Intrinsic capacity as a determinant of quality of life trajectories in older Europeans: A sex- and region-sensitive longitudinal analysis using SHARE

Intrinsic capacity as a determinant of quality of life trajectories in older Europeans: A sex- and region-sensitive longitudinal analysis using SHARE | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intrinsic capacity as a determinant of quality of life trajectories in older Europeans: A sex- and region-sensitive longitudinal analysis using SHARE Rafael Llorens-Ortega, Carmen Bertran-Noguer, Dolors Juvinyà-Canal, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9211395/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Understanding determinants of quality of life (QoL) in older adults is crucial in aging societies. Intrinsic capacity (IC), combining physical and mental capacities, may influence QoL changes, but evidence on specific IC domains and QoL is limited. This study examines associations between IC domains and two-year QoL changes in older Europeans, focusing on sex and regional differences. This study integrates factor and network analytical approaches to examine IC as a multidimensional system. Methods : Data from 11,493 adults aged ≥50 from 13 European countries in SHARE Waves 5 and 6 (2013–2015) were analyzed. IC was operationalized across five domains: mobility, cognition, psychological well-being, sensory function, and vitality. Exploratory factor analysis validated IC’s multidimensional structure. Network analysis assessed domain interrelations and links to QoL (CASP-12). Sex and regional differences were explored via stratified analyses and ANOVA. Results : IC domains formed a coherent multidimensional construct. Psychological well-being and mobility showed the strongest associations with QoL. Depressive symptoms and fatigue correlated negatively with CASP-12 (r = −0.284 and −0.324, p < 0.001). Cognitive and mobility domains had weaker but significant links. Over two years, modest IC declines paralleled QoL changes. Women and individuals in Southern and Eastern Europe exhibited greater IC deficits and lower QoL. Conclusions : Intrinsic capacity significantly influences short-term QoL changes in older Europeans. Psychological and mobility domains are key targets for interventions. Addressing sex and regional disparities in IC may improve well-being and reduce inequalities in aging populations. Figures Figure 1 Figure 2 Figure 3 Introduction Population aging is a global phenomenon that is profoundly impacting countries worldwide, particularly those with higher levels of development, due to increased life expectancy and declining fertility rates (WHO, 2024). Demographic projections estimate that the proportion of individuals aged 65 and older will rise from 9.7% in 2023 to 16.4% by 2050, with Europe being one of the most affected regions (WHO, 2024). This accelerated demographic shift presents significant challenges for public health and social systems, increasing the demand for strategies that promote healthy aging and enhance the quality of life (QoL) of older adults (United Nations, 2021 ). Quality of life is widely recognized as a multidimensional construct influenced not only by health status but also by social participation, autonomy, and broader societal conditions (McGregor et al., 2009; Galloway, 2006 ). In response to this challenge, the World Health Organization (WHO) has proposed a model of healthy aging that emphasizes the importance of functional ability, defined as the dynamic interaction between an individual’s intrinsic capacity and their environment (WHO, 2015). This model recognizes that aging is shaped not only by biological factors but also by psychosocial and environmental determinants (WHO, 2020). Within this framework, intrinsic capacity (IC), conceptualized by the WHO as the composite of an individual’s physical and mental capacities, plays a pivotal role in shaping trajectories of quality of life (QoL) in older adults, representing a paradigm shift from disease-centered models to a function-centered approach in healthy aging (WHO, 2015). IC is operationalized through five key domains as proposed in the WHO ICOPE framework: mobility, cognitive capacity, sensory function, psychological well-being, and vitality (WHO, 2019). Recent research has demonstrated that assessing IC offers a more comprehensive and nuanced understanding of healthy aging and its implications for QoL (Angelsen et al., 2024 ; Chhetri, 2022). Nonetheless, challenges remain in the development and validation of large-scale tools to measure these domains reliably (Rojano et al., 2023). Promoting active and healthy aging requires targeted interventions aimed at preserving and enhancing these capacities, thereby supporting physical, psychological, and social well-being. Social participation has also been identified as an important determinant of well-being and functional outcomes in later life, contributing to both health and quality-of-life trajectories (Oshio et al., 2024 ). Studies such as that by Takeda et al. ( 2024 ) have highlighted that improvements in socioeconomic conditions and access to healthcare can help maintain IC even in the presence of chronic conditions (Takeda et al., 2024 ). Furthermore, active social participation has been positively associated with higher IC and improved QoL, underscoring the need to reduce social isolation and strengthen support networks (López-Ortiz et al., 2022 ). Sex disparities in aging and their impact on QoL remain a critical area of inquiry. Recent studies have identified persistent sex inequalities in health, education, and QoL among older adults (Ahrenfeldt & Möller, 2021 ; Salinas-Rodríguez et al., 2024 ). Older women are more likely to experience poorer health outcomes and lower QoL compared to men (Llorens-Ortega et al., 2024 ). In this context, IC plays a pivotal role in shaping QoL trajectories, justifying a sex-sensitive analytical approach. The Survey of Health, Ageing and Retirement in Europe (SHARE) is one of the most comprehensive longitudinal databases available, offering valuable insights into how social, economic, and health-related factors influence QoL among older adults across diverse European contexts (Börsch-Supan et al., 2013 ). Its multidisciplinary and longitudinal design enables the exploration of the interplay between social determinants of health, IC, and other relevant variables in shaping QoL outcomes. It is essential to recognize that intrinsic capacity does not evolve in isolation, but is strongly shaped by social determinants of health, including education, socioeconomic status, living conditions, and access to healthcare and social support (Marmot, 2020; WHO, 2015). These structural factors influence both the preservation and decline of functional domains, and their unequal distribution contributes to disparities in aging outcomes. Evidence consistently shows that women, while living longer, are more likely to experience multimorbidity, functional limitations, and poorer self-rated health than men (Crimmins et al., 2019; Oksuzyan et al., 2019). Similarly, cross-national studies demonstrate that older adults in Southern and Eastern Europe, where welfare systems are less comprehensive and socioeconomic inequalities more pronounced, face a higher risk of functional decline and reduced quality of life compared to those in Northern and Continental Europe (Börsch-Supan et al., 2013 ; Llorens-Ortega et al., 2024 ). Therefore, given that IC encompasses key functional domains, its preservation is hypothesized to directly influence trajectories of QoL in older adults. We hypothesize that declines in intrinsic capacity domains will predict deteriorations in QoL over two years, with women and residents of Southern and Eastern Europe experiencing disproportionately greater declines. By integrating functional domains of intrinsic capacity with social determinants of health in a cross-national European sample, this study contributes to the growing literature examining how biological, psychological, and social factors jointly shape quality-of-life trajectories in later life. Although limited to two waves, this longitudinal design allows examination of short-term change patterns in intrinsic capacity and their association with QoL dynamics. Materials and Methods Study Design This study is a prospective, analytical cohort study based on population-level data from the Survey of Health, Ageing and Retirement in Europe (SHARE) (Börsch-Supan et al., 2013 ; Malter, 2015 ). SHARE is a multinational longitudinal study that investigates health, socioeconomic, and demographic factors among non-institutionalized adults aged 50 and older. Participants are interviewed biennially, enabling international comparisons and longitudinal analyses of aging-related factors. To minimize bias SHARE employs standardized data collection procedures, including harmonized questionnaires and probabilistic representative sampling to ensure representativeness across countries. Data for this study were drawn from Waves 5 (2013) and 6 (2015), which provide the most consistent and comparable measures of the intrinsic capacity domains across countries and allow for a robust longitudinal design. Although more recent waves are available, key variables were not fully harmonized in later datasets, and attrition was higher compared with the 2013–2015 interval, reducing response rates and completeness of variables of interest which could bias longitudinal analyses. Study Population In Wave 5 (2013), a total of 59,421 individuals were surveyed across 13 European countries: Germany, Austria, Belgium, Denmark, Slovenia, Spain, Estonia, France, Italy, Luxembourg, Sweden, Switzerland, and the Czech Republic. Inclusion criteria for this study required participants to be aged 50 or older, reside in one of the 13 selected countries, consent to participate, have taken part in both consecutive waves under analysis, possess complete data for all variables of interest, and not be institutionalized at the time of the interviews. A total of 11,493 individuals met these criteria. The remaining participants were excluded due to attrition, death, non-participation in the subsequent wave, or missing data in key variables. Figure 1 presents the flow diagram detailing the total number of respondents in Wave 5, exclusions due to non-participation or missing data, and the final analytical sample. To assess potential bias arising from non-participation in Wave 6, a comparative analysis was conducted using Propensity Score Matching (PSM). Participants and non-participants were matched based on key sociodemographic variables such as sex, age, and geographic region. This approach ensured that the analytical sample was representative of the general Wave 5 population, with only minor differences in age and a slightly lower proportion of women. Quality of life scores (CASP-12) were compared between matched groups, revealing no statistically significant differences. The participant selection methodology is detailed in a previous study (Llorens-Ortega et al., 2024 ) and is available for download via GitHub (Vila, J., 2024 ). Supplementary details of this analysis are provided in Table S1 . For the analysis of regional differences in the evolution of QoL and IC, countries were grouped into four regional clusters based on the 2023 Eurostat report on welfare models in Europe (European Commission, 2023 ): Northern Europe: Denmark and Sweden (Social Democratic regimes) Continental Europe: Austria, Germany, Belgium, France, Luxembourg, and Switzerland (Corporatist regimes) Southern Europe: Spain and Italy (Southern European regimes) Eastern Europe: Slovenia, Estonia, and the Czech Republic (Post-Socialist regimes) This grouping was selected to reflect welfare regime typologies that influence social and health policies, although some heterogeneity within clusters may exist. Data Collection Procedures Data were collected through Computer-Assisted Personal Interviews (CAPI), with an average duration of 90 minutes. Interviews were conducted in participants’ homes using standardized questionnaires covering a wide range of topics, including IC domains, social determinants of health, health conditions, and socioeconomic factors. Design weights were applied to enhance the representativeness of the findings (Malter, 2015 ). Interviewers received standardized training and quality control procedures were implemented to ensure data reliability. Design weights were applied in all analyses using the appropriate weighting procedures in SPSS and R to account for sampling design and non-response. All data are publicly available for scientific use at www.share-project.org . Study Variables Outcome Variable: Quality of Life (QoL). The primary outcome was QoL, assessed with the CASP-12 scale (Wiggins et al., 2008 ), which measures subjective well-being across four domains: Control, Autonomy, Self-realization, and Pleasure. Each domain includes three items rated on a 4-point Likert scale (1 = never to 4 = often), yielding a total score from 12 to 48, with higher values indicating better QoL. Following previous studies, we classified scores into four categories: 12–34 (low), 35–37 (moderate), 38–39 (high), and 40–48 (very high). The CASP-12 has shown high reliability (Cronbach’s α = 0.84) in older populations (Hyde et al., 2003 ; Pérez-Rojo et al., 2018 ). Explanatory Variables: Intrinsic Capacity Domains. Following WHO’s ICOPE guidelines (WHO, 2019), we considered five domains of intrinsic capacity (IC): locomotion, sensory, cognition, psychological well-being, and vitality (Rojano & Luque et al., 2023; Angelsen et al., 2024 ). Each domain was dichotomized according to the presence or absence of limitations. Locomotion was measured using SHARE items PH046 (difficulty walking 100 m) and PH047 (difficulty climbing stairs), both recoded into “no difficulty” versus “any limitation.” Additional indicators of functional limitation Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) were also dichotomized. Sensory function was assessed through perceived vision (PH043) and hearing (PH044) difficulties, each coded as “no difficulty” versus “any impairment.” Cognition was defined as a composite variable integrating episodic memory (CF003, CF006) and spatiotemporal orientation (CF103, CF113, CF114); participants with impairment in either dimension was considered cognitively limited. Psychological well-being was measured with the EURO-D scale (Maskileyson et al., 2021 ; Portellano-Ortiz et al., 2018 ), where scores ≥ 4 indicate depression. Vitality was operationalized through Body Mass Index (BMI, PH012, PH013), categorized into underweight (< 18.5), normal (18.5–24.9), overweight (25–29.9), and obesity (≥ 30). For analysis, BMI was dichotomized as “no weight-related issues” (18.5–24.9) versus “weight-related issues” (< 18.5 or ≥ 25). While BMI is an indirect and partial indicator of vitality, it has been widely used in epidemiological research as a proxy for metabolic and nutritional status in large population-based datasets. In the absence of direct measures of energy balance, sarcopenia, or inflammatory biomarkers within SHARE, BMI provides a standardized and comparable operationalization across countries. Nevertheless, we acknowledge that vitality is a multidimensional construct, and future research should incorporate broader physiological indicators to enhance construct validity. Covariates: Social Determinants of Health (SDH) and Sociodemographics. To contextualize QoL, we included covariates covering SDH, sociodemographic variables, health behaviors, self-perceived health, and comorbidity. SDH included sex, age groups (50–64, 65–74, 75–84, ≥ 85), education (ISCED levels: low, medium, high), financial situation (“ease of making ends meet” and “receipt of financial assistance”), and region of residence (Northern, Continental, Eastern, Southern Europe). Sociodemographic factors included marital status, household composition, and employment status. Health behaviors included physical activity (vigorous activity ≥ once/week vs. less often (Reitlo et al., 2018 ; Quiroz Mora et al., 2018 ), alcohol consumption, and tobacco use. Self-perceived health (PH003) was dichotomized into “good” (good/very good) versus “poor” (fair, poor, very poor). Comorbidity was coded as none versus two or more chronic conditions. All covariates were selected based on prior literature demonstrating their relevance to aging outcomes and quality of life. Statistical Analysis Continuous variables were presented as means and standard deviations, while categorical variables were described using absolute and relative frequencies. Univariate comparisons were conducted using Student’s t-tests for continuous variables and Chi-square tests for categorical variables. Subsequently, all variables of interest were recoded into dichotomous variables to reflect the presence (1) or absence (0) of functional, cognitive, sensory, or mobility-related limitations, following an adverse event-oriented approach. Each variable was recategorized so that a value of 1 indicated a negative outcome, such as perceived difficulty, chronic condition, or poor performance, based on established cut-off points from the literature or statistical criteria (Salazar Estrada et al., 2019 ; Salinas-Rodríguez et al., 2022 ; Berk, 2016 ). Using this harmonized coding, a composite intrinsic capacity index was calculated by summing the dichotomized variables. This index reflects the accumulation of deficits across domains, where higher scores indicate lower intrinsic capacity (i.e., more domains with limitations), and lower scores represent higher intrinsic capacity. This approach aligns with previous methodologies that assess functional aging through deficit accumulation or frailty indices (Cesari et al., 2018 ; Ofori-Asenso et al., 2019 ; Burn et al., 2018 ), and with studies examining the decline of intrinsic capacity (Rodríguez-Laso et al., 2023 ; Rojano & Luque et al., 2023). Although the deficit-accumulation strategy resembles approaches used in frailty research, intrinsic capacity differs conceptually in that it focuses on the preservation of functional domains rather than vulnerability to adverse outcomes. While frailty emphasizes risk accumulation, intrinsic capacity emphasizes the level of retained physical and mental capacities. Therefore, the present index should be interpreted as a functional capacity gradient rather than a frailty score. This recoding strategy enabled more effective operationalization of variables for subsequent statistical analyses. Wave 5 data were used for exploratory factor analysis (EFA), while Wave 6 data were employed to assess the predictive validity of the intrinsic capacity instrument. Exploratory Factor Analysis (EFA) To identify the underlying structure of the constructs assessed, an EFA was conducted using the Weighted Least Squares (WLS) method, appropriate for dichotomous and ordinal data. A polychoric-tetrachoric correlation matrix was used to estimate relationships among categorical variables. The optimal number of factors to retain was determined through Parallel Analysis based on factor analysis, complemented by inspection of the scree plot. To facilitate interpretation, an oblique rotation (Promax) was applied, given the expected correlation among latent factors. Factor loadings greater than 0.40 were considered significant. Model adequacy was evaluated by examining uniqueness values, and variables with values exceeding 0.70, such as the vitality domain, were excluded from the final model. Network Analysis (NA) Network analysis was selected because intrinsic capacity is conceptualized as a multidimensional and interactive system rather than a set of independent predictors. Traditional regression approaches assume unidirectional relationships and latent variable structures, whereas network models allow examination of conditional dependencies and the relative centrality of domains within a complex system (Borsboom, 2022 ). This approach is particularly suitable for aging research, where functional domains interact dynamically and may reinforce or buffer each other over time. Network Analysis was performed using the Huge Estimator approach to explore the structural relationships within intrinsic capacity, minimizing model overfitting and examining how key indicators of mobility, sensory health, mental health, and cognitive function interrelate (Borsboom, 2022 ). Additionally, Ebiglasso was used to explore directional associations between intrinsic capacity domains and QoL, acknowledging that causal inference cannot be established within the present observational design. This advanced tool enabled modeling of complex, non-linear relationships among variables, facilitating