{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:10:27Z","timestamp":1792789827329,"version":"4.5.0"},"reference-count":37,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swedish Energy Agency","id":[{"id":"https:\/\/ror.org\/0359z7n90","id-type":"ROR","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/0359z7n90","id-type":"ROR","asserted-by":"publisher"}]},{"name":"M\u00e4lardalen University","id":[{"id":"https:\/\/ror.org\/033vfbz75","id-type":"ROR","asserted-by":"crossref"}]},{"id":[{"id":"https:\/\/ror.org\/033vfbz75","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Turkish Health Institutes","award":["33987"],"award-info":[{"award-number":["33987"]}]},{"name":"Kocaeli University Scientific Research Projects Division","award":["FKA-2026-5195"],"award-info":[{"award-number":["FKA-2026-5195"]}],"id":[{"id":"https:\/\/ror.org\/0411seq30","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Kocaeli University Scientific Research Projects Division","award":["FBA-2026-4982"],"award-info":[{"award-number":["FBA-2026-4982"]}],"id":[{"id":"https:\/\/ror.org\/0411seq30","id-type":"ROR","asserted-by":"crossref"}]},{"award":["FKA-2026-5195"],"award-info":[{"award-number":["FKA-2026-5195"]}],"id":[{"id":"https:\/\/ror.org\/0411seq30","id-type":"ROR","asserted-by":"publisher"}]},{"award":["FBA-2026-4982"],"award-info":[{"award-number":["FBA-2026-4982"]}],"id":[{"id":"https:\/\/ror.org\/0411seq30","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Background\/Objectives: Multidimensional lifestyle interventions that combine healthy diet, physical activity, and psychosocial support are central to the effective self-management of type 1 and type 2 diabetes. Although digital health tools can improve lifestyle monitoring, existing AI-based dietary assessment methods often struggle to estimate food quantity accurately because the food size and scale are difficult to determine from images. This can lead to inconsistent estimates of food mass and energy content. This paper aimed to develop and technically evaluate PC2FoodNet, an AI-based framework for food recognition and physics-constrained physical quantity estimation, and to integrate dietary assessment and exercise support within the proposed AI-assisted Diabetes Care (AIDCare) mHealth platform for multidisciplinary lifestyle support. Methods: The AIDCare platform incorporates AI-assisted nutrition and professionally guided exercise modules to support personalized lifestyle management for individuals with diabetes. For dietary assessment, PC2FoodNet uses an EfficientNetV2-S backbone with multi-task regression and food-density priors to jointly model food volume, mass, and energy. A nutrition professional-in-the-loop approach supports the development and review of individualized diet plans and the assessment of patients\u2019 daily key nutrient intake according to individual nutritional requirements. The exercise module includes professionally designed exercises using Unreal Engine\u2019s MetaHuman plugin. The model was evaluated using four-fold stratified cross-validation on a dataset of 22,070 images covering 40 Turkish food categories. For physical quantity modeling, volume, mass, and energy targets were derived from standardized category-level references anchored to a 100 g reference portion rather than from image-specific ground-truth measurements. Results: PC2FoodNet achieved a mean Top-1 classification accuracy of 94.39% \u00b1 0.47% and Top-5 accuracy of 98.90% \u00b1 0.18% across the four folds. The corresponding Macro-F1 and Weighted-F1 scores were 93.14% \u00b1 0.59% and 94.16% \u00b1 0.36%, respectively. For the standardized category-level reference targets, the physics-constrained regression framework achieved mean MAE and RMSE values of 2.14 mL and 3.01 mL for reference volume, 21.65 g and 30.17 g for reference mass, and 41.20 kcal and 57.55 kcal for reference energy across all 22,070 pooled out-of-fold predictions. The confidence-based referral mechanism identified uncertain cases for clinical review, resulting in a clinical referral rate of 23.81%. Conclusions: AIDCare combines physics-constrained AI-based dietary assessment with personalized physical activity and continuous psychosocial support. A nutrition professional remains involved when AI predictions are uncertain, reducing the risks associated with fully automated lifestyle recommendations. The proposed approach provides a practical foundation for safer, more personalized, and evidence-based diabetes lifestyle management.