Introduction

Over the last decades, social gerontologists have introduced varying perspectives ranging from the early activity theory (Havighurst 1961) to the current paradigm of active ageing. Successful ageing—one of the leading theories to emerge (Rowe and Kahn 1987)—is also known as productive ageing, healthy ageing, or active ageing (Walker 2002). Some authors use these terms interchangeably (Fernández-Ballesteros 2008), while others distinguish between the different meanings (Walker 2002). All three concepts share certain features, such as their use of gerontological knowledge to build a positive conception of ageing (Foster and Walker 2015). Nonetheless, the construct of active ageing was formulated to transmit a broader concept than healthy ageing and productive ageing (Foster and Walker 2013; World Health Organisation [WHO] 2002). It includes a multidimensional view of health as measured by physical, mental, and social well-being (Peel et al. 2004), as well as the productivity of older adults to society (Bass et al. 1993). Moreover, activity is viewed as a broad domain, represented by participation in social, economic, cultural, physical, and routine activities (WHO 2002). Thus, all significant activities that improve the well-being of individuals and families, local communities, and society are part of “active ageing” (Foster and Walker 2015; Walker 2002).

The concept of active ageing has an increasingly important role not only in research, but in policy and society as well, due largely to the WHO’s (2002) multidimensional model. The European Commission (EC) has recently adopted the paradigm to face the challenge of the ageing population (Boudiny 2013). Both organisations address active ageing mainly at the population level; this means they are based on the global count of older people and promote potential contextual elements to increase opportunities to age actively. However, while the WHO’s active ageing policy considers healthy lifestyles in its conceptualisation, the EC fosters older people’s contribution to society in terms of productive activity, working longer, lifelong learning, and remaining active after retirement (Oxley 2009).

Several debates have emerged in the research on active ageing. The first reflects the lack of agreement on its definition (Boudiny 2013) and confusion about its components and determinants (Paúl et al. 2012; Tareque et al. 2013). For instance, sometimes terms of definition and determinants are blended together in the same study, conflating the definition of active ageing with its determinants (Paúl et al. 2012; Tareque et al. 2013). Moreover, some variables such as social support, life satisfaction, or coping styles (referring to the way people face difficult situations) are used in different studies as components, determinants, or even results of active ageing (Blanco 2010; Fernández-Ballesteros 2008; Perales et al. 2014). Though currently the majority of academics stress a multidimensional conception of well-being, it is not clear what variables should reflect it. Some authors define it through status variables such as physical health and functionality, cognitive state, positive affect, and/or social relationships, as perceived by older people (Caprara et al. 2013; Fernández-Ballesteros 2008; Bowling 2008; Stenner et al. 2011). Others refer to more processual variables when defining active ageing, considering continuous participation in different activities from labour force participation to engaging in social activities and daily life routines (Fernández-Mayoralas et al. 2015; Stenner et al. 2011).

A second debate concerns the inclusion of leisure activities in the concept of active ageing. Mainstream research considers only productive activities, both paid and unpaid work, that create social worth (Rowe and Kahn 1997). These activities seem to be important not only from the perspective of researchers and policy-makers but also subjectively by older people themselves (Stenner et al. 2011). However, an exclusive focus on productive activities has several shortcomings, namely reverting to the precursor concept of productive ageing, and neglecting alternative pathways of ageing actively (Boudiny and Mortelmans 2011). To address these drawbacks, various authors emphasise the incorporation of leisure activities for several reasons. First, formal productive engagement does not suit everyone (Stenner et al. 2011). When released from the responsibilities of middle age, for some older adults leisure is a way to re-engage with life (Clarke and Warren 2007). Second, different patterns of leisure activities can enhance or impede participation in productive activities, such as volunteering (Dury et al. 2015), raising the possibility that high participation in leisure can be incompatible with social participation. Third, leisure is subjectively important for older adults, who point to its multiple beneficial effects (Bowling 2008). These include improved cognitive and physical states, and compensation for social losses such as death of one’s life partner (Boudiny and Mortelmans 2011; Silverstein and Parker 2002).

