan, S., Wirth, M. D., Ortaglia, A., Alvarado, C. R., Shivappa, N., Hurley, T. G., & Hébert, J. R. (2018) · Scholar Commons (University of South Carolina) · Cited by 135 · see citing works
eButton
Sun, M., Burke, L. E., Mao, Z., Chen, Y., Chen, H.-C., Bai, Y., Li, Y., Li, C., & Jia, W. (2014) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2593069.2596678 · Cited by 135 · see citing works
Accuracy of a PDA-based dietary assessment program
Beasley, J., Riley, W. T., & Jean-Mary, J. (2005) · Nutrition · doi:10.1016/j.nut.2004.11.006 · Cited by 124 · see citing works
A review of the design and validation of web- and computer-based 24-h dietary recall tools
Timon, C., van den Barg, R., Blain, R. J., Kehoe, L., Evans, K., Walton, J., Flynn, A., & Gibney, E. R. (2016) · Nutrition Research Reviews · doi:10.1017/s0954422416000172 · Cited by 117 · see citing works
Self-Monitoring of Dietary Intake by Young Women: Online Food Records Completed on Computer or Smartphone Are as Accurate as Paper-Based Food Records but More Acceptable
Hutchesson, M., Rollo, M. E., Callister, R., & Collins, C. E. (2014) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2014.07.036 · Cited by 102 · see citing works
Automatic food detection in egocentric images using artificial intelligence technology
Jia, W., Li, Y., Qu, R., Baranowski, T., Burke, L. E., Zhang, H., Bai, Y., Mancino, J., Xu, G., Mao, Z., et al. (2018) · Public Health Nutrition · doi:10.1017/s1368980018000538 · Cited by 102 · see citing works
Measuring diet in the 21st century: use of new technologies
Cade, J. E. (2017) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · Cited by 102 · see citing works
Evaluation of a PDA-based dietary assessment and intervention program: a randomized controlled trial
Beasley, J. M., Riley, W. T., Davis, A., & Singh, J. (2008) · Journal of the American College of Nutrition · doi:10.1080/07315724.2008.10719701 · Cited by 99 · see citing works
An End-to-End Image-Based Automatic Food Energy Estimation Technique Based on Learned Energy Distribution Images: Protocol and Methodology
Fang, S., Shao, Z., Kerr, D. A., Boushey, C. J., & Zhu, F. M. (2019) · Nutrients · doi:10.3390/nu11040877 · Cited by 94 · see citing works
Evaluation 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
Using Hand-Held Computer Technologies to Improve Dietary Intake
Atienza, A. A., King, A. C., Oliveira, B. M., Ahn, D. K., & Gardner, C. D. (2008) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2008.01.034 · Cited by 82 · see citing works
Energy intake estimation from counts of chews and swallows
Fontana, J. M., HIGGINS, J. A., Schuckers, S., Bellisle, F., Pan, Z., Melanson, E. L., Neuman, M. R., & Sazonov, E. E. (2014) · Appetite · doi:10.1016/j.appet.2014.11.003 · Cited by 77 · see citing works
Examining the Utility of a Bite-Count–Based Measure of Eating Activity in Free-Living Human Beings
Scisco, J. L., Muth, E. R., & Hoover, A. W. (2013) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2013.09.017 · Cited by 77 · see citing works
Automatic food intake detection based on swallowing sounds
Makeyev, O., Lopez‐Meyer, P., Schuckers, S., Besio, W. G., & Sazonov, E. E. (2012) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2012.03.005 · Cited by 73 · see citing works
A pilot study to determine whether using a lightweight, wearable micro-camera improves dietary assessment accuracy and offers information on macronutrients and eating rate
Pettitt, C., Liu, J., Kwasnicki, R. M., Yang, G., Preston, T., & Frost, G. S. (2015) · British Journal Of Nutrition · doi:10.1017/s0007114515004262 · Cited by 66 · see citing works
Traditional methods <i>v.</i> new technologies – dilemmas for dietary assessment in large-scale nutrition surveys and studies: a report following an international panel discussion at the 9th International Conference on Diet and Activity Methods (ICDAM9), Brisbane, 3 September 2015
Amoutzopoulos, B., Steer, T., Roberts, C., Cade, J. E., Boushey, C. J., Collins, C. E., Trolle, E., Boer, E. J. D., Ziauddeen, N., van Rossum, C., et al. (2018) · Journal of Nutritional Science · doi:10.1017/jns.2018.4 · Cited by 62 · see citing works
Comparison between Human and Bite-Based Methods of Estimating Caloric Intake
Salley, J. N., Hoover, A. W., Wilson, M. L., & Muth, E. R. (2016) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2016.03.007 · Cited by 57 · see citing works
Narrative Review of New Methods for Assessing Food and Energy Intake
Herrera, M. C. A., & Chan, C. B. (2018) · Nutrients · doi:10.3390/nu10081064 · Cited by 56 · see citing works
Assessing the Usability of the Automated Self-Administered Dietary Assessment Tool (ASA24) among Low-Income Adults
Kupis, J., Johnson, S. R., Hallihan, G. M., & Olstad, D. L. (2019) · Nutrients · doi:10.3390/nu11010132 · Cited by 54 · see citing works
Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment
Farooq, M., Doulah, A., Parton, J., McCrory, M. A., HIGGINS, J. A., & Sazonov, E. E. (2019) · Nutrients · doi:10.3390/nu11030609 · Cited by 45 · see citing works
Development of a Hand-Held Personal Digital Assistant–Based Food Diary with Food Photographs for Japanese Subjects
Fukuo, W., Yoshiuchi, K., Ohashi, K., Togashi, H., Sekine, R., Kikuchi, H., Sakamoto, N., Inada, S., Sato, F., Kadowaki, T., et al. (2009) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2009.04.013 · Cited by 44 · see citing works
Promising approaches of computer-supported dietary assessment and management—Current research status and available applications
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
A Systematic Review of Technology-Driven Methodologies for Estimation of Energy Intake
Doulah, A., McCrory, M. A., HIGGINS, J. A., & Sazonov, E. E. (2019) · IEEE Access · doi:10.1109/access.2019.2910308 · Cited by 33 · see citing works
Agreement between Two Methods of Dietary Data Collection in Male Adolescent Academy-Level Soccer Players
Briggs, M. A., Rumbold, P. L. S., Cockburn, E., Russell, M. A., & Stevenson, E. (2015) · Nutrients · doi:10.3390/nu7075262 · Cited by 26 · see citing works
The Feasibility of Personal Digital Assistants (PDAs) to Collect Dietary Intake Data in Low-income Pregnant Women
Fowles, E. R., & Gentry, B. (2008) · Journal of Nutrition Education and Behavior · doi:10.1016/j.jneb.2007.08.015 · Cited by 24 · see citing works

