  Despite their prevalence in eHealth applications for behavior change,
persuasive messages tend to have small effects on behavior. Conditions or
states (e.g., confidence, knowledge, motivation) and characteristics (e.g.,
gender, age, personality) of persuadees are two promising components for more
effective algorithms for choosing persuasive messages. However, it is not yet
sufficiently clear how well considering these components allows one to predict
behavior after persuasive attempts, especially in the long run. Since
collecting data for many algorithm components is costly and places a burden on
users, a better understanding of the impact of individual components in
practice is welcome. This can help to make an informed decision on which
components to use. We thus conducted a longitudinal study in which a virtual
coach persuaded 671 daily smokers to do preparatory activities for quitting
smoking and becoming more physically active, such as envisioning one's desired
future self. Based on the collected data, we designed a Reinforcement Learning
(RL)-approach that considers current and future states to maximize the effort
people spend on their activities. Using this RL-approach, we found, based on
leave-one-out cross-validation, that considering states helps to predict both
behavior and future states. User characteristics and especially involvement in
the activities, on the other hand, only help to predict behavior if used in
combination with states rather than alone. We see these results as supporting
the use of states and involvement in persuasion algorithms. Our dataset is
available online.
