College Mental Health Project

Depression is common and debilitating disorder which, among college students, is predictive of significant problems including higher rates of dropout and suicidal behaviors. Despite viable psychotherapeutic and pharmacological options, the majority of depressed college students, like others in the general population, do not pursue treatment. Further, barriers to care, both those involving practical issues and psychological factors, lead to high attrition rates from treatment, resulting in modest effect sizes in effectiveness trials. In reaction to these alarming figures and as a means of increasing accessibility and retention, effective internet-based treatment for depression have been developed and tested. Despite increased availability, response to internet-based treatment continues to vary substantially, yet, controlled trials show that a meaningful proportion of patients who receive internet-based therapy recover. Identifying individuals with a high likelihood of responding to internet-based treatment would enable clinicians to target this inexpensive treatment only to the patients with a high probability of responding; allowing more intensive treatments to be reserved for patients who would not respond to internet-based therapy. The development of a system to make this determination would represent a major advance and address an unmet need.

ICare is an online depression treatment that has been adapted for college students (e.g., language used, problems discussed, embedded images). Prior work by the developers of ICare have tested the internet-based treatment in diverse samples of depressed adults, and prior meta-analytic research has demonstrated that psychological treatments for depression in college students are as effective relative to trials carried out among depressed adults. The overarching goal of the study is to: (a) test whether depressed college students utilize ICare, (b) identify psychosocial and clinical characteristics that increase the likelihood of ICare utilization, and (c) identify multivariate characteristics that predict treatment response.