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Factors associated with eating behaviors in older adults from a socioecological model perspective
Background The eating behaviors of older adults are associated with multiple factors. To promote older adults' healthy diets, it is imperative to comprehensively study the factors associated with the eating behaviors of the aging population group. This study aimed to probe the associated factor...
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description | Background The eating behaviors of older adults are associated with multiple factors. To promote older adults' healthy diets, it is imperative to comprehensively study the factors associated with the eating behaviors of the aging population group. This study aimed to probe the associated factors of older adults' eating behaviors from a socioecological model (SEM) perspective. Methods In 2021, a cross-sectional survey was performed to recruit participants in China. The survey data were analyzed using a multivariate generalized linear model to identify the factors associated with eating behaviors in older adults. Standardized regression coefficients ([beta]) and 95% confidence intervals (CIs) were estimated using a multivariate generalized linear model. Results The survey contained 1147 valid older adult participants. Multivariate generalized linear model results showed that older adults with older age [aged 71-80 ([beta] = -0.61), [greater than or equal to] 81 ([beta] = -1.12)], conscientiousness personality trait ([beta] = -0.27), and higher family health levels ([beta] = -0.23) were inclined to have better eating behaviors. The older adults with higher education levels [junior high school and high school ([beta] = 1.03), junior college and above ([beta] = 1.71)], higher general self-efficacy ([beta] = 0.09), more severe depression symptoms ([beta] = 0.24), and employment ([beta] = 0.82) tended to have poorer eating behaviors. Conclusions This study identified factors that are specifically associated with older adults' eating behaviors from an SEM perspective. The comprehensive multiple-angle perspective consideration may be a valuable idea for studying healthy eating behaviors in older adults. Keywords: Eating behavior, Older adults, Socioecological model, China |
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To promote older adults' healthy diets, it is imperative to comprehensively study the factors associated with the eating behaviors of the aging population group. This study aimed to probe the associated factors of older adults' eating behaviors from a socioecological model (SEM) perspective. Methods In 2021, a cross-sectional survey was performed to recruit participants in China. The survey data were analyzed using a multivariate generalized linear model to identify the factors associated with eating behaviors in older adults. Standardized regression coefficients ([beta]) and 95% confidence intervals (CIs) were estimated using a multivariate generalized linear model. Results The survey contained 1147 valid older adult participants. Multivariate generalized linear model results showed that older adults with older age [aged 71-80 ([beta] = -0.61), [greater than or equal to] 81 ([beta] = -1.12)], conscientiousness personality trait ([beta] = -0.27), and higher family health levels ([beta] = -0.23) were inclined to have better eating behaviors. The older adults with higher education levels [junior high school and high school ([beta] = 1.03), junior college and above ([beta] = 1.71)], higher general self-efficacy ([beta] = 0.09), more severe depression symptoms ([beta] = 0.24), and employment ([beta] = 0.82) tended to have poorer eating behaviors. Conclusions This study identified factors that are specifically associated with older adults' eating behaviors from an SEM perspective. The comprehensive multiple-angle perspective consideration may be a valuable idea for studying healthy eating behaviors in older adults. Keywords: Eating behavior, Older adults, Socioecological model, China</description><identifier>ISSN: 1471-2458</identifier><identifier>EISSN: 1471-2458</identifier><identifier>DOI: 10.1186/s12889-023-16651-2</identifier><identifier>PMID: 37670266</identifier><language>eng</language><publisher>London: BioMed Central Ltd</publisher><subject>Adults ; Aged ; Aging ; Anxiety ; China ; Confidence intervals ; Eating ; Eating behavior ; Education ; Food and nutrition ; Food habits ; Generalized linear models ; Health aspects ; Likert scale ; Malnutrition ; Multivariate analysis ; Older adults ; Older people ; Personality ; Personality traits ; Population ; Population studies ; Public health ; Questionnaires ; Regression coefficients ; Sample size ; Social aspects ; Social ecology ; Social support ; Socioecological model ; Statistical analysis ; Statistical models ; Surveys</subject><ispartof>BMC public health, 2023-09, Vol.23 (1), p.1-1726, Article 1726</ispartof><rights>COPYRIGHT 2023 BioMed Central Ltd.</rights><rights>2023. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>BioMed Central Ltd., part of Springer Nature 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c541t-b0655a9ff3598e7e8f5c530add40334709df60be656470b87f0330885a792a533</citedby><cites>FETCH-LOGICAL-c541t-b0655a9ff3598e7e8f5c530add40334709df60be656470b87f0330885a792a533</cites><orcidid>0000-0002-8846-7515 ; 0000-0003-0658-7589 ; 0000-0002-0507-6034 ; 0000-0001-7814-8011 ; 0000-0001-9607-313X ; 0000-0002-0371-4004</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/PMC10481492/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2865397351?