The third debate revolves around the frequently occurring dichotomy between active and passive activities (Boudiny and Mortelmans 2011). Typically, because of their proven benefits, only active leisure activities are considered important for active ageing, such as hobbies, sports, travelling, and creative activities (Avramov and Maskova 2003; Colcombe and Kramer 2003). This focus on active participation is partly confirmed by older people’s own perceptions; they contrast being active with being passive as a rationale for an agentive attitude (Stenner et al. 2011; Litwin and Shiovitz-Ezra 2006). However, this means enhancing mainly young-old preferences (Boudiny and Mortelmans 2011). Nonetheless, many older people consider that “ordinary” activities usually classified as passive, such as crossword puzzles, are more representative of their involvement with life (Clarke and Warren 2007). Moreover, much older people spend more time in home-based and family-related leisure, showing a certain change in people’s activities as they age, perhaps due to alterations in preferences and constraints (Boudiny 2013).

In defining activities, lifelong learning and the use of Information and Communication Technologies (ICT) have received special attention from researchers and policy-makers (EC 2008). Lifelong learning has been found to be an effective way to age actively (Tam 2011). Due to its beneficial role in promoting well-being (Walker 2002) through developing social contacts and postponing the onset of mental problems associated with ageing (Phillipson and Ogg 2010), it can be included in the definitions of active ageing as an independent component. Furthermore, the use of ICT is important as it enables older people to stay connected to society and to their social networks (Zaidi et al. 2013), providing them with enhanced cognitive opportunities and compensating for age-related losses (Boudiny and Mortelmans 2011).

Conceptualising active ageing

There are relatively few studies that have measured active ageing in its broad and inclusive conception, following the criteria and principles established in its creation (Marsillas 2016). This is due to the partial operationalisation of the variable, which include only discrete aspects such as employment, social participation, and, less frequently, leisure activities. Moreover, in some studies, active ageing is represented by a dichotomous variable created through compliance with a list of criteria of health-related variables (Fernández-Ballesteros et al. 2006). Yet, this measurement is too narrow since it generally limits active ageing to a few people, excluding the frail and people with disabilities from the definition and thus failing to fulfil the principles of active ageing (Walker 2002).

Studies that adopt a more multidimensional definition of active ageing include objective and subjective perceptions of health, functionality, cognitive, affective, and social status (Fernández-Ballesteros et al. 2006; Perales et al. 2014), thus referring to the health concept in a multidimensional and broad manner. It has also been defined by different participation variables such as leisure (Fernández-Mayoralas et al. 2015), social participation (Perales et al. 2014), and lifelong learning (Tam 2011). However, even though the use of ICT is mainly considered as a predictor of active ageing (Gjevjon et al. 2014), in this study it is proposed as a component since it improves older people’s well-being and increases their engagement with life (Boudiny and Mortelmans 2011).

Measuring active ageing

In the past few years, some instruments have been developed to measure active ageing. From the population perspective, the Active Ageing Index (Zaidi et al. 2013) was created with the collaboration of the EC and the United Nations Economic Commission for Europe (UNECE). This index targets policy-makers and aims to measure the amount of active ageing at a country level based on 22 indicators organised in four domains: (1) employment; (2) participation in society; (3) independent, healthy, and secure living; and (4) capacity and enabling environment for active ageing. Information for each indicator comes from secondary data sources for 28 European countries, such as the European Social Survey. The index sheds light on the effectiveness of existing strategies and points out the environmental elements that can be improved to increase opportunities to age actively. However, it cannot be used to measure active ageing on an individual level, since its indicators aim for macro-level measurements such as healthy life expectancy, and the result provided is a construct based on the aggregate number of older people that meet different indicators.

Other authors have developed measurement tools to capture the individual perspective, such as Tareque et al. (2013). Their index is based on the WHO’s model, using the three determinants of active ageing: health (referring to physical health and absence of disabilities, as well as physical activities), participation (participation with family, workforce, and in clubs/groups), and security (physical and financial security). The shortcomings of this index include the restricted scope of health, almost completely focusing on the physical dimension and excluding some important variables such as cognitive, affective, and social health. Furthermore, the authors stress that this index measures the determinants of active ageing, even though it is called an active ageing index and is supposed to measure the active ageing concept.

Three main research gaps emerge from this literature review. First, there is a lack of models that account for active ageing in its multidimensional and inclusive conception. Existing measurements are focused either on health variables or on productive participation, which are not fully representative of older people’s ways of engagement. Second, the possible effect of active ageing on life satisfaction has not received sufficient attention from policy-makers and researchers (Walker 2002). Though the relationship of some activities to life satisfaction has been studied (Neugarten et al. 1961) and is partly related to the socio-gerontological literature of the 1950s which argues for a positive correlation between active lifestyles and life satisfaction (Boudiny and Mortelmans 2011), it remains unclear how active ageing relates to this outcome. Finally, even though some authors mention coping styles (Fernández-Ballesteros 2008) and these have been theoretically included in active ageing models as predictors, they are rarely studied empirically in this field.

