Predictors of adherence by adolescents to a cognitive behaviour therapy website in school and community-based settings

Background: There have been no previous studies of the variables that predict adherence to online depression and anxiety intervention programs among adolescents. However, research of traditionally delivered intervention programs for a variety of health conditions in adolescence suggests that health knowledge, type and level of symptomatology, race, socioeconomic status, treatment setting, and support may predict adherence. Objective: The aim was to compare adherence rates and identify the predictors of adherence to a cognitive behavior therapy website in two adolescent samples that were offered the program in different settings and under different conditions of support. Methods: The first adolescent sample consisted of 1000 school students who completed the MoodGYM program in a classroom setting over five weeks as part of a randomized controlled trial. The second sample consisted of 7207 adolescents who accessed the MoodGYM program spontaneously and directly through the open access URL. All users completed a brief survey before the start of the program that measured background characteristics, depression history, symptoms of depression and anxiety, and dysfunctional thinking. Results: Adolescents in the school-based sample completed significantly more online exercises (mean = 9.38, SD = 6.84) than adolescents in the open access community sample (mean = 3.10, SD = 3.85; t1088.62 = -28.39, P < .001). A multiple linear regression revealed that school-based setting (P < .001) and female gender (P < .001) were predictive of greater adherence, as were living in a rural area (P < .001) and lower pre-test anxiety (P = .04) scores for the school-based sample and higher pre-test depression scores (P = .01) for the community sample. A history of depression (P = .33) and pre-test warpy thoughts scores (P = .35) were not predictive of adherence in the school-based or community sample. Conclusion: Adherence is greater in monitored settings, and the predictors of adherence differ between settings. Understanding these differences may improve program effectiveness and efficiency