Application of Digital Nutrition Technologies in Adult Weight Management
Abstract
Obesity and overweight remain major global public health challenges. In this paper, we examine current applications of digital nutrition technologies in weight management using the scoping review framework of Arksey and O'Malley. Visual recognition technologies have evolved from early-stage food image recognition into more advanced systems, although the vast diversity of food types continues to pose challenges for accuracy and generalizability. The combination of wearable devices and non-wearable sensors has increased the diversity of collected data and improved user comfort. Data integration and analysis tools, such as digital virtual twins, support personalized interventions by integrating multidimensional data. In addition, emerging applications have demonstrated encouraging clinical outcomes. However, existing studies have limitations, such as small sample sizes, high levels of information bias, and low evidence quality. Barriers, such as limited accessibility and high initial costs, further constrain the scalability of digital nutrition tools. Future studies should focus on expanding sample sizes, improving population diversity, and developing effective assessment methods and adjustments for specific target populations. Privacy and data security concerns must also be addressed to ensure safe implementation. Overall, digital nutrition technologies offer a promising approach to weight management, but continuous efforts in research and cost reduction are needed to fully realize their potential in weight management and to promote health for all.
Keywords: Nutrition assessment, Weight management, Wearable electronic devices, Mobile health, Overweight, Review
Full Text:
PDFReferences
National Health Commission of the PRC. Report on the nutrition and chronic diseases status of Chinese residents 2020. Acta Nutrimenta Sinica, 2020, 42(6): 521.
World Obesity Federation. World Obesity Atlas 2025. London: World Obesity Federation, 2025.(2025-03-04) [2025-05-10]. https://data. worldobesity.org/publications/world-obesity-atlas-2025-v6.pdf. COLLINS F S, VARMUS H. A new initiative on precision medicine. N Engl J Med, 2015, 372(9): 793-795. doi: 10.1056/NEJMp1500523.
World Health Organisation, Food and Agriculture Organization of the United Nations. Preparation and use of food-based dietary guidelines. WHO technical report series. Geneva: World Health Organization, 1998: 880. (1998-03-19) [2025-05-10]. https://www.who.int/publications/i/item/ 9241208805.
RIESENBERG D, PEETERS A, BACKHOLER K, et al. Exploring the effects of added sugar labels on food purchasing behaviour in Australian parents: an online randomised controlled trial. PLoS One, 2022, 17(8): e0271435. doi: 10.1371/journal.pone.0271435.
TOMPKINS J, PIACENZA F, HARRINGTON P, et al. Impact of nutritional interventions on quality of life in schizophrenia spectrum disorders: a scoping review. Ir J Psychol Med, 2025: 1-6. doi: 10.1017/ipm.2025.3.
ANGHEL R, BÎLTEANU L, FOLEA A R, et al. Assessing the impact of nutritional status on the quality of life in head and neck cancer patients-the need for comprehensive digital tools. Cancers (Basel), 2025, 17(7): 1128. doi: 10.3390/cancers17071128.
ZHANG L, WANG F, TASHIRO S, et al. Effects of dietary approaches and exercise interventions on gestational diabetes mellitus: a systematic review and Bayesian network meta-analysis. Adv Nutr, 2024, 15(12): 100330. doi: 10.1016/j.advnut.2024.100330.
ZHENG J L, JIANG S, Li X G, et al. Application and prospect of digital technology on personalized precision nutrition. Chin J Prev Med, 2022, 56(12): 1872-1879. doi: 10.3760/cma.j.cn112150-20220628-00669. ARKSEY H, O'MALLEY L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol, 2005, 8(1): 19-32. doi: 10.1080/ 1364557032000119616.
POLLOCK D, ALEXANDER L, MUNN Z, et al. Moving from consultation to co-creation with knowledge users in scoping reviews: guidance from the JBI Scoping Review Methodology Group. JBI Evid Synth, 2022, 20(4): 969-979. doi: 10.11124/jbies-21-00416.
HIGGINS J P T, THOMAS J, CHANDLER J, et al. Cochrane handbook for systematic reviews of interventions. 2nd Edition. Chichester (UK): John Wiley and Sons, 2019.
PHALLE A, GOKHALE D. Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications. Front Nutr, 2025, 12: 1518466. doi: 10.3389/fnut. 2025.1518466.
DAO M C, SUBAR A F, WARTHON-MEDINA M, et al. Dietary assessment toolkits: an overview. Public Health Nutr, 2019, 22(3): 404-418. doi: 10.1017/S1368980018002951.
PORCIELLO G, DI LAURO T, LUONGO A, et al. Optimizing nutritional care with machine learning: identifying sarcopenia risk through body composition parameters in cancer patients--insights from the NUTritional and Sarcopenia RIsk SCREENing Project (NUTRISCREEN). Nutrients, 2025, 17(8): 1376. doi: 10.3390/nu17081376.