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,25731,27901,27902,36989,36990,44566,53766,53768</link.rule.ids></links><search><creatorcontrib>Wang, Xue</creatorcontrib><creatorcontrib>Wu, Yibo</creatorcontrib><creatorcontrib>Miao, Juanxia</creatorcontrib><creatorcontrib>Pu, Keping</creatorcontrib><creatorcontrib>Ming, Wai-Kit</creatorcontrib><creatorcontrib>Zang, Shuang</creatorcontrib><title>Factors associated with eating behaviors in older adults from a socioecological model perspective</title><title>BMC public health</title><description>Background The eating behaviors of older adults are associated with multiple factors. To promote older adults' healthy diets, it is imperative to comprehensively study the factors associated with the eating behaviors of the aging population group. This study aimed to probe the associated factors of older adults' eating behaviors from a socioecological model (SEM) perspective. Methods In 2021, a cross-sectional survey was performed to recruit participants in China. The survey data were analyzed using a multivariate generalized linear model to identify the factors associated with eating behaviors in older adults. Standardized regression coefficients ([beta]) and 95% confidence intervals (CIs) were estimated using a multivariate generalized linear model. Results The survey contained 1147 valid older adult participants. Multivariate generalized linear model results showed that older adults with older age [aged 71-80 ([beta] = -0.61), [greater than or equal to] 81 ([beta] = -1.12)], conscientiousness personality trait ([beta] = -0.27), and higher family health levels ([beta] = -0.23) were inclined to have better eating behaviors. The older adults with higher education levels [junior high school and high school ([beta] = 1.03), junior college and above ([beta] = 1.71)], higher general self-efficacy ([beta] = 0.09), more severe depression symptoms ([beta] = 0.24), and employment ([beta] = 0.82) tended to have poorer eating behaviors. Conclusions This study identified factors that are specifically associated with older adults' eating behaviors from an SEM perspective. The comprehensive multiple-angle perspective consideration may be a valuable idea for studying healthy eating behaviors in older adults. Keywords: Eating behavior, Older adults, Socioecological model, China</description><subject>Adults</subject><subject>Aged</subject><subject>Aging</subject><subject>Anxiety</subject><subject>China</subject><subject>Confidence intervals</subject><subject>Eating</subject><subject>Eating behavior</subject><subject>Education</subject><subject>Food and nutrition</subject><subject>Food habits</subject><subject>Generalized linear models</subject><subject>Health aspects</subject><subject>Likert scale</subject><subject>Malnutrition</subject><subject>Multivariate analysis</subject><subject>Older adults</subject><subject>Older people</subject><subject>Personality</subject><subject>Personality traits</subject><subject>Population</subject><subject>Population studies</subject><subject>Public health</subject><subject>Questionnaires</subject><subject>Regression coefficients</subject><subject>Sample size</subject><subject>Social aspects</subject><subject>Social ecology</subject><subject>Social support</subject><subject>Socioecological model</subject><subject>Statistical analysis</subject><subject>Statistical models</subject><subject>Surveys</subject><issn>1471-2458</issn><issn>1471-2458</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNptkktv1DAUhSMEog_4A6wssWGT4redFaoqSitVYgNr68a5nvEoiQc7M6j_Hk-nAgYhL3x9fc5nXes0zTtGrxiz-mNh3NqupVy0TGvFWv6iOWfS1EIq-_Kv-qy5KGVDKTNW8dfNmTDaUK71eQO34JeUC4FSko-w4EB-xmVNEJY4r0iPa9jHgyDOJI0DZgLDblwKCTlNBMjBldCnMa2ih5FMacCRbDGXLfol7vFN8yrAWPDt837ZfL_9_O3mrn34-uX-5vqh9Uqype2pVgq6EITqLBq0QXklKAyDpEJIQ7shaNqjVroeemtCbVNrFZiOgxLisrk_cocEG7fNcYL86BJE99RIeeUgL9GP6KTgxmhJK9BKC8YG4D12VFEFwfa-sj4dWdtdP-HgcV4yjCfQ05s5rt0q7R2j0jLZ8Ur48EzI6ccOy-KmWDyOI8yYdsVxq5mWQnNape__kW7SLs_1rw4qJTojFPujWkGdIM4h1Yf9AequjRaKs07oqrr6j6quAafo04wh1v6JgR8NPqdSMobfQzLqDilzx5S5mjL3lDLHxS-kt8He</recordid><startdate>20230905</startdate><enddate>20230905</enddate><creator>Wang, Xue</creator><creator>Wu, Yibo</creator><creator>Miao, Juanxia</creator><creator>Pu, Keping</creator><creator>Ming, Wai-Kit</creator><creator>Zang, Shuang</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7T2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AN0</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>L6V</scope><scope>M0S</scope><scope>M1P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-8846-7515</orcidid><orcidid>https://orcid.org/0000-0003-0658-7589</orcidid><orcidid>https://orcid.org/0000-0002-0507-6034</orcidid><orcidid>https://orcid.org/0000-0001-7814-8011</orcidid><orcidid>https://orcid.org/0000-0001-9607-313X</orcidid><orcidid>https://orcid.org/0000-0002-0371-4004</orcidid></search><sort><creationdate>20230905</creationdate><title>Factors associated with eating behaviors in older adults from a socioecological model perspective</title><author>Wang, Xue ; Wu, Yibo ; Miao, Juanxia ; Pu, Keping ; Ming, Wai-Kit ; Zang, Shuang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c541t-b0655a9ff3598e7e8f5c530add40334709df60be656470b87f0330885a792a533</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Adults</topic><topic>Aged</topic><topic>Aging</topic><topic>Anxiety</topic><topic>China</topic><topic>Confidence intervals</topic><topic>Eating</topic><topic>Eating behavior</topic><topic>Education</topic><topic>Food and nutrition</topic><topic>Food habits</topic><topic>Generalized linear models</topic><topic>Health aspects</topic><topic>Likert scale</topic><topic>Malnutrition</topic><topic>Multivariate analysis</topic><topic>Older adults</topic><topic>Older people</topic><topic>Personality</topic><topic>Personality traits</topic><topic>Population</topic><topic>Population studies</topic><topic>Public health</topic><topic>Questionnaires</topic><topic>Regression coefficients</topic><topic>Sample size</topic><topic>Social aspects</topic><topic>Social ecology</topic><topic>Social support</topic><topic>Socioecological model</topic><topic>Statistical analysis</topic><topic>Statistical models</topic><topic>Surveys</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Xue</creatorcontrib><creatorcontrib>Wu, Yibo</creatorcontrib><creatorcontrib>Miao, Juanxia</creatorcontrib><creatorcontrib>Pu, Keping</creatorcontrib><creatorcontrib>Ming, Wai-Kit</creatorcontrib><creatorcontrib>Zang, Shuang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>ProQuest - Health & Medical Complete保健、医学与药学数据库</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>British Nursing Database</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Engineering Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>ProQuest Engineering Database</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Open Access: DOAJ - Directory of Open Access Journals</collection><jtitle>BMC public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Xue</au><au>Wu, Yibo</au><au>Miao, Juanxia</au><au>Pu, Keping</au><au>Ming, Wai-Kit</au><au>Zang, Shuang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Factors associated with eating behaviors in older adults from a socioecological model perspective</atitle><jtitle>BMC public health</jtitle><date>2023-09-05</date><risdate>2023</risdate><volume>23</volume><issue>1</issue><spage>1</spage><epage>1726</epage><pages>1-1726</pages><artnum>1726</artnum><issn>1471-2458</issn><eissn>1471-2458</eissn><abstract>Background The eating behaviors of older adults are associated with multiple factors. To promote older adults' healthy diets, it is imperative to comprehensively study the factors associated with the eating behaviors of the aging population group. This study aimed to probe the associated factors of older adults' eating behaviors from a socioecological model (SEM) perspective. Methods In 2021, a cross-sectional survey was performed to recruit participants in China. The survey data were analyzed using a multivariate generalized linear model to identify the factors associated with eating behaviors in older adults. Standardized regression coefficients ([beta]) and 95% confidence intervals (CIs) were estimated using a multivariate generalized linear model. Results The survey contained 1147 valid older adult participants. Multivariate generalized linear model results showed that older adults with older age [aged 71-80 ([beta] = -0.61), [greater than or equal to] 81 ([beta] = -1.12)], conscientiousness personality trait ([beta] = -0.27), and higher family health levels ([beta] = -0.23) were inclined to have better eating behaviors. The older adults with higher education levels [junior high school and high school ([beta] = 1.03), junior college and above ([beta] = 1.71)], higher general self-efficacy ([beta] = 0.09), more severe depression symptoms ([beta] = 0.24), and employment ([beta] = 0.82) tended to have poorer eating behaviors. Conclusions This study identified factors that are specifically associated with older adults' eating behaviors from an SEM perspective. The comprehensive multiple-angle perspective consideration may be a valuable idea for studying healthy eating behaviors in older adults. Keywords: Eating behavior, Older adults, Socioecological model, China</abstract><cop>London</cop><pub>BioMed Central Ltd</pub><pmid>37670266</pmid><doi>10.1186/s12889-023-16651-2</doi><orcidid>https://orcid.org/0000-0002-8846-7515</orcidid><orcidid>https://orcid.org/0000-0003-0658-7589</orcidid><orcidid>https://orcid.org/0000-0002-0507-6034</orcidid><orcidid>https://orcid.org/0000-0001-7814-8011</orcidid><orcidid>https://orcid.org/0000-0001-9607-313X</orcidid><orcidid>https://orcid.org/0000-0002-0371-4004</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adults Aged Aging Anxiety China Confidence intervals Eating Eating behavior Education Food and nutrition Food habits Generalized linear models Health aspects Likert scale Malnutrition Multivariate analysis Older adults Older people Personality Personality traits Population Population studies Public health Questionnaires Regression coefficients Sample size Social aspects Social ecology Social support Socioecological model Statistical analysis Statistical models Surveys |
title | Factors associated with eating behaviors in older adults from a socioecological model perspective |
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