Aim

The aim of this paper is threefold. First, it seeks to develop a new measurement tool based on a model of active ageing focused at the individual level, constructed by using two broad categories of variables: processual and status variables, which group together the most important dimensions found in the scientific literature. In so doing, a recurrent problem in the literature will be overcome, namely the partial study of the concept of active ageing. Second, this paper explores the relationship of active ageing to satisfaction with life. Finally, we explore the possible predictive role effect of coping strategies related to active ageing.

To arrive at these goals, the paper tests the following hypotheses: (1) active ageing can be defined as a higher-order construct, composed of two broad categories of variables (status and processual), (2) active ageing has a positive path on life satisfaction, and (3) coping styles have a predictive role in active ageing.

Method

Design and sampling

The study methodology was based on a survey of a representative sample of community-dwelling residents aged 60 and over in Galicia, Spain (804,403 inhabitants, 29.2% of the total population). Structured interviews were conducted by experienced psychologists using a questionnaire. The sampling selection was made through the county register, and a two-stage sampling was chosen: conglomerates for the selection of the first-level units (municipalities) and quotas according to the habitat (urban/semi-urban vs. rural/semi-rural), gender, and age group (60–74 years vs. 75 or older) for the selection of the second-level units (individuals). No personal data were requested, guaranteeing anonymity and confidentiality of the answers. Participation in the study was voluntary, and a 6% of the participants who initially accepted to be part of the study did not finish the interview.

The final sample was composed of 404 individuals (176 men and 228 women; mean age = 72.6 range = 60–94), recruited directly by interviewers in different community facilities, regarding those venues where people of different profiles usually attended. In this sense, we included social centres, which are oriented to older people to meet in order to have a coffee, do exercise, read newspapers, or arranging issues related to the municipality, as well as clinics, around the hospitals or markets. Regarding the habitat, 59.2% are residents of a rural/semi-rural area, whereas 40.8% are from urban/semi-urban area. Thirty per cent of respondents did not complete primary studies, 32.9% completed primary education, 21.0% secondary education, and 16.1% tertiary studies. In terms of marital status, 9.2% were single, 58.1% were married, 3.0% were divorced, and 29.7% were widowed.

Variables and measures

The variables included in the questionnaire were chosen based on a literature review (Marsillas 2016) and assessed the ten broad dimensions of: (1) health (objective and subjective health), (2) functionality (basic and instrumental daily activities), (3) cognitive status, (4) affective status, (5) social status (social and family perceived support, frequency of outdoor social contact), (6) ICT use, (7) lifelong learning, (8) employment, (9) social participation, (10) leisure activities, as well as coping styles (active and external), life satisfaction, and socio-demographic variables (age, gender, habitat, marital status, education, income).

The dimensions of active ageing were measured using different scales. Functionality was evaluated by Barthel Index (Cronbach’s alpha = 0.83) (Mahoney and Barthel 1965) and Lawton and Brody Scale (Cronbach’s alpha = 0.92) (Lawton and Brody 1969); cognitive status was measured by the Mini-Examen Cognoscitivo, the Spanish version of Minimental State Examination (Cronbach’s alpha = 0.73) (Lobo et al. 1999); affective status was measured by the positive affect scale of the Affective Balance Scale (Cronbach’s alpha = 0.76) (Godoy-Izquierdo et al. 2008); different leisure activities were measured using items from Scarmeas et al. (2003) and by adding two more items; social participation and employment were assessed with several items from the Active Ageing Index (Zaidi et al. 2013). Coping styles were measured by seven items chosen from the Spanish version of the Brief COPE Inventory (Vargas-Manzanares et al. 2010); life satisfaction was measured by the Satisfaction with Life Scale (Cronbach’s alpha = 0.76) (Diener et al. 1985). Health was assessed through seven items created for this study, ICT use was measured by three items including one from Zaidi et al. (2013), and social status was evaluated by a scale created for this study (Cronbach’s alpha = 0.83) by combining selected items from Zaidi et al. (2013), the Spanish version of Duke-UNC-11 scale (Bellón et al. 1996a), and modified items from the Spanish version of family APGAR (Bellón et al. 1996b). Specific items are shown in Table 1.

