{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T15:01:28Z","timestamp":1793718088877,"version":"4.6.1"},"reference-count":40,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T00:00:00Z","timestamp":1583712000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Psychol. Med."],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec id=\"S0033291719003933_sec_a1\"><jats:title>Background<\/jats:title><jats:p>Loneliness is a growing public health issue in the developed world. Among older adults, loneliness is a particular challenge, as the older segment of the population is growing and loneliness is comorbid with many mental as well as physical health issues. Comorbidity and common cause factors make identifying the antecedents of loneliness difficult, however, contemporary machine learning techniques are positioned to tackle this problem.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291719003933_sec_a2\" sec-type=\"methods\"><jats:title>Methods<\/jats:title><jats:p>This study analyzed four cohorts of older individuals, split into two age groups \u2013 45\u201369 and 70\u201379 \u2013 to examine which common psychological and sociodemographic are associated with loneliness at different ages. Gradient boosted modeling, a machine learning technique, and regression models were used to identify and replicate associations with loneliness.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291719003933_sec_a3\" sec-type=\"results\"><jats:title>Results<\/jats:title><jats:p>In all cohorts, higher emotional stability was associated with lower loneliness. In the older group, social circumstances such as living alone were also associated with higher loneliness. In the younger group, extraversion's association with lower loneliness was the only other confirmed relationship.<\/jats:p><\/jats:sec><jats:sec id=\"S0033291719003933_sec_a4\" sec-type=\"conclusions\"><jats:title>Conclusions<\/jats:title><jats:p>Different individual and social factors might underlie loneliness differences in distinct age groups. Machine learning methods have the potential to unveil novel associations between psychological and social variables, particularly interactions, and mental health outcomes.<\/jats:p><\/jats:sec>","DOI":"10.1017\/s0033291719003933","type":"journal-article","created":{"date-parts":[[2020,3,9]],"date-time":"2020-03-09T07:02:55Z","timestamp":1583737375000},"page":"991-1000","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":32,"title":["Generational differences in loneliness and its psychological and sociodemographic predictors: an exploratory and confirmatory machine learning study"],"prefix":"10.1017","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7053-4209","authenticated-orcid":false,"given":"Drew","family":"Altschul","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Iveson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian J.","family":"Deary","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2020,3,9]]},"reference":[{"key":"S0033291719003933_ref3","unstructured":"Banks, J. , Karlsen, S. , & Oldfield, Z. (2003). Socio-economic position. In M. Marmot, J. Banks, R. Blundell, C. Lessof, & J. Nazroo (Eds.), Health, wealth and lifestyles of the older population in England. 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