Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses
Background Lifestyle risk factors, such as unhealthy diet, physical inactivity or tobacco smoking can have detrimental effects on health and well-being. Therefore, it is important to examine multiple lifestyle risk factors instead of single ones. Cluster analysis allows the combination of single hea...
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| description | Background Lifestyle risk factors, such as unhealthy diet, physical inactivity or tobacco smoking can have detrimental effects on health and well-being. Therefore, it is important to examine multiple lifestyle risk factors instead of single ones. Cluster analysis allows the combination of single health behaviors in order to recognize distinguished behavior patterns. This study aimed to evaluate lifestyle patterns of general adult population in northwest of Iran with particular focus on dietary patterns, physical activity, and smoking status. Methods The current cross-sectional study consists of 525 adults aged 18–64 years from East-Azarbaijan Iran. Latent class analysis (LCA) was applied to recognize patterns of lifestyle behaviors with ingredients of diet, physical activity, and smoking status. Dietary intake was assessed using a validated food frequency questionnaire and dietary patterns were derived using factor analysis. Biochemical parameters including fasting blood sugar (FBS), serum lipids, liver enzyme and serum 25(OH)-D3 were measured with commercial ELIZA kits. Results Mean ages of participants were 42.90 ± 11.89 years. Using principal component analysis (PCA) three major dietary patterns were extracted including traditional dietary pattern (e.g. nuts and dry fruits), unhealthy dietary pattern (e.g. fast foods, refined grains) and the healthy dietary patterns (e.g. fruits, vegetables). Using LCA, three classes of lifestyles pattern were identified: 1st class was characterized by a healthy dietary pattern, moderate physical activity, and low probability of smoking. 2nd class was characterized by a traditional dietary pattern, low level of physical activity and low probability of smoking and 3rd class was characterized by a unhealthy dietary pattern, low level of physical activity and low probability of smoking and further analysis found that there were significant differences in body mass index (BMI), Waist-to-hip ratio (WHR), FBS, Hemoglobin (Hb), education levels and anxiety status between classes (P |
| doi_str_mv | 10.1371/journal.pone.0236242 |
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Therefore, it is important to examine multiple lifestyle risk factors instead of single ones. Cluster analysis allows the combination of single health behaviors in order to recognize distinguished behavior patterns. This study aimed to evaluate lifestyle patterns of general adult population in northwest of Iran with particular focus on dietary patterns, physical activity, and smoking status. Methods The current cross-sectional study consists of 525 adults aged 18–64 years from East-Azarbaijan Iran. Latent class analysis (LCA) was applied to recognize patterns of lifestyle behaviors with ingredients of diet, physical activity, and smoking status. Dietary intake was assessed using a validated food frequency questionnaire and dietary patterns were derived using factor analysis. Biochemical parameters including fasting blood sugar (FBS), serum lipids, liver enzyme and serum 25(OH)-D3 were measured with commercial ELIZA kits. Results Mean ages of participants were 42.90 ± 11.89 years. Using principal component analysis (PCA) three major dietary patterns were extracted including traditional dietary pattern (e.g. nuts and dry fruits), unhealthy dietary pattern (e.g. fast foods, refined grains) and the healthy dietary patterns (e.g. fruits, vegetables). Using LCA, three classes of lifestyles pattern were identified: 1st class was characterized by a healthy dietary pattern, moderate physical activity, and low probability of smoking. 2nd class was characterized by a traditional dietary pattern, low level of physical activity and low probability of smoking and 3rd class was characterized by a unhealthy dietary pattern, low level of physical activity and low probability of smoking and further analysis found that there were significant differences in body mass index (BMI), Waist-to-hip ratio (WHR), FBS, Hemoglobin (Hb), education levels and anxiety status between classes (P <0.05). Conclusion This study attempts to classify Iranian adults by their own health behavior. Healthcare professionals should be aware of associations between different lifestyle risk factors and health promotion strategies should further focus on multiple behaviors at the same time. In our country, more studies about the adult population are needed to support the observed findings of our study and therefore allow for a certain generalization of the observations.