Baseline Participant Characteristics from 2011 CHARLS Study

A collection of baseline characteristics of participants from the 2011 wave of the China Health and Retirement Longitudinal Study (CHARLS). It was used to assess the risk of sleep duration on geriatric sarcopenia and explore its moderating role with BMI. The dataset is 13.5 KB in size.

Use Cases

• Analyze the association between sleep duration categories (e.g., sufficient sleep, sleep deprivation, oversleeping) and geriatric sarcopenia risk.
• Investigate the moderating effect of sleep duration on the relationship between BMI categories (underweight, overweight, obesity) and sarcopenia.
• Conduct subgroup analysis for results stability, potentially by age or gender as implied by the study conclusions.
• Perform logistic regression to assess the risk of sleep duration for geriatric sarcopenia using baseline participant data.
• Apply Cox regression analyses to evaluate time-to-event relationships involving sleep duration, BMI, and sarcopenia risk.

Strengths

• Data is derived from the established China Health and Retirement Longitudinal Study (CHARLS) database.
• The dataset supports analysis of multiple sleep duration categories (four abnormal types) and BMI classifications.
• It is shared under a permissive CC BY 4.0 license, allowing for reuse and modification.

Limitations

• The dataset is very small at 13.5 KB, indicating limited scope and likely a small sample size or aggregated summary statistics.
• Specific column names, row counts, and sample data are unavailable, limiting understanding of the data structure and granularity.
• The data is from 2011 and may not reflect current population health trends.

Provenance

Source

China Health and Retirement Longitudinal Study (CHARLS) 2011 wave.

Collection Method

Data were extracted from the CHARLS database for the 2011, 2013, and 2015 waves; this file appears to contain baseline characteristics from 2011.

Time Range

Primary baseline data from 2011.

Freshness

The metadata was last updated in 2026, but the underlying data is from the 2011 CHARLS wave.

Geography

China.