Relationship between intrinsic capacity domains and the evolution of quality of life in older adults in Europe: a differential approach between men and women based on the SHARE study.

Relationship between intrinsic capacity domains and the evolution of quality of life in older adults in Europe: a differential approach between men and women based on the SHARE study. | 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 Relationship between intrinsic capacity domains and the evolution of quality of life in older adults in Europe: a differential approach between men and women based on the SHARE study. rafael llorens-ortega This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252801/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 Introduction This study explores the relationship between the domains of intrinsic capacity and quality of life in older adults in Europe, with particular focus on sex and regional differences. It confirms that intrinsic capacity is a multidimensional construct involving interconnected components such as mobility, cognitive function, mental health, and general health. Methods An exploratory factor analysis was conducted using data from the SHARE study (Waves 5-6), a longitudinal multinational project. The analysis focused on 11,493 older adults aged 50 and above, residing in 13 European countries. Sociodemographic, health, and socio-economic factors were considered, including variables like mobility difficulties, cognitive performance, depressive symptoms, and self-reported health. The study used harmonized surveys and representative probabilistic sampling to ensure comparability across countries. Results The results show significant differences between men and women, with women experiencing greater deterioration in key domains such as cognition, mobility, and mental health. Women exhibited higher levels of cognitive decline, which is linked to longer life expectancy and greater exposure to chronic diseases. Social determinants, such as education level and economic status, were found to have a significant impact on QoL and intrinsic capacity, with women in socially vulnerable situations showing higher rates of mental health deterioration, chronic diseases, and economic decline. Regional differences also played a role, with notable variations in health outcomes across european regions. Conclusion Mental health, mobility, and cognition are key determinants of intrinsic capacity and quality of life in older adults. This study highlights the importance of multidimensional approaches and interventions tailored to sex and regional differences to promote healthy aging. Sociology Intrinsic Capacity Quality of Life Healthy Aging Mental Health Cognition Mobility Social Determinants Gender Older Adults. Figures Figure 1 Figure 2 Contributions to the Literature Until now, intrinsic capacity has been mainly addressed in a unidimensional way, focusing on isolated physical or mental aspects. This study contributes a more comprehensive perspective by confirming that intrinsic capacity is a multidimensional construct involving mobility, cognitive function, mental and general health, and sensory function. Moreover, although previous research has highlighted gender disparities in aging, this study offers new findings on how women experience greater deterioration in key areas such as cognition and mental health. It also emphasizes the influence of social determinants, particularly for women, showing how economic and educational inequalities affect the quality of life of older adults. In summary, this study expands the understanding of intrinsic capacity, highlighting the need for multidimensional approaches and gender- and context-specific intervention strategies, representing a valuable contribution to future research and public health policies. Introduction The aging of the global population is a worldwide reality affecting both developed and developing countries (United Nations, 2019 ). Demographic projections indicate that the global population aged 65 and older will increase from 9.7% in 2022 to 16.4% by 2050, with Europe being one of the most affected regions (WHO, 2024). This accelerated aging presents significant social and public health challenges, increasing the need for strategies to promote healthy aging and improve the quality of life (QoL) of older adults (WHO, 2021). In response to this challenge, the World Health Organization (WHO) developed the healthy aging model, which emphasizes the importance of functional capacity, defined as the balance between physical, mental, and social conditions (WHO, 2015) (Wilk et al., 2022 ). This model highlights that aging is not only influenced by biological factors but also by psychosocial and environmental determinants (WHO, 2020). Within this framework, intrinsic capacity (IC) has emerged as a key concept, defined as the set of physical, mental, and social capacities that enable a person to maintain their independence and functionality over time (Hu et al., 2023 ) (Beard et al., 2016 ) (Takeda et al., 2024 ). IC includes five domains: locomotor capacity (mobility and muscle strength), cognitive capacity (memory and orientation), sensory capacity (vision and hearing), psychological capacity (emotional well-being), and vitality (energy and ability to perform daily activities) (Angelsen et al., 2024 ) (WHO, 2019). Recent studies have shown that the assessment of IC provides a more comprehensive perspective on healthy aging and its impact on quality of life (Chhetri, 2022). However, challenges remain in the validation of tools to measure these domains on a large scale (Rojano & Luque et al., 2023) (Salinas-Rodríguez et al., 2024 ). Additionally, sex differences in aging remain a central area of research, as various studies have identified inequalities in health, educational level, and quality of life between men and women (Ahrenfeldt & Möller, 2021 ). In general, women are more vulnerable to experiencing poorer health and lower quality of life compared to men (Llorens-Ortega et al., 2024 ). However, the role of IC in the evolution of quality of life, with a focus on gender, remains a less explored area. In this regard, the Survey of Health, Ageing, and Retirement in Europe (SHARE) study provides a valuable source of longitudinal data that allows for examining how social, economic, and health conditions influence the evolution of IC and QoL in older adults across different European countries (Börsch-Supan et al., 2013 ). This study facilitates the identification of regional and gender patterns, enabling the analysis of the relationship between IC and QoL over time and across different geographic and demographic contexts. Hypothesis The evolution of quality of life in older adults in Europe is influenced by their intrinsic capacity. It is expected that significant differences will be found between men and women, as well as across different regions of Europe, with a less favorable evolution in women and in the southern and eastern areas of the continent. Objective To analyze the relationship between the domains of intrinsic capacity and the evolution of quality of life in older adults in Europe, considering the differences between men and women and the geographical region of residence in a longitudinal study. Materials and Methods Design This is a prospective, analytical cohort study based on population data, framed within the SHARE project. Data collected in waves 5 (2013) and 6 (2015), were used (Börsch-Supan et al., 2013) (Malter, 2015). SHARE is a multinational longitudinal study that examines health, socio-economic, and demographic aspects in non-institutionalized older adults aged 50 and above. Since its inception in 2004, SHARE has expanded its reach to 17 European countries, including Germany, Austria, Belgium, Denmark, France, Greece, the Netherlands, Hungary, Italy, Slovenia, Spain, Estonia, Sweden, Switzerland, Poland, the Czech Republic, and Ireland. Participants are interviewed biannually, allowing for international comparisons and examining the evolution of factors related to aging. To minimize biases and ensure comparability between countries, SHARE implements standardized data collection procedures, with harmonized surveys and representative probabilistic sampling (Börsch-Supan et al., 2013). The study has a multidisciplinary and longitudinal approach, addressing the social, economic, and public health dimensions of aging in Europe. Study Population The population analyzed in this study consists of adults aged 50 years or older residing in 13 European countries included in SHARE wave 5. The selected countries are: Germany, Austria, Belgium, Denmark, Slovenia, Spain, Estonia, France, Italy, Luxembourg, Sweden, Switzerland, and the Czech Republic. The inclusion criteria were: being 50 years or older at the start of the study, having participated in the consecutive selected waves (5 and 6), voluntarily consenting to participate in the study, and not being institutionalized at the time of the interview. The exclusion criteria considered the absence of complete data on the key analysis variables and lack of follow-up in the selected waves. A total of 11,493 respondents met these inclusion criteria. For the analysis of regional differences in the evolution of quality of life and intrinsic capacity, countries were grouped into four regional clusters, based on the Eurostat report of 2023 (European Commission, 2023) on welfare models in Europe: 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). Data Collection Procedures Information was obtained through structured interviews in the participants' homes, assisted by computer (CAPI), with an average duration of 90 minutes. The standardized questionnaires collected data on the domains of intrinsic capacity and other factors relevant to quality of life. In addition to the standard questionnaires, the interviews also addressed socio-economic and demographic issues, allowing for the collection of information on sex, age, employment status, education level, and health conditions of the participants. To improve the representativeness of the results, design weights calibrated to the collected data were applied (Malter, 2015). The data is publicly available to the scientific community, facilitating access and replication of the study. www.share-project.org Study Variables Outcome Variable The main variable of this study was quality of life, assessed using the CASP-12 scale (Wiggins et al., 2008). The multidimensional model for CASP-12 has potential to be used as a tool to assess quality of life in older adults. This scale, validated in older populations, evaluates subjective well-being through four dimensions: Control, Autonomy, Satisfaction, and Self-realization. Each dimension includes three items, with responses on a Likert scale ranging from 1 (never) to 4 (often). The total score ranges from 12 to 48, with higher values indicating better quality of life. The following categories were established: 12-34: Low quality of life; 35-37: Moderate; 38-39: High; 40-48: Very high. According to previous studies, CASP-12 shows high internal consistency (Cronbach's α = 0.84), supporting its reliability in this study (Hyde et al., 2003) (Pérez-Rojo et al., 2018). Explanatory Variables This study considered the five domains of intrinsic capacity according to the WHO's ICOPE guidelines (Rojano and Luque et al., 2023) (World Health Organization, 2019) (Angelsen et al., 2024), in addition to the sex variable. Various validated scales or those used in previous studies were employed to assess the domains of intrinsic capacity (Lin-Chieh Meng, 2023). Locomotor capacity was assessed through mobility difficulties, Basic Activities of Daily Living (BADL) (Bonilla-Barrera et al., 2025), and Instrumental Activities of Daily Living (IADL) (Pérez-Castejón & Formiga, 2024). Cognitive capacity was measured using immediate and delayed word recall and space-time orientation (Evans et al., 2019). Sensory capacity was evaluated by perceived difficulty in vision and hearing (Pittman et al., 2022). Psychological capacity: The assessment of depressive symptoms in this study was conducted using the EURO-D scale, validated in European research on depression in older adults (Maskileyson et al., 2021) (Portellano-Ortiz et al., 2018). This scale includes 12 items, with a maximum score of 12 (high symptom presence) and a minimum of 0 (absence of symptoms), establishing a cutoff point of 4 to identify depression. For this study, four items were selected as theoretically relevant and with good psychometric stability: depression, fatigue, irritability, and interests. Other items were excluded as they grouped into somatic or cognitive dimensions that were less pertinent to the instrumental health model. The selected dimensions were: Affective: Depression (core symptom) and irritability (associated with comorbid anxiety). Somatic and motivational: Fatigue (key in geriatric depression, linked to daily functionality) and Interests (assesses anhedonia). Other symptoms such as sleep problems, appetite, pessimism, guilt, or concentration difficulties were excluded due to their lower specificity in discriminating mental health in older adults. Vitality or general health was assessed through self-reported health using a four-category scale: excellent/very good, good, fair, and poor. Additionally, the number of chronic diseases was recorded, categorized as none, 1 to 2, and 3 or more. The internal consistency of the intrinsic capacity measurements showed a Cronbach’s α of 0.833, indicating high reliability. Covariates To analyze quality of life in older adults, sociodemographic, economic, and lifestyle factors influencing intrinsic capacity were included. Sociodemographic and Economic Variables: Age (interest groups): 1. 50-64 years (economically active); 2. 65-74 years (recent retirees); 3. 75-84 years (older adults); 4. ≥85 years (advanced age). Educational level: According to the International Standard Classification of Education (ISCED) (United Nations Educational Scientific and Cultural Organization, 2011). 