RESEARCH ARTICLE
Active ageing profiles among older adults in
Spain: A Multivariate analysis based on SHARE
study
Fermina Rojo-PerezID1*, Vicente Rodriguez-Rodriguez1, Maria-Angeles Molina-Martinez2,
Gloria Fernandez-Mayoralas1, Diego Sanchez-GonzalezID3, Jose-Manuel Rojo-Abuin4,
Alba Ayala5, Carmen Rodriguez-BlazquezID6, Amaia Calderon-Larrañaga7, Oscar Ribeiro8,
Maria-João Forjaz9
1 Grupo de Investigacion sobre Envejecimiento (GIE), IEGD, CSIC, Madrid, España, 2 Departamento de
Personalidad, Evaluacio´n y Tratamiento Psicolo´gico, Facultad de Psicologı´a, Universidad Nacional de
Educacio´n a Distancia (UNED), Madrid, España, 3 Departamento de Geografı´a, Universidad Nacional de
Educacio´n a Distancia (UNED), Madrid, España, 4 Unidad de Analisis Estadistico (UAE), CCHS, CSIC,
Madrid, España, 5 Departamento de Estadı´stica, Universidad Carlos III de Madrid (UC3M), Madrid, España,
6 National Centre of Epidemiology and CIBERNED, Carlos III Institute of Health (ISCIII), Madrid, España,
7 Aging Research Center, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska
Institutet & Stockholm University, Solna, Sweden, 8 Center for Health Technology and Services Research
(CINTESIS), Department of Education and Psychology of the University of Aveiro, Aveiro, Portugal,
9 National Centre of Epidemiology and REDISSEC, Carlos III Institute of Health (ISCIII), Madrid, España
* fermina.rojo@csic.es
Abstract
Background
Following the active ageing model based on the Health, Lifelong Learning, Participation and
Security pillars, this research has a twofold objective: i) to classify older adults according to
active ageing profiles, taking into account the four pillars, and ii) to ascertain the relationship
between the profiles and personal and contextual factors, as well as well-being and quality
of life in old age.
Methods
A study sample of 5,566 Spanish older adults who participated in wave 6 of the Survey of
Health, Ageing and Retirement in Europe (SHARE) was included. Data were analysed in dif-
ferent steps applying several statistical analyses (Principal Component, Cluster, Discrimi-
nant, Multiple Correspondence and bivariate analysis with Pearson chi-square and
ANOVA).
Results
Five older adult profiles were obtained (I: with moderate activity; II: quasi-dependents; III:
with active ageing-limiting conditions; IV: with diverse and balanced activity; V: with excel-
lent active ageing conditions). The first three profiles were characterised by subjects with a
high average age, low educational level, who were retired or housewives, and who per-
ceived a moderate level of loneliness, satisfaction with the social network and quality of life,
PLOS ONE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 1 / 29
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Rojo-Perez F, Rodriguez-Rodriguez V,
Molina-Martinez M-A, Fernandez-Mayoralas G,
Sanchez-Gonzalez D, Rojo-Abuin J-M, et al. (2022)
Active ageing profiles among older adults in Spain:
A Multivariate analysis based on SHARE study.
PLoS ONE 17(8): e0272549. https://doi.org/
10.1371/journal.pone.0272549
Editor: Marı´a del Carmen Valls Martı´nez, University
of Almerı´a, SPAIN
Received: May 17, 2022
Accepted: July 21, 2022
Published: August 4, 2022
Copyright: © 2022 Rojo-Perez et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: Data Availability
Statement. The data underlying the results
presented in the study are distributed by SHARE-
ERIC (Survey of Health, Ageing and Retirement in
Europe – European Research Infrastructure
Consortium) to registered users through the
SHARE Research Data Center website only (http://
www.share-project.org/data-access.html). Access
to the data collected and generated in the SHARE
projects is provided free of charge for scientific
purposes, subject to European Union and national
as well as having a larger family network, but living in small households or alone. In contrast,
the latter two profiles showed better personal and contextual conditions, well-being and
quality of life.
Discussion and conclusions
The multidimensional approach to active ageing followed in this article has revealed the
presence of several older adult profiles, which are confined to groups with better or worse
active ageing conditions. In this context, if ageing is a process that reflects the previous way
of life, intervention priorities will have to consider actions that promote better conditions dur-
ing the life cycle.
Introduction and backgrounds
Population ageing is a global phenomenon with important regional differences. Scientific evi-
dence had already pointed to this trend across the board [1]. In Europe, the over-65s represent
more than a quarter of the population, having overtaken the number of teenagers and young
people aged between 15 and 24 years old [2]. In Spain, the percentage of older adults currently
stands at 20%, and is set to keep on rising to 36.8% in 2050 [2].
Different ageing studies take very approaches to the concept, from the pathological
approach to an active or successful ageing perspective [3, 4]. The ageing process does not
occur randomly, but instead is conditioned by biological, psychological, social and contextual
factors that influence ageing trajectories [5]. At older ages, these conditioning factors can trig-
ger health problems, disability and dependence [6], limiting the quality of life of ageing people
[7, 8]. The challenge is to fight disease and disability, trying to delay them in order to live inde-
pendently for as long as possible [9]. The population’s longevity, together with its life expec-
tancy and life trajectory, are key elements for considering that the ageing process is essentially
heterogeneous [10]. This vision of old age is reflected in the strategies for promoting positive
ageing trajectories [11], which enhance people’s autonomy and independence and their capac-
ities for action as social agents. An example of this is the World Health Organization (WHO)
Active Ageing (AA) paradigm [12, 13]. There are also numerous studies that underscore the
importance of specific aspects such as learning [14, 15] and the use of information and com-
munication technologies [16] in empowering older adults.
AA is construed as the process of optimising opportunities for Health, Lifelong Learning,
Participation and Security in order to improve ageing people’s quality of life [17]. The model,
which underpins a wide range of studies [12, 13], identifies determinants such as culture, gen-
der, personal, behavioural and economic factors as well as the physical and social environment
and health and social services. General research proves the consistency of a multifactorial
model [18]. Different studies base their approach on three key premises: 1) AA is defined as
multidimensional, both in its pillars and in the indicators that make up each of them; 2) these
dimensions are influenced by a variety of cross-cutting, personal and contextual determinants;
and 3) the result of the AA process is an improvement in quality of life [5, 19].
The WHO model-based AA analysis follows three main guidelines. The first emphasises
the four pillars, though they are not developed to the same extent, as a result of the different
amount of available data used in the quantitative methodology, as well as the inclusion of the
Lifelong Learning pillar after the seminal model (2002 versus 2015). Thus, Health is a widely
studied pillar [20], which can be used as an enabling factor for AA [21, 22] or as an outcome
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 2 / 29
data protection laws as well as the publicly
available Conditions of Use (http://www.share-
project.org/). The authors of this publication did
not have any special privileges to access or use the
data. http://www.share-project.org/home0.html
http://www.share-project.org/data-documentation/
waves-overview/wave-6.html.
Funding: Research of this paper is a part of i) the
QASP research project, funded by the Institute of
Health Carlos III, Intramural Strategical Action in
Health AESI 2018 (PI18CIII/00046); ii) it has also
been partially funded by REDISSEC (RD16/0005/
0002 and RD16/0001/0005, co-funded by
European Regional Development Fund/European
Social Fund “A way to make Europe” / ”Investing in
your future”) projects; iii) the R&D Activities
Program ENCAGEn-CM (H2019/HUM-5698)
funded by the Community of Madrid and co-funded
by the European Social Fund; iv) the ENVACES
R&D+i project (MINECO-FEDER, ref. CSO2015-
64115-R). Authors acknowledge support of the
publication fee by the CSIC Open Access
Publication Support Initiative through its Unit of
Information Resources for Research (URICI). The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
consistent with an active way of ageing [23–26]. The second pillar, Participation, tends to be
very widely represented in any of its meanings, from the most general, engaging in activities
[27–30], to other more specific, but highly relevant, activities such as volunteer work [31–36].
Many of these studies refer to participation to highlight its effects on health, well-being and
quality of life. Participation is linked to remaining physically active [23], developing social net-
works [37, 38] or staying on the job market [39] and contributing to productive activities [40].
The other pillars, Lifelong Learning [14, 15] or Security in its different facets [41] do not attract
as much progress in the scientific literature, perhaps, again, because the available data do not
favour it.
The second guideline is intertwined with the first because certain dimensions are often val-
ued as interactive factors in determining whether a person is actively ageing [42]. Thus, along
with the most frequently used purely demographic and individual determinants, such as age
and gender [18, 21, 43–45], great importance is attached to others, such as education [46], per-
sonal traits of a psychological nature [47], motivation [19, 29] or social and cultural values
[46]. Recently, environmental factors for AA, such as collaborative housing [48] or nursing
homes as places for care [27], or other factors in the social and political context that explain
social inequality, have gained importance [43, 49]. Finally, outcome variables to which AA has
been related are also acquiring relevance, such as happiness, life satisfaction, well-being and
quality of life [5, 19, 24, 50], to explain regional disparity in Europe according to their social,
cultural and institutional differences [23, 45, 51].
In this context, the AA model has become part of a stream of research that explores its
capacity to generate types of people and geographical spaces, measuring the degree of activity,
by means of quantitative variables [28, 50, 52]. In some cases, specific instruments have been
used, such as the AA index as a measure of inequality in general or between countries [45, 53–
58]. Other studies have attempted to operationalise the AA model empirically, without reach-
ing a consensus on how to formulate it [59]. At the same time, there are disparities in the types
and number of variables and measures and instruments considered in different quantitative
models [18, 60, 61] and qualitative approaches [29, 41]. In Europe, the SHARE dataset is
becoming a reference framework for the comparative study between countries, especially in its
longitudinal perspective and with regard to the study of some of the AA pillars and determi-
nants [62, 63]. In short, and taking into account the AA model’s possible limitations, the
importance of its multidimensionality marks a line of research that aims to take a more posi-
tive approach [21, 64], identifying groups of older adults according to how they age. In this
context, this study has aimed to i) establish profiles of older adults in Spain according to AA
pillars, and ii) examine the relationship between these profiles and personal and contextual
factors, as well as well-being and quality of life.
Material and methods
Data source, study design and sample
This paper has used the Survey of Health, Ageing and Retirement in Europe (SHARE) dataset
[63, 65, 66], a representative study of the European countries in which it is carried out. SHARE
project became the first European Research Infrastructure Consortium (ERIC) for running a
large-scale survey. The survey is reviewed and approved by the Ethics Council of the Max
Planck Society, and data collection procedures are subject to continuous ethics review.
SHARE-ERIC’s activities related to human subjects research are guided by international
research ethics principles such as the Respect Code of Practice for Socio-Economic Research
(professional and ethical guidelines for the conduct of socio-economic research) and respects
the Declaration of Helsinki in terms of anonymity of the participants and obtaining written
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 3 / 29
consents. Ever since it was created in 2004, SHARE has become an infrastructure for research-
ing and understanding population ageing in Europe from a life-course perspective using a
multidimensional and longitudinal approach, although not all countries participate in this net-
work. The target population is anyone aged 50 and over who lives in the country in question
on a regular basis. Information is collected through Computer-Assisted Personal Interviewing
(CAPI), and is harmonised using a questionnaire that is translated into national languages.
Data is collected and produced by modules, and an identifier is used to link data (individuals,
households) by wave and/or by module. More information on how the data are collected, eligi-
bility population, sampling procedures and documentation for each participant country, types
of respondents, ethical standards, sampling and other technical issues can be seen in the edited
book by Bo¨rsch-Supan and Ju¨rges [67].
This cross-sectional study is based on wave 6 (SHARE W6), release 6.1.0 (http://www.
share-project.org/data-documentation/share-data-releases.html) [68], in which 17 European
countries and Israel took part. The fieldwork was completed in 2015 [69]. The data are struc-
tured in a set of thematic files, which have been fully reviewed for the selection of the variables
of interest in this research. Where available, variables, indices or scales generated by the
SHARE project were also used [68]; otherwise, the original variables were used. Participants
were retained for Spain (n = 5,566).
In line with the AA model and its pillar structure, variables from all four pillars, (Health,
Lifelong Learning, Participation and Security) [17, 42] were used. The authors together
reviewed the files, selected the variables and their assignment to the four pillars in order to
reach a consensus. Thus, physical, sensory and mental health, functioning, support and health
services utilization variables were selected for Health. Lifelong Learning consists of informa-
tion about educational or training courses and skills. Participation is devoted to information
on leisure and community activities participation. Finally, the Security pillar includes variables
related to household economics. Variables reporting personal characteristics, area of residence
and perceptions of well-being and quality of life, as well as life satisfaction, were also used. For
the study of quality of life, we used the CASP-12 scale, a revised and adapted version of the
CASP-19 instrument on quality of life in older adults. This is a synthetic indicator, based on
the theory of human needs [70, 71], which measures the extent to which older adults’ needs
are satisfied in four dimensions: control, autonomy, self-fulfilment and pleasure. The instru-
ment facilitates comparability regardless of the context in which the information is collected
[72]. Its discriminatory and explanatory capacity [73] shows that it is an effective tool for mea-
suring quality of life in old age beyond the physical and mental capacities of older adults [74].
The variables selected and used, as well as their characteristics, can be seen in S1 Table. Var-
iables with more than 10% of cases with missing value after weighting were eliminated from
the analyses, except for the scale of social connectedness [66, 75] that reported 10.6% of miss-
ing cases. Information regarding financial gifts and help was discarded from the analysis due
to high floor effect that can lead to biased results [76].
