Correlates of quality of life, happiness and life satisfaction among European adults older than 50 years: A machine-learning approach

How does internet use affect the sense of gain in older adults? A moderated mediation model

In the context of the digital era, Internet use is of major significance for enhancing the sense of gain among elderly people and enabling them to share in the development dividend of the digital society. Based on CGSS 2021 (N = 2,929), this study explores the impact of Internet use on the sense of gain in the elderly, and a moderated mediation model was constructed to explore the mediating role of perceived social justice and the potential moderating role of social status. The results showed that (1) Internet use in general significantly enhanced elderly's sense of gain. Among this group, economic sense of gain (ESG) was significantly enhanced, political sense of gain (PSG) was positively affected but not significantly, while security sense of gain (SSG) was negatively affected. (2) A sense of social equity plays a mediating role in the impact of Internet use on the sense of gain among older adults. (3) Social status does not play a moderating role in the latter half of the path of the mediation model. Meanwhile, heterogeneity analysis revealed that Internet use positively and significantly affects the sense of gain among elderly people with low household incomes, high levels of literacy, an urban household registration, and a spouse. The research conclusion of this article provides strong empirical support for solving the problem of digital integration among the elderly and effectively enhancing their sense of achievement.

Personality traits and quality of life: a cross-sectional study in a middle-aged Iranian general population

INTRODUCTION

Despite considerable research on the association between Personality Traits (PT) and Quality of Life (QoL) in patients and older adults, this association remains poorly understood among the middle-aged general population.This investigation examines how each PT is associated with total QoL and its dimensions.

METHODS

The present investigation utilized data collected from a cross-sectional survey involving 786 families in Isfahan (644 female/wife respondents), Iran. QoL and PT were assessed using the validated WHOQOL-BREF and NEO-FFI questionnaires. Other data including demographic and socioeconomic status (SES) were also collected. Statistical analyses included bivariate correlation and simple and multiple linear and logistic regressions.

RESULTS

Mean value of Psychological health [Mean difference = -4.34, P = 0.003], Physical health [mean difference = -3.93, P = 0.004], and total score of QoL [mean difference = -3.21, P = 0.049] were all significantly lower in women than men. Higher SES score was consistently associated with greater QoL scores (r > 0, P < 0.05). The physical domain and total QoL scores have been negatively correlated with the spouse's age (r < 0, P < 0.05). In crude and adjusted models, higher Neuroticism scores were inversly associated with higher scores of all QoL domains and total QoL (OR < 1, P < 0.05, for all models) while others personality traits except Openness showed a direct association (OR > 1, P < 0.05). Linear regression analysis also confirmed that higher Neuroticism scores were linked to poorer QoL scores (Betacoefficient < 0, P < 0.05), while all other traits, except Openness, showed a positive association (Betacoefficient > 0, P < 0.05).

CONCLUSION

This study provides robust evidence about the significant association of PT with QoL outcomes in middle aged people. This significant association highlights the importance of considering these traits in clinical applications, as tailored interventions based on personality profiles can effectively enhance the well-being of middle-aged individuals.

Machine learning in the prediction of human wellbeing

Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents' self-reported wellbeing. We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using data from 2010 to 2018. We make three contributions. First, we show that ML algorithms can indeed yield better predictive performance than standard approaches, and establish an upper bound on the predictability of wellbeing scores with survey data. Second, we use ML to identify the key drivers of evaluative wellbeing. We show that the variables emphasised in the earlier intuition- and theory-based literature also appear in ML analyses. Third, we illustrate how ML can be used to make a judgement about functional forms, including the existence of satiation points in the effects of income and the U-shaped relationship between age and wellbeing.