Flagged first, then by citations

Sorted by citations

Artificial intelligence in nutrition research: perspectives on current and future applications
Côté, M., & Lamarche, B. (2021) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2021-0448 · Cited by 94 · see citing works
AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review
Shonkoff, E., Cara, K. C., Pei, X. (., Chung, M., Kamath, S., Panetta, K., & Hennessy, E. (2023) · Annals of Medicine · doi:10.1080/07853890.2023.2273497 · Cited by 77 · see citing works
Prognostic role of nutritional status in elderly patients hospitalized for COVID-19: a monocentric study
Recinella, G., Marasco, G., Serafini, G., Maestri, L., Bianchi, G. P., Forti, P., & Zoli, M. (2020) · Aging Clinical and Experimental Research · doi:10.1007/s40520-020-01727-5 · Cited by 52 · see citing works
Advancing sustainability in the food and nutrition system: a review of artificial intelligence applications
Namkhah, Z., Fatemi, S. F., Mansoori, A., Nosratabadi, S., Ghayour‐Mobarhan, M., & Sobhani, S. R. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1295241 · Cited by 46 · see citing works
Deep-Learning-Assisted Multi-Dish Food Recognition Application for Dietary Intake Reporting
Liu, Y., Onthoni, D. D., Mohapatra, S., Irianti, D., & Sahoo, P. K. (2022) · Electronics · doi:10.3390/electronics11101626 · Cited by 45 · see citing works
Artificial intelligence assisted food science and nutrition perspective for smart nutrition research and healthcare
Joshi, S., Bisht, B., Kumar, V., Singh, N., Begum, J. P. S., Singh, N., & Kumar, S. (2023) · Systems Microbiology and Biomanufacturing · doi:10.1007/s43393-023-00200-4 · Cited by 44 · see citing works
School-based healthy eating interventions for adolescents aged 10–19 years: an umbrella review
Samad, N., Bearne, L., Noor, F. M., Akter, F., & Parmar, D. (2024) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-024-01668-6 · Cited by 41 · see citing works
Novel dietary interventions for adolescents with obesity: A narrative review
Hoare, J. K., Jebeile, H., Garnett, S. P., & Lister, N. B. (2021) · Pediatric Obesity · doi:10.1111/ijpo.12798 · Cited by 26 · see citing works
Marine natural products and human immunity: novel biomedical resources for anti-infection of SARS-CoV-2 and related cardiovascular disease
Hu, C. (2024) · Natural Products and Bioprospecting · doi:10.1007/s13659-024-00432-4 · Cited by 24 · see citing works
Overview of objective measurement technologies for nutrition research, food-related consumer and marketing research
Rantala, E., Balatsas-Lekkas, A., Sözer, N., & Pennanen, K. (2022) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2022.05.006 · Cited by 16 · see citing works
A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation
Jia, W., Ren, Y., Li, B., Beatrice, B., Que, J., Cao, S., Wu, Z., Mao, Z., Lo, B. P. L., Anderson, A. K., et al. (2022) · Sensors · doi:10.3390/s22041493 · Cited by 15 · see citing works
Minimum days estimation for reliable dietary intake information: findings from a digital cohort
Singh, R. P., Verest, M. T. E., & Salathé, M. (2025) · European Journal of Clinical Nutrition · doi:10.1038/s41430-025-01644-8 · Cited by 14 · see citing works
Application of the Updated WCRF/AICR Cancer Prevention Score as an Outcome for Cancer Survivors Participating in a Tailored and Intensive Dietary and Physical Activity Intervention
Hill, E. B., Grainger, E. M., Young, G. S., Clinton, S. K., & Spees, C. (2022) · Nutrients · doi:10.3390/nu14224751 · Cited by 14 · see citing works
An Overview of Traditional and Novel Tools to Assess Diet
Esquivel, M. K., & Lozano, C. P. (2024) · American Journal of Lifestyle Medicine · doi:10.1177/15598276241240690 · Cited by 12 · see citing works
Assessing the Initial Validity of the PortionSize App to Estimate Dietary Intake Among Adults: Pilot and Feasibility App Validation Study
Saha, S., Panizza, C. E., Broyles, S. T., Martin, C. K., & Apolzan, J. W. (2022) · JMIR Formative Research · doi:10.2196/38283 · Cited by 11 · see citing works
Major dietary patterns in the United Kingdom Women's Cohort Study showed no evidence of prospective association with pancreatic cancer risk
