Introduction Many health organizations have promoted the importance of the health-related benefits of physical fitness and physical activity. Studies have evaluated effective public health practice aiming to understand the cognition of physical activity among youths and adolescents. However, studies investigating the level of cognition and knowledge of physical fitness among Asian adults are lacking. Purpose This study aimed to investigate the self-awareness level of physical fitness and exercise prescription and the demand for physical fitness assessment among Taiwanese adults.

Background Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics and hospitals, in which treating a patient mostly requires only one type of resource, a physical examination set typically requires multiple types of resources. Traditional methods that do not consider set-resource relationship thus may be inapplicable. Methods We formulate a stochastic mathematical programming model that maximizes the expected net reward, which is the examination revenue minus overage cost. A complete search algorithm and a greedy search algorithm are designed to search for optimal booking limits for all examination sets. To estimate the late cancellation probability for each individual consumer, we apply logistic regression to identify significant factors affecting the probability. After clustering is used to estimate individual probabilities, Monte Carlo simulation is conducted to generate probability distributions for the number of consumers without late cancellations. A discrete-event simulation is performance to evaluate the effectiveness of our proposed solution. Results We collaborate with a leading physical examination center to collect real data to evaluate our proposed overbooking policies. We show that the proposed overbooking policy may significantly increase the expected net reward. Our simulation results also help us understand the impact of overbooking on the expected number of customers and expected overage. A sensitivity analysis is conducted to demonstrate that the benefit of overbooking is insensitive to the accuracy of cost estimation. A Pareto efficiency analysis gives practitioners suggestions regarding policy determination considering multiple performance indications. Conclusions Our proposed overbooking policies may greatly enhance the overall performance of a physical examination center.

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Background: Late cancellation of physical examination has a severe impact on the profit of a healthcare center since it is often too late to ll the vacancy. A booking control policy that considers overbooking is then one natural solution.Case presentation: In this study, we consider a healthcare center providing different examination sets using dierent resources. As each resource has its unique cost, revenue, and capacity, the optimal booking limits of all examination sets are hard to be calculated. We propose a probabilistic optimization model that maximizes the expected prot given the late cancellation probability of each type of customer, where the probabilities are estimated through logistic regression and customer grouping using historical booking and cancellation records. To test the performance of our proposed solution, we collaborate with a leading healthcare center. We simulate the presence and absence of customers generated by historical records and compare different strategies of overbooking.Conclusions: Through the experiment, we show that our method can significantly increase the expected profit of the healthcare center by around 11%.