Statistical analysis
All analyses have been run with SPSS v26. Based on the cross-sectional calibrated weights for
individuals, that reproduced the size of the national target population [68], relative or normal-
ised weights have been calculated by dividing the weight by the mean of weights and preserv-
ing the sample size [77]. When using the normalised weights, the estimates of means and
proportions are correct and the test statistics are not affected.
The variables have been recoded so that more positive conditions take higher values. How-
ever, other variables operate in the opposite direction (i.e. the higher the value, the worse the
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 4 / 29
condition) (S1 Table), such as perception of loneliness, units of alcohol consumed, limitations
(activities of daily living, instrumental activities, mobility), depression, use of the hospital ser-
vice (number of times and nights), number of illnesses, number of medications taken, number
of technical aids used (such as a cane or walking stick, a zimmer frame or walker, etc.), pain
intensity scale, number of reported pains and number of frailty symptoms.
The statistical analysis was carried out in five phases:
a. Firstly, the factor analysis technique was applied with the Principal Component Analysis
(PCA) extraction method [78] to examine the relationship between the variables selected
for the conformation of the AA pillars and reduce their dimensionality. Due to the com-
plexity of the study objective and, especially, the high number of variables required, a PCA
was carried out for each thematic set of variables according to the AA pillars (Health, Life-
long Learning, Participation and Security). The factor scores of the 18 Principal Compo-
nents (PC) of the four AA pillars obtained were retained in the data file to be used in the
next analytical phase.
b. The second phase consisted of applying Cluster Analysis (CLA) to obtain a homogeneous
grouping of older adult subjects according to each of the AA pillars, using the K-Means
algorithm, where “k” refers to the number of groups specified a priori by the analyst [78].
Due to the high number of PC, and following the analytical method of grouping variables
from the previous PCA, a CLA was performed for each AA pillar. In the Health, Lifelong
Learning and Security pillars, the initial cluster centroids were chosen randomly by the pro-
gramme (default option). However, in the Participation pillar the solution chosen by the
programme was not satisfactory, as almost all participants were clustered around the mean.
Thus, taking into account the factor structure, a solution was proposed in which the initial
centroids were provided so that they saturated in the first four PC of the pillar (PC-11 to
PC-14), leaving the last one (PC-15) in the mean (see components of this pillar in S2
Table). The classification obtained by CLA was validated by Discriminant Analysis.
c. The clusters resulting from CLA for each AA pillar were used in a third analytical phase to
obtain the types of the cluster categories by applying Multiple Correspondence Analysis
(MCA). This multivariate method is similar to PCA but for categorical variables, and allows
us to ascertain the type of variables from a multidimensional perspective [79]. MCA analy-
ses relationships between variables by representing the categories in a multidimensional
space [80], so that the distance between categories is used to establish the degree of similar-
ity and plot a perceptual graph [81], in which proximity between categories indicates associ-
ation, while remoteness is interpreted as independence.
d. Using the MCA category types, the fourth step was to assign each subject or participant in
the study to the corresponding AA profile, using the mean of the categories that formed
each type in the two MCA dimensions. These means served as centroids in performing a
subsequent CLA without centroid updates, so that each subject was assigned to the closest
type of categories. This resulted in a classification of subjects by AA profiles to be used in
the subsequent analytical phase.
e. Finally, to address the second objective of this study, i.e. to determine the relationship
between AA profiles and socio-demographic factors and quality of life conditions, bivariate
statistical analysis was applied (contingency tables with χ2 test) with categorical variables.
Furthermore, with the scale variables, an ANOVA (with Bonferroni test for multiple com-
parisons) was conducted to compare the AA profiles in each independent variable. Statisti-
cal significance levels were set at p< .05.
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 5 / 29
Results
Sample characteristics
The sample consisted of 5,566 participants (Table 1),with a mean age of 67.2 years, (Minimum,
Min: 51; Maximum, Max: 106; Mean Standard Error, MSE: 15), 53.8% of whom were women.
This sample remained in the education system for an average of 8.8 years (Min: 0; Max: 25;
MSE: 0.1), such that 39.4% of the people completed their primary education or the first stage
of basic education, and 23% secondary basic education (lower or second stage). As regards
activity, 38.8% of the subjects were retired, but almost a quarter remained active, and slightly
less than 3 out of 10 were engaged in housework. Two thirds of the older adults were married
or living with a partner, and the average household size was 2.2 members.
In residential environment terms, almost two thirds of the older adults resided in large or
small towns and half of them occupied dwellings in block buildings (3 or more flats), though
more than 4 out of 10 reported living in a block building.
As for other living conditions, older adults showed a mean loneliness score of 3.7 (Min: 3;
Max: 9; MSE: 3.7) (the higher the index, the higher the loneliness), and a mean quality of life
score (CASP-12) of 36.1 (Min: 12; Max: 48; MSE: 36.1) (higher numbers implying better qual-
ity of life). The level of satisfaction with life in general and with the social network obtained
mean values of 7.5 and 8.9, respectively (measured on a scale from 0: completely dissatisfied to
10: completely satisfied).
Investigating the relationship among the variables
The PCA performed for each AA pillar provided 18 PC (S2 Table). The Health Pillar-related
PCA was formed by 8 PC, explaining 65.7% of the variance. The Lifelong Learning showed 2
PC explaining an 81.2% of the variance. In the Participation, 29% of the participants had no
complete information, thus each variable with missing values was replaced with the mean of
the variable; 5 PC were retained explaining a 69.9% of the variance. Finally, in the Security Pil-
lar formed by economic variables, an overall 69.3% of the variance was explained by 3 PC.
Grouping participants based on the active ageing pillars
Applying CLA with the PC for each AA pillar resulted in 17 clusters (Table 2).
a. The CLA performed over the Health Pillar’s (H) main components resulted in 5 clusters:
H-1: Need of help for functioning (grouped 32.2% of the subjects); H-2: Moderate health
(51.5%); H-3: Bad health (6.6%); H-4: Unhealthy habits (8.9%); H-5: High hospital use
(0.8%). Consumption of a protein diet and alcohol is a main component that did not stand
out in any of the homogeneous groups, as in all of them it is around the mean. Based on
Discriminant Analysis we observed that 98.2% of originally grouped participants were cor-
rectly classified.
b. The CLA performed over the Lifelong Learning Pillar’s (L) main components grouped sub-
jects in 4 clusters: L-6: Low competence (classified 51.7% of cases); L-7: Competence and
training involvement (3.2%); L-8: High competence (42.3%) as the opposite group to L-6;
L-9: High training involvement (2.8%). 99% of originally grouped participants were cor-
rectly classified.
c. The CLA performed over the Participation Pillar resulted in 4 clusters (P), named as fol-
lows: P-10: Low social connectedness / moderate volunteering (34.9% of cases); P-11: Low
physical & moderate social-political activities (20.9%); P-12: Physical activities / social
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 6 / 29
Table 1. Characteristics of the sample.
Frequency (valid cases) Descriptive statistics
Variables and categories N % Min Max Mean MSE
Age in 2015 5566 51 106 67.2 0.15
Gender 5566
Male 2570 46.2
Female 2996 53.8
Years of education 5566 0 25 8.8 0.068
Level of education (based on the ISCED 1997) 5566
Level 0 –Pre-Primary education 722 13.0
Level 1 –Primary education or First stage of basic education 2194 39.4
Level 2 –Secondary basic education (lower or second stage) 1281 23,0
Levels 3–4 –Secondary education (upper & post) 747 13.4
Levels 5–6– Tertiary education (first & second) 622 11.2
Marital status 5566
Married, living with spouse 3714 66.7
Registered partnership 76 1.4
Married, not living with spouse 59 1.1
Never married 427 7.7
Divorced 302 5.4
Widowed 988 17.7
Current job situation 5521
Retired 2140 38.8
Employed or self-employed 1338 24.2
Unemployed 409 7.4
Permanently sick 226 4.1
Homemaker 1250 22.6
Other 158 2.9
Household size 5566 1 10 2.4 0.016
Household type 5566
Single household 1797 32.3
Household in couple & others 3769 67.7
Number of children 5566 0 12 2.2 0.02
Number of grandchildren 5566 0 20 2.1 0.037
Area of building 5200
Rural areas 372 7.2
Large/small towns 3262 62.7
A big cities/metropolitan areas 1566 30.1
Type of building 5231
Farm/family house/double house 2527 48.3
Building with 3+ flats or high-rise 2670 51,1
Housing with services for elderly/nursing home 34 0.6
Housing tenure regime 5521
Owner 5103 92.4
Others (cooperative, tenant, subtenant, rent free) 418 7.6
Loneliness (short version of R-UCLA Loneliness Scale) (high is lonely) 5213 3 9 3.7 0.019
Social network satisfaction (high is more satisfaction) 5252 0 10 8,9 0.017
Life satisfaction (high is more satisfaction) 5566 0 10 7.5 0.025
(Continued)
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 7 / 29
connectedness (28.2%); P-13: Cognitive activities (16.0%). The 96.5% of originally grouped
participants were correctly classified.
d. The CLA performed over the Security Pillar’s main components captured 4 clusters: S-14:
Optimal household economy (classified 3.1% of cases); S-15: Self-assessed high economic
status (38.9%); S-16: Self-assessed low economic status (57.8%) contrasts with the previous
group; S-17: High value of non-liquid assets (0.2%). 99.6% of originally grouped partici-
pants were correctly classified.
Active ageing profiles
The MCA gave a perceptual map with the solution obtained in the clustering of profiles
according to their AA pillar-related living condition (Fig 1), and shows the distribution of the
cluster categories on the plane formed by the coordinate axes. The figure showed two dimen-
sions with eigenvalues, or part of the variance explained in each dimension, higher than 1
(dimension 1: 1.721; dimension 2: 1.245) and together they accounted for 37.1% of the model
variability. The relationship of the categories on the plane shows several different subject pro-
files which, by convention, have been listed starting with the lowest dimension 1 or x-axis
value, resulting in a counter-clockwise grouping. The first three profiles are to be found on the
lowest scores of dimension 1, as opposed to the rest of the profiles, to be found on the positive
values.
Profile I (people with moderate activity) included subjects with a low participation in phys-
ical activities, together with a moderate frequency of social and political tasks, and at the same
time an optimal economic assessment of their household according to the level of income.
Profile II (quasi-dependent persons) was characterised by poor health and functioning
conditions and frailty and, consequently, by the use of health services (primary and hospital
care), and the need for help from others in activities of daily living, as well as the use of techni-
cal aids. This profile is only associated with the Health pillar.
Profile III (people with active ageing-limiting conditions) grouped subjects with low self-
assessed competence in reading, writing and computer skills, low consumption of fruit/vegeta-
bles and high consumption of alcoholic beverages, as well as a moderate perception of sensory
health (unhealthy habits cluster), low self-perception of household economic status, low social
connectedness and moderate participation in voluntary activities. This profile is associated
with the four AA pillars.
Profile IV (people with diverse and balanced activity) was characterised by grouping sub-
jects with a high level of competence in reading, writing and computer skills, a moderate state
of health together with no need for technical or care aids, and a high frequency of participation
in activities requiring moderate or vigorous physical ability, low participation in cognitive
activities and moderate to high social cohesion.
Profile V (people with excellent active ageing conditions) brought together subjects who
were relatively more heterogeneous than in the previous profiles, so the cluster category
Table 1. (Continued)
Frequency (valid cases) Descriptive statistics
Variables and categories N % Min Max Mean MSE
CASP-12 index for Quality of Life and Wellbeing (high is better QoL) 5087 12 48 36.1 0.091
In shadow, scale variables.
Min: Minimum; Max: Maximum; MSE: Mean Standard Error.
https://doi.org/10.1371/journal.pone.0272549.t001
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 8 / 29
Table 2. Cluster analysis through k-means method.
Cluster and final cluster centres
Principal Components of Health
Pillar
H-1: Need of help for
functioning
H-2: Moderate health H-3: Bad health H-4: Unhealthy
habits
H-5: High
hospital use
PCH-1: Bad health 0.456 -0.425 1.271 -0.117 -0.053
PCH-2: Bad functioning 0.385 -0.247 -0.210 0.148 0.515
PCH-3: Good cognitive functioning -0.187 0.138 0.091 -0.139 -0.550
PCH-4: High use of hospital services -0.288 -0.133 1.725 -0.162 7.687
PCH-5: Good sensory health -0.216 0.079 -0.215 0.464 0.253
PCH-6: Do not need help / Do not
use of technical aids for activities
-0.938 0.582 0.293 -0.159 -0.427
PCH-7: High protein diet -0.066 0.014 -0.043 0.209 -0.218
PCH-8: High green-dairy/Low
alcohol consumption
0.296 0.236 0.095 -2.537 0.135
Number of Weighted Cases in each
Cluster (valid cases: 5555) N (%)
1788 (32.2) 2865 (51.5) 365 (6.6) 492 (8.9) 45 (0.8)
98,2% of original grouped cases correctly classified.
Cluster and final cluster centres
Principal Components of Learning
Pillar
L-6: Low competence L-7: Competence & training
involvement
L-8: High competence L-9: High training
involvement
PCL-9: Good writing/reading/ICTs
Skills
-0.743 0.558 0.865 0.011
PCL-10: Educational-training
involvement
-0.219 1.692 -0.205 5.145
Number of Weighted Cases in each
Cluster (valid cases: 5184) N (%)
2680 (51.7) 165 (3.2) 2193 (42.3) 147 (2.8)
99,0% of original grouped cases correctly classified.
