MotiVAtor : Analyzing Physical Activity Study Data in Lab and Life through Visual Analytics

Abstract

The complexity of intervention studies to assess physical activity (PA) is increasing, resulting in vast amounts of data being recorded in laboratory settings. Recent studies extend datasets with measurements outside the lab using wearable devices, allowing for a bridge to be built between the lab and real-life applications. Such heterogeneous, multigranular datasets impose various challenges for data analysis and visualization, and require tailored approaches to support domain experts. Contrarily, it enables data-driven hypothesis generation, which is particularly valuable in interdisciplinary contexts where theory-driven approaches fall short due to a lack of well-established theoretical foundations. While lab conditions are extensively handled in existing visual interfaces, measurements in everyday life situations are often neglected, yet have an essential impact on the capturing of PA. To facilitate exploration of this data, we propose a visual analytics application consisting of multiple linked views comprising a BiPlot, Variable Distribution plots, and a dense-pixel visualization allowing experts to generate novel interdisciplinary hypotheses based on laboratory and everyday life measurements. We evaluate our application by conducting an expert study with an end user of the application, showcasing the application’s benefits to support experts in solving tasks regarding exploration, pattern identification, association, and comparison.