Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods

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Project Summary Our prosed study will address critical gaps in the literature and practice of informed consent in digital health research. We will leverage the existing Digital Health Checklist (DHC) tool by expanding the consent prototype building component to incorporate what is meaningful to research participants. This study involves co-designing a meaningful informed consent prototype with participants to produce and test a digital health consent blueprint to increase capacity for understanding the function of algorithms used in behavioral interventions. These advances in the DHC tool will contribute to the evidence-base to support the process of informing prospective participants about digital health research. This study will leverage an established decision support tool developed for digital health researchers. The DHC was informed through an iterative design process involving behavioral scientists, regulators, IRB members, ethicists, and clinician-researchers and is grounded in accepted principles of research ethics, namely respect for persons, beneficence and justice, and incorporates four orthogonal domains including: (1) Access and Usability, (2) Risks and Benefits, (3) Privacy, and (4) Data Management. Inspired by an effectiveness-implementation design process, we will test and co-design an interactive consent form with prospective research participants. This human centered participatory design approach will expose unique concerns when asked to use a digital technology to gather personal health information. The proposed work will systematically study and actively respond to critical ethical, legal/regulatory and social implications (ELSI) applied to digital health research - specifically our ability to convey accessible study information such that informed consent transpires. This research will directly benefit our parent R01, will contribute to the literature on informed consent and have potential implications for other personalization algorithms for behavior change, such as those used in industry. Co-designing innovative decision support tools that can be used by researchers, algorithm developers, IRBs, and participants will foster shared decision making at the earliest stages of digital health research and algorithm creation.