Keywords

Health information technology (HIT), Mental Health Care, Patient-Centered Care, mHealth Technologies

Abstract

The use of patient-generated health data (PGHD) in the treatment of depression can provide valuable insights into patients' everyday lives and the success of the employed therapy. Young adults are an interesting target group for using PGHD, as they are tech-savvy and, therefore, particularly suited to the use of technologies such as PGHD. Although technological advances enable users to collect a multitude of different PGHD types, not all of them are relevant for depression. Similarly, there are types of PGHD that are associated with a great deal of effort when collecting them and are, therefore, not suitable for all users. Therefore, we identified different user types based on their data preferences and analyzed constructs from the UTAUT questionnaire to identify factors for the diffusion of PGHD in depression care among young adults. To achieve this, we analyzed data from 218 survey responses. Using a subsequent cluster analysis, we identified four different user types: “Balanced Trackers,” “Mental Trackers,” “Minimalist Trackers,” and “Proactive Trackers.” Based on these clusters, we show different possibilities for which user group and which types of PGHD are best suited and which factors are important for the diffusion of PGHD. Our preliminary results indicate that behavioral intention varies between clusters. At the same time, factors such as effort expectancy, performance expectancy, and facilitating conditions are generally high in all groups, suggesting ease of use and perceived benefits of PGHD in depression treatment, while social influence seems to have only a limited impact on user acceptance.

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