<\/jats:p>","DOI":"10.3390\/nu18193207","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T12:46:29Z","timestamp":1790599589000},"page":"3207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Assisted Nutrition Estimation and Exercise Support for Integrated Lifestyle Management in Patients with Diabetes"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0934-1069","authenticated-orcid":false,"given":"Muhammad","family":"Jamil","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"},{"name":"Wireless Information and Intelligent Systems (WINS) Research Center,  Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5694-8042","authenticated-orcid":false,"given":"Adnan","family":"Kavak","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"},{"name":"Wireless Information and Intelligent Systems (WINS) Research Center,  Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5590-0784","authenticated-orcid":false,"given":"Hossein","family":"Fotouhi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, M\u00e4lardalen University (MDU), 72123 V\u00e4ster\u00e5s, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5503-0450","authenticated-orcid":false,"given":"Md.","family":"Rashed","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5340-6106","authenticated-orcid":false,"given":"Emre","family":"Gezer","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Metabolism, Faculty of Medicine,  Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0933-654X","authenticated-orcid":false,"given":"Alpaslan Burak","family":"\u0130nner","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"},{"name":"Wireless Information and Intelligent Systems (WINS) Research Center,  Kocaeli University, 41001 \u0130zmit, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9999-868X","authenticated-orcid":false,"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sogang University, Seoul 04107, Republic of Korea"},{"name":"Research Center for Interneural Computing, China Medical University, Taichung 40402, Taiwan"},{"name":"Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"project-administration","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"American Diabetes Association Professional Practice Committee (2025). 5. Facilitating Positive Health Behaviors and Well-Being to Improve Health Outcomes: Standards of Care in Diabetes\u20142025. Diabetes Care, 48, S86\u2013S127. Erratum in Diabetes Care\u00a02025, 48, 665. https:\/\/doi.org\/10.2337\/dc25-er04a.","DOI":"10.2337\/dc25-er04a"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2557","DOI":"10.1007\/s40279-024-02057-6","article-title":"The Best Exercise Modality and Dose to Reduce Glycosylated Hemoglobin in Patients with Type 2 Diabetes: A Systematic Review with Pairwise, Network, and Dose\u2013Response Meta-Analyses","volume":"54","author":"Liang","year":"2024","journal-title":"Sport. Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104884","DOI":"10.1016\/j.ijnurstu.2024.104884","article-title":"Interventions and Behaviour Change Techniques for Improving Physical Activity Level in Working-Age People (18\u201360 Years) with Type 2 Diabetes: A Systematic Review and Network Meta-Analysis","volume":"160","author":"Zhao","year":"2024","journal-title":"Int. J. Nurs. Stud."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Xue, H., Zhang, L., Shi, Y., Zhang, H., Zhang, C., Liu, Y., Tan, W., and Liu, Y. (2025). The Effectiveness of Digital Health Intervention on Glycemic Control and Physical Activity in Patients with Type 2 Diabetes: A Systematic Review and Meta-Analysis. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1630588"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112907","DOI":"10.1016\/j.diabres.2025.112907","article-title":"Effects of Continuous Glucose Monitoring on Dietary Behavior and Physical Activity: A Systematic Review and Meta-Analysis","volume":"229","author":"Zhang","year":"2025","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105091","DOI":"10.1016\/j.ebiom.2024.105091","article-title":"Which Behaviour Change Techniques Work Best for Diabetes Self-Management Mobile Apps? Results from a Systematic Review and Meta-Analysis of Randomised Controlled Trials","volume":"103","author":"Tarricone","year":"2024","journal-title":"eBioMedicine"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e54557","DOI":"10.2196\/54557","article-title":"Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review","volume":"26","author":"Zheng","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2273497","DOI":"10.1080\/07853890.2023.2273497","article-title":"AI-Based Digital Image Dietary Assessment Methods Compared to Humans and Ground Truth: A Systematic Review","volume":"55","author":"Shonkoff","year":"2023","journal-title":"Ann. Med."