Digital Mental Health Service for Non-Treatment Seeking Young Adults

项目摘要 18 - 24岁的年轻人比任何其他成年人都经历更高水平的心理健康问题 组在美国，超过四分之一的年轻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人。 治疗，如心理治疗或药物治疗。然而，有证据表明， 有兴趣使用数字心理健康干预（DMHI），如移动的和基于网络的应用程序，以支持 症状自我管理和技能建设。虽然有证据表明心理健康应用程序可以有效地 降低症状严重程度，它们需要用户打开和使用应用程序的动力，这有助于 高辍学率。解决辍学问题的主要方法是使用人类教练， 提高参与度 短信通过手机上最常用的应用程序到达，因此很可能会 被观看。在短信干预的初步工作表明，良好的坚持，因为努力是低，但 效果不一致。信息可能会被认为是偏离目标或非个人的， 来传达更复杂的信息该项目旨在通过开发和试点一个 一个个性化的短信平台，使用机器学习来定制短信，以个人的 需求和偏好，以及URL链接，以提供对心理教育内容的访问， 消息，当该内容的长度超过消息的限制时。该项目将包括A 与美国最大的心理健康倡导组织“美国心理健康”合作。 该项目的主要目标是：（1）为年轻人开发一种自适应的消息服务， 根据个人的需求和偏好个性化信息和心理教育内容，（2） 采用序贯多分配随机治疗（SMART）设计进行可行性试验， 将评价（a）自适应、个性化的信息传递干预措施在减少 相对于静态版本的参与;以及（B）人类指导是否导致更大的症状减轻 和参与，相对于一个无指导的实施。 该项目将在短期内使我们能够确定这种干预的可行性，包括是否 我们的适应性干预影响治疗心理和参与目标， 痛苦它还将提供初步信息，说明有针对性的、低成本的、可扩展的模式的可行性。 针对可能需要完全自动化之外的额外支持的用户的强度辅导 干预这将使我们为我们的长期目标做好准备， 我们在一个在线社区环境中的适应性干预试验。

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.