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.

翻译：尽管说服性信息在电子健康应用的行为改变领域广泛存在，但其对行为的影响通常较小。被说服者的状态（如信心、知识、动机）和特征（如性别、年龄、个性）是构建更有效的说服信息选择算法的两个潜在要素。然而，目前尚不充分明确的是，综合考虑这些要素能否有效预测说服尝试后的行为，尤其是长期行为表现。由于收集多种算法要素的数据成本高昂且会给用户带来负担，深入了解各要素在实践中的影响将有助于决策者明智地选择需采用的要素。为此，我们开展了一项纵向研究，由虚拟教练说服671名每日吸烟者进行戒烟准备活动（如想象理想中的未来自我），并提升其身体活动水平。基于采集的数据，我们设计了一种强化学习方法，通过考虑当前和未来状态来最大化用户在活动中的投入。通过留一法交叉验证，我们发现考虑状态有助于同时预测行为与未来状态；而用户特征（尤其是对活动的参与度）仅在与状态联合使用时才能预测行为，单独使用时则效果不佳。这些结果支持在说服算法中纳入状态与参与度指标。我们的数据集已公开在线。