Personalization in Digital Psychological Interventions for Young Adults

Personalized digital psychological interventions (DPI) seek to provide solutions tailored to user requirements. DPI Personalization has been found to increase user engagement which can improve treatment outcomes. Recognizing different dimensions of personalization (content, method, and the actor/s doing the personalization), this research reviewed past studies of personalized DPIs for young adults. Identifying the dimensions of DPI personalization that have been studied is an important step in enhancing personalization, which subsequently can increase low user engagement and therefore enhance treatment of mental health. A structured literature review searched six academic databases with adjusted search phrases and included participants' age (18-26 years), publication years (2010-2019), and language (English). Out of 879 collected articles, 25 relevant articles were analyzed using a framework to extract details of personalization. The analysis revealed low rates of studies on personalization of DPIs in this age group. Personalization dimensions were mostly evident in content (75%), using segmented personalization (91%) by providers (83%), not users. This gap suggests further studies on DPI personalization are warranted. Future research must focus more on co-design and collecting users' personal, social, and technical preferences to enhance personalization quality. This can provoke young people's engagement through smarter and real-time personalization based on user characteristics and preferences to enhance usage and achieve better clinical outcomes.

Automated summary

Personalized digital psychological interventions (DPIs) aim to provide customized solutions based on user requirements, and personalization has been shown to increase user engagement and improve treatment outcomes. However, there is a lack of research on personalized DPIs for young adults, with most studies focusing on content personalization by providers rather than users. Future research should focus on co-design and understanding users' personal preferences to enhance personalization quality and increase user engagement, leading to better clinical outcomes.