Novel physical performance-based models for activities of daily living disability prediction among Chinese older community population: a nationally representative survey in China

Association between oral health and physical performance in Brazilian older adults: SABE cohort study

BACKGROUND

Oral health is recognized as integral to general health and impaired dentition status may affect physical performance among older adults. This study evaluated the longitudinal association between clinical and self-reported oral health measures and physical performance (outcome) in Brazilian older adults.

METHODS

This was a longitudinal study that used data from the second (year 2006), third (year 2010) and fourth (year 2015) waves of the Health Well-being and Aging Study conducted in Brazil. Physical performance, evaluated using the Short Physical Performance Battery (SPPB), was the dependent variable. Independent variables of interest were the number of teeth, presence of periodontal pocket, use of dental prostheses, and poor perceived oral health. The association between oral health measures and physical function was analyzed using generalized estimating equations with an ordinal regression model.

RESULTS

In the total sample, every additional tooth was associated with a greater chance of achieving a higher score on the SPPB test. Individuals wearing dental prostheses had higher chances of having higher scores than those not wearing them. In the analyses for the dentate sample, the presence of a periodontal pocket was not associated with SPPB and the increase in the number of teeth increased the chance of achieving a higher score.

CONCLUSION

A greater number of teeth, and using dentures, were associated with higher physical performance. Periodontal disease was not associated with the outcome.

Physical activity as a predictor of activities of daily living in older adults: a longitudinal study in China

Objective

This study aimed to assess the prevalence of physical activity and its association with the progression of difficulty performing activities of daily living among older adults in China.

Methods

A population-based prospective cohort study based on China Family Panel Studies (CFPS) data was conducted in 2018 and 2020. This study used a logistic model to empirically estimate the effects on daily living activities among older adults.

Results

A total of 2073 older adults aged 60 years and above were included, 78.0% of whom did not exercise. The logistic regression model revealed several predictive factors for activity of daily living decline among older adults. These included residence status (OR = 0.672; 95% CI 0.519-0.869; p = 0.002), age (OR = 0.307; 95% CI 0.169-0.557; p < 0.001), ethnicity (OR = 0.511; 95% CI 0.338-0.773; p = 0.001), education level (OR = 2.180; 95% CI 1.366-3.479; p < 0.001), job (OR = 0.601; 95% CI 0.447-0.810; p = 0.001), chronic disease (OR = 0.769; 95% CI 0.604-0.978; p = 0.032) and physical activity (less: OR = 0.464; 95% CI 0.300-0.720; p = 0.001; adequate: OR = 0.512; 95% CI 0.321-0.816; p = 0.005).

Conclusion

Our findings indicate that insufficient physical activity is particularly acute among the older adults. Physical activity has emerged as a significant predictor of decreased daily living activities among older adults. Our research underscores that less and adequate physical activity can prevent a reduction in daily living activities, in contrast to a lack of exercise. The most effective threshold for daily exercise frequency is a session per day, while the ideal exercise duration is 15 min. Additionally, the desired intensity for exercise is characterized by rapid breathing and a noticeable heartbeat, accompanied by slight perspiration. Community nurses play a pivotal role in providing health education on daily exercise to the older adults. It is crucial for nurses in community hospitals to closely monitor the daily exercise habits of the older adults, actively disseminate the benefits of exercise, and enhance their current exercise regimens through effective health education, ultimately improving their quality of life.

Risk prediction models for disability in older adults: a systematic review and critical appraisal

BACKGROUND

The amount of prediction models for disability in older adults is increasing but the prediction performance of different models varies greatly, and the quality of prediction models is still unclear.

OBJECTIVES

To systematically review and critically appraise the studies on risk prediction models for disability in older adults.

METHODS

A systematic literature search was conducted on PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), and Wanfang Database, published up until June 30, 2023. Data were extracted according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability of the included studies. In addition, all included studies were evaluated for clinical value.

RESULTS

A total of 5722 articles were initially retrieved from databases, 16 studies and 17 prediction models were finally included after screening. The sample sizes of studies ranged from 420 to 90,889. Model development methods mainly included logistic regression analysis, Cox proportional hazards regression, and machine learning methods. The C statistic or area under the curve (AUC) of models ranged from 0.650 to 0.853, and nine models had C statistic/AUC higher than 0.75. Age, chronic disease, gender, self-rated health, body mass index (BMI), drinking, smoking and education level were the most common predictors. According to the PROBAST, all included studies were at high risk of bias, and 10 studies were at high concerns for applicability. Only two studies reported following the Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement. After evaluation, only two models reached the standard of clinical value.

CONCLUSION

Although most of the included prediction models had acceptable discrimination, the overall quality and clinical value of the current studies were poor. In the future, researchers should follow the TRIPOD statement and PROBAST checklist to develop prediction models with larger sample sizes, more reasonable study designs, and more scientific analysis methods, to improve the predictive performance and application value.

TRIAL REGISTRATION

The review protocol was registered in PROSPERO (registration ID: CRD42023446657).

Mortality and disability risk among older adults unable to complete grip strength and physical performance tests: a population-based cohort study from China

BACKGROUND

The link between low grip strength, diminished physical performance, and adverse health outcomes in older adults has been well-established. However, the impact of older adults who cannot complete these tests on disability and mortality rates remains unexplored without longitudinal study.

METHODS

We collected data from the China Health and Retirement Longitudinal Study (CHARLS). Participants aged 60-101 were enrolled at baseline. We analyzed the prevalence of populations unable to complete handgrip strength (HGS), gait speed (GS), and five times chair stand test (FTCST). Completing risk models were used to estimate the risk of mortality and disability over seven years.