1. Low (ISCED 0-2, basic education or lower); 2. Medium (ISCED 3-4, upper secondary); 3. High (ISCED 5-6, tertiary). Marital status: 1. Living with a partner; 2. Living alone (Urbano Contreras et al., 2021). Economic difficulties to make ends meet: SHARE determines the economic level using the variable “getting by financially,” with options:1. Very easily; 2. Quite easily; 3. With difficulty; 4. With great difficulty. For analysis in this study, it was dichotomized as (1. no difficulty: 2. with difficulty) (Dávila Quintana & González López-Valcárcel, 2009) (Lesende, 2014). Healthy Lifestyle Variables: Physical activity: It was assessed according to the scale used in the SHARE study, which categorizes vigorous activity into four levels: 1. More than once a week: 2. Once a week; 3. Once to three times a month; 4. Almost never or never. For this study, categories 1 and 2 were grouped as 1. active, while 3 and 4 were categorized as 2. Inactive (Reitlo et al., 2018) (Quiroz Mora et al., 2018). Alcohol consumption: 1. Does not drink; 2. Drinks 1 to 4 days a week; 3. Drinks every day (Valencia Martín JL, 2020). Tobacco consumption: 1. Smokes; 2. Does not smoke (Jimenez-Ruiz et al., 2018). Statistical Analysis Descriptive and frequency analyses were performed to characterize the sample. Subsequently, all variables of interest were recoded into dichotomous variables, where a value of 1 indicated a positive outcome in the assessment (positive screen result). The recoding was performed as follows: Chronic diseases: 0. none or one disease; 1. two or more diseases. Vision: 0. no difficulty; 1. with difficulty; Hearing: 0. no difficulty; 1. with difficulty. Self-perceived health: Values from 1 to 3 were recategorized as 0 (good health), while values from 4 to 5 were recoded as 1 (poor health). Memory and orientation(Recall 1, Recall 2, and orientation): The lower 20th percentile represented poor memory and orientation (1), while higher values indicated good ability (0). Mobility: 0. no difficulty; 1. with difficulty (values 1, 2, 3, and 4). Basic Activities of Daily Living (BADL) and Instrumental Activities of Daily Living (IADL): 0. no difficulty; 1. with difficulty (values 1, 2, and 3). Total score was calculated by summarizing the values of each variable. This recoding allowed a better operationalization of the variables for their subsequent statistical analysis. For the exploratory factor analysis, wave 5 was used, while wave 6 was employed to analyze the predictive validity of the intrinsic capacity instrument. To identify the underlying structure of the constructs assessed, an Exploratory Factor Analysis (EFA) was conducted using the Weighted Least Squares (WLS) method as the factorization technique, given that the data include dichotomous and ordinal variables. A polychoric-tetrachoric correlation matrix was used to estimate the relationships between categorical variables. The optimal number of factors to retain was determined using a Parallel Analysis based on Factor Analysis (FA), complemented by inspection of the scree plot. To facilitate interpretation of the factors, an oblique rotation (Promax) was applied, as some correlation between the underlying factors was expected. Factors with loadings greater than 0.40 were considered significant, and the adequacy of the model was evaluated by reviewing the uniqueness of the variables, eliminating those with values above 0.70. In this study, Network Analysis (NA) was employed using the Huge Estimator approach to analyze the structure of intrinsic capacity, exploring how the key indicators of mobility, health, mental health, and cognitive function interrelate. On the other hand, Ebiglaso was used to delve deeper into exploring causal relationships between intrinsic capacity factors and their impact on quality of life (CASP). This advanced tool allowed for modeling complex and non-linear relationships between variables, facilitating the identification of the critical factors that have the most influence on quality of life. All analyses were performed using the SHARE weighted data and carried out with SPSS-25 (Statistical Package for the Social Sciences, IBM Corp. Armonk, NY, USA), R version 4.3.0, a statistical computing language and environment from the Foundation for Statistical Computing, Vienna, Austria, and JASP-Stats software (version 0.19.3). A 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 the materials related to the SHARE project, including Wave 5 and its subsequent waves (Waves 6 and 7). The committee has certified that the research project, along with its procedures, meets the highest international ethical standards. Moreover, strict measures have been adopted to ensure the confidentiality and privacy of the data and information provided by the participants, in accordance with the "Helsinki Declaration" and the International Ethical Guidelines for Biomedical Research Involving Human Subjects. Furthermore, written informed consent was obtained from all participants involved in the study, who voluntarily agreed to participate during the interviews. Results Demographic, healthy lifestyle, and quality of life by sex (Wave 5) The results show significant differences between men and women across various characteristics. On average, men were slightly older than women (64.4 vs. 63.3 years). There were more women in the 50–64 age group (54.4% vs. 50.0% in men), reflecting a higher female representation among younger adults. Regarding educational level, 37% of women had a low educational level, compared to 32.9% of men. Men were more likely to be married or in a registered partnership (80.7% vs. 71.2%), and women had a higher proportion of widows (13.4% vs. 4.4%). Economically, 31.5% of women reported financial difficulties, compared to 26.7% of men. Regarding lifestyle habits, 57.6% of men engaged in regular physical activity, compared to 50.2% of women. Furthermore, alcohol consumption and smoking were more prevalent among men, with 28.9% drinking almost daily compared to 11.5% of women, and 56% of men smoking daily compared to 37.1% of women. Finally, quality of life measured with the CASP-12 scale was slightly higher in men (38.6 vs. 38.0) (See Table 1 for more details and significances). Table 1 Demographic differences, 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%) Education 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/Registered Partner 4443 (71.2%) 4241 (80.7%) 8684 (75.6%) Divorced/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%) Can make ends meet 4337 (69.5%) 3853 (73.3%) 8190 (71.3%) < 0.001 Received help from others (outside 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 Drinks alcohol daily: < 0.001 No/1–2 per month 3801 (61.0%) 1950 (37.1%) 5751 (50.0%) 1–4 days/week 1719 (27.6%) 1790 (34.0%) 3509 (30.5%) Almost daily 716 (11.5%) 1517 (28.9%) 2233 (19.4%) Smoke daily 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: ANOVA for continuous variables, Chi-square for categorical variables. Factor analysis of intrinsic capacity variables To explore the factorial structure of intrinsic capacity (IC), the results of the Kaiser-Meyer-Olkin (KMO) sample adequacy test indicated an acceptable overall model adequacy (MSA = 0.694), with individual values ranging from 0.570 to 0.907. Additionally, the Bartlett's test of sphericity was significant ( χ² = 28,717.015, df = 32, p < .001), confirming the adequacy of the data for factor analysis. The factor analysis revealed a four-factor structure, explaining together 53.6% of the total variance. In the rotated solution, Factor 1 showed significant loadings on variables related to locomotor capacity (mobility, basic activities of daily living, and instrumental activities of daily living), while Factor 2 was primarily composed of variables associated with depressive symptoms (depression, irritability, fatigue, and interests). Factor 3 captured variables related to memory and orientation (immediate and delayed word recall and space-time orientation), and Factor 4 reflected aspects of general health and sensory function (vision, hearing, chronic diseases, and self-reported health) (See Table 2 for more details). Table 2 Factor analysis of intrinsic capacity variables Factor Loadings Factor 1 Factor 2 Factor 3 Factor 4 Uniqueness BDLA 0.824 0.325 IADL 0.799 0.355 Mobility 0.784 0.385 Depression (part of EURO-D) 0.794 0.437 Irritability (part of EURO-D) 0.648 0.639 Fatigue (part of EURO-D) 0.630 0.518 Interests (part of EURO-D) 0.582 0.544 Immediate word recall 0.729 0.448 Delayed word recall 0.746 0.489 Orientation 0.676 0.565 Vision 0.781 0.443 Chronic diseases 0.776 0.451 Self-perceived health 0.477 0.427 Note. The applied rotation method is Promax. BDLA: Basic Daily Living Activities IADL: Instrumental Activities of Daily Living To confirm the structure of intrinsic capacity, the results from the network analysis (NA) show that the network exhibits a sparsity of 0.359, indicating that 35.9% of possible connections are absent. This suggests a differentiated structure in measuring intrinsic capacity, rather than a single global dimension. The variable "Health" shows the highest values in centrality measures (betweenness: 1.954; closeness: 1.084; strength: 1.485; expected influence: 1.590), positioning it as a key node in the network. "Chronic diseases" also showed high expected influence (1.213), indicating its relevance in the structure of the instrument. Variables like "Interests," "Irritability," and "Space-time orientation" showed negative values in centrality, suggesting a lower impact on the global network (see Fig. 1 ). Correlations for Intrinsic Capacity Domains Pearson correlations were performed to evaluate the relationships between the quality of life and well-being index (CASP) and various health, mental state, cognition, and mobility variables. The CASP showed a significant negative correlation with the perception of good health ( r = -0.378, p < .001), the presence of chronic diseases ( r = -0.168, p < .001), and general health ( r = -0.317, p < .001), indicating that poorer physical health is associated with lower quality of life. Negative correlations were also observed between the CASP and depressive symptoms ( r = -0.284, p < .001), fatigue ( r = -0.324, p < .001), irritability ( r = -0.230, p < .001), and loss of interests ( r = -0.244, p < .001), suggesting that higher levels of emotional distress are related to lower quality of life. Regarding cognitive function, the CASP negatively correlated with performance on recall and orientation tasks, although with lower correlation values ( r between − 0.108 and − 0.162, p < .001), indicating a mild association between lower quality of life and worse cognitive performance. With respect to mobility, quality of life showed a weak negative correlation with the presence of motor difficulties ( r = -0.041, p < .001) and with activities of daily living (ADL) and instrumental activities of daily living (IADL) ( r between − 0.019 and − 0.035, p < .05), suggesting that mobility limitations have a lesser impact on quality of life compared to physical and mental health. The results show that poorer physical and mental health is strongly associated with lower quality of life, while cognitive function and mobility have weaker relationships with this indicator (for more details, see supplementary material Table S1). Network analysis of interactions between quality of life and intrinsic capacity domains. The network analysis was conducted with five nodes and ten non-null connections, with a dispersion of 0.000, indicating that all variables are related to each other (see Fig. 2 ). Regarding centrality measures, the quality of life and well-being index (CASP) presented the highest values in betweenness (1.789), closeness (0.856), and strength (0.846), indicating that it is a key node in the network. However, its expected influence was negative (-1.759), suggesting that its relationship with other variables could be determined by negative associations. On the other hand, total mobility showed the lowest values in closeness (-1.715) and strength (-1.613), suggesting lower connectivity within the network. In contrast, total health showed positive values in closeness (0.498), strength (0.650), and expected influence (0.684), indicating its relevance within the network structure. Regarding clustering measures, CASP and total health had the highest values in the Onnela coefficient (0.733 and 0.665, respectively), indicating a higher interconnection with other variables. Total mental health and cognitive status showed positive values in the Zhang index (0.663 and 0.934, respectively), suggesting higher cohesion in the network. In contrast, total mobility had the lowest value in Onnela (-1.712), reflecting lower integration within the network. These results suggest that CASP is a central node in the network, influenced by physical and mental health variables. Mobility, in contrast, seems to play a less significant role in the overall network structure (see Fig. 2 ). Intrinsic capacity analysis by sex The results suggest significant differences between men and women in the areas of total health, mental health, and cognition. In total health, men ( M = 0.740; SD = 0.96) scored significantly higher than women ( M = 0.697; SD = 0.93), t (11835) = 2.437, p = 0.015, Cohen's d = 0.045). In mental health, women ( M = 1.163; SD = 1.21) showed a significantly higher score than men ( M = 0.884; SD = 1.02), t (11449) = -13.854, p < 0.001, with a moderate effect (Cohen's d = -0.260). In cognition, men ( M = 0.609; SD = 0.85) scored significantly higher than women ( M = 0.518; SD = 0.82), t (11835) = 5.913, p < 0.001, Cohen's d = 0.109) (see Table 3 for more details). Table 3 Intrinsic capacity analysis by sex. Independent samples t-test Independent Samples T-Test t df p Cohen's d SE Cohen's d Total Health 2.437 11835 0.015 ᵃ 0.045 0.018 Total Mental Health -13.854 11449 < .001 ᵃ -0.260 0.019 Total Cognitive 5.913 11835 < .001 ᵃ 0.109 0.018 Total Mobility -1.383 11491 0.167 -0.026 0.019 Note. Student's t-test. ᵃ Brown-Forsythe test is significant (p < .05), suggesting a violation of the equal variance assumption Descriptives Group Descriptives Group N Mean SD SE Coefficient of variation Total Health 0 5399 0.740 0.964 0.013 1.303 1 6438 0.697 0.930 0.012 1.333 Total Mental Health 0 5213 0.884 1.012 0.014 1.144 1 6238 1.163 1.121 0.014 0.964 Total Cognitive 0 5399 0.609 0.852 0.012 1.398 1 6438 0.518 0.828 0.010 1.598 Total Mobility 0 5257 0.350 0.671 0.009 1.915 1 6236 0.368 0.678 0.009 1.843 Note : Student's t-test results comparing domains of intrinsic capacity between men (0) and women (1). Significant differences in mental health and cognition (p < .001). Brown-Forsythe test indicates violation of equality of variance in variables marked with ᵃ. Total intrinsic capacity by sex and region The ANOVA analysis showed that sex has a significant effect on the total IC score (F (1, 11485) = 9.575, p = 0.002, η² = 0.0008), indicating that women scored slightly higher than men. A significant effect of the region was also found (F (3, 11485) = 74.813, p < 0.001, η² = 0.019), suggesting differences in scores based on geographic location. Additionally, a significant interaction between gender and region was observed (F (3, 11485) = 2.825, p = 0.037, η² = 0.0007), indicating that the relationship between gender and the IC score varies by region. Post hoc analyses revealed that women in region South scored significantly higher than men in the same region ( p = 0.001). Significant differences between regions were also found in region North had the lowest scores compared to the others ( p < 0.001) (see Table 4 ). Table 4 ANOVA of total intrinsic capacity by sex and region ANOVA - TOTAL Intrinsic Capacity Cases 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 48.293.823 11485 4.205 Note. Type III Sum of Squares Descriptive Descriptive - TOTAL Intrinsic Capacity Region Sex N Mean SD SE Coefficient of variation 1. South 0 1271 2.760 2.080 0.058 0.754 1 1499 3.077 2.372 0.061 0.771 2. East 0 653 2.989 2.209 0.086 0.739 1 880 2.955 2.193 0.074 0.742 3. Continental 0 2060 2.601 2.013 0.044 0.774 1 2383 2.752 1.970 0.040 0.716 4. North 0 1273 2.148 1.798 0.050 0.837 1 1474 2.225 1.889 0.049 0.849 Note: Results of fixed effects ANOVA (Type III sums of squares) for total intrinsic ability. Significant differences by sex (p = 0.002) and region (p < .001) are observed, as well as a sex ✻ region interaction (p = 0.037). The effect size (η²) indicates a small influence of these variables. Men (0) and women (1). Variables related to employment, economic situation, and living arrangement by sex The ANOVA analysis showed a significant effect of employment type on the total IC score, F (6, 11830) = 168.692, p < 0.001, ω² = 0.078, indicating that occupation influences the scores obtained. Post hoc analyses revealed that self-employed individuals ( M = 1.828) scored significantly lower than retirees ( M = 3.298), employed individuals ( M = 2.977), unemployed individuals ( M = 2.771), those with medical disability ( M = 3.912), homemakers ( M = 2.956), and the "other" group ( M = 2.982) ( p < 0.001 in multiple comparisons). Additionally, those with medical disability obtained the highest scores, significantly higher than employees, self-employed individuals, and the unemployed ( p < 0.001) (see supplementary material Table S2). Regarding the economic situation, the results showed a significant effect of this variable, F (3, 11,574) = 146.513, p < .001, η² = 0.037. Participants who reported more difficulty making ends meet (category 1) had the highest mean score on IC ( M = 3.622, SD = 2.322), while those who reported no difficulty making ends meet (category 4) had the lowest mean ( M = 2.291, SD = 1.877) (see supplementary material Table S3). Living with a partner and sex, the results indicated a significant main effect of living with a partner, F (1, 11,833) = 144.323, p < .001, η² = 0.012, suggesting differences in IC according to cohabitation status. A significant effect of sex was also found, F (1, 11,833) = 16.190, p < .001, η² = 0.001, and a significant interaction between both variables, F (1, 11,833) = 14.098, p < .001, η² = 0.001. Descriptive analyses showed that women living alone had the highest average IC score ( M = 3.252, SD = 2.257), followed by men living alone ( M = 2.879, SD = 2.068). In contrast, both men ( M = 2.482, SD = 