Operationalizing Behavioral Theory for mHealth: Dynamics, Context, and Personalization

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Abstract This Administrative Supplement expands on the Specific Aims of the parent U01 award by proposing to harmonize data across the Micro-Randomized Trials (MRTs) involved in the Intensive Longitudinal Health Behaviors Network (ILHBN) to study engagement in digital trials and just-in-time interventions. This parent project (HeartSteps II, U01CA229445) is part of the ILHBN, established to collaboratively study factors that influence dynamic health behaviors in individuals’ daily lives using intensive longitudinal data (ILD) collection and novel analytic methods. The network includes seven U01 projects and one U24 Research Coordinating Center (RCC). The effectiveness of digital interventions is heavily reliant upon the participant’s engagement with the digital behavior change intervention (DBCI). To achieve behavior change, the participant needs to engage with the interface components of the DBCI app (e.g., app page views, logins), as well as the behavior change techniques (e.g., activity planning, weight-logging, diary entries) employed in the DBCI. There are tremendous opportunities to advance theory-driven behavior change interventions by sharing and analyzing ILD across the ILHBN network interventions. To maximize the utility of these data to advance the science of mobile health engagement and to support researchers interested in developing and testing new methods for analyzing complex, real-world behavioral data, we propose an Administrative Supplement that augments the work of the parent grant with the following new specific aims; 1): Curate and share data, paradata, and metadata with members of the ILHBN, and 2) Operationalize and Model Engagement in JIT interventions. This project will the first to investigate the patterns and correlates and causes of changes in engagement in Just-In-Time, mobile interventions over time. It will also support the curation of data to be contributed to the ILHBN flagship database. These data will support the development of models with the potential to lead to insights about a range of dynamic behavioral processes including engagement with mobile technologies in general. Finally, this Supplement will support the development of dynamic computational models of engagement in JITAIs and the tools to run them. Together, these activities move the field of ILD and engagement with ILD and JITAIs forward substantially.