This study aims to investigate the association of lifestyle values with lifestyle and quality of life among community-dwelling late middle-aged and older adults. Methods: This retrospective cross-sectional study analyzed secondary data of 200 late middle-aged and older adults. Lifestyle and lifestyle values were measured by Yonsei Lifestyle Profile (YLP) and YLP-Value, respectively. The mediator was quality of life, and a mediation analysis was conducted to examine the relationship between lifestyle values, lifestyle, and quality of life. Our study then repeated the analysis by dividing lifestyle into three components: physical activity, nutrition/eating habit, and activity participation. Results: Mediation analysis revealed significant associations across all pathways (all p<.001). The lifestyle values were associated with lifestyle (β=.40). Both lifestyle value (β=.40) and lifestyle (β=.18) were positively associated with quality of life. The indirect effect of lifestyle values on quality of life, mediated by lifestyle, accounted for 14.58% of the total effect, with the direct effect constituting 85.42%. Conclusion: Among the three components of lifestyle, a mediation effect was observed between lifestyle values, nutrition/eating habit, and quality of life. Our results indicate the need for education to increase lifestyle values within lifestyle programs, emphasizing the significance of lifestyle values in enhancing both lifestyle and quality of life among late middle-aged and older adults. Health professionals may integrate the results into interventions by recognizing the importance of lifestyle values.

Objectives: This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone. Methods: Data were extracted from 3112 older adults participating in the 2020 Korea Senior Survey. We employed 5 ML models to classify the life satisfaction of older adults living alone: logistic Lasso regression, decision tree-based classification and regression tree (CART), C5.0, random forest, and extreme gradient boost (XGBoost). The variables used as predictors included demographics, health status, functional abilities, environmental factors, and activity participation. The performance of these ML models was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, we assessed the significance of variable importance as indicated by the final classification models. Results: Out of the 1411 older adults living alone, 45.3% expressed satisfaction with their lives. The XGBoost model surpassed the performance of other models, achieving an F1-score of 0.72 and an AUC of 0.75. According to the XGBoost model, the five most important variables influencing life satisfaction were overall community satisfaction, self-rated health, opportunities to interact with neighbors, proximity to a child, and satisfaction with residence. Conclusions Overall satisfaction with the community environment emerged as the most significant predictor of life satisfaction among older adults living alone. These findings indicate that enhancing the supportiveness of the community environment could improve life satisfaction for this demographic.

Objectives: This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone. Methods: Data were extracted from 3112 older adults participating in the 2020 Korea Senior Survey. We employed 5 ML models to classify the life satisfaction of older adults living alone: logistic Lasso regression, decision tree-based classification and regression tree (CART), C5.0, random forest, and extreme gradient boost (XGBoost). The variables used as predictors included demographics, health status, functional abilities, environmental factors, and activity participation. The performance of these ML models was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, we assessed the significance of variable importance as indicated by the final classification models. Results: Out of the 1411 older adults living alone, 45.3% expressed satisfaction with their lives. The XGBoost model surpassed the performance of other models, achieving an F1-score of 0.72 and an AUC of 0.75. According to the XGBoost model, the five most important variables influencing life satisfaction were overall community satisfaction, self-rated health, opportunities to interact with neighbors, proximity to a child, and satisfaction with residence. Conclusions Overall satisfaction with the community environment emerged as the most significant predictor of life satisfaction among older adults living alone. These findings indicate that enhancing the supportiveness of the community environment could improve life satisfaction for this demographic.

Objectives: This study aimed to identify factors associated with life satisfaction by developing machine learning (ML) models to predict life satisfaction in older adults living alone. Methods: Data were extracted from 3112 older adults participating in the 2020 Korea Senior Survey. We employed 5 ML models to classify the life satisfaction of older adults living alone: logistic Lasso regression, decision tree-based classification and regression tree (CART), C5.0, random forest, and extreme gradient boost (XGBoost). The variables used as predictors included demographics, health status, functional abilities, environmental factors, and activity participation. The performance of these ML models was evaluated based on accuracy, precision, recall, F1-score, and area under the curve (AUC). Additionally, we assessed the significance of variable importance as indicated by the final classification models. Results: Out of the 1411 older adults living alone, 45.3% expressed satisfaction with their lives. The XGBoost model surpassed the performance of other models, achieving an F1-score of 0.72 and an AUC of 0.75. According to the XGBoost model, the five most important variables influencing life satisfaction were overall community satisfaction, self-rated health, opportunities to interact with neighbors, proximity to a child, and satisfaction with residence. Conclusions Overall satisfaction with the community environment emerged as the most significant predictor of life satisfaction among older adults living alone. These findings indicate that enhancing the supportiveness of the community environment could improve life satisfaction for this demographic.

Objective: This study explored the intricate relationships among the International Dysphagia Diet Standardisation Initiative (IDDSI) framework, swallowing function, and nutritional status, grounded in the Occupational Therapy Practice Framework (OTPF). This study underscores the need for personalized nutritional management and integrated approaches in patient care by analyzing the clinical application of the IDDSI framework, its correlation with dysphagia risk and nutritional status, and the influence of personal and environmental factors. Methods: This literature review adhered to the PRISMA guidelines and employed the PICO strategy to search the following databases: PubMed, Embase, the Korean Studies Information Service System (KISS), and the Research Information Sharing Service (RISS). The search terms included “dysphagia,” “swallowing disorder,” “deglutition,” “International Dysphagia Diet Standardisation Initiative,” “International Dysphagia Diet Standardization Initiative,” and “IDDSI.” Studies published up to December 2023 that had applied the IDDSI framework to patients requiring modified diets and analyzed variables related to both dysphagia risk and nutritional status were included. Following screening and quality assessment using JBI checklists, seven studies were ultimately selected. Results: The findings reveal that the IDDSI framework transcends simple dietary texture classification, evolving into a tailored management strategy that reflects individual clinical characteristics and care environments. The essential components for this evolution include systematic staff training programs, multidisciplinary team collaboration, financial support, and cultural localization strategies. The application of the IDDSI framework ensures safe eating, and enhances the nutritional status and quality of life of patients. Conclusion Future studies should aim to evaluate the effectiveness of applying the IDDSI framework across diverse cultural contexts and empirically analyze policy and phased implementation strategies that can overcome financial and environmental constraints.

