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护理学报 ›› 2024, Vol. 31 ›› Issue (23): 8-12.doi: 10.16460/j.issn1008-9969.2024.23.008

• 研究生园地 • 上一篇    下一篇

养老机构老年人认知功能与身体衰弱变化的交叉滞后分析

邓岚心, 张宇, 李敏锐, 高钰琳   

  1. 南方医科大学 护理学院, 广东 广州 510515
  • 收稿日期:2024-07-16 出版日期:2024-12-10 发布日期:2025-01-08
  • 通讯作者: 高钰琳(1974-),女,江西南昌人,硕士,副教授。E-mail: gyl@smu.edu.cn
  • 作者简介:邓岚心(2000-),女,广东东莞人,本科学历,硕士研究生在读。
  • 基金资助:
    老年长期照护教育部重点实验室(海军军医大学)开放课题基金(LNYB-2023-02);南方医科大学2023年研究生科研创新项目(无编号);2021年度广东省医学科研基金项目(A2021286)

Cross-lagged analysis of cognitive function and physical frailty changes in elderly residents of nursing homes

DENG Lan-xin, ZHANG Yu, LI Min-rui, GAO Yu-lin   

  1. School of Nursing, Southern Medical University, Guangzhou 510515, China
  • Received:2024-07-16 Online:2024-12-10 Published:2025-01-08

摘要: 目的 探讨养老机构老年人身体衰弱和认知功能之间随时间变化的趋势,并分析二者的时间序列关系。方法 采用分层随机抽样法,选取2022年9月—12月(T1)、2023年6月—9月(T2)期间广州市某养老机构的512名老年人作为研究对象。使用衰弱表型量表和蒙特利尔认知评估量表评估其身体衰弱和认知功能状况。使用重复测量方差分析探讨不同时间点二者的性别差异。采用Spearman相关性分析评估二者的关联性,并使用Mplus 8.3建立交叉滞后模型分析其双向关系。结果 养老机构老年人认知功能呈下降趋势,两个时间点的身体衰弱得分具有显著的正相关性。自回归结果显示,身体衰弱可以显著预测未来的身体衰弱(β=0.567,P<0.001),认知功能可以显著预测未来的认知功能(β=0.680,P<0.001)。交叉滞后模型显示,认知功能对身体衰弱具有显著负向预测作用(β=-0.146,P=0.002),而身体衰弱对认知功能预测作用不显著(β=0.064,P=0.134)。结论 养老机构中认知功能低的老年人在未来更易出现身体衰弱,认知功能也将继续下降,身体衰弱症状将加重。医护人员应加强对养老机构老年人认知状态和身体衰弱的监测,并通过综合护理措施,例如认知训练、运动锻炼、营养支持等,延缓认知功能下降和衰弱进程,促进健康老化。

关键词: 衰弱, 认知功能, 交叉滞后模型, 养老机构, 老年人健康, 纵向研究

Abstract: Objective To investigate the temporal trends in physical frailty and cognitive function among elderly residents in nursing homes and to analyze the time-series relationship between them. Methods A stratified random sampling was used to select 512 elderly residents in a nursing home in Guangzhou from September to December 2022 (T1) and from June to September 2023 (T2). The Frailty Phenotype Scale and the Montreal Cognitive Assessment Scale were employed to assess their physical frailty and cognitive function. Repeated measures ANOVA was applied to explore gender differences over time, while Spearman correlation analysis to assess the relationship between frailty and cognitive function. A cross-lagged panel model was constructed using Mplus 8.3 to analyze the bidirectional relationship between the two variables. Results Cognitive function among the elderly in nursing homes showed a declining trend, and frailty scores at both time points were significantly positively correlated. Autoregressive results indicated that frailty significantly predicted future frailty (β=0.567, P<0.001), while cognitive function significantly predicted future cognitive function (β=0.680, P<0.001). The cross-lagged model revealed that cognitive function had a significant negative predictive effect on future frailty (β=-0.146, P=0.002), whereas frailty did not significantly predict future cognitive function (β=0.064, P=0.134). Conclusion Elderly residents in nursing homes with lower cognitive function are more likely to experience future physical frailty, with a continued decline in cognitive function and worsening frailty symptoms. Healthcare professionals should strengthen the monitoring of cognitive function and physical frailty for elderly residents in nursing homes and implement comprehensive care interventions such as cognitive training, physical exercise, and nutritional support to slow the progression of cognitive decline and frailty, thereby promoting healthy aging.

Key words: frailty, cognitive function, cross-lagged model, nursing home, elderly health, longitudinal study

中图分类号: 

  • R473.2
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