Dropout and completion from an online intervention aimed at increasing psychological well-being and reducing depression, anxiety and stress
Method Stream: Other
ECs Only 14 EC (standard, no new/own data collection. Applicable in case of a clinical internship)
Description:
The prevalence of anxiety and depression continues to increase worldwide. Online interventions can provide psychological support to many people at once, without time and staff constraints. The effectiveness of those interventions has been observed in several studies across different populations for more than two decades (Pauley et al., 2021). However, dropout from such interventions remains an ongoing issue (Duhne et al., 2022), and research on dropout from online interventions is limited.
In this project, you will have access to a large dataset from an online intervention aimed at increasing psychological well-being and decreasing symptoms of anxiety, depression and stress. This dataset includes pre-assessment scores, instruments, and measurements conducted before each session that measure participants' positive and negative affect. Furthermore, you will compare the results of participants who completed the intervention with those who dropped out to analyse the difference in their scores.
This study will help to better understand whether participants' assessment scores can predict who will drop out and who will complete an intervention, so we can take action to prevent this.
References
Duhne, P. G. S., Delgadillo, J., & Lutz, W. (2022). Predicting early dropout in online versus face-to-face guided self-help: A machine learning approach. Behaviour Research and Therapy, 159, 104200.
Pauley, D., Cuijpers, P., Papola, D., Miguel, C., & Karyotaki, E. (2023). Two decades of digital interventions for anxiety disorders: a systematic review and meta-analysis of treatment effectiveness. Psychological medicine, 53(2), 567-579.
Who are we looking for?
Students interested in online interventions, dropout and innovative methods.
What do we offer?
Access to a large database