Developmental Trajectory of Frailty in Chinese Elderly People：an Analysis Based on the Latent Growth Model
Frailty is a prominent manifestation of aging. Frailty in Chinese older people has been studied mostly using cross-sectional designs, but its developmental trajectory has been rarely studied using longitudinal designs.
To examine the developmental trajectory and associated factors of frailty in Chinese older people using the data of four national waves of China Health and Retirement Longitudinal Study (CHARLS) .
The data of this study obtained from four national waves〔2011 (the baseline survey), and 2013, 2015 and 2018 (follow-up surveys) 〕 of CHARLS, which was initially conducted in 2011, and was followed by tracking once every 2 to 3 years with multi-stage PPS sampling for middle-aged and elderly groups in 28 provincial administrative regions of China, covering 150 counties and 450 villages. The surveyees were coded, and matched, then 2 267 cases (≥60 years old) involved in the four waves of surveys were selected as the sample. Frailty was assessed by the frailty index (FI). Mplus was used to construct three types of unconditional latent growth models, and the optimal fitting model was selected to determine the developmental trajectory of frailty of Chinese older people, and was used to develop the conditional latent growth model. The effects of time-invariant factors (gender, education level) and time-varying factors (physical activity, smoking, alcohol consumption, sleep) on frailty were examined.
The latent growth model with undefined curve fit the data better, and was selected as the optimal model to determine the frailty development trajectory. The results of χ2 (3) =36.16, CFI=0.992, TLI=0.984, RMSEA=0.070, SRMR=0.022, indicating that the frailty prevalence in older adults showed a trend of curvilinear increase. The values of intercept (initial level), slope (growth), and the variation of them of the model were significantly higher than 0 (P<0.01), indicating that there were significant individual differences in the initial level and growth rate of frailty. Gender and education level were negatively associated with the initial level of frailty (β=-0.113, -0.173, P<0.01). They were also negatively associated with the growth of frailty (β=-0.181, -0.151, P<0.01). Compared with men, women had higher initial level and faster growth rate of frailty (P<0.05). Compared to those with higher education level, those with lower education level had higher initial level and faster growth rate of frailty (P<0.05). Physical activity and sleep were negatively associated with frailty in all waves of surveys (P<0.05). Smoking was positively associated with frailty in 2011, 2015, 2018 waves of surveys (P<0.05). Alcohol consumption was positively associated with frailty in 2013 and 2015 waves of surveys (P<0.05) .
The frailty in Chinese older people showed a trajectory of curvilinear increase, and its initial level and growth rate had significant individual differences. Comparatively speaking, being female and having lower education level were associated with increased risk of having frailty. Moderate- and high-level physical activity and adequate sleep were associated with decreased risk of having frailty or alleviating frailty. Long-term smoking and drinking too much could exacerbate frailty.
Study on the Relationship between the Risk of Elderly Diabetes and Physical Activity
The prevalence of elderly diabetes is increasing in China recently, with lower cure and control rates. Some epidemiology studies have shown that physical activity (PA) can alleviate the prevalence and the development of elderly diabetes. However, the effects of different types and intensities of PA and their metabolic equivalent on elderly diabetes have not been clarified, and there are few related studies.
To understand the size and trend of the relationship between the risk of elderly diabetes and PA, in order to provide a scientific basis for formulating strategies for the prevention and control of elderly diabetes.
Based on the stratified cluster random sampling, a total of 13 022 elderly people aged 60 and above who had registered residence actually lived continuously for more than 1 year in Gangzha District, Tongzhou District, Qidong City, Haimen City, Rugao City, and Haian City of Jiangsu Province were selected from October 2017 to June 2018, including 2 698 diabetic patients. 2 698 non-diabetes people in the control group were matched with a age 1∶1 propensity score. The general demographic characters, laboratory indicators were collected, behavioral lifestyles such as physical activity and sitting time were investigated refer to the International Physical Activity Scale. Multivariate conditional Logistic regression and spline regression models were used to analyze the association between PA and elderly diabetes.
Multivariate Logistic regression analysis showed that with the Leisure physical activity (LPA) and occupational physical activity (OPA) increased by one intensity level, the risk of elderly diabetes decreased by 18% and 24% (P<0.000 1). The higher the metabolic equivalent of LPA, OPA, commuting physical activity (CPA), and total PA, the lower the risk of elderly diabetes (P<0.000 1). Daily steps ≥6 000 were associated with a 66% lower risk of diabetes (P<0.000 1), compared with daily steps <6 000. The longer thesitting time per week, the higher the risk of elderly diabetes (P<0.05). The results of spline regression showed that with the increased metabolic equivalent of OPA, the risk of elderly diabetes decreased monotonously. With the increase of metabolic equivalent of CPA, LPA, and total PA, the risk of elderly diabetes decreased first and then leveled off. The risk of different genders was consistent with the total population.
Moderate and severe LPA and OPA can reduce the risk of elderly diabetes, the effect of reducing the risk of elderly diabetes is enhanced, and the increased range increases significantly at first and then tends to flatten. The recommended LPA, CPA and total PA of 28 MET-h/w, 18 MET-h/w and 45 MET-h/w are optimal, respectively.