Cluster and final cluster centres
Principal Components of
Participation Pillar
P-10: Low social
connectedness / Moderate
volunteering
P-11: Low physical &
moderate social-political
activities
P-12: Physical activities /
Social connectedness
P-13: Cognitive
activities
PCP-11: High frequency of cognitive
activities performance
-0.320 -0.250 -0.420 1.750
PCP-12: High frequency of physical
activities performance
0.090 -1.390 0.830 0.160
PCP-13: High frequency of social &
political activities involvement
-0.260 0.590 -0.010 -0.200
PCP-14: High frequency of
volunteering activities performance
0.420 -0.190 -0.210 -0.290
PCP-15: High social connectedness -0.660 0.180 0.660 0.020
Number of Weighted Cases in each
Cluster (valid cases: 5566) N (%)
1938 (34.9) 1164 (20.9) 1570 (28.2) 893 (16.0)
96,5% of original grouped cases correctly classified.
Cluster and final cluster centres
Principal Components of Security
Pillar
S-14: Optimal household
economy
S-15: Self-assessed high
economic status
S-16: Self-assessed low
economic status
S-17: High value of
non-liquid assets
PCS-16: High self-perception of the
household economic status
-0.855 0.979 -0.609 -1.560
PCS-17: High value of household
non-financial/non-liquid assets &
expenditure
-0.346 0.054 -0.054 11.790
PCS-18: High household economic
health
4.565 0.005 -0.247 -0.899
(Continued)
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 9 / 29
centroids are further apart (see Fig 1). This profile represents subjects with a high frequency of
educational or training courses attendance and of performing cognitive activities, a very high
level of financial assets or illiquid goods, as well as a high economic self-positioning of the
household.
In the perceptual map there were two clusters, H-5 (older adults with high hospital service
use) and L-7 (people with high level of competence and participation in learning courses) not
clustered and distanced from the centre of the coordinate axes (0,0; 0,0), which showed little
association with other AA profiles.
Profile I clustered 12.7% of participants, profile II 8.1%, profile III 32.0%, profile IV 30.4%
and profile V 12.8%. The non-grouped clusters (H-5 and L-7) classified 0.8% and 3.2% of the
participants, respectively.
Relationship between active ageing profiles and sociodemographic,
contextual, and quality of life conditions
The results indicated a statistically significant association with all the variables analysed, and
showed two behaviours depending on whether the mean values of the AA profiles were above
or below the overall mean value (Fig 2) (see also the model and key results in S1 Fig). Thus,
subjects in profiles I, II and III, as well as cluster H-5, were characterised by being older than
average, having been in the education system for fewer years (well below the average of 8.8
years), showing a higher than average level of perceived loneliness, and lower satisfaction with
their social network (except profile III) and life in general, as well as lower quality of life and
subjective well-being. In home and family terms, these subjects live in small households
(except profile III), and reported having an above-average number of children and grandchil-
dren (2.2 and 2.1, respectively).
At the other end of the scale were profiles IV and V together with cluster L-7. These persons
were younger (below average, 67.2 years), stayed in the educational system for more than 8.8
years on average (cluster L-7 being an extreme case as it almost doubled this average value), did
not report perceiving loneliness, and achieved a higher level of satisfaction with the social net-
work and with life as a whole, as well as a higher quality of life and subjective wellbeing.
Table 3 shows the comparison of means between the profiles, where values not sharing the
same subscript (a, b, c, d, e) are significantly different. Consequently, age-related differences
were observed between all AA profiles, except for cluster H-5, which showed no statistically
significant differences with profile I (they share the same subscript), and cluster L-7, which
also showed no differences with profile IV.
Two scenarios were also observed in terms of categorical variables (Table 4): on the one
hand, subjects in profiles I, II and III and cluster H-5, and, on the other, profiles IV and V
and cluster L-7, although with some specific differences, such as those concerning gender,
Table 2. (Continued)
Cluster and final cluster centres
Principal Components of Health
Pillar
H-1: Need of help for
functioning
H-2: Moderate health H-3: Bad health H-4: Unhealthy
habits
H-5: High
hospital use
Number of Weighted Cases in each
Cluster (valid cases: 5521) N (%)
172 (3.1) 2150 (38.9) 3190 (57.8) 10 (0.2)
99,6% of original grouped cases correctly classified.
Notes: PC: Principal Component. Clusters are numbered correlatively through all clusters obtained in the analysis: H: Cluster of Health Pillar. L: Cluster of Learning
Pillar. P: Cluster of Participation Pillar. S: Cluster of Security Pillar.
https://doi.org/10.1371/journal.pone.0272549.t002
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 10 / 29
with a predominance of women in almost all profiles, of widowed people and to a lesser extent
of single and divorced people. In line with the lower number of years spent in the educational
system, there were more subjects from the first set of profiles (I, II, III, group H-5) in the lower
levels of education, with a predominance of retirees and people with some kind of disability
(permanently sick), unemployed and people engaged in housework, living in single-person
Fig 1. Plot of category clusters.
https://doi.org/10.1371/journal.pone.0272549.g001
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 11 / 29
Fig 2. Active ageing profiles according to personal and contextual characteristics.
https://doi.org/10.1371/journal.pone.0272549.g002
Table 3. Active ageing profiles according to personal and contextual characteristics (t-test with post-hoc Bonferroni correction for means comparisons).
Profiles of Active Ageing (% of cases)
Personal and contextual variables (mean
values)
Profile I
(12.7%)
Profile II
(8.1%)
Profile III
(32.0%)
Profile IV
(30.4%)
Profile V
(12.8%)
Not grouped (H-
5) (0.8%)
Not grouped (L-
7) (3.2%)
Mean
Age in 2015 77.6a 69.8b 67.8c 62.4d 65.9e 77.8a 61.1d 67.2
Years of education 6.4a 6.9a,b 7.3b 10.1c 11.2d 6.9a,b 15.4e 8.8
Loneliness (short version of R-UCLA
Loneliness Scale) (high is lonely)
4.5a 4.5a 3.8b 3.4c 3.5c 4.6a 3.4c 3.7
Social network satisfaction (high is more
satisfaction)
8.6a 8.8a,b 9.0b 9.0b,c 9.0b,d 8.8a,b 9.1b,e 8.9
Life satisfaction (high is more satisfaction) 7.1a 6.6b 7.3c 7.9d 8.0d 7.1a,b,c 8.5e 7.5
CASP-12 index for Quality of Life and
Wellbeing (high is better QoL)
30.8a 32.6b 34.5c 38.5d 38.9d 30.3a,b 40.9e 36.1
Household size 2.1a 2.3a,b 2.4b 2.7c 2.3a,b 2.2a,b,c 2.3a,b 2.4
Number of children 2.6a 2.5a 2.3b 2.0c 2.0c,d 2.3a,b,c,d 1.6d 2.2
Number of grandchildren 3.7a 2.9b 2.3c 1.4d 1.7d 3.0a,b,c 0.7e 2.1
Notes: Values in the same row and subtable not sharing the same subscript (a, b, c, d, e) are significantly different at p<0.05 in the two-sided test of equality for column
means. Cells with no subscript are not included in the test. Tests assume equal variances (1).
(1) Tests are adjusted for all pairwise comparisons within a row of each innermost subtable using the Bonferroni correction.
In bold, values above the mean of the total population. In shadow, not grouped clusters.
Profile I: people with moderate activity; Profile II: quasi-dependent persons; Profile III: people with active ageing-limiting conditions; Profile IV: people with diverse and
balanced activity; Profile V: people with excellent active ageing conditions
https://doi.org/10.1371/journal.pone.0272549.t003
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 12 / 29
Table 4. Active ageing profiles according to personal and contextual categorical characteristics (contingency table; % within active ageing profile).
Variables and categories (% of
cases)
Profile I
(12.7%)
Profile II
(8.1%)
Profile III
(32.0%)
Profile IV
(30.4%)
Profile V
(12.8%)
Not grouped
(H-5) (0.8%)
Not grouped
(L-7) (3.2%)
%
Gender χ2 78.780 p-
value = 0.000Male 35.9 41.2 46.0 53.4 45.5 31.1 38.6 46.2
Female 64.1 58.8 54.0 46.6 54.5 68.9 61.4 53.8
Marital status χ2 486.783 p-
value = 0.000Married, living with spouse 57.8 54.2 67.1 73.1 71.4 55.6 54.5 66.8
Registered partnership 0.1 0.4 0.2 3.5 1.1 2.2 0.0 1.3
Married, not living with spouse 1.1 1.1 1.0 0.9 1.8 0.0 0.0 1.1
Never married 3.2 10.0 9.6 6.8 5.9 6.7 15.3 7.7
Divorced 1.3 6.7 5.4 7.4 3.4 0.0 8.0 5.4
Widowed 36.4 27.5 16.7 8.2 16.3 35.6 22.2 17.8
Education (ISCED 1997 coding) χ2 1351.534 p-
value = 0.0000 Level 0 –Pre-Primary
education
30.6 18.2 17.4 4.7 3.2 22.2 0.6 13.0
Level 1 –Primary education or
First stage of basic education
45.0 50.9 49.3 29.9 31.0 46.7 12.5 39.4
Level 2 –Secondary (Lower) or
second stage of basic education
14.3 24.0 20.3 31.4 22.9 24.4 2.3 23.0
Levels 3–4 –Secondary (Upper &
Post) education
5.5 1.8 8.2 20.7 19.7 4.4 33.5 13.4
Levels 5–6– Tertiary (First &
Second) education
4.7 5.1 4.8 13.3 23.2 2.2 51.1 11.2
Current job situation χ2 807.523 p-
value = 0.000Retired 45.6 50.4 41.4 30.6 43.1 34.1 17.6 38.8
Employed or self-employed 5.1 8.4 19.8 36.4 27.0 4.5 61.4 24.2
Unemployed 6.6 7.8 7.8 9.8 1.7 0.0 8.0 7.4
Permanently sick 9.8 6.7 4.9 0.8 3.1 13.6 0.0 4.1
Homemaker 23.2 24.2 24.5 20.8 23.5 38.6 7.4 22.6
Other 9.6 2.4 1.6 1.5 1.7 9.1 5.7 2.8
Household type χ2 250.154 p-
value = 0.000Single household 48.2 47.4 33.0 20.5 28.9 37.8 47.5 32.3
Household in couple & others 51.8 52.6 67.0 79.5 71.1 62.2 52.5 67.7
Area of building χ2 108.021 p-
value = 0.000Rural areas 7.6 6.2 8.7 5.4 7.5 11.6 6.6 7.2
Large/small towns 59.5 66.5 65.4 66.8 48.2 60.5 59.2 62.7
A big cities/metropolitan areas 32.9 27.3 25.9 27.8 44.4 27.9 34.2 30.1
Type of building χ2 110.210 p-
value = 0.000Farm/family house/double house 52.7 51.3 48.0 51.1 36.8 50.0 47.0 48.3
Building with 3+ flats or high-
rise
44.6 48.7 51.5 48.5 63.0 45.5 53.0 51.0
Housing with services for
elderly/nursing home
2.7 0.0 0.5 0.4 0.1 4.5 0.0 0.7
Housing tenure (owner, others) χ2 30.090 p-
value = 0.000Owner 91.1 87.3 92.5 93.5 92.7 93.2 98.3 92.4
Others (cooperative, tenant,
subtenant, rent free)
8.9 12.7 7.5 6.5 7.3 6.8 1.7 7.6
Profile I: people with moderate activity; Profile II: quasi-dependent persons; Profile III: people with active ageing-limiting conditions; Profile IV: people with diverse and
balanced activity; Profile V: people with excellent active ageing conditions.
In shadow, not grouped clusters.
https://doi.org/10.1371/journal.pone.0272549.t004
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 13 / 29
households, more in single-family dwellings and even in old people’s homes (profile I and
cluster H-5). Profiles IV and V and cluster L-7 were characterised by having a high level of
education, being married or divorced, still in employment, although they were also retired or
engaged in housework, lived in households with their partner, or in households with their
partner and with other people.
As for the size of the area of residence, almost two thirds of the population lived in
medium-sized and large urban areas, and no clearly distinct pattern was observed between
profiles I, II and III versus IV and V, as was the case with the type of residence building (3 or
more storey-buildings in more than half of the cases). As for dwelling tenure status, more than
9 out of 10 older persons owned their dwelling, a status that was higher in profiles III to V,
and clusters H-5 and L-7.
Discussion
This study establishes older adult profiles according to the four AA pillars in Spain, and exam-
ines relationships between AA profiles and personal and contextual factors, well-being and
quality of life. By doing so, it fills a gap in previous research: in scientific literature, the lack of
consensus on formulating the AA model has been conditioned by discrepancies in materials
and methods from a multidimensional perspective [59, 60].
As regards the first objective, consideration was given to AA’s multidimensionality based
on the construction of four pillar-related indicators: Health, Lifelong Learning, Participation
and Security. The aim was to overcome reductionist approaches that do not address the theo-
retical model or others that do not distinguish between construct criteria and determinants
[64], in order to consider other analytical or methodological approaches, such as the genera-
tion of the AA index [82] the empirical validation of the AA model [18] or the study of AA
and its impact on survival [24].
Each pillar was built with multiple indicators from the SHARE dataset [63, 65, 66]. Other
authors, working with the same survey [24, 74, 83] or with other data [18], arrived at a similar
selection of health domain-related indicators (diseases, dependency and physical or cognitive
functioning) [84]. This paper has expanded the health-related indicators by adding others
linked to the use of medical services [85], nutrition [86] and alcohol consumption [87]; all of
them have been related to functionality, morbidity and/or mortality [85–87]. As regards the
Participation pillar, SHARE provides information on the type of activities carried out and
their frequency, usually considered by most authors [88–92]. For the Lifelong Learning pillar,
consideration was given to basic skills (reading, writing or computer use) and involvement in
training activities [93], while in the Security pillar, measures related mainly to financial secu-
rity were considered, due to the limitation of the source. Other studies have approached Secu-
rity as a pillar of manageable living conditions, such as physical security in the face of
dependency [94], or the intuitive and lay understanding of older adults themselves [41].
