When No One is Looking: Patient Trajectories on Waiting Lists in Digital Mental Health Interventions
Method Stream: Literature Review
ECs: Only 14 EC (standard, no new/own data collection. Applicable in case of a clinical internship)
Description:
Objective:
This thesis project explores the natural trajectory of patients allocated to waiting-list control groups within digital mental health interventions for anxiety and depression. Specifically, it investigates whether improvements observed in control conditions can be attributed to regression to the mean or spontaneous recovery.
Approach:
The student will utilize existing meta-analytic data from randomized controlled trials (RCTs), focusing exclusively on participants assigned to waiting lists. By employing statistical methods such as Minimal Clinically Important Difference (MCID), the student will quantify the proportion of participants showing clinically meaningful improvement without active intervention. Subsequently, factors such as clinical severity (clinical vs. subclinical populations), duration of the waiting period, and demographic or methodological trial characteristics will be systematically analyzed to identify their moderating influence on patient trajectories.
Expected Outcomes:
This research will clarify the extent and nature of spontaneous improvement in waiting-list conditions, contributing into methodological considerations for future digital mental health trials.