2.008) and women ( M = 2.495, SD = 2.017) living with their partners had lower scores (see supplementary material Table S4). Comparisons between waves A significant difference in the means of intrinsic capacity between the years 2013 and 2015 was found. The t-test for related samples revealed that the mean in 2013 ( M = 2.766, SD = 2.131) was significantly higher than in 2015 ( M = 2.628, SD = 2.072), t (11836) = 7.698, p < .001. However, the effect size was small (Cohen's d = 0.071). Discussion This study has explored the relationship between the domains of intrinsic capacity and quality of life in older adults, with special attention to sex and regional differences within the European context. The results suggest the multifaceted nature of intrinsic capacity, which involves several interconnected domains, such as mobility, basic activities of daily living (BADLs), Instrumental activities of daily living (IADLs), cognitive function, depressive symptoms, general health, and sensory function. Through Exploratory Factor Analysis (EFA), it was confirmed that intrinsic capacity is not a unidimensional construct, but rather composed of components grouped according to their relevance, such as mobility and mental health. This multidimensional structure highlights the need for a comprehensive approach to assess and address intrinsic capacity in older adults, considering both the physical and psychological aspects of aging (Leitón Espinoza et al., 2021 ; Cruz-Peralta & González-Celis, 2023). In relation to sex differences, the results indicate that women experience greater deterioration in several key domains of intrinsic capacity. This is consistent with previous studies that have documented that older women tend to face greater decline in areas such as cognition, mobility, and mental health compared to men, due to a combination of biological and sociocultural factors. In particular, women show greater cognitive decline, which may be linked to longer life expectancy, which exposes them to a higher risk of chronic diseases and multimorbidity (San José Laporte, 2012 ; Zaninotto et al., 2009 ). This pattern has been widely discussed in the literature, noting that women experience greater cognitive and physical decline, especially in lower socioeconomic contexts, which aligns with the findings of Llorens-Ortega et al. ( 2024 ), who document cognitive decline and physical health as factors that primarily affect women in situations of social disadvantage. On the other hand, men showed less deterioration in areas such as sensorial health, especially. This reflects a broader trend observed in other studies, which have shown that men tend to have better recovery in areas such as sensorial health compared to women, especially in more developed regions (Alfonso Silguero et al., 2014 ). However, women face faster deterioration in areas related to cognition and locomotion, highlighting gender disparities in the evolution of functional capacity and quality of life (Concha-Cisternas et al., 2021 ; Díaz-Alonso et al., 2021 ). Mental health, and particularly depression, has been a key domain that shows significantly higher prevalence in women over time. Depression, identified as a factor that accelerates functional decline, is a particularly relevant issue in the female population. Our results align with previous studies that have documented an increase in depressive symptoms among women, which may be linked to both biological factors and the accumulation of psychosocial stress throughout their lives (Portellano-Ortiz et al., 2018 ; Jalali et al., 2024 ). The interaction between factors such as social isolation, lack of emotional support, and the higher caregiving burden may further exacerbate the negative effects of depression on the quality of life of older women (Lee et al., 2022 ; Courtin & Knapp, 2017 ). A key aspect of this study has been the incorporation of social determinants of health in the assessment of intrinsic capacity. Factors such as education level, marital status, and economic status have consistently been associated with functional decline and quality of life in older adults (Steptoe & Zaninotto, 2020 ; Bielderman et al., 2015 ). In our study regions (North, South, East, and Continental), we observed that women in situations of social and economic vulnerability showed greater deterioration in mental health, a significant increase in chronic diseases, and greater economic decline. This pattern reinforces the conclusions of recent studies that highlight how social inequalities affect older women more, especially in contexts of poverty or lack of access to healthcare services (Bacigalupe et al., 2022 ; Spiers et al., 2022 ). The network analysis revealed that general health and chronic diseases are the most central factors in the intrinsic capacity network, reflecting their significant influence on other domains. Self-reported health, in particular, emerged as a central component of intrinsic capacity, notably influencing both mobility and cognitive function. However, mobility and cognitive function, while relevant, had a more modest impact on quality of life compared to physical and mental health. These findings align with previous studies that have found that physical and mental health are key determinants of quality of life in older adults (Gutiérrez-Robledo et al., 2021 ; Sugimoto et al., 2022 ). In summary, the results of this study underscore the complex relationship between intrinsic capacity and quality of life in older adults, highlighting the importance of considering both the physical and psychological aspects of aging. Sex differences and social determinants of health should be key factors when designing interventions to promote healthy aging. The findings suggest that, in addition to physical health, mental health, especially cognition and mobility, should be priority intervention areas, with an approach tailored to the specific needs of older men and women, as well as to regional disparities. These results provide valuable implications for the development of public policies and public health strategies to improve the quality of life and well-being of older adults in Europe. Study Limitations Although the results obtained are valuable, there are some limitations that should be considered. First, the use of secondary data from the SHARE survey may involve certain recall and self-reported biases that could have affected the accuracy of the intrinsic capacity measurements. Additionally, the geographically limited sample reduces the ability to generalize these results to other cultural contexts outside Europe. Future studies could benefit from more diverse samples and the inclusion of longitudinal data to assess changes more thoroughly in quality of life and intrinsic capacity over time. Conclusions This study has provided a deeper understanding of the relationship between the domains of intrinsic capacity and quality of life in older adults in Europe, highlighting the complexity and multifaceted nature of intrinsic capacity, as well as the influence of socioeconomic, geographic, and sex factors on the health and well-being of older adults. The main conclusions are as follows: Multidimensional intrinsic capacity : Intrinsic capacity is a multifaceted construct that encompasses key domains such as mobility, activities of daily living, instrumental activities of daily living, cognitive function, mental health, general health, and sensory function. This finding reinforces the need for integrated and multidimensional approaches in the assessment and promotion of healthy aging, considering both the physical and psychological aspects of aging. Impact of health and mental health : Physical and mental health, especially general health, and depressive symptoms, were identified as the most influential factors in intrinsic capacity and quality of life in older adults. The higher prevalence of depressive symptoms and greater cognitive decline in women highlight the importance of addressing mental health as part of a comprehensive approach to healthy aging. Sex differences in intrinsic capacity and quality of life : Significant disparities were observed between men and women in various domains of intrinsic capacity. Women showed greater deterioration in areas such as cognition, mobility, and mental health compared to men. These results suggest that interventions should be tailored to the specific needs of each sex, with particular attention to mental health and cognition for older women. Social determinants and inequalities : Sociodemographic factors, such as education level, marital status, and economic situation, had a significant impact on intrinsic capacity and quality of life, particularly among women in situations of social and economic vulnerability. These social inequalities exacerbate functional decline and mental health, underscoring the need for public policies that address socioeconomic disparities to improve the well-being of older adults. Implications for public health policies : The findings of this study have important implications for the design of public health policies and programs targeting older adults. It is essential to develop strategies that address mobility, cognitive and mental health, as well as social inequalities, to improve the quality of life of older adults. Interventions should be adapted to gender and geographic differences to ensure that the specific needs of each subgroup of the population are addressed. In conclusion, this study emphasizes the importance of a holistic and inclusive approach to the assessment and promotion of healthy aging. Intrinsic capacity should be considered a broad and multifactorial concept, involving both physical and psychological aspects, and should be addressed differently depending on gender and individuals' socioeconomic conditions. Public policies and health interventions must be tailored to these realities to improve the quality of life of older adults in Europe. 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., & Abizanda Soler, P. (2014). Enfermedad crónica, mortalidad, discapacidad y pérdida de movilidad en ancianos españoles: estudio FRADEA. Revista Española de Geriatría y Gerontología , 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 Bacigalupe, A., González-Rábago, Y., & Jiménez-Carrillo, M. (2022). Desigualdad de género y medicalización de la salud mental: factores socioculturales determinantes desde el análisis de percepciones expertas. Atención Primaria , 54 (7), 102378. https://doi.org/10.1016/j.aprim.2022.102378 Beard, J. R., Officer, A., de Carvalho, I. A., Sadana, R., Pot, A. M., Michel, J.-P., Lloyd-Sherlock, P., Epping-Jordan, J. E., Peeters, G. M. E. E. (Geeske), Mahanani, W. R., Thiyagarajan, J. A., & Chatterji, S. (2016). The World report on ageing and health: a policy framework for healthy ageing. The Lancet , 387 (10033), 2145–2154. https://doi.org/10.1016/S0140-6736(15)00516-4 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 Bonilla-Barrera, C. H., Bea Berges, L., & Seco-Calvo, J. (2025). Desnutrición y obesidad en mayores autónomos y determinantes de salud relacionados con ambos trastornos nutricionales. Atención Primaria , 57 (1), 103078. https://doi.org/10.1016/j.aprim.2024.103078 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 Cacciatore, S., Marzetti, E., Calvani, R., Picca, A., Salini, S., Russo, A., Tosato, M., & Landi, F. (2024). Intrinsic capacity and recent falls in adults 80 years and older living in the community: results from the ilSIRENTE Study. Aging Clinical and Experimental Research , 36 (1), 169. https://doi.org/10.1007/s40520-024-02822-7 Cao, X., Tian, Y., Chen, H., Li, S., & Zhou, J. (2024). The Global Research Trends on Intrinsic Capacity of Older Adults: A Bibliometric and Visual Analysis of Papers Published During 2015–2023. Journal of Multidisciplinary Healthcare , Volume 17 , 3323–3339. https://doi.org/10.2147/JMDH.S471324 Collins, K. H. , S. B. , S. C. , R. R. A. , H. W. , C. R. , & M. D. A. (2016). Association of body mass index (BMI) and percent body fat among BMI-defined non-obese middle-aged individuals: Insights from a population-based Canadian sample. Canadian Journal of Public Health , 107 (6) , 520–525. https://www.jstor.org/stable/90006523 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 Dávila Quintana, C. D., & González López-Valcárcel, B. (2009). Crisis económica y salud. Gaceta Sanitaria , 23 (4), 261–265. https://doi.org/10.1016/j.gaceta.2009.04.003 Di Masso Tarditti, M., Ezquerra Samper, S., & Rivera-Ferre, M. G. (2021). Mujeres en la Economía Social y Solidaria: ¿alternativas socioeconómicas para todas? CIRIEC-España, Revista de Economía Pública, Social y Cooperativa , 102 , 123–159. https://doi.org/10.7203/CIRIEC-E.102.17557 Díaz-Alonso, J., Bueno-Pérez, A., Toraño-Ladero, L., Caballero, F. F., López-García, E., Rodríguez-Artalejo, F., & Lana, A. (2021). Limitación auditiva y fragilidad social en hombres y mujeres mayores. Gaceta Sanitaria , 35 (5), 425–431. https://doi.org/10.1016/j.gaceta.2020.08.007 European Commission. (n.d.). Eurostat Regional Yearbook 2023 . Retrieved September 5, 2024, from https://ec.europa.eu/eurostat/ Evans, I. E. M., Martyr, A., Collins, R., Brayne, C., & Clare, L. (2019). Social Isolation and Cognitive Function in Later Life: A Systematic Review and Meta-Analysis. Journal of Alzheimer’s Disease , 70 (s1), S119–S144. https://doi.org/10.3233/JAD-180501 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 Hu, X., Ruan, J., Zhang, W., Chen, J., Bao, Z., Ruan, Q., & Yu, Z. (2023). The overall and domain-specific quality of life of Chinese community-dwelling older adults: the role of intrinsic capacity and disease burden. Frontiers in Psychology , 14 . https://doi.org/10.3389/fpsyg.2023.1190800 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 needs 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 Jimenez-Ruiz, C. A., Pascual Lledó, J. F., Cícero Guerrero, A., Cristóbal Fernández, M., Mayayo Ulibarri, M., & Villar Laguna, C. (2018). Análisis de la calidad de vida en pacientes con enfermedad pulmonar obstructiva crónica (EPOC) que dejan de fumar. Medicina de Familia. SEMERGEN , 44 (5), 310–315. https://doi.org/10.1016/j.semerg.2017.08.003 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 Lesende, I. M. (2014). Crisis económica y vulnerabilidad social en personas mayores. Atención Primaria , 46 (2), 55–57. https://doi.org/10.1016/j.aprim.2013.11.001 Lin-Chieh Meng, H.-M. C. W.-H. L. W.-J. L. C.-K. L. C.-H. L. F.-Y. H. L.-K. C. . (2023). Multi-Trajectories of Intrinsic Capacity Decline and Their Impact on Age-Related Outcomes: A 20-Year National Longitudinal Cohort Study. Aging and Disease . https://doi.org/10.14336/AD.2023.1115-1 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), 401. https://doi.org/10.1057/s41599-024-02899-5 Malter, F. and A. B.-S. (2015). 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 Moreiras Tuny, O., Cuadrado Vives, C., de la Calle, S. del P., & Rodríguez Sangrador, M. (2007). Determinantes nutricionales de un envejecimiento sano: Proyecto HALE (Healthy Ageing: Longitudinal Study in Europe). Clínica e Investigación En Arteriosclerosis , 19 (1), 30–36. https://doi.org/10.1016/S0214-9168(07)74168-2 OMS. (2015). Organización Mundial de la Salud. (2015). Informe mundial sobre el envejecimiento y la salud. https://apps.who.int/iris/handle/10665/186463 OMS. (2021). “Decenio del Envejecimiento Saludable (2021-2030).” https://www.who.int/es/initiatives/decade-of-healthy-ageing OMS. (2024). Perspectivas de población mundial. 2024: Resumen de resultados. Departamento de Asuntos Económicos y Sociales de las Naciones Unidas; 2024. https://population.un.org/wpp/ Payne, M. (2021). Social Division and Later Life: Difference, Diversity and Inequality, Chris Gilleard and Paul Higgs. The British Journal of Social Work , 51 (1), 376–378. https://doi.org/10.1093/bjsw/bcaa123 Pérez-Castejón, J. M., & Formiga, F. (2024). Fragilidad y capacidad intrínseca; del concepto teórico a la aplicabilidad clínica. Revista Española de Geriatría y Gerontología , 59 (5), 101483. https://doi.org/10.1016/j.regg.2024.101483 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 Pittman, C., Nieman, C. L., Reed, N. S., Swenor, B. K., & Willink, A. (2022). Sensory Impairment and the Odds of Adverse Consequences of Unmet Needs for Care Among Older Adults. The Journals of Gerontology: Series B , 77 (5), 983–991. https://doi.org/10.1093/geronb/gbab065 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 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 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., & Salvà Casanovas, A. (2023). 