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Phalle, A., and Gokhale, D. (2025). Navigating Next-Gen Nutrition Care Using Artificial Intelligence-Assisted Dietary Assessment Tools\u2014A Scoping Review of Potential Applications. Front. Nutr., 12.","DOI":"10.3389\/fnut.2025.1518466"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"104408","DOI":"10.1016\/j.tifs.2024.104408","article-title":"Computer Vision and Deep Learning-Based Approaches for Detection of Food Nutrients\/Nutrition: New Insights and Advances","volume":"146","author":"Kaushal","year":"2024","journal-title":"Trends Food Sci. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/RBME.2023.3283149","article-title":"A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems","volume":"17","author":"Konstantakopoulos","year":"2024","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, D., Zuo, E., Wang, D., He, L., Dong, L., and Lu, X. (2025). Deep Learning in Food Image Recognition: A Comprehensive Review. Appl. Sci., 15.","DOI":"10.3390\/app15147626"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"American Diabetes Association Professional Practice Committee (2025). 7. Diabetes Technology: Standards of Care in Diabetes\u20142025. Diabetes Care, 48, S146\u2013S166. Erratum in Diabetes Care\u00a02025, 48, 666. https:\/\/doi.org\/10.2337\/dc25-er04b.","DOI":"10.2337\/dc25-er04b"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e125","DOI":"10.1016\/S2589-7500(22)00233-3","article-title":"Effectiveness, Reach, Uptake, and Feasibility of Digital Health Interventions for Adults with Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomised Controlled Trials","volume":"5","author":"Moschonis","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11795514241246419","DOI":"10.1177\/11795514241246419","article-title":"The Role of Digital Health Technology Interventions in the Prevention of Type 2 Diabetes Mellitus: A Systematic Review","volume":"17","author":"Nguyen","year":"2024","journal-title":"Clin. Med. Insights Endocrinol. Diabetes"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1007\/s10916-024-02098-4","article-title":"Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery","volume":"48","author":"Ouanes","year":"2024","journal-title":"J. Med. Syst."},{"key":"ref_17","first-page":"101145","article-title":"Benefits and Harms Associated with the Use of AI-Related Algorithmic Decision-Making Systems by Healthcare Professionals: A Systematic Review","volume":"48","author":"Wilhelm","year":"2025","journal-title":"Lancet Reg. Health"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e54826","DOI":"10.2196\/54826","article-title":"The Role of Smartwatch Technology in the Provision of Care for Type 1 or 2 Diabetes Mellitus or Gestational Diabetes: Systematic Review","volume":"12","author":"Fellas","year":"2024","journal-title":"JMIR MHealth UHealth"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Albalawi, H.F.A. (2024). The Role of Tele-Exercise for People with Type 2 Diabetes: A Scoping Review. Healthcare, 12.","DOI":"10.3390\/healthcare12090917"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Dor\u00e9, B., Gaudreault, A., Everard, G., Ayena, J.C., Abboud, A., Robitaille, N., and Batcho, C.S. (2023). Acceptability, Feasibility, and Effectiveness of Immersive Virtual Technologies to Promote Exercise in Older Adults: A Systematic Review and Meta-Analysis. Sensors, 23.","DOI":"10.3390\/s23052506"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bilika, P., Karampatsou, N., Stavrakakis, G., Paliouras, A., Theodorakis, Y., Strimpakos, N., and Kapreli, E. (2023). Virtual Reality-Based Exercise Therapy for Patients with Chronic Musculoskeletal Pain: A Scoping Review. Healthcare, 11.","DOI":"10.3390\/healthcare11172412"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Vecchio, M., Chiaramonte, R., Buccheri, E., Tomasello, S., Leonforte, P., Rescifina, A., Ammendolia, A., Longo, U.G., and de Sire, A. (2025). Metaverse-Aided Rehabilitation: A Perspective Review of Successes and Pitfalls. J. Clin. Med., 14.","DOI":"10.3390\/jcm14020491"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9932","DOI":"10.1109\/TPAMI.2023.3237871","article-title":"Large