Category: Paper

Machine learning methods have the potential to enable the development of more personalized approaches to treatment of mental health conditions. Digital treatment delivery may be particularly amenable to the application of these techniques. I am delighted to be a co-author of a paper from a cross-disciplinary international team, published in World Psychiatry. The paper examines the potential of machine learning to provide outcome predictions for mental health treatment, as well as considering ethical challenges surrounding the use of these techniques.… Read more

The original vision of Ecological Momentary Interventions (EMI) – brief interventions delivered in the moment during daily life – were put forward over a decade ago. In the interim, the arrival of the smartphone has made these interventions far more feasible to deploy. At this point, it is timely to examine whether the original vision of these systems has been realised, and furthermore has the concept of EMI shifted to incorporate further possibilities opened up by these technologies? With Stephen Schueller, University of California, Irvine, and Prof.… Read more

Smartphones have made it easier than ever to ask people questions “in the moment” about their feelings and activities, as they go about their daily lives. This approach, described as Ecological Momentary Assessment, is a topic of interest to researchers in a range of disciplines, including HCI, design, psychology, and mental health. But what are our options in designing these systems? In this inter-disciplinary narrative review of 342 papers, published in Interacting with Computers, we examine the challenges, choices and options facing designers of these systems.… Read more

Engagement is one of the primary challenges in the design of digital health interventions, however interaction with these interventions is complex, and can be difficult to characterise. In this paper, we explore the use of machine learning techniques to analyse engagment in digital mental health interventions. The work was carried out in collaboration with researchers in Microsoft Research, SilverCloud Health, and TCD School of Psychology.

Chien I, Enrique A, Palacios J, Regan, T., Keegan, D., Carter, D., Tschiatschek S., Nori, A.,… Read more

Machine Learning can be expected to have a significant impact on the field of mental health in the coming years. Researchers in computing have been exploring the potential of these systems, looking at a variety of data sources ranging from sensor based systems to electronic health records. In this paper, we review work in the computing literature, and advocate for a more human-centered approach, in order to ethically and effectively realise the potential of these systems.

A. Thieme, D. Belgrave & G.… Read more

Using personal data in mHealth apps for public health requires navigating a range of design tensions, and the area of perinatal mental health is a particularly complex setting. We explore the balance between the many concerns and constraints affecting the design of these systems (both patient and clinician interfaces), in a new (open access) paper in the International Journal of Human-Computer Studies https://doi.org/10.1016/j.ijhcs.2019.102373. The work was carried out as part of a collaboration between ourselves at Trinity College Dublin and researchers at the School of Public Health at Imperial College London, with Marguerite Barry at UCD & Cecily Morrison at Microsoft Research Cambridge.… Read more