The analytical procedure for handling all the information was planned and executed in
successive phases, starting with the identification of patterns of relationships between variables
based on PCA. The CLA conducted for each AA pillar has reflected how different older adults
are as they grow older, revealing a wide variety of old age states, as the result of a process in
which opportunities are used unequally within each pillar, as stated in the very definition of
AA [17, 42]. Furthermore, this very powerful analytical technique is influenced by the data set
used and the research strategy [95]. The resultant classification is similar to that obtained in
other available reports [96]. For instance, more than half of the subjects were classified in the
"moderate health" cluster of the Health pillar. According to the National Health Survey [97],
45.5% of older adults in Spain regard their health to be good or very good. Slightly more than
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 14 / 29
half of the older adults were grouped in the "low competence" cluster of the Lifelong Learning
pillar; according to the same report, there is a predominance of older adults with primary edu-
cation and no education [96]. As for the Security pillar classification, the fact that almost 60%
of the subjects are grouped in the "Self-assessed low economic status" cluster could be
explained by the volume of inactive population in the sample studied (around 24% were still
employed) and the effect of retirement on income [98]. This is in line, firstly, with the individ-
uals’ own self-perception of their financial resources [99] and the reported difficulties in
making ends meet, which are particularly noticeable in Eastern and Southern European coun-
tries [100], and, secondly, with the fact that they are close to the poverty line [96] which could
be a limiting factor in promoting AA and enhancing the quality of life of older adults.
An optimal combination of the Health, Lifelong Learning, Participation and Security pillars
will be key to achieving AA. Most authors hold this assumption and there have been attempts
to build it into empirical models [101–103], yet few have succeeded in showing the interdepen-
dence between some pillars and others to show profiles of older adults ageing along diverse tra-
jectories. This paper has demonstrated the interdependence of the pillars, giving five main
profiles, which in turn were related to personal and contextual factors as well as to measures
of well-being and quality of life.
Worth noting is that engaging in activities is present in all profiles except the one defined
only by the Health pillar (profile II). Health, through the multiple measures used, either as a
factor or as an outcome, is another area that is closely linked with the activity profiles. Indeed,
a low perception of health, a limited level of functioning and unhealthy habits lead to less
activity among older adults [28, 104], while more favourable health conditions encourage lei-
sure and participation activities, such as volunteer work [33, 105]. This is probably due to the
fact that Participation is a cornerstone of the AA framework [92, 106] and is a defining ele-
ment, as opposed to other related terms such as successful, productive, or positive [47, 84].
Moderate or high participation is related to moderate health and economic conditions. These
variables are associated in a multitude of studies and their interdependence is clearly evident
[52, 107, 108]. Health and Security seem to be the necessary elements [22, 41] underpinning
Participation [106]. In addition, better conditions in the Lifelong Learning pillar [15] were
related to higher activity profiles.
By analysing profiles of older adults in Spain, this research has identified a wide range of
factors that give them interpretative consistency, but which do not always match those offered
in AA literature, either because they do not follow the same theoretical basis or because they
do not always use the same analytical methodology. That is why the AA profiles are con-
structed with quantitative methods that combine independent variables, generally at the indi-
vidual level, with others that express the results of the AA process or other related facts, such as
quality of life or subjective well-being as outcome variables. As a consequence, it is often hard
to clearly distinguish between dimensions, determinants and outcomes, because the analyses
are too closely linked to the available data. However, so of the many different factors that influ-
ence older adults’ active behaviours are far more prevalent than others.
The basic demographic variables, age and gender, are part of the most common interpre-
tative construct because they feature in all studies, whatever their type. Age plays a preferential
role, yet it tends to act in two directions: firstly, by appearing in the least active groups [109]
and, secondly, by influencing the reduction in the number and type of activities as the popula-
tion ages [28, 52], although it is not always documented to work this way [110]. Similarly, the
age variable shows a different association by type of active elderly, depending on whether the
activities are carried out at home (at older ages) such as family help or home maintenance, or
outside the home (at younger ages) such as volunteer work [95]. The fact is that, by including
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 15 / 29
other age-related variables, this activity trajectory is also related to living without a partner,
with lower economic income [104] and a decrease in personal well-being [50].
Women’s involvement is greater in some specific profiles, such as those involving caring
for people or activities in the home, or less when it comes to profiles of people still linked to
the world of work or volunteer work [95, 109–111]. In the case of Spain, the life trajectory of
these post-Civil War (post-1939) generations, marks an appreciable difference in gender roles,
although recently women seem to be more interested in carrying out ’novel’ and motivating
activities, which are more rewarding and which allow them to recover a role hitherto not usu-
ally assigned to them [29]. Men of these generations behave more conservatively and are more
attached to the closer and less active social community space.
The level of education, measured by the number of years spent in the system and the level
attained, is another factor that conditions the activity profile, through general rules: a lower
educational level tends to be associated with less activity [28, 110] and less rewarding or moti-
vating activities, but of a compulsory nature in the family sphere [110]. The profiles obtained
also show intergenerational educational level-related gains. For instance, profiles IV and V are
more defined by the Lifelong Learning, with younger ages and a higher level of education. Dif-
ferent studies point to the country’s older adults having higher levels of education, making it
possible to reduce the gender gap in old age [112].
From a life course perspective, the population studied includes people who are old enough
to be retired from work or who are carrying out household tasks, as the main activity-related
groups. Both can guide their transition into retirement through a variety of possibilities [113],
from those requiring remuneration to those undertaken on a voluntary basis [114] or to main-
tain intergenerational care relationships [115]. However, activity-relatedness is not a factor in
many AA studies, probably because of the limited ability to discriminate if the vast majority of
the population is already retired or because it is mediated by other variables such as age [116].
However, this factor becomes relevant when analysed together with many others to relate AA
to quality of life [48, 56].
Another way of influencing activity is through concomitant variables, such as level of
income, so that education and economy are associated in determining activity profiles [105],
or marital status to indicate that people who live alone and have a low level of education
behave in a similar way [95]. Precisely, beyond marital status, the form of cohabitation, the
size of the household and having children and grandchildren are relevant variables in the
differential characterisation of activity profiles. The key could be found in whether there are
children (or even grandchildren) in the household, or within the family network but living
outside the household, in more or less close environments and with more or less frequent con-
tacts in an ascending familialism or supportive-at-distance typology [117]. In the first case, a
larger household size and reporting having few children and grandchildren is consistent with a
profile of younger people, and, in general, men, people living in a couple and with others, pos-
sibly children yet to be emancipated, who maintain a diverse and balanced activity (profile
IV). Something similar happens with profile V, but in this case they would be women. At the
same time, having more children and grandchildren corresponds to low activity profiles (pro-
files I and II): people living alone, in smaller households, older and, above all, women. Yet hav-
ing more children and grandchildren could also tend to lead to activities in the home or
family care environment that compete with other leisure and participation activities for the
person’s available time, in order to reconcile tasks of different types and nature [110]. The lat-
ter could be the case of the profile of limiting conditions for AA (profile III), which is observed
among not very old women who say that they have more children and grandchildren both
inside and outside the home, and which would also correspond to a descending familialism
typology and activity based on intergenerational family solidarity provided by older women
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 16 / 29
[118]. In any case, profiles I, II and III show higher reported loneliness, compared to lower
scores for profiles IV and V, which would be related not so much to the size of the family net-
work but rather to other factors such as increasing age and changes and lost in marital status,
income, self-rate health, cognitive functioning and depression [119, 120], aspects that are also
related to limiting conditions for AA and maintaining a good quality of life [121].
Personal motivation (or a lack thereof) as well as personal rewards (life satisfaction) and
social rewards (social networks, avoidance of loneliness) also contribute to understanding
the active behaviour of older adults [106]. It has been found that having a higher number of
people in one’s social network is associated with higher levels of activity, while a less dense net-
work is associated with lower activity, although perceived support may act in the opposite
direction [28, 104]. On the other hand, the importance of the social and community environ-
ment in which the activities are carried out must be assessed as a mechanism for reinforcing
them [106].
As regards other contextual conditions, older adults tend to reside in cities, especially
medium-sized ones [122], which mirrors the process of urbanisation and demographic ageing
[123–125]. In this study, no homogeneous pattern has been observed according to the two
large profile groups, such that both profile I and V subjects reside in large urban and metropol-
itan areas, while the remaining ones do so in smaller cities. In any case, the trend towards
urbanisation has led to the development of a specific city friendliness programme in order to
optimise the living conditions and quality of life of older adults [30, 126].
With respect to the residential environment, home ownership is the most significant
regime in Spain compared to other neighbouring countries [127], and among the older popu-
lation it reaches higher proportions in line with their age and the time they have had to acquire
it [128]. The results show that people with the worst AA conditions (profiles I, II and III,
located in the low scores of dimension 1 of the perceptual map) showed slightly lower percent-
ages of ownership compared to the profiles of better positioned subjects, in accordance with
their greater purchasing power. In relation to the type of residence dwelling, two situations
were observed; on the one hand, older adults with a moderate active profile, living to a greater
extent on a farm or in family housing, in line with their location in smaller residential areas,
and, on the other hand, the profiles of younger people with better AA conditions, living in
housing in block buildings in line with their settlement in large cities and metropolitan areas.
In Spain, part of the older population faces the problems of an ageing housing stock character-
ised by a lower level of amenities (lifts, heating, air conditioning) and the need for renovations,
which worsen their isolation, hinder the desire to grow old at home with autonomy and inde-
pendence, and jeopardise the promotion of AA [128, 129].
Other factors may also influence the level of activity, but their effects are not differentiated
because they are incorporated into the more general variables. Something very similar happens
when we try to measure the impact of carrying out more or less activities of one type or
another on personal well-being, quality of life or satisfaction with it. These are very general
social and multidimensional constructs, in which it is not the influence of all their condition-
ing factors is not easily identifiable, and their effects may be contradictory depending on the
research design and the data used [50, 130]. The relationship between AA and personal well-
being (including life satisfaction, quality of life, satisfaction with social networks, absence of
perceived loneliness) has been highlighted in the profiles of older adults who are more compe-
tent and with better personal and contextual conditions to have a high level of activity, in line
with the high association of these constructs [19, 92].
Constructing an ageing model based on a broad set of variables, in order to identify profiles
of older adults with different degrees of activity, is a significantly increasing trend in the litera-
ture, and one that uses a methodology based on individual data with multidimensional
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 17 / 29
variables: some that measure different activities, the "process" variables [130] while others mea-
sure the person’s situation and which the AA model accepts as determining factors. Yet the
multidimensional approach is also entails far more complex, as this paper has shown with
regard to the construction of AA profiles. The use of quantitative data, from SHARE or other
European and North American databases, has highlighted the potential of this classification
strategy, both in terms of the activities analysed and the determinants that serve to explain the
types of activity and/or profiles of older adults, measured from different perspectives (individ-
ual, countries) and supported by different theories [28, 38, 50, 52, 92, 109, 110, 131–133]. This
paper has also revealed a far from negligible diversity of results influenced by the population
samples and the variables selected and available for analysis [104, 133, 134]. Furthermore, one
must not lose sight of the interpretative capacity of using qualitative information in the study
of AA profiles [29]. The tendency, however, is that the WHO AA model is not usually consid-
ered as the reference to be followed in studies on activity profiles and older adults, and when it
is, not all dimensions and determinants are covered [133]. It is much more common to use
various unidimensional, multidimensional or behavioural models, according to Boudiny
[135], based on successful, healthy or productive ageing theories, using specific sources that do
not make it easy to standardise results. The sample of studies cited above are good evidence of
this.
Limitations and future lines
It must be noted that this research was subject to certain limitations. The first stems from the
difficulty of finding data on AA [136]. This study used a database, the SHARE project, which is
characterised by its rich multidimensional design, and the fact that it studies a large number of
countries, thus permitting cross-sectional and longitudinal comparative studies. However, this
survey is not designed to specifically survey AA. So, from a thematic approach, this dataset
does not offer all the information defined in the AA paradigm [17, 42]. In this regard, an
unequal number of variables have been used per pillar, which also conditions the different
number of variables involved in its construction, on the one hand, and a possible bias in the
results, on the other.
The larger number of indicators available in the SHARE survey matches the areas of great-
est scientific development within AA, namely Health and Participation, with a lower presence
of questions related to the Lifelong Learning and Security pillars, despite their proven rele-
vance in positive ageing trajectories [14, 103, 137].
As regards the variables selected in the Participation pillar, almost 30% of the participants
had missing values. Therefore, during the PCA of this pillar, these values were replaced by the
mean of the variable. This may have influenced the results obtained for this pillar in the first
CLA run, as almost 95% of the cases were grouped around a single cluster. This was the reason
why, for this set of variables, the initial centroids were user-defined.
Another limitation of the study is the database date, 2015 year. SHARE-ERIC Consortium
carried out two subsequent waves in 2017 (wave 7) and 2019 (wave 8). Wave 7 lacked the
appropriate information about social networks as a relevant domain in AA. The wave 8 did
not have the data available at the time of execution of our research because the field work was
interrupted by the COVID-19 pandemic [138]. This paper, based on wave 6, showed the gen-
eration of active ageing profiles, and we assume that the changes in the two following waves
would be smaller due to the short difference in dates. However, the COVID-19 pandemic out-
break could have altered older adults behaviors and quality of life due to i) the consequences of
the impact of the disease, and ii) the measures imposed for preventing the spread of the coro-
navirus, showed by other research [139–145]. In this sense, the SHARE-ERIC designed and
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 18 / 29
developed the SHARE—COVID19 survey (1 and 2 waves) (http://www.share-project.org/
share-covid19.html) with the objective of identifying the lockdown effects over older-adult
population. This is a future line of research to examine the possible impact of the pandemic on
active ageing behaviors.
