Association of physical activity with socio-economic status and chronic disease in older adults in China: cross-sectional findings from the survey of CLASS 2020 after the outbreak of COVID-19.

BACKGROUND: In 2021, China had a population of 264·01 million individuals over the age of 60, indicating a high prevalence of chronic diseases. Among older adults, physical inactivity (PI) is a significant risk factor for chronic diseases. However, few studies have been conducted on the correlation of physical activity (PA) with the economic status, geography and chronic disease risks in Chinese elderly. The objectives of this study were to better understand the distribution of PA among older adults in China and its relationship with economic status, geography, and chronic disease risks. METHODS: This study utilized data from the China Longitudinal Aging Social Survey (CLASS) in 2020, post-COVID-19. The study employed a stratified, multistage, probabilistic sampling approach and included 11,396 adults over the age of 59 from 28 provinces in China. Data on demographics, the duration and intensity of PA, history of diseases and personalized factors influencing PA were collected via structured interviews by researchers. In this study, we conducted a comprehensive analysis, employing a range of statistical methods including descriptive analysis, Wilcoxon rank-sum tests, Bayesian networks, and chi-square tests. RESULTS: The prevalence of PI among older adults over 59 in China is 28·82%. Significant regional differences were observed in the duration of PA at different intensities. Older adults residing in more economically developed areas were more likely to engage in moderate-to-vigorous physical activity (MVPA) and exhibited longer sedentary behavior. Economic status and urban-rural disparities consistently emerged as direct influential factors across all intensity types. Chronic disease risks were significantly lower in active older adults compared to inactive ones. Lack of social guidance, family support, and personal inclination towards sedentary behavior were the main personalized factors affecting PA among older adults, and these factors could be relatively easily modified. CONCLUSIONS: Economic status, geography, and living areas (urban and rural) significantly influenced the distribution of physical activities in China. Particularly, economic status and living areas acted as direct factors. Older adults reaching the recommended standards for PA had significantly lower chronic disease risks, highlighting the importance of improving personalized factors which are crucial for promoting PA.

Comparison of different prediction models for estimation of walking and running energy expenditure based on a wristwear three-axis accelerometer.

Objective: Objectively and efficiently measuring physical activity is a common issue facing the fields of medicine, public health, education, and sports worldwide. In response to the problem of low accuracy in predicting energy consumption during human motion using accelerometers, a prediction model for asynchronous energy consumption in the human body is established through various algorithms, and the accuracy of the model is evaluated. The optimal energy consumption prediction model is selected to provide theoretical reference for selecting reasonable algorithms to predict energy consumption during human motion. Methods: A total of 100 subjects aged 18-30 years participated in the study. Experimental data for all subjects are randomly divided into the modeling group (n = 70) and validation group (n = 30). Each participant wore a triaxial accelerometer, COSMED Quark pulmonary function tester (Quark PFT), and heart rate band at the same time, and completed the tasks of walking (speed range: 2 km/h, 3 km/h, 4 km/h, 5 km/h, and 6 km/h) and running (speed range: 7 km/h, 8 km/h, and 9 km/h) sequentially. The prediction models were built using accelerometer data as the independent variable and the metabolic equivalents (METs) as the dependent variable. To calculate the prediction accuracy of the models, root mean square error (RMSE) and bias were used, and the consistency of each prediction model was evaluated based on Bland-Altman analysis. Results: The linear equation, logarithmic equation, cubic equation, artificial neural network (ANN) model, and walking-and-running two-stage model were established. According to the validation results, our proposed walking-and-running two-stage model showed the smallest overall EE prediction error (RMSE = 0.76 METs, Bias = 0.02 METs) and the best performance in Bland-Altman analysis. Additionally, it had the lowest error in predicting EE during walking (RMSE = 0.66 METs, Bias = 0.03 METs) and running (RMSE = 0.90 METs, Bias < 0.01 METs) separately, as well as high accuracy in predicting EE at each single speed. Conclusion: The ANN-based walking-and-running two-stage model established by separating walking and running can better estimate the walking and running EE, the improvement of energy consumption prediction accuracy will be conducive to more accurate to monitor the energy consumption of PA.

A policy-driven multifaceted approach for early childhood physical fitness promotion: impacts on body composition and physical fitness in young Chinese children.

BACKGROUND: The prevalence of obesity increased while certain measures of physical fitness deteriorated in preschool children in China over the past decade. This study tested the effectiveness of a multifaceted intervention that integrated childcare center, families, and community to promote healthy growth and physical fitness in preschool Chinese children. METHODS: This 12-month study was conducted using a quasi-experimental pretest/posttest design with comparison group. The participants were 357 children (mean age = 4.5 year) enrolled in three grade levels in two childcare centers in Beijing, China. The intervention included: 1) childcare center intervention (physical activity policy changes, teacher training, physical education curriculum and food services training), 2) family intervention (parent education, internet website for support, and family events), and 3) community intervention (playground renovation and community health promotion events). The study outcome measures included body composition (percent body fat, fat mass, and muscle mass), Body Mass Index (BMI) and BMI z-score and physical fitness scores in 20-meter agility run (20M-AR), broad jump for distance (BJ), timed 10-jumps, tennis ball throwing (TBT), sit and reach (SR), balance beam walk (BBW), 20-meter crawl (20M-C)), 30-meter sprint (30M-S)) from a norm referenced test. Measures of process evaluation included monitoring of children's physical activity (activity time and intensity) and food preparation records, and fidelity of intervention protocol implementation. RESULTS: Children in the intervention center significantly lowered their body fat percent (-1.2%, p < 0.0001), fat mass (-0.55 kg, p <0.0001), and body weight (0.36 kg, p <0.02) and increased muscle mass (0.48 kg, p <0.0001), compared to children in the control center. They also improved all measures of physical fitness except timed 10-jumps (20M-AR: -0.74 seconds, p < 0.0001; BJ: 8.09 cm, p < 0.0001; TBT: 0.52 meters, p < 0.006; SR: 0.88 cm, p < 0.03; BBW: -2.02 seconds, p <0.0001; 30M-S: -0.45 seconds, p < 0.02; 20M-C: -3.36 seconds, p < 0.0001). Process evaluation data showed that the intervention protocol was implemented with high fidelity. CONCLUSIONS: The study demonstrated that a policy-driven multi-faceted intervention can improve preschool children's body composition and physical fitness. Program efficacy should be tested in a randomized trial. TRIAL REGISTRATION: ChiCTR-ONRC-14004143.