Table 1 Constructs and items
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Statistical analysis

First, multicollinearity was analysed and rejected by checking intercorrelations between variables (r < 0.5) and the variance inflation factor (VIF) for formative factors below the recommended value of 3.3 (Lowry and Gaskin 2014). Moreover, common method biases were analysed with Harman’s single-factor test, obtaining 21 distinct factors of which the highest one accounted for 14.33% of the variance of the model. This result combined with examining the correlation matrix of constructs ensured that the data did not suffer from these biases.

The three hypotheses were tested with partial least squares (PLS) regression. The PLS algorithm was performed due to its suitability for the exploratory analysis required for theory-building (Lowry and Gaskin 2014). A molar model was specified with twenty first-order constructs: one was formative (social participation) and the others reflective, ten second-order constructs, two third-order constructs, and one fourth-order construct. Following the suggestions of Lowry and Gaskin (2014), after deleting items that did not meet the statistical requirements in the measurement model, construct and convergent validity of reflective constructs were checked by analysing the significant loadings on each theoretical construct through the bootstrapping of 500 resamples. Discriminant validity was checked by analysing cross-loadings between indicators and first-order constructs as well as by comparing the correlations between the square root of the average variance extracted and other latent variables. Reliability was tested by a composite reliability indicator. Validity of the formative construct was tested by ensuring that weights were roughly equal and had significant t values. A fourth-order factor analysis was performed using the several-steps approach to achieve the fourth-order formative construct called active ageing. Afterwards, the predictive power of the model was tested by analysing the path coefficients of the model. The validity and suitability of the model were checked by calculating the significance of each path using a t test through a bootstrapping of 500 resamples. Analyses of the model were performed in SmartPLS 2.0. The level of significance used was 0.05.

Results

Measurement instrument of active ageing: validity

Figure 1 shows both the measurement and the structural model of the PLS analysis. Regarding the measurement model, twenty first-order constructs were created. In this first step, two items were removed from the model because they were not significant, indicated by italics values in Table 2: attending church as a social leisure activity and caring for older people as social participation. After that, factorial analysis was conducted again to obtain the significances and the psychometric properties of items are shown in Table 2. All factor loadings achieved the minimum threshold of 0.5 and showed adequate convergent validity by achieving a significant t statistic value. Then, higher-order factor constructs were created by higher-order factorial analysis with the step strategies, beginning with the second-order constructs and continuing with third- and fourth-order constructs. The active ageing construct is composed of two broad categories of variables. Processual variables capture the activity meaning of active ageing, while status variables relate more to the concept of health. Each is composed of five second-order constructs.

Fig. 1
figure 1

Measurement and structural model

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Table 2 Descriptive statistics for the scales
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All the latent variables had satisfactory psychometric properties, in terms of internal consistency, by showing values for composite reliability of each reflective latent variable above 0.7. Social participation was a formative scale, with similar weighting for each indicator and significant t values. The convergent and discriminant validities of the first-order construct were analysed by the matrix of loading and cross-loadings (this information is available from the authors). The loadings of the items are 0.1 higher for the latent variable to which they theoretically belong than for other variables. Discriminant validity was also examined by comparing the square root of the average variance extracted (AVE), indicated by the bold values placed in the diagonal, to the correlations with other variables (Table 3). Because diagonal values were higher than other correlations, evidence of discriminant values was revealed.

Table 3 Interconstruct correlations and square roots of AVE of first-order constructs
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Regarding the measurement model (Fig. 1), the two broad categories of variables called processual variables (PV) and status variables (SV) are significant third-order constructs, both with high weights over active ageing, the fourth-order construct. The status variables, referring to different elements related to the whole concept of health, showed a higher value (β = 0.74) than the processual variables, related to the adjective “active” in the construct (β = 0.40). All the variables included in the model proved significant; however, variables such as productive leisure activities, employment, and cognitive status showed lower weighting in their respective higher-order constructs. Nonetheless, since all of them were significant, they were maintained in the model. As for the processual variables, leisure activities, ICT use, and lifelong learning showed a higher weighting and social support, affect, and physical health achieved a higher importance regarding the status variables.

Background variables were also included in the model to explore how active ageing is related to variables such as age, gender, and education. In this case, gender did not achieve a significant path (β = −0.02), whereas age (β = −0.30) and education (β = 0.34) showed a significant relationship. This means that young-old people as well as higher education levels obtained higher levels of active ageing.

