Digital Strategies to Advance Help-Seeking - Aim 3

Overview

This proposal aims to establish a Digital Laboratory focused on advancing help-seeking and expediting treatment initiation in youth ages 12-29 who are at Clinical High-Risk (CHR) for developing psychosis. Leveraging the Health Action Process Approach (HAPA) model, this study will identify help-seeking subtypes in 25,000 youth who screen positive for psychosis-risk on Mental Health America's national online screening platform, iteratively develop and test theory and data-driven, personalized strategies to advance help-seeking using Micro-Randomized Trials and a Sequential Multiple Assignment Randomized Trial, identify the most accurate CHR screening threshold in an online environment, and link youth, when indicated, to local clinical care via AMP-SCZ, a NIH funded national network of CHR programs throughout the US. This academic-industry partnership aims to curate one of the largest datasets of youth with CHR, and to develop effective strategies to enhance early help-seeking, in a population where help-seeking is critical and a significant barrier to care.

This proposal aims to establish a Digital Laboratory focused on advancing help-seeking and expediting treatment initiation in youth (ages 12-29) who are at Clinical High-Risk (CHR) for developing psychosis. Despite the implementation of evidence-based early intervention services across the U.S., CHR youth wait months to years between symptom onset and receiving care, resulting in significantly worse outcomes. Current CHR identification strategies have relied on costly, inefficient, and outdated resources. Global efforts to expedite treatment initiation in CHR youth, have catalyzed unprecedented demand for innovative digital healthcare solutions aimed at increasing access to available and effective treatment options. Searching online represents one of the first proactive step towards treatment initiation, yet very little research to date has informed how best to support CHR youth as they engage in critical early online help-seeking steps towards care. To address this knowledge gap, this study involves a partnership between Mental Health America (MHA), the world's largest online mental health screening and advocacy organization, Strong365, an online resource hub dedicated to early psychosis education and care navigation, Accelerating Medicines Partnership - Schizophrenia (AMP-SCZ), a NIH-funded network of CHR programs across the US, and four academic partners with expertise in the assessment and management of CHR youth (Columbia, UCSF, UCD) online psychosis screening and care navigation (Columbia, UCD) and digital mental health engagement (UW, Columbia). Building on MHA's research partnership focused on digital mental health engagement (R01MH125179), this approach leverages the Health Action Process Approach (HAPA) model, which describes how individuals move from intention to action when considering behavioral change. This study will first characterize help-seeking subtypes in 25,000 youth who score above threshold for psychosis-risk on the Prodromal-Questionnaire (PQ-B), on MHA's national screening platform. Next, the investigators will use this large dataset to establish the PQ-B score most effective at identifying CHR youth online, given that the risk of false positives may be higher in online self-screening, and that optimal thresholds can vary depending upon sample prevalence, population, and setting. The investigators will then iteratively develop and test theory and data driven digital strategies using a series of Micro Randomized Trials (MRT) designed to advance help-seeking in CHR youth from pre-intention to intention, from intention to action, and from action to super-action by directly engaging HAPA target mechanisms: self-efficacy, perceived needs/risks, and outcome expectancies. The specific aims and hypotheses include: Aim 1: Characterize help-seeking patterns in 25,000 youth who score above PQ-B threshold. H1a: Youth will cluster into (1) pre-intenders (take the PQ-B and engage with educational content), (2) intenders (initiate a text exchange with a Strong365 peer navigator (3) actors (advance from texting to clinical assessment with a Strong365 clinician over phone/video) and (4) super-actors (advance from assessment to AMP-SCZ intake). Data will include online metadata (time spent online, # of resources viewed, time spent to complete the PQ-B, # of texts initiated/exchanged), self-report (demographics, symptom type and severity, PQ-B score, goals/needs, self-efficacy), and natural language. H1b (Strong365 only): Natural Language Processing (NLP) of data extracted from participant/provider interactions over text and video will identify linguistic markers of HAPA stages: intender, actor, super-actor. Models based on HAPA stages, along with behavioral features (i.e., message timing, frequency, response lag) will predict help-seeking advancement vs. disengagement. Top predictive features will be used to inform the crafting of help-seeking advancement strategies to be tested in MRTs (Aim 3). Aim 2: To ensure that those who complete the PQ-B are directed appropriately, this study will establish the most accurate threshold for identifying CHR online. H2: Using data from population-based PQ-B screening, the investigators predict that a total distress score of 20+ will generate the highest diagnostic odds ratio with a sensitivity of at least 80% online, as determined by remote clinical assessment. For the remainder of the study, the threshold score that maximizes specificity and sensitivity will be used. Aim 3: Iteratively develop, test, and select the most effective help-seeking advancement strategies using MRTs in 25,000 youth, at 3 timepoints along the pathway to care when youth are most likely to disengage (post PQ-B completion, post texting with a peer navigator, post remote clinical assessment). H3a: Digital strategies on the PQ-B results page will advance youth from online screening alone to texting (pre-intender to intender). Strategies to enhance task self-efficacy and outcome expectancies will be more effective than post-screening information as usual. H3b: Automated digital strategies delivered over text will advance youth from texting to remote clinical assessment (intender to actor). Strategies to enhance task self-efficacy and outcome expectancies will be more effective than control messages. H3c: Automated digital strategies delivered over text will advance youth from assessment to AMP-SCZ intake and treatment initiation, when indicated (actor to super-actor). Strategies to enhance maintenance and recovery self-efficacy will be more effective than control messages.