the identification of critical factors with the greatest influence on QoL. Given the ongoing methodological debate regarding the stability of centrality indices in psychological and health-related networks, the results should be interpreted primarily in terms of overall structural patterns rather than precise centrality rankings. The network was estimated to use regularization procedures to reduce spurious associations and overfitting; however, centrality metrics may be sensitive to sampling variability. Therefore, findings related to node importance are presented as exploratory and hypothesis-generating rather than definitive causal hierarchies. Software Used: All analyses were conducted using weighted SHARE data and performed with the following software: SPSS version 25 (IBM Corp., Armonk, NY, USA); R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria); JASP Stats version 0.19.3. The significance level was set at p < 0.05. Ethical Considerations The Ethics Committee of the Max Planck Society for the Advancement of Science conducted a thorough review of all materials related to the SHARE project, including Wave 5 and the subsequent Wave 6. The committee certified that the research project and its procedures comply with the highest international ethical standards. Strict measures were implemented to ensure the confidentiality and privacy of participants’ data, in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects. Written informed consent was obtained from all participants, who voluntarily agreed to participate during the interviews (Börsch-Supan et al., 2013 ). Participants were informed about the purpose of the study, data confidentiality, and their right to withdraw at any time without penalty. Data confidentiality and anonymity were rigorously maintained throughout data collection, processing, and analysis. Personal identifiers were removed prior to data access by researchers, and data were stored securely in compliance with data protection regulations. No individual participant data are reported in this study. This study did not involve any interventions or clinical trials; therefore, a clinical trial registration number is not applicable. Results Demographics, healthy lifestyle, and quality of life by sex (Wave 5) Statistically significant sex differences were found across multiple variables (p < 0.001). Men were slightly older on average than women (64.4 vs. 63.3 years). Women were more represented in the 50–64 age group (54.4% vs. 50.0%). Educational disparities were evident, with 37.0% of women having low education compared to 32.9% of men. Marital status differed substantially, with men more often married or partnered (80.7% vs. 71.2%) and women more frequently widowed (13.4% vs. 4.4%). Economic difficulties were more prevalent among women (31.5% vs. 26.7%). In terms of health behaviors, men reported higher levels of physical activity (57.6% vs. 50.2%), but also greater prevalence of daily alcohol consumption (28.9% vs. 11.5%) and smoking (56.0% vs. 37.1%). Quality of life (QoL), measured by CASP-12, was significantly higher among men than women (38.6 vs. 38.0; p < 0.001). These findings indicate consistent sex differences in socioeconomic conditions and health behaviors. See Table 1 for detailed results. Table 1. Sociodemographic characteristics, healthy lifestyle, and quality of life by Sex (Wave 5) Variable % Women (N=6236) Men (N=5257) Total (N=11493) p-value Demographic Data: Age (SD) 63.3 (10.2) 64.4 (9.36) 63.8 (9.83) <0.001 Age Group: <0.001 50-64 3392 (54.4%) 2629 (50.0%) 6021 (52.4%) 65-74 1753 (28.1%) 1668 (31.7%) 3421 (29.8%) 75-84 885 (14.2%) 819 (15.6%) 1704 (14.8%) 85+ 206 (3.30%) 141 (2.68%) 347 (3.02%) Educational Level: <0.001 Low 2310 (37.0%) 1727 (32.9%) 4037 (35.1%) Medium 2326 (37.3%) 2037 (38.7%) 4363 (38.0%) High 1600 (25.7%) 1493 (28.4%) 3093 (26.9%) Marital Status: <0.001 Married or registered partner 4443 (71.2%) 4241 (80.7%) 8684 (75.6%) Divorced or separated 638 (10.2%) 432 (8.22%) 1070 (9.31%) Single 319 (5.12%) 350 (6.66%) 669 (5.82%) Widowed 836 (13.4%) 234 (4.45%) 1070 (9.31%) Able to make ends meet 4337 (69.5%) 3853 (73.3%) 8190 (71.3%) <0.001 Received help from others (outside the household) 1174 (18.8%) 833 (15.8%) 2007 (17.5%) <0.001 Healthy Lifestyle: Physical Activity 3132 (50.2%) 3027 (57.6%) 6159 (53.6%) <0.001 Daily Alcohol Consumption: <0.001 None/1-2 times per month 3801 (61.0%) 1950 (37.1%) 5751 (50.0%) 1-4 days per week 1719 (27.6%) 1790 (34.0%) 3509 (30.5%) Almost daily 716 (11.5%) 1517 (28.9%) 2233 (19.4%) Daily Smoking 2314 (37.1%) 2944 (56.0%) 5258 (45.7%) <0.001 Quality of Life: CASP (SD) 38.0 (6.37) 38.6 (6.17) 38.3 (6.29) <0.001 Note: Statistical tests used: ANOVA for continuous variables and Chi-square for categorical variables. Exploratory factor analysis of intrinsic capacity variables An Exploratory Factor Analysis (EFA) was conducted to uncover the latent structure of intrinsic capacity, using variables related to mobility, cognition, psychological well-being, sensory function, and vitality. Sampling adequacy was acceptable (overall KMO = 0.601; individual values 0.486–0.964), and Bartlett’s test of sphericity (χ² (66) = 138,861, p < 0.001) confirmed the suitability of the data for factor analysis. Five components with eigenvalues greater than 1 were extracted, jointly explaining 73.05% of the total variance. After Promax (oblique) rotation, the factor structure corresponded to the WHO’s conceptualization of intrinsic capacity, comprising: Mobility : locomotor limitations together with ADL and IADL performance, Psychological state : depressive symptoms, including fatigue, irritability, and loss of interest. Cognition : immediate and delayed word recall, together with orientation tasks. Sensory function : self-reported vision and hearing difficulties. Vitality : BMI and BMI categories. The covariance matrix of factor scores indicated negligible covariances (≈ 0), indicating that, although theoretically correlated, the extracted domains showed low empirical overlap in this sample. This structure provides empirical support for the multidimensional nature of intrinsic capacity and validates its operationalization in this dataset. Moreover, the high proportion of explained variance underscores the robustness of the factorial model as a basis for constructing a synthetic index of intrinsic capacity in comparative aging research. See Table 2 for detailed factor loadings and component structure. Table 2. Factor analysis of intrinsic capacity variables Variable Factor 1 (Mobility) Factor 2 (Psychological State) Factor 3 (Cognition) Factor 4 (Sensory Function) Factor 5 (Vitality) Uniqueness Mobility problems 0.865 0.325 ADL 0.867 0.385 IADL 0.787 0.355 Depression scale (EuroD) 0.486 0.437 Immediate word recall 0.928 0.448 Delayed word recall 0.928 0.489 Orientation in time and space 0.676 0.565 Vision difficulty 0.908 0.443 Hearing difficulty 0.854 0.451 BMI 0.961 0.934 BMI categories 0.964 0.934 Note: Rotated factor loadings are shown (Promax, oblique rotation). Each variable is primarily associated with a single domain of intrinsic capacity as defined by the WHO: mobility, psychological state, cognition, sensory function, and vitality. Activities of Daily Living (ADL). Instrumental Activities of Daily Living (IADL); BMI: Body Mass Index Network analysis of intrinsic capacity structure To further examine the structural organization of IC, a Network Analysis (NA) was performed to explore the interactions between IC domains and QoL. The network exhibited a sparsity of 35.9%, indicating that more than one-third of the possible edges were absent, thus supporting the multidimensional nature of IC rather than a single global dimension. Mobility emerged as the most central domain, exhibiting the highest betweenness (1.954), closeness (1.084), strength (1.485), and expected influence (1.590), underscoring its pivotal role in intrinsic capacity. A strong connection was observed between vitality and fatigue, reflecting their close interdependence. Sensory and psychological domains showed weaker connectivity, suggesting more domain-specific effects. The network visualization is provided in Figure 2. Correlations between intrinsic capacity domains and quality of life Pearson correlation analyses demonstrated that psychological variables had the strongest negative associations with QoL: depressive symptoms (r = −0.284, p < .001), fatigue (r = −0.324, p < .001), irritability (r = −0.230, p < .001), and loss of interest (r = −0.244, p < .001). Cognitive performance showed weaker but significant negative correlations with CASP-12 scores (r range = −0.108 to −0.162, all p < 0.001). Mobility variables exhibited small but significant negative correlations (r range = −0.019 to −0.041, p < 0.05). These findings highlight mental health as the strongest direct correlate of QoL, with mobility playing a structurally central role. The full correlation matrix is available in Supplementary Table S2. Network analysis of interactions between quality of life and intrinsic capacity domains A second network analysis including quality of life (CASP-12) and IC domains revealed complete interconnectivity among nodes (dispersion = 0.000). CASP-12 emerged as the most central node, with the highest betweenness (1.789), closeness (0.856), and strength (0.846), but a negative expected influence (−1.759) indicates that these connections primarily reflected inverse relationships with IC deficits. Among domains, Mobility showed the lowest connectivity (closeness = −1.715; strength = −1.613), whereas Sensory function demonstrated higher integration (closeness = 0.498; strength = 0.650). Clustering measures reinforced this pattern, with CASP and Sensory presenting the highest Onnela coefficients (0.733 and 0.665), while Mobility displayed the weakest integration (Onnela = −1.712). Psychological and cognitive domains showed moderate cohesion (Zhang index = 0.663 and 0.934). The vitality domain was excluded, as BMI alone did not capture the multidimensional nature of vitality, making it an inadequate proxy in this context. See Figure 3 for the network graph. Sex differences in intrinsic capacity domains Independent samples t-tests revealed significant sex differences in sensory function, mental health, and cognition. Men had slightly higher sensory scores (M = 0.740) than women (M = 0.697; p = 0.015, d = 0.045). Women exhibited greater emotional distress (mental health domain) than men (M = 1.163 vs. 0.884; p < 0.001, d = −0.260). Cognitive performance was higher in men (M = 0.609) compared to women (M = 0.518; p < 0.001, d = 0.109). No significant sex difference was found in mobility (p = 0.167). Detailed statistics are provided in Table 3. Table 3. Analysis of intrinsic capacity by sex. Independent samples t-Test Domain t df p Cohen's d SE Cohen’s d Sensory 2.437 11835 0.015ᵃ 0.045 0.018 Mental Health -13.854 11449 < .001ᵃ -0.260 0.019 Cognition 5.913 11835 < .001ᵃ 0.109 0.018 Mobility -1.383 11491 0.167 -0.026 0.019 Descriptive statistics by group Domain Group N Mean SD CV Sensory Men 5399 0.740 0.964 1.303 Sensory Women 6438 0.697 0.930 1.333 Mental Health Men 5213 0.884 1.012 1.144 Mental Health Women 6238 1.163 1.121 0.964 Cognition Men 5399 0.609 0.852 1.398 Cognition Women 6438 0.518 0.828 1.598 Mobility Men 5257 0.350 0.671 1.915 Mobility Women 6236 0.368 0.678 1.843 Note: Results from Student's t-test comparing intrinsic capacity domains between men and women. Significant differences were found in mental health, cognition (p < .001), and sensory domain (p = 0.015). Brown-Forsythe test indicates violation of homogeneity of variances in variables marked with ᵃ. SD: Standard Deviation; SE: Standard Error; CV: Coefficient of Variation. Total intrinsic capacity by sex and region ANOVA results showed significant main effects of sex (F (1, 11,485) = 9.575, p = 0.002, η² = 0.0008) and region (F (3, 11,485) = 74.813, p < 0.001, η² = 0.019), as well as a significant sex × region interaction (F (3, 11,485) = 2.825, p = 0.037, η² = 0.0007). Women had slightly higher IC deficit scores overall. Northern Europe exhibited the lowest deficits, while Southern Europe had the highest. Post hoc tests confirmed women in Southern Europe had significantly higher deficits than men in the same region (p = 0.001). See Table 4 for details. Table 4. ANOVA of total intrinsic capacity by sex and region Source Sum of Squares df Mean Square F p η² Sex 40.261 1 40.261 9.575 0.002 8.164×10⁻⁴ Region 943.753 3 314.584 74.813 < .001 0.019 Sex ✻ Region 35.640 3 11.880 2.825 0.037 7.227×10⁻⁴ Residuals 48293.823 11485 4.205 Descriptive statistics – Total Intrinsic Capacity Region Sex N Mean SD SE CV South Men (0) 1271 2.760 2.080 0.058 0.754 South Women (1) 1499 3.077 2.372 0.061 0.771 East Men (0) 653 2.989 2.209 0.086 0.739 East Women (1) 880 2.955 2.193 0.074 0.742 Continental Men (0) 2060 2.601 2.013 0.044 0.774 Continental Women (1) 2383 2.752 1.970 0.040 0.716 North Men (0) 1273 2.148 1.798 0.050 0.837 North Women (1) 1474 2.225 1.889 0.049 0.849 Note: Results from fixed-effects ANOVA (Type III Sum of Squares) for total intrinsic capacity. Significant differences were observed by sex (p = 0.002) and region (p < .001), as well as a sex ✻ region interaction (p = 0.037). Effect size (η²) indicates a small influence of these variables. Men (0) and Women (1). SD: Standard Deviation; SE: Standard Error; CV: Coefficient of Variation. Socioeconomic and household influences Intrinsic capacity varied significantly by socioeconomic status and household composition. Employment status had a strong effect (F (6, 11,830) = 168.692, p < 0.001, ω² = 0.078). Self-employed individuals had the lowest IC scores (M = 1.828), while those with medical disabilities had the highest (M = 3.912), significantly exceeding retirees, employees, and the unemployed (all p < 0.001). Detailed comparisons are provided in Supplementary Table S3. Economic difficulty showed a clear gradient (F (3, 11,574) = 146.513, p < 0.001, η² = 0.037). Participants reporting severe financial difficulty had the highest mean IC scores (M = 3.622), whereas those with no difficulty had the lowest (M = 2.291), reflecting better functional capacity (see Table 5 ). Table 5. Economic difficulty and intrinsic capacity Cases Sum of Squares df Mean Square F p η² Economic Difficulty 1,798,701 3 599,567 146.513 < .001 0.037 Residuals 47,363,578 11,574 4,092 Note: Type III Sum of Squares. Descriptive Statistics – TOTAL Intrinsic Capacity Economic Difficulty N Mean SD SE Coefficient of Variation 1. Severe difficulty 759 3.622 2.322 0.084 0.641 2. Some difficulty 2,239 3.070 2.211 0.047 0.720 3. Slight difficulty 3,204 2.596 2.044 0.036 0.787 4. No difficulty 5,376 2.291 1.877 0.026 0.820 Note: Descriptive statistics of total intrinsic capacity according to financial difficulty. N = sample size; SD = standard deviation; SE = standard error. The coefficient of variation indicates the relative variability within each category. Household composition influenced outcomes as well. Living with a partner was associated with significantly lower IC scores (F (1, 11,833) = 144.323, p < 0.001, η² = 0.012). Women living alone had the highest deficits (M = 3.252), followed by men living alone (M = 2.879). By contrast, both men (M = 2.482) and women (M = 2.495) living with a partner had lower scores, indicating fewer accumulated deficits. Significant interaction effects (F (1, 11,833) = 14.098, p < 0.001) suggest that cohabitation affects men and women differently (See Table 6 for further details.) Table 6. Living with a spouse/partner, sex, and intrinsic capacity Cases Sum of Squares df Mean Square F p η² Living with a spouse/partner 608,903 1 608,903 144.323 < .001 0.012 Sex 68,307 1 68,307 16.190 < .001 0.001 Living with a spouse/partner ✻ Sex 59,479 1 59,479 14.098 < .001 0.001 Residuals 49,923,645 11,833 4,219 Note: Type III Sum of Squares. Descriptive Statistics – TOTAL Intrinsic Capacity Sex Living with a Spouse/Partner N Mean SD SE Coefficient of Variation Male Yes 4,550 2.482 2.008 0.030 0.809 Male No 849 2.879 2.068 0.071 0.718 Female Yes 4,706 2.495 2.017 0.029 0.808 Female No 1,732 3.252 2.257 0.054 0.694 Note: Descriptive statistics of total intrinsic capacity by sex and cohabitation with a spouse/partner. N = sample size; SD = standard deviation; SE = standard error. The coefficient of variation indicates the relative variability within each category. 1 = Yes (Lives with spouse/partner), 3 = No (Does not live with spouse/partner). Change between waves (2013–2015) Paired-samples analysis revealed a significant increase in mean IC scores between 2013 (M = 2.628, SD = 2.072) and 2015 (M = 2.766, SD = 2.131), t (11,836) = 7.698, p < 0.001, Cohen’s d = 0.071, indicating a small but measurable decline in intrinsic capacity over two years. Women exhibited greater deficit accumulation, especially in psychological domains. Regional disparities persisted, with Northern Europe showing better outcomes. Older adults aged 75+ experienced sharper declines, particularly in mobility and mental health. Supplementary Table S3 provides complete data. Discussion This study provides robust empirical evidence that intrinsic capacity domains are closely associated with quality-of-life trajectories in older adults across European regions. The findings underscore the multifaceted and multidimensional nature of intrinsic capacity, encompassing interconnected domains such as mobility (including ADL and IADL), cognitive function, mental health, and sensory function. Importantly, these findings offer strong empirical support for the WHO healthy aging framework, which conceptualizes intrinsic capacity as dynamically shaped by environmental and social determinants. The observed sex and regional disparities illustrate that functional aging is not solely biologically driven but deeply embedded within broader welfare and socioeconomic contexts. These results are consistent with recent research, such as the meta-analysis conducted by Zhou and Ma ( 2022 ), which emphasized that intrinsic capacity should be understood as a complex, multidimensional construct rather than a unidimensional entity. The originality of this study lies in the joint application of EFA and Network Analysis to intrinsic capacity, together with a systematic sex- and region-based comparison, which has been scarcely addressed in previous literature. Through EFA, the study confirmed that intrinsic capacity comprises distinct but interrelated components clustering according to their functional relevance—namely sensory function, cognition, mobility, and mental health. This finding reinforces the need for integrated assessment approach that considers both physical and psychological aspects of aging (Cruz-Peralta & González-Celis, 2023 ; Leitón Espinoza et al., 2021 ). However, the vitality domain, operationalized solely through Body Mass Index (BMI), presented notable limitations. While BMI is a useful epidemiological measure, it fails to capture the complexity of factors influencing intrinsic capacity and quality of life (Leitón Espinoza et al., 2021 ). In fact, the high uniqueness values observed in the factor analysis, particularly for BMI and its categories, indicated that these variables did not cluster effectively with others, suggesting that BMI alone is insufficient to represents vitality adequately. Regarding sex differences, the results indicated that women showed a higher accumulation of deficits across several key domains, particularly cognition and mental health. This pattern aligns with previous studies showing that older women tend to face more significant declines in these areas compared to men (Pavez Lizarraga et al., 2023 ). A combination of biological and sociocultural factors appears to play a critical role in this deterioration, as women’s longer life expectancy increases their exposure to chronic diseases and multimorbidity (Au et al., 2017 ; San José Laporte, 2012 ). The findings of this study reinforce those of Llorens-Ortega et al. ( 2024 ), who documented cognitive and physical decline, particularly among older women from socially disadvantaged backgrounds. Conversely, men showed less deterioration in sensory health, reflecting a trend observed in other studies suggesting sex differences in sensory function (Alfonso Silguero et al., 2014 ). Overall, women’s higher accumulation of deficits, particularly in cognition and psychological domains, highlights persistent sex disparities in functional capacity and quality of life trajectories (Kaur et al., 2024 ; Concha-Cisternas et al., 2021 ; Díaz-Alonso et al., 2021 ). Mental health, particularly depression, emerged as a key domain with significantly higher prevalence among women over time. Depression is known to accelerate