Background: Obesity prevalence has become one of the most prominent issues in global public health. Physical activity has been recognized as a key player in the obesity epidemic. Objectives: The objectives of this study are to (1) examine the relationship between physical activity and weight status and (2) assess the performance and predictive power of a set of popular machine learning and traditional statistical methods. Methods: National Health and Nutrition Examination Survey (NHANES, 2003 to 2006) data were used. A total of 7162 participants met our inclusion criteria (3682 males and 3480 females), with average age ranging from 48.6 (normal weight) to 52.1 years old (overweight). Eleven classifying algorithms—including logistic regression, naïve Bayes, Radial Basis Function (RBF), local k-nearest neighbors (k-NN), classification via regression (CVR), random subspace, decision table, multiobjective evolutionary fuzzy classifier, random tree, J48, and multilayer perceptron—were implemented and evaluated, and they were compared with traditional logistic regression model estimates. Results: With physical activity and basic demographic status, of all methods analyzed, the random subspace classifier algorithm achieved the highest overall accuracy and area under the receiver operating characteristic (ROC) curve (AUC). The duration of vigorous-intensity activity in one week and the duration of moderate-intensity activity in one week were important attributes. In general, most algorithms showed similar performance. Logistic regression was middle-ranking in terms of overall accuracy, sensitivity, specificity, and AUC among all methods. Conclusions: Physical activity was an important factor in predicting weight status, with gender, age, and race/ethnicity being less but still essential factors associated with weight outcomes. Tailored intervention policies and programs should target the differences rooted in these demographic factors to curb the increase in the prevalence of obesity and reduce disparities among sub-demographic populations.

This study systematically examines the diffusion of the discrete event simulation (DES) approach in health services and health care management by examining relevant factors such as research areas, channels with the objective of promoting the application of DES in the health field. We examined 483 journal papers referencing this approach that were published in 230 journals during 1981 to 2014. The application of DES has extended from health service operational research evaluation to the assessment of interventions in diverse health arenas. The increase in the number of adopters (paper authors) of DES and the increase in number of related channels (journals publishing DES-related articles) are highly correlated, which suggests an increase of DES-related publications in health research. The same conclusion is reached, that is, an increased diffusion of DES in health research, when we focus on the temporal trends of the channels and adopters. The applications of DES in health research cover 22 major areas based on our categorization. The expansion in the health areas also suggests to a certain extent the rapid diffusion of DES in health research.

Cohn et al. (2019) conducted a wallet drop experiment in 40 countries to measure “civic honesty around the globe,” which has received worldwide attention but also sparked controversies over using the email response rate as the sole metric of civic honesty. Relying on the lone measurement may overlook cultural differences in behaviors that demonstrate civic honesty. To investigate this issue, we conducted an extended replication study in China, utilizing email response and wallet recovery to assess civic honesty. We found a significantly higher level of civic honesty in China, as measured by the wallet recovery rate, than reported in the original study, while email response rates remained similar. To resolve the divergent results, we introduce a cultural dimension, individualism versus collectivism, to study civic honesty across diverse cultures. We hypothesize that cultural differences in individualism and collectivism could influence how individuals prioritize actions when handling a lost wallet, such as contacting the wallet owner or safeguarding the wallet. In reanalyzing Cohn et al.’s data, we found that email response rates were inversely related to collectivism indices at the country level. However, our replication study in China demonstrated that the likelihood of wallet recovery was positively correlated with collectivism indicators at the provincial level. Consequently, relying solely on email response rates to gauge civic honesty in cross-country comparisons may neglect the vital individualism versus collectivism dimension. Our study not only helps reconcile the controversy surrounding Cohn et al.'s influential field experiment but also furnishes a fresh cultural perspective to evaluate civic honesty.