Value of the Short Physical Performance Battery (SPPB) in predicting fall and fall-induced injury among old Chinese adults

Author:

Li Weiqiang1,Rao Zhenzhen1,Fu Yanhong1,Schwebel David C.2,Li Li1,Ning Peishan1,Huang Jiaqi1,Hu Guoqing1

Affiliation:

1. Central South University

2. University of Alabama at Birmingham

Abstract

Abstract Background: The short physical performance battery (SPPB) is an easy-to-use tool for fall risk prediction, but its predictive value among community dwellers has not been examined through a large-sample longitudinal study. Methods: We analyzed five-round follow-up data (2, 3, 4, 5, 7 years) of the China Health and Retirement Longitudinal Study (CHARLS) (2011-2018). The Cochran-Armitage trend test examined trends in fall incidence rate across SPPB performance levels. Multivariable logistic regression and negative binomial regression models were fitted to examine associations between SPPB performance and subsequent fall and fall-induced injury. The goodness-of-fit and area under the receiver operating curve (AUC) were used together to quantify the value of the SPPB in predicting fall and fall-induced injury among community-dwelling older adults. Results: The CHARLS study included 9279, 6153, 4142, 4148, and 3583 eligible adults aged 60 years and older in the five included follow-up time periods. SPPB performance was associated with fall and fall-induced injury in two or three of the five follow-up time periods (P<0.05). The goodness-of-fit for all predictive models was poor, with both Cox-Snell R2 and Nagelkerke R2 under 0.10 and AUCs of 0.53-0.57 when using only SPPB as a predictor and with both Cox-Snell R2 and Nagelkerke R2 lower than 0.12 and AUCs of 0.61-0.67 when using SPPB, demographic variables, and self-reported health conditions as predictors together. Sex and age-specific analyses displayed highly similar results. Conclusions: The use of SPPB together with demographic variables and self-reported health conditions does not appear to offer good predictive performance for falls or fall-induced injuries among community-dwelling older Chinese adults.

Publisher

Research Square Platform LLC

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3