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 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 San José Laporte, A. (2012). La valoración de la multimorbilidad en personas de edad avanzada. Un área importante de la valoración geriátrica integral. Revista Española de Geriatría y Gerontología , 47 (2), 47–48. https://doi.org/10.1016/j.regg.2011.12.001 Soria Romero, Z., & Montoya Arce, B. J. (2017). Envejecimiento y factores asociados a la calidad de vida de los adultos mayores en el Estado de México. Papeles de Población , 23 (93), 59–93. https://doi.org/10.22185/24487147.2017.93.022 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. (2019). United Nations. Departament of Economic and Social Affairs. Popiulation division. World Population Prospect: Highlights . https://population.un.org/wpp/Publications/Files/WPP2019 Highlights.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 Urbano Contreras, A., Martínez González, R. A., & Iglesias García, M. T. (2021). Revisión de la investigación sobre relaciones de pareja en países hispanohablantes (2000-2018). Interdisciplinaria Revista de Psicología y Ciencias Afines , 38 (3), 25–48. https://doi.org/10.16888/interd.2021.38.3.2 Valencia Martín JL, G. I. S. G. L. C. G. F. S. C. M. B. B. B. (2020). EPISODIOS DE CONSUMO INTENSIVO DE ALCOHOL “BINGE DRINKING”: RETOS EN SU DEFINICIÓN E IMPACTO EN SALUD. Rev Esp Salud Pública. , 94 , 1–17. https://www.sanidad.gob.es/biblioPublic/publicaciones/recursos_propios/resp/revista_cdrom/VOL94/C_ESPECIALES/RS94C_202011170.pdf 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. Social Indicators Research , 89 (1), 61–77. https://doi.org/10.1007/s11205-007-9220-5 Wilk, P., Ruiz-Castell, M., Bohn, T., Fagherazzi, G., Nicholson, K., Moran, V., Makovski, T., Pi Alperin, M., Stranges, S., & Samouda, H. (2022). Association between functional limitation and quality of life among older adults with multimorbidity in Luxembourg. European Journal of Public Health , 32 (Supplement_3). https://doi.org/10.1093/eurpub/ckac129.292 World Health Organization. (1995). Physical status: The use and interpretation of anthropometry. . World Health Organization Technical Report Series, 854, 1-452. PMID: 8594834. https://europepmc.org/article/MED/8594834 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 Zaninotto, P., Falaschetti, E., & Sacker, A. (2009). Age trajectories of quality of life among older adults: results from the English Longitudinal Study of Ageing. Quality of Life Research , 18 (10), 1301–1309. https://doi.org/10.1007/s11136-009-9543-6 Additional Declarations The authors declare no competing interests. Supplementary Files suplementarymaterial.docx Relationship between intrinsic capacity domains and the evolution of quality of life in older adults in Europe: a differential approach between men and women based on the SHARE study. 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-6252801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":430505002,"identity":"5735a145-0d68-4c38-b1e8-5fbc52f6c277","order_by":0,"name":"rafael llorens-ortega","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYFACxgcMDwpgLAYJBgbmAwYwERyA2YAhwQDGYpCQYGBLgIqwEdbCBrSCgbAW8/bDjB8SDGzy+aXbn1V8bLOoY2Bj3gwUYZDnn9+AVYvMmWRmiQSDNMuZc86Y3ZzZBnIYWxlQhMFwxjHstkgw5B8AKjhsYHAjh+02L0iLfI8ZyGEJDLi08D9m/gHSYn8j/VnxX7AtPMYghyXI49IikcwGsUUiwYyZEaLFQAIcAji1PGazAPrFQOJGjrFkzzkJyTaIXyQMNx5LwOGwZOYbHypsDPhnpD/88KOsjp8fFGJAEXm5wwewW4MB2GDhMgpGwSgYBaOAfAAAM6lPh0I80TYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0657-4975","institution":"universidad autonoma de barcelona","correspondingAuthor":true,"prefix":"","firstName":"rafael","middleName":"","lastName":"llorens-ortega","suffix":""}],"badges":[],"createdAt":"2025-03-18 11:51:59","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6252801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6252801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78824622,"identity":"461cc100-6b06-409d-9443-686c4ca44d10","added_by":"auto","created_at":"2025-03-19 12:17:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork analysis of intrinsic capacity domains and quality of life variables.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Network of relationships between different domains of intrinsic capacity and quality of life variables in older adults. The nodes represent individual variables, and the links indicate the strength and direction of their relationships. Links in blue represent positive associations, whereas links in red 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)\u003c/p\u003e\n\u003cp\u003eBasic Daily Living Activities (BDLA)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6252801/v1/d921b24959127cf8d5451365.png"},{"id":78824623,"identity":"d94ff970-0141-4b0d-b81d-d35d382edee8","added_by":"auto","created_at":"2025-03-19 12:17:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62612,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNetwork analysis of interactions between quality of life and intrinsic capacity domains.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: network of relationships between the quality-of-life index (CASP-12) and key domains of intrinsic capacity in older adults: mobility, general health, mental health and cognition. The lines indicate the direction and magnitude of the associations, where blue links represent positive relationships and red links represent negative relationships. The thickness of the lines reflects the intensity of the connection between variables.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6252801/v1/cb707e3538260996d5873aca.png"},{"id":78826004,"identity":"c2f795d9-4689-478a-a4bb-2e287120553b","added_by":"auto","created_at":"2025-03-19 12:33:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1507258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6252801/v1/d111917c-eaf3-4cb4-9156-8440a50c2a41.pdf"},{"id":78824625,"identity":"b3fe94ca-704c-42fa-b18e-b8bea76a27ef","added_by":"auto","created_at":"2025-03-19 12:17:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":31423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between intrinsic capacity domains and the evolution of quality of life in older adults in Europe: a differential approach between men and women based on the SHARE study.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"suplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6252801/v1/7ba326d31759a3a326e8949c.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eRelationship between intrinsic capacity domains and the evolution of quality of life in older adults in Europe: a differential approach between men and women based on the SHARE study.\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Contributions to the Literature","content":"\u003cp\u003eUntil now, intrinsic capacity has been mainly addressed in a unidimensional way, focusing on isolated physical or mental aspects. This study contributes a more comprehensive perspective by confirming that intrinsic capacity is a multidimensional construct involving mobility, cognitive function, mental and general health, and sensory function.\u003c/p\u003e\n\u003cp\u003eMoreover, although previous research has highlighted gender disparities in aging, this study offers new findings on how women experience greater deterioration in key areas such as cognition and mental health. It also emphasizes the influence of social determinants, particularly for women, showing how economic and educational inequalities affect the quality of life of older adults.\u003c/p\u003e\n\u003cp\u003eIn summary, this study expands the understanding of intrinsic capacity, highlighting the need for multidimensional approaches and gender- and context-specific intervention strategies, representing a valuable contribution to future research and public health policies.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe aging of the global population is a worldwide reality affecting both developed and developing countries (United Nations, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Demographic projections indicate that the global population aged 65 and older will increase from 9.7% in 2022 to 16.4% by 2050, with Europe being one of the most affected regions (WHO, 2024). This accelerated aging presents significant social and public health challenges, increasing the need for strategies to promote healthy aging and improve the quality of life (QoL) of older adults (WHO, 2021).\u003c/p\u003e \u003cp\u003eIn response to this challenge, the World Health Organization (WHO) developed the healthy aging model, which emphasizes the importance of functional capacity, defined as the balance between physical, mental, and social conditions (WHO, 2015) (Wilk et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This model highlights that aging is not only influenced by biological factors but also by psychosocial and environmental determinants (WHO, 2020). Within this framework, intrinsic capacity (IC) has emerged as a key concept, defined as the set of physical, mental, and social capacities that enable a person to maintain their independence and functionality over time (Hu et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (Beard et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) (Takeda et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIC includes five domains: locomotor capacity (mobility and muscle strength), cognitive capacity (memory and orientation), sensory capacity (vision and hearing), psychological capacity (emotional well-being), and vitality (energy and ability to perform daily activities) (Angelsen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (WHO, 2019).\u003c/p\u003e \u003cp\u003eRecent studies have shown that the assessment of IC provides a more comprehensive perspective on healthy aging and its impact on quality of life (Chhetri, 2022). However, challenges remain in the validation of tools to measure these domains on a large scale (Rojano \u0026amp; Luque et al., 2023) (Salinas-Rodr\u0026iacute;guez et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, sex differences in aging remain a central area of research, as various studies have identified inequalities in health, educational level, and quality of life between men and women (Ahrenfeldt \u0026amp; M\u0026ouml;ller, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In general, women are more vulnerable to experiencing poorer health and lower quality of life compared to men (Llorens-Ortega et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the role of IC in the evolution of quality of life, with a focus on gender, remains a less explored area.\u003c/p\u003e \u003cp\u003eIn this regard, the Survey of Health, Ageing, and Retirement in Europe (SHARE) study provides a valuable source of longitudinal data that allows for examining how social, economic, and health conditions influence the evolution of IC and QoL in older adults across different European countries (B\u0026ouml;rsch-Supan et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This study facilitates the identification of regional and gender patterns, enabling the analysis of the relationship between IC and QoL over time and across different geographic and demographic contexts.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHypothesis\u003c/strong\u003e \u003cp\u003eThe evolution of quality of life in older adults in Europe is influenced by their intrinsic capacity. It is expected that significant differences will be found between men and women, as well as across different regions of Europe, with a less favorable evolution in women and in the southern and eastern areas of the continent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eObjective\u003c/strong\u003e \u003cp\u003eTo analyze the relationship between the domains of intrinsic capacity and the evolution of quality of life in older adults in Europe, considering the differences between men and women and the geographical region of residence in a longitudinal study.\u003c/p\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a prospective, analytical cohort study based on population data, framed within the SHARE project. Data collected in waves 5 (2013) and 6 (2015), were used (B\u0026ouml;rsch-Supan et al., 2013) (Malter, 2015). SHARE is a multinational longitudinal study that examines health, socio-economic, and demographic aspects in non-institutionalized older adults aged 50 and above.\u003c/p\u003e\n\u003cp\u003eSince its inception in 2004, SHARE has expanded its reach to 17 European countries, including Germany, Austria, Belgium, Denmark, France, Greece, the Netherlands, Hungary, Italy, Slovenia, Spain, Estonia, Sweden, Switzerland, Poland, the Czech Republic, and Ireland. Participants are interviewed biannually, allowing for international comparisons and examining the evolution of factors related to aging.\u003c/p\u003e\n\u003cp\u003eTo minimize biases and ensure comparability between countries, SHARE implements standardized data collection procedures, with harmonized surveys and representative probabilistic sampling (B\u0026ouml;rsch-Supan et al., 2013). The study has a multidisciplinary and longitudinal approach, addressing the social, economic, and public health dimensions of aging in Europe.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe population analyzed in this study consists of adults aged 50 years or older residing in 13 European countries included in SHARE wave 5. The selected countries are: Germany, Austria, Belgium, Denmark, Slovenia, Spain, Estonia, France, Italy, Luxembourg, Sweden, Switzerland, and the Czech Republic.\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria were: being 50 years or older at the start of the study, having participated in the consecutive selected waves (5 and 6), voluntarily consenting to participate in the study, and not being institutionalized at the time of the interview.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria considered the absence of complete data on the key analysis variables and lack of follow-up in the selected waves. A total of 11,493 respondents met these inclusion criteria.\u003c/p\u003e\n\u003cp\u003eFor the analysis of regional differences in the evolution of quality of life and intrinsic capacity, countries were grouped into four regional clusters, based on the Eurostat report of 2023 (European Commission, 2023) on welfare models in Europe: 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation was obtained through structured interviews in the participants\u0026apos; homes, assisted by computer (CAPI), with an average duration of 90 minutes. The standardized questionnaires collected data on the domains of intrinsic capacity and other factors relevant to quality of life. In addition to the standard questionnaires, the interviews also addressed socio-economic and demographic issues, allowing for the collection of information on sex, age, employment status, education level, and health conditions of the participants. To improve the representativeness of the results, design weights calibrated to the collected data were applied (Malter, 2015). The data is publicly available to the scientific community, facilitating access and replication of the study. www.share-project.org\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Variable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main variable of this study was quality of life, assessed using the CASP-12 scale (Wiggins et al., 2008). The multidimensional model for CASP-12 has potential to be used as a tool to assess quality of life in older adults. This scale, validated in older populations, evaluates subjective well-being through four dimensions: Control, Autonomy, Satisfaction, and Self-realization.