Internet-delivered Cognitive Behavioral Treatment of Depression and Anxiety in Latin American College Students

Summary

Participation Requirements

Description

This study is designed to detect, engage, and experimentally evaluate the effects of internet-based cognitive behavior therapy (e-CBT) to treat college students in two Latin American low-middle income countries (LMICs; Colombia and Mexico) with major depressive disorder and/or generalized anxiety di...

This study is designed to detect, engage, and experimentally evaluate the effects of internet-based cognitive behavior therapy (e-CBT) to treat college students in two Latin American low-middle income countries (LMICs; Colombia and Mexico) with major depressive disorder and/or generalized anxiety disorder, with or without other comorbid common mental disorders (CMDs). Detection and engagement will be based on inexpensive internet-based recruitment and administration of self-report surveys. The intervention will be based on inexpensive e-CBT to address the low resources in LMICs. The study builds on prior research by collaborators in the WHO World Mental Health (WMH) survey consortium and WMH International College Study (WMH-ICS) initiative documenting high prevalence, impairment, and unmet need for treatment of CMDs among college students around the world and significant effects of e-CBT in treating these disorders in high income countries. First, the investigators will carry out a pragmatic clinical trial with students seeking treatment for CMDs at student clinics in universities in Colombia and Mexico. Students on waitlists for student clinic services will be offered a possibility of receiving guided or self-guided e-CBT immediately while staying on the list. 33% of the students with CMD who express interest will be randomized to guided e-CBT, another 33% to self-guided e-CBT, and the remaining 33% to treatment as usual (TAU), where the latter is defined as remaining on the waiting list. Short-term aggregate intervention effects will be assessed 90 days after randomization and longer-term effects 12 months after randomization. The investigators will then use ensemble machine learning methods to predict heterogeneity of treatment effects of e-CBT versus TAU and develop a precision treatment rule (PTR) to predict which students will respond best to which intervention. A SMART design will then be implemented in which 50% of a second cohort of students from the same sample frame (i.e., students seeking treatment for CMDs at student clinics in the participating universities) will be randomized to the treatment arm predicted to be optimal by the PTR and the other 50% to randomization across arms. Additionally, the investigators will use outreach to recruit students into the trial who might not otherwise seek treatment from student clinics. Two procedures will be used here. In the first, students will be recruited from annual WMH-ICS internet-based mental health needs assessment surveys that will be carried out with a probability sample of students to estimate unmet need for treatment and barriers to treatment. Students who respond to these surveys and meet inclusion criteria and are not in treatment will be randomized to guided e-CBT, self-guided e-CBT, and treatment as usual in student clinics to determine if access to guided and self-guided e-CBT reduces the unmet need for treatment among students with CMDs who fail to seek treatment at student health clinics. The second will expand recruitment by sending emails to random subsamples of students notifying them of the availability of the internet-based interventions and inviting them to participate in a trial. Students that respond to this announcement will then be recruited and randomized into the same three arms as among students that participated in the annual WMH-ICS survey.

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