Active ageing profiles among older adults in Spain: A Multivariate analysis based on SHARE study

RESEARCH ARTICLE Active ageing profiles among older adults in Spain: A Multivariate analysis based on SHARE study Fermina Rojo-PerezID 1 *, Vicente Rodriguez-Rodriguez 1 , Maria-Angeles Molina-Martinez 2 , Gloria Fernandez-Mayoralas 1 , Diego Sanchez-GonzalezID 3 , Jose-Manuel Rojo-Abuin 4 , Alba Ayala 5 , Carmen Rodriguez-BlazquezID 6 , Amaia Calderon-Larrañaga 7 , Oscar Ribeiro 8 , Maria-João Forjaz 9 1Grupo de Investigacion sobre Envejecimiento (GIE), IEGD, CSIC, Madrid, España, 2Departamento de Personalidad, Evaluacio ´n y Tratamiento Psicolo ´gico, Facultad de Psicologı´a, Universidad Nacional de Educacio ´n a Distancia (UNED), Madrid, España, 3Departamento de Geografı ´a, Universidad Nacional de Educacio ´n a Distancia (UNED), Madrid, España, 4Unidad de Analisis Estadistico (UAE), CCHS, CSIC, Madrid, España, 5Departamento de Estadı ´stica, Universidad Carlos III de Madrid (UC3M), Madrid, España, 6National Centre of Epidemiology and CIBERNED, Carlos III Institute of Health (ISCIII), Madrid, España, 7Aging Research Center, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet & Stockholm University, Solna, Sweden, 8Center for Health Technology and Services Research (CINTESIS), Department of Education and Psychology of the University of Aveiro, Aveiro, Portugal, 9National Centre of Epidemiology and REDISSEC, Carlos III Institute of Health (ISCIII), Madrid, España *[email protected] 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 different steps applying several statistical analyses (Principal Component, Cluster, Discriminant, 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 excellent 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 perceived 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 SHAREERIC (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 during the life cycle. Introduction and backgrounds Population ageing is a global phenomenon with important regional differences. Scientific evidence 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 trigger 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 independently for as long as possible [9]. The population’s longevity, together with its life expectancy 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 capacities 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 communication 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, gender, 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.shareproject.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. CSO201564115-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 networks [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 valued 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], personal 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 reaching 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 determinants [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 positive 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 researching and understanding population ageing in Europe from a life-course perspective using a multidimensional and longitudinal approach, although not all countries participate in this network. 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, eligibility 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 structured 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 information 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 instrument 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 measuring 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. Variables 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 missing 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 normalised weights have been calculated by dividing the weight by the mean of weights and preserving 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. However, 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 service (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 complexity 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, Lifelong Learning, Participation and Security). The factor scores of the 18 Principal Components (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 programme (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 analyses 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 similarity and plot a perceptual graph [81], in which proximity between categories indicates association, 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 comparisons) was conducted to compare the AA profiles in each independent variable. Statistical 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 quality 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 Pillar 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 correctly classified. b. The CLA performed over the Lifelong Learning Pillar’s (L) main components grouped subjects 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 correctly classified. c. The CLA performed over the Participation Pillar resulted in 4 clusters (P), named as follows: 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 participants 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 dimensions 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 profiles 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 physical 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 technical aids. This profile is only associated with the Health pillar. Profile III (people with active ageing-limiting conditions) grouped subjects with low selfassessed competence in reading, writing and computer skills, low consumption of fruit/vegetables 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 subjects 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 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 usually 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 motivating 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. Different 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 maintain 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 contacts 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, possibly 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 (profiles I and II): people living alone, in smaller households, older and, above all, women. Yet having 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 latter 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 network 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 network 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 environment 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 metropolitan 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 population 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 percentages 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 characterised 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 independence, 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 conditioning 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 wellbeing (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 competent 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 literature, 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 measure 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 (individual, 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 considered 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 greatest 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 relevance 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 generation 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 outbreak 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 coronavirus, 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 methodological 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 widespread trend, there are two other areas that would require more attention, such as crossreferencing and further triangulation studies. Both would stem from longitudinal type analyses, albeit constrained by the availability of adequate data, and the use of combined quantitative-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 prepost 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, environmental interventions, based on designs and adjustments of the residential and community environment, such as environmental adaptations, relevant assistive technologies, and environmental 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 previous 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 European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006062193; 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-COHESION: GA N˚870628; SERISS: GA N˚ 654221; SSHOC: GA N˚ 823782) and by DG Employment, 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 Institute 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 comments 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 Rodriguez-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 Rodriguez-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-Angeles 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. UNUnited Nations, Department of Economic and Social Affairs, Population Division. World Population 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 LongTerm Care Expenditure in Four European Union Member States: The Influence of Demographic Scenarios. 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, functional 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, MolinaMazo R. La calidad de vida en ancianos polimedicados con multimorbilidad. Revista Española de Geriatrı ´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, 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. 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 revisio ´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-ygenero-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 envejecimiento desde una perspectiva popular. 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. 307–26. Available from: https://editorial.tirant.com/co/libro/envejecimientoactivo-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-calidadde-vida-y-genero-9788418329142 17. WHOWorld Health Organization. Active Ageing: A Policy Framework. Geneva: World Health Organization; 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 Gerontology 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, Fuentes-Pumarola C, et al. Prevalence and related factors of Active and Healthy Ageing in Europe according 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-genero9788418329142 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 A ˆge ´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 leisure 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 interpretan 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. Environmental 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/s11266020-00267-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 Participation: 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, RojoPe ´rez F, editors. Envejecimiento Activo, Calidad de Vida y Ge ´nero Las miradas acade ´mica, institucional 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-BRInternational 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 informantes 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-genero9788418329142 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 variables 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 calidad 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-calidadde-vida-y-genero-9788418329142 50. Ramia I, Voicu M. Life Satisfaction and Happiness Among Older Europeans: The Role of Active Ageing. 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 Wellbeing 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/10.1007/978-3319-69909-7_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 Epidemiology. 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 version: 7.1.0. SHARE-ERIC. Data set. 2019. Available from: http://www.share-project.org/datadocumentation/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–Methodology. 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. MEAMunich 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 Center for the Economics of Aging (MEA), the Max Planck Institute for Social Law and Social Policy (MPISOC); 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. Influence 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. S ˇimkovic 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. Visualization 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 Gerontology, 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