functional decline, particularly in females. Previous research has emphasized the central role of psychological well-being in shaping perceived quality of life, identifying mental health as a key determinant of subjective well-being (Kim, 2025). Our findings are consistent with studies documenting increased depressive symptoms among older women, attributable to a combination of biological factors and the accumulation of psychosocial stressors throughout life (Jalali et al., 2024 ; Portellano-Ortiz et al., 2018 ). Moreover, factors such as social isolation, lack of emotional support, and greater caregiving burden exacerbate depression’s negative impact on older women’s quality of life (Courtin & Knapp, 2017 ; Lee et al., 2022 ). These results reinforce the urgent need for sex-sensitive public health policies, including enhanced access to community-based mental health services, systematic depression screening in primary care, and tailored psychosocial support for vulnerable women. The inclusion of social determinants of health (SDH) in this study was essential to understanding how factors such as educational level, marital status, and economic situation are consistently associated with functional decline and quality of life in older adults (Bielderman et al., 2015 ; Steptoe & Zaninotto, 2020 ). Across the European regions analyzed, women in socially and economically vulnerable situations exhibited greater deterioration in mental health, a significant increase in chronic conditions, and more pronounced economic decline. This pattern aligns with recent studies showing that social inequalities disproportionately affect older women, particularly in contexts of poverty or limited access to healthcare (Bacigalupe et al., 2022 ; Spiers et al., 2022 ). Similarly, previous research has shown that social participation and broader social conditions play a critical role in shaping well-being and functional trajectories among older adults (Oshio et al., 2024 ). From a policy perspective, these findings highlight the importance of reducing socioeconomic inequalities to mitigate IC decline. Interventions should include income protection for older adults, universal access to healthcare and long-term care services, and programs that address social isolation and support for those living alone (Lu et al., 2025 ). Importantly, harmonizing social protection policies across European regions could reduce disparities observed between Northern and Southern/Eastern Europe. The network analysis elucidated the underlying structure of intrinsic capacity, revealing mobility as a structurally central domain, while psychological and sensory domains showed stronger direct associations with quality of life. Although mobility and cognition are important domains, their impact on quality of life appears to be more modest compared to physical and mental health. This finding aligns with previous studies demonstrating that physical and mental health are key determinants of quality of life in older adults (Geigl et al., 2023 ; Gutiérrez-Robledo et al., 2021 ; Sugimoto et al., 2022 ). These results suggest that interventions prioritizing mental well-being and physical resilience may yield the most significant improvements in overall QoL. Strengths and limitations Among the strengths of this study is the use of a large representative sample from 13 European countries, a short-term longitudinal and comparative perspective across two consecutive waves. Additionally, the inclusion of social determinants of health enriched the analysis, allowing for the identification of socioeconomic and geographic disparities. However, the study also presents several limitations. The use of self-reported data may introduce bias, particularly in domains such as mental health and mobility. Second, although longitudinal data were used, the observational design does not allow full disentanglement of bidirectional relationships between intrinsic capacity domains and quality of life. Psychological well-being and QoL may mutually influence each other over time, and causal inferences should therefore be interpreted cautiously. Furthermore, the vitality domain’s operationalization via BMI is limited as this measure does not fully capture the multidimensional nature of vitality. Future studies should incorporate additional indicators to provide a more accurate representation of this domain. The relatively short follow-up period (2 years) may underestimate longer-term intrinsic capacity trajectories. Additionally, although regularized network models reduce overfitting, the stability of centrality indices may vary across samples, and replication in independent cohorts is warranted. Residual confounding cannot be fully excluded despite adjustment for multiple covariates. Moreover, variability in health policy implementation across European countries may have influenced the results. This suggests that future research should include longer longitudinal data and more diverse samples to better assess changes in quality of life and intrinsic capacity over time. Public health implications The findings of this study have direct implications for public health policy. Addressing disparities in intrinsic capacity and quality of life requires integrated strategies at both the individual and structural levels: Mental health interventions : Implement systematic depression screening in primary care, expand psychological services tailored for older adults, and adopt sex-sensitive approaches that consider caregiving burdens and social isolation. Mobility promotion : Develop community-based exercise programs, implement fall prevention strategies, and promote urban planning that fosters age-friendly environments to support physical resilience. Nutritional and vitality programs : Design initiatives that go beyond Body Mass Index (BMI) to address malnutrition, frailty, and energy balance, incorporating comprehensive assessments of vitality. Reducing regional disparities : Advocate for EU-level policies that harmonize access to health and long-term care services, with targeted support for Southern and Eastern European regions where deficits are more pronounced. Social and economic protection : Strengthen financial security through pensions and assistance programs, promote social participation, and provide support for individuals living alone, especially widowed or divorced women. By integrating clinical, behavioral, and policy-oriented interventions, these measures could help reduce sex and regional disparities, ultimately promoting equitable and healthy aging across Europe. Conceptually, this study contributes to bridge the gap between the WHO intrinsic capacity framework and the social determinants of health perspective. It demonstrates that intrinsic capacity domains are not only biologically grounded but also socially patterned across sex and welfare regimes. Therefore, healthy aging must be understood within broader structural contexts, recognizing intrinsic capacity as a socially embedded functional construct shaped by life-course inequalities. Conclusions This study provides empirical evidence that intrinsic capacity (IC) is a multidimensional construct consistently associated with quality of life (QoL) in older adults across Europe. Sex-related differences were observed, with women showing higher accumulation of deficits particularly in cognition and mental health domains, while regional patterns showed that Southern and Eastern Europe accumulated more deficits compared to Northern regions. Social determinants such as education and economic status were also associated with these disparities. Together, these findings highlight the need for targeted actions addressing sex and regional inequalities in aging. Strengthening mental health care and mobility support may represent promising strategic areas for preserving intrinsic capacity and improving quality of life in older adults. Abbreviations ADL: Activities of Daily Living BMI: Body Mass Index CASP-12: Control, Autonomy, Self-realization, Pleasure scale EFA: Exploratory Factor Analysis EURO-D: European Depression Scale IADL: Instrumental Activities of Daily Living IC: Intrinsic capacity NA: Network Analysis QoL: Quality of life SDH: Social Determinants of Health SHARE: Survey of Health, Ageing and Retirement in Europe Declarations Acknowledgements This research utilizes data from waves SHARE 5 and 6 https://doi.org/10.6103/SHARE.w5.700, https://doi.org/10.6103/SHARE.w6.700. For methodological details, please refer to Börsch-Supan et al. (2013). https://doi.org/10.1093/ije/dyt088. Availability of data and materials The data used in this study are publicly available through the Survey of Health, Ageing and Retirement in Europe (SHARE) database (https://www.share-project.org) for scientific use upon registration. The code used for sample selection and matching procedures is available on GitHub: https://github.com/JoanVilaDomenech/Matching/. Additional supplementary materials related to this study are available upon request from the corresponding author. Funding Data collection in SHARE has been primarily funded by the European Commission through the Fifth Framework Programme (QLK6-CT-2001-00360), the Sixth Framework Programme (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), and the Seventh Framework Programme (SHARE-PREP: No. 211909, SHARE-LEAP: No. 227822, SHARE M4: No. 261982). Additional funding has been provided by 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 various national funding sources (see www.share-project.org for a full list). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Contributions All authors contributed to the study conception and design. Specifically, contributions are as follows (CRediT taxonomy): Conceptualization: RLO, CBN, DJC; Methodology: RLO, DJC, CBF; Formal analysis: RLO, JGO; Writing – original draft preparation: RLO; Writing – review and editing: all authors; Supervision: CBN Corresponding Author Correspondence to Rafael Llorens-Ortega: [email protected] Affiliation: EUIT University Center, Autonomous University of Barcelona, Cerdanyola del Vallès, Spain. Ethical Declarations Conflict of Interest: The authors declare no conflicts of interest. Consent for Publication: This study does not contain any individual participant data. All authors confirm that they have no financial or non-financial competing interests related to this work. Ethics Approval The Ethics Council of the Max Planck Society for the Advancement of Science thoroughly reviewed all materials related to the SHARE project, including Waves 5 and subsequent waves. The committee certified that the research project, its procedures, and the measures taken to ensure confidentiality and participant privacy comply with international ethical standards, in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects. Informed Consent: Informed consent was obtained from all individual participants included in the study during the interview process. Participants voluntarily agreed to participate and were informed about the study’s aims, procedures, and data confidentiality. Clinical trial number: not applicable. References Ahrenfeldt, L. J., & Möller, S. (2021). The Reciprocal Relationship between Socioeconomic Status and Health and the Influence of Sex: A European SHARE-Analysis Based on Structural Equation Modeling. International Journal of Environmental Research and Public Health , 18 (9), 5045. https://doi.org/10.3390/ijerph18095045 Alfonso Silguero, S. A., Martínez-Reig, M., Gómez Arnedo, L., Juncos Martínez, G., Romero Rizos, L., & Soler, A., P (2014). Chronic disease, mortality, disability, and loss of mobility in Spanish elderly: FRADEA study. Spanish Journal of Geriatrics and Gerontology , 49 (2), 51–58. https://doi.org/10.1016/j.regg.2013.05.007 Angelsen, A., Nakrem, S., Zotcheva, E., Strand, B. H., & Strand, L. B. (2024). Health-promoting behaviors in older adulthood and intrinsic capacity 10 years later: the HUNT study. Bmc Public Health , 24 (1), 284. https://doi.org/10.1186/s12889-024-17840-3 Au, B., Dale-McGrath, S., & Tierney, M. C. (2017). Sex differences in the prevalence and incidence of mild cognitive impairment: A meta-analysis. Ageing Research Reviews , 35 , 176–199. https://doi.org/10.1016/j.arr.2016.09.005 Bacigalupe, A., González-Rábago, Y., & Jiménez-Carrillo, M. (2022). Gender inequality and medicalization of mental health: sociocultural determinants from the analysis of expert perceptions. Primary Care , 54 (7), 102378. https://doi.org/10.1016/j.aprim.2022.102378 Berk, R. A. (2016). Classification and Regression Trees (CART) (pp. 129–186). https://doi.org/10.1007/978-3-319-44048-4_3 Bielderman, A., de Greef, M. H. G., Krijnen, W. P., & van der Schans, C. P. (2015). Relationship between socioeconomic status and quality of life in older adults: a path analysis. Quality of Life Research , 24 (7), 1697–1705. https://doi.org/10.1007/s11136-014-0898-y Borsboom, D. (2022). Possible Futures for Network Psychometrics. Psychometrika , 87 (1), 253–265. https://doi.org/10.1007/s11336-022-09851-z Börsch-Supan, A., Brandt, M., Hunkler, C., Kneip, T., Korbmacher, J., Malter, F., Schaan, B., Stuck, S., & Zuber, S. (2013). Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). International Journal of Epidemiology , 42 (4), 992–1001. https://doi.org/10.1093/ije/dyt088 Burn, R., Hubbard, R. E., Scrase, R. J., Abey-Nesbit, R. K., Peel, N. M., Schluter, P. J., & Jamieson, H. A. (2018). A frailty index derived from a standardized comprehensive geriatric assessment predicts mortality and aged residential care admission. BMC Geriatrics , 18 (1), 319. https://doi.org/10.1186/s12877-018-1016-8 Cesari, M., Araujo de Carvalho, I., Amuthavalli Thiyagarajan, J., Cooper, C., Martin, F. C., Reginster, J. Y., Vellas, B., & Beard, J. R. (2018). Evidence for the Domains Supporting the Construct of Intrinsic Capacity. The Journals of Gerontology: Series A , 73 (12), 1653–1660. https://doi.org/10.1093/gerona/gly011 Concha-Cisternas, Y., Vargas-Vitoria, R., & Celis-Morales, C. (2021). Morphophysiological changes and fall risk in the older adult: a review of the literature. Salud Uninorte , 36 (2), 450–470. https://doi.org/10.14482/sun.36.2.618.97 Courtin, E., & Knapp, M. (2017). Social isolation, loneliness and health in old age: a scoping review. Health & Social Care in the Community , 25 (3), 799–812. https://doi.org/10.1111/hsc.12311 de Cruz-Peralta, M. J., & González-Celis, A. L. (2023). Interventions to improve quality of life in older adults: systematic review with IOP questions. Psychology and Health , 33 (2), 415–426. https://doi.org/10.25009/pys.v33i2.2824 Chhetri, J. K., Han, R. H., Ma, L., Jiang, Y., Peng, D., Ma, J. P., & Cesari, M. (2022). Intrinsic Capacity as a Determinant of Physical Resilience: Implications for Healthy Aging. The Journal of Nutrition Health & Aging , 25 (1), 136–144. https://doi.org/10.1007/s12603-021-1629-z Díaz-Alonso, J., Bueno-Pérez, A., Toraño-Ladero, L., Caballero, F. F., López-García, E., Rodríguez-Artalejo, F., & Lana, A. (2021). Hearing limitation and social frailty in older men and women. Gaceta Sanitaria , 35 (5), 425–431. https://doi.org/10.1016/j.gaceta.2020.08.007 Dosil-Díaz, C., Pinazo-Hernandis, S., Pereiro, A. X., & Facal, D. (2024). The impact of the COVID-19 pandemic on nursing home professionals: results of the RESICOVID project. Psicologia: Reflexão e Crítica , 37 (1), 11. https://doi.org/10.1186/s41155-023-00284-w European Commission (2023). Eurostat Regional Yearbook 2023 . https://ec.europa.eu/eurostat/ Galloway, S. (2006). Cultural Participation and Individual Quality of Life: A Review of Research Findings. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-006-9002-3 Geigl, C., Loss, J., Leitzmann, M., & Janssen, C. (2023). Social factors of health-related quality of life in older adults: a multivariable analysis. Quality of Life Research , 32 (11), 3257–3268. https://doi.org/10.1007/s11136-023-03472-4 Gutiérrez-Robledo, L. M., García-Chanes, R. E., & Pérez-Zepeda, M. U. (2021). Screening intrinsic capacity and its epidemiological characterization: a secondary analysis of the Mexican Health and Aging Study. Revista Panamericana de Salud Pública , 45 , 1. https://doi.org/10.26633/RPSP.2021.121 Hyde, M., Wiggins, R. D., Higgs, P., & Blane, D. B. (2003). A measure of quality of life in early old age: The theory, development and properties of a need’s satisfaction model (CASP-19). Aging & Mental Health , 7 (3), 186–194. https://doi.org/10.1080/1360786031000101157 Jagadish, K., & Chhetri, R. H. H. L. M. J. P. M. P. C. (2022). Capacidad intrínseca y envejecimiento saludable. Age and Ageing , 51 (11). https://doi.org/https://doi.org/10.1093/ageing/afac239 Jalali, A., Ziapour, A., Karimi, Z., Rezaei, M., Emami, B., Kalhori, R. P., Khosravi, F., Sameni, J. S., & Kazeminia, M. (2024). Global prevalence of depression, anxiety, and stress in the elderly population: a systematic review and meta-analysis. BMC Geriatrics , 24 (1), 809. https://doi.org/10.1186/s12877-024-05311-8 Kaur, A., Fouad, M. H., Pozzebon, C., Behlouli, H., Rajah, M. N., & Pilote, L. (2024). Sex Differences in the Association Between Vascular Risk Factors and Cognitive Decline. JACC: Advances , 3 (7), 100930. https://doi.org/10.1016/j.jacadv.2024.100930 Lee, S. H., Lee, H., & Yu, S. (2022). Effectiveness of Social Support for Community-Dwelling Elderly with Depression: A Systematic Review and Meta-Analysis. Healthcare , 10 (9), 1598. https://doi.org/10.3390/healthcare10091598 Leitón Espinoza, Z. E., Fajardo-Ramos, E., López-González, Á., Martínez-Villanueva, R. M., & Villanueva-Benites, M. E. (2021). Cognition and Functional Capacity in the Elderly Adult. Salud Uninorte , 36 (1), 124–139. https://doi.org/10.14482/sun.36.1.618.97 Llorens-Ortega, R., Bertran-Noguer, C., Juvinyà-Canals, D., Garre-Olmo, J., & Bosch-Farré, C. (2024). Influence of social determinants of health in the evolution of the quality of life of older adults in Europe: A comparative analysis between men and women. Humanities and Social Sciences Communications , 11 (1). https://doi.org/10.1057/s41599-024-02899-5 López-Ortiz, S., Lista, S., Peñín-Grandes, S., Pinto-Fraga, J., Valenzuela, P. L., Nisticò, R., Emanuele, E., Lucia, A., & Santos-Lozano, A. (2022). Defining and assessing intrinsic capacity in older people: A systematic review and a proposed scoring system. Ageing Research Reviews , 79 , 101640. https://doi.org/10.1016/j.arr.2022.101640 Lu, S., Chui, C., & Lum, T. (2025). A Chain Mediation Model Unveiling the Effectiveness of Timebanking on Quality of Life in Later Life. Applied Research in Quality of Life. https://doi.org/10.1007/s11482-025-10503-4 Malter, F. (2015). and A. B.