{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T05:43:34Z","timestamp":1790401414455,"version":"4.1.0"},"reference-count":55,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T00:00:00Z","timestamp":1761696000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82173503"],"award-info":[{"award-number":["82173503"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Program on Basic Research Project of China","award":["2019FY101001"],"award-info":[{"award-number":["2019FY101001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Background: Over the past several decades, the prevalence of gestational diabetes mellitus (GDM) has risen markedly worldwide, posing serious threats to both maternal and child health by increasing adverse pregnancy outcomes and long-term metabolic risks. Developing effective risk prediction tools for early detection and intervention has become the most important clinical priority in this field. The current GDM prediction models primarily rely on non-modifiable factors, for example age and body mass index, while modifiable factors such as lifestyle and health literacy, although strongly associated with GDM, have not been fully utilized in risk assessment. This study sought to establish and validate a nomogram prediction model combining modifiable and non-modifiable risk factors, with the goal of identifying high-risk Chinese pregnant women with GDM at an early stage and promoting targeted prevention and personalized prenatal management. Methods: A multicenter study was conducted across 7 maternal health institutions in Southern China (2021\u20132023), enrolling 806 singleton pregnant women (14\u201323+6 weeks). The collected data included sociodemographic, clinical history, and modifiable factors collected through validated questionnaires: dietary quality, physical activity level, sleep quality, and nutrition and health literacy. GDM was diagnosed via 75 g oral glucose tolerance test at 24\u201328 weeks. Predictive factors were identified through multi-variable logistic regression. A nomogram model was developed (70% modeling group) and validated (30% validation group). Receiver operator characteristic curves, calibration curves, and decision curve analysis were used to evaluate the prediction ability, the degree of calibration, and the clinical benefit of the model, respectively. Results: The finalized risk prediction model included non-modifiable factors such as maternal age, pre-pregnancy weight, and maternal polycystic ovary syndrome, as well as modifiable factors including dietary quality, physical activity level, sleep quality, nutrition and health literacy. The application of the nomogram in the modeling group and the validation groups showed that the model had high stability, favorable predictive ability, good calibration effect and clinical practicality. Conclusions: Overall, the integrated model demonstrates significant clinical utility as it facilitates the prompt identification of individuals at heightened risk and offers actionable targets for personalized interventions. In terms of future implementation, this model can be integrated into prenatal care as a rapid scoring table during early pregnancy consultations or incorporated into mobile health applications. This approach fosters precise prevention strategies for GDM in maternal health by emphasizing nutrition and health literacy, supplemented by coordinated adjustments in diet, physical activity, and sleep.<\/jats:p>","DOI":"10.3390\/nu17213400","type":"journal-article","created":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:50:19Z","timestamp":1761785419000},"page":"3400","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Prediction of Gestational Diabetes Mellitus: A Nomogram Model Incorporating Lifestyle, Nutrition and Health Literacy Factors"],"prefix":"10.3390","volume":"17","author":[{"given":"Minghan","family":"Fu","sequence":"first","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Menglu","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhencheng","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laidi","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lishan","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7732-3073","authenticated-orcid":false,"given":"Ye","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1633-6313","authenticated-orcid":false,"given":"Zhixu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Maternal, Child and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China"},{"name":"The Institute of Nutrition and Food Science, Nanjing Medical University, Nanjing 211166, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100330","DOI":"10.1016\/j.advnut.2024.100330","article-title":"Effects of Dietary Approaches and Exercise Interventions on Gestational Diabetes Mellitus: A Systematic Review and Bayesian Network Meta-analysis","volume":"15","author":"Zhang","year":"2024","journal-title":"Adv. Nutr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109050","DOI":"10.1016\/j.diabres.2021.109050","article-title":"IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group\u2019s Criteria","volume":"183","author":"Wang","year":"2022","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/jdi.12854","article-title":"Prevalence of gestational diabetes mellitus in mainland China: A systematic review and meta-analysis","volume":"10","author":"Gao","year":"2019","journal-title":"J. Diabetes Investig."