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0236242</identifier><identifier>PMID: 32701986</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Adults ; Behavior ; Biology and Life Sciences ; Body mass ; Body mass index ; Body size ; Chronic illnesses ; Cluster analysis ; Data collection ; Diet ; Dietary intake ; Exercise ; Factor analysis ; Fast food ; Food intake ; Fruits ; Health promotion ; Health risks ; Hemoglobin ; Latent class analysis ; Lifestyles ; Lipids ; Low level ; Medicine and Health Sciences ; Methods ; Nuts ; Pattern recognition ; Physical activity ; Physical Sciences ; Population ; Population studies ; Principal components analysis ; Questionnaires ; Research and Analysis Methods ; Risk analysis ; Risk factors ; Serum lipids ; Smoking ; Social Sciences ; Sociodemographics ; Studies ; Tobacco ; Tobacco smoking ; Well being</subject><ispartof>PloS one, 2020-07, Vol.15 (7), p.e0236242-e0236242</ispartof><rights>2020 Vajdi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Vajdi et al 2020 Vajdi et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c433t-db8ec354a9ee4c82e47a4d8691b1932c666e5bfe3923f75a87d7e7914327b8993</citedby><cites>FETCH-LOGICAL-c433t-db8ec354a9ee4c82e47a4d8691b1932c666e5bfe3923f75a87d7e7914327b8993</cites><orcidid>0000-0002-7036-6900</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377498/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7377498/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids></links><search><contributor>Shiels, Paul Gerard</contributor><creatorcontrib>Vajdi, Mahdi</creatorcontrib><creatorcontrib>Nikniaz, Leila</creatorcontrib><creatorcontrib>Pour Asl, Asghar Mohammad</creatorcontrib><creatorcontrib>Abbasalizad Farhangi, Mahdieh</creatorcontrib><title>Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses</title><title>PloS one</title><description>Background Lifestyle risk factors, such as unhealthy diet, physical inactivity or tobacco smoking can have detrimental effects on health and well-being. Therefore, it is important to examine multiple lifestyle risk factors instead of single ones. Cluster analysis allows the combination of single health behaviors in order to recognize distinguished behavior patterns. This study aimed to evaluate lifestyle patterns of general adult population in northwest of Iran with particular focus on dietary patterns, physical activity, and smoking status. Methods The current cross-sectional study consists of 525 adults aged 18–64 years from East-Azarbaijan Iran. Latent class analysis (LCA) was applied to recognize patterns of lifestyle behaviors with ingredients of diet, physical activity, and smoking status. Dietary intake was assessed using a validated food frequency questionnaire and dietary patterns were derived using factor analysis. Biochemical parameters including fasting blood sugar (FBS), serum lipids, liver enzyme and serum 25(OH)-D3 were measured with commercial ELIZA kits. Results Mean ages of participants were 42.90 ± 11.89 years. Using principal component analysis (PCA) three major dietary patterns were extracted including traditional dietary pattern (e.g. nuts and dry fruits), unhealthy dietary pattern (e.g. fast foods, refined grains) and the healthy dietary patterns (e.g. fruits, vegetables). Using LCA, three classes of lifestyles pattern were identified: 1st class was characterized by a healthy dietary pattern, moderate physical activity, and low probability of smoking. 2nd class was characterized by a traditional dietary pattern, low level of physical activity and low probability of smoking and 3rd class was characterized by a unhealthy dietary pattern, low level of physical activity and low probability of smoking and further analysis found that there were significant differences in body mass index (BMI), Waist-to-hip ratio (WHR), FBS, Hemoglobin (Hb), education levels and anxiety status between classes (P <0.05). Conclusion This study attempts to classify Iranian adults by their own health behavior. Healthcare professionals should be aware of associations between different lifestyle risk factors and health promotion strategies should further focus on multiple behaviors at the same time. In our country, more studies about the adult population are needed to support the observed findings of our study and therefore allow for a certain generalization of the observations.</description><subject>Adults</subject><subject>Behavior</subject><subject>Biology and Life Sciences</subject><subject>Body mass</subject><subject>Body mass index</subject><subject>Body size</subject><subject>Chronic illnesses</subject><subject>Cluster analysis</subject><subject>Data collection</subject><subject>Diet</subject><subject>Dietary intake</subject><subject>Exercise</subject><subject>Factor analysis</subject><subject>Fast food</subject><subject>Food intake</subject><subject>Fruits</subject><subject>Health promotion</subject><subject>Health risks</subject><subject>Hemoglobin</subject><subject>Latent class analysis</subject><subject>Lifestyles</subject><subject>Lipids</subject><subject>Low level</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Nuts</subject><subject>Pattern recognition</subject><subject>Physical activity</subject><subject>Physical Sciences</subject><subject>Population</subject><subject>Population studies</subject><subject>Principal components analysis</subject><subject>Questionnaires</subject><subject>Research and Analysis Methods</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Serum lipids</subject><subject>Smoking</subject><subject>Social Sciences</subject><subject>Sociodemographics</subject><subject>Studies</subject><subject>Tobacco</subject><subject>Tobacco smoking</subject><subject>Well 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patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses</title><author>Vajdi, Mahdi ; Nikniaz, Leila ; Pour Asl, Asghar Mohammad ; Abbasalizad Farhangi, Mahdieh</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c433t-db8ec354a9ee4c82e47a4d8691b1932c666e5bfe3923f75a87d7e7914327b8993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adults</topic><topic>Behavior</topic><topic>Biology