KIRK D, KOK E, TUFANO M, et al. Machine learning in nutrition research. Adv Nutr, 2022, 13(6): 2573-2589. doi: 10.1016/j.advnut.2023.03.001.
ZEEVI D, KOREM T, ZMORA N, et al. Personalized nutrition by prediction of glycemic responses. Cell, 2015, 163(5): 1079-1094. doi: 10. 1016/j.cell.2015.11.001.
RUF A, KOCH E D, EBNER-PRIEMER U, et al. Studying microtemporal, within-person processes of diet, physical activity, and related factors using the APPetite-Mobile-App: feasibility, usability, and validation study. J Med Internet Res, 2021, 23(7): e25850. doi: 10.2196/25850.
HILL J O, WYATT H R, PETERS J C. Energy balance and obesity. Circulation, 2012, 126(1): 126-132. doi: 10.1161/CIRCULATIONAHA. 111.087213.
CAI L L. Application of continuous glucose monitoringand actigraph to compare the effect oflow-carbohydrate diet and exercise on bodyweight and metabolism in overweight andobese adults. Shanghai: Shanghai Jiao Tong University, 2020. doi: 10.27307/d.cnki.gsjtu.2020.000104.
PETTITT C, LIU J, KWASNICKI R M, et al. 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, 2016, 115(1): 160-167. doi: 10.1017/S0007114515004262. SenseCam. Microsoft Research. (2023-11-28) [2025-05-18]. http://research.microsoft.com/en-us/um/cambridge/projects/sensecam/.
MARCANO-OLIVIER M, ERJAVEC M, HORNE P J, et al. Measuring lunchtime consumption in school cafeterias: a validation study of the use of digital photography. Public Health Nutr, 2019, 22(10): 1745-1754. doi: 10.1017/S136898001900048X.
BUDININGSARI D, SYAHRIAN F. Validity of a digital photo-based dietary assessment tool: development and initial evaluation. Nutr Health, 2024: 2601060241239095. doi: 10.1177/02601060241239095.
DUHÉ A F, GILMORE L A, BURTON J H, et al. The remote food photography method accurately estimates dry powdered foods-the source of calories for many infants. J Acad Nutr Diet, 2016, 116(7): 1172-1177. doi: 10.1016/j.jand.2016.01.011.
ZHANG Q, HE C, QIN W, et al. Eliminate the hardware: mobile terminals-oriented food recognition and weight estimation system. Front Nutr, 2022, 9: 965801. doi: 10.3389/fnut.2022.965801.
MA P, LAU C P, YU N, et al. Image-based nutrient estimation for Chinese dishes using deep learning. Food Res Int, 2021, 147: 110437. doi: 10.1016/j.foodres.2021.110437.
SAZONOV E S, MAKEYEV O, SCHUCKERS S, et al. Automatic detection of swallowing events by acoustical means for applications of monitoring of ingestive behavior. IEEE Trans Biomedical Eng, 2010, 57(3): 626-633. doi: 10.1109/TBME.2009.2033037.
SEN S, SUBBARAJU V, MISRA A, et al. The case for smartwatch-based diet monitoring//2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). St. Louis, USA: IEEE, 2015: 585-590. doi: 10.1109/PERCOMW.2015. 7134103.
ZHOU B, CHENG J Y, SUNDHOLM M, et al. Smart table surface: a novel approach to pervasive dining monitoring//2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). St. Louis, USA: IEEE, 2015: 155-162. doi: 10.1109/PERCOM.2015.7146522.
FAROOQ M, SAZONOV E. A novel wearable device for food intake and physical activity recognition. Sensors (Basel), 2016, 16(7): 1067. doi: 10. 3390/s16071067.
CHEN J, BERKMAN W, BARDOUH M, et al. The use of a food logging app in the naturalistic setting fails to provide accurate measurements of nutrients and poses usability challenges. Nutrition, 2019, 57: 208-216. doi: 10.1016/j.nut.2018.05.003.
SEMPIONATTO J R, KHORSHED A A, AHMED A, et al. Epidermal enzymatic biosensors for sweat vitamin C: toward personalized nutrition. ACS Sens, 2020, 5(6): 1804-1813. doi: 10.1021/acssensors.0c00604.
YANG Y, SONG Y, BO X, et al. A laser-engraved wearable sensor for sensitive detection of uric acid and tyrosine in sweat. Nat Biotechnol, 2020, 38(2): 217-224. doi: 10.1038/s41587-019-0321-x.
MUMMAH S, ROBINSON T N, MATHUR M, et al. Effect of a mobile app intervention on vegetable consumption in overweight adults: a randomized controlled trial. Int J Behav Nutr Phys Act, 2017, 14(1): 125. doi: 10.1186/s12966-017-0563-2.
Refbacks
- There are currently no refbacks.
