Use of tailoring features and reasons for dropout in a guided internet-based transdiagnostic individually-tailored cognitive behavioral therapy for symptoms of depression and/or anxiety in college students

Transdiagnostic individually-tailored digital interventions reduce symptoms of depression and anxiety in adults with moderate effects. However, research into these approaches for college students is scarce and contradicting. In addition, the exact reasons for intervention dropout in this target group are not well known, and the use of individually-tailored intervention features, such as optional modules, has not yet been explored. The current study aimed to (1) investigate reasons for dropout from a guided internet-based transdiagnostic individually-tailored intervention for college students assessed in a randomized controlled trial (RCT) and (2) evaluate whether participants used tailoring features intended for their baseline symptoms. A sample of college students with mild to moderate depression and/or anxiety symptoms (n = 48) in the Netherlands (partially) followed a guided internet-based transdiagnostic individually-tailored intervention. We contacted those who did not complete the entire intervention (n = 29) by phone to report the reasons for intervention dropout. Further, we descriptively explored the use of tailoring features (i.e., depression versus anxiety trajectory) and optional modules of the intervention in the whole sample. We identified a range of person- and intervention-related reasons for intervention dropout, most commonly busy schedules, needs for different kinds of help, or absence of personal contact. Furthermore, only less than half of the participants used the individually-tailoring features to address the symptoms they reported as predominant. In conclusion, digital interventions clear about the content and targeted symptoms, tested in user research could prevent dropout and create reasonable expectations of the intervention. Participants would benefit from additional guidance when using tailoring features of digital interventions, as they often do not choose the tailoring features targeting their baseline symptoms.