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e067946","DOI":"10.1136\/bmj-2021-067946","article-title":"Gestational diabetes mellitus and adverse pregnancy outcomes: Systematic review and meta-analysis","volume":"377","author":"Ye","year":"2022","journal-title":"BMJ"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1097\/GCO.0000000000000901","article-title":"Update on gestational diabetes and adverse pregnancy outcomes","volume":"35","author":"Ugwudike","year":"2023","journal-title":"Curr. Opin. Obstet. Gynecol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tseng, S.T., Lee, M.C., Tsai, Y.T., Lu, M.C., Yu, S.C., Tsai, I.J., Lee, I.T., and Yan, Y.H. (2023). Risks after Gestational Diabetes Mellitus in Taiwanese Women: A Nationwide Retrospective Cohort Study. Biomedicines, 11.","DOI":"10.3390\/biomedicines11082120"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nijs, H., and Benhalima, K. (2020). Gestational Diabetes Mellitus and the Long-Term Risk for Glucose Intolerance and Overweight in the Offspring: A Narrative Review. J. Clin. Med., 9.","DOI":"10.3390\/jcm9020599"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Thapa, S., Ahmed, K.Y., Bizuayehu, H.M., Huda, M.M., Chalise, B., Bore, M.G., Belachew, S.A., Hassen, T.A., Amsalu, E., and Shifti, D.M. (2024). Trends and social determinants of the obesity epidemic among reproductive-age women in ten Asian countries. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-73522-5"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.4239\/wjd.v14.i10.1541","article-title":"Establishment and evaluation of a risk prediction model for gestational diabetes mellitus","volume":"14","author":"Lin","year":"2023","journal-title":"World J. Diabetes"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e70334","DOI":"10.1096\/fj.202402129R","article-title":"Development and validation of a risk prediction model for gestational diabetes mellitus in women of advanced maternal age during the first trimester","volume":"39","author":"Tang","year":"2025","journal-title":"FASEB J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ding, Y., Xu, F., Zhong, C., Tong, L., Li, F., Li, Q., Chen, R., Zhou, X., Li, X., and Cui, W. (2021). Association between Chinese Dietary Guidelines Compliance Index for Pregnant Women and Risks of Pregnancy Complications in the Tongji Maternal and Child Health Cohort. Nutrients, 13.","DOI":"10.3390\/nu13030829"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.4093\/dmj.2024.0280","article-title":"The Preventive Effect of Physical Activity on Gestational Diabetes Mellitus: A Korean Longitudinal Prospective Cohort Study","volume":"49","author":"Lim","year":"2025","journal-title":"Diabetes Metab. J."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.sleep.2024.04.003","article-title":"Association between nighttime sleep duration, midday napping, and sleep quality during early pregnancy and risk of gestational diabetes mellitus: A prospective cohort study in China","volume":"119","author":"Lai","year":"2024","journal-title":"Sleep Med."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sakai, H., Kawata, R., Adhikari, R., Thapa, Y.O., and Bhandari, T.R. (2024). Effectiveness of art-based health education on anemia and health literacy among pregnant women in Western Nepal: A randomized controlled trial. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0281789"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tang, F., Zhong, X., Liu, S., Guo, X., and Li, D. (2023). Pathway analysis of the impact of health literacy, social support and self-efficacy on self-management behaviors in pregnant women with gestational diabetes mellitus. Front. Public Health, 11.","DOI":"10.3389\/fpubh.2023.1188072"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1186\/s12944-023-01904-1","article-title":"Development and validation of a nomogram prediction model for hypertension-diabetes comorbidity based on chronic disease management in the community","volume":"22","author":"Wu","year":"2023","journal-title":"Lipids Health Dis."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, Z., Kang, S., and Kang, H. (2024). Development and validation of nomograms for predicting cardiovascular disease risk in patients with prediabetes and diabetes. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-71904-3"},{"key":"ref_18","first-page":"5602","article-title":"A nomogram for predicting breast cancer based on hematologic and ultrasound parameters","volume":"15","author":"Liu","year":"2023","journal-title":"Am. J. Transl. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e173","DOI":"10.1016\/S1470-2045(14)71116-7","article-title":"Nomograms in oncology: More than meets the eye","volume":"16","author":"Balachandran","year":"2015","journal-title":"Lancet Oncol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, L., Zhu, Q., Wang, Z., Tao, Y., Liu, H., Tang, F., Liu, S.M., and Zhang, Y. (2023). Establishment and validation of a predictive nomogram for gestational diabetes mellitus during early pregnancy term: A retrospective study. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1087994"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, F., Yang, S., Zhang, Y., Yang, X., Zhang, C., and Fan, J. (2020). Nomogram for prediction of gestational diabetes mellitus in urban, Chinese, pregnant women. BMC Pregnancy Childbirth, 20.","DOI":"10.1186\/s12884-019-2703-y"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Qiu, M., Guo, L., Li, Y., Xie, Z., Yang, W., Ouyang, L., Yin, J., Zhou, Y., and Fu, M. (2024). Association between maternal weight perception before and during pregnancy and postpartum depression status in Southern China. Nutrients, 16.","DOI":"10.3390\/nu16213696"},{"key":"ref_23","unstructured":"(2013). Criteria of Weight for Adults (Standard No. WS\/T 428-2013)."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s12937-015-0044-x","article-title":"Reproducibility and relative validity of a semi-quantitative food frequency questionnaire for Chinese pregnant women","volume":"14","author":"Zhang","year":"2015","journal-title":"Nutr. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1111\/jhn.12844","article-title":"Development and validation of a photographic atlas of food portions for accurate quantification of dietary intakes in China","volume":"34","author":"Ding","year":"2021","journal-title":"J. Hum. Nutr. Diet."},{"key":"ref_26","unstructured":"Yang, Y. (2018). China Food Composition Tables, Peking University Medical Press. [6th ed.]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1016\/j.puhe.2015.03.024","article-title":"Reliability and relative validity of three physical activity questionnaires in Taizhou population of China: The Taizhou Longitudinal Study","volume":"129","author":"Hu","year":"2015","journal-title":"Public Health"},{"key":"ref_28","first-page":"260","article-title":"Analysis on reliability and validity of the Pittsburgh sleep quality index","volume":"43","author":"Lu","year":"2014","journal-title":"Chongqing Med."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ding, Y., Li, G., Shi, X., Wang, M., Peng, Y., Deng, H., Yang, Z., Liang, Q., and Wang, Z. (2023). Correlation of lifestyle behaviors during pregnancy with postpartum depression status of puerpera in the rural areas of South China. Front. Public Health, 11.","DOI":"10.3389\/fpubh.2023.1304226"},{"key":"ref_30","unstructured":"Obstetrics Subgroup, Chinese Society of Obstetrics and Gynecology, Chinese Medicine Association, Chinese Society of Perinatal Medicine, Chinese Medicine Association, and Commitee of Pregnancy with Diabetes Mellitus, China Maternal and Child Health Association (2022). Guideline of diagnosis and treatment of hyperglycemia in pregnancy (2022). Zhonghua Fu Chan Ke Za Zhi, 57, 3\u201312."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Iwasaki, K., Lalani, B., Kahng, J., Carapeto, P., Sanjines, S., Hela, F., Abarca, C., Tsuji, T., Darcy, J., and Bartke, A. (2023). Decreased IGF1R attenuates senescence and improves function in pancreatic \u03b2-cells. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1203534"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, J., Cao, Q., Mao, C., Xu, J., Li, Y., Mu, Y., Huang, G., Chen, D., Deng, X., and Xu, T. (2025). Development and validation of a prediction model for gestational diabetes mellitus risk among women from 8 to 14 weeks of gestation in Western China. BMC Pregnancy Childbirth, 25.","DOI":"10.1186\/s12884-025-07442-1"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1111\/jog.15380","article-title":"A clinical model and nomogram for early prediction of gestational diabetes based on common maternal demographics and routine clinical parameters","volume":"48","author":"Zhang","year":"2022","journal-title":"J. Obstet. Gynaecol. Res."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1042\/CS20190266","article-title":"Placental function in maternal obesity","volume":"134","author":"Kelly","year":"2020","journal-title":"Clin. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bernhardt, G.V., Shivappa, P., Bernhardt, K., Bhat, S., Pinto, J.R.T., Jhancy, M., and Kumar, S. (2022). Markers of inflammation in obese pregnant women: Adenosine deaminase and high sensitive C-reactive protein. Eur. J. Obstet. Gynecol. Reprod. Biol. X, 16.","DOI":"10.1016\/j.eurox.2022.100167"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"121069","DOI":"10.1016\/j.lfs.2022.121069","article-title":"Polycystic ovary syndrome (PCOS) increases the risk of subsequent gestational diabetes mellitus (GDM): A novel therapeutic perspective","volume":"310","author":"Choudhury","year":"2022","journal-title":"Life Sci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1097\/MED.0000000000000769","article-title":"Gestational diabetes and other adverse pregnancy outcomes in polycystic ovary syndrome","volume":"29","author":"Selen","year":"2022","journal-title":"Curr. Opin. Endocrinol. Diabetes Obes."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1530\/EC-18-0076","article-title":"Polycystic ovary syndrome and risk factors for gestational diabetes","volume":"7","author":"Mustaniemi","year":"2018","journal-title":"Endocr. Connect."