Despite these limitations, the research also has certain strengths, including the methodo-
logical design to address the study of a large dataset of different types of data. Consequently,
the successive analytical procedure phases have been expressly planned and executed for the
proposed objectives.
In the multiple and diverse AA studies, there is room for future and novel developments
stemming from their conceptualisation and progress and from the aforementioned limitations
to achieve more precise diagnoses. Some of the possible improvements in these studies should
come from the need to establish comparative frameworks between countries, differentiated by
their social, cultural and political model, thus overcoming the reductionism imposed by
research anchored in, for example, developed countries. Although this is an increasingly wide-
spread trend, there are two other areas that would require more attention, such as cross-
referencing and further triangulation studies. Both would stem from longitudinal type analy-
ses, albeit constrained by the availability of adequate data, and the use of combined quantita-
tive-qualitative methodologies, which would make it easier to compare the two visions and
provide deeper insight into the views and experiences of older adults.
Finally, there is another area for improvement in AA research, derived from the use of pre-
post methodology, which enables psychosocial and environmental interventions to first assess
and then improve the behaviour of older adults. These interventions also can contribute to the
development of actions aimed at promoting AA at an individual and collective level, by
encouraging healthy lifestyles, developing active and passive preventive safety strategies in old
age, and promoting active participation and continuous learning [30, 146]. In turn, environ-
mental interventions, based on designs and adjustments of the residential and community
environment, such as environmental adaptations, relevant assistive technologies, and environ-
mental and behavioral safety strategies, can favour changes in behaviors and promote better
environmental adaptation of old people, encouraging outdoor activities, participation and
social relationships, and reducing the risk of social isolation and loneliness [147, 148]. This
knowledge would underpin the application of public policies aimed at promoting AA as a
mechanism for consolidating quality of life in the ageing process.
Conclusions
This research has revealed the presence of various profiles of older adults according to their
levels of AA in Spain. Following the pillars of the seminal WHO model and its subsequent
complementation and applying various analytical statistical techniques, five profiles of people
have been obtained: with moderate activity, quasi-dependents, with limiting AA conditions,
with diverse and balanced activity, and with excellent AA conditions. The first three profiles
accounted for more than half of the population, their main features being their higher average
age, lower level of education, being retired, living in small households but having had more
children and grandchildren, showing a greater perception of loneliness and lower quality of
life. On the other hand, profile IV and V subjects were the mirror image of the previous
profiles.
With the results obtained, the older adult subjects can be classified into profiles, which
could serve as a basis for establishing intervention priorities, although this is not the object of
this study. However, the main priority would be to address the foundations for better living
conditions in old age throughout the life cycle, from educational stages, working age,
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 19 / 29
retirement age, or other stages with specific needs. As a process, ageing reflects a person’s pre-
vious way of life [10] and, as a society, possible differences in the life course will lead to social
inequalities [149], which are at the origin of a different level of AA. As the older adults group
grows with the arrival of generations with better living conditions, this age group is likely to
achieve better AA profiles in the near future.
Supporting information
S1 Table. Selected variables according to active ageing pillars, and personal and contextual
information.
(PDF)
S2 Table. Factor analysis by principal component analysis extraction method.
(PDF)
S1 Fig. Active ageing profiles model and key results.
(PDF)
Acknowledgments
This paper uses data from SHARE Wave 6 (DOI: 10.6103/SHARE.w6.710), see Bo¨rsch-Supan
et al. (63) for methodological details. The SHARE data collection has been funded by the Euro-
pean Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-
062193; COMPARE: CIT5-CT-2005-028857; SHARELIFE: CIT4-CT-2006-028812), FP7
(SHARE-PREP: GA N˚211909; SHARE-LEAP: GA N˚ 227822; SHARE M4: GA N˚261982;
DASISH: GA N˚ 283646) and Horizon 2020 (SHARE-DEV3: GA N˚ 676536; SHARE-COHE-
SION: GA N˚870628; SERISS: GA N˚ 654221; SSHOC: GA N˚ 823782) and by DG Employ-
ment, Social Affairs & Inclusion. Additional funding from the German Ministry of Education
and Research, the Max Planck Society for the Advancement of Science, the U.S. National Insti-
tute on Aging (U01_AG09740-13S2; P01_AG005842; P01_AG08291; P30_AG12815;
R21_AG025169; Y1-AG-4553-01; IAG_BSR06-11; OGHA_04–064; HHSN271201300071C)
and from various national funding sources is gratefully acknowledged (see www.share-project.
org). The authors also wish to thank the Plos One editor and reviewers for their valuable com-
ments and efforts towards improving the manuscript.
Author Contributions
Conceptualization: Fermina Rojo-Perez, Vicente Rodriguez-Rodriguez, Maria-Angeles
Molina-Martinez, Gloria Fernandez-Mayoralas, Diego Sanchez-Gonzalez, Maria-João
Forjaz.
Formal analysis: Fermina Rojo-Perez, Jose-Manuel Rojo-Abuin, Alba Ayala, Carmen Rodri-
guez-Blazquez.
Funding acquisition: Fermina Rojo-Perez, Gloria Fernandez-Mayoralas, Maria-João Forjaz.
Methodology: Fermina Rojo-Perez, Vicente Rodriguez-Rodriguez.
Supervision: Vicente Rodriguez-Rodriguez, Diego Sanchez-Gonzalez.
Writing – original draft: Fermina Rojo-Perez, Vicente Rodriguez-Rodriguez, Maria-Angeles
Molina-Martinez, Gloria Fernandez-Mayoralas, Diego Sanchez-Gonzalez, Carmen Rodri-
guez-Blazquez.
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 20 / 29
Writing – review & editing: Fermina Rojo-Perez, Vicente Rodriguez-Rodriguez, Maria-Ange-
les Molina-Martinez, Gloria Fernandez-Mayoralas, Diego Sanchez-Gonzalez, Jose-Manuel
Rojo-Abuin, Alba Ayala, Carmen Rodriguez-Blazquez, Amaia Calderon-Larrañaga, Oscar
Ribeiro, Maria-João Forjaz.
References
1. Christensen K, Doblhammer G, Rau R, Vaupel JW. Ageing populations: the challenges ahead. The
Lancet. 2009; 374(9696):1196–208. https://doi.org/10.1016/S0140-6736(09)61460-4 PMID:
19801098
2. UN- United Nations, Department of Economic and Social Affairs, Population Division. World Popula-
tion Ageing 2019 (ST/ESA/SER.A/444). New York: United Nations, Department of Economic and
Social Affairs, Population Division; 2020. Available from: https://www.un.org/en/development/desa/
population/publications/pdf/ageing/WorldPopulationAgeing2019-Report.pdf
3. Baltes PB, Smith J. New frontiers in the future of aging: from successful aging of the young old to the
dilemmas of the fourth age. Gerontology. 2003; 49(2):123–35. https://doi.org/10.1159/000067946
PMID: 12574672
4. Rowe JW, Kahn RL. Successful aging. The Gerontologist. 1997; 37(4):433–40. https://doi.org/10.
1093/geront/37.4.433 PMID: 9279031
5. Ferna´ndez-Ballesteros R, Sa´nchez-Izquierdo M, Santacreu M. Active Aging and Quality of Life. In:
Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, editors. Handbook of Active Ageing and Quality of Life From
Concepts to Applications. Cham: Springer, International Handbooks of Quality-of-Life series; 2021.
p. 15–42. https://doi.org/10.1007/978-3-030-58031-5_2
6. Costa-Font J, Wittenberg R, Patxot C, Comas-Herrera A, Gori C, di Maio A, et al. Projecting Long-
Term Care Expenditure in Four European Union Member States: The Influence of Demographic Sce-
narios. Social Indicators Research. 2008; 86(2):303–21. https://doi.org/10.1007/s11205-007-9140-4
7. Akosile CO, Mgbeojedo UG, Maruf FA, Okoye EC, Umeonwuka IC, Ogunniyi A. Depression, func-
tional disability and quality of life among Nigerian older adults: Prevalences and relationships. Archives
of Gerontology and Geriatrics. 2018; 74:39–43. https://doi.org/10.1016/j.archger.2017.08.011 PMID:
28954240
8. Naveiro-Rilo J-C, Diez-Jua´rez D, Flores-Zurutuza M-L, Javierre-Pe´rez P, Alberte-Pe´rez C, Molina-
Mazo R. La calidad de vida en ancianos polimedicados con multimorbilidad. Revista Española de Ger-
iatrı´a y Gerontologı´a. 2014; 49(4):158–64. https://doi.org/10.1016/j.regg.2013.10.004 PMID:
24529640
9. Zaidi A, Gasior K, Zolyomi E, Schmidt A, Rodrigues R, Marin B. Measuring active and healthy ageing
in Europe. Journal of European Social Policy. 2017; 27(2):138–57. https://doi.org/10.1177/
0958928716676550
10. Kalache A, Voelcher I, Louvison M. Active Aging and the Longevity Revolution. In: Rojo-Pe´rez F, Fer-
na´ndez-Mayoralas G, editors. Handbook of Active Ageing and Quality of Life From Concepts to Appli-
cations. Cham: Springer, International Handbooks of Quality-of-Life series; 2021. p. 43–62. https://
doi.org/10.1007/978-3-030-58031-5_3
11. Walker A, Maltby T. Active ageing: A strategic policy solution to demographic ageing in the European
Union. International Journal of Social Welfare. 2012; 21(Suppl. 1):S117–S30. https://doi.org/10.1111/
j.1468-2397.2012.00871.x
12. Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, Rodrı´guez-Rodrı´guez V. Active Ageing and Quality of Life: A
Systematized Literature Review. In: Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, editors. Handbook of
Active Ageing and Quality of Life From Concepts to Applications. Cham: Springer, International
Handbooks of Quality-of-Life series; 2021. p. 63–96. https://doi.org/10.1007/978-3-030-58031-5_4
13. Rojo-Pe´rez F, Gallardo-Peralta L, Ferna´ndez-Mayoralas G, Rodrı´guez-Rodrı´guez V, Montes de Oca
Zavala V, Prieto-Flores ME, et al. Envejecimiento activo y buen envejecer en Iberoame´rica. Una revi-
sio´n bibliogra´fica. In: Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, editors. Envejecimiento Activo, Calidad
de Vida y Ge´nero Las miradas acade´mica, institucional y social. Valencia: Tirant lo Blanch; 2021. p.
175–211. Available from: https://editorial.tirant.com/co/libro/envejecimiento-activo-calidad-de-vida-y-
genero-9788418329142
14. Molina MA´ , Schettini R. Lifelong Learning and Quality of Life. In: Rojo-Pe´rez F, Ferna´ndez-Mayoralas
G, editors. Handbook of Active Ageing and Quality of Life From Concepts to Applications. Cham:
Springer, International Handbooks of Quality-of-Life series; 2021. p. 111–9. https://doi.org/10.1007/
978-3-030-58031-5_6
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 21 / 29
15. Schettini R, Molina-Martı´nez MA´ , Gallardo Peralta LP. Formacio´n continua en el proceso de envejeci-
miento desde una perspectiva popular. In: Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, editors. Envejeci-
miento Activo, Calidad de Vida y Ge´nero Las miradas acade´mica, institucional y social. Valencia:
Tirant lo Blanch; 2021. p. 307–26. Available from: https://editorial.tirant.com/co/libro/envejecimiento-
activo-calidad-de-vida-y-genero-9788418329142
16. Martı´n Palomo MT, Muñoz Terro´n JM. Tecnologı´as de la Informacio´n y de la Comunicacio´n en el
envejecimiento activo. In: Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, editors. Envejecimiento Activo,
Calidad de Vida y Ge´nero Las miradas acade´mica, institucional y social. Valencia: Tirant lo Blanch;
2021. p. 409–528. Available from: https://editorial.tirant.com/co/libro/envejecimiento-activo-calidad-
de-vida-y-genero-9788418329142
17. WHO- World Health Organization. Active Ageing: A Policy Framework. Geneva: World Health Organi-
zation; 2002. 1–60 p. Available from: http://apps.who.int/iris/bitstream/10665/67215/1/WHO_NMH_
NPH_02.8.pdf
18. Pau´l C, Ribeiro O, Teixeira L. Active ageing: An empirical approach to the WHO model. Current Ger-
ontology and Geriatrics Research. 2012(382972):1–10. https://doi.org/10.1155/2012/382972 PMID:
23193396
19. Rojo-Pe´rez F, Ferna´ndez-Mayoralas G. Comprensio´n y autovaloracio´n de la Calidad de Vida como
medida de resultado del Envejecimiento Activo. In: Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, editors.
Envejecimiento Activo, Calidad de Vida y Ge´nero Las miradas acade´mica, institucional y social.
Valencia: Tirant lo Blanch; 2021. p. 743–801. Available from: https://editorial.tirant.com/co/libro/
envejecimiento-activo-calidad-de-vida-y-genero-9788418329142
20. Win HH, Nyunt TW, Lwin KT, Zin PE, Nozaki I, Bo TZ, et al. Cohort profile: healthy and active ageing
in Myanmar (JAGES in Myanmar 2018): a prospective population-based cohort study of the long-term
care risks and health status of older adults in Myanmar. BMJ Open. 2020; 10(10):e042877. https://doi.
org/10.1136/bmjopen-2020-042877 PMID: 33130574
21. Bosch-Farre´ C, Garre-Olmo J, Bonmatı´-Tomàs A, Malago´n-Aguilera M-C, Gelabert-Vilella S, Fuen-
tes-Pumarola C, et al. Prevalence and related factors of Active and Healthy Ageing in Europe accord-
ing to two models: Results from the Survey of Health, Ageing and Retirement in Europe (SHARE).
