Prevalence and factors associated with frailty and pre-frailty in the older adults in China: a national cross-sectional study

Study on the current status and influencing factors of physical activity in pre-frail rural empty-nest older adults

OBJECTIVE

To evaluate the current status of physical activity and its influencing factors among pre-frail rural empty-nest older adults, and to provide targeted recommendations for improving the quality of life for rural empty-nest older adults.

METHODS

A purposive sampling method, considering convenience, was used to select participants. Between May and December 2023, a questionnaire survey was conducted among pre-frail rural empty-nest older adults in Chaoyang County, Liaoning Province. The survey included a demographic information form, a lifestyle behavior questionnaire, a nutrition risk assessment scale, and a physical activity scale for the older adults.

RESULTS

A total of 522 pre-frail older adults were included in this study. The median score for physical activity was 162.5 (109.0, 229.0), with walking (98.5%) and household physical activities (85.8%) being the predominant forms of activity. Logistic regression analysis revealed that the presence of hypertension (OR = 1.537), coronary heart disease (OR = 1.490), respiratory diseases (OR = 1.534), osteoarthritis (OR = 1.726), and malnutrition (OR = 1.637) were independent risk factors for low physical activity levels in pre-frail rural empty-nest older adults (P < 0.05).

CONCLUSION

Physical activity levels among pre-frail rural empty-nest older adults are low, with walking and household activities being the primary forms of exercise. Community healthcare providers should enhance physical activity management for this population, conduct health education on chronic diseases, help foster healthy eating habits, and prevent the risk of malnutrition. These measures will help improve physical activity levels and potentially delay or even reverse the frailty state.

CLINICAL TRIAL NUMBER

Not applicable.

The Association Between Urbanization and Frailty Status in China

Background: A frailty index (FI) can identify individuals with frailty in a population of interest. Previous literature suggests a need for frailty assessment methods for older adults in China and that urbanization may impact frailty status. We used a FI to examine the association between frailty and urbanization as living in a less urbanized area may put older adults at a higher risk frailty and poor healthcare outcomes. Methods: We included adults aged 55 years and older (n = 7695) from the China Health and Nutrition Survey (2018). The FI was based on health outcomes correlating with a deficit score divided by number of health items: robust (<0.08), pre-frail (0.08-0.24), and frail (≥0.25). We used multinomial logistic regression models to examine associations between urbanization tertile (low, medium, and high) and frailty, using our novel FI. We also conducted sub-analyses examining how urbanization level modifies the relationship between frailty status and region of residence, and education and income levels. Results: Living in an area of low versus high urbanization was associated with higher odds of frail versus robust (1.5; 1.2-2.0), and pre-frail versus robust (1.6; 1.4-2.0) status in the fully adjusted model. Generally, higher odds of worse frailty status (e.g., pre-frail or frail) was associated with lower tertiles of urbanization for region, income, and education when compared to the highest urbanization tertile. Conclusions: A FI can help identify specific characteristics that may benefit from individualized interventions to counteract frailty. Living in less urbanized areas was associated with higher odds of pre-frailty and frailty. Inclusion of urbanization level, geographic residence, and social determinants of health in FI development can help identify older adults most at risk of frailty and contribute to individual and policy-level frailty prevention interventions.

Machine Learning Models for Frailty Classification of Older Adults in Northern Thailand: Model Development and Validation Study

Background

Frailty is defined as a clinical state of increased vulnerability due to the age-associated decline of an individual's physical function resulting in increased morbidity and mortality when exposed to acute stressors. Early identification and management can reverse individuals with frailty to being robust once more. However, we found no integration of machine learning (ML) tools and frailty screening and surveillance studies in Thailand despite the abundance of evidence of frailty assessment using ML globally and in Asia.

Objective

We propose an approach for early diagnosis of frailty in community-dwelling older individuals in Thailand using an ML model generated from individual characteristics and anthropometric data.

Methods

Datasets including 2692 community-dwelling Thai older adults in Lampang from 2016 and 2017 were used for model development and internal validation. The derived models were externally validated with a dataset of community-dwelling older adults in Chiang Mai from 2021. The ML algorithms implemented in this study include the k-nearest neighbors algorithm, random forest ML algorithms, multilayer perceptron artificial neural network, logistic regression models, gradient boosting classifier, and linear support vector machine classifier.

Results

Logistic regression showed the best overall discrimination performance with a mean area under the receiver operating characteristic curve of 0.81 (95% CI 0.75-0.86) in the internal validation dataset and 0.75 (95% CI 0.71-0.78) in the external validation dataset. The model was also well-calibrated to the expected probability of the external validation dataset.

