Digital Mental Health Service for Non-Treatment Seeking Young Adults

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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.