On the Difficulty of Predicting Engagement with Digital Health for Substance Use

Journal article

Digital interventions can be an important instrument in treating substance use disorder. However, most digital mental health interventions suffer from early, frequent user dropout. Early prediction of engagement would allow identification of individuals whose engagement with digital interventions may be too limited to support behaviour change, and subsequently offering them support. To investigate this, we used machine learning models to predict different metrics of real-world engagement with a digital cognitive behavioural therapy intervention widely available in UK addiction services. Our predictor set consisted of baseline data from routinely-collected standardised psychometric measures. Areas under the ROC curve, and correlations between predicted and observed values indicated that baseline data do not contain sufficient information about individual patterns of engagement.

Authors: Günther, F; Yau, C; Elison-Davies, S; Wong, D

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: IOS Press
• Journal: Studies in Health Technology and Informatics
• Volume: 302
• Pages: 967-971
• Publication date: 2023-05-18
• DOI: 10.3233/shti230319
• ISSN: 0926-9630
• ISBN: 9781643683881
• Language: English
• Keywords: Identification (biology); Clinical psychology; Applied psychology; Addiction; Intervention (counseling); Substance use; Health care; Psychiatry; Dropout (neural networks); Set (abstract data type); Computer science; Psychology; Digital health; Psychological intervention; Baseline (sea); Mental health; Machine learning