Quality of Life Among Older Adults With Frailty: A Structural Equation Modeling.

PURPOSE: Frailty is a complex age-related clinical condition among older adults. Quality of life (QOL) among older adults with frailty is an important topic of interest. The current cross-sectional study aimed to develop a structural equation model to identify factors affecting QOL. METHOD: A total of 180 older adults with frailty were recruited from general units in two hospitals in Fuzhou, China. Data were collected using the World Health Organization Quality of Life Brief Version, Pittsburgh Sleep Quality Index, Geriatric Depression Scale-15, and Simplified Coping Style Questionnaire. Structural equation modeling was used to analyze data. RESULTS: The final model exhibited a good fit. Poor sleep quality was associated with depression, passive coping style, and poor QOL. Depression was also associated with poor QOL. Passive coping style was associated with better QOL. CONCLUSION: This study demonstrated that sleep quality has direct and indirect effects on QOL of older adults with frailty through depression and passive coping style. Recommendations are that care providers pay attention to biological and physiological variables, symptom status, and functional status of older adults with frailty. This study also provides a theoretical basis for developing interventions that may lead to improvements in QOL among older adults with frailty. [Journal of Psychosocial Nursing and Mental Health Services, 62(5), 49-55.].

Logistic regression technique is comparable to complex machine learning algorithms in predicting cognitive impairment related to post intensive care syndrome.

To evaluate the performance of machine learning (ML) models and to compare it with logistic regression (LR) technique in predicting cognitive impairment related to post intensive care syndrome (PICS-CI). We conducted a prospective observational study of ICU patients at two tertiary hospitals. A cohort of 2079 patients was screened, and finally 481 patients were included. Seven different ML models were considered, decision tree (DT), random forest (RF), XGBoost, neural network (NN), naïve bayes (NB), and support vector machine (SVM), and compared with logistic regression (LR). Discriminative ability was evaluated by area under the receiver operating characteristic curve (AUC), calibration belt plots, and Hosmer-Lemeshow test was used to assess calibration. Decision curve analysis was performed to quantify clinical utility. Duration of delirium, poor Richards-Campbell sleep questionnaire (RCSQ) score, advanced age, and sepsis were the most frequent and important candidates risk factors for PICS-CI. All ML models showed good performance (AUC range: 0.822-0.906). NN model had the highest AUC (0.906 [95% CI 0.857-0.955]), which was slightly higher than, but not significantly different from that of LR (0.898 [95% CI 0.847-0.949]) (P > 0.05, Delong test). Given the overfitting and complexity of some ML models, the LR model was then used to develop a web-based risk calculator to aid decision-making ( https://model871010.shinyapps.io/dynnomapp/ ). In a low dimensional data, LR may yield as good performance as other complex ML models to predict cognitive impairment after ICU hospitalization.

Prevalence of Frailty and Associated Factors Among Hospitalized Older Adults: A Cross-Sectional Study.

This study aimed to estimate the prevalence of frailty and associated factors among hospitalized older adults. It consisted of 184 hospitalized older adults recruited between October 2019 to January 2020. We used the FRAIL scale, Geriatric Depression Scale-15, and the Pittsburg Sleep Quality Index to collect data. Descriptive statistics, independent group t-test, Chi-square (χ2) tests, and logistic regression were applied to statistical analysis. It found that the prevalence of frailty among hospitalized older adults was 39.7%. Depression (Mild: OR = 5.312, 95% CI [2.384-11.833]; Moderate: OR = 6.630, 95% CI [2.077-21.160]) and low activities of daily living (ADL) (Slight dependence: OR = 5.667, 95% CI [1.308-24.557]; Moderate dependence: OR = 15.188, 95% CI [3.342-69.016]; Severe dependence: OR = 5.872, 95% CI [2.645-13.038]) were independent predictors of frailty. Future studies on the interventions to reduce depression, improve ADL and delay the progression of frailty are encouraged. We should focus more on ADL, emotional and psychological state of hospitalized older adults to prevent frailty.

Factors associated with self-management behaviors of patients with chronic obstructive pulmonary disease.

AIMS: To examine self-management behaviors level and discuss the associated factors among chronic obstructive pulmonary disease (COPD) patients. METHODS: A descriptive study design was used. A convenience sample of 124 COPD patients was recruited from three hospitals in Fuzhou. Self-management Behaviors, Social Support Rating Scale and The Family APGAR Score were used to collect data. Descriptive statistics, one-way analysis of variance (ANOVA), Pearson correlation and multiple linear regression were used for data analysis. RESULTS: The overall score of self-management behaviors ranged 4-41, with a mean score of 21.26 ± 7.72. Pearson correlation analyses and ANOVA revealed that age, course of disease, education, marital status, family function, subjective support, objective support, and availability of support were positively correlated with overall self-management behaviors (p < .05). Multiple linear regression analyses revealed that subjective support and availability of support significantly predicted self-management behaviors. CONCLUSIONS: The level of self-management behaviors of COPD patients was suboptimal. We suggest that people in the patients' social network should provide support for them, and help them use available resources to improve the level of self-management behaviors.

Caregiver burden among informal caregivers of hospitalized patients with frailty: A cross-sectional survey.

This study aimed to investigate the status of caregiver burden among informal caregivers of hospitalized frail older patients and explore the associated factors. A total of 191 frail older patients and their informal caregivers were recruited from the inpatient units of the three teaching hospitals. We collected data using the FRAIL scale, the Zarit Burden Interview, and the Simplified Coping Style Questionnaire. We used descriptive statistics, Pearson correlation coefficients, one-way analysis of variance, and multiple linear regression for the data analysis. The caregivers of hospitalized frail older patients experienced a moderate caregiver burden (26.476±11.289). The regression model for caregiver burden was significant. Male caregivers, taking care of patients before hospitalization, experiencing negative emotions, and negative coping styles were significantly associated with caregiver burden, F (7, 183) = 15.13, p <. 001. The findings suggest that early identification of caregiver burden and focused interventions for alleviating caregiver burden are needed.

Attitude of Nurses Toward the Care of Older Adults in China.

Introduction: The aim of this study was to assess the attitudes of registered nurses toward older adults in China. Method: An online questionnaire was sent to registered nurses of five hospitals. The sample included 1,367 registered nurses in this study. Kogan's Attitudes toward Older People Scale and Facts of Aging Quiz were used to collect data. Descriptive statistics, independent t-tests, one-way analysis of variance and logistic regression were used for data analysis. Results: Registered nurses held a positive attitude toward older adults (155.09 ± 21.94). The experience of being cared by older adults (odds ratio [OR] = 1.545, p = .007) and relationship with older adults (OR = 2.440, p = .000) were associated with the attitudes of registered nurses. Discussion: Registered nurses in China held a positive attitude toward older adults. The results contribute to care practice for the older adults in hospitals.