Changing hearts and minds: theorizing how, when, and under what conditions three social influence implementation strategies work.

Background: Opinion leadership, educational outreach visiting, and innovation championing are commonly used strategies to address barriers to implementing innovations and evidence-based practices in healthcare settings. Despite voluminous research, ambiguities persist in how these strategies work and under what conditions they work well, work poorly, or work at all. The current paper develops middle-range theories to address this gap.

Modeling engagement with a digital behavior change intervention (HeartSteps II): An exploratory system identification approach.

Objective: Digital behavior change interventions (DBCIs) are feasibly effective tools for addressing physical activity. However, in-depth understanding of participants' long-term engagement with DBCIs remains sparse. Since the effectiveness of DBCIs to impact behavior change depends, in part, upon participant engagement, there is a need to better understand engagement as a dynamic process in response to an individual's ever-changing biological, psychological, social, and environmental context.

Did we personalize? Assessing personalization by an online reinforcement learning algorithm using resampling.

There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about when to treat and how to treat based on the user's context (e.g.

Effect-Invariant Mechanisms for Policy Generalization.

Policy learning is an important component of many real-world learning systems. A major challenge in policy learning is how to adapt efficiently to unseen environments or tasks. Recently, it has been suggested to exploit invariant conditional distributions to learn models that generalize better to unseen environments.

Conducting Research at the Intersection of HCI and Health: Building and Supporting Teams with Diverse Expertise to Increase Public Health Impact.

Research at the intersection of human-computer interaction (HCI) and health is increasingly done by collaborative cross-disciplinary teams. The need for cross-disciplinary teams arises from the interdisciplinary nature of the work itself-with the need for expertise in a health discipline, experimental design, statistics, and computer science, in addition to HCI. This work can also increase innovation, transfer of knowledge across fields, and have a higher impact on communities.