Application of the MOST Preparation Phase: A 2³ Factorial Trial to Pilot a Physical Activity Support Program for Adults with Overweight/Obesity
Danielle Ostendorf

Dr. Danielle Ostendorf’s presentation highlights her NIH-funded Career Development Award project, which focuses on the development and pilot testing of the Move+ physical activity support program for adults with overweight or obesity. This presentation is focused on Aim 2 of her proposal which involves an ongoing 12-month 2³ pilot factorial trial. The Move+ program includes group-based classes, individualized coaching sessions, mental guided imagery, and online fitness memberships. Using the Preparation Phase from the Multiphase Optimization Strategy (MOST) framework, the study is focused on evaluating the acceptability and feasibility of the intervention components and establishing feasibility of the research protocol, in preparation for a fully-powered factorial trial to evaluate effectiveness of Move+ intervention components (MOST Optimization Phase).

Mobile Assistance for Regulating Smoking (MARS): Results from a Micro-Randomized Trial
Lindsey Potter

Tobacco is a leading cause of preventable death and disability. Self-regulatory activities are key components in tobacco cessation interventions, but poor engagement is a major barrier. Just-In-Time Adaptive Interventions (JITAIs) can be leveraged to enhance real-time, real-world engagement in self-regulatory strategies, but systematic investigation of how and when to intervene during a quit attempt is lacking. The MARS micro-randomized trial investigated whether and under what conditions prompting to engage with self-regulatory strategies via a mobile app, and what type of strategies, increased real-time, real-world engagement. We discuss lessons learned and next steps for optimizing JITAIs for smoking cessation.

Optimizing and personalizing digital meditation training through hybrid factorial and micro-randomized designs
Simon Goldberg

Digital meditation training has the potential to reduce depression and anxiety at scale. However, current implementations produce small effect sizes and have low rates of engagement. This talk presents our work using hybrid factorial and micro-randomized designs to optimize digital meditation training. Across multiple studies, we are examining candidate components, including various forms of lightweight human support and digital elements such as digital prompts and short videos. The hybrid design allows us to test multiple components simultaneously as well as to examine both baseline and time-varying participant characteristics (i.e., moderators) that can guide treatment delivery. Ultimately, this work is aimed at developing second-generation meditation apps that include maximally effective components that are personalized to a given individual.