Session: Innovative Uses of Technology for Data Collection and Delivery of Behavioral Preventive Interventions (Society for Prevention Research 26th Annual Meeting)

A considerable body of research through randomized control trials indicates that evidence-based interventions (EBIs) improve a range of mental health and behavioral outcomes, including prevention or reduction of depression and anxiety, substance use, and HIV. When EBIs are implemented in community settings, intervention effects are often diminished due to a lack of quality of delivery, lack of adequate monitoring and supervision. Efficacy and effectiveness trials typically rely on human-based methods of implementation monitoring. These methods for research involve conducting independent observations, with extensive training and supervision of coders to maintain interrater reliability. Although these approaches are more effective than other commonly used methods, such as facilitator self-report, they are still cost-prohibitive and impractical in community settings.

In this paper symposium, we discuss how technology can provide an unobtrusive method to facilitate monitoring of and data collection in behavioral interventions. In some cases, these technologies allow for real-time monitoring of important indicators, such as stress or engagement, that can provide insight into how the intervention is delivered and how it can be tailored to the real-time need of intervention participants.

The first paper, "Exploring the use of a wrist-worn activity monitor to aid mindfulness practice and improve mood" exploits mobile technology to suggest intervention participants to engage in mindfulness practice real-time. The ability to monitor stress where participants are improves prevention science delivery.

The second paper, "Network Canvas: An Innovative and Intuitive Network Data Capture Tool for Prevention Research" presents a method for data collection of sexually connected networks that facilitates recall and accuracy of sexual event reporting. This tool also facilitates the graphical display of sexual networks that can inform and tailor existing interventions for HIV prevention.

The third paper, "Developing a computational theory of linguistic alignment to measure engagement in mHealth HIV prevention interventions" provides a proof of concept of how to exploit linguistic features of interventions participants to measure engagement and to tailor content of the intervention.

Together, these technologies can optimize prevention science by providing real-time engagement monitoring, delivery and tailoring of preventive behavioral interventions.