Integrating Insights About Human Movement Patterns from Digital Data into Psychological Science

Understanding people’s movement patterns has many important applications, from analysing habits and social behaviours, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social media interactions. Existing research largely uses data-driven modelling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalise on what movement data can convey about associated thoughts, feelings, attitudes and behaviour. This article outlines trends in current research and discusses how psychologists can better address theoretical and methodological challenges in future work, while capitalising on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve our ability to make predictions about individuals’ or groups’ movement patterns, and that such interdisciplinary research has the capacity to advance psychological theory.