We present a series of mHealth applications and studies pursued as part of the Fittle+ project. This program of research has the dual aims of (1) bringing scalable evidence-based behavior-change interventions to mHealth and evaluating them and (2) developing theoretically based predictive models to better understand the dynamics of the impact of these interventions on achieving behavior-change goals. Our approach in the Fittle+ systems rests on the idea that to master the complex fabric of a new healthy lifestyle, one must weave together a new set of healthy habits that over-ride the old unhealthy habits. To achieve these aims, we have developed a series of mHealth platforms that provide scaffolding interventions: Behavior-change techniques and associated mHealth interactions (e.g., SMS reminders; chatbot dialogs; user interface functionality; etc.) that provide additional support to the acquisition and maintenance of healthy habits. We present experimental evidence collected so far for statistically significant improvements in behavior change in eating, exercise, and physical activity for the following scaffolding interventions: guided mastery, teaming, self-affirmation, and implementation intentions. We also present predictive computational ACT-R models of daily individual behavior goal success for data collected in guided mastery and implementation intention studies that address goal-striving and habit formation mechanisms.

我们介绍了一系列移动医疗应用程序和研究，作为Fittle +项目的一部分。该研究计划的双重目标是（1）将可扩展的基于证据的行为改变干预措施引入mHealth并对其进行评估，以及（2）开发基于理论的预测模型以更好地了解这些干预措施对实现行为的影响的动态变化-改变目标。我们在Fittle +系统中的方法基于这样的想法：要掌握一种新的健康生活方式的复杂结构，就必须编织一套新的健康习惯，以取代过去的不健康习惯。为了实现这些目标，我们开发了一系列的mHealth平台，它们提供了脚手架干预措施：行为改变技术和相关的mHealth交互（例如SMS提醒，聊天机器人对话框，用户界面功能等），为获取和维护健康习惯提供了额外的支持。我们目前提供的实验证据表明，对于下列脚手架干预措施，饮食，运动和体育锻炼的行为改变具有统计学上的显着改善：指导性的掌握，团队合作，自我肯定和实施意图。我们还针对在指导性掌握和实施意向研究中收集的数据提供了日常个人行为目标成功的预测性计算ACT-R模型，这些模型解决了目标达成和习惯形成机制。