Shyam, S., Greenwood, D. C., Mai, C., Tan, S. S., Yusof, B. N. M., Moy, F. M., & Cade, J. E. (2023) · Nutrition Research · doi:10.1016/j.nutres.2023.07.007 · Cited by 9 · see citing works
Are commonly used lab‐based measures of food value and choice predictive of self‐reported real‐world snacking? An ecological momentary assessment study
Masterton, S., Hardman, C. A., Boyland, E. J., Robinson, E., Makin, H., & Jones, A. (2022) · British Journal of Health Psychology · doi:10.1111/bjhp.12622 · Cited by 8 · see citing works
Capturing children food exposure using wearable cameras and deep learning
Elbassuoni, S., Ghattas, H., Ati, J. E., Zoughby, Y., Semaan, A. T., Akl, C., Trabelsi, T., Talhouk, R., Gharbia, H. B., Shmayssani, Z., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000211 · Cited by 7 · see citing works
Use of digital food records in clinical settings: lessons in translation from a randomised controlled trial
Barnett, A., Mayr, H. L., Keating, S. E., Conley, M. M., Webb, L., Jegatheesan, D. K., Staudacher, H. M., Macdonald, G. A., Kelly, J. T., Campbell, K. L., et al. (2024) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.13389 · Cited by 7 · see citing works
Selecting foods from FNDDS when calculating food intake: Does the reference matter?
Saha, S., Lozano, C. P., Broyles, S. T., Apolzan, J. W., & Martin, C. K. (2023) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2023.105733 · Cited by 6 · see citing works
Artificial intelligence in nutrition research
Côté, M., & Lamarche, B. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15688-5.00031-0 · Cited by 5 · see citing works
Using Voice Assistant Systems to Improve Dietary Recall among Older Adults: Perspectives of Registered Dietitians
Shah, R. R., Dixon, C. C., Fowler, M. J., Driesse, T. M., Liang, X., Summerour, C. E., Gross, D. C., Spangler, H., Lynch, D. H., & Batsis, J. A. (2024) · Journal of Nutrition in Gerontology and Geriatrics · doi:10.1080/21551197.2024.2302619 · Cited by 5 · see citing works
Evaluating the Validity of the PortionSize Smartphone Application for Estimating Dietary Intake in Free-Living Conditions: A Pilot Study
Diktas, H. E., Lozano, C. P., Saha, S., Broyles, S. T., Martin, C. K., & Apolzan, J. W. (2024) · Journal of Nutrition Education and Behavior · doi:10.1016/j.jneb.2024.05.226 · Cited by 5 · see citing works
Avoiding Reactions Outside the Home: Challenges, Strategies, and Opportunities to Enhance Dining Out Experiences of People with Food Hypersensitivities
Nunes, F., Almeida, J., Chung, C.-F., & Verdezoto, N. X. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445662 · Cited by 5 · see citing works
Dietary Behavior Assessments in Children—A Mixed-Method Research Exploring the Perspective of Pediatric Dieticians on Innovative Technologies
de Gooijer, F. J., Lasschuijt, M. P., Wit, R. F., Feskens, E. J. M., Brouwer-Brolsma, E. M., & Camps, G. (2023) · Current Developments in Nutrition · doi:10.1016/j.cdnut.2023.100091 · Cited by 5 · see citing works
The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings
Kok, E. J., Chauhan, A., Tufano, M., Feskens, E. J. M., & Camps, G. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1520674 · Cited by 4 · see citing works
Using Popular Foods Consumed to Inform Development of Digital Tools for Dietary Assessment and Monitoring
Chen, J., Grech, A. L., & Allman-Farinelli, M. A. (2022) · Nutrients · doi:10.3390/nu14224822 · Cited by 4 · see citing works
Development and User Experience Evaluation of an Experience Sampling-Based Dietary Assessment Method
Verbeke, J., & Matthys, C. (2024) · Current Developments in Nutrition · doi:10.1016/j.cdnut.2024.104479 · Cited by 3 · see citing works
Comparing self-reported energy intake using an online dietary tool with energy expenditure by an activity tracker
Vasileiou, A., Searle, D., Larsen, S. C., Magkos, F., Horgan, G. W., Stubbs, R. J., Santos, I., Palmeira, A. L., & Heitmann, B. L. (2023) · Nutrition · doi:10.1016/j.nut.2023.112258 · Cited by 3 · see citing works
Tracking Nutrient Intake with AI: How AI Can Help Monitor and Optimize Nutrient Consumption
