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 Accelerating Medicines Partnership - Schizophrenia (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.
Aim 1: Characterize help-seeking patterns in 25,000 youth who score above Prodromal-Questionnaire (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.
Study Type
OBSERVATIONAL
Enrollment
25,000
Columbia University Irving Medical Center
New York, New York, United States
RECRUITINGAim 1: Proportion of participants in each help-seeking category
Data from participants, including 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 will be used to cluster participants into 4 categories: (1) Pre-intenders (take the PQ-B); (2) Intenders (initiate a text exchange with a peer navigator; (3) Actors (advance to clinical assessment); and (4) Super-actors (advance to intake).
Time frame: 1 year
Aim 2: Threshold score for identifying Clinical High-Risk Youth online
This score will be determined using data from population-based PQ-B screening. A total distress score of 20+ is predicted to 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, a threshold score that maximizes specificity and sensitivity will be used.
Time frame: 1 year
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