标题： 现实世界对自适应心理健康干预的接受度：一项真实环境研究的结果 显示英文标题

标题： Real-World Receptivity to Adaptive Mental Health Interventions: Findings from an In-the-Wild Study

摘要： 移动健康（mHealth）技术的兴起使得利用被动获取的智能手机数据实时监测和干预心理健康状况成为可能。 在这些能力的基础上，即时自适应干预（JITAIs）旨在适时提供个性化支持，根据用户的不断变化的情境和需求进行调整。 尽管之前的研究已经探讨了情境如何影响用户对通用通知和一般mHealth信息的反应，但相对较少的工作研究了情境对其参与实际心理健康干预的影响。 此外，虽然现有研究大多集中在检测用户何时可能从干预中受益，但较少关注理解接受度，即用户参与并执行干预的意愿和能力。 在本研究中，我们通过两个组成部分来研究用户接受度：接受度（承认或参与提示）和可行性（在情境约束下采取行动的能力）。 我们使用定制的Android应用LogMe对70名学生进行了为期两周的实地研究，该应用收集被动传感器数据和主动情境报告以触发心理健康干预。 自适应干预模块是使用Thompson Sampling构建的，这是一种强化学习算法。 我们提出了四个与智能手机功能和自我报告情境相关的研究问题，探讨自适应强化学习方法是否可以通过最大化综合接受度奖励来优化干预的传递。 我们的结果表明，几种被动获取的数据显著影响了用户对干预的接受度。 我们的发现为设计情境感知、自适应的干预提供了见解，这些干预不仅及时，而且在现实环境中可操作。 显示英文摘要

摘要： The rise of mobile health (mHealth) technologies has enabled real-time monitoring and intervention for mental health conditions using passively sensed smartphone data. Building on these capabilities, Just-in-Time Adaptive Interventions (JITAIs) seek to deliver personalized support at opportune moments, adapting to users' evolving contexts and needs. Although prior research has examined how context affects user responses to generic notifications and general mHealth messages, relatively little work has explored its influence on engagement with actual mental health interventions. Furthermore, while much of the existing research has focused on detecting when users might benefit from an intervention, less attention has been paid to understanding receptivity, i.e., users' willingness and ability to engage with and act upon the intervention. In this study, we investigate user receptivity through two components: acceptance(acknowledging or engaging with a prompt) and feasibility (ability to act given situational constraints). We conducted a two-week in-the-wild study with 70 students using a custom Android app, LogMe, which collected passive sensor data and active context reports to prompt mental health interventions. The adaptive intervention module was built using Thompson Sampling, a reinforcement learning algorithm. We address four research questions relating smartphone features and self-reported contexts to acceptance and feasibility, and examine whether an adaptive reinforcement learning approach can optimize intervention delivery by maximizing a combined receptivity reward. Our results show that several types of passively sensed data significantly influenced user receptivity to interventions. Our findings contribute insights into the design of context-aware, adaptive interventions that are not only timely but also actionable in real-world settings.