Depression and anxiety are highly comorbid and costly diseases. Evidence-based psychotherapy is the first-line treatment but is underutilized and not scalable. Digital mental health interventions (DMHIs), delivered via the internet and/or mobile apps, have evolved as efficacious and potentially scalable treatments. To date, however, effectiveness in routine care is limited by insufficient patient engagement. In order to achieve the transformative potential of DMHIs, we must identify strategies to keep patients engaged without adding human support in a form that would limit scalability. Automated motivational push messaging (AMM) and light-touch human coach support (CS) offer two such strategies. The proposed research tests these strategies, while drawing preliminary conclusions about a hypothesized model of DMHI engagement based on the technology adoption and treatment adherence literature. The model posits that two systems-level constructs (social influence and facilitating conditions) and three patient-level constructs (attitude, self-efficacy, habit strength) drive DMHI engagement. In Study 1 (N=20), I will employ user-centered design to develop and refine a set AMMs targeting the three hypothesized patient-level engagement-driving constructs (Aim 1). In Study 2, I will recruit N=76 primary care patients with depression and/or anxiety via provider referral to an 8-week 2x2 factorial clinical trial whereby participants will all receive access to a DMHI with known efficacy and be randomized to an engagement strategy condition (i.e., a previously-validated CS protocol, newly-developed AMM, both or neither). To further understand how AMMs function, message delivery in the AMM arms will be micro-randomized: each day participants will be randomized to receive a message or not, such that they receive an average of 4.2 messages/week. Micro- randomization allows causal inference about the near-term impact of message delivery (i.e., are AMMs a cue to action) and the relationship between message impact and context (e.g., time of day the message is delivered). Measured outcome data will comprise level of engagement (operationalized as minutes of DMHI use), weekly self-reports on the five engagement-driving constructs, and weekly self-reports of clinical outcomes. I will test im pacts of each strategy on m easured outcom e data (Aim 2) and explore the hypothesized relationships between engagement-driving constructs and DMHI engagement (Aim 3). Clinical outcomes will be assessed, however, consistent with the experimental therapeutics model, this research leverages a DMHI with known efficacy, allowing the focus to be an upstream target (patient engagement) rather than the clinical outcomes themselves. The overarching goal is to influence the target so as to ultimately enhance clinical effectiveness. This project will build my expertise in clinical trial design and build my proficiency in user-centered design (i.e., rapid, prototype testing via field studies) and data science (i.e., analysis of intensive, correlated longitudinal data) methods commonly applied in DMHI optimization research. Findings will lay a foundation for R01s aimed at optimizing DMHIs for engagement, and ultimately effectiveness, when integrated into routine care.
Mobile app mental health interventions are accessible and effective when used at the recommended dose; but when implemented in routine care, patients do not stay engaged long enough to see clinical benefits. The proposed research evaluates what drives patient engagement and tests the impact of two strategies? automated motivational push messaging and coach support?to improve engagement with an evidence-based mobile app intervention for depression and/or anxiety. Findings will lay the foundation for a program of research focused on optimizing mobile app interventions for engagement and effectiveness.
