基于理论的习惯建模增强行为预测
Theory-based Habit Modeling for Enhancing Behavior Prediction
摘要：
习惯的心理学理论认为，当一个强烈的习惯通过行为重复形成时，它可以在相同的环境中自动触发行为。考虑到习惯和行为之间的相互关系，改变生活方式行为（例如，刷牙）在很大程度上是一项打破旧习惯并创造新的健康习惯的任务。因此，表示用户的习惯强度对于行为改变支持系统（BCSS）非常有用，例如，预测行为或决定干预何时达到其预期效果。然而，习惯强度是不能直接观察到的，现有的自我报告措施对用户来说是繁重的。在本文中，建立在最近的习惯形成的计算模型，我们提出了一种方法，使智能系统计算习惯强度的基础上观察到的行为。使用两项干预研究的数据对使用计算的习惯强度进行行为预测的假设优势进行了测试，我们训练参与者每天刷牙两次，持续三周，并使用加速度计监测他们的行为。通过分层交叉验证，我们发现，对于预测未来刷牙行为的任务，计算的习惯强度明显优于自我报告的习惯强度（在两项研究中），并且也优于基于过去行为频率的模型（在较大的第二项研究中）。我们的研究结果为我们基于理论的用户习惯建模方法提供了初步支持，并鼓励使用习惯计算来提供个性化和适应性干预。
英文摘要：
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.
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