Personalisation is key to creating successful digital health applications. Recent evidence links personality and preference for digital experience — suggesting that psychometric traits can be a promising basis for personalisation of digital mental health services. However, there is still little quantitative evidence from actual app usage. In this study, we explore how different personality types engage with different intervention content in a commercial mental health application. Specifically, we collected the Big Five personality traits alongside the app usage data of 126 participants using a mobile mental health app for seven days. We found that personality traits significantly correlate with the engagement and user ratings of different intervention content. These findings open a promising research avenue that can inform the personalised delivery of digital mental health content and the creation of recommender systems, ultimately improving the effectiveness of mental health interventions.

Recommender Systems have not been explored to a great extent for improving health and subjective wellbeing. Recent advances in mobile technologies and user modelling present the opportunity for delivering such systems, however the key issue is understanding the drivers of subjective wellbeing at an individual level. In this paper we propose a novel approach for deriving personalized activity recommendations to improve subjective wellbeing by maximizing the congruence between activities and personality traits. To evaluate the model, we leveraged a rich dataset collected in a smartphone study, which contains three weeks of daily activity probes, the Big-Five personality questionnaire and subjective wellbeing surveys. We show that the model correctly infers a range of activities that are 'good' or 'bad' (i.e. that are positively or negatively related to subjective wellbeing) for a given user and that the derived recommendations greatly match outcomes in the real-world.