View clinical trials related to Age Problem.
Filter by:Background and purposes: Square-stepping exercise (SSE) has been proved to be an effective intervention for motor and cognitive function in older adults. However, the underlying mechanism of SSE still remains undetermined. Therefore, the aim of this study is to elucidate the possible mechanism of SSE in healthy older adults. Methods: This is a cross-sectional study. Inclusion criteria are: (1) age between 65 and 90 years, (2) no frailty indicated by Fried frailty criteria, (3) mini-mental state examination score≧24 and Montreal Cognitive Assessment score≧26, (4) ability to walk independently for 1 min. Brain activation differences between SSE patterns and usual walking, as well as relationships between brain activity, cognitive function, physical performance and SSE performance will be examined. This study will address both cognitive and motor aspects of possible mechanism in SSE. SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) will be used to analyze the collected data in this study. One-way ANOVA with repeated measures is used to evaluate the differences in brain activation among usual walking, SSE-pattern 1, and SSE-pattern 2, with Bonferroni test for post hoc analysis. The Pearson correlation coefficient will be used to establish the relationships between brain activity and SSEs performance, between cognitive function and SSEs performance, and between motor function and SSEs performance. The significant level is set at p< .05.
The purpose of this project is to combine a novel posturogrpahy based on HTC VIVE trackers and hybrid machine learning and deep learning algorithms to establish a set of simple, convenient and valid fall risk assessment tool. This observational and follow up study will community elderly aged over 60 years old. The investigators will collect demographic data, questionnaire surveys, traditional balance tests and the tracker-based posturography to obtain the trunk stability parameters in different standing task. The fall risk will be classified according to self-reported falls n the past one year and verified in a 6-month follow up. The investigators will evaluate the performance of different hybrid machine learning and deep learning algorithm to extract the important features of multiple posturographic parameters and select an optimal model. The investigators will use the receiver operating characteristic curve analysis to compute the sensitivity, specificity and accuracy of different algorithms for risk classification and also compare the performance with traditional balance assessment tools.
Register for the study of the prevalence and burden of diseases, risk factors and outcomes of hospitalizations in older age groups in the countries of Eurasia.