Mechanisms and Predictors of Change in App-Based Mindfulness Training for Adolescents

Mindfulness-based smartphone apps have surged in popularity in recent years. Headspace - among the most popular of these platforms - has over 42 million users. Recent surveys indicate that 11% of U.S. adolescents have used mindfulness apps as a means of coping with anxiety or depressive symptoms, which increase substantially during the adolescent years. A growing body of research implicates rumination as being a transdiagnostic risk factor involved in the development of depression and anxiety in youth. Critically, mindfulness meditation has shown significant promise in targeting rumination, and ultimately improving depressive and anxiety symptoms. Mindfulness apps offer a convenient and cost-effective means for accessing mindfulness training, while being interactive and engaging for youth. Despite their growing popularity among teens, strikingly little research has been conducted on these apps. Two critical questions have yet to be addressed, which are strongly aligned with the NCCIH Strategic Plan: (1) what are the underlying neural and cognitive mechanisms that account for the beneficial effects of these apps and (2) for whom is app-based mindfulness well-suited. To address these gaps, adolescents (ages 13-18) will be randomly assigned to an app-delivered mindfulness course vs. an active control condition and will complete pre- and post-intervention resting state functional magnetic resonance imaging (fMRI) scans to probe static and dynamic functional connectivity within - and between - brain networks implicated in mindfulness training and rumination. In addition, cognitive tasks will be administered at pre- and post-intervention to assess attentional control abilities putatively enhanced by mindfulness training. Finally, mindfulness skills and changes in rumination will be assessed via smartphone-based ecological momentary assessment (EMA). First, the investigators will test whether changes in (1) brain functional connectivity (dynamic resting state functional connectivity via coactivation pattern analysis), (2) attentional control (Correct NoGo % on the SART task) and (3) acquisition and use of mindfulness skills (assessed via EMA) mediate between-group (i.e., app vs. control) differences in the reduction of rumination. Second, the investigators will test whether a machine learning model incorporating baseline clinical, demographic, and psychosocial characteristics can be used to identify which adolescents are predicted to benefit from app-based mindfulness training. Ultimately, such an algorithm may inform individual risk-benefit assessments that could be used to objectively communicate the probability of experiencing positive vs. adverse outcomes to users prior to engaging with a mindfulness app. Collectively, results are expected to advance (1) our understanding of the underlying mechanisms that account for the beneficial effects of app-based mindfulness training and (2) our ability to predict which adolescents are well-suited to these increasingly popular apps.