Digital Mental Health Service for Non-Treatment Seeking Young Adults

项目总结 18-24岁的年轻人经历的心理健康问题比其他任何成年人都要高 一群人。在生活在美国的所有年轻人中，超过四分之一的人患有心理健康问题。 不幸的是，他们是最不可能寻求或接受传统面对面交流的成年年龄组 治疗如面对面的心理治疗或药物治疗。然而，有证据表明，它们是， 有兴趣使用数字心理健康干预(DMHI)，如移动和基于网络的应用程序，以支持 症状自我管理和技能培养。虽然有证据表明心理健康应用程序可以有效地 降低症状严重性，他们需要用户打开和使用应用程序的动机，这有助于 高辍学率。解决辍学问题的主要方法是使用人工教练，这 提高参与度。 短信通过手机上最常用的应用程序发送，因此很可能 被查看。在短信干预方面的初步工作表明，由于努力程度较低，依从性良好，但 有效性一直不一致。信息可能被认为是脱离目标或非人性化的，这是困难的 以传达更复杂的信息。该项目旨在通过开发和试点一种 个性化文本消息收发平台，使用机器学习根据个人的 需求和偏好，以及URL链接，以提供对心理教育内容的访问以进行情景设置 消息，当内容长度超过消息的限制时。该项目将包括一个 与美国最大的心理健康倡导组织--美国心理健康组织建立伙伴关系。 该项目的主要目标是：(1)为年轻人开发一种自适应消息传送服务， 根据个人的需要和偏好个性化信息和心理教育内容，(2) 使用序贯多分配随机治疗(SMART)设计进行可行性试验， 将评估(A)自适应、个性化消息传递干预在减少 相对于静态版本的参与度；以及(B)人工指导是否会导致更大程度的症状缓解 和参与度，相对无指导的实施。 这一项目将使我们能够在短期内确定这一干预的可行性，包括 我们的适应性干预影响治疗心理和参与目标，并减少心理 苦恼。它还将提供有关定向、低成本、可扩展模式的可行性的初步信息。 为可能需要更多支持的用户提供强化培训，而不仅仅是全自动 干预。这将为我们的长期目标做好准备，即进行完全有效的随机对照 我们的适应性干预在在线社区环境中的试验。

PROJECT SUMMARY Young adults aged 18-24 experience higher levels of mental health problems than any other adult age group. Over one quarter of all young adults living in the United States suffer from a mental health condition. Unfortunately, they are the adult age group who are least likely to seek or receive traditional face-to-face treatments such as in-person psychotherapy or pharmacotherapy. There is evidence that they are, however, interested in using digital mental health interventions (DMHIs), such as mobile and web-based apps, to support symptom self-management and skill building. While evidence suggests mental health apps can effectively reduce symptom severity, they require motivation from the user to open and use the app, which contributes to the high dropout rates. The primary method of addressing dropout has been the use of human coaching, which boosts engagement. SMS text messages arrive through the most commonly used app on the phone, and are therefore likely to be viewed. Initial work in text message interventions suggests good adherence, as effort is low, but effectiveness has been inconsistent. Messages can be perceived as off-target or impersonal, and it is difficult to convey more complex information. This project aims to address these problems by developing and piloting a personalized text messaging platform that uses machine learning to tailor SMS messages to an individual’s needs and preferences, and URL links to provide access to psychoeducational content to contextualize messages, when the length of that content exceeds the limitations of messages. This project will include a partnership with Mental Health America, the nation’s largest mental health advocacy organization. The primary goals of the project are to: (1) Develop an adaptive messaging service for young adults that personalizes messages and psychoeducational content to the needs and preferences of an individual, (2) Conduct a feasibility trial using a sequential multiple assignment randomized treatment (SMART) design, which will evalutate (a) the effectiveness of an adaptive, personalized messaging intervention in reducing engagement relative to a static version; and (b) whether human coaching results in greater symptom reduction and engagement, relative an unguided implementation. This project will, in the near term, allow us to determine the feasibility of this intervention, including whether our adaptive intervention affects treatment psychological and engagement targets, and reduces psychological distress. It will also provide preliminary information on the feasibility of a scalable model of targeted, low- intensity coaching for users who may require additional support above and beyond a fully automated intervention. This will prepare us for our longer-term goal of conducting a fully-powered randomized controlled trial of our adaptive intervention in an online community setting.