Just-in-Time Adaptive Interventions to Optimize Adolescent Mental Health Treatments

Presenter:

Mary Rooney, Ph.D.
Division of Services and Intervention Research

Rationale:

The proposed concept aims to solicit applications focused on enhancing the clinical potency of established adolescent mental health treatments using developmentally informed and theoretically grounded just-in-time adaptive intervention (JITAI) augmentations. While JITAI augmentations could be applied to established interventions across the lifespan, this concept focuses specifically on adolescence. Adolescence is a developmental period characterized by heightened risk for new onset mental illness or worsening mental disorder symptoms, poor engagement with face-to-face interventions, and peak engagement with technology. JITAI augmentations have the potential to capitalize on adolescents’ near ubiquitous use of technology to facilitate symptom reduction and behavior change by providing content via an intrinsically motivating platform, facilitating skills practice in real world settings, tailoring the intervention to match the adolescent’s needs and preferences, providing in-the-moment feedback and reinforcement, and scaffolding adolescents in real time to mitigate impairments. While JITAIs have been discussed as a “future direction” in mental health research for some time, the increasing availability of powerful mobile and sensing technologies has now made it feasible to test the clinical impact of these intervention augmentations today.

Goal and Approach:

The goal of this concept is to encourage research that seeks to utilize JITAI augmentations to enhance the clinical impact of established digital health interventions on adolescent mental disorder symptoms and impairments. A transdisciplinary approach involving clinical, developmental, and mental health researchers; engineers; computer scientists; human-computer interaction specialists; and key stakeholders is strongly encouraged. 

Study designs would be informed by developmental science; be grounded in a clearly specified empirical model of behavior change; adhere to the NIMH experimental therapeutics approach to clinical trials; and, provide a strong rationale for the type, timing, and intensity of the selected interventions. Specifically, the approach should address key JITAI design principles, including intervention decision points, intervention options, tailoring variables, decision rules, and sustained intervention engagement and adherence. Areas of focus might include in-the-moment behavior change approaches that promote the use of transdiagnostic intervention strategies in daily life (e.g., exposure, behavioral activation, or emotion regulation skills), in-the-moment interventions that provide scaffolding and promote skill use at opportune moments to mitigate the cognitive impairments associated with a specific disorder (e.g., attention deficit hyperactivity disorder, autism, depression), or passive approaches for monitoring risk or vulnerability states associated with a specific mental illness to promote responsive, proactive help seeking behavior or self-management strategies.

Applications should also highlight how the proposed study will provide the field with generalizable knowledge about factors that drive adolescent engagement, motivation, and behavior change in the digital health space, and practical challenges and ethical considerations related to the study population. For example, factors related to intervention use within the context of schoolwide student mobile phone policies, parental concerns about limiting adolescent cell phone use when the phone is a treatment device, and engagement factors associated with the extended use of digital health interventions.

Anticipated Outcomes and Products:

It is anticipated that research generated from this concept would produce robust effectiveness data to support future definitive trials; generalizable knowledge about factors driving adolescent engagement, adherence, motivation, and behavior change; and approaches for deriving clinically actionable data from complex smartphone- or sensor-derived data.