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One slope does not fit all: longitudinal trajectories of quality of life in older adulthood

Ágnes Szabó, Martin Hyde Orcid Logo, Andy Towers

Quality of Life Research, Volume: 30, Issue: 8, Pages: 2161 - 2170

Swansea University Author: Martin Hyde Orcid Logo

Abstract

PurposeMaintaining or improving quality of life (QoL) in later life has become a major policy objective. Yet we currently know little about how QoL develops at older ages. The few studies that have modelled QoL change across time for older adults have used ‘averaged’ trajectories. However, this igno...

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Published in: Quality of Life Research
ISSN: 0962-9343 1573-2649
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa56664
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fullrecord <?xml version="1.0"?><rfc1807><datestamp>2021-08-16T10:36:36.4788618</datestamp><bib-version>v2</bib-version><id>56664</id><entry>2021-04-16</entry><title>One slope does not fit all: longitudinal trajectories of quality of life in older adulthood</title><swanseaauthors><author><sid>fce212ae306f4f36b2c328ec89c5da9b</sid><ORCID>0000-0002-9955-8121</ORCID><firstname>Martin</firstname><surname>Hyde</surname><name>Martin Hyde</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2021-04-16</date><deptcode>PHAC</deptcode><abstract>PurposeMaintaining or improving quality of life (QoL) in later life has become a major policy objective. Yet we currently know little about how QoL develops at older ages. The few studies that have modelled QoL change across time for older adults have used &#x2018;averaged&#x2019; trajectories. However, this ignores the variations in the way QoL develops between groups of older adults.MethodsWe took a theoretically informed &#x2018;capabilities approach&#x2019; to measuring QoL. We used four waves of data, covering 6 years, from the New Zealand Health, Work and Retirement Study (NZHWR) (N&#x2009;=&#x2009;3223) to explore whether distinct QoL trajectories existed. NZHWR is a nationally representative longitudinal study of community-dwelling adults aged 50&#x2009;+&#x2009;in New Zealand. Growth mixture modelling was applied to identify trajectories over time and multinomial regressions were calculated to test baseline differences in demographic variables (including age, gender, ethnicity, education and economic living standards).ResultsWe found five QoL trajectories: (1) high and stable (51.94%); (2) average and declining (22.74%); (3) low and increasing (9.62%); (4) low and declining (10.61%); (5) low and stable (5.09%). Several differences across profiles in baseline demographic factors were identified, with economic living standards differentiating between all profiles.ConclusionsThe trajectory profiles demonstrate that both maintaining and even improving QoL in later life is possible. This has implications for our capacity to develop nuanced policies for diverse groups of older adults.</abstract><type>Journal Article</type><journal>Quality of Life Research</journal><volume>30</volume><journalNumber>8</journalNumber><paginationStart>2161</paginationStart><paginationEnd>2170</paginationEnd><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>0962-9343</issnPrint><issnElectronic>1573-2649</issnElectronic><keywords>Capabilities approach; CASP; Latent class growth analysis; Longitudinal; Quality of life; Trajectory analysis</keywords><publishedDay>1</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2021</publishedYear><publishedDate>2021-08-01</publishedDate><doi>10.1007/s11136-021-02827-z</doi><url/><notes/><college>COLLEGE NANME</college><department>Public Health</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>PHAC</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2021-08-16T10:36:36.4788618</lastEdited><Created>2021-04-16T08:27:53.7811147</Created><path><level id="1">College of Human and Health Sciences</level><level id="2">Centre for Innovative Ageing</level></path><authors><author><firstname>&#xC1;gnes</firstname><surname>Szab&#xF3;</surname><order>1</order></author><author><firstname>Martin</firstname><surname>Hyde</surname><orcid>0000-0002-9955-8121</orcid><order>2</order></author><author><firstname>Andy</firstname><surname>Towers</surname><order>3</order></author></authors><documents><document><filename>56664__20219__33545d747db64b949b03d21786ad9dc9.pdf</filename><originalFilename>Szabo Hyde Towers - One slope does not fit all - accepted version.pdf</originalFilename><uploaded>2021-06-22T18:21:24.7936985</uploaded><type>Output</type><contentLength>357021</contentLength><contentType>application/pdf</contentType><version>Accepted Manuscript</version><cronfaStatus>true</cronfaStatus><embargoDate>2022-04-11T00:00:00.0000000</embargoDate><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2021-08-16T10:36:36.4788618 v2 56664 2021-04-16 One slope does not fit all: longitudinal trajectories of quality of life in older adulthood fce212ae306f4f36b2c328ec89c5da9b 0000-0002-9955-8121 Martin Hyde Martin Hyde true false 2021-04-16 PHAC PurposeMaintaining or improving quality of life (QoL) in later life has become a major policy objective. Yet we currently know little about how QoL develops at older ages. The few studies that have modelled QoL change across time for older adults have used ‘averaged’ trajectories. However, this ignores the variations in the way QoL develops between groups of older adults.MethodsWe took a theoretically informed ‘capabilities approach’ to measuring QoL. We used four waves of data, covering 6 years, from the New Zealand Health, Work and Retirement Study (NZHWR) (N = 3223) to explore whether distinct QoL trajectories existed. NZHWR is a nationally representative longitudinal study of community-dwelling adults aged 50 + in New Zealand. Growth mixture modelling was applied to identify trajectories over time and multinomial regressions were calculated to test baseline differences in demographic variables (including age, gender, ethnicity, education and economic living standards).ResultsWe found five QoL trajectories: (1) high and stable (51.94%); (2) average and declining (22.74%); (3) low and increasing (9.62%); (4) low and declining (10.61%); (5) low and stable (5.09%). Several differences across profiles in baseline demographic factors were identified, with economic living standards differentiating between all profiles.ConclusionsThe trajectory profiles demonstrate that both maintaining and even improving QoL in later life is possible. This has implications for our capacity to develop nuanced policies for diverse groups of older adults. Journal Article Quality of Life Research 30 8 2161 2170 Springer Science and Business Media LLC 0962-9343 1573-2649 Capabilities approach; CASP; Latent class growth analysis; Longitudinal; Quality of life; Trajectory analysis 1 8 2021 2021-08-01 10.1007/s11136-021-02827-z COLLEGE NANME Public Health COLLEGE CODE PHAC Swansea University 2021-08-16T10:36:36.4788618 2021-04-16T08:27:53.7811147 College of Human and Health Sciences Centre for Innovative Ageing Ágnes Szabó 1 Martin Hyde 0000-0002-9955-8121 2 Andy Towers 3 56664__20219__33545d747db64b949b03d21786ad9dc9.pdf Szabo Hyde Towers - One slope does not fit all - accepted version.pdf 2021-06-22T18:21:24.7936985 Output 357021 application/pdf Accepted Manuscript true 2022-04-11T00:00:00.0000000 true eng
title One slope does not fit all: longitudinal trajectories of quality of life in older adulthood
spellingShingle One slope does not fit all: longitudinal trajectories of quality of life in older adulthood
Martin Hyde
title_short One slope does not fit all: longitudinal trajectories of quality of life in older adulthood
title_full One slope does not fit all: longitudinal trajectories of quality of life in older adulthood
title_fullStr One slope does not fit all: longitudinal trajectories of quality of life in older adulthood
title_full_unstemmed One slope does not fit all: longitudinal trajectories of quality of life in older adulthood
title_sort One slope does not fit all: longitudinal trajectories of quality of life in older adulthood
author_id_str_mv fce212ae306f4f36b2c328ec89c5da9b
author_id_fullname_str_mv fce212ae306f4f36b2c328ec89c5da9b_***_Martin Hyde
author Martin Hyde
author2 Ágnes Szabó
Martin Hyde
Andy Towers
format Journal article
container_title Quality of Life Research
container_volume 30
container_issue 8
container_start_page 2161
publishDate 2021
institution Swansea University
issn 0962-9343
1573-2649
doi_str_mv 10.1007/s11136-021-02827-z
publisher Springer Science and Business Media LLC
college_str College of Human and Health Sciences
hierarchytype
hierarchy_top_id collegeofhumanandhealthsciences
hierarchy_top_title College of Human and Health Sciences
hierarchy_parent_id collegeofhumanandhealthsciences
hierarchy_parent_title College of Human and Health Sciences
department_str Centre for Innovative Ageing{{{_:::_}}}College of Human and Health Sciences{{{_:::_}}}Centre for Innovative Ageing
document_store_str 1
active_str 0
description PurposeMaintaining or improving quality of life (QoL) in later life has become a major policy objective. Yet we currently know little about how QoL develops at older ages. The few studies that have modelled QoL change across time for older adults have used ‘averaged’ trajectories. However, this ignores the variations in the way QoL develops between groups of older adults.MethodsWe took a theoretically informed ‘capabilities approach’ to measuring QoL. We used four waves of data, covering 6 years, from the New Zealand Health, Work and Retirement Study (NZHWR) (N = 3223) to explore whether distinct QoL trajectories existed. NZHWR is a nationally representative longitudinal study of community-dwelling adults aged 50 + in New Zealand. Growth mixture modelling was applied to identify trajectories over time and multinomial regressions were calculated to test baseline differences in demographic variables (including age, gender, ethnicity, education and economic living standards).ResultsWe found five QoL trajectories: (1) high and stable (51.94%); (2) average and declining (22.74%); (3) low and increasing (9.62%); (4) low and declining (10.61%); (5) low and stable (5.09%). Several differences across profiles in baseline demographic factors were identified, with economic living standards differentiating between all profiles.ConclusionsThe trajectory profiles demonstrate that both maintaining and even improving QoL in later life is possible. This has implications for our capacity to develop nuanced policies for diverse groups of older adults.
published_date 2021-08-01T04:12:21Z
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