Digital Strategies to Advance Help-Seeking Aim 1 and 2

Recruiting

• Conditions: Clinical High Risk; Early Psychosis; First Episode Psychosis

• Registration Number: NCT06774430
• Lead Sponsor: Columbia University

Brief Summary

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.

Detailed Description

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 Timeline

• Study First Submitted: 2025-01-13
• Study First Submitted QC: 2025-01-13
• Study First Posted: 2025-01-14
• Status Verified: 2025-04
• Study Start: 2025-04-07
• Last Update Submitted: 2025-04-29
• Last Update Posted: 2025-05-02
• Primary Completion: 2028-05-31
• Study Completion: 2028-05-31

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 25000

Inclusion Criteria

• Ages 12-29 years
• Living within a 50-mile radius of a US based AMP-SCZ site
• Able to complete the English language PQ-B on MHA's screening platform

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Aim 1: Proportion of participants in each help-seeking category	1 year	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).
Aim 2: Threshold score for identifying Clinical High-Risk Youth online	1 year	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.

Trial Locations

Locations (1)

Columbia University Irving Medical Center; 🇺🇸 New York, New York, United States