PLOS One. 2018; 13(10):1–19. https://doi.org/10.1371/journal.pone.0206353 PMID: 30372472
22. Rodrı´guez-Bla´zquez C, Martin S, Forjaz MJ. Factores determinantes de la salud en el envejecimiento
activo. In: Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, editors. Envejecimiento Activo, Calidad de Vida y
Ge´nero Las miradas acade´mica, institucional y social. Valencia: Tirant lo Blanch; 2021. p. 213–29.
Available from: https://editorial.tirant.com/co/libro/envejecimiento-activo-calidad-de-vida-y-genero-
9788418329142
23. Fass E, Schlesinger T. The Relation of Physical Activity and Self-Rated Health in Older Age—Cross
Country Analysis Results from SHARE. Journal of Population Ageing. 2020; 13(3):347–64. https://doi.
org/10.1007/s12062-019-09242-w
24. Hijas-Go´mez AI, Ayala A, Rodrı´guez-Garcı´a MP, Rodrı´guez-Bla´zquez C, Rodrı´guez-Rodrı´guez V,
Rojo-Pe´rez F, et al. The WHO Active Ageing Pillars and its association with survival: findings from a
population-based study in Spain. Archives of Gerontology and Geriatrics. 2020; 90(104114):1–15.
https://doi.org/10.1016/j.archger.2020.104114 PMID: 32526561
25. Litwin H, Schwartz E, Damri N. Cognitively Stimulating Leisure Activity and Subsequent Cognitive
Function: A SHARE-based Analysis. The Gerontologist. 2017; 57(1): 940–8. https://doi.org/10.1093/
geront/gnw084 PMID: 27117305
26. Sirven N, Debrand T. La Participation Sociale des Personnes Aˆ ge´es en Europe. Instrument du «Bien
Vieillir » ou Facteur d’Ine´galite´s Sociales de Sante´? [Social participation of elderly people in Europe:
An instrument of "healthy ageing" or a factor in the social inequality of health?]. Retraite et Socie´te´.
2013; 65(2):59–80. https://doi.org/10.3917/rs.065.0059
27. Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, Martı´nez-Martı´n P, Prieto-Flores M-E, Rodrı´guez-Bla´zquez
C, Martı´n-Garcı´a S, et al. Active ageing and quality of life: factors associated with participation in lei-
sure activities among institutionalized older adults, with and without dementia. Aging & Mental Health.
2015; 19(11):1031–41. https://doi.org/10.1080/13607863.2014.996734 PMID: 25584744
28. Morrow-Howell N, Putnam M, Lee YS, Greenfield JC, Inoue M, Chen H. An Investigation of Activity
Profiles of Older Adults. The Journals of Gerontology, Series B: Psychological Sciences and Social
Sciences. 2014; 69(5):809–21. https://doi.org/10.1093/geronb/gbu002 PMID: 24526690
29. Rodrı´guez-Rodrı´guez V, Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, Prieto-Flores M-E. ¿Co´mo interpre-
tan el envejecimiento activo las personas mayores en España? Evidencias desde una perspectiva no
profesional. Aula Abierta. 2018; 47(1):67–78. https://doi.org/10.17811/rifie.47.1.2018.67–78
30. Sa´nchez-Gonza´lez D, Rojo-Pe´rez F, Rodrı´guez-Rodrı´guez V, Ferna´ndez-Mayoralas G. Environmen-
tal and Psychosocial Interventions in Age-Friendly Communities and Active Ageing: A Systematic
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 22 / 29
Review. International Journal of Environmental Research and Public Health. 2020; 17(22):8305.
https://doi.org/10.3390/ijerph17228305 PMID: 33182710
31. Haski-Leventhal D. Elderly Volunteering and Well-Being: A Cross-European Comparison Based on
SHARE Data. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations. 2009; 20
(4):388–404. https://doi.org/10.1007/s11266-009-9096-x
32. Morawski L, Okulicz-Kozaryn A, Strzelecka M. Elderly Volunteering in Europe: The Relationship
Between Volunteering and Quality of Life Depends on Volunteering Rates. VOLUNTAS: International
Journal of Voluntary and Nonprofit Organizations. 2020 first online;. https://doi.org/10.1007/s11266-
020-00267-w
33. Papa R, Cutuli G, Principi A, Scherer S. Health and Volunteering in Europe: A Longitudinal Study.
Research on Aging. 2019; 41(7):670–96. https://doi.org/10.1177/0164027519834939 PMID:
30845894
34. Potocnik K, Sonnentag S. A longitudinal study of well-being in older workers and retirees: The role of
engaging in different types of activities. Journal of Occupational and Organizational Psychology. 2013;
86(4):497–521. https://doi.org/10.1111/joop.12003
35. Principi A, Galenkamp H, Papa R, Socci M, Suanet B, Schmidt A, et al. Do predictors of volunteering
in older age differ by health status? European Journal of Ageing. 2016; 13(2):91–102. https://doi.org/
10.1007/s10433-016-0377-0 PMID: 28804374
36. Serrat R, Scharf T, Villar F, Go´mez C. Fifty-Five Years of Research Into Older People’s Civic Participa-
tion: Recent Trends, Future Directions. The Gerontologist. 2020; 60(1):e38–e51. https://doi.org/10.
1093/geront/gnz021 PMID: 30889249
37. Litwin H, Stoeckel K. Engagement and social capital as elements of active ageing: an analysis of older
europeans. Socilogia e Politiche Sociali. 2014; 17(3):9–31. https://doi.org/10.3280/SP2014-003002
38. Rossi G, Boccacin L, Bramanti D, Meda SG. Active ageing: Intergenerational relationships and social
generativity. In: Riva G, Ajmone Marsan P, Grassi C, editors. Active Ageing and Healthy Living: A
Human Centered Approach in Research and Innovation as Source of Quality of Life. Amsterdam:
IOS Press. Series Studies in Health and Technology, vol. 203; 2014. p. 57–68. https://doi.org/10.
3233/978-1-61499-425-1-57
39. Di Gessa G, Grundy E. The relationship between active ageing and health using longitudinal data from
Denmark, France, Italy and England. Journal of Epidemiology and Community Health. 2014; 68
(3):261–7. https://doi.org/10.1136/jech-2013-202820 PMID: 24272919
40. Wahrendorf M, Siegrist J. Are changes in productive activities of older people associated with changes
in their well-being? Results of a longitudinal European study. European Journal of Ageing. 2010; 7
(2):59–68. https://doi.org/10.1007/s10433-010-0154-4 PMID: 28798618
41. Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, Rodrı´guez-Rodrı´guez V, Sa´nchez-Roma´n M, Schettini R,
Rodrı´guez-Bla´zquez C, et al. Marco teo´rico y estudio de diseño e implementacio´n de investigacio´n
cualitativa en Envejecimiento Activo, Calidad de Vida y Ge´nero. In: Ferna´ndez-Mayoralas G, Rojo-
Pe´rez F, editors. Envejecimiento Activo, Calidad de Vida y Ge´nero Las miradas acade´mica, institucio-
nal y social. Valencia: Tirant lo Blanch; 2021. p. 48–126. Available from: https://editorial.tirant.com/
co/libro/envejecimiento-activo-calidad-de-vida-y-genero-9788418329142
42. ILC-BR- International Longevity Centre Brazil. Active Ageing: A Policy Framework in Response to the
Longevity Revolution. Rio de Janeiro: International Longevity Centre Brazil; 2015. 116 p. Available
from: https://ilcbrazil.org.br/wp-content/uploads/2020/07/FINAL-executive-summary-04-v1.1.pdf
43. Molina MA´ , Cañadas-Reche JL, Serrano-del-Rosal R. Social Participation of the Elders in Europe:
The Influence of Individual and Contextual Variables. Ageing International. 2018; 43(2):190–206.
https://doi.org/10.1007/s12126-017-9300-z
44. Sa´nchez-Roma´n M, Ferna´ndez-Mayoralas G. Ge´nero y envejecimiento activo: la visio´n de los infor-
mantes clave en instituciones, equipos profesionales y personas mayores usuarias de servicios. In:
Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, editors. Envejecimiento Activo, Calidad de Vida y Ge´nero Las
miradas acade´mica, institucional y social. Valencia: Tirant lo Blanch; 2021. p. 585–616. Available
from: https://editorial.tirant.com/co/libro/envejecimiento-activo-calidad-de-vida-y-genero-
9788418329142
45. Steinmayr D, Weichselbaumer D, Winter-Ebmer R. Gender Differences in Active Ageing: Findings
from a New Individual-Level Index for European Countries. Social Indicators Research. 2020; 151
(2):691–721. https://doi.org/10.1007/s11205-020-02380-1
46. Hank K, Erlinghagen M. Dynamics of volunteering in older Europeans. The Gerontologist. 2010; 50
(2):170–8. https://doi.org/10.1093/geront/gnp122 PMID: 19666783
47. Molina-Martı´nez MA, Schettini R, Ferna´ndez-Ferna´ndez V, Gallardo-Peralta L. El papel de las vari-
ables psicolo´gicas en el envejecimiento activo. Perspectiva no acade´mica. In: Ferna´ndez-Mayoralas
G, Rojo-Pe´rez F, editors. Envejecimiento Activo, Calidad de Vida y Ge´nero Las miradas acade´mica,
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 23 / 29
institucional y social. Valencia: Tirant lo Blanch; 2021. p. 473–95. Available from: https://editorial.
tirant.com/co/libro/envejecimiento-activo-calidad-de-vida-y-genero-9788418329142
48. Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, Rodrı´guez-Rodrı´guez V, Lardie´s-Bosque R, Prieto-Flores
ME, Gallardo-Peralta LP, et al. Contextos residenciales, envejecimiento activo y calidad de vida. Un
ana´lisis a microescala en España. In: Sempere Souvannavong JD, Corte´s Samper C, Cutillas Orgile´s
E, Valero Escandell JR, editors. Poblacio´n y territorio España tras la crisis de 2008. Granada:
COMARES; 2020. p. 191–208. Available from: http://rua.ua.es/dspace/handle/10045/115389
49. Lardie´s Bosque R. Sociedad, instituciones y polı´ticas: factores para envejecer activamente y con cali-
dad de vida en España. In: Ferna´ndez-Mayoralas G, Rojo-Pe´rez F, editors. Envejecimiento Activo,
Calidad de Vida y Ge´nero Las miradas acade´mica, institucional y social. Valencia: Tirant lo Blanch;
2021. p. 397–438. Available from: https://editorial.tirant.com/co/libro/envejecimiento-activo-calidad-
de-vida-y-genero-9788418329142
50. Ramia I, Voicu M. Life Satisfaction and Happiness Among Older Europeans: The Role of Active Age-
ing. Social Indicators Research. 2022; 160:667–87. https://doi.org/10.1007/s11205-020-02424-6
51. Erlinghagen M, Hank K. The participation of older Europeans in volunteer work. Ageing & Society.
2006; 26(4):567–84. https://doi.org/10.1017/S0144686X06004818
52. Arpino B, Bordone V. Active Ageing Typologies: A Latent Class Analysis of the Older Europeans. In:
Zaidi A, Harper S, Howse K, Lamura G, Perek-Białas J, editors. Building Evidence for Active Ageing
Policies: Active Ageing Index and its Potential. Singapore: Springer Singapore; 2018. p. 295–311.
https://doi.org/10.1007/978-981-10-6017-5_14
53. Au DWH, Woo J, Zaidi A. Extending the Active Ageing Index to Hong Kong Using a Mixed-Method
Approach: Feasibility and Initial Results. Journal of Population Ageing. 2021; 14(1):53–68. https://doi.
org/10.1007/s12062-020-09275-6
54. Barslund M, Von Werder M, Zaidi A. Inequality in active ageing: evidence from a new individual-level
index for European countries. Ageing & Society. 2019; 39(3):541–67. https://doi.org/10.1017/
S0144686X17001052
55. Rodrı´guez-Rodrı´guez V, Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, Morillo R, Forjaz MJ, Prieto-Flores
ME. Active Ageing Index: Application to Spanish Regions. Journal of Population Ageing. 2017; 10
(1):25–40. https://doi.org/10.1007/s12062-016-9171-1
56. Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, Rodrı´guez-Rodrı´guez V. Active Ageing and Personal Wellbe-
ing Among Older Adults in Spain. In: Maggino F, editor. Encyclopedia of Quality of Life and Well-Being
Research. Cham: Springer International Publishing; 2020. p. 1–10. https://doi.org/10.1007/978-3-
319-69909-7_4001–2
57. Varlamova M, Sinyavskaya O. Active Ageing Index in Russia—Identifying Determinants for Inequality.
Journal of Population Ageing. 2020; 14(1):69–90. https://doi.org/10.1007/s12062-020-09277-4
58. Um J, Zaidi A, Parry J, Xiong Q. Capturing gendered aspects of active aging in China: Insights drawn
from the Active Aging Index in comparison with EU countries. Asian Social Work and Policy Review.
2020; 15(1):47–59. https://doi.org/10.1111/aswp.12218
59. A´ lvarez-Garcı´a J, Dura´n-Sa´nchez A, Del Rı´o-Rama MDlC, Garcı´a-Ve´lez DF. Active Ageing: Mapping
of Scientific Coverage. International Journal of Environmental Research and Public Health. 2018;
15:1–21. https://doi.org/10.3390/ijerph15122727 PMID: 30513943
60. Naah FL, Njong AM, Kimengsi JN. Determinants of active and healthy ageing in sub-saharan africa:
Evidence from Cameroon. International Journal of Environmental Research and Public Health. 2020;
17(9):3038. https://doi.org/10.3390/ijerph17093038 PMID: 32349334
61. Thanakwang K, Isaramalai S, Hatthakit U. Development and psychometric testing of the active aging
scale for Thai adults. Clinical Interventions in Aging. 2014; 9:1211–21. https://doi.org/10.2147/CIA.
