Association between midday napping and long-term trajectories of cognitive function among middle-aged and older Chinese adults
Abstract
Background The prevalence of dementia has become an increasingly important public health priority. This study investigated the association between midday napping and long-term trajectories of cognitive function in middle-aged and older Chinese adults. Methods Among 4648 participants aged 45+ years extracted from the China Health and Retirement Longitudinal Study (CHARLS). The components of the Telephone Interview of Cognitive Status battery (TICS-10) was used to assess cognitive function. Group-based trajectory modelling (GBTM) was used to identify long-term trajectories of cognitive function. Multinomial logistic regression model was used to estimate risk ratios (RRs) and 95% confidence intervals (CIs). Results Three distinct long-term trajectories of cognitive function reflected patterns of rapid decline, slow decline, and stable. The RR (95% CI) for rapid decline was 1.45 (1.05–2.01) for 0 minutes, 1.49 (1.05–2.12) for 31–90 minutes, and 2.19 (1.41–3.42) for >90 minutes compared with midday napping 1–30 minutes. The RR (95% CI) for slow decline was 1.22 (1.02–1.47) for 0 minutes, 1.27 (1.04–1.55) for 31–90 minutes, and 1.80 (1.38–2.35) for >90 minutes compared with midday napping 1–30 minutes. In addition, the increased risk of cognitive decline that transferred from >90 to 31–90 minutes, switched from 31–90 to >90 minutes, and persisted in >90 minutes compared with midday napping 1–30 minutes, especially rapid decline. Conclusions There was a longitudinal association between no and long (>30 minutes) midday napping and long-term trajectories of cognitive decline, especially rapid decline. The study is a 4-year observational in nature and provides limited evidence for establishing causal relationships.
Article Details
Authors (5)
Jinghong Huang
Dongrui Peng
Yutong Zhang
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences
Yanan Zhang
Xiaohui Wang