Conclusions

Our findings showed that our models have the potential to be utilized as a screening tool using simple, accessible demographic and explainable clinical variables in Thai community-dwelling older persons to identify individuals with frailty who require early intervention to become physically robust.

The relationship among socioeconomic status, social support and frailty: is there a gender difference?

OBJECTIVE

This study aimed to determine the relationship among socioeconomic status, social support and frailty, and its gender difference.

METHODS

Education and income were combined to indicate the socioeconomic status. The Social Support Rating Scale (SSRS) was used to measure the level of social support. Frailty was measured by the FRAIL Scale. Mediation effects were analyzed using the PROCESS 4.1 macro in SPSS version 26.0.

RESULTS

Among the 936 participants, socioeconomic status had a direct effect on frailty (effect = - 0.088, 95% CI: - 0.142, - 0.021). Social support was an indirect pathway for the relationship between socioeconomic status and frailty (effect = - 0.011, 95% CI: - 0.023, - 0.003), accounting for 11.11% of the total effect. Stratified by gender, we found that the total, direct and indirect effects of socioeconomic status on frailty were significant only in the female subsample.

CONCLUSION

Overall, there was a significant association between socioeconomic status and frailty among the rural older adults, and social support mediated this relationship. However, there were gender differences in the association among socioeconomic status, social support and frailty. Specifically, the correlation between socioeconomic status and frailty and the mediating role of social support were found only in the female subsample. The public health sector should focus on the rural older adults with low socioeconomic status and lack of social support, taking targeted interventions to avoid and delay the occurrence and progress of frailty.

Risk prediction model of physical frailty for a rural older population: a cross-sectional study in Hunan Province, China

Introduction

Physical frailty is a common medical syndrome characterized by low muscle strength, low endurance, and reduced physiological function that leads to significantly negative health outcomes in older adults. This study investigated the risk variables among rural older adults in Hunan Province, China, and developed a physical frailty prediction model to inform policymaking to enhance their health and well-being.

Methods

This study was conducted from July 22 to September 3, 2022. A total of 291 participants were recruited using stratified cluster random sampling from five large villages in Hunan Province. Frailty screening was performed based on the Fatigue, Resistance, Ambulation, Illnesses, and Loss of Weight (FRAIL) scale, Geriatric Depression Scale 15-item version (GDS-15), Falls Efficacy Scale-International (FES-I), and Mini Nutrition Assessment-Short Form (MNA-SF). A logistic regression analysis was performed to identify the predictive factors for physical frailty and develop a physical frailty prediction model based on the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, and Youden index.

Results

The physical frailty prevalence among rural older adults in Hunan Province was 21.31% (n = 62). Household income and expenditure [odds ratio (OR): 1.826, 95% confidence interval (CI): 1.142-2.918], physical exercise frequency (OR: 1.669, 95% CI: 1.137-2.451), depressive symptoms (OR: 9.069, 95% CI: 3.497-23.516), and fear of falling (OR: 3.135, 95% CI: 1.689-5.818) were identified as significant predictors of physical frailty in rural older individuals. The AUC for the frailty predictive model was 0.860 (95% CI: 0.805, 0.914). The sensitivity and specificity at the optimal cutoff value were 80.6 and 76.0%, respectively, with a Youden index of 0.566.

Conclusion

The prediction model constructed in this study demonstrated promise as a potential tool for evaluating physical frailty risk in older adults, which can contribute to healthcare providers' screenings for high-risk populations. Further multidimensional and experimental intervention studies should be conducted to prevent the occurrence and delay the progression of physical frailty in older adults.

Development of a Frailty Prediction Model Among Older Adults in China: A Cross-Sectional Analysis Using the Chinese Longitudinal Healthy Longevity Survey

AIMS

To identify the risk factors associated with frailty among older adults in China and develop a predictive model for assessing their frailty risk.

DESIGN

Secondary cross-sectional analysis.

METHODS

The 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) provided data for this study. A total of 9006 participants were included in the analysis. Their general demographic, socioeconomic status and health behaviour risk factors were collected in the CLHLS. Frailty was assessed using the Frailty Index. A visual nomogram model was constructed based on independent predictors identified using multivariate analysis. The nomogram's discrimination and calibration capabilities were evaluated using the C-statistics and calibration curves. A 1000-times resampling enhanced bootstrap method was performed for internal validation of the nomogram.

RESULTS

The results showed that living in rural settings, having a primary education level, having a spouse, having basic living security, smoking, drinking, exercising and social activities were protective factors against frailty. Increasing age, being underweight or obese, adverse self-assessed economic status and poor sleep quality were risk factors of frailty. The AUC values of the internal validation set were 0.830. The calibration curve was close to ideal. The Brier score was 0.122. The above results showed that the nomogram model had a good predictive performance.