Scale Visual Food Recognition","volume":"45","author":"Min","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e51432","DOI":"10.2196\/51432","article-title":"Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review","volume":"26","author":"Chotwanvirat","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7577","DOI":"10.1109\/JBHI.2024.3417280","article-title":"Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis","volume":"28","author":"Lo","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"293","DOI":"10.3233\/MNM-230039","article-title":"Development and Evaluation of Food Photograph Series Software for Portion Size Estimation among Urban North Indian Adults","volume":"16","author":"Sharma","year":"2023","journal-title":"Mediterr. J. Nutr. Metab."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kim, J.H., Lee, D.S., and Kwon, S.K. (2023). Food Classification and Meal Intake Amount Estimation through Deep Learning. Appl. Sci., 13.","DOI":"10.3390\/app13095742"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111943","DOI":"10.1016\/j.jfoodeng.2024.111943","article-title":"Image-Based Volume Estimation for Food in a Bowl","volume":"372","author":"Jia","year":"2024","journal-title":"J. Food Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shao, Z., Vinod, G., He, J., and Zhu, F. (2023, January 10\u201314). An End-to-End Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image. Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME), Brisbane, Australia.","DOI":"10.1109\/ICME55011.2023.00166"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Magid, B., Ibrahim, M., Kawashti, Y.A., Mohamed, M., Sabry, M., Hindy, H., Khaled, M., and Mohamed, W. (2023, January 21\u201323). CalorieMe: An Image-Based Calorie Estimator System. Proceedings of the 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS), Cairo, Egypt.","DOI":"10.1109\/ICICIS58388.2023.10391113"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1016\/j.procs.2025.03.248","article-title":"Enhancing Food Analysis with Attention-Based Deep Learning: Ingredients, Recipes, and Calorie Estimation","volume":"260","author":"Gautam","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e70116","DOI":"10.1111\/1750-3841.70116","article-title":"Image-Based Food Groups and Portion Prediction by Using Deep Learning","volume":"90","author":"Nogay","year":"2025","journal-title":"J. Food Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"100978","DOI":"10.1016\/j.focha.2025.100978","article-title":"Diet Engine: A Real-Time Food Nutrition Assistant System for Personalized Dietary Guidance","volume":"7","author":"Saad","year":"2025","journal-title":"Food Chem. Adv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Velombe, J.C., Bayraktar, S., Kavak, A., Jamil, M., \u0130nner, A.B., Srivastava, G., and Fotouhi, H. (2026). A Hybrid CNN\u2013MLLM Architecture for Image-Based Nutrition Estimation and Advisory Insulin Decision Support in Type 1 Diabetes. Nutrients, 18.","DOI":"10.20944\/preprints202605.0623.v1"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"G\u00f6z, F., Jamil, M., Kavak, A., Bayraktar, S., Do\u011fru, A.C., Srivastava, G., and Fotouhi, H. (2026). A Confidence-Aware Hybrid Vision\u2013Language Framework for Food Recognition and Nutritional Monitoring. Nutrients, 18.","DOI":"10.3390\/nu18152449"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Cheng, S.T., Lyu, Y.J., and Teng, C. (2025). Image-Based Nutritional Advisory System: Employing Multimodal Deep Learning for Food Classification and Nutritional Analysis. Appl. Sci., 15.","DOI":"10.3390\/app15094911"},{"key":"ref_37","first-page":"11","article-title":"Development of Software for Predicting the Thermophysical Properties of Foods Based on Their Composition Using the Choi\u2013Okos Model","volume":"41","author":"Puruncaja","year":"2026","journal-title":"Int. J. Spec. Educ."}],"container-title":["Nutrients"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6643\/18\/19\/3207\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:27:22Z","timestamp":1790742442000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6643\/18\/19\/3207"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,28]]},"references-count":37,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["nu18193207"],"URL":"https:\/\/doi.org\/10.3390\/nu18193207","relation":{},"ISSN":["2072-6643"],"issn-type":[{"value":"2072-6643","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,28]]}}}