Baduanjin exercise for balance function in community-dwelling older adults with cognitive frailty: a randomized controlled trial protocol

Effects of different Chinese traditional exercises on sleep quality and mental health of adults: systematic review and meta-analysis

OBJECTIVE

To evaluate the effects of traditional Chinese exercises on sleep quality and mental health in adults with different health statuses. METHOD   : This review was registered a priori (CRD42023382188) and executed according to PRISMA statement guidelines. We searched the databases CNKI, China Biology Medicine disc (CBM), Wangfang, PubMed, Web of Science, and Embase from the date of database creation to March 2023. Two researchers independently screened the literature, extracted the data, evaluated the methodological quality of the included literature, and then used Revman 5.4 and Stata 17.0 for the corresponding statistical analysis and graphing.

RESULTS

Thirty-four randomized controlled trials (RCTs) were included, with a total sample of 2563 adults (1276 in the experimental group and 1287 in the control group). The meta-analysis results showed that traditional Chinese exercise significantly improved sleep quality in different groups [WMD =  - 1.58, 95% CI =  - 2.17, - 1.00, P < 0.00001] and that Badaanjin was better than Taijiquan in improving sleep quality. For mental health, traditional Chinese exercise had good effects on anxiety [WMD =  - 1.66, 95% CI =  - 2.64, - 0.69, P = 0.0009] and depression [WMD =  - 1.67, 95% CI =  - 3.23, - 0.11, P = 0.04].

CONCLUSION

Traditional Chinese exercises can improve the quality of sleep and mental health in different groups of people and are highly recommended for good health effects.

Frailty Prevention Care Management Program (FPCMP) on Frailty and Health Function in Community-Dwelling Older Adults: A Quasi-Experimental Trial Protocol

BACKGROUND

Frailty often results from deteriorating muscle strength and decreased physical function in older adults. Frailty includes not only physical components, but also psychological and social aspects. Previous research has shown that exercise programs, especially resistance exercises combined with nutritional care, can reduce frailty.

OBJECTIVES

This study aimed to develop a Frailty Prevention Care Management Program that prevents frailty and improves physical activity and nutrition compared to usual care for community-dwelling older adults.

METHODS

A quasi-experimental and single-blinded trial with a non-equivalent control group using a before-after design will be performed involving Frailty Prevention Care Management Program interventions, taking place both at the communities. Participants will be divided into two different intervention groups and two control groups. All groups will be assessed three times: at baseline, immediately after the intervention, and 3 months post intervention. A total of 72 community-dwelling older adults are recruited. This intervention includes an exercise program (design TRX program) and nutritional education. The control group will not receive any specific exercise training. The primary outcome shall comprise the effect of the Frailty Prevention Care Management Program on frailty using the Taiwanese version of the Tilburg frailty indicator. Secondary outcomes include the effect of physical activity using the Senior Fitness Test and nutrition measures using the Mini Nutritional Assessment-Short Form. A generalized estimating equation is constructed to analyze the effects of the intervention.

CONCLUSIONS

This trial will provide vital information to guide interventions to improve outcomes (frailty, physical activity, and nutrition) and inform the integration of nutrition and TRX exercises in community-dwelling older adults.

Machine Learning Models to Predict Future Frailty in Community-Dwelling Middle-Aged and Older Adults: The ELSA Cohort Study

BACKGROUND

Machine learning (ML) models can be used to predict future frailty in the community setting. However, outcome variables for epidemiologic data sets such as frailty usually have an imbalance between categories, that is, there are far fewer individuals classified as frail than as nonfrail, adversely affecting the performance of ML models when predicting the syndrome.

METHODS

A retrospective cohort study with participants (50 years or older) from the English Longitudinal Study of Ageing who were nonfrail at baseline (2008-2009) and reassessed for the frailty phenotype at 4-year follow-up (2012-2013). Social, clinical, and psychosocial baseline predictors were selected to predict frailty at follow-up in ML models (Logistic Regression, Random Forest [RF], Support Vector Machine, Neural Network, K-nearest neighbor, and Naive Bayes classifier).

RESULTS

Of all the 4 378 nonfrail participants at baseline, 347 became frail at follow-up. The proposed combined oversampling and undersampling method to adjust imbalanced data improved the performance of the models, and RF had the best performance, with areas under the receiver-operating characteristic curve and the precision-recall curve of 0.92 and 0.97, respectively, specificity of 0.83, sensitivity of 0.88, and balanced accuracy of 85.5% for balanced data. Age, chair-rise test, household wealth, balance problems, and self-rated health were the most important frailty predictors in most of the models trained with balanced data.

CONCLUSIONS

ML proved useful in identifying individuals who became frail over time, and this result was made possible by balancing the data set. This study highlighted factors that may be useful in the early detection of frailty.