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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. To succeed at a collaborative project, researchers must effectively form and maintain a team that has the right expertise, integrate research perspectives and work practices, align individual and team goals, and secure funding to support the research. However, successfully operating as a team has been challenging for HCI researchers, and can be limited due to a lack of training, shared vocabularies, lack of institutional incentives, support from funding agencies, and more; which significantly inhibits their impact. This workshop aims to draw on the wealth of individual experiences in health project team collaboration across the CHI community and beyond. By bringing together different stakeholders involved in HCI health research, together, we will identify needs experienced during interdisciplinary HCI and health collaborations. We will identify existing practices and success stories for supporting team collaboration and increasing HCI capacity in health research. We aim for participants to leave our workshop with a toolbox of methods to tackle future team challenges, a community of peers who can strive for more effective teamwork, and feeling positioned to make the health impact they wish to see through their work. 

Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies. We study the effect of context inference error and partial observability on the ability to learn effective policies. Our results show that the propagation of uncertainty from context inferences is critical to improving intervention efficacy as context uncertainty increases, while policy gradient algorithms can provide remarkable robustness to partially observed behavioral state information.