Testing Scalable, Single-Session Interventions for Adolescent Depression in the context of COVID-19

Project Summary/Abstract States and localities nationwide are taking unprecedented steps to reduce public health threats posed by COVID-19, including school closures affecting >50 million youth. The pandemic has also caused families extreme financial hardship, sudden unemployment, and distress. This combination of collective trauma, socialisolation, and economic recession drastically increases risk for adolescent major depression (MD): already thelead cause of disability in youth. However, youth MD treatments face problems of potency and accessibility. Up to 65% of youth receiving MD treatment fail to respond, partly due to MD's heterogeneity: an MD diagnosis reflects >1400 possible symptom combinations, highlighting the need for treatments matched to personal need. Treatment accessibility issues are similarly severe. Before the pandemic, < 50% of youth with MD accessed any treatment at all; newfound financial strain will further preclude families' capacity to afford care for their children. It is thus critical to identify effective, scalable strategies to buffer against youth MD in the context of COVID-19, along with strategies to match such interventions with youth most likely to benefit. This project will integrate machine learning approaches and large-scale SSI research to rapidly test potent, accessible strategies for reducing adolescent MD during COVID-19. Via the largest-ever SSI trial (N=1,200 youth with elevated MD symptoms, ages 12-16), Aim 1 is to test whether (1) evidence-based SSIs improve proximal targets (e.g., hopelessness and perceived agency, which has predicted longer-term SSI response) and 3-month clinical outcomes (MD severity) during the COVID-19 pandemic, and (2) whether SSIs targeting cognitive versus behavioral MD symptoms are most impactful in this context. In a fully-online trial, youths recruited from across the U.S. will be randomized to 1 of 3 self-administered SSIs: a behavioral activation SSI,targeting behavioral MD symptoms (anhedonia; activity withdrawal); an SSI teaching growth mindset, the belief that personal traits are malleable, targeting cognitive MD symptoms (e.g. hopelessness); or a control SSI. Per baseline, post-SSI, and 3-month follow-up data, we will test each SSI's relative benefits, versus the control, in the context of COVID-19. Results will reveal whether SSIs targeting behavioral versus cognitive symptoms differentially reduce overall MD severity in this context. Aim 2 is to test whether (and, if so, which of) SSIs can impact COVID-19 specific trauma and anxiety symptoms, informing whether novel, COVID-19-tailored supports may be needed to reduce pandemic-specific mental health sequelae. Aim 3 is to test person-level and contextual predictors of SSI response, via machine-learning techniques, regardless of overall intervention effects observed. Given MD's heterogeneity, we will test whether baseline symptoms (e.g., having more severe cognitive or behavioral MD symptoms) predict response to SSIs targeting different symptom types. We will also test exposure to COVID-19-related adversities (e.g. parent job loss; loved one hospitalized for COVID-19) and general disadvantage (e.g. family low-income; racial minority status) forecast SSI response.