Association Between Sleep parameters and physical, psychological and cognitive multi-morbidities Analysis in China

Research Hypothesis:

This study tested the hypothesis that unhealthy sleep parameters (short or long sleep duration, extended daytime napping, and poor sleep quality) are associated with a higher prevalence and incidence of physical, psychological, and cognitive multimorbidity (PPC-MM) in middle-aged and older adults in China.

What the Data Shows and Key Findings:

This dataset, derived from the China Health and Retirement Longitudinal Study (CHARLS), enables analysis of the sleep-PPC-MM relationship.
Cross-sectional analysis​ (n=10,821) showed that compared to 6-8 hours of sleep, short sleep (<6 hours) significantly increased the odds of PPC-MM (OR=1.80, 95% CI 1.51-2.13). Poor sleep quality was a stronger risk factor (OR=2.73, 95% CI 2.30-3.24).
Longitudinal analysis​ (n=6,106, baseline PPC-MM-free) found that baseline short sleep (HR=1.22, 95% CI 1.06-1.42) and poor sleep quality (HR=1.43, 95% CI 1.24-1.64) predicted a higher incidence of PPC-MM over time. Daytime napping was not significant in fully adjusted models.
Data Interpretation and Usage:

This processed dataset includes sleep parameters, PPC-MM status (a composite of physical conditions, psychological distress, and cognitive impairment), and key covariates (e.g., age, sex, BMI). The findings provide robust evidence that poor sleep is an independent risk factor for complex multimorbidity. Researchers can use this dataset to:
Validate the reported associations.
Conduct further subgroup or interaction analyses.
Use it as a benchmark for comparative studies.
How the Data was Gathered:

Data come from nationally representative CHARLS waves (2011-2019), collected via standardized interviews. This dataset is a cleaned and structured subset for analyzing sleep and PPC-MM.

Usage Notes:
Observational data; causal inference should be cautious.
Self-reported measures may be subject to bias.
Data is anonymized and provided in an analysis-ready format (e.g., CSV) with a codebook.