Active ageing and life satisfaction

Figure 1 shows a model of the relationship between active ageing, considered a fourth-order factor construct, and life satisfaction. The path coefficient was quite high and significant (β = 0.52). The determination coefficient showed a moderated value of R 2 = 0.27. This means that 27% of the variance of life satisfaction is explained by active ageing, and a rise of one unit of active ageing entails a rise of 0.52 in life satisfaction. Both concepts have a moderated relationship but with high predictive power.

Coping styles, active ageing, and life satisfaction

Figure 2 shows a model of the relationship between coping styles, active ageing, and life satisfaction. In so doing, the role of coping strategies on active ageing was explored. The path coefficients were both positive and significant. The active coping styles showed a meaningful path coefficient (β = 0.40), whereas the external coping styles showed a lower path coefficient (β = 0.14). The determinant coefficient of both styles on active ageing was moderated (R 2 = 0.18). Thus, this model has moderated predictive power. When adding the background variables to the model, the determination coefficient achieved R 2 = 0.39.

Fig. 2
figure 2

Coping, active ageing, and life satisfaction

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Discussion

This study was developed to explore the concept of active ageing and its relationship to other concepts commonly used in gerontological research. The paper contributes to the empirical literature on active ageing, proposing an innovative, empirical approach to this construct. The most important contributions of this study are (1) the development of a valid instrument to measure active ageing at an individual level as a higher-order construct composed of two broad categories of multidimensional variables, (2) the finding of a positive and high relationship between active ageing and life satisfaction, and (3) the higher significant relationship of active coping styles compared to external coping styles with active ageing.

As to the first contribution, in this study active ageing is constructed based on previous iterations of the concept (healthy and productive ageing) but also incorporating a broader view of the activities included and encompassing people with disabilities as active agers (Walker 2002). The results demonstrate that active ageing can be measured at an individual level, unifying the components promoted by policy-makers, researchers, and older people’s own perspectives on active ageing. Based on our findings, we can assert that active ageing is a higher-order construct, composed of two broad categories of variables: status and processual variables. Status variables include elements related to health as a multidimensional concept, considering physical, psychological, and social variables. These findings are consistent with authors who study active ageing such as Bowling (2008) and Caprara et al. (2013). However, including health in active ageing has been rejected by some scholars due to its frequent restriction to physical components and neglect of other important elements (Boudiny 2013; Davey 2002). In our model, we defined health broadly (physical, mental, cognitive, and social) because of its importance as part of active ageing (Bowling 2005; Fernández-Ballesteros 2008), but we agree that it cannot be the only axis of the concept, since it is neither sufficient nor indispensable to ageing actively (Clarke and Warren 2007; Stenner et al. 2011). Social variables represent the most important status variable, as shown by authors who demonstrate the value of social relationships in later life (Schulz and Heckhausen 1996), especially emotionally close bonds (Berg 2008).

Processual variables represent and unify different dimensions of active ageing, including both productive and leisure activities. This is in line with the view of older people themselves when defining active ageing (Bowling 2008; Stenner et al. 2011) and supports mainstream ideas about productive activities as defended by policy-makers, in terms of employment and social participation (EC 1999) as well as leisure activities, mainly advocated by researchers (Boudiny 2013; Hasmanová 2011). Although both are important, leisure activities contribute the most to processual variables, agreeing with the argument of some authors (Boudiny and Mortelmans 2011; Clarke and Warren 2007). In our model, we included different types of leisure activities, even those traditionally considered rather passive such as TV watching (Avramov and Maskova 2003).

The importance of ICT use is also demonstrated as a processual variable and part of the concept of active ageing, coinciding with authors such as Boudiny and Mortelmans (2011). Subsequently, as stated by previous authors who refer to the benefits of the use of ICT (Boudiny and Mortelmans 2011; Small et al. 2009), our results are in line with the current encouragement of their use, thus providing empirical support to its inclusion as a component of the active ageing concept. We found lifelong learning to be another important dimension which influences older people’s well-being (Walker 2002). Considering productive activities in terms of social participation, only caring for older people did not seem a satisfactory fit in the model. A possible reason is that long-term care of either ill or dependent older people can affect the psychological well-being (Boudiny 2013) or physical and mental health of their caregivers (Boudiny and Mortelmans 2011; Morrow-Howell 2000).

