  Psychological theories of habit posit that when a strong habit is formed
through behavioral repetition, it can trigger behavior automatically in the
same environment. Given the reciprocal relationship between habit and behavior,
changing lifestyle behaviors (e.g., toothbrushing) is largely a task of
breaking old habits and creating new and healthy ones. Thus, representing
users' habit strengths can be very useful for behavior change support systems
(BCSS), for example, to predict behavior or to decide when an intervention
reaches its intended effect. However, habit strength is not directly observable
and existing self-report measures are taxing for users. In this paper, built on
recent computational models of habit formation, we propose a method to enable
intelligent systems to compute habit strength based on observable behavior. The
hypothesized advantage of using computed habit strength for behavior prediction
was tested using data from two intervention studies, where we trained
participants to brush their teeth twice a day for three weeks and monitored
their behaviors using accelerometers. Through hierarchical cross-validation, we
found that for the task of predicting future brushing behavior, computed habit
strength clearly outperformed self-reported habit strength (in both studies)
and was also superior to models based on past behavior frequency (in the larger
second study). Our findings provide initial support for our theory-based
approach of modeling user habits and encourages the use of habit computation to
deliver personalized and adaptive interventions.
