Frontiers in Aging Neuroscience (Jan 2023)

Association between cognitive frailty and falls among older community dwellers in China: A Chinese longitudinal healthy longevity survey-based study

  • Huihe Chen,
  • Lanhui Huang,
  • Wei Xiang,
  • Yu Liu,
  • Jian-Wen Xu

DOI
https://doi.org/10.3389/fnagi.2022.1048961
Journal volume & issue
Vol. 14

Abstract

Read online

BackgroundThe combined effect of cognitive impairment (CoI) and frailty on falls is controversial. This study aimed to explore whether older adults with cognitive frailty (CF) were at a higher risk of falls than those with only CoI or frailty and to present a fall prediction model based on CF.MethodsA total of 4,067 adults aged ≥ 60 years were included from the Chinese Longitudinal Healthy Longevity Survey through face-to-face interviews. Cognitive function and frailty were assessed using the mini-mental state examination scale and frailty index, respectively. Logistic regression was used to determine fall-associated risk factors and develop a fall prediction model. A nomogram was then plotted. The model performance was evaluated using the area under the curve (AUC), concordance index (C-index), and calibration curve. All analyses were performed using SPSS and R statistical packages.ResultsThe prevalence of CF and falls were 1.4 and 19.4%, respectively. After adjusting for covariates, the odds ratio of CF, frailty only, and CoI only for falls were 2.27 (95% CI: 1.29–3.97), 1.41 (95% CI: 1.16–1.73), and 0.99 (95% CI: 0.43–2.29), respectively. CF, sex, age, hearing difficulty, depression, anxiety, disability in instrumental activities of daily living, and serious illness in the past 2 years were independently associated with falls. A prediction model based on these factors yielded an AUC of 0.646 and a C-index of 0.641.ConclusionCognitive frailty (CF) exerted a cumulative effect on falls than did CoI or frailty alone. Joint assessments of cognitive function and frailty status may be beneficial for fall risk screening in community. A prediction model using CF as a factor could be helpful for this process.

Keywords