Examining subjective well-being during pregnancy and its association with pregnancy intendedness among women in Nigeria: A population-based cross-sectional multilevel study

In this study, we examined the patterns of subjective well-being (SWB) measures among pregnant women and quantified the extent to which pregnancy intendedness is associated with low SWB measures during pregnancy. We analyzed data from the 2021 Nigeria Multiple Indicator Cluster Survey comprising 3,491 pregnant women. The associations between pregnancy intention and low SWB measures (unhappiness, low life satisfaction [LS] and diminished optimism) were determined by fitting series of multilevel logistic regression models with random intercepts. Among pregnant women in our sample 20%, 37.5% and 9.6%, reported being unhappy, experiencing low LS and having diminished optimism, respectively. However, we found no significant association between pregnancy intention and being unhappy (mistimed: adjusted odds ratio [aOR] = 1.19, 95% CI = 0.88-1.60; unwanted: aOR = 1.16, 95% CI = 0.71-1.91), experiencing low LS (mistimed: aOR = 1.07, 95% CI = 0.83-1.37; unwanted: aOR = 1.06, 95% CI = 0.69-1.65) and having diminished optimism (mistimed: aOR = 1.22, 95% CI = 0.82-1.82; unwanted: aOR = 1.07, 95% CI = 0.56-2.04). Findings from the study suggest that pregnant women in Nigeria who reported having either a mistimed or unwanted pregnancy were just as likely to report being unhappy, experience low LS and have diminished optimism as women whose pregnancy was intended.

A hybrid self-supervised model predicting life satisfaction in South Korea

OBJECTIVE

Life satisfaction pertains to an individual's subjective evaluation of their life quality, grounded in their personal criteria. It stands as a crucial cognitive aspect of subjective wellbeing, offering a reliable gauge of a person's comprehensive wellbeing status. In this research, our objective is to develop a hybrid self-supervised model tailored for predicting individuals' life satisfaction in South Korea.

METHODS

We employed the Busan Metropolitan City Social Survey Data in 2021, a comprehensive dataset compiled by the Big Data Statistics Division of Busan Metropolitan City. After preprocessing, our analysis focused on a total of 32,390 individuals with 51 variables. We developed the self-supervised pre-training TabNet model as a key component of this study. In addition, we integrated the proposed model with the Local Interpretable Model-agnostic Explanation (LIME) technique to enhance the ease and intuitiveness of interpreting local model behavior.

RESULTS

The performance of our advanced model surpassed conventional tree-based ML models, registering an AUC of 0.7778 for the training set and 0.7757 for the test set. Furthermore, our integrated model simplifies and clarifies the interpretation of local model actions, effectively navigating past the intricate nuances of TabNet's standard explanatory mechanisms.

CONCLUSION

Our proposed model offers a transparent understanding of AI decisions, making it a valuable tool for professionals in the social sciences and psychology, even if they lack expertise in data analytics.

Predicting life satisfaction using machine learning and explainable AI

Life satisfaction is a crucial facet of human well-being. Hence, research on life satisfaction is incumbent for understanding how individuals experience their lives and influencing interventions targeted at enhancing mental health and well-being. Life satisfaction has traditionally been measured using analog, complicated, and frequently error-prone methods. These methods raise questions concerning validation and propagation. However, this study demonstrates the potential for machine learning algorithms to predict life satisfaction with a high accuracy of 93.80% and a 73.00% macro F1-score. The dataset comes from a government survey of 19000 people aged 16-64 years in Denmark. Using feature learning techniques, 27 significant questions for assessing contentment were extracted, making the study highly reproducible, simple, and easily interpretable. Furthermore, clinical and biomedical large language models (LLMs) were explored for predicting life satisfaction by converting tabular data into natural language sentences through mapping and adding meaningful counterparts, achieving an accuracy of 93.74% and macro F1-score of 73.21%. It was found that life satisfaction prediction is more closely related to the biomedical domain than the clinical domain. Ablation studies were also conducted to understand the impact of data resampling and feature selection techniques on model performance. Moreover, the correlation between primary determinants with different age brackets was analyzed, and it was found that health condition is the most important determinant across all ages. The best performing Machine Learning model trained in this study is deployed on a public server, ensuring unrestricted usage of the model. We highlight the advantages of machine learning methods for predicting life satisfaction and the significance of XAI for interpreting and validating these predictions. This study demonstrates how machine learning, large language models and XAI can jointly contribute to building trust and understanding in using AI to investigate human behavior, with significant ramifications for academics and professionals working to quantify and comprehend subjective well-being.