-S. SHARE Wave 5: Innovations & Methodology. https://share-eric.eu/fileadmin/user_upload/Methodology_Volumes/Method_vol5_31March2015.pdf Maskileyson, D., Seddig, D., & Davidov, E. (2021). The EURO-D Measure of Depressive Symptoms in the Aging Population: Comparability Across European Countries and Israel. Frontiers in Political Science , 3 . https://doi.org/10.3389/fpos.2021.665004 Ofori-Asenso, R., Chin, K. L., Mazidi, M., Zomer, E., Ilomaki, J., Zullo, A. R., Gasevic, D., Ademi, Z., Korhonen, M. J., LoGiudice, D., Bell, J. S., & Liew, D. (2019). Global Incidence of Frailty and Prefrailty Among Community-Dwelling Older Adults. JAMA Network Open , 2 (8), e198398. https://doi.org/10.1001/jamanetworkopen.2019.8398 Oshio, T., Sugiyama, K., & Ashida, T. (2024). Can Social Participation Reduce and Postpone the Need for Long-Term Care? Evidence from a 17-Wave Nationwide Survey in Japan. Applied Research in Quality of Life . https://doi.org/10.1007/s11482-024-10288-y Pavez Lizarraga, A., Vanegas López, J., & Flores Alvarado, S. (2023). Analysis of age, sex, and memory self-perception in cognitive impairment in older adults. Medical Journal of Chile , 151 (10), 1288–1294. https://doi.org/10.4067/s0034-98872023001001288 Pérez-Rojo, G., Martín, N., Noriega, C., & López, J. (2018). Psychometric properties of the CASP-12 in a Spanish older community dwelling sample. Aging & Mental Health , 22 (5), 700–708. https://doi.org/10.1080/13607863.2017.1292208 Portellano-Ortiz, C., Garre-Olmo, J., Calvó-Perxas, L., & Conde-Sala, J. L. (2018). Depression and associated variables in people over 50 years in Spain. Revista de Psiquiatria y Salud Mental , 11 (4), 216–226. https://doi.org/10.1016/j.rpsm.2016.10.003 Quiroz Mora, C. A., Serrato, D. M., & Bergonzoli Pelaez, G. (2018). Factors associated with adherence to physical activity in patients with chronic non-communicable diseases. Journal of Public Health , 20 (4), 460–464. https://doi.org/10.15446/rsap.v20n4.62959 Reitlo, L. S., Sandbakk, S. B., Viken, H., Aspvik, N. P., Ingebrigtsen, J. E., Tan, X., Wisløff, U., & Stensvold, D. (2018). Exercise patterns in older adults instructed to follow moderate- or high-intensity exercise protocol – the generation 100 study. BMC Geriatrics , 18 (1), 208. https://doi.org/10.1186/s12877-018-0900-6 Rodríguez-Laso, Á., García-García, F. J., & Rodríguez-Mañas, L. (2023). The ICOPE Intrinsic Capacity Screening Tool: Measurement Structure and Predictive Validity of Dependence and Hospitalization. The Journal of Nutrition Health and Aging , 27 (10), 808–816. https://doi.org/10.1007/s12603-023-1985-y Rojano i Luque, X., Blancafort-Alias, S., Prat Casanovas, S., Forné, S., Martín Vergara, N., Fabregat Povill, P., Vila Royo, M., Serrano, R., Sanchez-Rodriguez, D., Vílchez Saldaña, M., Martínez, I., Domínguez López, M., Riba Porquet, F., & Intxaurrondo González, A. (2023). & Salvà Casanovas, A. Identification of decreased intrinsic capacity: Performance of diagnostic measures of the ICOPE Screening tool in community dwelling older people in the VIMCI study. BMC Geriatrics , 23 (1), 106. https://doi.org/10.1186/s12877-023-03799-0 Salazar Estrada, J. G., Gutiérrez Strauss, A. M., Aranda Beltrán, C., & Ramírez Ramírez, S. (2019). Psychometric properties of the Satisfaction with Life Scale, in workers of the manufacturing industry. Psicología Desde El Caribe , 35 (3), 197–209. https://doi.org/10.14482/psdc.35.3.150.15 Salinas-Rodríguez, A., Fernández-Niño, J. A., Rivera-Almaraz, A., & Manrique-Espinoza, B. (2024). Intrinsic capacity trajectories and socioeconomic inequalities in health: the contributions of wealth, education, gender, and ethnicity. International Journal for Equity in Health , 23 (1), 48. https://doi.org/10.1186/s12939-024-02136-0 Salinas-Rodríguez, A., González-Bautista, E., Rivera-Almaraz, A., & Manrique-Espinoza, B. (2022). Longitudinal trajectories of intrinsic capacity and their association with quality of life and disability. Maturitas , 161 , 49–54. https://doi.org/10.1016/j.maturitas.2022.02.005 San José Laporte, A. (2012). The assessment of multimorbidity in the elderly. An important area of comprehensive geriatric assessment. Spanish Journal of Geriatrics and Gerontology , 47 (2), 47–48. https://doi.org/10.1016/j.regg.2011.12.001 Spiers, G. F., Liddle, J. E., Stow, D., Searle, B., Whitehead, I. O., Kingston, A., Moffatt, S., Matthews, F. E., & Hanratty, B. (2022). Measuring older people’s socioeconomic position: a scoping review of studies of self-rated health, health service and social care use. Journal of Epidemiology and Community Health , 76 (6), 572–579. https://doi.org/10.1136/jech-2021-218265 Steptoe, A., & Zaninotto, P. (2020). Lower socioeconomic status and the acceleration of aging: An outcome-wide analysis. Proceedings of the National Academy of Sciences , 117 (26), 14911–14917. https://doi.org/10.1073/pnas.1915741117 Sugimoto, T., Arai, H., & Sakurai, T. (2022). An update on cognitive frailty: Its definition, impact, associated factors and underlying mechanisms, and interventions. Geriatrics & Gerontology International , 22 (2), 99–109. https://doi.org/10.1111/ggi.14322 Takeda, C., Barreto, P. D. S., & Vellas, B. (2024). Intrinsic Capacity. In Frailty (pp. 23–29). Springer International Publishing. https://doi.org/10.1007/978-3-031-57361-3_5 United Nations (2021). United Nations. Population Fund. (2021). Aging in the 21st Century: A Celebration and a Challenge . https://www.unfpa.org/sites/default/files/pub-pdf/Ageing%20Report%20Executive%20Summary%20SPANISH%20Final_0.pdf United Nations Educational Scientific and Cultural Organization (2011). International Standard Classification of Education: ISCED 2011 . http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf Vila, J. (2024). Matching function, GitHub repository . https://github.com/JoanVilaDomenech/Matching/ Wiggins, R. D., Netuveli, G., Hyde, M., Higgs, P., & Blane, D. (2008). The Evaluation of a Self-enumerated Scale of Quality of Life (CASP-19) in the Context of Research on Ageing: A Combination of Exploratory and Confirmatory Approaches. (The evaluation of a self-enumerated scale of quality of life (CASP-19 …. Social Indicators Research , 89 (1), 61–77. https://doi.org/10.1007/s11205-007-9220-5 World Health Organization (2015). World Health Organization. (2015). World Report on Ageing and Health. https://apps.who.int/iris/handle/10665/186463 World Health Organization (2024). World population prospects. 2024: Summary of results. United Nations Department of Economic and Social Affairs; 2024. https://population.un.org/wpp/ World Health Organization (2019). Integrated care for older people (ICOPE): guidance for person-centred assessment and pathways in primary care . https://iris.who.int/handle/10665/326843 World Health Organization (2020). Decade of healthy ageing: the global strategy and action plan on ageing and health 2016–2020: towards a world in which everyone can live a long and healthy life: report by the Director-General. https://iris.who.int/handle/10665/355618 Zhou, Y., & Ma, L. (2022). Intrinsic Capacity in Older Adults: Recent Advances. Aging and Disease , 13 (2), 353. https://doi.org/10.14336/AD.2021.0818 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial..docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9211395","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611336015,"identity":"c32fa5e8-ea0a-4af5-bfb9-38eb146d5a5a","order_by":0,"name":"Rafael Llorens-Ortega","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYFACxgcMDwpgLAYJBgbmAwYwERyA2YAhwQDGYpCQYGBLgIqwEdbCBrSCgbAW8/bDjB8SDGzy+aXbn1V8bLOoY2Bj3gwUYZDnn9+AVYvMmWRmiQSDNMuZc86Y3ZzZBnIYWxlQhMFwxjHstkgw5B8AKjhsYHAjh+02L0iLfI8ZyGEJDLi08D9m/gHSYn8j/VnxX7AtPMYghyXI49IikcwGsUUiwYyZEaLFQAIcAji1PGazAPrFQOJGjrFkzzkJyTaIXyQMNx5LwOGwZOYbHypsDPhnpD/88KOsjp8fFGJAEXm5wwewW4MB2GDhMgpGwSgYBaOAfAAAM6lPh0I80TYAAAAASUVORK5CYII=","orcid":"","institution":"EUIT University Center, Autonomous University of Barcelona (UAB)","correspondingAuthor":true,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Llorens-Ortega","suffix":""},{"id":611336016,"identity":"130cf556-18a8-44e5-9751-72f9c5f8937c","order_by":1,"name":"Carmen Bertran-Noguer","email":"","orcid":"","institution":"University of Girona","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"","lastName":"Bertran-Noguer","suffix":""},{"id":611336017,"identity":"9a8bc651-471f-4cc1-b763-38244fb96ae0","order_by":2,"name":"Dolors Juvinyà-Canal","email":"","orcid":"","institution":"University of Girona","correspondingAuthor":false,"prefix":"","firstName":"Dolors","middleName":"","lastName":"Juvinyà-Canal","suffix":""},{"id":611336018,"identity":"4cb93bad-f935-4eef-9717-892f27a0c517","order_by":3,"name":"Josep Garre-Olmo","email":"","orcid":"","institution":"University of Girona","correspondingAuthor":false,"prefix":"","firstName":"Josep","middleName":"","lastName":"Garre-Olmo","suffix":""},{"id":611336019,"identity":"9f0e914b-ac8e-481b-9f4a-7652684fc04b","order_by":4,"name":"Cristina Bosch-Farré","email":"","orcid":"","institution":"University of Girona","correspondingAuthor":false,"prefix":"","firstName":"Cristina","middleName":"","lastName":"Bosch-Farré","suffix":""}],"badges":[],"createdAt":"2026-03-24 11:40:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9211395/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9211395/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105353306,"identity":"d4f4d23d-0591-499e-bc5a-d36718f6d3c6","added_by":"auto","created_at":"2026-03-25 06:17:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32882,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the sample selection process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9211395/v1/5e7927485e1cfa48a2505488.png"},{"id":105353305,"identity":"7d2b66cb-768b-45d5-8a71-d0286ec7354b","added_by":"auto","created_at":"2026-03-25 06:17:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130139,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork analysis of intrinsic capacity domains and quality of life\u003c/p\u003e\n\u003cp\u003eNote: Network of relationships between different domains of intrinsic capacity and quality of life variables in older adults. Nodes represent individual variables, and links indicate the strength and direction of their relationships. Blue links represent positive associations, while red links indicate negative associations. The thickness of the lines reflects the magnitude of the association.\u003c/p\u003e\n\u003cp\u003eInstrumental Activities of Daily Living (IADL)\u003cstrong\u003e; \u003c/strong\u003eBasic Activities of Daily Living (BADL)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9211395/v1/c7da33244fbe99b14acd39bf.png"},{"id":105353307,"identity":"212bd686-d83b-4b1d-9a17-e7196de49cd0","added_by":"auto","created_at":"2026-03-25 06:17:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58061,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork Analysis of the Interactions Between Quality of Life and the Domains of Intrinsic Capacity.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9211395/v1/c4c3f4adc09bd889852470e0.png"},{"id":106816308,"identity":"91333c63-9d1c-4f04-abd7-d6b67628a517","added_by":"auto","created_at":"2026-04-13 17:25:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1824369,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9211395/v1/a2e09cd7-2532-4e4e-8ba1-033a2c9233e4.pdf"},{"id":105353308,"identity":"0ef73d30-42ab-4439-8945-49b6f627c9c4","added_by":"auto","created_at":"2026-03-25 06:17:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":30877,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial..docx","url":"https://assets-eu.researchsquare.com/files/rs-9211395/v1/894ddbd90a37cf1e38de51b0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intrinsic capacity as a determinant of quality of life trajectories in older Europeans: A sex- and region-sensitive longitudinal analysis using SHARE","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation aging is a global phenomenon that is profoundly impacting countries worldwide, particularly those with higher levels of development, due to increased life expectancy and declining fertility rates (WHO, 2024). Demographic projections estimate that the proportion of individuals aged 65 and older will rise from 9.7% in 2023 to 16.4% by 2050, with Europe being one of the most affected regions (WHO, 2024). This accelerated demographic shift presents significant challenges for public health and social systems, increasing the demand for strategies that promote healthy aging and enhance the quality of life (QoL) of older adults (United Nations, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Quality of life is widely recognized as a multidimensional construct influenced not only by health status but also by social participation, autonomy, and broader societal conditions (McGregor et al., 2009; Galloway, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn response to this challenge, the World Health Organization (WHO) has proposed a model of healthy aging that emphasizes the importance of functional ability, defined as the dynamic interaction between an individual\u0026rsquo;s intrinsic capacity and their environment (WHO, 2015). This model recognizes that aging is shaped not only by biological factors but also by psychosocial and environmental determinants (WHO, 2020). Within this framework, intrinsic capacity (IC), conceptualized by the WHO as the composite of an individual\u0026rsquo;s physical and mental capacities, plays a pivotal role in shaping trajectories of quality of life (QoL) in older adults, representing a paradigm shift from disease-centered models to a function-centered approach in healthy aging (WHO, 2015). IC is operationalized through five key domains as proposed in the WHO ICOPE framework: mobility, cognitive capacity, sensory function, psychological well-being, and vitality (WHO, 2019).\u003c/p\u003e \u003cp\u003eRecent research has demonstrated that assessing IC offers a more comprehensive and nuanced understanding of healthy aging and its implications for QoL (Angelsen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chhetri, 2022). Nonetheless, challenges remain in the development and validation of large-scale tools to measure these domains reliably (Rojano et al., 2023). Promoting active and healthy aging requires targeted interventions aimed at preserving and enhancing these capacities, thereby supporting physical, psychological, and social well-being. Social participation has also been identified as an important determinant of well-being and functional outcomes in later life, contributing to both health and quality-of-life trajectories (Oshio et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Studies such as that by Takeda et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have highlighted that improvements in socioeconomic conditions and access to healthcare can help maintain IC even in the presence of chronic conditions (Takeda et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, active social participation has been positively associated with higher IC and improved QoL, underscoring the need to reduce social isolation and strengthen support networks (L\u0026oacute;pez-Ortiz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSex disparities in aging and their impact on QoL remain a critical area of inquiry. Recent studies have identified persistent sex inequalities in health, education, and QoL among older adults (Ahrenfeldt \u0026amp; M\u0026ouml;ller, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Salinas-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Older women are more likely to experience poorer health outcomes and lower QoL compared to men (Llorens-Ortega et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, IC plays a pivotal role in shaping QoL trajectories, justifying a sex-sensitive analytical approach.\u003c/p\u003e \u003cp\u003eThe Survey of Health, Ageing and Retirement in Europe (SHARE) is one of the most comprehensive longitudinal databases available, offering valuable insights into how social, economic, and health-related factors influence QoL among older adults across diverse European contexts (B\u0026ouml;rsch-Supan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Its multidisciplinary and longitudinal design enables the exploration of the interplay between social determinants of health, IC, and other relevant variables in shaping QoL outcomes.\u003c/p\u003e \u003cp\u003eIt is essential to recognize that intrinsic capacity does not evolve in isolation, but is strongly shaped by social determinants of health, including education, socioeconomic status, living conditions, and access to healthcare and social support (Marmot, 2020; WHO, 2015). These structural factors influence both the preservation and decline of functional domains, and their unequal distribution contributes to disparities in aging outcomes. Evidence consistently shows that women, while living longer, are more likely to experience multimorbidity, functional limitations, and poorer self-rated health than men (Crimmins et al., 2019; Oksuzyan et al., 2019). Similarly, cross-national studies demonstrate that older adults in Southern and Eastern Europe, where welfare systems are less comprehensive and socioeconomic inequalities more pronounced, face a higher risk of functional decline and reduced quality of life compared to those in Northern and Continental Europe (B\u0026ouml;rsch-Supan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Llorens-Ortega et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Therefore, given that IC encompasses key functional domains, its preservation is hypothesized to directly influence trajectories of QoL in older adults. We hypothesize that declines in intrinsic capacity domains will predict deteriorations in QoL over two years, with women and residents of Southern and Eastern Europe experiencing disproportionately greater declines.\u003c/p\u003e \u003cp\u003eBy integrating functional domains of intrinsic capacity with social determinants of health in a cross-national European sample, this study contributes to the growing literature examining how biological, psychological, and social factors jointly shape quality-of-life trajectories in later life. Although limited to two waves, this longitudinal design allows examination of short-term change patterns in intrinsic capacity and their association with QoL dynamics.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eThis study is a prospective, analytical cohort study based on population-level data from the Survey of Health, Ageing and Retirement in Europe (SHARE) (B\u0026ouml;rsch-Supan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Malter, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). SHARE is a multinational longitudinal study that investigates health, socioeconomic, and demographic factors among non-institutionalized adults aged 50 and older. Participants are interviewed biennially, enabling international comparisons and longitudinal analyses of aging-related factors. To minimize bias SHARE employs standardized data collection procedures, including harmonized questionnaires and probabilistic representative sampling to ensure representativeness across countries.\u003c/p\u003e \u003cp\u003eData for this study were drawn from Waves 5 (2013) and 6 (2015), which provide the most consistent and comparable measures of the intrinsic capacity domains across countries and allow for a robust longitudinal design. Although more recent waves are available, key variables were not fully harmonized in later datasets, and attrition was higher compared with the 2013\u0026ndash;2015 interval, reducing response rates and completeness of variables of interest which could bias longitudinal analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eIn Wave 5 (2013), a total of 59,421 individuals were surveyed across 13 European countries: Germany, Austria, Belgium, Denmark, Slovenia, Spain, Estonia, France, Italy, Luxembourg, Sweden, Switzerland, and the Czech Republic. Inclusion criteria for this study required participants to be aged 50 or older, reside in one of the 13 selected countries, consent to participate, have taken part in both consecutive waves under analysis, possess complete data for all variables of interest, and not be institutionalized at the time of the interviews.\u003c/p\u003e \u003cp\u003eA total of 11,493 individuals met these criteria. The remaining participants were excluded due to attrition, death, non-participation in the subsequent wave, or missing data in key variables.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the flow diagram detailing the total number of respondents in Wave 5, exclusions due to non-participation or missing data, and the final analytical sample.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo assess potential bias arising from non-participation in Wave 6, a comparative analysis was conducted using Propensity Score Matching (PSM). Participants and non-participants were matched based on key sociodemographic variables such as sex, age, and geographic region. This approach ensured that the analytical sample was representative of the general Wave 5 population, with only minor differences in age and a slightly lower proportion of women. Quality of life scores (CASP-12) were compared between matched groups, revealing no statistically significant differences. The participant selection methodology is detailed in a previous study (Llorens-Ortega et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and is available for download via GitHub (Vila, J., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Supplementary details of this analysis are provided in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFor the analysis of regional differences in the evolution of QoL and IC, countries were grouped into four regional clusters based on the 2023 Eurostat report on welfare models in Europe (European Commission, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNorthern Europe: Denmark and Sweden (Social Democratic regimes)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eContinental Europe: Austria, Germany, Belgium, France, Luxembourg, and Switzerland (Corporatist regimes)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSouthern Europe: Spain and Italy (Southern European regimes)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEastern Europe: Slovenia, Estonia, and the Czech Republic (Post-Socialist regimes)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis grouping was selected to reflect welfare regime typologies that influence social and health policies, although some heterogeneity within clusters may exist.