Scholarlink

Background <p>Frailty state is dynamic and reversible, but there is a lack of clarity about the patterns of frailty transition and its influencing factors. This study aimed to investigate frailty transitions and the impact of sociodemographic and behavioral factors on these transitions in Chinese middle-aged and older adults.</p> Methods <p>We used five-wave data from the China Health and Retirement Longitudinal Study (CHARLS), and 20,140 Chinese adults aged ≥ 45 years were included. Frailty was assessed using a 35-item frailty index. A multi-state Markov model was used to systematically analyze the transition patterns of frailty states (robust, prefrail, frail) and death, and to explore the associations of sociodemographic and behavioral factors with frailty transitions.</p> Results <p>Among the 20,140 participants at baseline, the proportions of robust, prefrailty, and frailty were 30.4%, 51.8%, and 17.8%, respectively. In the short term, participants tended to remain in their original state, while over time, the probability of deterioration and recovery increased significantly. Increasing age [HR (hazard ratio): 1.02; 95% CI (confidence interval): 1.02–1.02], being female (1.39, 1.27–1.53), living in a rural area (1.34, 1.26–1.42), being illiterate (1.21, 1.12–1.30), current smoking (1.11, 1.01–1.21), and sleep &lt; 6 or &gt; 8 h (1.23, 1.16–1.30) increased the risk of frailty deterioration. While current drinking (0.92, 0.86–0.99) reduced the risk of frailty deterioration. Stratified analyses by age and gender showed consistent results with the main analysis.</p> Conclusions <p>Targeted interventions should be developed for at-risk populations and intervenable behavioral factors should be taken to slow or reverse the progression of frailty.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Associations of sociodemographic and behavioral factors with frailty transition patterns: a multi-state Markov analysis of the China Health and Retirement Longitudinal Study (CHARLS)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月8日

查看全文
  • Kai Zhang,
  • Yi Zhang,
  • Weizheng Kong,
  • Xiaolin Hu,
  • Lirong Chai,
  • Dongfeng Zhang,
  • Junning Fan

摘要

Background

Frailty state is dynamic and reversible, but there is a lack of clarity about the patterns of frailty transition and its influencing factors. This study aimed to investigate frailty transitions and the impact of sociodemographic and behavioral factors on these transitions in Chinese middle-aged and older adults.

Methods

We used five-wave data from the China Health and Retirement Longitudinal Study (CHARLS), and 20,140 Chinese adults aged ≥ 45 years were included. Frailty was assessed using a 35-item frailty index. A multi-state Markov model was used to systematically analyze the transition patterns of frailty states (robust, prefrail, frail) and death, and to explore the associations of sociodemographic and behavioral factors with frailty transitions.

Results

Among the 20,140 participants at baseline, the proportions of robust, prefrailty, and frailty were 30.4%, 51.8%, and 17.8%, respectively. In the short term, participants tended to remain in their original state, while over time, the probability of deterioration and recovery increased significantly. Increasing age [HR (hazard ratio): 1.02; 95% CI (confidence interval): 1.02–1.02], being female (1.39, 1.27–1.53), living in a rural area (1.34, 1.26–1.42), being illiterate (1.21, 1.12–1.30), current smoking (1.11, 1.01–1.21), and sleep < 6 or > 8 h (1.23, 1.16–1.30) increased the risk of frailty deterioration. While current drinking (0.92, 0.86–0.99) reduced the risk of frailty deterioration. Stratified analyses by age and gender showed consistent results with the main analysis.

Conclusions

Targeted interventions should be developed for at-risk populations and intervenable behavioral factors should be taken to slow or reverse the progression of frailty.