ABSTRACT
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.
ABSTRACT
Vigorous-intensity leisure-time physical activity, such as marathon, has become increasingly popular, but its effect on immune functions and health is poorly understood. Here, we performed scRNA-seq analysis of peripheral blood mononuclear cells (PBMCs) after a bout of symptom-limited cardiopulmonary exercise (CPX) test or marathon. Time-series single-cell analysis revealed the detailed series of landscapes of immune cells in response to short and long vigorous-intensity activities. Reduction of effective T cells was observed with the cell migration and motility pathways enriched in circulation following marathon. Baseline values of PBMCs abundance were reached around 1 h after CPX and 24 h following marathon, but longer time was required for expression recovery of cytotoxicity genes. The ratio of effector/naive T cells was found to change uniformly among the participants and could serve as a better indicator for exercise intensity than the CD4+/CD8+ T cell ratio. Moreover, we identified time-dependent monocyte state transitions after marathon.