\u003c/p\u003e\n\u003ch3\u003eData Collection Procedures\u003c/h3\u003e\n\u003cp\u003eData were collected through Computer-Assisted Personal Interviews (CAPI), with an average duration of 90 minutes. Interviews were conducted in participants\u0026rsquo; homes using standardized questionnaires covering a wide range of topics, including IC domains, social determinants of health, health conditions, and socioeconomic factors. Design weights were applied to enhance the representativeness of the findings (Malter, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Interviewers received standardized training and quality control procedures were implemented to ensure data reliability. Design weights were applied in all analyses using the appropriate weighting procedures in SPSS and R to account for sampling design and non-response. All data are publicly available for scientific use at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.share-project.org\" target=\"_blank\"\u003ewww.share-project.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.share-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eStudy Variables\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eOutcome Variable: Quality of Life (QoL).\u003c/b\u003e The primary outcome was QoL, assessed with the CASP-12 scale (Wiggins et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which measures subjective well-being across four domains: Control, Autonomy, Self-realization, and Pleasure. Each domain includes three items rated on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;never to 4\u0026thinsp;=\u0026thinsp;often), yielding a total score from 12 to 48, with higher values indicating better QoL. Following previous studies, we classified scores into four categories: 12\u0026ndash;34 (low), 35\u0026ndash;37 (moderate), 38\u0026ndash;39 (high), and 40\u0026ndash;48 (very high). The CASP-12 has shown high reliability (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;0.84) in older populations (Hyde et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; P\u0026eacute;rez-Rojo et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cb\u003eExplanatory Variables: Intrinsic Capacity Domains.\u003c/b\u003e Following WHO\u0026rsquo;s ICOPE guidelines (WHO, 2019), we considered five domains of intrinsic capacity (IC): locomotion, sensory, cognition, psychological well-being, and vitality (Rojano \u0026amp; Luque et al., 2023; Angelsen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Each domain was dichotomized according to the presence or absence of limitations. Locomotion was measured using SHARE items PH046 (difficulty walking 100 m) and PH047 (difficulty climbing stairs), both recoded into \u0026ldquo;no difficulty\u0026rdquo; versus \u0026ldquo;any limitation.\u0026rdquo; Additional indicators of functional limitation Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) were also dichotomized. Sensory function was assessed through perceived vision (PH043) and hearing (PH044) difficulties, each coded as \u0026ldquo;no difficulty\u0026rdquo; versus \u0026ldquo;any impairment.\u0026rdquo; Cognition was defined as a composite variable integrating episodic memory (CF003, CF006) and spatiotemporal orientation (CF103, CF113, CF114); participants with impairment in either dimension was considered cognitively limited. Psychological well-being was measured with the EURO-D scale (Maskileyson et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Portellano-Ortiz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), where scores\u0026thinsp;\u0026ge;\u0026thinsp;4 indicate depression. Vitality was operationalized through Body Mass Index (BMI, PH012, PH013), categorized into underweight (\u0026lt;\u0026thinsp;18.5), normal (18.5\u0026ndash;24.9), overweight (25\u0026ndash;29.9), and obesity (\u0026ge;\u0026thinsp;30). For analysis, BMI was dichotomized as \u0026ldquo;no weight-related issues\u0026rdquo; (18.5\u0026ndash;24.9) versus \u0026ldquo;weight-related issues\u0026rdquo; (\u0026lt;\u0026thinsp;18.5 or \u0026ge;\u0026thinsp;25). While BMI is an indirect and partial indicator of vitality, it has been widely used in epidemiological research as a proxy for metabolic and nutritional status in large population-based datasets. In the absence of direct measures of energy balance, sarcopenia, or inflammatory biomarkers within SHARE, BMI provides a standardized and comparable operationalization across countries. Nevertheless, we acknowledge that vitality is a multidimensional construct, and future research should incorporate broader physiological indicators to enhance construct validity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCovariates: Social Determinants of Health (SDH) and Sociodemographics.\u003c/b\u003e To contextualize QoL, we included covariates covering SDH, sociodemographic variables, health behaviors, self-perceived health, and comorbidity. SDH included sex, age groups (50\u0026ndash;64, 65\u0026ndash;74, 75\u0026ndash;84, \u0026ge;\u0026thinsp;85), education (ISCED levels: low, medium, high), financial situation (\u0026ldquo;ease of making ends meet\u0026rdquo; and \u0026ldquo;receipt of financial assistance\u0026rdquo;), and region of residence (Northern, Continental, Eastern, Southern Europe). Sociodemographic factors included marital status, household composition, and employment status. Health behaviors included physical activity (vigorous activity\u0026thinsp;\u0026ge;\u0026thinsp;once/week vs. less often (Reitlo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Quiroz Mora et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), alcohol consumption, and tobacco use. Self-perceived health (PH003) was dichotomized into \u0026ldquo;good\u0026rdquo; (good/very good) versus \u0026ldquo;poor\u0026rdquo; (fair, poor, very poor). Comorbidity was coded as none versus two or more chronic conditions.\u003c/p\u003e \u003cp\u003eAll covariates were selected based on prior literature demonstrating their relevance to aging outcomes and quality of life.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were presented as means and standard deviations, while categorical variables were described using absolute and relative frequencies. Univariate comparisons were conducted using Student\u0026rsquo;s t-tests for continuous variables and Chi-square tests for categorical variables.\u003c/p\u003e \u003cp\u003eSubsequently, all variables of interest were recoded into dichotomous variables to reflect the presence (1) or absence (0) of functional, cognitive, sensory, or mobility-related limitations, following an adverse event-oriented approach. Each variable was recategorized so that a value of 1 indicated a negative outcome, such as perceived difficulty, chronic condition, or poor performance, based on established cut-off points from the literature or statistical criteria (Salazar Estrada et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Salinas-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Berk, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Using this harmonized coding, a composite intrinsic capacity index was calculated by summing the dichotomized variables. This index reflects the accumulation of deficits across domains, where higher scores indicate lower intrinsic capacity (i.e., more domains with limitations), and lower scores represent higher intrinsic capacity. This approach aligns with previous methodologies that assess functional aging through deficit accumulation or frailty indices (Cesari et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ofori-Asenso et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Burn et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and with studies examining the decline of intrinsic capacity (Rodr\u0026iacute;guez-Laso et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rojano \u0026amp; Luque et al., 2023). Although the deficit-accumulation strategy resembles approaches used in frailty research, intrinsic capacity differs conceptually in that it focuses on the preservation of functional domains rather than vulnerability to adverse outcomes. While frailty emphasizes risk accumulation, intrinsic capacity emphasizes the level of retained physical and mental capacities. Therefore, the present index should be interpreted as a functional capacity gradient rather than a frailty score. This recoding strategy enabled more effective operationalization of variables for subsequent statistical analyses. Wave 5 data were used for exploratory factor analysis (EFA), while Wave 6 data were employed to assess the predictive validity of the intrinsic capacity instrument.\u003c/p\u003e \u003cp\u003eExploratory Factor Analysis (EFA)\u003c/p\u003e \u003cp\u003eTo identify the underlying structure of the constructs assessed, an EFA was conducted using the Weighted Least Squares (WLS) method, appropriate for dichotomous and ordinal data. A polychoric-tetrachoric correlation matrix was used to estimate relationships among categorical variables. The optimal number of factors to retain was determined through Parallel Analysis based on factor analysis, complemented by inspection of the scree plot. To facilitate interpretation, an oblique rotation (Promax) was applied, given the expected correlation among latent factors.\u003c/p\u003e \u003cp\u003eFactor loadings greater than 0.40 were considered significant. Model adequacy was evaluated by examining uniqueness values, and variables with values exceeding 0.70, such as the vitality domain, were excluded from the final model.\u003c/p\u003e \u003cp\u003eNetwork Analysis (NA)\u003c/p\u003e \u003cp\u003eNetwork analysis was selected because intrinsic capacity is conceptualized as a multidimensional and interactive system rather than a set of independent predictors. Traditional regression approaches assume unidirectional relationships and latent variable structures, whereas network models allow examination of conditional dependencies and the relative centrality of domains within a complex system (Borsboom, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This approach is particularly suitable for aging research, where functional domains interact dynamically and may reinforce or buffer each other over time. Network Analysis was performed using the Huge Estimator approach to explore the structural relationships within intrinsic capacity, minimizing model overfitting and examining how key indicators of mobility, sensory health, mental health, and cognitive function interrelate (Borsboom, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, Ebiglasso was used to explore directional associations between intrinsic capacity domains and QoL, acknowledging that causal inference cannot be established within the present observational design. This advanced tool enabled modeling of complex, non-linear relationships among variables, facilitating the identification of critical factors with the greatest influence on QoL.\u003c/p\u003e \u003cp\u003eGiven the ongoing methodological debate regarding the stability of centrality indices in psychological and health-related networks, the results should be interpreted primarily in terms of overall structural patterns rather than precise centrality rankings. The network was estimated to use regularization procedures to reduce spurious associations and overfitting; however, centrality metrics may be sensitive to sampling variability. Therefore, findings related to node importance are presented as exploratory and hypothesis-generating rather than definitive causal hierarchies.\u003c/p\u003e \u003cp\u003eSoftware Used: All analyses were conducted using weighted SHARE data and performed with the following software: SPSS version 25 (IBM Corp., Armonk, NY, USA); R version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria); JASP Stats version 0.19.3. The significance level was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e The Ethics Committee of the Max Planck Society for the Advancement of Science conducted a thorough review of all materials related to the SHARE project, including Wave 5 and the subsequent Wave 6. The committee certified that the research project and its procedures comply with the highest international ethical standards. Strict measures were implemented to ensure the confidentiality and privacy of participants\u0026rsquo; data, in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects. Written informed consent was obtained from all participants, who voluntarily agreed to participate during the interviews (B\u0026ouml;rsch-Supan et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Participants were informed about the purpose of the study, data confidentiality, and their right to withdraw at any time without penalty.\u003c/p\u003e \u003cp\u003eData confidentiality and anonymity were rigorously maintained throughout data collection, processing, and analysis. Personal identifiers were removed prior to data access by researchers, and data were stored securely in compliance with data protection regulations. No individual participant data are reported in this study.\u003c/p\u003e \u003cp\u003eThis study did not involve any interventions or clinical trials; therefore, a clinical trial registration number is not applicable.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDemographics, healthy lifestyle, and quality of life by sex (Wave 5)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistically significant sex differences were found across multiple variables (p \u0026lt; 0.001). Men were slightly older on average than women (64.4 vs. 63.3 years). Women were more represented in the 50\u0026ndash;64 age group (54.4% vs. 50.0%). Educational disparities were evident, with 37.0% of women having low education compared to 32.9% of men. Marital status differed substantially, with men more often married or partnered (80.7% vs. 71.2%) and women more frequently widowed (13.4% vs. 4.4%). Economic difficulties were more prevalent among women (31.5% vs. 26.7%). In terms of health behaviors, men reported higher levels of physical activity (57.6% vs. 50.2%), but also greater prevalence of daily alcohol consumption (28.9% vs. 11.5%) and smoking (56.0% vs. 37.1%). Quality of life (QoL), measured by CASP-12, was significantly higher among men than women (38.6 vs. 38.0; p \u0026lt; 0.001). These findings indicate consistent sex differences in socioeconomic conditions and health behaviors. See \u003cstrong\u003eTable\u003c/strong\u003e 1 for detailed results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Sociodemographic characteristics, healthy lifestyle, and quality of life by Sex (Wave 5)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"width: 4.2e+2pt;border: none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen (N=6236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen (N=5257)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTotal (N=11493)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic Data:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.3 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e64.4 (9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.8 (9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; 50-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;3392 (54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2629 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6021 (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; 65-74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;1753 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1668 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3421 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; 75-84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;885 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e819 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1704 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; 85+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;206 (3.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e141 (2.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347 (3.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEducational Level:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2310 (37.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1727 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4037 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2326 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2037 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4363 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1600 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1493 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3093 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried or registered partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4443 (71.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4241 (80.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8684 (75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDivorced or separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e638 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e432 (8.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1070 (9.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e319 (5.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e350 (6.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e669 (5.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e836 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e234 (4.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1070 (9.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAble to make ends meet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4337 (69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3853 (73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8190 (71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eReceived help from others (outside the household)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1174 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e833 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2007 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Lifestyle:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Physical Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3132 (50.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3027 (57.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6159 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDaily Alcohol Consumption:\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNone/1-2 times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3801 (61.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1950 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5751 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1-4 days per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1719 (27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1790 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3509 (30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlmost daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e716 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1517 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2233 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDaily Smoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2314 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2944 (56.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5258 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eQuality of Life:\u003c/strong\u003e CASP (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.0 (6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.6 (6.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.3 (6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Statistical tests used: ANOVA for continuous variables and Chi-square for categorical variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExploratory factor analysis of intrinsic capacity variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAn Exploratory Factor Analysis (EFA) was conducted to uncover the latent structure of intrinsic capacity, using variables related to mobility, cognition, psychological well-being, sensory function, and vitality. Sampling adequacy was acceptable (overall KMO = 0.601; individual values 0.486\u0026ndash;0.964), and Bartlett\u0026rsquo;s test of sphericity (\u0026chi;\u0026sup2; (66) = 138,861, p \u0026lt; 0.001) confirmed the suitability of the data for factor analysis.\u003c/p\u003e\n\u003cp\u003eFive components with eigenvalues greater than 1 were extracted, jointly explaining 73.05% of the total variance. After Promax (oblique) rotation, the factor structure corresponded to the WHO\u0026rsquo;s conceptualization of intrinsic capacity, comprising:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eMobility\u003c/strong\u003e: locomotor limitations together with ADL and IADL performance,\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003ePsychological state\u003c/strong\u003e: depressive symptoms, including fatigue, irritability, and loss of interest.