Regarding the hypothesis about the relationship between active ageing and life satisfaction, we found a positive path of active ageing on life satisfaction. In this sense, a generally high life satisfaction was evident in this sample, similarly to other previous studies including general population of older people (van Beuningen 2012; Vázquez et al. 2013). The reason could be that life satisfaction derives from the cognitive evaluation of one’s life, where individual regulative strategies can alter experiences and living conditions into a subjective reality (Ferring et al. 2004; Ferring and Filipp 2000). In addition, when people age, their way of obtaining life satisfaction may change as well, with many older people, for instance, preferring emotionally close relationships to other social activities (Berg 2008). Nonetheless, it was found lower life satisfaction in other samples in specific situations, such as reduced self-care capacity or older caregivers (Borg et al. 2006; Vitaliano et al. 1991). The results of this study adds to a certain extent to the line of research which has demonstrated that the assessment of life satisfaction can be influenced by some factors, such as poor self-reported health, low self-care capacity, or low satisfaction with social support, modifying its perception and decreasing it (Borg et al. 2006; Good et al. 2011).

Finally, regarding the third hypothesis about the role of coping styles, we can say that active and external coping styles predict an active process of ageing. However, active coping strategies show a higher value as a predictor. These findings are similar to those in the argument for agent capacities and the pro-active coping with obstacles (Ouwehand et al. 2007), considered important psychological abilities that improve the way people age.

Despite the findings, our study also has some inherent limitations. First, the cross-sectional nature of the research does not permit the verification of the causal relationship among variables. Each component of active ageing can also act as a predictor (Hasmanová 2011). Given that active ageing is an unobservable construct, it is measured by proxies. Distinguishing between its determinants and components depends on the choice of each author. However, our model is based on the knowledge provided by different agents (policy-makers, researchers, and older people) and as such is a good representation of the diverse components of active ageing presented in the literature.

Second, we wanted to know the influence of active ageing on the cognitive, subjective component of well-being, life satisfaction, but it may not be the best outcome variable for the model of active ageing proposed here. In future research, it would be interesting to include quality of life as an outcome variable (WHO 2002). Third, most of the variables are assessed by self-reporting; thus, subjective perceptions can influence the results (Fernández-Ballesteros 2011). Nevertheless, in our research the validity can be ascertained by comparing it to the objectively measured equivalent variables, such as the specification of the social network in the case of perceived social support. Additionally, we used a culturally homogeneous sample, and therefore, further studies are needed to validate this model in other cultures.

Finally, including more antecedents or predictor variables with a long-term effect covering the multilevel model (Fernández-Ballesteros 2008) as well as studying the socio-demographic differences about the paths of the variables could reveal interesting results. This study, however, was carried out to explore this individual-level approach in a broader, inclusive way and to try to construct a theory based on empirical research. The final aim was to complement the population perspective of active ageing, which traditionally promotes productive activities, focusing on the individual variables likely to be modified by individual-level intervention, such as cognitive stimulation, and promoting interest in intergenerational activities.

Our study’s findings can be considered a step forward in clarifying the debates in the literature and unifying different approaches to studying active ageing at the individual level. The results support the hypothesis of the inclusion of both status and processual variables as components of active ageing, thus encompassing the different spheres of a person’s life. In so doing, the duality between scientific and policy fields is somewhat reconciled. We included multidimensional variables to try to diminish the restrictive standard as well as to account for the heterogeneity of older people (Boudiny and Mortelmans 2011). To improve the potential of this paradigm, different considerations should be weighed. First, opportunities to age actively and make free decisions should be enhanced, instead of creating subtle forms of obligation (Hasmanová 2011). For this to be achieved, active ageing should be promoted using a twofold approach: intervention in both socio-political and environmental arenas and individual spheres by increasing people’s awareness of the benefits of ageing actively. Second, considering active ageing as a concept applicable to the whole lifespan is another important step, by starting the emphasis on the first half of life and continuing with the potential of older people (WHO 2002).

The findings of this study also allow us to say that active ageing is an important concept for people’s lives in terms of life satisfaction and that active coping styles are related to higher possibilities of ageing actively. It would be interesting to design an intervention to promote active coping styles and to test the hypothesis of an association between both concepts. In conclusion, the active ageing paradigm is moving in a positive direction. By considering additional components such as those proposed here, it can be of even greater benefit both to individuals and entire societies.