Comparison of personality traits and successful aging in older Taiwanese

Definitions of Ageing According to the Perspective of the Psychology of Ageing: A Scoping Review

In the last decades, the scientific interest in ageing has increased due to the progressive ageing of the global population and due to the importance of guaranteeing the elder people and the next generations a good quality of life and biopsychological well-being. However, nowadays, there is not a common and accepted definition of ageing. This situation may refer to the complexity and relevance of the ageing topic and it means that the concept of ageing needs to be understood in a deeper way as a multidimensional and complex process that includes different elements distinctive. The main goal of this review is to explore the definitions and conceptual models of ageing according to a psychological point of view, through a biopsychosocial approach, that integrates biological, psychological, and social aspects with the main goal of a better understanding of the complexity of the process itself.

METHODS

We conducted a review of the literature through PubMed, Scopus, Web of Science and Google Scholar databases, and we followed the PRISMA-ScR guidelines, analysing papers written in English between 2002 and 2023.

RESULTS

The review showed different conceptual models of ageing, including Rowe and Kahn's successful ageing model, the World Health Organization's models, and others like the "Selection, Optimization and Compensation" Model. Also, the determinants and predictors of ageing have been analysed highlighting the individual variability and the multidimensional nature of ageing. The geographic diversity of the included studies allowed for the analysis of cultural, socioeconomic, and environmental differences in the conceptualisation of ageing.

CONCLUSIONS

The results emphasise the importance of targeted interventions and programs aimed at promoting well-being during ageing, considering the complexity and multidimensionality of the ageing process.

Prediction of successful aging using ensemble machine learning algorithms

BACKGROUND

Aging is a chief risk factor for most chronic illnesses and infirmities. The growth in the aged population increases medical costs, thus imposing a heavy financial burden on families and communities. Successful aging (SA) is a positive and qualitative view of aging. From a biomedical perspective, SA is defined as the absence of diseases or disability disorders. This is distinct from normal aging, which is associated with age-related deterioration in physical and cognitive functions. From a social perspective, SA highlights life satisfaction and individual well-being, usually attained through socialization. It is an abstract and multidimensional concept surrounded by imprecision about its definition and measurement. Our study attempted to find the most effective features of SA as defined by Rowe and Kahn's theory. The determined features were used as input parameters of six machine learning (ML) algorithms to create and validate predictive models for SA.

METHODS

In this retrospective study, the raw data set was first pre-processed; then, based on the data of a sample of 983, five basic ML techniques including artificial neural network, decision tree, support vector machine, Naïve Bayes, and k-nearest neighbors (K-NN) with one ensemble method (that gathers 30 K-NN algorithms as weak learners) were trained. Finally, the prediction result was yielded using the majority vote method based on the output of the generated base models.

RESULTS

The experimental results revealed that the predictive system has been more successful in predicting SA with a 93% precision, 92.40% specificity, 87.80% sensitivity, 90.31% F-measure, 89.62% accuracy, and a ROC of 96.10%, using a five-fold cross-validation procedure.

CONCLUSIONS

Our results showed that ML techniques potentially have satisfactory performance in supporting the SA-related decisions of social and health policymakers. The KNN-based ensemble algorithm is superior to the other ML models in classifying people into SA and non-SA classes.

The Prospective Association of Personality Traits and Successful Aging

We used data (N = 928) from ORANJ BOWL, a six-wave panel of adults (aged 50-74 at baseline) to address the association between personality and successful aging at two points in time, 8 years apart. Regressions examined the associations between Wave 2 neuroticism, extraversion, openness, agreeableness, and conscientiousness and Wave 3 subjective success, functional ability, pain, and chronic conditions. Models tested personality traits independently and then simultaneously, with interactions. Confirmatory analyses used Waves 5/6 data. All traits but openness were individually associated with successful aging at both time points. When testing traits simultaneously, only neuroticism and extraversion were consistently associated with subjective success, with an interaction at Waves 2/3. Neuroticism (Waves 2/3) and conscientiousness (Waves 5/6) were associated with functional ability. Neuroticism was associated with pain (Waves 2/3). Personality was not associated with chronic conditions. These analyses set up future work examining relationships between change in personality and change in successful aging.

Applying machine learning methods to develop a successful aging maintenance prediction model based on physical fitness tests

AIM

The purpose of this study was to develop a machine learning prediction model for successful aging (SA) based on physical fitness tests.

METHODS

A total of 3657 community-dwelling adults aged ≥60 years from Nanchang city were recruited in this study. A 3-year follow-up test was carried out for all the participants to determine whether they turn to non-SA. Developed questionnaires and physical fitness tests were used to obtain overall health condition, balance, agility, speed, reactions and gait. Four machine learning models (logistic regression, deep learning, random forest and gradient boosting decision tree) were applied to develop the prediction models, the analyzed sample was 890.

RESULTS

The baseline prevalence of successful aging was 26.99%, The average annual incidence rate of SA to non-SA was 11.04%. There were significant differences between the SA and non-SA groups for all physical fitness tests at baseline. The accuracy and area under the curve of all four machine learning models was >85%, the positive predictive value and sensitivity was >75%, and the specificity was >86% on the average. The deep learning model outperformed the other model, with area under the curve 90.00%, accuracy 89.3%, positive predictive value 85.8% and specificity 93.1%, respectively. Compared with other models, the logistic regression model performed best in sensitivity. Age, arm curl, 30-s sit-to-stand and reaction time were important predictors in all models.

CONCLUSION

The deep learning model is ideal in the prediction of SA maintenance, and the corresponding physical fitness interventions are essential to ensuring SA. Geriatr Gerontol Int 2020; ••: ••-••.