The development of innovative wearable technologies has raised great interest in new means of data collection in healthcare and biopharmaceutical research and development. Multiple applications for wearables have been identified in a number of therapeutic areas; however, researchers face many challenges in the clinic, including scientific methodology as well as regulatory, legal, and operational hurdles. To facilitate further evaluation and adoption of these technologies, we highlight methodological and logistical considerations for implementation in clinical trials, including key elements of analytical and clinical validation in the specific context of use (COU). Additionally, we provide an assessment of the maturity of the field and successful examples of recent clinical experiments.

Technology is changing how we practice medicine. Sensors and wearables are getting smaller and cheaper, and algorithms are becoming powerful enough to predict medical outcomes. Yet despite rapid advances, healthcare lags behind other industries in truly putting these technologies to use. A major barrier to entry is the cross-disciplinary approach required to create such tools, requiring knowledge from many people across many fields. We aim to drive the field forward by unpacking that barrier, providing a brief introduction to core concepts and terms that define digital medicine. Specifically, we contrast “clinical research” versus routine “clinical care,” outlining the security, ethical, regulatory, and legal issues developers must consider as digital medicine products go to market. We classify types of digital measurements and how to use and validate these measures in different settings. To make this resource engaging and accessible, we have included illustrations and figures throughout that we hope readers will borrow from liberally. This primer is the first in a series that will accelerate the safe and effective advancement of the field of digital medicine.

BackgroundThe growing consensus that most valuable data source for biomedical discoveries is derived from human samples is clearly reflected in the growing number of translational medicine and translational sciences departments across pharma as well as academic and government supported initiatives such as Clinical and Translational Science Awards (CTSA) in the US and the Seventh Framework Programme (FP7) of EU with emphasis on translating research for human health.MethodsThe pharmaceutical companies of Johnson and Johnson have established translational and biomarker departments and implemented an effective knowledge management framework including building a data warehouse and the associated data mining applications. The implemented resource is built from open source systems such as i2b2 and GenePattern.ResultsThe system has been deployed across multiple therapeutic areas within the pharmaceutical companies of Johnson and Johnsons and being used actively to integrate and mine internal and public data to support drug discovery and development decisions such as indication selection and trial design in a translational medicine setting. Our results show that the established system allows scientist to quickly re-validate hypotheses or generate new ones with the use of an intuitive graphical interface.ConclusionsThe implemented resource can serve as the basis of precompetitive sharing and mining of studies involving samples from human subjects thus enhancing our understanding of human biology and pathophysiology and ultimately leading to more effective treatment of diseases which represent unmet medical needs.

We assessed the performance of two US Food and Drug Administration ( FDA ) 510(k)‐cleared wearable digital devices and the operational feasibility of deploying them to augment data collection in a 10‐day residential phase I clinical trial. The Phillips Actiwatch Spectrum Pro (Actiwatch) was used to assess mobility and sleep, and the Vitalconnect HealthPatch MD (HealthPatch) was used for monitoring heart rate ( HR ), respiratory rate ( RR ), and surface skin temperature ( ST ). We measured data collection rates, compared device readouts with anticipated readings and conventional in‐clinic measures, investigated data limitations, and assessed user acceptability. Six of nine study participants consented; completeness of data collection was adequate (> 90% for four of six subjects). A good correlation was observed between the HealthPatch device derived and in‐clinic measures for HR (Pearson r = 0.71; P = 2.2e‐16) but this was poor for RR ( r = 0.08; P = 0.44) and ST ( r = 0.14; P = 0.14). Manual review of electrocardiogram strips recorded during reported episodes of tachycardia > 180 beats/min showed that these were artefacts. The HealthPatch was judged to be not fit‐for‐purpose because of artefacts and the need for time‐consuming manual review. The Actiwatch device was suitable for monitoring mobility, collecting derived sleep data, and facilitating the interpretation of vital sign data. These results suggest the need for fit‐for‐purpose evaluation of wearable devices prior to their deployment in drug development studies.