\u003c/p\u003e\n\u003cp\u003eEach dimension includes three items, with responses on a Likert scale ranging from 1 (never) to 4 (often). The total score ranges from 12 to 48, with higher values indicating better quality of life. The following categories were established: 12-34: Low quality of life; 35-37: Moderate; 38-39: High; 40-48: Very high. According to previous studies, CASP-12 shows high internal consistency (Cronbach\u0026apos;s \u0026alpha; = 0.84), supporting its reliability in this study (Hyde et al., 2003) (P\u0026eacute;rez-Rojo et al., 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExplanatory Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study considered the five domains of intrinsic capacity according to the WHO\u0026apos;s ICOPE guidelines (Rojano and Luque et al., 2023) (World Health Organization, 2019) (Angelsen et al., 2024), in addition to the sex variable.\u003c/p\u003e\n\u003cp\u003eVarious validated scales or those used in previous studies were employed to assess the domains of intrinsic capacity (Lin-Chieh Meng, 2023). Locomotor capacity was assessed through mobility difficulties, Basic Activities of Daily Living (BADL) (Bonilla-Barrera et al., 2025), and Instrumental Activities of Daily Living (IADL) (P\u0026eacute;rez-Castej\u0026oacute;n \u0026amp; Formiga, 2024). Cognitive capacity was measured using immediate and delayed word recall and space-time orientation (Evans et al., 2019). Sensory capacity was evaluated by perceived difficulty in vision and hearing (Pittman et al., 2022).\u003c/p\u003e\n\u003cp\u003ePsychological capacity: The assessment of depressive symptoms in this study was conducted using the EURO-D scale, validated in European research on depression in older adults (Maskileyson et al., 2021) (Portellano-Ortiz et al., 2018). This scale includes 12 items, with a maximum score of 12 (high symptom presence) and a minimum of 0 (absence of symptoms), establishing a cutoff point of 4 to identify depression. For this study, four items were selected as theoretically relevant and with good psychometric stability: depression, fatigue, irritability, and interests.\u003c/p\u003e\n\u003cp\u003eOther items were excluded as they grouped into somatic or cognitive dimensions that were less pertinent to the instrumental health model. The selected dimensions were: Affective: Depression (core symptom) and irritability (associated with comorbid anxiety). Somatic and motivational: Fatigue (key in geriatric depression, linked to daily functionality) and Interests (assesses anhedonia).\u003c/p\u003e\n\u003cp\u003eOther symptoms such as sleep problems, appetite, pessimism, guilt, or concentration difficulties were excluded due to their lower specificity in discriminating mental health in older adults.\u003c/p\u003e\n\u003cp\u003eVitality or general health was assessed through self-reported health using a four-category scale: excellent/very good, good, fair, and poor. Additionally, the number of chronic diseases was recorded, categorized as none, 1 to 2, and 3 or more. The internal consistency of the intrinsic capacity measurements showed a Cronbach\u0026rsquo;s \u0026alpha; of 0.833, indicating high reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCovariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze quality of life in older adults, sociodemographic, economic, and lifestyle factors influencing intrinsic capacity were included.\u003c/p\u003e\n\u003cp\u003eSociodemographic and Economic Variables:\u003c/p\u003e\n\u003cp\u003eAge (interest groups): 1. 50-64 years (economically active); 2. 65-74 years (recent retirees); 3. 75-84 years (older adults); 4. \u0026ge;85 years (advanced age).\u003c/p\u003e\n\u003cp\u003eEducational level: According to the International Standard Classification of Education (ISCED) (United Nations Educational Scientific and Cultural Organization, 2011).\u003cbr\u003e\u0026nbsp;1. Low (ISCED 0-2, basic education or lower); 2. Medium (ISCED 3-4, upper secondary); 3. High (ISCED 5-6, tertiary).\u003c/p\u003e\n\u003cp\u003eMarital status: 1. Living with a partner; 2. Living alone (Urbano Contreras et al., 2021).\u003c/p\u003e\n\u003cp\u003eEconomic difficulties to make ends meet: SHARE determines the economic level using the variable \u0026ldquo;getting by financially,\u0026rdquo; with options:1. Very easily; 2. Quite easily; 3. With difficulty; 4. With great difficulty. For analysis in this study, it was dichotomized as (1. no difficulty: \u0026nbsp;2. with difficulty) (D\u0026aacute;vila Quintana \u0026amp; Gonz\u0026aacute;lez L\u0026oacute;pez-Valc\u0026aacute;rcel, 2009) (Lesende, 2014).\u003c/p\u003e\n\u003cp\u003eHealthy Lifestyle Variables:\u003c/p\u003e\n\u003cp\u003ePhysical activity: It was assessed according to the scale used in the SHARE study, which categorizes vigorous activity into four levels: 1. More than once a week: 2. Once a week; 3. Once to three times a month; 4. Almost never or never. For this study, categories 1 and 2 were grouped as 1. active, while 3 and 4 were categorized as 2. Inactive (Reitlo et al., 2018) (Quiroz Mora et al., 2018).\u003c/p\u003e\n\u003cp\u003eAlcohol consumption: 1. Does not drink; 2. Drinks 1 to 4 days a week; 3. Drinks every day (Valencia Mart\u0026iacute;n JL, 2020).\u003c/p\u003e\n\u003cp\u003eTobacco consumption: 1. Smokes; 2. Does not smoke (Jimenez-Ruiz et al., 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive and frequency analyses were performed to characterize the sample. Subsequently, all variables of interest were recoded into dichotomous variables, where a value of 1 indicated a positive outcome in the assessment (positive screen result). The recoding was performed as follows:\u003c/p\u003e\n\u003cp\u003eChronic diseases: 0. none or one disease; 1. two or more diseases.\u003c/p\u003e\n\u003cp\u003eVision: 0. no difficulty; 1. with difficulty; Hearing: 0. no difficulty; 1. with difficulty.\u003c/p\u003e\n\u003cp\u003eSelf-perceived health: Values from 1 to 3 were recategorized as 0 (good health), while values from 4 to 5 were recoded as 1 (poor health).\u003c/p\u003e\n\u003cp\u003eMemory and orientation(Recall 1, Recall 2, and orientation): The lower 20th percentile represented poor memory and orientation (1), while higher values indicated good ability (0).\u003c/p\u003e\n\u003cp\u003eMobility: 0. no difficulty; 1. with difficulty (values 1, 2, 3, and 4).\u003c/p\u003e\n\u003cp\u003eBasic Activities of Daily Living (BADL) and Instrumental Activities of Daily Living (IADL): 0. no difficulty; 1. with difficulty (values 1, 2, and 3). Total score was calculated by summarizing the values of each variable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis recoding allowed a better operationalization of the variables for their subsequent statistical analysis.\u003c/p\u003e\n\u003cp\u003eFor the exploratory factor analysis, wave 5 was used, while wave 6 was employed to analyze the predictive validity of the intrinsic capacity instrument.\u003c/p\u003e\n\u003cp\u003eTo identify the underlying structure of the constructs assessed, an Exploratory Factor Analysis (EFA) was conducted using the Weighted Least Squares (WLS) method as the factorization technique, given that the data include dichotomous and ordinal variables. A polychoric-tetrachoric correlation matrix was used to estimate the relationships between categorical variables.\u003c/p\u003e\n\u003cp\u003eThe optimal number of factors to retain was determined using a Parallel Analysis based on Factor Analysis (FA), complemented by inspection of the scree plot. To facilitate interpretation of the factors, an oblique rotation (Promax) was applied, as some correlation between the underlying factors was expected.\u003c/p\u003e\n\u003cp\u003eFactors with loadings greater than 0.40 were considered significant, and the adequacy of the model was evaluated by reviewing the uniqueness of the variables, eliminating those with values above 0.70.\u003c/p\u003e\n\u003cp\u003eIn this study,\u0026nbsp;Network Analysis (NA)\u0026nbsp;was employed using the Huge Estimator approach to analyze the structure of intrinsic capacity, exploring how the key indicators of mobility, health, mental health, and cognitive function interrelate. On the other hand,\u0026nbsp;Ebiglaso\u0026nbsp;was used to delve deeper into exploring causal relationships between intrinsic capacity factors and their impact on quality of life (CASP). This advanced tool allowed for modeling complex and non-linear relationships between variables, facilitating the identification of the critical factors that have the most influence on quality of life.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using the SHARE weighted data and carried out with SPSS-25 (Statistical Package for the Social Sciences, IBM Corp. Armonk, NY, USA), R version 4.3.0, a statistical computing language and environment from the Foundation for Statistical Computing, Vienna, Austria, and JASP-Stats software (version 0.19.3). A significance level was set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Ethics Committee of the Max Planck Society for the Advancement of Science conducted a thorough review of the materials related to the SHARE project, including Wave 5 and its subsequent waves (Waves 6 and 7). The committee has certified that the research project, along with its procedures, meets the highest international ethical standards. Moreover, strict measures have been adopted to ensure the confidentiality and privacy of the data and information provided by the participants, in accordance with the \u0026quot;Helsinki Declaration\u0026quot; and the International Ethical Guidelines for Biomedical Research Involving Human Subjects.\u003c/p\u003e\n\u003cp\u003eFurthermore, written informed consent was obtained from all participants involved in the study, who voluntarily agreed to participate during the interviews.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eDemographic, healthy lifestyle, and quality of life by sex (Wave 5)\u003c/h2\u003e\n \u003cp\u003eThe results show significant differences between men and women across various characteristics. On average, men were slightly older than women (64.4 vs. 63.3 years). There were more women in the 50\u0026ndash;64 age group (54.4% vs. 50.0% in men), reflecting a higher female representation among younger adults. Regarding educational level, 37% of women had a low educational level, compared to 32.9% of men. Men were more likely to be married or in a registered partnership (80.7% vs. 71.2%), and women had a higher proportion of widows (13.4% vs. 4.4%). Economically, 31.5% of women reported financial difficulties, compared to 26.7% of men.\u003c/p\u003e\n \u003cp\u003eRegarding lifestyle habits, 57.6% of men engaged in regular physical activity, compared to 50.2% of women. Furthermore, alcohol consumption and smoking were more prevalent among men, with 28.9% drinking almost daily compared to 11.5% of women, and 56% of men smoking daily compared to 37.1% of women. Finally, quality of life measured with the CASP-12 scale was slightly higher in men (38.6 vs. 38.0) (See Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e for more details and significances).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic differences, healthy lifestyle, and quality of life by sex (Wave 5)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable %\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWomen N\u0026thinsp;=\u0026thinsp;6236\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMen N\u0026thinsp;=\u0026thinsp;5257\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal N\u0026thinsp;=\u0026thinsp;11493\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic Data\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.3 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.4 (9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.8 (9.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3392 (54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2629 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6021 (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1753 (28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1668 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3421 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u0026ndash;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e885 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e819 (15.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1704 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206 (3.30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e141 (2.68%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e347 (3.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2310 (37.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1727 (32.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4037 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2326 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2037 (38.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4363 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1600 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1493 (28.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3093 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital Status\u003c/strong\u003e:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/Registered Partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4443 (71.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4241 (80.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8684 (75.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced/Separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e638 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e432 (8.22%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1070 (9.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319 (5.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350 (6.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e669 (5.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e836 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234 (4.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1070 (9.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCan make ends meet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4337 (69.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3853 (73.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8190 (71.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReceived help from others (outside household)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1174 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e833 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2007 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy Lifestyle\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003ePhysical Activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3132 (50.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3027 (57.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6159 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrinks alcohol daily:\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo/1\u0026ndash;2 per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3801 (61.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1950 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5751 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;4 days/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1719 (27.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1790 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3509 (30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlmost daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e716 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1517 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2233 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoke daily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2314 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2944 (56.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5258 (45.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuality of Life: CASP (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.0 (6.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.6 (6.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.3 (6.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNote: Statistical tests: ANOVA for continuous variables, Chi-square for categorical variables.