Exploring influential factors in the self-assessment of life satisfaction among Chinese elderly: a structural equation modeling analysis

The aging problem is becoming more and more prominent globally. Attention to the quality of life and related health improvement among the elderly has become an important issue in modern society. This study utilized a tracking survey conducted in 2017-2018, involving 9,327 Chinese older adults, to examine health influencing factors, and applied structural equation modeling to analyze the influencing factors on the self-assessment of life satisfaction among older adults in different regions (cities, counties, and villages) in China. This study revealed that economic status, psychological status, personal situation, life behaviors, and child care are important influences on older people's self- assessed life satisfaction. There is a positive correlation between economic status, psychological status, child care and the results of the self-assessment of life satisfaction of the elderly. Psychological status and child care have a greater impact on the self-assessment of life satisfaction among the elderly in urban areas compared to villages and towns. The influence of economic status on the self-assessment of life satisfaction of the elderly is lower in urban areas than in rural areas. There is a significant difference in the influence of personal situations on the self-assessment of life satisfaction among the elderly. Additionally, older individuals tend to report higher levels of self-assessment of life satisfaction. Furthermore, female elderly individuals tend to report higher levels of satisfaction compared to males. Findings from this study indicate that improving health self-assessment in older adults requires targeted efforts based on different geographic areas of life and the age stages of older adults, and more attention needs to be paid to men who are just entering old age.

Effective analysis of job satisfaction among medical staff in Chinese public hospitals: a random forest model

Objective

This study explored the factors and influence degree of job satisfaction among medical staff in Chinese public hospitals by constructing the optimal discriminant model.

Methods

The participant sample is based on the service volume of 12,405 officially appointed medical staff from different departments of 16 public hospitals for three consecutive years from 2017 to 2019. All medical staff (doctors, nurses, administrative personnel) invited to participate in the survey for the current year will no longer repeat their participation. The importance of all associated factors and the optimal evaluation model has been calculated.

Results

The overall job satisfaction of medical staff is 25.62%. The most important factors affecting medical staff satisfaction are: Value staff opinions (Q10), Get recognition for your work (Q11), Democracy (Q9), and Performance Evaluation Satisfaction (Q5). The random forest model is the best evaluation model for medical staff satisfaction, and its prediction accuracy is higher than other similar models.

Conclusion

The improvement of medical staff job satisfaction is significantly related to the improvement of democracy, recognition of work, and increased employee performance. It has shown that improving these five key variables can maximize the job satisfaction and motivation of medical staff. The random forest model can maximize the accuracy and effectiveness of similar research.

Determinants of the happiness of adolescents: A leisure perspective

Leisure plays a key role in the happiness of youth. Studies have shown that various factors of leisure, such as the type, the time, the cost, and the space, have an influence on the adolescents' happiness. However, little is known about which of these factors is a major factor in youth's happiness. The purpose of this study is to explore the leisure factors that determine happiness in adolescents by examining the relationship between happiness and various leisure factors. The study used the method of machine learning to analyze national statistical data, National Leisure Activity Survey. The data used in this study were from the National Leisure Activity Survey 2019, which is a national statistic produced by the Ministry of Culture, Sports and Tourism in the Republic of Korea. The analysis found that leisure perceptions, academic and leisure balance, and public leisure space have a very important impact on the adolescents' well-being. The findings of this research may contribute to a better understanding of leisure and happiness in adolescents, and will also help adolescents make better use of their leisure time, leading to better leisure lives, and ultimately contribute to raising their level of happiness.

V. Confirmatory factor analysis of the effect of daily-living on the happiness of community-dwelling older adults in Chile

This study examined the effect of the human-functioning dimension on happiness among community-dwelling older adults (OAs) in Chile. Questionnaires were used for data collection from a sample of 785 OAs of both sexes attending healthcare institutions. Exploratory factor analysis was performed using parallel analysis and oblique rotation. Confirmatory factor analysis and structural equation modeling were conducted using the maximum likelihood and unweighted least squares methods. Goodness-of-fit analyses were performed by considering absolute and respective incremental fit indices. The relationships between the functioning and happiness factors were all significant at the 1% level, indicating that functioning impacts happiness. The ratios of the variances between both constructs were identical to those of the covariances, indicating consistency between the models, with similarities and equalities in the estimation of their parameters. The modeling confirms a direct relationship between activities of daily living functioning and happiness. Given that a lack of functioning significantly affects OAs' happiness and quality of life, this relationship is consistent with the available theory. These findings may contribute to the formulation of social and health policies regarding OAs in Chile and other Latin American countries.

