Appropriate controls for digital therapeutic clinical trials: A narrative review of control conditions in clinical trials of digital therapeutics (DTx) deploying psychosocial, cognitive, or behavioral content

Abstract

Digital therapeutics (DTx) are software programs that treat a disease or condition. Increasingly, DTx are part of medical care, and in the US healthcare system they are regulated by the FDA as Software as a Medical Device (SaMD). Randomized controlled trials (RCT) remain a key evidence generation step for most DTx. However, developing a unified approach to the design of appropriate control conditions has been a challenge for two main reasons: (1) inheriting control condition definitions from pharmacotherapy and medical device RCT that may not directly apply, and (2) challenges in establishing control conditions for psychosocial interventions that build the core of many DTx. In our critical review we summarize different approaches to control conditions and patient blinding in RCT evaluating DTx with psychosocial, cognitive or behavioral content. We identify control condition choices, ranging from very minimal digital controls to more complex and stringent digital applications that contain aspects of "fake" therapy, general wellness content or games. Our review of RCTs reveals room for improvement in describing and naming control conditions more consistently. We further discuss challenges in defining placebo controls for DTx and ways in which control choices may have a therapeutic effect. While no one-size-fits-all control conditions and study designs will apply to all DTx, we propose points to consider for defining appropriate digital control conditions. At the same time, given the rapid iterative development and optimization of DTx, treatments with low risk profile may be evaluated with minimal digital controls followed by extensive real-world effectiveness trials.

Keywords: control conditions; digital clinical trials; digital health; mHealth; placebo control; psychology; sham; software as a medical device.

Figure 1

Control conditions. Control condition stringency can be distinguished with regards to what aspects are controlled for. For example, waitlist control conditions will control for aspects of natural disease progression and being regularly assessed in a trial. More stringent sham or placebo controls will also contain aspects of a digital control, such as engagement tools, digital disease management tools, such as wellness content or trackers. DTx may also try and establish a working alliance (common factors). Thus, stringent digital sham conditions may not be fully inactive.

Figure 2

Potential choices in designing a digital control condition for DTx.