Whig, P., Kasula, B. Y., Yathiraju, N., Jain, A., & Sharma, S. (2025) · Springer eBooks · doi:10.1007/978-981-96-2120-0_6 · Cited by 3 · see citing works
Record or Recall? Exploring Self-Reported Dietary Assessment Methods for Office Workers during the COVID-19 Work-from-Home Period
Pan, S., Brouwer-Brolsma, E. M., Ren, X., Vos, S. B., Brombacher, A., & Lucassen, D. A. (2022) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3546155.3546649 · Cited by 2 · see citing works
Towards Healthy Diets and Sustainable Nutritional Behavior: Identifying Design Opportunities for Technology-Supported Malnutrition Care
Alberts, J. W., Vastenburg, M. H., Nap, H. H., & IJsselsteijn, W. A. (2026) · Journal of Ageing and Longevity · doi:10.3390/jal6030050 · Cited by 1 · see citing works
Reliability of a frequency method for assessing vegetable intake using photos among college students: a smart phone approach
Suthar, H., Thiagarajah, K., Karaye, I. M., Lopez-Ixta, Z. T., & Bhurosy, T. (2023) · Journal of American College Health · doi:10.1080/07448481.2023.2266034 · Cited by 1 · see citing works
A Data-Driven Methodology and Workflow Process Leveraging Research Electronic Data Capture (REDCap) to Coordinate and Accelerate the Implementation of Personalized Microbiome-Based Nutrition Approaches in Clinical Research
Tourab, H., Torrego-Ellacuría, M., Sanz, L. L., Anchuelo, A. C., Gómez‐Garré, D., González, S. S., Luaces, M., Barbancho, B. M., Mayol, J. M., Cabrera-Umpiérrez, M. F., et al. (2024) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-59717-6_10 · Cited by 1 · see citing works
A systematic review & updated meta-analysis on the association between higher consumption of ultra processed foods and risk of diabetes and its complications: Response Letter
de Souza, M. H. G., Moura, F. S., Lima, L. C. V., & Amaral, M. J. M. (2025) · Metabolism · doi:10.1016/j.metabol.2025.156155 · Cited by 1 · see citing works
Artificial intelligence-assisted mobile dietary assessment: time to expand the standard toolkit?
Engle‐Stone, R. (2024) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2024.09.008 · Cited by 1 · see citing works
Ocean & seas: Novel biomedical resources for anti-infection of SARS-CoV-2Preprint
Hu, C. (2024) · doi:10.22541/au.170667171.10307493/v1
Nutrition’s Route to Behaviour and Vice Versa: Longitudinal Links from Early Life to Adolescence
Willemsen, Y. (2024) · Radboud University Press eBooks · doi:10.54195/9789493296374
Relative Validity of the Food Recording Smartphone App Libro in Young People Vulnerable to Eating Disorder: A Preliminary Cross-Over Study
Basso, M., Zhang, L., Savva, G. M., Kadosh, K. C., & Τράκα, Μ. (2025) · Nutrients · doi:10.3390/nu17111823
Integrating Technology and Culture: Smartphone Validation of a Food Frequency Questionnaire for Nutrient Intake Estimates in the Adult Population of Trinidad and Tobago
Foster-Nicholas, L. A., Dyett, P. A., Heskey, C. E., Shavlik, D. J., & Segovia‐Siapco, G. (2025) · Current Developments in Nutrition · doi:10.1016/j.cdnut.2025.107500
Protocol of the validation of the experience sampling-based dietary assessment method (ESDAM) against doubly labeled water, urinary protein, and biomarkers
Verbeke, J., Verbeke, K., der Schueren, B. V., Vangoitsenhoven, R., & Matthys, C. (2025) · Nutrition Journal · doi:10.1186/s12937-025-01232-3
Development and Cognitive Validation of a Culturally Appropriate Diet Diary for Sri Lankan PopulationPreprint
Rajakaruna, V., Wijesinghe, C. J., & Kasturiratne, A. (2026) · Research Square · doi:10.21203/rs.3.rs-8647233/v1
Designing Social Support for Dietary Self-Monitoring: A Mixed-Method Pilot of a Chatbot-Integrated Buddy System
Wintraecken, K., & Lü, Y. (2026) · International Digital Public Health Conference (DPH) · doi:10.1109/dph70309.2026.11650539
Exploration of the Application of Artificial Intelligence-Based Nutrition Intervention Systems
潘, 宇. (2026) · Hans Journal of Food and Nutrition Science · doi:10.12677/hjfns.2026.155062
Turkish Research Dietitians Prioritize Strengthening Traditional Methods Before Digital Transition: A Qualitative Study
Guney-Coskun, M., Weech, M., Fallaize, R., Hwang, F., Lovegrove, J. A., & Kalkan, İ. (2026) · Journal of Nutrition · doi:10.1016/j.tjnut.2026.101897

