Background Trouble sleeping is one of the major health issues nowadays. Current evidence on the correlation between muscle quality and trouble sleeping is limited. Methods A cross-sectional study design was applied and participants aged from 18 to 60 years in the National Health and Nutrition Examination Survey (NHANES) 2011–2014 was used for analysis. Muscle quality index (MQI) was quantitatively calculated as handgrip strength (HGS, kg) sum/ arm and appendicular skeletal muscle mass (ASM, kg) by using the sum of the non-dominant hand and dominant hand. Sleeping data was obtained by interviews and self-reported by individuals. The main analyses utilized weighted multivariable logistic regression models according to the complex multi-stage sampling design of NHANES. Restricted cubic spline model was applied to explore the non-linear relationship between MQI and trouble sleeping. Moreover, subgroup analyses concerning sociodemographic and lifestyle factors were conducted in this study. Results 5143 participants were finally included in. In the fully adjusted model, an increased level of MQI was significantly associated with a lower odds ratio of trouble sleeping, with OR = 0.765, 95% CI: (0.652,0.896), p = 0.011. Restricted cubic spline showed a non-linear association between MQI and trouble sleeping. However, it seemed that the prevalence of trouble sleeping decreased with increasing MQI until it reached 2.362, after which the odds ratio of trouble sleeping reached a plateau. Subgroup analyses further confirmed that the negative association between the MQI and trouble sleeping was consistent and robust across groups. Conclusion Overall, this study revealed that MQI can be used as a reliable predictor in odds ratio of trouble sleeping. Maintaining a certain level of muscle mass would be beneficial to sleep health. However, this was a cross-sectional study, and causal inference between MQI and trouble sleeping was worthy of further exploration.
Traditional Chinese medicine (TCM) plays a major role in preventing and treating the disease, however, it is also facing a slice of challenges as fewer choices of TCM treatment. Although lifestyles and health conditions might be paramount influencing factors for the choice of TCM treatment, the relative evidence is scarce. The current observational study was designed to evaluate this association. A total of 24,173 Chinese individuals with a mean age of 47.3 years from the Chinese Family Panel Studies 2014 were selected. The choice of TCM treatment was acquired by the self-report questionnaire. Latent class analysis was employed to identify clusters of lifestyles and health conditions. The binary logistic regression model was employed to examine the association between lifestyles, health conditions and the choice of TCM treatment. Lifestyles and health conditions were classified into 3 classes with latent class analysis, healthy group, unhealthy behavior group, and physical inactivity group. After controlling for potential confounding factors, the results showed individuals in unhealthy behavior group (odds ratio = 1.51, 95% confidence interval: 1.35–1.68, P < .001) or physical inactivity group (odds ratio = 1.11, 95% confidence interval: 1.02–1.22, P = .019) were more likely to visit TCM doctors than healthy group. Sex-specific difference was observed, the relationship still existed among the males. The current study revealed the relationship between lifestyles, health conditions and the choice of TCM treatment. This will provide evidence for the TCM development and provide support for further research.
Background Sitting time and physical activity are related to renal function among type 2 diabetes mellitus (T2DM); however, the mechanism of how it contributes to renal function is not well understood. The current study attempts to explore the relationship between sitting time and renal function among T2DM patients, with a particular focus on the mediating role of physical activity. Methods This research uses the data of 1761 Chinese T2DM patients from Ningxia Province. Sitting time and physical activity were obtained during a face-to-face survey, and renal function was assessed by the estimated glomerular filtration rate (eGFR). The bootstrap method is used to test the mediating effect. Results The research found that sitting time was negatively associated with eGFR and physical activity after controlling for covariates. Physical activity was positively associated with eGFR. Physical activity has mediated the relationship between sitting time and eGFR among T2DM patients (explaining 16.1% of the total variance). Conclusion The present findings suggest that sitting time negatively affects eGFR among T2DM patients and provides new evidence that physical activity could attenuate the association between sitting time and eGFR. Hence, intervention strategies focusing on sitting time and physical activity should be paid more attention in the future.
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