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eFactor analysis of intrinsic capacity variables\u003c/h2\u003e\n \u003cp\u003eTo explore the factorial structure of intrinsic capacity (IC), the results of the Kaiser-Meyer-Olkin (KMO) sample adequacy test indicated an acceptable overall model adequacy (MSA\u0026thinsp;=\u0026thinsp;0.694), with individual values ranging from 0.570 to 0.907. Additionally, the Bartlett\u0026apos;s test of sphericity was significant (\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e = 28,717.015, \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), confirming the adequacy of the data for factor analysis.\u003c/p\u003e\n \u003cp\u003eThe factor analysis revealed a four-factor structure, explaining together 53.6% of the total variance. In the rotated solution, Factor 1 showed significant loadings on variables related to locomotor capacity (mobility, basic activities of daily living, and instrumental activities of daily living), while Factor 2 was primarily composed of variables associated with depressive symptoms (depression, irritability, fatigue, and interests). Factor 3 captured variables related to memory and orientation (immediate and delayed word recall and space-time orientation), and Factor 4 reflected aspects of general health and sensory function (vision, hearing, chronic diseases, and self-reported health) (See Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for more details).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFactor analysis of intrinsic capacity variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor Loadings\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUniqueness\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBDLA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.325\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIADL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.355\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepression (part of EURO-D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIrritability (part of EURO-D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFatigue (part of EURO-D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterests (part of EURO-D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmediate word recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDelayed word recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOrientation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.443\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-perceived health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.427\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNote. The applied rotation method is Promax.\u003c/p\u003e\n \u003cp\u003eBDLA: Basic Daily Living Activities\u003c/p\u003e\n \u003cp\u003eIADL: Instrumental Activities of Daily Living\u003c/p\u003e\n \u003cp\u003eTo confirm the structure of intrinsic capacity, the results from the network analysis (NA) show that the network exhibits a sparsity of 0.359, indicating that 35.9% of possible connections are absent. This suggests a differentiated structure in measuring intrinsic capacity, rather than a single global dimension.\u003c/p\u003e\n \u003cp\u003eThe variable \u0026quot;Health\u0026quot; shows the highest values in centrality measures (betweenness: 1.954; closeness: 1.084; strength: 1.485; expected influence: 1.590), positioning it as a key node in the network. \u0026quot;Chronic diseases\u0026quot; also showed high expected influence (1.213), indicating its relevance in the structure of the instrument. Variables like \u0026quot;Interests,\u0026quot; \u0026quot;Irritability,\u0026quot; and \u0026quot;Space-time orientation\u0026quot; showed negative values in centrality, suggesting a lower impact on the global network (see Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eCorrelations for Intrinsic Capacity Domains\u003c/h2\u003e\n \u003cp\u003ePearson correlations were performed to evaluate the relationships between the quality of life and well-being index (CASP) and various health, mental state, cognition, and mobility variables.\u003c/p\u003e\n \u003cp\u003eThe CASP showed a significant negative correlation with the perception of good health (\u003cem\u003er\u003c/em\u003e = -0.378, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), the presence of chronic diseases (\u003cem\u003er\u003c/em\u003e = -0.168, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and general health (\u003cem\u003er\u003c/em\u003e = -0.317, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that poorer physical health is associated with lower quality of life. Negative correlations were also observed between the CASP and depressive symptoms (\u003cem\u003er\u003c/em\u003e = -0.284, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), fatigue (\u003cem\u003er\u003c/em\u003e = -0.324, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), irritability (\u003cem\u003er\u003c/em\u003e = -0.230, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), and loss of interests (\u003cem\u003er\u003c/em\u003e = -0.244, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that higher levels of emotional distress are related to lower quality of life.\u003c/p\u003e\n \u003cp\u003eRegarding cognitive function, the CASP negatively correlated with performance on recall and orientation tasks, although with lower correlation values (\u003cem\u003er\u003c/em\u003e between \u0026minus;\u0026thinsp;0.108 and \u0026minus;\u0026thinsp;0.162, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating a mild association between lower quality of life and worse cognitive performance.\u003c/p\u003e\n \u003cp\u003eWith respect to mobility, quality of life showed a weak negative correlation with the presence of motor difficulties (\u003cem\u003er\u003c/em\u003e = -0.041, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and with activities of daily living (ADL) and instrumental activities of daily living (IADL) (\u003cem\u003er\u003c/em\u003e between \u0026minus;\u0026thinsp;0.019 and \u0026minus;\u0026thinsp;0.035, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), suggesting that mobility limitations have a lesser impact on quality of life compared to physical and mental health. The results show that poorer physical and mental health is strongly associated with lower quality of life, while cognitive function and mobility have weaker relationships with this indicator (for more details, see supplementary material Table S1).\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\u003eThe network analysis was conducted with five nodes and ten non-null connections, with a dispersion of 0.000, indicating that all variables are related to each other (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eRegarding centrality measures, the quality of life and well-being index (CASP) presented the highest values in betweenness (1.789), closeness (0.856), and strength (0.846), indicating that it is a key node in the network. However, its expected influence was negative (-1.759), suggesting that its relationship with other variables could be determined by negative associations.\u003c/p\u003e\n \u003cp\u003eOn the other hand, total mobility showed the lowest values in closeness (-1.715) and strength (-1.613), suggesting lower connectivity within the network. In contrast, total health showed positive values in closeness (0.498), strength (0.650), and expected influence (0.684), indicating its relevance within the network structure.\u003c/p\u003e\n \u003cp\u003eRegarding clustering measures, CASP and total health had the highest values in the Onnela coefficient (0.733 and 0.665, respectively), indicating a higher interconnection with other variables. Total mental health and cognitive status showed positive values in the Zhang index (0.663 and 0.934, respectively), suggesting higher cohesion in the network. In contrast, total mobility had the lowest value in Onnela (-1.712), reflecting lower integration within the network. These results suggest that CASP is a central node in the network, influenced by physical and mental health variables. Mobility, in contrast, seems to play a less significant role in the overall network structure (see Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eIntrinsic capacity analysis by sex\u003c/h2\u003e\n \u003cp\u003eThe results suggest significant differences between men and women in the areas of total health, mental health, and cognition. In total health, men (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.740; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96) scored significantly higher than women (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.697; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93), t (11835)\u0026thinsp;=\u0026thinsp;2.437, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015, Cohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). In mental health, women (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.163; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.21) showed a significantly higher score than men (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.884; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.02), \u003cem\u003et\u003c/em\u003e (11449) = -13.854, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, with a moderate effect (Cohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e = -0.260). In cognition, men (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.609; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.85) scored significantly higher than women (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.518; \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.82), \u003cem\u003et\u003c/em\u003e (11835)\u0026thinsp;=\u0026thinsp;5.913, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Cohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.109) (see Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e for more details).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntrinsic capacity analysis by sex. Independent samples t-test\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eIndependent Samples T-Test\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE Cohen\u0026apos;s d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Mental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Cognitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eᵃ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Student\u0026apos;s t-test.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eᵃ Brown-Forsythe test is significant (p\u0026thinsp;\u0026lt;\u0026thinsp;.05), suggesting a violation of the equal variance assumption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescriptives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"8\"\u003e\n \u003cp\u003e\u003cem\u003eGroup Descriptives\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient of variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Mental Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.144\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Cognitive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.598\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Mobility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.915\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\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 \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: Student\u0026apos;s t-test results comparing domains of intrinsic capacity between men (0) and women (1). Significant differences in mental health and cognition (p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Brown-Forsythe test indicates violation of equality of variance in variables marked with ᵃ.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eTotal intrinsic capacity by sex and region\u003c/h2\u003e\n \u003cp\u003eThe ANOVA analysis showed that sex has a significant effect on the total IC score (F (1, 11485)\u0026thinsp;=\u0026thinsp;9.575, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003e\u0026eta;\u0026sup2;\u003c/em\u003e = 0.0008), indicating that women scored slightly higher than men. A significant effect of the region was also found (F (3, 11485)\u0026thinsp;=\u0026thinsp;74.813, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003e\u0026eta;\u0026sup2;\u003c/em\u003e = 0.019), suggesting differences in scores based on geographic location. Additionally, a significant interaction between gender and region was observed (F (3, 11485)\u0026thinsp;=\u0026thinsp;2.825, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037, \u003cem\u003e\u0026eta;\u0026sup2;\u003c/em\u003e = 0.0007), indicating that the relationship between gender and the IC score varies by region.\u003c/p\u003e\n \u003cp\u003ePost hoc analyses revealed that women in region South scored significantly higher than men in the same region (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Significant differences between regions were also found in region North had the lowest scores compared to the others (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (see Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eANOVA of total intrinsic capacity by sex and region\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eANOVA - TOTAL Intrinsic Capacity\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026eta;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.164\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e943.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex ✻ Region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.227\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResiduals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.293.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Type III Sum of Squares\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e\u003cem\u003eDescriptive - TOTAL Intrinsic Capacity\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoefficient of variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. South\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.771\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.739\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Continental\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. North\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003eNote: Results of fixed effects ANOVA (Type III sums of squares) for total intrinsic ability. Significant differences by sex (p\u0026thinsp;=\u0026thinsp;0.002) and region (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) are observed, as well as a sex ✻ region interaction (p\u0026thinsp;=\u0026thinsp;0.037). The effect size (\u0026eta;\u0026sup2;) indicates a small influence of these variables. Men (0) and women (1).\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eVariables related to employment, economic situation, and living arrangement by sex\u003c/h2\u003e\n \u003cp\u003eThe ANOVA analysis showed a significant effect of employment type on the total IC score, F (6, 11830)\u0026thinsp;=\u0026thinsp;168.692, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003e\u0026omega;\u0026sup2;\u003c/em\u003e = 0.078, indicating that occupation influences the scores obtained.\u003c/p\u003e\n \u003cp\u003ePost hoc analyses revealed that self-employed individuals (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.828) scored significantly lower than retirees (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.298), employed individuals (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.977), unemployed individuals (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.771), those with medical disability (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.912), homemakers (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.956), and the \u0026quot;other\u0026quot; group (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.982) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in multiple comparisons). Additionally, those with medical disability obtained the highest scores, significantly higher than employees, self-employed individuals, and the unemployed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (see supplementary material Table S2).