{"title":"孤独感的临床和生活方式预测因素:一项为期两年的纵向研究","authors":"Thyago Antonelli-Salgado , Bruno Braga Montezano , Thiago Henrique Roza , Vitória Bouvier , Aline Zimerman , Lucas Tavares Noronha , Grasiela Marcon , Maurício Scopel Hoffmann , André Russowsky Brunoni , Ives Cavalcante Passos","doi":"10.1016/j.jpsychires.2024.11.025","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>While loneliness is a global public health problem, the literature lacks studies assessing loneliness predictors in low- and middle-income countries. Therefore, we aimed to analyze clinical and lifestyle predictors of loneliness.</div></div><div><h3>Methods</h3><div>We conducted a 2-year longitudinal study in Brazil based on a snowball sample and online surveys (baseline: May 6 to June 6, 2020). We assessed clinical and lifestyle predictors of loneliness using multiple regression models. The analyses were adjusted for several sociodemographic variables and weighted for attrition and sampling procedures.</div></div><div><h3>Results</h3><div>The study included a nationwide sample of 473 participants (18–75 years; 87.1% females). After adjusting for sociodemographic factors, we identified as risk factors: depressive symptoms (RR: 1.214; 95%CI: 1.08–1.36; p = 0.001), anxiety symptoms (RR:1.191; 95%CI: 1.04–1.35; p = 0.007), alcohol abuse (RR: 1.579; 95%CI: 1.32–1.88; p < 0.001), and cannabis use (RR: 1.750; 95%CI: 1.25–2.39; p < 0.001). More than 150 min/week of physical activity (RR: 0.177; 95%CI: 0.07–0.34; p < 0.001) and good/excellent quality of family relationships (RR: 0.73; 95%CI: 0.60–0.87; p < 0.001) and sleep (RR: 0.483; 95%CI: 0.39–0.59; p < 0.001) were protective factors.</div></div><div><h3>Conclusion</h3><div>Several clinical factors (depression, anxiety, alcohol, and cannabis) have been identified as risk factors for loneliness, while lifestyle factors (physical activity, better quality of sleep, and family relationships) have been associated with a lower incidence of loneliness. Addressing clinical and lifestyle factors may therefore be essential to preventing loneliness.</div></div>","PeriodicalId":16868,"journal":{"name":"Journal of psychiatric research","volume":"180 ","pages":"Pages 482-488"},"PeriodicalIF":3.7000,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical and lifestyle predictors of loneliness: A two-year longitudinal study\",\"authors\":\"Thyago Antonelli-Salgado , Bruno Braga Montezano , Thiago Henrique Roza , Vitória Bouvier , Aline Zimerman , Lucas Tavares Noronha , Grasiela Marcon , Maurício Scopel Hoffmann , André Russowsky Brunoni , Ives Cavalcante Passos\",\"doi\":\"10.1016/j.jpsychires.2024.11.025\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>While loneliness is a global public health problem, the literature lacks studies assessing loneliness predictors in low- and middle-income countries. Therefore, we aimed to analyze clinical and lifestyle predictors of loneliness.</div></div><div><h3>Methods</h3><div>We conducted a 2-year longitudinal study in Brazil based on a snowball sample and online surveys (baseline: May 6 to June 6, 2020). We assessed clinical and lifestyle predictors of loneliness using multiple regression models. The analyses were adjusted for several sociodemographic variables and weighted for attrition and sampling procedures.</div></div><div><h3>Results</h3><div>The study included a nationwide sample of 473 participants (18–75 years; 87.1% females). After adjusting for sociodemographic factors, we identified as risk factors: depressive symptoms (RR: 1.214; 95%CI: 1.08–1.36; p = 0.001), anxiety symptoms (RR:1.191; 95%CI: 1.04–1.35; p = 0.007), alcohol abuse (RR: 1.579; 95%CI: 1.32–1.88; p < 0.001), and cannabis use (RR: 1.750; 95%CI: 1.25–2.39; p < 0.001). More than 150 min/week of physical activity (RR: 0.177; 95%CI: 0.07–0.34; p < 0.001) and good/excellent quality of family relationships (RR: 0.73; 95%CI: 0.60–0.87; p < 0.001) and sleep (RR: 0.483; 95%CI: 0.39–0.59; p < 0.001) were protective factors.</div></div><div><h3>Conclusion</h3><div>Several clinical factors (depression, anxiety, alcohol, and cannabis) have been identified as risk factors for loneliness, while lifestyle factors (physical activity, better quality of sleep, and family relationships) have been associated with a lower incidence of loneliness. Addressing clinical and lifestyle factors may therefore be essential to preventing loneliness.</div></div>\",\"PeriodicalId\":16868,\"journal\":{\"name\":\"Journal of psychiatric research\",\"volume\":\"180 \",\"pages\":\"Pages 482-488\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of psychiatric research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0022395624006459\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PSYCHIATRY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of psychiatric research","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022395624006459","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PSYCHIATRY","Score":null,"Total":0}
Clinical and lifestyle predictors of loneliness: A two-year longitudinal study
Background
While loneliness is a global public health problem, the literature lacks studies assessing loneliness predictors in low- and middle-income countries. Therefore, we aimed to analyze clinical and lifestyle predictors of loneliness.
Methods
We conducted a 2-year longitudinal study in Brazil based on a snowball sample and online surveys (baseline: May 6 to June 6, 2020). We assessed clinical and lifestyle predictors of loneliness using multiple regression models. The analyses were adjusted for several sociodemographic variables and weighted for attrition and sampling procedures.
Results
The study included a nationwide sample of 473 participants (18–75 years; 87.1% females). After adjusting for sociodemographic factors, we identified as risk factors: depressive symptoms (RR: 1.214; 95%CI: 1.08–1.36; p = 0.001), anxiety symptoms (RR:1.191; 95%CI: 1.04–1.35; p = 0.007), alcohol abuse (RR: 1.579; 95%CI: 1.32–1.88; p < 0.001), and cannabis use (RR: 1.750; 95%CI: 1.25–2.39; p < 0.001). More than 150 min/week of physical activity (RR: 0.177; 95%CI: 0.07–0.34; p < 0.001) and good/excellent quality of family relationships (RR: 0.73; 95%CI: 0.60–0.87; p < 0.001) and sleep (RR: 0.483; 95%CI: 0.39–0.59; p < 0.001) were protective factors.
Conclusion
Several clinical factors (depression, anxiety, alcohol, and cannabis) have been identified as risk factors for loneliness, while lifestyle factors (physical activity, better quality of sleep, and family relationships) have been associated with a lower incidence of loneliness. Addressing clinical and lifestyle factors may therefore be essential to preventing loneliness.
期刊介绍:
Founded in 1961 to report on the latest work in psychiatry and cognate disciplines, the Journal of Psychiatric Research is dedicated to innovative and timely studies of four important areas of research:
(1) clinical studies of all disciplines relating to psychiatric illness, as well as normal human behaviour, including biochemical, physiological, genetic, environmental, social, psychological and epidemiological factors;
(2) basic studies pertaining to psychiatry in such fields as neuropsychopharmacology, neuroendocrinology, electrophysiology, genetics, experimental psychology and epidemiology;
(3) the growing application of clinical laboratory techniques in psychiatry, including imagery and spectroscopy of the brain, molecular biology and computer sciences;