Stay Slim or Get Fat?: An Examination of the "Jolly Fat" Effect in Chinese Older Adults

Does weight impact adolescent mental health? Evidence from China

In this paper, we examine the causal impact of weight on adolescent mental health. Using the China Family Panel Studies, we find significant negative effects of adolescent weight, instrumented by cohort-level parental body mass index (BMI), on mental illness. In particular, a one standard deviation increase in adolescent BMI z-score decreases the K6 score by 0.766 (or 0.232 standard deviations). This finding is contrary to recent evidence from adults. We find this contrast can partly be explained by the different impacts of adolescent weight on self-image and social relationships. Unlike adults, heavy adolescents feel that they are popular among peers in China.

Longitudinal Relationship between Cognitive Function and Health-Related Quality of Life among Middle-Aged and Older Patients with Diabetes in China: Digital Usage Behavior Differences

BACKGROUND

Cognitive function and health-related quality of life (HRQoL) are important issues in diabetes care. According to the China Association for Aging, it is estimated that by 2030, the number of elderly people with dementia in China will reach 22 million. The World Health Organization reports that by 2044, the number of people with diabetes in China is expected to reach 175 million.

METHODS

Cohort analyses were conducted based on 854 diabetic patients aged ≥45 years from the third (2015) and fourth (2018) survey of the China Health and Retirement Longitudinal Study (CHARLS). Correlation analysis, repeated-measures variance analysis, and cross-lagged panel models were used to measure the difference in digital usage behavior in the established relationship.

RESULTS

The results show that the cognitive function of middle-aged and older diabetic patients is positively correlated with HRQoL. HRQoL at T1 could significantly predict cognitive function at T2 (PCS: B = 0.12, p < 0.01; MCS: B = 0.14, p < 0.01). This relationship is more associated with individual performance than digital usage behavior.

CONCLUSIONS

Unidirectional associations may exist between cognitive function and HRQoL among middle-aged and older Chinese diabetes patients. In the future, doctors and nurses can recognize the lowering of self-perceived HRQoL of middle-aged and older diabetic patients, and thus draw more attention to their cognitive function, in turn strengthening the evaluation, detection, and intervention of their cognitive function.

Differences in Health-Related Quality of Life and Its Associated Factors Among Older Adults in Urban and Rural Areas

Purpose

Urban-rural health disparity is one of the most prominent challenges in China today. The goal of this study is to find differences in health-related quality of life (HRQoL) and its associated factors among older people in urban and rural areas.

Methods

A multi-stage stratified sampling method was conducted in Shanxi Province, with a total of 3250 older adults participated in this cross-sectional survey. HRQoL was assessed using the Chinese version of the EQ-5D-5L. Tobit regression models were employed to identify associated factors for HRQoL.

Results

The mean EQ-5D utility score of the total sample was 0.87± 0.23, with a statistically significant difference observed between urban (0.89 ± 0.22) and rural areas (0.86 ± 0.23). Obesity (Coe=−0.10, p=0.021) and nutrition awareness (Coe=−0.14, p=0.009) were two unique associated factors to rural older adults’ HRQoL. While, age (Urban: Coe=0.13, p=0.001; Rural: Coe=−0.019, p<0.001), socioeconomic status (Urban: Coe=0.13, p<0.001; Rural: Coe=0.14, p<0.001), number of chronic non-communicable diseases (Urban: Coe=−0.20, p<0.001; Rural: Coe=−0.15, p<0.001), sleep quality (Urban: Coe=−0.22, p<0.001; Rural: Coe=0.15, p<0.001) and daytime sleepiness (Urban: Coe=−0.13, p<0.001; Rural: Coe=−0.13, p<0.001) were found to be associated with HRQoL regardless of the residential area.

Conclusion

This study suggested that rural older adults are facing HRQoL disadvantages compared to those in urban area. Accordingly, more attention should be devoted to rural older adults’ HRQoL, particularly to the unique factors like body weight and nutrition awareness. Targeted policies and interventions should be implemented to improve HRQoL and bridge the urban-rural HRQoL gap.