\u003c/p\u003e\n \u003cp\u003eRegarding the economic situation, the results showed a significant effect of this variable, F (3, 11,574)\u0026thinsp;=\u0026thinsp;146.513, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003e\u0026eta;\u0026sup2;\u003c/em\u003e = 0.037. Participants who reported more difficulty making ends meet (category 1) had the highest mean score on IC (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.622, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.322), while those who reported no difficulty making ends meet (category 4) had the lowest mean (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.291, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.877) (see supplementary material Table S3).\u003c/p\u003e\n \u003cp\u003eLiving with a partner and sex, the results indicated a significant main effect of living with a partner, F (1, 11,833)\u0026thinsp;=\u0026thinsp;144.323, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003e\u0026eta;\u0026sup2;\u003c/em\u003e = 0.012, suggesting differences in IC according to cohabitation status. A significant effect of sex was also found, F (1, 11,833)\u0026thinsp;=\u0026thinsp;16.190, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u003cem\u003e\u0026eta;\u0026sup2;\u003c/em\u003e = 0.001, and a significant interaction between both variables, F (1, 11,833)\u0026thinsp;=\u0026thinsp;14.098, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, \u0026eta;\u0026sup2; = 0.001.\u003c/p\u003e\n \u003cp\u003eDescriptive analyses showed that women living alone had the highest average IC score (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.252, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.257), followed by men living alone (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.879, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.068). In contrast, both men (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.482, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.008) and women (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.495, SD\u0026thinsp;=\u0026thinsp;2.017) living with their partners had lower scores (see supplementary material Table S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eComparisons between waves\u003c/h2\u003e\n \u003cp\u003eA significant difference in the means of intrinsic capacity between the years 2013 and 2015 was found. The t-test for related samples revealed that the mean in 2013 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.766, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.131) was significantly higher than in 2015 (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.628, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.072), \u003cem\u003et\u003c/em\u003e (11836)\u0026thinsp;=\u0026thinsp;7.698, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001. However, the effect size was small (Cohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.071).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study has explored the relationship between the domains of intrinsic capacity and quality of life in older adults, with special attention to sex and regional differences within the European context. The results suggest the multifaceted nature of intrinsic capacity, which involves several interconnected domains, such as mobility, basic activities of daily living (BADLs), Instrumental activities of daily living (IADLs), cognitive function, depressive symptoms, general health, and sensory function. Through Exploratory Factor Analysis (EFA), it was confirmed that intrinsic capacity is not a unidimensional construct, but rather composed of components grouped according to their relevance, such as mobility and mental health. This multidimensional structure highlights the need for a comprehensive approach to assess and address intrinsic capacity in older adults, considering both the physical and psychological aspects of aging (Leit\u0026oacute;n Espinoza et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cruz-Peralta \u0026amp; Gonz\u0026aacute;lez-Celis, 2023).\u003c/p\u003e \u003cp\u003eIn relation to sex differences, the results indicate that women experience greater deterioration in several key domains of intrinsic capacity. This is consistent with previous studies that have documented that older women tend to face greater decline in areas such as cognition, mobility, and mental health compared to men, due to a combination of biological and sociocultural factors. In particular, women show greater cognitive decline, which may be linked to longer life expectancy, which exposes them to a higher risk of chronic diseases and multimorbidity (San Jos\u0026eacute; Laporte, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zaninotto et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). This pattern has been widely discussed in the literature, noting that women experience greater cognitive and physical decline, especially in lower socioeconomic contexts, which aligns with the findings of Llorens-Ortega et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who document cognitive decline and physical health as factors that primarily affect women in situations of social disadvantage.\u003c/p\u003e \u003cp\u003eOn the other hand, men showed less deterioration in areas such as sensorial health, especially. This reflects a broader trend observed in other studies, which have shown that men tend to have better recovery in areas such as sensorial health compared to women, especially in more developed regions (Alfonso Silguero et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, women face faster deterioration in areas related to cognition and locomotion, highlighting gender disparities in the evolution of functional capacity and quality of life (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, and particularly depression, has been a key domain that shows significantly higher prevalence in women over time. Depression, identified as a factor that accelerates functional decline, is a particularly relevant issue in the female population. Our results align with previous studies that have documented an increase in depressive symptoms among women, which may be linked to both biological factors and the accumulation of psychosocial stress throughout their lives (Portellano-Ortiz et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jalali et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The interaction between factors such as social isolation, lack of emotional support, and the higher caregiving burden may further exacerbate the negative effects of depression on the quality of life of older women (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Courtin \u0026amp; Knapp, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA key aspect of this study has been the incorporation of social determinants of health in the assessment of intrinsic capacity. Factors such as education level, marital status, and economic status have consistently been associated with functional decline and quality of life in older adults (Steptoe \u0026amp; Zaninotto, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bielderman et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In our study regions (North, South, East, and Continental), we observed that women in situations of social and economic vulnerability showed greater deterioration in mental health, a significant increase in chronic diseases, and greater economic decline. This pattern reinforces the conclusions of recent studies that highlight how social inequalities affect older women more, especially in contexts of poverty or lack of access to healthcare services (Bacigalupe et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Spiers et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe network analysis revealed that general health and chronic diseases are the most central factors in the intrinsic capacity network, reflecting their significant influence on other domains. Self-reported health, in particular, emerged as a central component of intrinsic capacity, notably influencing both mobility and cognitive function. However, mobility and cognitive function, while relevant, had a more modest impact on quality of life compared to physical and mental health. These findings align with previous studies that have found that physical and mental health are key determinants of quality of life in older adults (Guti\u0026eacute;rrez-Robledo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sugimoto et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn summary, the results of this study underscore the complex relationship between intrinsic capacity and quality of life in older adults, highlighting the importance of considering both the physical and psychological aspects of aging. Sex differences and social determinants of health should be key factors when designing interventions to promote healthy aging. The findings suggest that, in addition to physical health, mental health, especially cognition and mobility, should be priority intervention areas, with an approach tailored to the specific needs of older men and women, as well as to regional disparities. These results provide valuable implications for the development of public policies and public health strategies to improve the quality of life and well-being of older adults in Europe.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStudy Limitations\u003c/h2\u003e \u003cp\u003eAlthough the results obtained are valuable, there are some limitations that should be considered. First, the use of secondary data from the SHARE survey may involve certain recall and self-reported biases that could have affected the accuracy of the intrinsic capacity measurements. Additionally, the geographically limited sample reduces the ability to generalize these results to other cultural contexts outside Europe. Future studies could benefit from more diverse samples and the inclusion of longitudinal data to assess changes more thoroughly in quality of life and intrinsic capacity over time.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study has provided a deeper understanding of the relationship between the domains of intrinsic capacity and quality of life in older adults in Europe, highlighting the complexity and multifaceted nature of intrinsic capacity, as well as the influence of socioeconomic, geographic, and sex factors on the health and well-being of older adults. The main conclusions are as follows:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eMultidimensional intrinsic capacity\u003c/strong\u003e: Intrinsic capacity is a multifaceted construct that encompasses key domains such as mobility, activities of daily living, instrumental activities of daily living, cognitive function, mental health, general health, and sensory function. This finding reinforces the need for integrated and multidimensional approaches in the assessment and promotion of healthy aging, considering both the physical and psychological aspects of aging.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eImpact of health and mental health\u003c/strong\u003e: Physical and mental health, especially general health, and depressive symptoms, were identified as the most influential factors in intrinsic capacity and quality of life in older adults. The higher prevalence of depressive symptoms and greater cognitive decline in women highlight the importance of addressing mental health as part of a comprehensive approach to healthy aging.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSex differences in intrinsic capacity and quality of life\u003c/strong\u003e: Significant disparities were observed between men and women in various domains of intrinsic capacity. Women showed greater deterioration in areas such as cognition, mobility, and mental health compared to men. These results suggest that interventions should be tailored to the specific needs of each sex, with particular attention to mental health and cognition for older women.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eSocial determinants and inequalities\u003c/strong\u003e: Sociodemographic factors, such as education level, marital status, and economic situation, had a significant impact on intrinsic capacity and quality of life, particularly among women in situations of social and economic vulnerability. These social inequalities exacerbate functional decline and mental health, underscoring the need for public policies that address socioeconomic disparities to improve the well-being of older adults.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eImplications for public health policies\u003c/strong\u003e: The findings of this study have important implications for the design of public health policies and programs targeting older adults. It is essential to develop strategies that address mobility, cognitive and mental health, as well as social inequalities, to improve the quality of life of older adults. Interventions should be adapted to gender and geographic differences to ensure that the specific needs of each subgroup of the population are addressed.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn conclusion, this study emphasizes the importance of a holistic and inclusive approach to the assessment and promotion of healthy aging. Intrinsic capacity should be considered a broad and multifactorial concept, involving both physical and psychological aspects, and should be addressed differently depending on gender and individuals\u0026apos; socioeconomic conditions. Public policies and health interventions must be tailored to these realities to improve the quality of life of older adults in Europe.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\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. https://doi.org/10.3390/ijerph18095045\u003c/li\u003e\n\u003cli\u003eAlfonso Silguero, S. A., Mart\u0026iacute;nez-Reig, M., G\u0026oacute;mez Arnedo, L., Juncos Mart\u0026iacute;nez, G., Romero Rizos, L., \u0026amp; Abizanda Soler, P. (2014). Enfermedad cr\u0026oacute;nica, mortalidad, discapacidad y p\u0026eacute;rdida de movilidad en ancianos espa\u0026ntilde;oles: estudio FRADEA. \u003cem\u003eRevista Espa\u0026ntilde;ola de Geriatr\u0026iacute;a y Gerontolog\u0026iacute;a\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(2), 51\u0026ndash;58. https://doi.org/10.1016/j.regg.2013.05.007\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1186/s12889-024-17840-3\u003c/li\u003e\n\u003cli\u003eBacigalupe, A., Gonz\u0026aacute;lez-R\u0026aacute;bago, Y., \u0026amp; Jim\u0026eacute;nez-Carrillo, M. (2022). Desigualdad de g\u0026eacute;nero y medicalizaci\u0026oacute;n de la salud mental: factores socioculturales determinantes desde el an\u0026aacute;lisis de percepciones expertas. \u003cem\u003eAtenci\u0026oacute;n Primaria\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(7), 102378. https://doi.org/10.1016/j.aprim.2022.102378\u003c/li\u003e\n\u003cli\u003eBeard, J. R., Officer, A., de Carvalho, I. A., Sadana, R., Pot, A. M., Michel, J.