Session: PLENARY SESSION III, Mobile Health (mHealth) in Prevention Science: Assessment, Intervention, and Analysis. (Society for Prevention Research 27th Annual Meeting)

4-020 PLENARY SESSION III, Mobile Health (mHealth) in Prevention Science: Assessment, Intervention, and Analysis.

Friday, May 31, 2019: 10:15 AM-11:45 AM
Grand Ballroom A (Hyatt Regency San Francisco)
Stephanie T. Lanza, Nicholas B. Allen, Fiona C. Baker and Holly Jimison
Chair: Stephanie T. Lanza, PhD, The Pennsylvania State University


Fiona C. Baker, PhD, Senior Program Director, Human Sleep Research Program, SRI International

Holly Jimison, PhD, Professor of the Practice, Khoury College of Computer Sciences and Bouve College of Health Services, Northeastern University

Nicholas Allen, PhD, Ann Swindells Professor, Director, Center for Digital Mental Health, Director of Clinical Training, Department of Psychology, University of Oregon

This year, we, as prevention scientists, are examining potential opportunities presented by “big data” in the world around us. During today’s plenary session, we focus on the potential for mobile health, or mHealth, to advance prevention science. Data in mHealth may be derived from any number of sources, including smart phones used to collect active or passive data, wearable sensors that can detect moments of stress, social media data that can reveal emerging trends or outbreaks across the country, and more. The goal of this plenary session is to showcase the use of mHealth in prevention-related studies and to spark further discussion of the potential, the limitations, and the challenges that lie ahead.

The first speaker will discuss the use of modern passive sensors to gain a deep understanding of sleep and associated health behaviors in adolescents. She presents results from two national studies that have integrated wearable devices, and also comments on the validity, reliability, and interpretability of “big data” in prevention research.

The second speaker will focus on the short-term prediction of suicide risk as he discusses new opportunities to harness computational techniques to examine intensive data from real-time monitoring using smart phones, wearable computing, and smart home technology. Opportunities to harness this class of predictive models in prevention research is discussed, along with potential ethical challenges in using these approaches.

The third speaker discusses the use of wearable devices to derive real-time heart rate variability and electrodermal activity, which might provide triggers for interventions that target moments of stress. Coaching interventions to address moments of stress hold promise for preventing a variety of health behaviors and conditions.

Presenter 1

Speaker: Fiona C. Baker, PhD

Title: Applying technology to study sleep, substance use, and other behaviors in adolescents: What we are learning in the ABCD and NCANDA studies

Sleep is fundamental for health across ages and plays a key role in establishing optimal physical and mental health during adolescence. There are dramatic changes in sleep duration and timing across adolescence, considered a normal part of development. Many adolescents, however, have extreme reductions in sleep and/or poor quality sleep that impact their mental health and daytime functioning, and are risk factors for alcohol and other substance use. Wearables have the potential to expand the characterization and evaluation of sleep in the adolescent population, to enable better recognition of when sleep deviates from normal. They provide accessibility to an unprecedented amount of information about daily sleep and other behaviors collected in the natural environment for an extended duration, without active engagement from users and without the need of specialized technicians to process the data. In this presentation, I will present data from a wearable used to track sleep in adolescents in the Adolescent Brain and Cognitive Development (ABCD) study, and discuss advantages and limitations of its use. I will also discuss work done in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), which is embarking on efforts to link daily sleep and physical activity (wearables) with frequent self-reported assessments of substance use, mood, and stressors, using a customized app. The app allows enhanced characterization of substance use with greater granularity than can be obtained with annual clinical interviews, and can allow investigation of the daily impact of substance use on behaviors like sleep. Applying technology to study sleep and other behaviors in adolescence can quickly expand our knowledge base, however, in this growing area of research, questions remain about validity, accuracy and reliability, and interpretation of ‘big data’ being generated should not be over-extended.

Presenter 2

Speaker: Nicholas Allen, PhD

Title: Prevention of suicidal thoughts and behaviors in adolescents: Can recent developments in technology and computational science provide a breakthrough?

Suicide is one of the leading causes of death among adolescents, and developing effective methods of prevention has been hampered by poor predictive methods, especially short-term prediction of suicidal thoughts and behaviors (STBs). Currently, the most robust predictors of STBs are demographic or clinical indicators that have relatively weak predictive value. However, there is an emerging literature on short-term prediction of suicide risk that has identified a number of promising candidates, including (but not limited to) rapid escalation of: (a) emotional distress, (b) social dysfunction (i.e., bullying, rejection), and (c) sleep disturbance. In this presentation I will explore how to capitalize on recent developments in intensive real-time monitoring methods of objective behavior and computational analysis in order to address these fundamental problems. We now have the capacity to use: (a) smartphone, wearable computing, and smart home technology to conduct intensive longitudinal assessments monitoring of putative risk factors with minimal participant burden and (b) modern computational techniques to develop predictive algorithms for STBs. Although these approaches have enormous potential to create new knowledge, the current literature base is minimal. Moreover, passive monitoring of risk for STBs raises complex ethical issues that will need to be resolved before large scale preventative applications are feasible. However, smartphone, wearable, and smart home technology may provide one point of access that might facilitate both early identification and intervention implementation, and thus, represents a key area for future STB research.

Presenter 3

Speaker: Holly B Jimison, PhD, FACMI, Northeastern University

Title: Monitoring Stress in a Dynamic Real-World Environment

Stress has been recognized as an important risk factor for many health-related conditions, including cardiovascular disease, diabetes, asthma, sleep disorders, depression and back pain. Stress has also been shown to precipitate unhealthy behaviors, such as smoking or overeating. Dynamic measures of stress in a real-world environment can be an important tool for health behavior change interventions. With new wearable sensors for measuring heart rate variability and electrodermal activity, we now have an opportunity to detect stressful events and anticipate the need for just-in-time automated coaching. New research methods have provided tools for users of the technology to develop insights on stress triggers and coaching methods for providing stress interventions, such as cognitive behavioral therapy and deep breathing exercises.

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