Understanding the impact of sisu on workforce and well-being: A machine learning-based analysis

This study investigates the construct of sisu, a Finnish attribute representing mental resilience and fortitude when confronted with difficult situations. By leveraging advanced analytical methods and explainable Artificial Intelligence, we gain insights into how sisu factors influence well-being, work efficiency, and overall health. We investigate how the beneficial aspects of sisu contribute significantly to mental and physical health, satisfaction, and professional accomplishments. Conversely, we analyze the harmful sisu and its adverse impacts on the same domains. Our findings, including intriguing trends related to age, educational level, emotional states, and gender, pave the way for developing tailored solutions and initiatives to nurture the beneficial aspects of sisu and curtail the damaging consequences of sisu within professional settings and personal welfare.

Quality of life and socio-demographic factors associated with nutritional risk in Brazilian community-dwelling individuals aged 80 and over: cluster analysis and ensemble methods

Introduction

The aim of the present study was to use cluster analysis and ensemble methods to evaluate the association between quality of life, socio-demographic factors to predict nutritional risk in community-dwelling Brazilians aged 80 and over.

Methods

This cross-sectional study included 104 individuals, both sexes, from different community locations. Firstly, the participants answered the sociodemographic questionnaire, and were sampled for anthropometric data. Subsequently, the Mini-Mental State Examination (MMSE) was applied, and Mini Nutritional Assessment Questionnaire (MAN) was used to evaluate their nutritional status. Finally, quality of life (QoL) was assessed by a brief version of World Health Organizations' Quality of Life (WHOQOL-BREF) questionnaire and its older adults' version (WHOQOL-OLD).

Results

The K-means algorithm was used to identify clusters of individuals regarding quality-of-life characteristics. In addition, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms were used to predict nutritional risk. Four major clusters were derived. Although there was a higher proportion of individuals aged 80 and over with nutritional risk in cluster 2 and a lower proportion in cluster 3, there was no statistically significant association. Cluster 1 showed the highest scores for psychological, social, and environmental domains, while cluster 4 exhibited the worst scores for the social and environmental domains of WHOQOL-BREF and for autonomy, past, present, and future activities, and intimacy of WHOQOL-OLD.

Conclusion

Handgrip, household income, and MMSE were the most important predictors of nutritional. On the other hand, sex, self-reported health, and number of teeth showed the lowest levels of influence in the construction of models to evaluate nutritional risk. Taken together, there was no association between clusters based on quality-of-life domains and nutritional risk, however, predictive models can be used as a complementary tool to evaluate nutritional risk in individuals aged 80 and over.

Physical Activity, Functional Status, and Quality of Life Among Older Adults in India

Introduction

Physical activity and social engagement protect older adults against functional decline and improve their quality of life. Physical inactivity poses an increased risk for noncommunicable diseases. Globally, one in four adults is physically inactive. Enhancing the physical activity of older adults is crucial not only for increasing their life expectancy but also for improving their functional status and quality of life.

Objective

This study aimed to evaluate the physical activity, functional status, and quality of life of older adults attending the medical outpatient department of a tertiary care hospital in Chennai, Tamil Nadu, India.

Methods

The study adopted a descriptive cross-sectional research design. A total of 100 female and male patients above 65 years of age with comorbid conditions attending the medical outpatient department participated in the study. The sociodemographic and clinical characteristics of these patients were assessed. In addition, the Rapid Assessment of Physical Activity, Katz Index of Independence in Activities of Daily Living, Lawton Instrumental Activities of Daily Living Scale, and Older People Quality of Life Questionnaire were used to measure the physical activity, functional status, and quality of life of patients. The data were analyzed using descriptive and inferential statistics.

