Joan Domènech-Abella1,2,3, Jaime Perales4, Elvira Lara1,2,3, Maria Victoria Moneta1,2,5, Ana Izquierdo2,6, Laura Alejandra Rico-Uribe2,6, Jordi Mundó3, Josep Maria Haro1,2,7. 1. 1 Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain. 2. 2 Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Salud Mental, Madrid, Spain. 3. 3 Universitat de Barcelona, Spain. 4. 4 University of Kansas Medical Center, Kansas City, USA. 5. 5 Sant Joan de Déu Foundation, Barcelona, Spain. 6. 6 Universidad Autónoma de Madrid, Spain. 7. 7 Instituto de Investigación Sanitaria Princesa (IP), Madrid, Spain.
Abstract
OBJECTIVE: Successful aging (SA) refers to maintaining well-being in old age. Several definitions or models of SA exist (biomedical, psychosocial, and mixed). We examined the longitudinal association between various SA models and sociodemographic factors, and analyzed the patterns of change within these models. METHOD: This was a nationally representative follow-up in Spain including 3,625 individuals aged ≥50 years. Some 1,970 individuals were interviewed after 3 years. Linear regression models were used to analyze the survey data. RESULTS: Age, sex, and occupation predicted SA in the biomedical model, while marital status, educational level, and urbanicity predicted SA in the psychosocial model. The remaining models included different sets of these predictors as significant. In the psychosocial model, individuals tended to improve over time but this was not the case in the biomedical model. CONCLUSION: The biomedical and psychosocial components of SA need to be addressed specifically to achieve the best aging trajectories.
OBJECTIVE: Successful aging (SA) refers to maintaining well-being in old age. Several definitions or models of SA exist (biomedical, psychosocial, and mixed). We examined the longitudinal association between various SA models and sociodemographic factors, and analyzed the patterns of change within these models. METHOD: This was a nationally representative follow-up in Spain including 3,625 individuals aged ≥50 years. Some 1,970 individuals were interviewed after 3 years. Linear regression models were used to analyze the survey data. RESULTS: Age, sex, and occupation predicted SA in the biomedical model, while marital status, educational level, and urbanicity predicted SA in the psychosocial model. The remaining models included different sets of these predictors as significant. In the psychosocial model, individuals tended to improve over time but this was not the case in the biomedical model. CONCLUSION: The biomedical and psychosocial components of SA need to be addressed specifically to achieve the best aging trajectories.
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