Dose-response research in digital health interventions: Concepts, considerations, and challenges

To optimize digital health interventions, intervention creators must determine what intervention dose will produce the most substantial health behavior change-the dose-response relationship-while minimizing harms or burden. In this article we present important concepts, considerations, and challenges in studying dose-response relationships in digital health interventions. We propose that interventions make three types of prescriptions: (1) intervention action prescriptions, prescriptions to receive content from the intervention, such as to read text or listen to audio; (2) participant action prescriptions, prescriptions to produce and provide content to the intervention, such as to send text messages or post intervention-requested photos on social media; and (3) behavioral target action prescriptions, prescriptions to engage in behaviors outside the intervention, such as changing food intake or meditating. Each type of prescription has both an intended dose (i.e., what the intervention actually prescribes) and an enacted dose (i.e., what portion of the intended dose is actually completed by the participant). Dose parameters of duration, frequency, and amount can be applied to each prescription type. We consider adaptive interventions and interventions with ad libitum prescriptions as examples of tailored doses. Researchers can experimentally manipulate the intended dose to determine the dose-response relationship. The enacted dose cannot be directly manipulated; however, we consider the applicability of "controlled concentration" research design to the study of enacted dose. We consider challenges in dose-response research in digital health interventions, including characterizing amount with self-paced activities and combining doses across modality. The presented concepts and considerations may help contribute to the optimization of digital health interventions. (PsycINFO Database Record (c) 2019 APA, all rights reserved).