Results

Among the participants, only 17% were active, and 35% were underactive for regular light activities. Approximately 81% did not perform any activity to improve their muscle strength or flexibility. In 56% of the participants, disability was present. The majority mentioned that they experienced difficulties in preparing food and shopping. Regarding the quality of life of the participants, the mean ± standard deviation score was 77.38 ± 9.03. Significant results were found in the domains of independence, control over life, and freedom. The overall quality of life was not related to leisure and activities, financial circumstances, the Instrumental Activities of Daily Living Scale score, the Rapid Assessment of Physical Activity 2 score, and the Rapid Assessment of Physical Activity 1 score. The quality of life was weakly related to emotional and psychological well-being, control over life, home and neighborhood, freedom, independence, and social relationships.

Conclusion

Limited physical activity and poor functional status impact the quality of life of older adults. Therefore, educational and physical interventions should be implemented at the hospital and community levels to improve the physical activity, functional status, and quality of life of older adults.

Understanding Key Predictors of Life Satisfaction in a Nationally Representative Sample of Koreans

The purpose of this study was to examine the factors that predict life satisfaction in a large representative sample of Koreans by analyzing data from the Gallup World Poll. The primary objective was to identify important predictors and suggest strategies to improve quality of life in Korea. The study used available Korean data from 2006 to 2017, which included 14,101 participants (mean age = 46.42). Predictors included demographic and psychological variables, with the Cantril Ladder of Life Scale serving as the outcome variable. The results show a decline in life satisfaction with advancing age, and that the relationship between life satisfaction and age varied by gender. Among the predictors examined, satisfaction with the standard of living and household income emerged as the most influential factors in determining life evaluation; other strong predictors included positive affect and negative affect, social support, gender, and education level. These results imply that, to increase life satisfaction, it is imperative to provide job opportunities and social services specifically targeted to individuals in low-income groups. In addition, it is crucial to implement tailored psychosocial interventions that address the unique developmental tasks and psychological challenges experienced by individuals according to their gender and life cycle stage.

What Is the Mediating Role of Communication Skills and Sexual Satisfaction between Job and Life Satisfaction of Healthcare Employees?

There are three areas of harmony in human life that are related to each other: "work environment", "love-to be loved-sexual area" and "social environment". Incompatibility and dissatisfaction in one area can also affect other areas. Therefore, the aim of this study is to examine the relationship between job satisfaction, life satisfaction, communication, and sexual satisfaction of healthcare employees. The data collected by questionnaires from 394 employees working in university hospitals in Turkey were analysed using SPSS and AMOS programs. The findings show that there is a positive relationship between the job and life satisfaction of employees of healthcare organizations. Additionally, the findings revealed that communication skills and sexual satisfaction have a mediating role between job satisfaction and life satisfaction of employees in healthcare organizations. Life satisfaction, sexual satisfaction, and relationships are some of the factors that should be considered by healthcare organizations. It would be beneficial to employees and the public if health policy makers implemented programmes to enhance job satisfaction.

Clinical decision support system for quality of life among the elderly: an approach using artificial neural network

Background

Due to advancements in medicine and the elderly population’s growth with various disabilities, attention to QoL among this age group is crucial. Early prediction of the QoL among the elderly by multiple care providers leads to decreased physical and mental disorders and increased social and environmental participation among them by considering all factors affecting it. So far, it is not designed the prediction system for QoL in this regard. Therefore, this study aimed to develop the CDSS based on ANN as an ML technique by considering the physical, psychiatric, and social factors.

Methods

In this developmental and applied study, we investigated the 980 cases associated with pleasant and unpleasant elderlies QoL cases. We used the BLR and simple correlation coefficient methods to attain the essential factors affecting the QoL among the elderly. Then three BP configurations, including CF-BP, FF-BP, and E-BP, were compared to get the best model for predicting the QoL.

Results

Based on the BLR, the 13 factors were considered the best factors affecting the elderly’s QoL at P < 0.05. Comparing all ANN configurations showed that the CF-BP with the 13-16-1 structure with sensitivity = 0.95, specificity  =  0.97, accuracy = 0.96, F-Score = 0.96, PPV = 0.95, and NPV = 0.97 gained the best performance for QoL among the elderly.

Conclusion

The results of this study showed that the designed CDSS based on the CFBP could be considered an efficient tool for increasing the QoL among the elderly.