Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.

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Over the last two decades, statistics educators have made important changes to introductory courses. Current guidelines emphasize developing statistical thinking in students and exposing them to the entire investigative process in the context of interesting research questions and real data. As a result, many concepts (confounding, multivariable models, study design, etc.) previously reserved only for higherlevel courses now appear in introductory courses. Despite these changes, causality is rarely discussed in introductory courses, except for warning students "correlation does not imply causation" or covering the special case of randomized controlled experiments. In this article, we argue causal inference concepts align well with statistics education guidelines for introductory courses by developing statistical and multivariable thinking, exposing students to many aspects of the investigative process, and fostering active learning. We discuss how to integrate causal inference concepts into introductory courses using causal diagrams and provide an illustrative example with youth smoking data. Through our website, we also provide a guided student activity and instructor resources. Supplementary materials for this article are available online.

Background The purpose of this study was to determine the dose-response association between habitual physical activity (PA) and cognitive function using a nationally representative dataset of U.S. older adults aged ≥ 60 years. Methods We used data from the 2011–2014 National Health and Nutrition Examination Survey (n = 2441, mean [SE] age: 69.1 [0.2] years, 54.7% females). Cognitive function was assessed using the Digit Symbol Substitution Test (DSST) and Animal Fluency Test (AFT). Habitual PA was collected using a tri-axial accelerometer worn on participants’ non-dominant wrist. PA was expressed as two metrics using monitor-independent movement summary (MIMS) units: the average of Daily MIMS (MIMS/day) and peak 30-minute MIMS (Peak-30MIMS; the average of the highest 30 MIMS mins/day). Sample weight-adjusted multivariable linear regression was performed to determine the relationship between each cognitive score and MIMS metric while adjusting for covariates. Results After controlling for covariates, for each 1000-unit increase in Daily MIMS, DSST score increased (β-coefficient [95% CIs]) by 0.67 (0.40, 0.93), while AFT score increased by 0.13 (0.04, 0.22); for each one-unit increase in Peak-30MIMS, DSST score increased by 0.56 (0.42, 0.70), while AFT score increased by 0.10 (0.05, 0.15), all p-values <0.001. When including both MIMS metrics in a single model, the association between Peak-30MIMS and cognitive scores remained significant (p-values <0.01), whereas Daily MIMS did not. Conclusions Our findings suggest that higher PA (both daily accumulated and peak effort) is associated with better cognitive function in the U.S. older adult population.