S66069 PMID: 25092971
62. Borsch-Supan A, Hank K, Jurges H, Schroder M. Introduction: empirical research on health, ageing
and retirement in Europe. Journal of European Social Policy. 2009; 19(4):293–300. https://doi.org/10.
1177/1350506809341510
63. Bo¨rsch-Supan A, Brandt M, Hunkler C, Kneip T, Korbmacher J, Malter F, et al. Data Resource Profile:
The Survey of Health, Ageing and Retirement in Europe (SHARE). International Journal of Epidemiol-
ogy. 2013; 42(1):992–1001. https://doi.org/10.1093/ije/dyt088 PMID: 23778574
64. Ferna´ndez-Ballesteros Garcı´a R, Zamarro´n Casinello MD, Lo´pez Bravo MD, Molina Martı´nez MA´ ,
Dı´ez Nicola´s J, Montero Lo´pez P, et al. Envejecimiento con e´xito: criterios y predictores. Psicothema.
2010; 22(4):641–7. PMID: 21044491
65. Bo¨rsch-Supan A. Survey of Health, Ageing and Retirement in Europe (SHARE) Wave 6. Release ver-
sion: 7.1.0. SHARE-ERIC. Data set. 2019. Available from: http://www.share-project.org/data-
documentation/waves-overview/wave-6.html. https://doi.org/10.6103/SHARE.w6.710
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 24 / 29
66. Malter F, Bo¨rsch-Supan A, editors. SHARE Wave 6: Panel innovations and collecting Dried Blood
Spots. Mu¨nchen: Munich Center for the Economics of Aging (MEA), the Max Planck Institute for
Social Law and Social Policy (MPISOC); 2017. Available from: http://www.share-project.org/uploads/
tx_sharepublications/201804_SHARE-WAVE-6_MFRB.pdf
67. Bo¨rsch-Supan A, Ju¨rges H, editors. The Survey of Health, Aging, and Retirement in Europe–Method-
ology. Mannheim: Research Institute for the Economics of Aging (MEA); 2005. Available from: http://
www.share-project.org/uploads/tx_sharepublications/SHARE_BOOK_METHODOLOGY_Wave1.pdf
68. MEA- Munich Center for the Economics of Aging. The Survey of Health, Ageing and Retirement in
Europe (SHARE). Release Guide 6.1.0. Mannheim: Mannheim Research Institute for the Economics
of Aging (MEA); March 29 2018. 84 p. Available from: http://www.share-project.org/fileadmin/pdf_
documentation/SHARELIFE_release_guide_6.1.0.pdf
69. Malter F, Schuller K, Bo¨rsch-Supan A. SHARE Compliance Profiles–Wave 6. Mu¨nchen: Munich Cen-
ter for the Economics of Aging (MEA), the Max Planck Institute for Social Law and Social Policy (MPI-
SOC); 2016. Available from: http://www.share-project.org/fileadmin/pdf_documentation/SHARE_
Wave6_ComplianceProfiles_v8.pdf
70. Doyal L, Gough I. A theory of human needs. Critical Social Policy. 1984; 4(10):6–38. https://doi.org/10.
1177/026101838400401002
71. Maslow AH. A theory of human motivation. Psychological Review. 1943; 50(4):370–96. https://doi.org/
10.1037/h0054346
72. Hyde M, Wiggins RD, Higgs P, Blane DB. A measure of quality of life in early old age: The theory,
development and properties of a Needs Satisfaction Model (CASP-19). Aging & Mental Health. 2003;
7(3):186–94. https://doi.org/10.1080/1360786031000101157 PMID: 12775399
73. Howel D. Interpreting and evaluating the CASP-19 quality of life measure in older people. Age and
Ageing. 2012; 41(5):612–7. https://doi.org/10.1093/ageing/afs023 PMID: 22391614
74. Ayala A, Rodrı´guez-Bla´zquez C, Caldero´n-Larrañaga A, Beridze G, Teixeira L, Arau´jo L, et al. Influ-
ence of Active and Healthy Ageing on Quality of Life Changes: Insights from the Comparison of Three
European Countries. International Journal of Environmental Research and Public Health. 2021; 18
(8):4152. https://doi.org/10.3390/ijerph18084152 PMID: 33919964
75. Litwin H, Stoeckel KJ. Social Network, Activity Participation, and Cognition: A Complex Relationship.
Research on Aging. 2016; 38(1):76–97. https://doi.org/10.1177/0164027515581422 PMID: 25878191
76. Sˇ imkovic M, Tra¨uble B. Robustness of statistical methods when measure is affected by ceiling and/or
floor effect. PLoS ONE. 2019; 14(8):e0220889. https://doi.org/10.1371/journal.pone.0220889 PMID:
31425561
77. Hahs-Vaughn DL. A Primer for Using and Understanding Weights With National Datasets. The Journal
of Experimental Education. 2005; 73(3):221–48. https://doi.org/10.3200/JEXE.73.3.221–248
78. Hair JFJ, Anderson RE, Tatham RL, Black WC. Multivariate data analysis. Upper Saddle River, New
Jersey: Prentice-Hall; 1998. 730 p.
79. Husson F, Josse J. Multiple Correspondence Analysis. In: Blasius J, Greenacre M, editors. Visualiza-
tion and Verbalization of Data. New York: Chapman and Hall/CRC; 2014. p. 163–81. https://doi.org/
10.1201/b16741
80. Di Franco G. Multiple correspondence analysis: one only or several techniques? Quality & Quantity.
2015; 50(3):1299–315. https://doi.org/10.1007/s11135-015-0206-0
81. Rodriguez-Sabate C, Morales I, Sanchez A, Rodriguez M. The Multiple Correspondence Analysis
Method and Brain Functional Connectivity: Its Application to the Study of the Non-linear Relationships
of Motor Cortex and Basal Ganglia. Front Neurosci. 2017; 11(345). https://doi.org/10.3389/fnins.2017.
00345 PMID: 28676738
82. Zaidi A, Gasior K, Hofmarcher MM, Lelkes O, Marin B, Rodrigues R, et al. Active Ageing Index 2012.
Concept, Methodology and Final Results. Vienna: European Centre Vienna; 2013. Available from:
https://www.euro.centre.org/downloads/detail/1401/1
83. Hank K. How "successful" do older Europeans age? Findings from SHARE. The Journals of Gerontol-
ogy, Series B: Psychological Sciences and Social Sciences. 2011; 66B(2):230–6. https://doi.org/10.
1093/geronb/gbq089 PMID: 21135069
84. Ferna´ndez-Ballesteros R, Molina M-A, Schettini R, Santacreu M. The semantic network of aging well.
In: Robine J-M, Jagger C, Crimmins EM, Robine J-M, Jagger C, Crimmins EM, editors. Annual review
of gerontology and geriatrics, Vol 33: Healthy longevity: A global approach. New York, NY, US:
Springer Publishing Co; 2013. p. 79–107.
85. Li CM, Lin CH, Li CI, Liu CS, Lin WY, Li TC, et al. Frailty status changes are associated with healthcare
utilization and subsequent mortality in the elderly population. BMC Public Health. 2021; 21(645).
https://doi.org/10.1186/s12889-021-10688-x PMID: 33794860
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 25 / 29
86. Jayanama K, Theou O, Godin J, Cahill L, Rockwood K. Association of fatty acid consumption with
frailty and mortality among middle-aged and older adults. Nutrition. 2020; 70:110610. https://doi.org/
10.1016/j.nut.2019.110610 PMID: 31743811
87. Stott DJ. Alcohol and mortality in older people: understanding the J-shaped curve. Age and Ageing.
2020; 49(3):332–3. https://doi.org/10.1093/ageing/afaa027 PMID: 32343789
88. Adams KB, Leibbrandt S, Moon H. A critical review of the literature on social and leisure activity and
wellbeing in later life. Ageing and Society. 2011; 31(4):683–712. https://doi.org/10.1017/
s0144686x10001091
89. Arpino B, Sole´-Auro´ A. Education Inequalities in Health Among Older European Men and Women:
The Role of Active Aging. Journal of Aging and Health. 2017; 31(1):185–208. https://doi.org/10.1177/
0898264317726390 PMID: 28823184
90. Boerio P, Garavaglia E, Gaia A. Active ageing in Europe: are changes in social capital associated with
engagement, initiation and maintenance of activity in later life? Ageing & Society. 2021:1–19. https://
doi.org/10.1017/s0144686x21001021
91. Lakomy´ M. Differences in social participation of older adults across European welfare regimes: Four-
teen years of SHARE data collection. International Sociology. 2021; 36(6):906–25. https://doi.org/10.
1177/0268580921993326
92. Rodrı´guez-Rodrı´guez V. Comprensio´n del envejecimiento activo segu´n contextos. In: Ferna´ndez-
Mayoralas G, Rojo-Pe´rez F, editors. Envejecimiento Activo, Calidad de Vida y Ge´nero Las miradas
acade´mica, institucional y social. Valencia: Tirant lo Blanch; 2021. p. 127–74. Available from: https://
editorial.tirant.com/co/libro/envejecimiento-activo-calidad-de-vida-y-genero-9788418329142
93. Golinowska S, Sowa A, Deeg D, Socci M, Principi A, Rodrigues R, et al. Participation in formal learning
activities of older Europeans in poor and good health. European Journal of Ageing. 2016; 13(2):115–
27. https://doi.org/10.1007/s10433-016-0371-6 PMID: 27358603
94. Wongsala M, Anbacken EM, Rosendahl S. Active ageing—perspectives on health, participation, and
security among older adults in northeastern Thailand—a qualitative study. BMC Geriatrics. 2021; 21
(1):41. https://doi.org/10.1186/s12877-020-01981-2 PMID: 33430777
95. Burr JA, Mutchler JE, Caro FG. Productive activity clusters among middle-aged and older adults: inter-
secting forms and time commitments. The Journals of Gerontology, Series B: Psychological Sciences
and Social Sciences. 2007; 62(4):S267–S75. https://doi.org/10.1093/geronb/62.4.s267 PMID:
17673540
96. Pe´rez Dı´az J, Abella´n Garcı´a A, Aceituno Nieto P, Ramiro Fariñas D. Un perfil de las personas
mayores en España, 2020. Indicadores estadı´sticos ba´sicos. Informes Envejecimiento en Red. 2020;
25:1–39. http://envejecimiento.csic.es/documentos/documentos/enred-indicadoresbasicos2020.pdf
97. MSCBS- Ministerio de Sanidad CyBS. Encuesta Nacional de Salud de España 2017. Madrid: Minis-
terio de Sanidad, Consumo y Bienestar Social; 2017. Available from: https://www.mscbs.gob.es/
estadEstudios/estadisticas/encuestaNacional/encuesta2017.htm
98. Bo¨rsch-Supan A, Brugiavini A, Ju¨rges H, Kapteyn A, Mackenbach J, Siegrist J, et al., editors. First
Results from the Survey of Health, Ageing and Retirement in Europe (2004–2007). Starting the Longi-
tudinal Dimension. Mannheim: Mannheim Research Institute for the Economics of Aging (MEA);
2008. Available from: http://www.share-project.org/uploads/tx_sharepublications/
BuchSHAREganz250808.pdf
99. Rodrı´guez-Rodrı´guez V, Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, Ahmed-Mohamed K, Lardie´s-Bos-
que R, Prieto-Flores ME, et al. Recursos econo´micos y calidad de vida en la poblacio´n mayor. Revista
Internacional de Sociologı´a. 2011; 69(1):195–227. https://doi.org/10.3989/ris.2009.11.26
100. Bo¨rsch-Supan A, Bristle J, Andersen-Ranberg K, Brugiavini A, Jusot F, Litwin H, et al., editors. Health
and socio-economic status over the life course First results from SHARE Waves 6 and 7. Olderbourg:
De Gruyter Publishers; 2019. https://doi.org/10.1515/9783110617245
101. Chansarn S. Active ageing of elderly people and its determinants: Empirical evidence from Thailand.
Asia-Pacific Social Science Review. 2012; 12(1):1–18.
102. Lai CKY, Chan EA, Chin KCW. Who are the healthy active seniors? A cluster analysis. BMC Geriat-
rics. 2014; 14(127):1–7. https://doi.org/10.1186/1471-2318-14-127 PMID: 25443864
103. Yang Y, Meng Y, Dong P. Health, Security and Participation: A Structural Relationship Modeling
among the Three Pillars of Active Ageing in China. International Journal of Environmental Research
and Public Health. 2020; 17(19):7255. https://doi.org/10.3390/ijerph17197255 PMID: 33020396
104. Mandl B, Millonig A, Friedl V. The Variety of the Golden Agers: Identifying Profiles of Older People for
Mobility Research. Transportation Research Board 92nd Annual Meeting; Washington, DC: TRB
committee ANB60 Safe Mobility of Older Persons; 2013.
