JITAI is an emerging technique with great potential to support health behavior by providing the right type and amount of support at the right time. A crucial aspect of JITAIs is properly timing the delivery of interventions, to ensure that a user is receptive and ready to process and use the support provided. Some prior works have explored the association of context and some user-specific traits on receptivity, and have built post-study machine-learning models to detect receptivity. For effective intervention delivery, however, a JITAI system needs to make in-the-moment decisions about a user's receptivity. To this end, we conducted a study in which we deployed machine-learning models to detect receptivity in the natural environment, i.e., in free-living conditions. We leveraged prior work regarding receptivity to JITAIs and deployed a chatbot-based digital coach -- Walkie -- that provided physical-activity interventions and motivated participants to achieve their step goals. The Walkie app included two types of machine-learning model that used contextual information about a person to predict when a person is receptive: a static model that was built before the study started and remained constant for all participants and an adaptive model that continuously learned the receptivity of individual participants and updated itself as the study progressed. For comparison, we included a control model that sent intervention messages at random times. The app randomly selected a delivery model for each intervention message. We observed that the machine-learning models led up to a 40% improvement in receptivity as compared to the control model. Further, we evaluated the temporal dynamics of the different models and observed that receptivity to messages from the adaptive

中文翻译：

---

检测自然环境中移动医疗干预的接受度

JITAI 是一种新兴技术，具有通过在正确的时间提供正确类型和数量的支持来支持健康行为的巨大潜力。JITAI 的一个关键方面是正确安排干预措施的时间，以确保用户能够接受并准备好处理和使用所提供的支持。一些先前的工作探索了上下文和一些用户特定特征对接受度的关联，并建立了研究后的机器学习模型来检测接受度。然而，为了有效地进行干预，JITAI 系统需要对用户的接受程度做出即时决策。为此，我们进行了一项研究，其中我们部署了机器学习模型来检测自然环境（即自由生活条件）中的接受度。我们利用先前关于接受 JITAI 的工作，并部署了一个基于聊天机器人的数字教练——Walkie——提供体育活动干预并激励参与者实现他们的步骤目标。Walkie 应用程序包括两种类型的机器学习模型，它们使用一个人的上下文信息来预测一个人何时愿意接受：一种是在研究开始之前建立的静态模型，并且对所有参与者保持不变，另一种是自适应模型，它不断学习个体参与者的接受度并随着研究的进展而自我更新。为了进行比较，我们包含了一个随机发送干预消息的控制模型。该应用程序为每条干预消息随机选择了一个传递模型。我们观察到，与控制模型相比，机器学习模型的接受度提高了 40%。此外，我们评估了不同模型的时间动态，并观察到对来自自适应模型的消息的接受度