{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T17:04:35Z","timestamp":1785776675514,"version":"3.56.0"},"reference-count":46,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education of the Republic of Korea","award":["NRF-2024S1A5A8026204"],"award-info":[{"award-number":["NRF-2024S1A5A8026204"]}]},{"name":"National Research Foundation of Korea","award":["NRF-2024S1A5A8026204"],"award-info":[{"award-number":["NRF-2024S1A5A8026204"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Objectives: Multidimensional dietary patterns provide a more comprehensive assessment of meal-related behavior than single behaviors, yet evidence on their variation across adulthood and association with health factors relevant to healthy aging is limited. This study examined meal-related behavior pattern distribution by age and identified predictors of unhealthy patterns. Methods: Data from 47,332 adults aged \u2265 18 years in the Korea National Health and Nutrition Examination Survey (2014\u20132022) were analyzed. Participants were divided into three age groups: young adults (18\u201339 years), middle-aged adults (40\u201364 years), and older adults (\u226565 years). Within each age group, participants were further classified into three categories based on their adherence to three key meal-related behaviors: High adherence (all three behaviors: regular breakfast, shared mealtimes, and infrequent eating out), Moderate adherence (two behaviors), or Low adherence (one or none). Group differences in sociodemographic, clinical variables, nutrient intake, biochemical markers, and lifestyle factors were tested using ANOVA and Pearson\u2019s chi-square, and predictors were identified with multinomial logistic regression. Results: Older adults most frequently showed the High adherence group (51.1%) but had the lowest prevalence of shared mealtimes (66.3%), suggesting social vulnerabilities despite healthy individual habits. Young adults had the highest Low pattern prevalence, which was primarily driven by infrequent breakfast and frequent eating out. Across all ages, poorer patterns were consistently associated with living alone, prolonged sedentary time, and adverse metabolic profiles. Middle-aged adults exhibited moderate adherence to healthy behaviors but showed the highest prevalence of chronic disease. In older adults, poorer patterns were associated with lower intakes of energy, carbohydrate, protein, and dietary fiber intake, alongside higher rates of living alone and sedentary behavior. Conclusions: Composite meal-related behaviors differed across age groups as follows: Young adults most frequently exhibited low adherence, middle-aged adults showed moderate adherence but bore the highest burden of chronic diseases, while older adults demonstrated high breakfast adherence and low frequency of eating out, but faced nutritional insufficiency and social vulnerability. These findings suggest the need for personalized interventions for each age group.<\/jats:p>","DOI":"10.3390\/nu17182982","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T14:10:19Z","timestamp":1758118219000},"page":"2982","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Composite Meal-Related Behaviors and Health Indicators: Insight from Large-Scale Nationwide Data on Korean Adults"],"prefix":"10.3390","volume":"17","author":[{"given":"Seongryu","family":"Bae","sequence":"first","affiliation":[{"name":"Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Republic of Korea"},{"name":"Digital Healthcare Institute, Dong-A University, Busan 49315, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1976-0005","authenticated-orcid":false,"given":"Hyuntae","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Health Sciences, The Graduate School of Dong-A University, Busan 49315, Republic of Korea"},{"name":"Digital Healthcare Institute, Dong-A University, Busan 49315, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ueno, S., Aung, M.N., Yuasa, M., Ishtiaq, A., Khin, E.T., Latt, T.S., Moolphate, S., Sato, S., and Tanigawa, T. 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