METHODS: Systematic review and meta-analysis.
METHODS: Older adults with dementia residing in the community, hospitals, and residential care facilities.
METHODS: MEDLINE, Embase, PsycINFO, CINAHL, SPORTDiscus, the Cochrane Library, and the PEDro databases were searched from inception until December 27, 2023 (PROSPERO registration number: CRD42022303670). Retrospective or prospective studies that evaluated the associations between physical performance measures and falls in older adults with dementia were included. A random effects model was used to calculate the standardized mean difference (SMD) and 95% CI for each physical performance measure between fallers and nonfallers. Sensitivity analyses were conducted on the longitudinal studies to determine the ability of physical performance measures to predict future falls.
RESULTS: Twenty-eight studies were included in this review (n = 3542). The 5-time chair stand test [SMD = 0.23 (0.01, 0.45)], the Berg Balance Scale [SMD = -0.52 (-0.87, -0.17)], postural sway when standing on the floor [SMD = 0.25 (0.07, 0.43)] and on a foam surface [SMD = 0.45 (0.25, 0.66)], and the Short Physical Performance Battery total score [SMD = -0.46 (-0.66, -0.27)] could discriminate fallers from nonfallers. Sensitivity analyses showed that gait speed could predict future falls in longitudinal cohort studies [SMD = -0.29 (-0.49, -0.08)]. Subgroup analyses showed that gait speed [SMD = -0.21 (-0.38, -0.05)] and the Timed Up and Go test [SMD = 0.54 (0.16, 0.92)] could identify fallers staying in residential care facilities or hospitals.
CONCLUSIONS: The 5-time chair stand test, the Berg Balance Scale, postural sway when standing on the floor and a foam surface, and the Short Physical Performance Battery can be used to predict falls in older adults with dementia. Gait speed and the Timed Up and Go test can be used to predict falls in institutionalized older adults with dementia. Clinicians are recommended to use these physical performance measures to assess fall risk in older adults with dementia.
方法:系统评价和荟萃分析。
方法:居住在社区的老年痴呆症患者,医院,和住宅护理设施。
方法:MEDLINE,Embase,PsycINFO,CINAHL,SPORTDiscus,Cochrane图书馆,和PEDro数据库从开始到2023年12月27日进行搜索(PROSPERO注册号:CRD42022303670)。回顾性或前瞻性研究评估了老年人痴呆症的身体表现指标与跌倒之间的关系。随机效应模型用于计算跌倒者和非跌倒者之间每个身体表现指标的标准化平均差(SMD)和95%CI。对纵向研究进行了敏感性分析,以确定物理性能指标预测未来跌倒的能力。
结果:本综述纳入28项研究(n=3542)。5次椅台试验[SMD=0.23(0.01,0.45)],Berg平衡量表[SMD=-0.52(-0.87,-0.17)],站在地板上[SMD=0.25(0.07,0.43)]和泡沫表面[SMD=0.45(0.25,0.66)]时的姿势摇摆,短物理性能电池总分[SMD=-0.46(-0.66,-0.27)]可以区分跌倒者和非跌倒者。敏感性分析表明,在纵向队列研究中,步态速度可以预测未来的跌倒[SMD=-0.29(-0.49,-0.08)]。亚组分析显示,步态速度[SMD=-0.21(-0.38,-0.05)]和TimedUpandGo测试[SMD=0.54(0.16,0.92)]可以识别留在住宅护理设施或医院的跌倒者。
结论:5次椅子站立测试,伯格平衡量表,站在地板和泡沫表面上时的姿势摇摆,短体能电池可用于预测老年痴呆症患者的跌倒。步态速度和TimedUpandGo测试可用于预测住院的老年痴呆症患者的跌倒。建议临床医生使用这些身体表现指标来评估患有痴呆症的老年人的跌倒风险。