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Evaluation ViewAttendees 12

Technology/Digital Health
Symposia

Symposium 41 - How technological innovation can be used to enhance CBT-based research and interventions: Applications of EMA, EMI, and Machine Learning

Friday, November 15, 2024
12:30 PM – 1:30 PM EST
Location: Liberty B, Level 3, Marriott Downtown
CE: 1

Keywords: Technology / Mobile Health, Change Process / Mechanisms,
Level of Familiarity: Moderate
Recommended Readings: Zainal, N. H., & Newman, M. G. (2024). Which client with generalized anxiety disorder benefits from a mindfulness ecological momentary intervention versus a self-monitoring app? Developing a multivariable machine learning predictive model. Journal of Anxiety Disorders, 102, 102825.,   Erickson, T. M., Lewis, J. A., Crouch, T. A., Singh, N. B., & Cummings, M. H. (2023). Interpersonal contrast avoidance as a mechanism for the maintenance of worry. Journal of Anxiety Disorders, 94, 102678.,   Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: incorporating mobile technology into psychosocial and health behaviour treatments. British journal of health psychology, 15(1), 1-39.,   Balaskas, A., Schueller, S. M., Cox, A. L., & Doherty, G. (2021). Ecological momentary interventions for mental health: A scoping review. PloS one, 16(3), e0248152.,  

Technological-based assessments and interventions have emerged as promising and more scalable alternatives to traditional retrospective self-report and therapeutic methods. As up to 70% of those experiencing mental health symptoms worldwide do not receive treatment (Thornicroft, 2007), technological-based assessments and interventions are needed to increase the accessibility and dissemination of psychological services.

One such technologically based assessment is ecological momentary assessment (EMA), which functions to address limitations in cross-sectional research by providing temporally sensitive, multimodal, context-specific, interactive, and generalizable assessment (Ebner-Priemer & Trull, 2009). As EMA provides insight into time-contingent aspects of conditions (e.g., examining activity level in depression based on time of day), EMA studies can be effectively translated to inform ecological momentary interventions (EMI). With the exponential increase in access to mobile devices worldwide, EMI interventions offer an accessible and scalable way to decrease the burden of mental illness (Kazdin et al., 2011).

While recent advancements of EMA and EMI show promise, it is important to consider individual differences and tailor these methods to address the needs of diverse populations. Indeed, while EMA and EMI have been applied to a variety of populations, not all EMI interventions will work well for everybody, and it is not yet clear how to mitigate participation barriers and improve treatment responsiveness in minoritized groups (Kennedy-Malone et al., 2022; Heron & Smyth, 2011). Machine learning (ML) algorithms are one novel approach to inform treatment decisions and provide personalized treatment recommendations (Dwyer et al., 2018), and can be used to examine differential benefits to EMI interventions (Zainal & Newman, 2024).

Therefore, the research in this symposium will (a) discuss the application of EMA techniques to study mechanisms of psychological conditions; (b) examine the use of ML to predict differential treatment responsiveness to an EMI; and (c) discuss the application of an EMI to meet the needs of marginalized communities when they face discrimination.

Learning Objectives:

At the end of this session, the learner will be able to:
  1. Present an overview of how tech-based assessment and interventions can increase accessibility and precision of psychological services
  2. Present data and evidence from two EMA studies indicating its utility in identifying temporally-sensitive mechanisms across psychopathology
  3. Describe a machine learning algorithm to predict differential treatment responsiveness to an EMI
  4. Explain a novel direction in the application of EMI to address discrimination in marginalized communities
  5. Describe various quantitative methods to examine EMA and longitudinal data

Presentations:

  • 12:30 PM – 1:30 PM EST
    (SYM 41) The Naturalistic Reinforcement of Obsessions and Compulsions: An Ecological Momentary Assessment Study

    Speaker: Valerie S. Swisher, B.S. (she/her/hers) – The Pennsylvania State University

    Co-author: Michelle G. Newman, B.S., M.A., Ph.D. (she/her/hers) – The Pennsylvania State University

  • 12:30 PM – 1:30 PM EST
    (SYM 41) Leveraging Ema-methods to Bridge the Contrast Avoidance Model and Contemporary Integrative Interpersonal Theory to Examine Maintenance Factors of Anxiety and Mood Disorders

    Speaker: Adam Calderon, B.S., M.A. – The Pennsylvania State University

    Co-author: Michelle G. Newman, B.S., M.A., Ph.D. (she/her/hers) – The Pennsylvania State University

  • 12:30 PM – 1:30 PM EST
    (SYM 41) Which Client with Generalized Anxiety Disorder Benefits from a Mindfulness Ecological Momentary Intervention versus a Self-monitoring App? Developing a Multivariable Machine Learning Model

    Speaker: Nur Hani Zainal, M.S., Ph.D. (she/her/hers) – National University of Singapore

    Co-author: Michelle G. Newman, B.S., M.A., Ph.D. (she/her/hers) – The Pennsylvania State University

  • 12:30 PM – 1:30 PM EST
    (SYM 41) Mixed Methods Evaluation of Brief Mobile Interventions Targeting Stress/anxiety About Experiences of Discrimination

    Speaker: Kaitlyn Petz, B.A. (she/her/hers) – University of Virginia

    Co-author: Kaitlyn Petz, B.A. (she/her/hers) – University of Virginia

    Co-author: Emma R. Toner, M.A. – University of Virginia

    Co-author: Mark Rucker, M.S. – University of Virginia

    Co-author: Emily Leventhal, B.A. – Icahn School of Medicine

    Co-author: Sarah Livermon, B.S. – University of Virginia

    Co-author: Benjamin Davidson, B.S. – University of Virginia

    Co-author: Mehdi Boukhechba, Ph.D. – Johnson & Johnson

    Co-author: Laura Barnes, PhD – University of Virginia

    Co-author: Bethany Teachman, Ph.D. – University of Virginia