CONCLUSIONS

A simple and fast frailty risk prediction model was developed in this study to help healthcare professionals screen older adults at high risk of frailty in China.

IMPACT

The frailty risk prediction model will assist healthcare professionals in risk management and decision-making and provide targeted frailty prevention interventions. Screening high-risk older adults and early intervention can reduce the risk of adverse outcomes and save medical expenses for older adults and society, thereby realising cost-effective planning of health resources and healthy ageing.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution. This study was a cross-sectional, secondary analysis of the CLHLS data.

Does Medical Insurance Integration Reduce Frailty Risk? Evidence From Rural Older Adults in China

OBJECTIVES

This study aimed to assess the impacts of China's health insurance integration reform on frailty among rural older adults.

METHODS

Nationally representative longitudinal data with 2,751 adults aged ≥60 years were analyzed from the China Health and Retirement Longitudinal Study 2011-2015. The integration of the rural New Cooperative Medical Scheme and urban Resident Basic Medical Insurance into the unified Urban and Rural Resident Basic Medical Insurance (URRBMI). Frailty Index (FI) summarizes 32 health deficits, quantifying frailty severity with a range of 0-1. Frailty is defined as FI ≥ 0.25, prefrailty as FI: 0.10-0.25, and robustness as FI < 0.10. Frailty worsening, stability, and improvement from 2011 to 2015 were assessed. Difference-in-differences and propensity score matched difference-in-differences models assessed URRBMI integration effects on frailty severity and risk (FI ≥ 0.25) among rural older adults.

RESULTS

URRBMI integration significantly reduced frailty severity by 15.16% and risk by 9.60% points among rural older adults. Reductions were greatest among initially prefrail individuals, with 27.49% lower frailty severity and a 17.62% point reduction in subsequent frailty onset risk after URRBMI integration. In contrast, no significant benefits were observed for initially robust or frail subgroups following integration. Analyses of frailty transitions corroborated selective benefits, with URRBMI integration lowering the risks of worsening frailty among prefrail but no significant reversal of frailty status among those initially frail or prefrail.

DISCUSSION

China's URRBMI integration selectively ameliorated frailty progression among rural older adults with prefrail status. Targeting integrated medical insurance policies toward high-risk populations may optimize frailty prevention effects.

Development of a nomogram for predicting acute pain among patients after abdominal surgery: A prospective observational study

AIMS

To develop a nomogram to provide a screening tool for recognising patients at risk of post-operative pain undergoing abdominal operations.

BACKGROUND

Risk prediction models for acute post-operative pain can allow initiating prevention strategies, which are valuable for post-operative pain management and recovery. Despite the increasing number of studies on risk factors, there were inconsistent findings across different studies. In addition, few studies have comprehensively explored predictors of post-operative acute pain and built prediction models.

DESIGN

A prospective observational study.

METHODS

A total of 352 patients undergoing abdominal operations from June 2022 to December 2022 participated in this investigation. A nomogram was developed for predicting the probability of acute pain after abdominal surgery according to the results of binary logistic regression. The nomogram's predictive performance was assessed by discrimination and calibration. Internal validation was performed via Bootstrap with 1000 re-samplings.

RESULTS

A total of 139 patients experienced acute post-operative pain following abdominal surgery, with an incidence of 39.49%. Age <60, marital status (unmarried, divorced, or widowed), consumption of intraoperative remifentanil >2 mg, indwelling of drainage tubes, poor quality sleep, high pain catastrophizing, low pain self-efficacy, and PCIA not used were predictors of inadequate pain control in patients after abdominal surgery. Using these variables, we developed a nomogram model. All tested indicators showed that the model has reliable discrimination and calibration.

CONCLUSIONS

This study established an online dynamic predictive model that can offer an individualised risk assessment of acute pain after abdominal surgery. Our model had good differentiation and calibration and was verified internally as a useful tool for risk assessment.

RELEVANCE TO CLINICAL PRACTICE

The constructed nomogram model could be a practical tool for predicting the risk of experiencing acute post-operative pain in patients undergoing abdominal operations, which would be helpful to realise personalised management and prevention strategies for post-operative pain.

REPORTING METHOD

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines were adopted in this study.

PATIENT OR PUBLIC CONTRIBUTION

Before the surgery, research group members visited the patients who met the inclusion criteria and explained the purpose and scope of the study to them. After informed consent, they completed the questionnaire. The patients' pain scores (VAS) were regularly assessed and documented by the bedside nurse for the first 3 days following surgery. Other information was obtained from medical records.