Authors: Zhao, X., Xu, X., Li, X., He, X., Yang, Y., & Zhu, S.
Affiliations: Children's Hospital of Zhejiang University (CN)
Emory University (US)
Johns Hopkins University (US)
ORCID: Zhao, Xueyin
Xu, Xiaochen
Li, Xiuyan
He, Xi
Yang, Yang
Zhu, Shankuan
Venue: European Journal of Clinical Nutrition
Publisher: Springer Science and Business Media LLC
Published: 2020-10-20
Volume: 75
Issue: 4
Pages: 582-587
ISSN: 0954-3007 (ISSN-L)
1476-5640
DOI: 10.1038/s41430-020-00779-0
OpenAlex: W3093913881
Cited by: 46
References: 46
Citations by year: 2026: 5 · 2025: 7 · 2024: 12 · 2023: 10 · 2022: 8 · 2021: 3 · 2020: 1
FWCI: 0.92
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: European Journal of Clinical Nutrition (publishedVersion)
PubMed (publishedVersion)
Funders: Cyrus Tang Foundation (419600-11102)
Topics: Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
Keywords: dietary assessment, mobile health
MeSH terms: Diet, Humans, Technology, Diet Records

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

Emerging trends of technology-based dietary assessment: a perspective study

Integrity signals

Claims the paper makes

Claims other papers make about this one

Cites

Cited by

Related

Record

About these checks