Performance measures predict onset of activity of daily living difficulty in community-dwelling older adults

功能指标可以预测社区居住老年人日常生活活动困难的发生。

阅读:1

Abstract

OBJECTIVES: To assess the predictive value of five performance-based measures for the onset of difficulty in activities of daily living (ADLs). DESIGN: A prospective cohort study; home visits every 6 months for 18 months. SETTING: Community-based. PARTICIPANTS: Community-dwelling older adults, n=110, (mean age 80.3+/-7.0; range 67-98) who reported no difficulty in basic ADLs. MEASUREMENTS: The Short Physical Performance Battery (SPPB), gait speed, Berg Balance Scale (BBS), grip strength, and Timed Up and Go Test (TUG) were evaluated at baseline. Seven ADL items were assessed at baseline and 6, 12, and 18 months. The onset of ADL disability was self-report of difficulty in any of the seven ADL items. Logistic regression models were fitted for each of the physical performance measures to predict onset of ADL difficulty at 6, 12, and 18 months. RESULTS: After controlling for age, comorbid conditions, and sex, the BBS was the most consistent and best predictor for the onset of ADL difficulty over an 18-month period (6 months, c-statistic=0.725, (95% confidence interval (CI)=0.60-0.85; 12 months, c-statistic=0.840 95% CI=0.75, 0.93; 18 months, c-statistic=0.821, 95% CI=0.71, 0.93). The SPPB showed excellent predictive value for the onset of difficulty at 12 months. Ninety-five, 89, and 75 older adults completed the 6, 12, and 18-month follow-up visits, respectively. CONCLUSION: BBS, followed by SPPB, TUG, gait speed, and grip strength, were predictive of the onset of ADL difficulty over an 18-month period in community-dwelling older adults. Screening nondisabled older adults with simple performance tests could allow clinicians to identify those at risk for ADL difficulty and may help to detect early functional decline.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。