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 26 / 29
105. da Silva Sousa NF, de Azevedo Barros MB. Level of active aging: Influence of environmental, social
and health-related factors. Archives of Gerontology and Geriatrics. 2020; 90:104094. https://doi.org/
10.1016/j.archger.2020.104094 PMID: 32485497
106. Rodrı´guez-Rodrı´guez V. Participacio´n en actividades para envejecer activamente. In: Ferna´ndez-
Mayoralas G, Rojo-Pe´rez F, editors. Envejecimiento Activo, Calidad de Vida y Ge´nero Las miradas
acade´mica, institucional y social. Valencia: Tirant lo Blanch; 2021. p. 231–66. Available from: https://
editorial.tirant.com/co/libro/envejecimiento-activo-calidad-de-vida-y-genero-9788418329142
107. Lee HY, Jang SN, Lee S, Cho SI, Park EO. The relationship between social participation and self-
rated health by sex and age: a cross-sectional survey. International Journal of Nursing Studies 2008;
45(7):1042–54. https://doi.org/10.1016/j.ijnurstu.2007.05.007 PMID: 17658532
108. Niedzwiedz CL, Richardson EA, Tunstall H, Shortt NK, Mitchell RJ, Pearce JR. The relationship
between wealth and loneliness among older people across Europe: Is social participation protective?
Preventive Medicine. 2016; 91:24–31. https://doi.org/10.1016/j.ypmed.2016.07.016 PMID: 27471027
109. Michèle J, Guillaume M, Alain T, Nathalie B, Claude F, Kamel G. Social and leisure activity profiles
and well-being among the older adults: a longitudinal study. Aging & Mental Health. 2019; 23(1):77–
83. https://doi.org/10.1080/13607863.2017.1394442 PMID: 29160718
110. Mergenthaler A, Sackreuther I, Staudinger UM. Productive activity patterns among 60–70-year-old
retirees in Germany. Ageing and Society. 2019; 39(6):1122–51. https://doi.org/10.1017/
S0144686X17001404
111. Ferna´ndez-Mayoralas G, Schettini R, Sa´nchez-Roma´n M, Rojo-Pe´rez F, Agullo´ MS, Forjaz MJ. El
papel del ge´nero en el buen envejecer. Una revisio´n sistema´tica desde la perspectiva cientı´fica.
Prisma Social. 2018(21):149–76.
112. Sole´-Auro´ A, Alcañiz M. Educational attainment, gender and health inequalities among older adults in
Catalonia (Spain). International Journal for Equity in Health. 2016; 15(1):126. https://doi.org/10.1186/
s12939-016-0414-9 PMID: 27491677
113. Foster L, Walker A. Gender and Active Ageing in Europe. European Journal of Ageing. 2013; 10(1):3–
10. https://doi.org/10.1007/s10433-013-0261-0 PMID: 28804278
114. Del Barrio Truchado E, Marsillas Rascado S, Sancho Castiello M. Del envejecimiento activo a la ciu-
dadanı´a activa: el papel de la amigabilidad. Aula Abierta. 2018; 47(1):37–44. https://doi.org/10.17811/
rifie.47.1.2018.37–44
115. Villar F, Serrat R. Aging at a development crossroad. In: Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, edi-
tors. Handbook of Active Ageing and Quality of Life From Concepts to Applications. Cham: Springer,
International Handbooks of Quality-of-Life series; 2021. p. 121–33. https://doi.org/10.1007/978-3-030-
58031-5_7
116. Rodrı´guez-Rodriguez V, Rojo-Pe´rez F, Ferna´ndez-Mayoralas G. Active ageing in Spain: Leisure,
Community Participation and Quality of Life. In: Rodriguez de la Vega L, Toscano W, editors. Hand-
book of Leisure, Physical Activity, Sports, Recreation, and Quality of Life. Cham: Springer Interna-
tional Publishing; 2018. p. 237–57. https://doi.org/10.1007/978-3-319-75529-8_14
117. Dykstra PA, Fokkema T. Relationships between parents and their adult children: a West European
typology of late-life families. Ageing and Society. 2011; 31(4):545–69. https://doi.org/10.1017/
S0144686X10001108
118. Petrova´ Kafkova´ M. Older People as Care Givers and Their Roles In Family in the Era of Active Age-
ing: Case of the Czech Republic. Studia Socjologiczne. 2015; 2(217):49–73.
119. Hajek A, Ko¨nig H-H. Which factors contribute to loneliness among older Europeans? Findings from
the Survey of Health, Ageing and Retirement in Europe: Determinants of loneliness. Archives of Ger-
ontology and Geriatrics. 2020; 89:104080. https://doi.org/10.1016/j.archger.2020.104080 PMID:
32371343
120. Puga D, Ferna´ndez-Carro C, Ferna´ndez-Abascal H. Multimorbidity, Social Networks and Health-
Related Wellbeing at the End of the Life Course. In: Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, editors.
Handbook of Active Ageing and Quality of Life From Concepts to Applications. Cham: Springer, Inter-
national Handbooks of Quality-of-Life series; 2021. p. 609–28. https://doi.org/10.1007/978-3-030-
58031-5_37
121. Beridze G, Ayala A, Ribeiro O, Ferna´ndez-Mayoralas G, Rodrı´guez-Bla´zquez C, Rodrı´guez-Rodrı´-
guez V, et al. Are Loneliness and Social Isolation Associated with Quality of Life in Older Adults?
Insights from Northern and Southern Europe. International Journal of Environmental Research and
Public Health. 2020; 17(22):8637. https://doi.org/10.3390/ijerph17228637 PMID: 33233793
122. Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, Rodrı´guez-Rodrı´guez V, Prieto-Flores ME, Rojo-Abuı´n JM.
Residential Environment of the Elderly People in Spain. Towards a Municipal Categorization. Boletı´n
de la Asociacio´n de Geo´grafos Españoles. 2007; (43):369–74. https://bage.age-geografia.es/ojs/
index.php/bage/article/view/596
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 27 / 29
123. Garcı´a Ballesteros A, Jime´nez Blasco BC. Envejecimiento y urbanizacio´n: implicaciones de dos pro-
cesos coincidentes. Investigaciones Geogra´ficas. 2016(89):58–73. https://doi.org/10.14350/rig.47362
124. Leeson GW. The Growth, Ageing and Urbanisation of our World. Journal of Population Ageing. 2018;
11(2):107–15. https://doi.org/10.1007/s12062-018-9225-7
125. Phillipson C, Grenier A. Urbanization and Ageing: Ageism, Inequality, and the Future of “Age-Friendly”
Cities. University of Toronto Quarterly. 2021; 90(2):225–41. https://doi.org/10.3138/utq.90.2.11
126. Beard JR, Petitot C. Ageing and urbanization: Can cities be designed to foster active ageing? Public
Health Reviews. 2010; 32(2):427–50. https://doi.org/10.1007/BF03391610
127. Kaas L, Kocharkov G, Preugschat E. Wealth Inequality and Homeownership in Europe. Annals of Eco-
nomics and Statistics. 2019; 136:27–54. https://doi.org/10.15609/annaeconstat2009.136.0027
128. Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, Rodrı´guez-Rodrı´guez V, Rojo-Abuı´n JM. The Environments
of Ageing in the Context of the Global Quality of Life among Older People Living in Family Housing. In:
Mollenkopf H, Walker A, editors. Quality of Life in Old Age International and Multi-disciplinary Perspec-
tives. Dordrecht: Springer, Social Indicators Research Series, Volume 31; 2007. p. 123–50. https://
doi.org/10.1007/978-1-4020-5682-6_8
129. Garcı´a-Valdez MT, Sa´nchez-Gonza´lez D, Roma´n-Pe´rez R. Envejecimiento y estrategias de adapta-
cio´n a los entornos urbanos desde la gerontologı´a ambiental [Aging and adaptation strategies to
urban environments from environmental gerontology]. Estudios Demogra´ficos y Urbanos. 2019; 34
(1):101–28. https://doi.org/10.24201/edu.v34i1.1810
130. Marsillas S, De Donder L, Kardol T, van Regenmortel S, Dury S, Brosens D, et al. Does active ageing
contribute to life satisfaction for older people? Testing a new model of active ageing. European Journal
of Ageing. 2017; 14(3):295–310. https://doi.org/10.1007/s10433-017-0413-8 PMID: 28936139
131. Avramov D, Maskova M. Active ageing in Europe. Strasbourg: Council of Europe; 2003. Available
from: https://book.coe.int/en/population-studies-series/2687-active-ageing-in-europe-volume-1-
population-series-no-41.html
132. Bramanti D, Meda SG, Rossi G. Active Ageing among the Generations: Towards an Age-Friendly
Society? In: Scabini E, Rossi G, editors. Living Longer A Resource for the family an Opportunity for
Society. Champaign-Illinois: Common Ground Research Networks; 2018. p. 1–34. Available from:
http://hdl.handle.net/10807/127134
133. Lee YS, Putnam M, Morrow-Howell N, Inoue M, Greenfield JC, Chen H. Consolidated Measures of
Activity among Older Adults: Results of a Three Data Set Comparison. Journal of Gerontological
Social Work. 2019; 62(5):502–20. https://doi.org/10.1080/01634372.2019.1582123 PMID: 30786817
134. Chen YC, Putnam M, Lee YS, Morrow-Howell N. Activity Patterns and Health Outcomes in Later Life:
The Role of Nature of Engagement. The Gerontologist. 2019; 59(4):698–708. https://doi.org/10.1093/
geront/gny023 PMID: 29659800
135. Boudiny K. ’Active ageing’: From empty rhetoric to effective policy tool. Ageing and Society. 2013; 33
(6):1077–98. https://doi.org/10.1017/S0144686X1200030X PMID: 23913994
136. Pe´rez Dı´az J, Abella´n Garcı´a A. “Active Ageing”: Its Relevance from an Historical Perspective. In:
Rojo-Pe´rez F, Ferna´ndez-Mayoralas G, editors. Handbook of Active Ageing and Quality of Later Life
From concepts to applications. Cham: Springer, series International Handbooks of Quality of Life;
2021. p. 171–84. https://doi.org/10.1007/978-3-030-58031-5_10
137. Ferna´ndez-Mayoralas G. Seguridad, envejecimiento activo y calidad de vida. In: Ferna´ndez-Mayora-
las G, Rojo-Pe´rez F, editors. Envejecimiento Activo, Calidad de Vida y Ge´nero Las miradas acade´-
mica, institucional y social. Valencia: Tirant lo Blanch; 2021. p. 267–306. Available from: https://
editorial.tirant.com/co/libro/envejecimiento-activo-calidad-de-vida-y-genero-9788418329142
138. Scherpenzeel A, Axt K, Bergmann M, Douhou S, Oepen A, Sand G, et al. Collecting survey data
among the 50+ population during the COVID-19 outbreak: The Survey of Health, Ageing and Retire-
ment in Europe (SHARE). Survey Research Methods. 2020; 14(2):217–21. https://doi.org/10.18148/
srm/2020.v14i2.7738
139. Angelinova Angelova M. Factors affecting the active life of people aged 50 and over in Europe before
and during the pandemic. Revista Inclusiones. 2021; 8:62–90.
140. Bertoni M, Celidoni M, Bianco CD, Weber G. How did European retirees respond to the COVID-19
pandemic? Economic Letters. 2021; 203:109853. https://doi.org/10.1016/j.econlet.2021.109853
141. Novais F, Cordeiro C, Caˆmara Pestana P, Coˆrte-Real B, Reynolds Sousa T, Delerue Matos A, et al.
The Impact of COVID-19 in Older People in Portugal: Results from the Survey of Health, Ageing and
Retirement (SHARE). Acta Me´dica Portuguesa. 2021; 34(11):761–6. https://doi.org/10.20344/amp.
16209 PMID: 34986084
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 28 / 29
142. Scheel-Hincke LL, Ahrenfeldt LJ, Andersen-Ranberg K. Sex differences in activity and health changes
following COVID-19 in Europe—results from the SHARE COVID-19 survey. European Journal of Pub-
lic Health. 2021; 31(6):1281–4. https://doi.org/10.1093/eurpub/ckab096 PMID: 34406382
143. SHARE-ERIC- The Survey of Health Ageing and Retirement in Europe, The European Research Infra-
structure Consortium. Results of the 1st SHARE Corona Survey; Project SHARE-COVID19 Munich:
SHARE-ERIC, (Project Number 101015924, Report no. 1); 2021. 36 p. https://doi.org/10.17617/2.
3356927
144. Spitzer S, Shaikh M, Weber D. Older Europeans’ health perception and their adaptive behavior during
the COVID-19 pandemic. European Journal of Public Health. 2022; 32(2):322–7. https://doi.org/10.
1093/eurpub/ckab221 PMID: 34978564
145. Vergauwen J, Delaruelle K, Dykstra PA, Bracke P, Mortelmans D. The COVID-19 pandemic and
changes in the level of contact between older parents and their non-coresident children: A European
study. Journal of Family Research. 2022; 34(1):512–37. https://doi.org/10.20377/jfr-695
146. Nelson G, Prilleltensky I, editors. Community Psychology: In Pursuit of Liberation and Well-Being.
New York: Palgrave Macmillan; 2005.
147. Clemson L, Mackenzie L, Ballinger C, Close JCC, Cumming RG. Environmental interventions to pre-
vent falls in community-dwelling older people: a meta-analysis of randomized trials. Journal of Aging
and Health. 2008; 20(8):954–71. https://doi.org/10.1177/0898264308324672 PMID: 18815408
148. Williams-Roberts H, Jeffery B, Johnson S, Muhajarine N. The effectiveness of healthy community
approaches on positive health outcomes in Canada and the United States. Social Sciences. 2016; 5
(1):1–21. https://doi.org/10.3390/socsci5010003
149. Komp K, Johansson S. Population ageing in a lifecourse perspective: developing a conceptual frame-
work. Ageing and Society. 2016; 36(9):1937–60. https://doi.org/10.1017/S0144686X15000756
PLOS ONE Active ageing profiles findings from SHARE
PLOS ONE | https://doi.org/10.1371/journal.pone.0272549 August 4, 2022 29 / 29