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCognition\u003c/strong\u003e: immediate and delayed word recall, together with orientation tasks.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSensory function\u003c/strong\u003e: self-reported vision and hearing difficulties.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVitality\u003c/strong\u003e: BMI and BMI categories.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe covariance matrix of factor scores indicated negligible covariances (\u0026asymp; 0), indicating that, although theoretically correlated, the extracted domains showed low empirical overlap in this sample. This structure provides empirical support for the multidimensional nature of intrinsic capacity and validates its operationalization in this dataset. Moreover, the high proportion of explained variance underscores the robustness of the factorial model as a basis for constructing a synthetic index of intrinsic capacity in comparative aging research. See \u003cstrong\u003eTable 2\u003c/strong\u003e for detailed factor loadings and component structure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Factor analysis of intrinsic capacity variables\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 1 (Mobility)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 2 (Psychological State)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 3 (Cognition)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 4 (Sensory Function)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor 5 (Vitality)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUniqueness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMobility problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDepression scale (EuroD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eImmediate word recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDelayed word recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOrientation in time and space\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVision difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHearing difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI categories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Rotated factor loadings are shown (Promax, oblique rotation). Each variable is primarily associated with a single domain of intrinsic capacity as defined by the WHO: mobility, psychological state, cognition, sensory function, and vitality. Activities of Daily Living (ADL). Instrumental Activities of Daily Living (IADL); BMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork analysis of intrinsic capacity structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further examine the structural organization of IC, a Network Analysis (NA) was performed to explore the interactions between IC domains and QoL. The network exhibited a sparsity of 35.9%, indicating that more than one-third of the possible edges were absent, thus supporting the multidimensional nature of IC rather than a single global dimension. Mobility emerged as the most central domain, exhibiting the highest betweenness (1.954), closeness (1.084), strength (1.485), and expected influence (1.590), underscoring its pivotal role in intrinsic capacity. \u0026nbsp;A strong connection was observed between vitality and fatigue, reflecting their close interdependence. Sensory and psychological domains showed weaker connectivity, suggesting more domain-specific effects. The network visualization is provided in Figure 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelations between intrinsic capacity domains and quality of life\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson correlation analyses demonstrated that psychological variables had the strongest negative associations with QoL: depressive symptoms (r = \u0026minus;0.284, p \u0026lt; .001), fatigue (r = \u0026minus;0.324, p \u0026lt; .001), irritability (r = \u0026minus;0.230, p \u0026lt; .001), and loss of interest (r = \u0026minus;0.244, p \u0026lt; .001). Cognitive performance showed weaker but significant negative correlations with CASP-12 scores (r range = \u0026minus;0.108 to \u0026minus;0.162, all p \u0026lt; 0.001). Mobility variables exhibited small but significant negative correlations (r range = \u0026minus;0.019 to \u0026minus;0.041, p \u0026lt; 0.05). These findings highlight mental health as the strongest direct correlate of QoL, with mobility playing a structurally central role. The full correlation matrix is available in Supplementary Table S2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork analysis of interactions between quality of life and intrinsic capacity domains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA second network analysis including quality of life (CASP-12) and IC domains revealed complete interconnectivity among nodes (dispersion = 0.000). CASP-12 emerged as the most central node, with the highest betweenness (1.789), closeness (0.856), and strength (0.846), but a negative expected influence (\u0026minus;1.759) indicates that these connections primarily reflected inverse relationships with IC deficits.\u003c/p\u003e\n\u003cp\u003eAmong domains, Mobility showed the lowest connectivity (closeness = \u0026minus;1.715; strength = \u0026minus;1.613), whereas Sensory function demonstrated higher integration (closeness = 0.498; strength = 0.650). Clustering measures reinforced this pattern, with CASP and Sensory presenting the highest Onnela coefficients (0.733 and 0.665), while Mobility displayed the weakest integration (Onnela = \u0026minus;1.712). Psychological and cognitive domains showed moderate cohesion (Zhang index = 0.663 and 0.934). The vitality domain was excluded, as BMI alone did not capture the multidimensional nature of vitality, making it an inadequate proxy in this context. See \u003cstrong\u003eFigure 3\u003c/strong\u003e for the network graph.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSex differences in intrinsic capacity domains\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent samples t-tests revealed significant sex differences in sensory function, mental health, and cognition. Men had slightly higher sensory scores (M = 0.740) than women (M = 0.697; p = 0.015, d = 0.045). Women exhibited greater emotional distress (mental health domain) than men (M = 1.163 vs. 0.884; p \u0026lt; 0.001, d = \u0026minus;0.260). Cognitive performance was higher in men (M = 0.609) compared to women (M = 0.518; p \u0026lt; 0.001, d = 0.109). No significant sex difference was found in mobility (p = 0.167). Detailed statistics are provided in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Analysis of intrinsic capacity by sex. Independent samples t-Test\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDomain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE Cohen\u0026rsquo;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSensory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.015ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-13.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001ᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDescriptive statistics by group\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDomain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSensory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSensory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Results from Student\u0026apos;s t-test comparing intrinsic capacity domains between men and women. Significant differences were found in mental health, cognition (p \u0026lt; .001), and sensory domain (p = 0.015). Brown-Forsythe test indicates violation of homogeneity of variances in variables marked with ᵃ.\u003cbr\u003e\u0026nbsp;SD: Standard Deviation; SE: Standard Error; CV: Coefficient of Variation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTotal intrinsic capacity by sex and region\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eANOVA results showed significant main effects of sex (F (1, 11,485) = 9.575, p = 0.002, \u0026eta;\u0026sup2; = 0.0008) and region (F (3, 11,485) = 74.813, p \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.019), as well as a significant sex \u0026times; region interaction (F (3, 11,485) = 2.825, p = 0.037, \u0026eta;\u0026sup2; = 0.0007). Women had slightly higher IC deficit scores overall. \u0026nbsp;Northern Europe exhibited the lowest deficits, while Southern Europe had the highest. Post hoc tests confirmed women in Southern Europe had significantly higher deficits than men in the same region (p = 0.001). See Table 4 for details.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. ANOVA of total intrinsic capacity by sex and region\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026eta;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.164\u0026times;10⁻⁴\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e943.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e314.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u0026nbsp;✻\u0026nbsp;Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.227\u0026times;10⁻⁴\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResiduals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48293.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics \u0026ndash; Total Intrinsic Capacity\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eContinental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eContinental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMen (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eWomen (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Results from fixed-effects ANOVA (Type III Sum of Squares) for total intrinsic capacity. Significant differences were observed by sex (p = 0.002) and region (p \u0026lt; .001), as well as a sex\u0026nbsp;✻\u0026nbsp;region interaction (p = 0.037). Effect size (\u0026eta;\u0026sup2;) indicates a small influence of these variables. Men (0) and Women (1).\u003cbr\u003e\u0026nbsp;SD: Standard Deviation; SE: Standard Error; CV: Coefficient of Variation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocioeconomic and household influences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntrinsic capacity varied significantly by socioeconomic status and household composition. Employment status had a strong effect (F (6, 11,830) = 168.692, p \u0026lt; 0.001, \u0026omega;\u0026sup2; = 0.078). Self-employed individuals had the lowest IC scores (M = 1.828), while those with medical disabilities had the highest (M = 3.912), significantly exceeding retirees, employees, and the unemployed (all p \u0026lt; 0.001). Detailed comparisons are provided in Supplementary Table S3. Economic difficulty showed a clear gradient (F (3, 11,574) = 146.513, p \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.037). Participants reporting severe financial difficulty had the highest mean IC scores (M = 3.622), whereas those with no difficulty had the lowest (M = 2.291), reflecting better functional capacity (see \u003cstrong\u003eTable 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Economic difficulty and intrinsic capacity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026eta;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEconomic Difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,798,701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e599,567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e146.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResiduals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47,363,578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11,574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Type III Sum of Squares.\u003c/p\u003e\n\u003cp\u003eDescriptive Statistics \u0026ndash; TOTAL Intrinsic Capacity\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEconomic Difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCoefficient of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1. Severe difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2. Some difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2,239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3. Slight difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3,204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4. No difficulty\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5,376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Descriptive statistics of total intrinsic capacity according to financial difficulty. N = sample size; SD = standard deviation; SE = standard error. The coefficient of variation indicates the relative variability within each category.\u003c/p\u003e\n\u003cp\u003eHousehold composition influenced outcomes as well. Living with a partner was associated with significantly lower IC scores (F (1, 11,833) = 144.323, p \u0026lt; 0.001, \u0026eta;\u0026sup2; = 0.012). Women living alone had the highest deficits (M = 3.252), followed by men living alone (M = 2.879). By contrast, both men (M = 2.482) and women (M = 2.495) living with a partner had lower scores, indicating fewer accumulated deficits. Significant interaction effects (F (1, 11,833) = 14.098, p \u0026lt; 0.001) suggest that cohabitation affects men and women differently (See \u003cstrong\u003eTable 6\u003c/strong\u003e for further details.)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Living with a spouse/partner, sex, and intrinsic capacity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026eta;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLiving with a spouse/partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e608,903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e608,903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e144.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLiving with a spouse/partner\u0026nbsp;✻\u0026nbsp;Sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59,479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59,479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt; .001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eResiduals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49,923,645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11,833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Type III Sum of Squares.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDescriptive Statistics \u0026ndash; TOTAL Intrinsic Capacity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border: none; width: 100%;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLiving with a Spouse/Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCoefficient of Variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4,706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.808\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1,732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Descriptive statistics of total intrinsic capacity by sex and cohabitation with a spouse/partner. N = sample size; SD = standard deviation; SE = standard error. The coefficient of variation indicates the relative variability within each category. 1 = Yes (Lives with spouse/partner), 3 = No (Does not live with spouse/partner).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChange between waves (2013\u0026ndash;2015)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePaired-samples analysis revealed a significant increase in mean IC scores between 2013 (M = 2.628, SD = 2.072) and 2015 (M = 2.766, SD = 2.131), t (11,836) = 7.698, p \u0026lt; 0.001, Cohen\u0026rsquo;s d = 0.071, indicating a small but measurable decline in intrinsic capacity over two years. Women exhibited greater deficit accumulation, especially in psychological domains. Regional disparities persisted, with Northern Europe showing better outcomes. Older adults aged 75+ experienced sharper declines, particularly in mobility and mental health. Supplementary Table S3 provides complete data.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides robust empirical evidence that intrinsic capacity domains are closely associated with quality-of-life trajectories in older adults across European regions. The findings underscore the multifaceted and multidimensional nature of intrinsic capacity, encompassing interconnected domains such as mobility (including ADL and IADL), cognitive function, mental health, and sensory function. Importantly, these findings offer strong empirical support for the WHO healthy aging framework, which conceptualizes intrinsic capacity as dynamically shaped by environmental and social determinants. The observed sex and regional disparities illustrate that functional aging is not solely biologically driven but deeply embedded within broader welfare and socioeconomic contexts.\u003c/p\u003e \u003cp\u003eThese results are consistent with recent research, such as the meta-analysis conducted by Zhou and Ma (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which emphasized that intrinsic capacity should be understood as a complex, multidimensional construct rather than a unidimensional entity. The originality of this study lies in the joint application of EFA and Network Analysis to intrinsic capacity, together with a systematic sex- and region-based comparison, which has been scarcely addressed in previous literature.\u003c/p\u003e \u003cp\u003eThrough EFA, the study confirmed that intrinsic capacity comprises distinct but interrelated components clustering according to their functional relevance\u0026mdash;namely sensory function, cognition, mobility, and mental health. This finding reinforces the need for integrated assessment approach that considers both physical and psychological aspects of aging (Cruz-Peralta \u0026amp; Gonz\u0026aacute;lez-Celis, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Leit\u0026oacute;n Espinoza et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the vitality domain, operationalized solely through Body Mass Index (BMI), presented notable limitations. While BMI is a useful epidemiological measure, it fails to capture the complexity of factors influencing intrinsic capacity and quality of life (Leit\u0026oacute;n Espinoza et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In fact, the high uniqueness values observed in the factor analysis, particularly for BMI and its categories, indicated that these variables did not cluster effectively with others, suggesting that BMI alone is insufficient to represents vitality adequately.\u003c/p\u003e \u003cp\u003eRegarding sex differences, the results indicated that women showed a higher accumulation of deficits across several key domains, particularly cognition and mental health. This pattern aligns with previous studies showing that older women tend to face more significant declines in these areas compared to men (Pavez Lizarraga et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A combination of biological and sociocultural factors appears to play a critical role in this deterioration, as women\u0026rsquo;s longer life expectancy increases their exposure to chronic diseases and multimorbidity (Au et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; San Jos\u0026eacute; Laporte, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings of this study reinforce those of Llorens-Ortega et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who documented cognitive and physical decline, particularly among older women from socially disadvantaged backgrounds. Conversely, men showed less deterioration in sensory health, reflecting a trend observed in other studies suggesting sex differences in sensory function (Alfonso Silguero et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Overall, women\u0026rsquo;s higher accumulation of deficits, particularly in cognition and psychological domains, highlights persistent sex disparities in functional capacity and quality of life trajectories (Kaur et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Concha-Cisternas et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; D\u0026iacute;az-Alonso et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMental health, particularly depression, emerged as a key domain with significantly higher prevalence among women over time. Depression is known to accelerate functional decline, particularly in females. Previous research has emphasized the central role of psychological well-being in shaping perceived quality of life, identifying mental health as a key determinant of subjective well-being (Kim, 2025). Our findings are consistent with studies documenting increased depressive symptoms among older women, attributable to a combination of biological factors and the accumulation of psychosocial stressors throughout life (Jalali et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Portellano-Ortiz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMoreover, factors such as social isolation, lack of emotional support, and greater caregiving burden exacerbate depression\u0026rsquo;s negative impact on older women\u0026rsquo;s quality of life (Courtin \u0026amp; Knapp, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results reinforce the urgent need for sex-sensitive public health policies, including enhanced access to community-based mental health services, systematic depression screening in primary care, and tailored psychosocial support for vulnerable women.