Nearly all of us carry a powerful computer throughout our waking hours in the form a smart phone. Remarkably, even the most basic iPhone has vastly more computational power than the entire set of computers that guided Apollo 11 to the moon. The ubiquity of smart phones has led to the easy (and potentially too easy) collection of vast amounts of data collected over time. Indeed, one of the most vexing current challenges in longitudinal data analysis is determining how to best fit meaningful statistical models to high-density repeated measures to test specific hypotheses of interest. For example, a recent article in the New York Times summarized two published studies that examined the impact of fitness tracking on health and well-being. In one study, 4000 subjects were followed over a decade and, on average, those who exercised at least 150 minutes-per-week were associated with a one-third decrease in premature death. In a second study, subjects who were paid cash to meet their exercise goals showed slight increases in their activity as recorded by the fitness trackers. However, an array of study limitations exist, not the least of which is validly establishing the proper causal direction of effect. Regardless, smart phones offer a plethora of exciting opportunities for the collection of high-density repeated measures data, yet the subsequent analysis and interpretation remains a challenge.