-P., Lloyd-Sherlock, P., Epping-Jordan, J. E., Peeters, G. M. E. E. (Geeske), Mahanani, W. R., Thiyagarajan, J. A., \u0026amp; Chatterji, S. (2016). The World report on ageing and health: a policy framework for healthy ageing. \u003cem\u003eThe Lancet\u003c/em\u003e, \u003cem\u003e387\u003c/em\u003e(10033), 2145\u0026ndash;2154. https://doi.org/10.1016/S0140-6736(15)00516-4\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1007/s11136-014-0898-y\u003c/li\u003e\n\u003cli\u003eBonilla-Barrera, C. H., Bea Berges, L., \u0026amp; Seco-Calvo, J. (2025). Desnutrici\u0026oacute;n y obesidad en mayores aut\u0026oacute;nomos y determinantes de salud relacionados con ambos trastornos nutricionales. \u003cem\u003eAtenci\u0026oacute;n Primaria\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e(1), 103078. https://doi.org/10.1016/j.aprim.2024.103078\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1093/ije/dyt088\u003c/li\u003e\n\u003cli\u003eCacciatore, S., Marzetti, E., Calvani, R., Picca, A., Salini, S., Russo, A., Tosato, M., \u0026amp; Landi, F. (2024). Intrinsic capacity and recent falls in adults 80 years and older living in the community: results from the ilSIRENTE Study. \u003cem\u003eAging Clinical and Experimental Research\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(1), 169. https://doi.org/10.1007/s40520-024-02822-7\u003c/li\u003e\n\u003cli\u003eCao, X., Tian, Y., Chen, H., Li, S., \u0026amp; Zhou, J. (2024). The Global Research Trends on Intrinsic Capacity of Older Adults: A Bibliometric and Visual Analysis of Papers Published During 2015\u0026ndash;2023. \u003cem\u003eJournal of Multidisciplinary Healthcare\u003c/em\u003e, \u003cem\u003eVolume 17\u003c/em\u003e, 3323\u0026ndash;3339. https://doi.org/10.2147/JMDH.S471324\u003c/li\u003e\n\u003cli\u003eCollins, K. H. , S. B. , S. C. , R. R. A. , H. W. , C. R. , \u0026amp; M. D. A. (2016). Association of body mass index (BMI) and percent body fat among BMI-defined non-obese middle-aged individuals: Insights from a population-based Canadian sample. \u003cem\u003eCanadian Journal of Public Health\u003c/em\u003e, \u003cem\u003e107 (6)\u003c/em\u003e, 520\u0026ndash;525. https://www.jstor.org/stable/90006523\u003c/li\u003e\n\u003cli\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. https://doi.org/10.14482/sun.36.2.618.97\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1111/hsc.12311\u003c/li\u003e\n\u003cli\u003eD\u0026aacute;vila Quintana, C. D., \u0026amp; Gonz\u0026aacute;lez L\u0026oacute;pez-Valc\u0026aacute;rcel, B. (2009). Crisis econ\u0026oacute;mica y salud. \u003cem\u003eGaceta Sanitaria\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(4), 261\u0026ndash;265. https://doi.org/10.1016/j.gaceta.2009.04.003\u003c/li\u003e\n\u003cli\u003eDi Masso Tarditti, M., Ezquerra Samper, S., \u0026amp; Rivera-Ferre, M. G. (2021). Mujeres en la Econom\u0026iacute;a Social y Solidaria: \u0026iquest;alternativas socioecon\u0026oacute;micas para todas? \u003cem\u003eCIRIEC-Espa\u0026ntilde;a, Revista de Econom\u0026iacute;a P\u0026uacute;blica, Social y Cooperativa\u003c/em\u003e, \u003cem\u003e102\u003c/em\u003e, 123\u0026ndash;159. https://doi.org/10.7203/CIRIEC-E.102.17557\u003c/li\u003e\n\u003cli\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). Limitaci\u0026oacute;n auditiva y fragilidad social en hombres y mujeres mayores. \u003cem\u003eGaceta Sanitaria\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(5), 425\u0026ndash;431. https://doi.org/10.1016/j.gaceta.2020.08.007\u003c/li\u003e\n\u003cli\u003eEuropean Commission. (n.d.). \u003cem\u003eEurostat Regional Yearbook 2023\u003c/em\u003e. Retrieved September 5, 2024, from https://ec.europa.eu/eurostat/\u003c/li\u003e\n\u003cli\u003eEvans, I. E. M., Martyr, A., Collins, R., Brayne, C., \u0026amp; Clare, L. (2019). Social Isolation and Cognitive Function in Later Life: A Systematic Review and Meta-Analysis. \u003cem\u003eJournal of Alzheimer\u0026rsquo;s Disease\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(s1), S119\u0026ndash;S144. https://doi.org/10.3233/JAD-180501\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1007/s11136-023-03472-4\u003c/li\u003e\n\u003cli\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. https://doi.org/10.26633/RPSP.2021.121\u003c/li\u003e\n\u003cli\u003eHu, X., Ruan, J., Zhang, W., Chen, J., Bao, Z., Ruan, Q., \u0026amp; Yu, Z. (2023). The overall and domain-specific quality of life of Chinese community-dwelling older adults: the role of intrinsic capacity and disease burden. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e. https://doi.org/10.3389/fpsyg.2023.1190800\u003c/li\u003e\n\u003cli\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 needs satisfaction model (CASP-19). \u003cem\u003eAging \u0026amp; Mental Health\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 186\u0026ndash;194. https://doi.org/10.1080/1360786031000101157\u003c/li\u003e\n\u003cli\u003eJagadish K 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). https://doi.org/https://doi.org/10.1093/ageing/afac239\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1186/s12877-024-05311-8\u003c/li\u003e\n\u003cli\u003eJimenez-Ruiz, C. A., Pascual Lled\u0026oacute;, J. F., C\u0026iacute;cero Guerrero, A., Crist\u0026oacute;bal Fern\u0026aacute;ndez, M., Mayayo Ulibarri, M., \u0026amp; Villar Laguna, C. (2018). An\u0026aacute;lisis de la calidad de vida en pacientes con enfermedad pulmonar obstructiva cr\u0026oacute;nica (EPOC) que dejan de fumar. \u003cem\u003eMedicina de Familia. SEMERGEN\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(5), 310\u0026ndash;315. https://doi.org/10.1016/j.semerg.2017.08.003\u003c/li\u003e\n\u003cli\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. https://doi.org/10.3390/healthcare10091598\u003c/li\u003e\n\u003cli\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. https://doi.org/10.14482/sun.36.1.618.97\u003c/li\u003e\n\u003cli\u003eLesende, I. M. (2014). Crisis econ\u0026oacute;mica y vulnerabilidad social en personas mayores. \u003cem\u003eAtenci\u0026oacute;n Primaria\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(2), 55\u0026ndash;57. https://doi.org/10.1016/j.aprim.2013.11.001\u003c/li\u003e\n\u003cli\u003eLin-Chieh Meng, H.-M. C. W.-H. L. W.-J. L. C.-K. L. C.-H. L. F.-Y. H. L.-K. C. . (2023). Multi-Trajectories of Intrinsic Capacity Decline and Their Impact on Age-Related Outcomes: A 20-Year National Longitudinal Cohort Study. \u003cem\u003eAging and Disease\u003c/em\u003e. https://doi.org/10.14336/AD.2023.1115-1\u003c/li\u003e\n\u003cli\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), 401. https://doi.org/10.1057/s41599-024-02899-5\u003c/li\u003e\n\u003cli\u003eMalter, F. and A. B.-S. (2015). \u003cem\u003eSHARE Wave 5: Innovations \u0026amp; Methodology. \u003c/em\u003e. https://share-eric.eu/fileadmin/user_upload/Methodology_Volumes/Method_vol5_31March2015.pdf\u003c/li\u003e\n\u003cli\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. https://doi.org/10.3389/fpos.2021.665004\u003c/li\u003e\n\u003cli\u003eMoreiras Tuny, O., Cuadrado Vives, C., de la Calle, S. del P., \u0026amp; Rodr\u0026iacute;guez Sangrador, M. (2007). Determinantes nutricionales de un envejecimiento sano: Proyecto HALE (Healthy Ageing: Longitudinal Study in Europe). \u003cem\u003eCl\u0026iacute;nica e Investigaci\u0026oacute;n En Arteriosclerosis\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1), 30\u0026ndash;36. https://doi.org/10.1016/S0214-9168(07)74168-2\u003c/li\u003e\n\u003cli\u003eOMS. (2015). \u003cem\u003eOrganizaci\u0026oacute;n Mundial de la Salud. (2015). Informe mundial sobre el envejecimiento y la salud.\u003c/em\u003e https://apps.who.int/iris/handle/10665/186463\u003c/li\u003e\n\u003cli\u003eOMS. (2021). \u003cem\u003e\u0026ldquo;Decenio del Envejecimiento Saludable (2021-2030).\u0026rdquo;\u003c/em\u003e https://www.who.int/es/initiatives/decade-of-healthy-ageing\u003c/li\u003e\n\u003cli\u003eOMS. (2024). \u003cem\u003ePerspectivas de poblaci\u0026oacute;n mundial. 2024: Resumen de resultados. Departamento de Asuntos Econ\u0026oacute;micos y Sociales de las Naciones Unidas; 2024.\u003c/em\u003e https://population.un.org/wpp/\u003c/li\u003e\n\u003cli\u003ePayne, M. (2021). Social Division and Later Life: Difference, Diversity and Inequality, Chris Gilleard and Paul Higgs. \u003cem\u003eThe British Journal of Social Work\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(1), 376\u0026ndash;378. https://doi.org/10.1093/bjsw/bcaa123\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Castej\u0026oacute;n, J. M., \u0026amp; Formiga, F. (2024). Fragilidad y capacidad intr\u0026iacute;nseca; del concepto te\u0026oacute;rico a la aplicabilidad cl\u0026iacute;nica. \u003cem\u003eRevista Espa\u0026ntilde;ola de Geriatr\u0026iacute;a y Gerontolog\u0026iacute;a\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(5), 101483. https://doi.org/10.1016/j.regg.2024.101483\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1080/13607863.2017.1292208\u003c/li\u003e\n\u003cli\u003ePittman, C., Nieman, C. L., Reed, N. S., Swenor, B. K., \u0026amp; Willink, A. (2022). Sensory Impairment and the Odds of Adverse Consequences of Unmet Needs for Care Among Older Adults. \u003cem\u003eThe Journals of Gerontology: Series B\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(5), 983\u0026ndash;991. https://doi.org/10.1093/geronb/gbab065\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1016/j.rpsm.2016.10.003\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1186/s12877-018-0900-6\u003c/li\u003e\n\u003cli\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., Intxaurrondo Gonz\u0026aacute;lez, A., \u0026amp; Salv\u0026agrave; Casanovas, A. (2023). 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. https://doi.org/10.1186/s12877-023-03799-0\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1186/s12939-024-02136-0\u003c/li\u003e\n\u003cli\u003eSan Jos\u0026eacute; Laporte, A. (2012). La valoraci\u0026oacute;n de la multimorbilidad en personas de edad avanzada. Un \u0026aacute;rea importante de la valoraci\u0026oacute;n geri\u0026aacute;trica integral. \u003cem\u003eRevista Espa\u0026ntilde;ola de Geriatr\u0026iacute;a y Gerontolog\u0026iacute;a\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(2), 47\u0026ndash;48. https://doi.org/10.1016/j.regg.2011.12.001\u003c/li\u003e\n\u003cli\u003eSoria Romero, Z., \u0026amp; Montoya Arce, B. J. (2017). Envejecimiento y factores asociados a la calidad de vida de los adultos mayores en el Estado de M\u0026eacute;xico. \u003cem\u003ePapeles de Poblaci\u0026oacute;n\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(93), 59\u0026ndash;93. https://doi.org/10.22185/24487147.2017.93.022\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1136/jech-2021-218265\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1073/pnas.1915741117\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1111/ggi.14322\u003c/li\u003e\n\u003cli\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. https://doi.org/10.1007/978-3-031-57361-3_5\u003c/li\u003e\n\u003cli\u003eUnited Nations. (2019). \u003cem\u003eUnited Nations. Departament of Economic and Social Affairs. Popiulation division. World Population Prospect: Highlights\u003c/em\u003e. https://population.un.org/wpp/Publications/Files/WPP2019 Highlights.pdf\u003c/li\u003e\n\u003cli\u003eUnited Nations Educational Scientific and Cultural Organization. (2011). \u003cem\u003eInternational Standard Classification of Education: ISCED 2011\u003c/em\u003e. http://uis.unesco.org/sites/default/files/documents/international-standard-classification-of-education-isced-2011-en.pdf\u003c/li\u003e\n\u003cli\u003eUrbano Contreras, A., Mart\u0026iacute;nez Gonz\u0026aacute;lez, R. A., \u0026amp; Iglesias Garc\u0026iacute;a, M. T. (2021). Revisi\u0026oacute;n de la investigaci\u0026oacute;n sobre relaciones de pareja en pa\u0026iacute;ses hispanohablantes (2000-2018). \u003cem\u003eInterdisciplinaria Revista de Psicolog\u0026iacute;a y Ciencias Afines\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(3), 25\u0026ndash;48. https://doi.org/10.16888/interd.2021.38.3.2\u003c/li\u003e\n\u003cli\u003eValencia Mart\u0026iacute;n JL, G. I. S. G. L. C. G. F. S. C. M. B. B. B. (2020). EPISODIOS DE CONSUMO INTENSIVO DE ALCOHOL \u0026ldquo;BINGE DRINKING\u0026rdquo;: RETOS EN SU DEFINICI\u0026Oacute;N E IMPACTO EN SALUD. \u003cem\u003eRev Esp Salud P\u0026uacute;blica. \u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e, 1\u0026ndash;17. https://www.sanidad.gob.es/biblioPublic/publicaciones/recursos_propios/resp/revista_cdrom/VOL94/C_ESPECIALES/RS94C_202011170.pdf\u003c/li\u003e\n\u003cli\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. \u003cem\u003eSocial Indicators Research\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e(1), 61\u0026ndash;77. https://doi.org/10.1007/s11205-007-9220-5\u003c/li\u003e\n\u003cli\u003eWilk, P., Ruiz-Castell, M., Bohn, T., Fagherazzi, G., Nicholson, K., Moran, V., Makovski, T., Pi Alperin, M., Stranges, S., \u0026amp; Samouda, H. (2022). Association between functional limitation and quality of life among older adults with multimorbidity in Luxembourg. \u003cem\u003eEuropean Journal of Public Health\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(Supplement_3). https://doi.org/10.1093/eurpub/ckac129.292\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (1995). Physical status: The use and interpretation of anthropometry. . \u003cem\u003eWorld Health Organization Technical Report Series, 854, 1-452. PMID: 8594834.\u003c/em\u003e https://europepmc.org/article/MED/8594834\u003c/li\u003e\n\u003cli\u003eWorld Health Organization. (2019). \u003cem\u003eIntegrated care for older people (\u0026lrm;ICOPE)\u0026lrm;: guidance for person-centred assessment and pathways in primary care\u003c/em\u003e. https://iris.who.int/handle/10665/326843\u003c/li\u003e\n\u003cli\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 https://iris.who.int/handle/10665/355618\u003c/li\u003e\n\u003cli\u003eZaninotto, P., Falaschetti, E., \u0026amp; Sacker, A. (2009). Age trajectories of quality of life among older adults: results from the English Longitudinal Study of Ageing. \u003cem\u003eQuality of Life Research\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(10), 1301\u0026ndash;1309. https://doi.org/10.1007/s11136-009-9543-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"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":"Intrinsic Capacity, Quality of Life, Healthy Aging, Mental Health, Cognition, Mobility, Social Determinants, Gender, Older Adults.","lastPublishedDoi":"10.21203/rs.3.rs-6252801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6252801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction\u003c/strong\u003e\u003cbr\u003e\nThis study explores the relationship between the domains of intrinsic capacity and quality of life in older adults in Europe, with particular focus on sex and regional differences. It confirms that intrinsic capacity is a multidimensional construct involving interconnected components such as mobility, cognitive function, mental health, and general health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003cbr\u003e\nAn exploratory factor analysis was conducted using data from the SHARE study (Waves 5-6), a longitudinal multinational project. The analysis focused on 11,493 older adults aged 50 and above, residing in 13 European countries. Sociodemographic, health, and socio-economic factors were considered, including variables like mobility difficulties, cognitive performance, depressive symptoms, and self-reported health. The study used harmonized surveys and representative probabilistic sampling to ensure comparability across countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cbr\u003e\nThe results show significant differences between men and women, with women experiencing greater deterioration in key domains such as cognition, mobility, and mental health. Women exhibited higher levels of cognitive decline, which is linked to longer life expectancy and greater exposure to chronic diseases. Social determinants, such as education level and economic status, were found to have a significant impact on QoL and intrinsic capacity, with women in socially vulnerable situations showing higher rates of mental health deterioration, chronic diseases, and economic decline. Regional differences also played a role, with notable variations in health outcomes across european regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nMental health, mobility, and cognition are key determinants of intrinsic capacity and quality of life in older adults. This study highlights the importance of multidimensional approaches and interventions tailored to sex and regional differences to promote healthy aging.\u003c/p\u003e","manuscriptTitle":"Relationship between intrinsic capacity domains and the evolution of quality of life in older adults in Europe: a differential approach between men and women based on the SHARE study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-19 12:17:28","doi":"10.21203/rs.3.rs-6252801/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 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":45851630,"name":"Sociology"}],"tags":[],"updatedAt":"2025-03-19T12:17:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-19 12:17:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6252801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6252801","identity":"rs-6252801","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}