RESULTS

A total of 3,768 participants were included in the analysis. The percentage of older adults unable to complete the GS and FTCST tests increased notably with age, from 2.68 to 8.90% and 2.60-20.42%, respectively. The proportion of older people unable to perform the HGS was relatively stable, ranging from 1.40 to 3.66%. Compared to older adults who can complete these tests, those who cannot perform FTCST face a significantly higher risk of mortality, with 49.1% higher risk [hazard ratio (HR) = 1.491, 95% CI = 1.156, 1.922; subdistribution hazard ratio (SHR) = 1.491, 95%CI = 1.135,1.958)]. Participants who were unable to complete the GS test had a higher risk of developing ADL disability, regardless of whether they were compared to the lowest-performing group (HR = 1.411, 95%CI = 1.037,1.920; SHR = 1.356, 95%CI = 1.030,1.785) or those who can complete the GS (HR = 1.727, 95%CI = 1.302,2.292; SHR = 1.541, 95%CI = 1.196,1.986). No statistically significant difference in the risk of developing ADL disability among older adults who were unable to complete the HGS test compared with either the poorest performing group (HR = 0.982, 95% CI = 0.578, 1.666; SHR = 1.025, 95% CI = 0.639, 1.642) or those who were able to complete the HGS test (HR = 1.008, 95% CI = 0.601, 1.688; SHR = 0.981, 95% CI = 0.619, 1.553). The risk of all-cause mortality was not significantly different for older adults who were unable to complete the HGS test compared to those with the worst performance (HR = 1.196, 95%CI = 0.709-2.020; SHR = 1.196, 95%CI = 0.674, 2.124) or those who were able to complete the test (HR = 1.462, 95%CI = 0.872-2.450; SHR = 1.462, 95%CI = 0.821,2.605).

CONCLUSION

The risks of adverse events faced by older adults unable to complete the tests vary, indicating the necessity for future research to conduct separate analyses on this high-risk population.

Association between disability in activities of daily living and phase angle in hemodialysis patients

BACKGROUND

Disability in activities of daily living (ADL) significantly increases the risk of mortality among patients undergoing hemodialysis. Malnutrition and decreased exercise capacity are closely correlated with ADL disability. Phase angle (PhA) has been proposed as a measure of nutritional status and exercise capacity. This study aims to investigate the prevalence of ADL disability in hemodialysis patients and its association with PhA.

METHODS

A prospective, observational study was conducted, involving hemodialysis patients treated between November 2019 and January 2020 in an affiliated hospital of Chinese university. ADL was measured using both basic ADL (BADL) scales and instrumental ADL (IADL) scales. PhA measurements were obtained using a BIA device while the patients were in the supine position after dialysis.

RESULTS

A total of 237 hemodialysis patients with a mean age of 60.01 ± 13.55 years were included in this study. The prevalence of disability in ADL was 43.5%. Multivariable analysis results showed a robust association between low PhA and disability in both BADL and IADL (for each unit decrease in PhA: odds ratio 4.83 [95% CI: 2.56-9.0], and 3.57 [95% CI: 2.14-5.95], respectively). The optimal cut-off values of PhA for disability in BADL and IADL were 4.8 and 5.4, with the area under the ROC curve (AUC) were 0.783 (0.727, 0.835) and 0.799 (0.743, 0.848), respectively.

CONCLUSIONS

Low PhA is strongly associated with disability in ADL in hemodialysis patients. These findings suggest that PhA may serve as a potentially objective measure of ADL disability in hemodialysis patients.

Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study

Background

Predicting disability risk in healthy older adults in China is essential for timely preventive interventions, improving their quality of life, and providing scientific evidence for disability prevention. Therefore, developing a machine learning model capable of evaluating disability risk based on longitudinal research data is crucial.

Methods

We conducted a prospective cohort study of 2,175 older adults enrolled in the China Health and Retirement Longitudinal Study (CHARLS) between 2015 and 2018 to develop and validate this prediction model. Several machine learning algorithms (logistic regression, k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, and XGBoost) were used to assess the 3-year risk of developing disability. The optimal cutoff points and adjustment parameters are explored in the training set, the prediction accuracy of the models is compared in the testing set, and the best-performing models are further interpreted.

Results

During a 3-year follow-up period, a total of 505 (23.22%) healthy older adult individuals developed disabilities. Among the 43 features examined, the LASSO regression identified 11 features as significant for model establishment. When comparing six different machine learning models on the testing set, the XGBoost model demonstrated the best performance across various evaluation metrics, including the highest area under the ROC curve (0.803), accuracy (0.757), sensitivity (0.790), and F1 score (0.789), while its specificity was 0.712. The decision curve analysis (DCA) indicated showed that XGBoost had the highest net benefit in most of the threshold ranges. Based on the importance of features determined by SHAP (model interpretation method), the top five important features were identified as right-hand grip strength, depressive symptoms, marital status, respiratory function, and age. Moreover, the SHAP summary plot was used to illustrate the positive or negative effects attributed to the features influenced by XGBoost. The SHAP dependence plot explained how individual features affected the output of the predictive model.

Conclusion

Machine learning-based prediction models can accurately evaluate the likelihood of disability in healthy older adults over a period of 3 years. A combination of XGBoost and SHAP can provide clear explanations for personalized risk prediction and offer a more intuitive understanding of the effect of key features in the model.