{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T12:46:00Z","timestamp":1791463560864,"version":"4.3.4"},"reference-count":29,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2024,9,25]],"date-time":"2024-09-25T00:00:00Z","timestamp":1727222400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100017170","name":"Thailand Science Research and Innovation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017170","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004704","name":"National Research Council of Thailand","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004704","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Geriatrics Gerontology Int"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:sec><jats:title>Aim<\/jats:title><jats:p>The purpose of this longitudinal study was to evaluate the prevalence and correlates of and the factors contributing to meal\u2010skipping in middle\u2010aged and older adults in Thailand.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We examined prospective cohort data from three consecutive waves of the Health, Aging and Retirement in Thailand (HART) study (analytic sample size\u2009=\u20092863) for participants 45\u2009years of age and older. Meal\u2010skipping was assessed with a one\u2010 and two\u2010day recall of breakfast, lunch, and dinner. We used generalized estimating equations analysis to evaluate the longitudinal relationships between sociodemographic, lifestyle, and health indicators and meal\u2010skipping between 2015 (wave 1), 2017 (wave 2), and 2020 (wave 3). Furthermore, binary logistic regression was used to establish the longitudinal association between meal\u2010skipping and the incidence of multiple chronic conditions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The prevalence of meal\u2010skipping was 6.2% in 2015, 16.2% in 2017, and 13.6% in 2020, and across study years the highest proportion of meal skipping was for lunch (6.0%), followed by breakfast (4.6%), and dinner (2.8%). In the final model, sociodemographic factors (higher education, urban residence, and lower subjective economic status), lifestyle factors (high alcohol use, current smoking, and obesity class II), and health indicators (probable depression and wearing dentures) were positively associated with meal\u2010skipping. In the adjusted logistic regression models, compared with no meal\u2010skipping, meal\u2010skipping in 2\u20133 study waves was associated with incident obesity and incident probable depression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Skipping meals was linked to a number of lifestyle variables and indicators of physical and mental illness. <jats:bold>Geriatr Gerontol Int 2024; 24: 1196\u20131202<\/jats:bold>.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ggi.14966","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T06:23:48Z","timestamp":1727331828000},"page":"1196-1202","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Prevalence, correlates, and health indicators associated with meal\u2010skipping among middle\u2010aged and older adults in Thailand: A national longitudinal 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