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Physical exercise can improve sleep quality. However, how to perform physical exercise to achieve the best possible improvements is not clear. In this article, we build predictive models based on volume real data collected from wearable devices to predict the sleep efficiency related to users’ daily exercise information. As far as we know, this is the first study to investigate insights of prediction of sleep efficiency from volume physical exercise data collected from real world.

Heart rate is a good measure for physical exercise as it accurately reflects exercise intensity and is easy to measure. If the heart rate response to a complete exercise session is predicted beforehand, information related to the exercise can be inferred, such as exercise intensity and calorie consumption. While most current heart rate prediction models are developed and tested for the scenarios of indoor running exercise or low running speed exercise, we adopt a nonlinear Ordinary Differential Equation (ODE) model for complete outdoor running exercise sessions to predict the heart rate response and identify the parameters of the model with machine learning algorithms. The proposed model enables us to predict a complete outdoor running exercise session instead of predicting the heart rate for a short duration. Model validation is carried out both on the training and testing sets. Our results show that the proposed model captures very stable prediction performance.

Physical exercise and sleep have independent, yet synergistic, impacts on the health. However, the effects of acute exercise level on sleep quality have not been well investigated. We utilize statistical methods to investigate the differences of exercise level between the good and bad sleep nights. Our results present a complex interrelation between physical exercise and sleep quality with analyzing large personal data sets collected from wearables. As far as we know, this is the first study to investigate insights of interrelation of physical exercise and sleep quality based on a big volume of data collected from wearable devices of real users.

Excitation-contraction coupling (ECC) is a process linking the electrical excitation of the muscle cell (myocyte) membrane to the contraction of the cell. In this study the possibilities of mathematical modeling were studied in current ECC research. Mathematical modeling was employed in two distinct ECC research areas, the enzymatic regulation of ECC and ECC during cardiac myocyte development. Despite the distinction, both of these are extremely complex biological systems characterized by diverse and partly contradictory reported experimental results, with a large part based on genetically engineered animal models. Novel mathematical models were developed for both of these research areas. The model of ventricular myocyte ECC with calmodulin-dependent protein kinase II (CaMKII)-mediated regulation faithfully reproduced the heart-rate dependent regulation of ECC. This regulation is thought to be the major effect of CaMKII-mediated regulation. The model of the embryonic ventricular myocyte provided the first comprehensive system analysis of how the embryonic heartbeat is generated at the cellular level. A similar type of model was also developed to show the notable differences between neonatal and adult ventricular myocyte ECC. The mathematical models of ECC presented in this study were further used to simulate ECC in genetically engineered myocytes. The cellular mechanisms of genetically engineered animal models could be better understood by employing mathematical modeling in parallel to experimental characterization of the animal model. It was found in simulations that the indirect consequences and the compensatory mechanisms induced by genetic modification may have a more significant effect on ECC than the direct consequences of the modification. To understand the overwhelming complexity of biological systems including ECC, competent system analysis tools, such as mathematical modeling, are required. The purpose of mathematical modeling is not to replace th