Objective: This study aimed to examine the association between receiving rehabilitation services and dysphagia recovery in the adult population of the United States (U.S.). We hypothesized that individuals receiving rehabilitation services would be more likely to report improved swallowing status. Methods: A retrospective cross-sectional design was used. The study included 868 adults with a history of swallowing problems who completed the 2012 National Health Interview Survey. A multinomial logistic regression model was employed to assess the relationship between a history of using rehabilitation services and current swallowing status compared to 12 months ago, controlling for demographic and clinical characteristics. Results: The majority of participants reported their swallowing status as “about the same” (n=468, 53.9%), followed by “better” (n=251, 28.9%) and “worse” (n=147, 17.2%) compared to 12 months ago. Adults who received rehabilitation services were more likely to have a better swallowing status compared to those with “about the same” status (odds ratio [OR] 1.553, 95% confidence interval [CI] 1.031-2.339). However, adults with depression and memory loss who received rehabilitation services were less likely to report improved swallowing status (OR 0.604, 95% CI 0.396-0.923; OR 0.488, 95% CI 0.299-0.796, respectively). Conclusion The findings demonstrate a strong association between receiving rehabilitation services and improvements in swallowing function among U.S. adults. While this is encouraging, the results also reveal that more work is needed to improve patient outcomes, namely, 1) improving access to rehabilitation care for patients with dysphagia, and 2) implementing targeted initiatives to improve outcomes in specific patient populations.

Background: Impaired executive function is common in older adults. This study examined the causal relationship between the use of information and communication technology (ICT) and executive function in older adults over time.Method: This study performed a secondary analysis of data from four waves (2016–2019) of the National Health and Aging Trends Study. A fixed-effect analysis was conducted to examine the effects of ICT on the executive function of older adults without dementia aged ≥65 years. This study analyzed data from 3,334 respondents. Results: We observed significant positive effects of ICT use on executive function over time (standardized β=0.043–0.045; 95% confidence interval, 0.001–0.043; p<0.05). Conclusion The current findings support the use of ICT as a protective approach to prevent decline in executive function in community-dwelling older adults.

Background: Impaired executive function is common in older adults. This study examined the causal relationship between the use of information and communication technology (ICT) and executive function in older adults over time.Method: This study performed a secondary analysis of data from four waves (2016–2019) of the National Health and Aging Trends Study. A fixed-effect analysis was conducted to examine the effects of ICT on the executive function of older adults without dementia aged ≥65 years. This study analyzed data from 3,334 respondents. Results: We observed significant positive effects of ICT use on executive function over time (standardized β=0.043–0.045; 95% confidence interval, 0.001–0.043; p<0.05). Conclusion The current findings support the use of ICT as a protective approach to prevent decline in executive function in community-dwelling older adults.

Background: Impaired executive function is common in older adults. This study examined the causal relationship between the use of information and communication technology (ICT) and executive function in older adults over time.Method: This study performed a secondary analysis of data from four waves (2016–2019) of the National Health and Aging Trends Study. A fixed-effect analysis was conducted to examine the effects of ICT on the executive function of older adults without dementia aged ≥65 years. This study analyzed data from 3,334 respondents. Results: We observed significant positive effects of ICT use on executive function over time (standardized β=0.043–0.045; 95% confidence interval, 0.001–0.043; p<0.05). Conclusion The current findings support the use of ICT as a protective approach to prevent decline in executive function in community-dwelling older adults.

OBJECTIVES: Few longitudinal studies have explored age-related differences in the relationship between lifestyle factors and cognitive decline. This study investigated lifestyle factors at baseline that slow the longitudinal rate of cognitive decline in young-old (55-64 years), middle-old (65-74 years), and old-old (75+ years) individuals. METHODS: We conducted an 11-year follow-up that included 6,189 older adults from the Korean Longitudinal Study of Aging, which is a cohort study of community-dwelling older Koreans. Lifestyle factors, including physical activity, social activity (SA), smoking, and alcohol consumption were assessed at baseline. Cognitive function was measured at 2-year intervals over 11 years. Latent growth modeling and multi-group analysis were performed. RESULTS: The influence of lifestyle factors on the rate of cognitive decline differed by age. Smoking at baseline (-0.05; 95% confidence interval [CI], -0.11 to -0.00, per study wave) accelerated cognitive decline in young-old individuals, whereas frequent participation in SA at baseline (0.02; 95% CI, 0.01 to 0.03, per study wave) decelerated cognitive decline in middle-old individuals. None of the lifestyle factors in this study decelerated cognitive decline in old-old individuals. CONCLUSIONS Cognitive strategies based on modifiable lifestyle factors such as smoking cessation in young-old individuals and frequent SA participation in middle-old age individuals may have great potential for preventing cognitive decline. Because the influence of lifestyle factors varied by age group, age-specific approaches are recommended to promote cognitive health.