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zakaria, H., Abusanana, S., Mussa, B.M., Al Dhaheri, A.S., Stojanovska, L., Mohamad, M.N., Saleh, S.T., Ali, H.I., and Cheikh Ismail, L. (2023). The Role of Lifestyle Interventions in the Prevention and Treatment of Gestational Diabetes Mellitus. Medicina, 59.","DOI":"10.3390\/medicina59020287"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Li, Y., Zheng, H., Jiang, W., Chi, Y., Meng, Y., Mo, Y., Chen, C., Qu, B., He, J., and Chen, F. (2025). Which dietary pattern should be adopt by age-appropriate pregnant women to avoid gestational weight gain and gestational diabetes mellitus. BMC Pregnancy Childbirth, 25.","DOI":"10.1186\/s12884-025-07801-y"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lindsay, K.L., Milone, G.F., Grobman, W.A., Haas, D.M., Mercer, B.M., Simhan, H.N., Saade, G.R., Silver, R.M., and Chung, J.H. (2022). Periconceptional diet quality is associated with gestational diabetes risk and glucose concentrations among nulliparous gravidas. Front. Endocrinol., 13.","DOI":"10.3389\/fendo.2022.940870"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Laredo-Aguilera, J.A., Gallardo-Bravo, M., Rabanales-Sotos, J.A., Cobo-Cuenca, A.I., and Carmona-Torres, J.M. (2020). Physical Activity Programs during Pregnancy Are Effective for the Control of Gestational Diabetes Mellitus. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17176151"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, H., Fang, X., Wong, T.H., Chan, S.N., Akinwunmi, B., Ming, W.K., Zhang, C.J.P., and Wang, Z. (2022). Physical Activity during Pregnancy: Comparisons between Objective Measures and Self-Reports in Relation to Blood Glucose Levels. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19138064"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e002622","DOI":"10.1136\/bmjdrc-2021-002622","article-title":"Effects of resistance exercise on blood glucose level and pregnancy outcome in patients with gestational diabetes mellitus: A randomized controlled trial","volume":"10","author":"Xie","year":"2022","journal-title":"BMJ Open Diabetes Res. Care"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"101436","DOI":"10.1016\/j.smrv.2021.101436","article-title":"Sleep disturbances during pregnancy and adverse maternal and fetal outcomes: A systematic review and meta-analysis","volume":"58","author":"Lu","year":"2021","journal-title":"Sleep Med. Rev."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.sleep.2019.11.1246","article-title":"Sleep quality and gestational diabetes in pregnant women: A systematic review and meta-analysis","volume":"67","author":"Zhu","year":"2020","journal-title":"Sleep Med."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Myoga, M., Tsuji, M., Tanaka, R., Shibata, E., Askew, D.J., Aiko, Y., Senju, A., Kawamoto, T., Hachisuga, T., and Araki, S. (2019). Impact of sleep duration during pregnancy on the risk of gestational diabetes in the Japan environmental and Children\u2019s study (JECS). BMC Pregnancy Childbirth, 19.","DOI":"10.1186\/s12884-019-2632-9"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1111\/dme.13155","article-title":"Sleep duration and quality, and risk of gestational diabetes mellitus in pregnant Chinese women","volume":"34","author":"Wang","year":"2017","journal-title":"Diabet. Med."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"103308","DOI":"10.1016\/j.midw.2022.103308","article-title":"The progression on the measurement instruments of maternal health literacy: A scoping review","volume":"109","author":"Chen","year":"2022","journal-title":"Midwifery"},{"key":"ref_50","first-page":"64","article-title":"Study on maternal and infant health literacy and influencing factors among maternal in Lanzhou, 2017","volume":"35","author":"Tan","year":"2021","journal-title":"Chin. Prim. Health Care"},{"key":"ref_51","first-page":"529","article-title":"Investigation and analysis on influencing factors of maternal and child health literacy of pregnant and parturient women in Jilin Province","volume":"35","author":"Wan","year":"2020","journal-title":"Matern. Child Health Care China"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yang, L., Cui, Y., Du, J., Liu, Z., Duan, Y., Qi, Q., Liu, H., and Zhang, M. (2024). Association between nutritional literacy and nutrition label use in Chinese community residents. Front. Nutr., 11.","DOI":"10.3389\/fnut.2024.1380310"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1017\/S1368980019001289","article-title":"Nutrition literacy predicts adherence to healthy\/unhealthy diet patterns in adults with a nutrition-related chronic condition","volume":"22","author":"Taylor","year":"2019","journal-title":"Public Health Nutr."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Nawabi, F., Krebs, F., Lorenz, L., Shukri, A., Alayli, A., and Stock, S. (2022). Health Literacy among Pregnant Women in a Lifestyle Intervention Trial. Int. J. Environ. Res. Public Health, 19.","DOI":"10.3390\/ijerph19105808"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"e40260","DOI":"10.1097\/MD.0000000000040260","article-title":"The relationship between health literacy and healthy lifestyle behaviors: A meta-analysis","volume":"103","author":"Elkin","year":"2024","journal-title":"Medicine"}],"container-title":["Nutrients"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6643\/17\/21\/3400\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:57:20Z","timestamp":1761785840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6643\/17\/21\/3400"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,29]]},"references-count":55,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["nu17213400"],"URL":"https:\/\/doi.org\/10.3390\/nu17213400","relation":{},"ISSN":["2072-6643"],"issn-type":[{"value":"2072-6643","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,29]]}}}