Course Title: Plenary G: Using Mobile Sensing to Assess Mental Health and Functioning: The Case of Suicide Prediction (Allen)

Credit Hours: 1

Instructor(s) Nicholas Allen

Nicholas Allen, PhD Ann Swindells Professor of Clinical Psychology Director, Center for Digital Mental Health Director of Clinical Training Department of Psychology University of Oregon
Plenary G: Using Mobile Sensing to Assess Mental Health and Functioning: The Case of Suicide Prediction
Abstract & Learning Objectives: Despite the fact that assessing client progress is fundamental to evidence-based treatment, many clinicians only use unstructured clinical assessment methods to assess progress. Mobile and wearable computing now allows new assessment methods that are ecological, continuous, and objective. For example, studies have shown that symptoms often vary markedly within individuals across time, and understanding this pattern of variation is critical to assessment of client status and treatment planning. Also, most current methods of assessment used in mental health treatment rely primarily in self report methods, and research has found that objective and self-report methods often show low correlation (e.g., such as in studies of sleep, contraceptive use, or substance use), suggesting the self-report data can only provide part of the clinical picture. Furthermore, self-report methods are burdensome for clients to complete (especially if they are required to do so regularly), so objective measures that can be captured without participant burden (e.g., by monitoring sensors that detect the client’s naturalistic patterns of use of the personal smart phones) may be a particularly compelling approach. In sum, an effective technology-assisted approach to routine clinical assessment that increases client compliance and provides dynamic assessment of both subjective and objective indices of mental health should improve both clinical processes and client outcomes. Moreover, such methods can be used to design just-in time interventions. In this presentation I will describe potential and pitfalls associated with these mobile and ubiquitous assessment methods, including issues of reliability, validity and ethical concerns, using the detection of suicide risk as a salient use case to demonstrate these issues. Upon conclusion of this course, learners will be able to:
  • Critique the pros and cons of self-report versus technology-assisted approaches to clinical assessment
  • Describe ethical concerns associated with using mobile sensing to assess mental health
  • Discuss the detection of suicide risk within the context of effective technology-assisted assessments
Speaker Biography: Dr. Nick Allen is the Ann Swindells Professor of Clinical Psychology at the University of Oregon, where he Director of Clinical Training. He is a leading researcher in the area of adolescent mental health, know especially for his work on adolescent onset depression. His work aims to understand the interactions between multiple risk factors for adolescent emergent mental health disorders, including stress, family processes, brain development, autonomic physiology, genetic risk, immunology, and sleep. More recently, his work has focused on translating risk factors identified in prospective longitudinal studies into innovative preventative approaches to adolescent mental health. For example, we have completed a large randomized controlled trial of a sleep improvement intervention that aims to prevent the onset of mental disorders during adolescence. We are also currently also conducting trials of other innovative preventative approaches (e.g., parenting, outdoor wilderness activities), aimed at early to mid-adolescence as a key inflection point in life for health trajectories. He is the Director of the Center for Digital Mental Health (, where his work focuses on the use of mobile and wearable technology to monitor risk for poor mental health, and his group has developed software tools that combine active and passive sensing methods to provide intensive longitudinal assessment of behavior with minimal participant burden. The ultimate aim of developing these technologies is to facilitate the development of a new generation of “just in time” behavioral interventions for early intervention and prevention of adolescent health problems