and Life Sciences</topic><topic>Body mass</topic><topic>Body mass index</topic><topic>Body size</topic><topic>Chronic illnesses</topic><topic>Cluster analysis</topic><topic>Data collection</topic><topic>Diet</topic><topic>Dietary intake</topic><topic>Exercise</topic><topic>Factor analysis</topic><topic>Fast food</topic><topic>Food intake</topic><topic>Fruits</topic><topic>Health promotion</topic><topic>Health risks</topic><topic>Hemoglobin</topic><topic>Latent class analysis</topic><topic>Lifestyles</topic><topic>Lipids</topic><topic>Low level</topic><topic>Medicine and Health Sciences</topic><topic>Methods</topic><topic>Nuts</topic><topic>Pattern recognition</topic><topic>Physical activity</topic><topic>Physical Sciences</topic><topic>Population</topic><topic>Population studies</topic><topic>Principal components analysis</topic><topic>Questionnaires</topic><topic>Research and Analysis Methods</topic><topic>Risk analysis</topic><topic>Risk factors</topic><topic>Serum lipids</topic><topic>Smoking</topic><topic>Social Sciences</topic><topic>Sociodemographics</topic><topic>Studies</topic><topic>Tobacco</topic><topic>Tobacco smoking</topic><topic>Well being</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vajdi, Mahdi</creatorcontrib><creatorcontrib>Nikniaz, Leila</creatorcontrib><creatorcontrib>Pour Asl, Asghar 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Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vajdi, Mahdi</au><au>Nikniaz, Leila</au><au>Pour Asl, Asghar Mohammad</au><au>Abbasalizad Farhangi, Mahdieh</au><au>Shiels, Paul Gerard</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses</atitle><jtitle>PloS one</jtitle><date>2020-07-23</date><risdate>2020</risdate><volume>15</volume><issue>7</issue><spage>e0236242</spage><epage>e0236242</epage><pages>e0236242-e0236242</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Background Lifestyle risk factors, such as unhealthy diet, physical inactivity or tobacco smoking can have detrimental effects on health and well-being. Therefore, it is important to examine multiple lifestyle risk factors instead of single ones. Cluster analysis allows the combination of single health behaviors in order to recognize distinguished behavior patterns. This study aimed to evaluate lifestyle patterns of general adult population in northwest of Iran with particular focus on dietary patterns, physical activity, and smoking status. Methods The current cross-sectional study consists of 525 adults aged 18–64 years from East-Azarbaijan Iran. Latent class analysis (LCA) was applied to recognize patterns of lifestyle behaviors with ingredients of diet, physical activity, and smoking status. Dietary intake was assessed using a validated food frequency questionnaire and dietary patterns were derived using factor analysis. Biochemical parameters including fasting blood sugar (FBS), serum lipids, liver enzyme and serum 25(OH)-D3 were measured with commercial ELIZA kits. Results Mean ages of participants were 42.90 ± 11.89 years. Using principal component analysis (PCA) three major dietary patterns were extracted including traditional dietary pattern (e.g. nuts and dry fruits), unhealthy dietary pattern (e.g. fast foods, refined grains) and the healthy dietary patterns (e.g. fruits, vegetables). Using LCA, three classes of lifestyles pattern were identified: 1st class was characterized by a healthy dietary pattern, moderate physical activity, and low probability of smoking. 2nd class was characterized by a traditional dietary pattern, low level of physical activity and low probability of smoking and 3rd class was characterized by a unhealthy dietary pattern, low level of physical activity and low probability of smoking and further analysis found that there were significant differences in body mass index (BMI), Waist-to-hip ratio (WHR), FBS, Hemoglobin (Hb), education levels and anxiety status between classes (P <0.05). Conclusion This study attempts to classify Iranian adults by their own health behavior. Healthcare professionals should be aware of associations between different lifestyle risk factors and health promotion strategies should further focus on multiple behaviors at the same time. In our country, more studies about the adult population are needed to support the observed findings of our study and therefore allow for a certain generalization of the observations.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>32701986</pmid><doi>10.1371/journal.pone.0236242</doi><orcidid>https://orcid.org/0000-0002-7036-6900</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adults Behavior Biology and Life Sciences Body mass Body mass index Body size Chronic illnesses Cluster analysis Data collection Diet Dietary intake Exercise Factor analysis Fast food Food intake Fruits Health promotion Health risks Hemoglobin Latent class analysis Lifestyles Lipids Low level Medicine and Health Sciences Methods Nuts Pattern recognition Physical activity Physical Sciences Population Population studies Principal components analysis Questionnaires Research and Analysis Methods Risk analysis Risk factors Serum lipids Smoking Social Sciences Sociodemographics Studies Tobacco Tobacco smoking Well being |
| title | Lifestyle patterns and their nutritional, socio-demographic and psychological determinants in a community-based study: A mixed approach of latent class and factor analyses |
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