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Article: Characteristics of urban neighbourhood environments and cognitive age in mid-age and older adults

TitleCharacteristics of urban neighbourhood environments and cognitive age in mid-age and older adults
Authors
KeywordsCognitive age
Machine learning
Neighbourhood socio-economic status
Parkland availability
Urban environments
Issue Date1-Sep-2023
PublisherElsevier
Citation
Health & Place, 2023, v. 83 How to Cite?
AbstractIn this cross-sectional study, we examined the extent to which features of the neighbourhood natural, built, and socio-economic environments were related to cognitive age in adults (N = 3418, Mage = 61 years) in Australia. Machine learning estimated an individual's cognitive age from assessments of processing speed, verbal memory, premorbid intelligence. A ’cognitive age gap’ was calculated by subtracting chronological age from predicted cognitive age and was used as a marker of cognitive age. Greater parkland availability and higher neighbourhood socio-economic status were associated with a lower cognitive age gap score in confounder- and mediator-adjusted regression models. Cross-sectional design is a limitation. Living in affluent neighbourhoods with access to parks maybe beneficial for cognitive health, although selection mechanisms may contribute to the findings.
Persistent Identifierhttp://hdl.handle.net/10722/346290
ISSN
2023 Impact Factor: 3.8
2023 SCImago Journal Rankings: 1.276

 

DC FieldValueLanguage
dc.contributor.authorSoloveva, Maria V-
dc.contributor.authorPoudel, Govinda-
dc.contributor.authorBarnett, Anthony-
dc.contributor.authorShaw, Jonathan E-
dc.contributor.authorMartino, Erika-
dc.contributor.authorKnibbs, Luke D-
dc.contributor.authorAnstey, Kaarin J-
dc.contributor.authorCerin, Ester-
dc.date.accessioned2024-09-14T00:30:21Z-
dc.date.available2024-09-14T00:30:21Z-
dc.date.issued2023-09-01-
dc.identifier.citationHealth & Place, 2023, v. 83-
dc.identifier.issn1353-8292-
dc.identifier.urihttp://hdl.handle.net/10722/346290-
dc.description.abstractIn this cross-sectional study, we examined the extent to which features of the neighbourhood natural, built, and socio-economic environments were related to cognitive age in adults (N = 3418, Mage = 61 years) in Australia. Machine learning estimated an individual's cognitive age from assessments of processing speed, verbal memory, premorbid intelligence. A ’cognitive age gap’ was calculated by subtracting chronological age from predicted cognitive age and was used as a marker of cognitive age. Greater parkland availability and higher neighbourhood socio-economic status were associated with a lower cognitive age gap score in confounder- and mediator-adjusted regression models. Cross-sectional design is a limitation. Living in affluent neighbourhoods with access to parks maybe beneficial for cognitive health, although selection mechanisms may contribute to the findings.-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofHealth & Place-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCognitive age-
dc.subjectMachine learning-
dc.subjectNeighbourhood socio-economic status-
dc.subjectParkland availability-
dc.subjectUrban environments-
dc.titleCharacteristics of urban neighbourhood environments and cognitive age in mid-age and older adults-
dc.typeArticle-
dc.identifier.doi10.1016/j.healthplace.2023.103077-
dc.identifier.pmid37451077-
dc.identifier.scopuseid_2-s2.0-85166471926-
dc.identifier.volume83-
dc.identifier.eissn1873-2054-
dc.identifier.issnl1353-8292-

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