\u003c/p\u003e \u003cp\u003eThe inclusion of social determinants of health (SDH) in this study was essential to understanding how factors such as educational level, marital status, and economic situation are consistently associated with functional decline and quality of life in older adults (Bielderman et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Steptoe \u0026amp; Zaninotto, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Across the European regions analyzed, women in socially and economically vulnerable situations exhibited greater deterioration in mental health, a significant increase in chronic conditions, and more pronounced economic decline. This pattern aligns with recent studies showing that social inequalities disproportionately affect older women, particularly in contexts of poverty or limited access to healthcare (Bacigalupe et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Spiers et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Similarly, previous research has shown that social participation and broader social conditions play a critical role in shaping well-being and functional trajectories among older adults (Oshio et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a policy perspective, these findings highlight the importance of reducing socioeconomic inequalities to mitigate IC decline. Interventions should include income protection for older adults, universal access to healthcare and long-term care services, and programs that address social isolation and support for those living alone (Lu et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Importantly, harmonizing social protection policies across European regions could reduce disparities observed between Northern and Southern/Eastern Europe.\u003c/p\u003e \u003cp\u003eThe network analysis elucidated the underlying structure of intrinsic capacity, revealing mobility as a structurally central domain, while psychological and sensory domains showed stronger direct associations with quality of life. Although mobility and cognition are important domains, their impact on quality of life appears to be more modest compared to physical and mental health. This finding aligns with previous studies demonstrating that physical and mental health are key determinants of quality of life in older adults (Geigl et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Guti\u0026eacute;rrez-Robledo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sugimoto et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These results suggest that interventions prioritizing mental well-being and physical resilience may yield the most significant improvements in overall QoL.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eAmong the strengths of this study is the use of a large representative sample from 13 European countries, a short-term longitudinal and comparative perspective across two consecutive waves. Additionally, the inclusion of social determinants of health enriched the analysis, allowing for the identification of socioeconomic and geographic disparities.\u003c/p\u003e \u003cp\u003eHowever, the study also presents several limitations. The use of self-reported data may introduce bias, particularly in domains such as mental health and mobility. Second, although longitudinal data were used, the observational design does not allow full disentanglement of bidirectional relationships between intrinsic capacity domains and quality of life. Psychological well-being and QoL may mutually influence each other over time, and causal inferences should therefore be interpreted cautiously. Furthermore, the vitality domain\u0026rsquo;s operationalization via BMI is limited as this measure does not fully capture the multidimensional nature of vitality. Future studies should incorporate additional indicators to provide a more accurate representation of this domain. The relatively short follow-up period (2 years) may underestimate longer-term intrinsic capacity trajectories. Additionally, although regularized network models reduce overfitting, the stability of centrality indices may vary across samples, and replication in independent cohorts is warranted. Residual confounding cannot be fully excluded despite adjustment for multiple covariates.\u003c/p\u003e \u003cp\u003eMoreover, variability in health policy implementation across European countries may have influenced the results. This suggests that future research should include longer longitudinal data and more diverse samples to better assess changes in quality of life and intrinsic capacity over time.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePublic health implications\u003c/h2\u003e \u003cp\u003eThe findings of this study have direct implications for public health policy. Addressing disparities in intrinsic capacity and quality of life requires integrated strategies at both the individual and structural levels:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMental health interventions\u003c/b\u003e: Implement systematic depression screening in primary care, expand psychological services tailored for older adults, and adopt sex-sensitive approaches that consider caregiving burdens and social isolation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMobility promotion\u003c/b\u003e: Develop community-based exercise programs, implement fall prevention strategies, and promote urban planning that fosters age-friendly environments to support physical resilience.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNutritional and vitality programs\u003c/b\u003e: Design initiatives that go beyond Body Mass Index (BMI) to address malnutrition, frailty, and energy balance, incorporating comprehensive assessments of vitality.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eReducing regional disparities\u003c/b\u003e: Advocate for EU-level policies that harmonize access to health and long-term care services, with targeted support for Southern and Eastern European regions where deficits are more pronounced.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSocial and economic protection\u003c/b\u003e: Strengthen financial security through pensions and assistance programs, promote social participation, and provide support for individuals living alone, especially widowed or divorced women.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eBy integrating clinical, behavioral, and policy-oriented interventions, these measures could help reduce sex and regional disparities, ultimately promoting equitable and healthy aging across Europe.\u003c/p\u003e \u003cp\u003eConceptually, this study contributes to bridge the gap between the WHO intrinsic capacity framework and the social determinants of health perspective. It demonstrates that intrinsic capacity domains are not only biologically grounded but also socially patterned across sex and welfare regimes. Therefore, healthy aging must be understood within broader structural contexts, recognizing intrinsic capacity as a socially embedded functional construct shaped by life-course inequalities.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides empirical evidence that intrinsic capacity (IC) is a multidimensional construct consistently associated with quality of life (QoL) in older adults across Europe. Sex-related differences were observed, with women showing higher accumulation of deficits particularly in cognition and mental health domains, while regional patterns showed that Southern and Eastern Europe accumulated more deficits compared to Northern regions. Social determinants such as education and economic status were also associated with these disparities.\u003c/p\u003e \u003cp\u003eTogether, these findings highlight the need for targeted actions addressing sex and regional inequalities in aging. Strengthening mental health care and mobility support may represent promising strategic areas for preserving intrinsic capacity and improving quality of life in older adults.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADL: Activities of Daily Living\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eCASP-12: Control, Autonomy, Self-realization, Pleasure scale\u003c/p\u003e\n\u003cp\u003eEFA: Exploratory Factor Analysis\u003c/p\u003e\n\u003cp\u003eEURO-D: European Depression Scale\u003c/p\u003e\n\u003cp\u003eIADL: Instrumental Activities of Daily Living\u003c/p\u003e\n\u003cp\u003eIC: Intrinsic capacity\u003c/p\u003e\n\u003cp\u003eNA: Network Analysis\u003c/p\u003e\n\u003cp\u003eQoL: Quality of life\u003c/p\u003e\n\u003cp\u003eSDH: Social Determinants of Health\u003c/p\u003e\n\u003cp\u003eSHARE: Survey of Health, Ageing and Retirement in Europe\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research utilizes data from waves SHARE 5 and 6 https://doi.org/10.6103/SHARE.w5.700, https://doi.org/10.6103/SHARE.w6.700. For methodological details, please refer to B\u0026ouml;rsch-Supan et al. (2013). https://doi.org/10.1093/ije/dyt088.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are publicly available through the Survey of Health, Ageing and Retirement in Europe (SHARE) database (https://www.share-project.org) for scientific use upon registration. The code used for sample selection and matching procedures is available on GitHub: https://github.com/JoanVilaDomenech/Matching/.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional supplementary materials related to this study are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData collection in SHARE has been primarily funded by the European Commission through the Fifth Framework Programme (QLK6-CT-2001-00360), the Sixth Framework Programme (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), and the Seventh Framework Programme (SHARE-PREP: No. 211909, SHARE-LEAP: No. 227822, SHARE M4: No. 261982). Additional funding has been provided by 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 various national funding sources (see www.share-project.org for a full list). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Specifically, contributions are as follows (CRediT taxonomy): Conceptualization: RLO, CBN, DJC; Methodology: RLO, DJC, CBF; Formal analysis: RLO, JGO; Writing \u0026ndash; original draft preparation: RLO; Writing \u0026ndash; review and editing: all authors; Supervision: CBN\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Rafael Llorens-Ortega: [email protected]\u003c/p\u003e\n\u003cp\u003eAffiliation: EUIT University Center, Autonomous University of Barcelona, Cerdanyola del Vall\u0026egrave;s, Spain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConflict of Interest: The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003eConsent for Publication: This study does not contain any individual participant data. All authors confirm that they have no financial or non-financial competing interests related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Council of the Max Planck Society for the Advancement of Science thoroughly reviewed all materials related to the SHARE project, including Waves 5 and subsequent waves. The committee certified that the research project, its procedures, and the measures taken to ensure confidentiality and participant privacy comply with international ethical standards, in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study during the interview process. Participants voluntarily agreed to participate and were informed about the study\u0026rsquo;s aims, procedures, and data confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhrenfeldt, L. J., \u0026amp; M\u0026ouml;ller, S. (2021). The Reciprocal Relationship between Socioeconomic Status and Health and the Influence of Sex: A European SHARE-Analysis Based on Structural Equation Modeling. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(9), 5045. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph18095045\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18095045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlfonso Silguero, S. A., Mart\u0026iacute;nez-Reig, M., G\u0026oacute;mez Arnedo, L., Juncos Mart\u0026iacute;nez, G., Romero Rizos, L., \u0026amp; Soler, A., P (2014). Chronic disease, mortality, disability, and loss of mobility in Spanish elderly: FRADEA study. \u003cem\u003eSpanish Journal of Geriatrics and Gerontology\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(2), 51\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.regg.2013.05.007\u003c/span\u003e\u003cspan address=\"10.1016/j.regg.2013.05.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAngelsen, A., Nakrem, S., Zotcheva, E., Strand, B. H., \u0026amp; Strand, L. B. (2024). Health-promoting behaviors in older adulthood and intrinsic capacity 10 years later: the HUNT study. \u003cem\u003eBmc Public Health\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-024-17840-3\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-17840-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAu, B., Dale-McGrath, S., \u0026amp; Tierney, M. C. (2017). Sex differences in the prevalence and incidence of mild cognitive impairment: A meta-analysis. \u003cem\u003eAgeing Research Reviews\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e, 176\u0026ndash;199. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arr.2016.09.005\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2016.09.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBacigalupe, A., Gonz\u0026aacute;lez-R\u0026aacute;bago, Y., \u0026amp; Jim\u0026eacute;nez-Carrillo, M. (2022). Gender inequality and medicalization of mental health: sociocultural determinants from the analysis of expert perceptions. \u003cem\u003ePrimary Care\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(7), 102378. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aprim.2022.102378\u003c/span\u003e\u003cspan address=\"10.1016/j.aprim.2022.102378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerk, R. A. (2016). \u003cem\u003eClassification and Regression Trees (CART)\u003c/em\u003e (pp. 129\u0026ndash;186). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-44048-4_3\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-44048-4_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBielderman, A., de Greef, M. H. G., Krijnen, W. P., \u0026amp; van der Schans, C. P. (2015). Relationship between socioeconomic status and quality of life in older adults: a path analysis. \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(7), 1697\u0026ndash;1705. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11136-014-0898-y\u003c/span\u003e\u003cspan address=\"10.1007/s11136-014-0898-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorsboom, D. (2022). Possible Futures for Network Psychometrics. \u003cem\u003ePsychometrika\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e(1), 253\u0026ndash;265. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11336-022-09851-z\u003c/span\u003e\u003cspan address=\"10.1007/s11336-022-09851-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB\u0026ouml;rsch-Supan, A., Brandt, M., Hunkler, C., Kneip, T., Korbmacher, J., Malter, F., Schaan, B., Stuck, S., \u0026amp; Zuber, S. (2013). Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e(4), 992\u0026ndash;1001. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyt088\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyt088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurn, R., Hubbard, R. E., Scrase, R. J., Abey-Nesbit, R. K., Peel, N. M., Schluter, P. J., \u0026amp; Jamieson, H. A. (2018). A frailty index derived from a standardized comprehensive geriatric assessment predicts mortality and aged residential care admission. \u003cem\u003eBMC Geriatrics\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 319. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-018-1016-8\u003c/span\u003e\u003cspan address=\"10.1186/s12877-018-1016-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCesari, M., Araujo de Carvalho, I., Amuthavalli Thiyagarajan, J., Cooper, C., Martin, F. C., Reginster, J. Y., Vellas, B., \u0026amp; Beard, J. R. (2018). Evidence for the Domains Supporting the Construct of Intrinsic Capacity. \u003cem\u003eThe Journals of Gerontology: Series A\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(12), 1653\u0026ndash;1660. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gerona/gly011\u003c/span\u003e\u003cspan address=\"10.1093/gerona/gly011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConcha-Cisternas, Y., Vargas-Vitoria, R., \u0026amp; Celis-Morales, C. (2021). Morphophysiological changes and fall risk in the older adult: a review of the literature. \u003cem\u003eSalud Uninorte\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(2), 450\u0026ndash;470. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14482/sun.36.2.618.97\u003c/span\u003e\u003cspan address=\"10.14482/sun.36.2.618.97\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCourtin, E., \u0026amp; Knapp, M. (2017). Social isolation, loneliness and health in old age: a scoping review. \u003cem\u003eHealth \u0026amp; Social Care in the Community\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(3), 799\u0026ndash;812. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/hsc.12311\u003c/span\u003e\u003cspan address=\"10.1111/hsc.12311\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Cruz-Peralta, M. J., \u0026amp; Gonz\u0026aacute;lez-Celis, A. L. (2023). Interventions to improve quality of life in older adults: systematic review with IOP questions. \u003cem\u003ePsychology and Health\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(2), 415\u0026ndash;426. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.25009/pys.v33i2.2824\u003c/span\u003e\u003cspan address=\"10.25009/pys.v33i2.2824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChhetri, J. K., Han, R. H., Ma, L., Jiang, Y., Peng, D., Ma, J. P., \u0026amp; Cesari, M. (2022). Intrinsic Capacity as a Determinant of Physical Resilience: Implications for Healthy Aging. \u003cem\u003eThe Journal of Nutrition Health \u0026amp; Aging\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 136\u0026ndash;144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12603-021-1629-z\u003c/span\u003e\u003cspan address=\"10.1007/s12603-021-1629-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD\u0026iacute;az-Alonso, J., Bueno-P\u0026eacute;rez, A., Tora\u0026ntilde;o-Ladero, L., Caballero, F. F., L\u0026oacute;pez-Garc\u0026iacute;a, E., Rodr\u0026iacute;guez-Artalejo, F., \u0026amp; Lana, A. (2021). Hearing limitation and social frailty in older men and women. \u003cem\u003eGaceta Sanitaria\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(5), 425\u0026ndash;431. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gaceta.2020.08.007\u003c/span\u003e\u003cspan address=\"10.1016/j.gaceta.2020.08.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDosil-D\u0026iacute;az, C., Pinazo-Hernandis, S., Pereiro, A. X., \u0026amp; Facal, D. (2024). The impact of the COVID-19 pandemic on nursing home professionals: results of the RESICOVID project. \u003cem\u003ePsicologia: Reflex\u0026atilde;o e Cr\u0026iacute;tica\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s41155-023-00284-w\u003c/span\u003e\u003cspan address=\"10.1186/s41155-023-00284-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEuropean Commission (2023). \u003cem\u003eEurostat Regional Yearbook 2023\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ec.europa.eu/eurostat/\u003c/span\u003e\u003cspan address=\"https://ec.europa.eu/eurostat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalloway, S. (2006). Cultural Participation and Individual Quality of Life: A Review of Research Findings. Applied Research in Quality of Life. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11482-006-9002-3\u003c/span\u003e\u003cspan address=\"10.1007/s11482-006-9002-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeigl, C., Loss, J., Leitzmann, M., \u0026amp; Janssen, C. (2023). Social factors of health-related quality of life in older adults: a multivariable analysis. \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(11), 3257\u0026ndash;3268. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11136-023-03472-4\u003c/span\u003e\u003cspan address=\"10.1007/s11136-023-03472-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuti\u0026eacute;rrez-Robledo, L. M., Garc\u0026iacute;a-Chanes, R. E., \u0026amp; P\u0026eacute;rez-Zepeda, M. U. (2021). Screening intrinsic capacity and its epidemiological characterization: a secondary analysis of the Mexican Health and Aging Study. \u003cem\u003eRevista Panamericana de Salud P\u0026uacute;blica\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e, 1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.26633/RPSP.2021.121\u003c/span\u003e\u003cspan address=\"10.26633/RPSP.2021.121\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHyde, M., Wiggins, R. D., Higgs, P., \u0026amp; Blane, D. B. (2003). A measure of quality of life in early old age: The theory, development and properties of a need\u0026rsquo;s satisfaction model (CASP-19). \u003cem\u003eAging \u0026amp; Mental Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 186\u0026ndash;194. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/1360786031000101157\u003c/span\u003e\u003cspan address=\"10.1080/1360786031000101157\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJagadish, K., \u0026amp; Chhetri, R. H. H. L. M. J. P. M. P. C. (2022). Capacidad intr\u0026iacute;nseca y envejecimiento saludable. \u003cem\u003eAge and Ageing\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(11). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1093/ageing/afac239\u003c/span\u003e\u003cspan address=\"10.1093/ageing/afac239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJalali, A., Ziapour, A., Karimi, Z., Rezaei, M., Emami, B., Kalhori, R. P., Khosravi, F., Sameni, J. S., \u0026amp; Kazeminia, M. (2024). Global prevalence of depression, anxiety, and stress in the elderly population: a systematic review and meta-analysis. \u003cem\u003eBMC Geriatrics\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(1), 809. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-024-05311-8\u003c/span\u003e\u003cspan address=\"10.1186/s12877-024-05311-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur, A., Fouad, M. H., Pozzebon, C., Behlouli, H., Rajah, M. N., \u0026amp; Pilote, L. (2024). Sex Differences in the Association Between Vascular Risk Factors and Cognitive Decline. \u003cem\u003eJACC: Advances\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(7), 100930. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jacadv.2024.100930\u003c/span\u003e\u003cspan address=\"10.1016/j.jacadv.2024.100930\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, S. H., Lee, H., \u0026amp; Yu, S. (2022). Effectiveness of Social Support for Community-Dwelling Elderly with Depression: A Systematic Review and Meta-Analysis. \u003cem\u003eHealthcare\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(9), 1598. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/healthcare10091598\u003c/span\u003e\u003cspan address=\"10.3390/healthcare10091598\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeit\u0026oacute;n Espinoza, Z. E., Fajardo-Ramos, E., L\u0026oacute;pez-Gonz\u0026aacute;lez, \u0026Aacute;., Mart\u0026iacute;nez-Villanueva, R. M., \u0026amp; Villanueva-Benites, M. E. (2021). Cognition and Functional Capacity in the Elderly Adult. \u003cem\u003eSalud Uninorte\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(1), 124\u0026ndash;139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14482/sun.36.1.618.97\u003c/span\u003e\u003cspan address=\"10.14482/sun.36.1.618.97\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLlorens-Ortega, R., Bertran-Noguer, C., Juviny\u0026agrave;-Canals, D., Garre-Olmo, J., \u0026amp; Bosch-Farr\u0026eacute;, C. (2024). Influence of social determinants of health in the evolution of the quality of life of older adults in Europe: A comparative analysis between men and women. \u003cem\u003eHumanities and Social Sciences Communications\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1057/s41599-024-02899-5\u003c/span\u003e\u003cspan address=\"10.1057/s41599-024-02899-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-Ortiz, S., Lista, S., Pe\u0026ntilde;\u0026iacute;n-Grandes, S., Pinto-Fraga, J., Valenzuela, P. L., Nistic\u0026ograve;, R., Emanuele, E., Lucia, A., \u0026amp; Santos-Lozano, A. (2022). Defining and assessing intrinsic capacity in older people: A systematic review and a proposed scoring system. \u003cem\u003eAgeing Research Reviews\u003c/em\u003e, \u003cem\u003e79\u003c/em\u003e, 101640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arr.2022.101640\u003c/span\u003e\u003cspan address=\"10.1016/j.arr.2022.101640\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, S., Chui, C., \u0026amp; Lum, T. (2025). A Chain Mediation Model Unveiling the Effectiveness of Timebanking on Quality of Life in Later Life. Applied Research in Quality of Life. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11482-025-10503-4\u003c/span\u003e\u003cspan address=\"10.1007/s11482-025-10503-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalter, F. (2015). and A. B.-S. \u003cem\u003eSHARE Wave 5: Innovations \u0026amp; Methodology.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://share-eric.eu/fileadmin/user_upload/Methodology_Volumes/Method_vol5_31March2015.pdf\u003c/span\u003e\u003cspan address=\"https://share-eric.eu/fileadmin/user_upload/Methodology_Volumes/Method_vol5_31March2015.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaskileyson, D., Seddig, D., \u0026amp; Davidov, E. (2021). The EURO-D Measure of Depressive Symptoms in the Aging Population: Comparability Across European Countries and Israel. \u003cem\u003eFrontiers in Political Science\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpos.2021.665004\u003c/span\u003e\u003cspan address=\"10.3389/fpos.2021.665004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOfori-Asenso, R., Chin, K. L., Mazidi, M., Zomer, E., Ilomaki, J., Zullo, A. R., Gasevic, D., Ademi, Z., Korhonen, M. J., LoGiudice, D., Bell, J. S., \u0026amp; Liew, D. (2019). Global Incidence of Frailty and Prefrailty Among Community-Dwelling Older Adults. \u003cem\u003eJAMA Network Open\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(8), e198398. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jamanetworkopen.2019.8398\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2019.8398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOshio, T., Sugiyama, K., \u0026amp; Ashida, T. (2024). Can Social Participation Reduce and Postpone the Need for Long-Term Care? Evidence from a 17-Wave Nationwide Survey in Japan. \u003cem\u003eApplied Research in Quality of Life\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11482-024-10288-y\u003c/span\u003e\u003cspan address=\"10.1007/s11482-024-10288-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePavez Lizarraga, A., Vanegas L\u0026oacute;pez, J., \u0026amp; Flores Alvarado, S. (2023). Analysis of age, sex, and memory self-perception in cognitive impairment in older adults. \u003cem\u003eMedical Journal of Chile\u003c/em\u003e, \u003cem\u003e151\u003c/em\u003e(10), 1288\u0026ndash;1294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4067/s0034-98872023001001288\u003c/span\u003e\u003cspan address=\"10.4067/s0034-98872023001001288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Rojo, G., Mart\u0026iacute;n, N., Noriega, C., \u0026amp; L\u0026oacute;pez, J. (2018). Psychometric properties of the CASP-12 in a Spanish older community dwelling sample. \u003cem\u003eAging \u0026amp; Mental Health\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(5), 700\u0026ndash;708. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13607863.2017.1292208\u003c/span\u003e\u003cspan address=\"10.1080/13607863.2017.1292208\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePortellano-Ortiz, C., Garre-Olmo, J., Calv\u0026oacute;-Perxas, L., \u0026amp; Conde-Sala, J. L. (2018). Depression and associated variables in people over 50 years in Spain. \u003cem\u003eRevista de Psiquiatria y Salud Mental\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(4), 216\u0026ndash;226. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rpsm.2016.10.003\u003c/span\u003e\u003cspan address=\"10.1016/j.rpsm.2016.10.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuiroz Mora, C. A., Serrato, D. M., \u0026amp; Bergonzoli Pelaez, G. (2018). Factors associated with adherence to physical activity in patients with chronic non-communicable diseases. \u003cem\u003eJournal of Public Health\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(4), 460\u0026ndash;464. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15446/rsap.v20n4.62959\u003c/span\u003e\u003cspan address=\"10.15446/rsap.v20n4.62959\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReitlo, L. S., Sandbakk, S. B., Viken, H., Aspvik, N. P., Ingebrigtsen, J. E., Tan, X., Wisl\u0026oslash;ff, U., \u0026amp; Stensvold, D. (2018). Exercise patterns in older adults instructed to follow moderate- or high-intensity exercise protocol \u0026ndash; the generation 100 study. \u003cem\u003eBMC Geriatrics\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1), 208. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-018-0900-6\u003c/span\u003e\u003cspan address=\"10.1186/s12877-018-0900-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez-Laso, \u0026Aacute;., Garc\u0026iacute;a-Garc\u0026iacute;a, F. J., \u0026amp; Rodr\u0026iacute;guez-Ma\u0026ntilde;as, L. (2023). The ICOPE Intrinsic Capacity Screening Tool: Measurement Structure and Predictive Validity of Dependence and Hospitalization. \u003cem\u003eThe Journal of Nutrition Health and Aging\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(10), 808\u0026ndash;816. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12603-023-1985-y\u003c/span\u003e\u003cspan address=\"10.1007/s12603-023-1985-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojano i Luque, X., Blancafort-Alias, S., Prat Casanovas, S., Forn\u0026eacute;, S., Mart\u0026iacute;n Vergara, N., Fabregat Povill, P., Vila Royo, M., Serrano, R., Sanchez-Rodriguez, D., V\u0026iacute;lchez Salda\u0026ntilde;a, M., Mart\u0026iacute;nez, I., Dom\u0026iacute;nguez L\u0026oacute;pez, M., Riba Porquet, F., \u0026amp; Intxaurrondo Gonz\u0026aacute;lez, A. (2023). \u0026amp; Salv\u0026agrave; Casanovas, A. Identification of decreased intrinsic capacity: Performance of diagnostic measures of the ICOPE Screening tool in community dwelling older people in the VIMCI study. \u003cem\u003eBMC Geriatrics\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 106. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12877-023-03799-0\u003c/span\u003e\u003cspan address=\"10.1186/s12877-023-03799-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalazar Estrada, J. G., Guti\u0026eacute;rrez Strauss, A. M., Aranda Beltr\u0026aacute;n, C., \u0026amp; Ram\u0026iacute;rez Ram\u0026iacute;rez, S. (2019). Psychometric properties of the Satisfaction with Life Scale, in workers of the manufacturing industry. \u003cem\u003ePsicolog\u0026iacute;a Desde El Caribe\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(3), 197\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14482/psdc.35.3.150.15\u003c/span\u003e\u003cspan address=\"10.14482/psdc.35.3.150.15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalinas-Rodr\u0026iacute;guez, A., Fern\u0026aacute;ndez-Ni\u0026ntilde;o, J. A., Rivera-Almaraz, A., \u0026amp; Manrique-Espinoza, B. (2024). Intrinsic capacity trajectories and socioeconomic inequalities in health: the contributions of wealth, education, gender, and ethnicity. \u003cem\u003eInternational Journal for Equity in Health\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12939-024-02136-0\u003c/span\u003e\u003cspan address=\"10.1186/s12939-024-02136-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalinas-Rodr\u0026iacute;guez, A., Gonz\u0026aacute;lez-Bautista, E., Rivera-Almaraz, A., \u0026amp; Manrique-Espinoza, B. (2022). Longitudinal trajectories of intrinsic capacity and their association with quality of life and disability. \u003cem\u003eMaturitas\u003c/em\u003e, \u003cem\u003e161\u003c/em\u003e, 49\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.maturitas.2022.02.005\u003c/span\u003e\u003cspan address=\"10.1016/j.maturitas.2022.02.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSan Jos\u0026eacute; Laporte, A. (2012). The assessment of multimorbidity in the elderly. An important area of comprehensive geriatric assessment. \u003cem\u003eSpanish Journal of Geriatrics and Gerontology\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(2), 47\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.regg.2011.12.001\u003c/span\u003e\u003cspan address=\"10.1016/j.regg.2011.12.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSpiers, G. F., Liddle, J. E., Stow, D., Searle, B., Whitehead, I. O., Kingston, A., Moffatt, S., Matthews, F. E., \u0026amp; Hanratty, B. (2022). Measuring older people\u0026rsquo;s socioeconomic position: a scoping review of studies of self-rated health, health service and social care use. \u003cem\u003eJournal of Epidemiology and Community Health\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(6), 572\u0026ndash;579. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/jech-2021-218265\u003c/span\u003e\u003cspan address=\"10.1136/jech-2021-218265\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteptoe, A., \u0026amp; Zaninotto, P. (2020). Lower socioeconomic status and the acceleration of aging: An outcome-wide analysis. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e(26), 14911\u0026ndash;14917. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1915741117\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1915741117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSugimoto, T., Arai, H., \u0026amp; Sakurai, T. (2022). An update on cognitive frailty: Its definition, impact, associated factors and underlying mechanisms, and interventions. \u003cem\u003eGeriatrics \u0026amp; Gerontology International\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(2), 99\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/ggi.14322\u003c/span\u003e\u003cspan address=\"10.1111/ggi.14322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakeda, C., Barreto, P. D. S., \u0026amp; Vellas, B. (2024). Intrinsic Capacity. In \u003cem\u003eFrailty\u003c/em\u003e (pp. 23\u0026ndash;29). Springer International Publishing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-57361-3_5\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-57361-3_5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations (2021). \u003cem\u003eUnited Nations. Population Fund. (2021). Aging in the 21st Century: A Celebration and a Challenge\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unfpa.org/sites/default/files/pub-pdf/Ageing%20Report%20Executive%20Summary%20SPANISH%20Final_0.pdf\u003c/span\u003e\u003cspan address=\"https://www.unfpa.org/sites/default/files/pub-pdf/Ageing%20Report%20Executive%20Summary%20SPANISH%20Final_0.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Educational Scientific and Cultural Organization (2011). \u003cem\u003eInternational Standard Classification of Education: ISCED 2011\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf\u003c/span\u003e\u003cspan address=\"http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVila, J. (2024). \u003cem\u003eMatching function, GitHub repository\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/JoanVilaDomenech/Matching/\u003c/span\u003e\u003cspan address=\"https://github.com/JoanVilaDomenech/Matching/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWiggins, R. D., Netuveli, G., Hyde, M., Higgs, P., \u0026amp; Blane, D. (2008). The Evaluation of a Self-enumerated Scale of Quality of Life (CASP-19) in the Context of Research on Ageing: A Combination of Exploratory and Confirmatory Approaches. (The evaluation of a self-enumerated scale of quality of life (CASP-19 \u0026hellip;. \u003cem\u003eSocial Indicators Research\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e(1), 61\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11205-007-9220-5\u003c/span\u003e\u003cspan address=\"10.1007/s11205-007-9220-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2015). \u003cem\u003eWorld Health Organization. (2015). World Report on Ageing and Health.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/186463\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/186463\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2024). \u003cem\u003eWorld population prospects. 2024: Summary of results. United Nations Department of Economic and Social Affairs; 2024.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://population.un.org/wpp/\u003c/span\u003e\u003cspan address=\"https://population.un.org/wpp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2019). \u003cem\u003eIntegrated care for older people (ICOPE): guidance for person-centred assessment and pathways in primary care\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/326843\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/326843\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization (2020). \u003cem\u003eDecade of healthy ageing: the global strategy and action plan on ageing and health 2016\u0026ndash;2020: towards a world in which everyone can live a long and healthy life: report by the Director-General.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/355618\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/355618\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Y., \u0026amp; Ma, L. (2022). Intrinsic Capacity in Older Adults: Recent Advances. \u003cem\u003eAging and Disease\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.14336/AD.2021.0818\u003c/span\u003e\u003cspan address=\"10.14336/AD.2021.0818\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9211395/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9211395/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Understanding determinants of quality of life (QoL) in older adults is crucial in aging societies. Intrinsic capacity (IC), combining physical and mental capacities, may influence QoL changes, but evidence on specific IC domains and QoL is limited. This study examines associations between IC domains and two-year QoL changes in older Europeans, focusing on sex and regional differences. This study integrates factor and network analytical approaches to examine IC as a multidimensional system.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Data from 11,493 adults aged ≥50 from 13 European countries in SHARE Waves 5 and 6 (2013–2015) were analyzed. IC was operationalized across five domains: mobility, cognition, psychological well-being, sensory function, and vitality. Exploratory factor analysis validated IC’s multidimensional structure. Network analysis assessed domain interrelations and links to QoL (CASP-12). Sex and regional differences were explored via stratified analyses and ANOVA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: IC domains formed a coherent multidimensional construct. Psychological well-being and mobility showed the strongest associations with QoL. Depressive symptoms and fatigue correlated negatively with CASP-12 (r = −0.284 and −0.324, p \u0026lt; 0.001). Cognitive and mobility domains had weaker but significant links. Over two years, modest IC declines paralleled QoL changes. Women and individuals in Southern and Eastern Europe exhibited greater IC deficits and lower QoL.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Intrinsic capacity significantly influences short-term QoL changes in older Europeans. Psychological and mobility domains are key targets for interventions. Addressing sex and regional disparities in IC may improve well-being and reduce inequalities in aging populations.\u003c/p\u003e","manuscriptTitle":"Intrinsic capacity as a determinant of quality of life trajectories in older Europeans: A sex- and region-sensitive longitudinal analysis using SHARE","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 06:17:31","doi":"10.21203/rs.3.rs-9211395/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63ea13ac-5f21-48d7-8c3f-1900e263ca82","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T17:25:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 06:17:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9211395","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9211395","identity":"rs-9211395","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}