Abstract
Digital health (DH) is the use of digital technologies and data analytics to understand health-related behaviors and enhance personalized clinical care. DH is increasingly being used in clinical trials, and an important field that could potentially benefit from incorporating DH into trial design is pharmacogenetics. Prospective pharmacogenetic trials typically compare a standard care arm to a pharmacogenetic-guided therapeutic arm. These trials often require large sample sizes, are challenging to recruit into, lack patient diversity, and can have complicated workflows to deliver therapeutic interventions to both investigators and patients. Importantly, the use of DH technologies could mitigate these challenges and improve pharmacogenetic trial design and operation. Some DH use cases include (1) automatic electronic health record-based patient screening and recruitment; (2) interactive websites for participant engagement; (3) home- and tele-health visits for patient convenience (e.g., samples for lab tests, physical exams, medication administration); (4) healthcare apps to collect patient-reported outcomes, adverse events and concomitant medications, and to deliver therapeutic information to patients; and (5) wearable devices to collect vital signs, electrocardiograms, sleep quality, and other discrete clinical variables. Given that pharmacogenetic trials are inherently challenging to conduct, future pharmacogenetic utility studies should consider implementing DH technologies and trial methodologies into their design and operation.
Highlights
Digital health (DH) broadly refers to the use of digital technologies and data analytics to understand health-related behaviors, which can be utilized to enable more personalized clinical care [1,2]
Randomized controlled trials (RCTs) can have extensive inclusion and exclusion criteria (IEC), which limit patient enrollment and can lead to trial results with limited generalizability. This can be problematic in a pharmacogenetic trial where studying ethnically under-represented patient populations has been noted as an unmet need in the pharmacogenetics field [8,9]. Given these challenges with implementing RCTs for pharmacogenetic interventions, pragmatic clinical trial designs have increasingly been pursued as an alternative utility study that is complementary to the traditional RCTs [10]
Subsequent sections in this review present use cases of DH technologies that could be applied to pharmacogenetic trials in an effort to improve trial design, lower costs, increase patient engagement, and measure outcomes
Summary
Digital health (DH) broadly refers to the use of digital technologies and data analytics to understand health-related behaviors, which can be utilized to enable more personalized clinical care [1,2]. Prospective pharmacogenetic trials usually compare a standard dosing arm (standard of care control) to a pharmacogenetic-guided arm, often involving a therapeutic algorithm. They are generally designed as single-blinded, where the participant is unaware of their arm, but the prescriber/investigator is aware, as blinding the prescriber presents many challenges when implementing medication adjustments, or if the pharmacogenetic-guided intervention is only a ‘recommendation’ to be tailored by physician judgement. The use of DH technologies offers significant potential for improving pharmacogenetic trial design, which may facilitate more efficient and cost-effective trial operations [6]
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