Wearables & Big Data In Clinical Trials — Where Do We Stand?

By Yvette Jansen, Grant Thornton

Patient behaviors and key patient information have become much easier to track with a multitude of sensors and real-time tracking devices at consumers’ fingertips. The most frequently used consumer wearables include ActiGraph, the Apple Watch, and FitBit. As of February 2020, clinicaltrials.gov shows that approximately 460 wearables studies are underway, and according to Kaiser Associates and Intel, 70 percent of clinical trials will incorporate sensors by 2025. Pharmaceutical and medical device companies have an opportunity to take advantage of these large data sets and developments to optimize both their clinical trials and their products to improve treatment efficacy.

Wearable data presents many advantages due to the depth and breadth of information collected. Theoretically, wearables can be used across therapeutic areas for deep phenotyping, detection and interpretation of adverse events, and clinical trial participant recruitment. Clinical trials depend on rich patient data. Collection in a physician’s office captures a snapshot of the participant’s data, i.e., one electrocardiogram or phenotype analysis. By contrast, wearables track consumer and patient data over large periods of time, resulting in rich data sets with the potential to reveal new insights.

Big Pharma, Tech Firms, Academia Move Implementation Forward

Patients and their personal data, collected in electronic health records (EHRs) as well as via personal wearables and medical devices, are critical components in the success of clinical trials. Artificial intelligence (AI) tools can support the analysis prior to and during the trial, and many life sciences organizations have started to see their value. For example, Novartis has created a proprietary machine learning predictive analytics platform called Nerve Live. To create the platform, Novartis partnered with QuantumBlack. Initially, QuantumBlack collected data from Formula One races and used machine learning to optimize race operations. With QuantumBlack’s help, Novartis piloted the platform to support its global drug development team’s plan to simulate country allocation scenarios for clinical studies. The platform tracks all data points of the 550 clinical studies running in parallel as well as uses analytical software to predict issues in the clinical trial execution.

Another example is the use of wearables as a key clinical trial objective. Janssen Pharmaceuticals and Apple announced a collaboration in January 2019, which resulted in the launch of a research study. The trial focuses on the use of the Apple Watch’s irregular rhythm notifications and the ECG app to improve atrial fibrillation outcomes and early detection. This multiyear research program, which will only be launched in the U.S., will be used for individuals over 65 years old.

(Editor’s note: AbbVie is another Big Pharma company at the forefront of implementing digital technologies into clinical trials, with 60 different initiatives underway — see “AbbVie Goes All-In On Wearables And Digital Technologies.”)

Technology companies have started to initiate these partnerships, too, and a clear example is Alphabet’s Verily, Google’s sister company. Verily kicked off Project Baseline in 2017, in collaboration with Duke and Stanford universities, to collect health data of participants over a four-year timeline. The collection of clinical consultation and survey data produced a large set of data, setting a baseline on “what it means to be healthy and the transition to disease.” The success of the project resulted in a partnership between Verily and Pfizer, Sanofi, Novartis, and Otsuka in mid-2019. Using Verily’s platform, patients and clinicians will be actively engaged in clinical trials to increase the speed of clinical research. The program aims to map out the human health baseline and carry out clinical studies — using technology developed under Verily – in disease areas such as cancer, mental health, diabetes, dermatology, and heart disease. The American Heart Association is using Verily’s platform for the Research Goes Red registry. After launching its marketing campaign, it received more than 26,000 registrations, including 8,500 providing consent for specific clinical trials and that are open to joining additional research opportunities through Project Baseline.

Recruitment and retention in clinical trials can specifically benefit from AI tools such as machine learning and natural language processing. These tools will find matches between specific patients and trials that are recruiting through integration with EHRs, medical devices, and wearables, and recommend these matches to doctors and patients (either in real time at the clinical consultation or as a notification on the patient’s wearable). In addition to recruitment, retention of participants is a current challenge. The application of AI to rich patient data presents the opportunity to track the patient’s compliance with the clinical trial’s adherence criteria. The data can be presented to clinical trial administrators, which allows them to notify the patient of retention risks and take predictive and preventive measures as opposed to reactive management.

Overcoming The Hurdles To Broader Adoption

While the use of wearables and AI tools creates the potential to simplify and streamline clinical research, it also highlights the need to similarly simplify existing processes, roles, and systems in life sciences companies. In advance of full usability of wearables in clinical trials, proof of concepts will continue to test and validate the feasibility of wearables in studies. Sponsors of studies should also continue to work — or partner with device makers — to evolve endpoint data collection and validation. Studies have found that having clinical scientists directly involved in clinical study design and conduct is highly beneficial.¹ However, R&D scientists are generally not familiar with devices, which creates a barrier for adoption of wearable technologies in drug development clinical trials. On the other hand, device engineers are not conversant with the drug development process and regulatory requirements for drug approvals. The solution would be to bring device engineers into clinical trials and drug development to educate R&D and enable adoption of device technologies.

Success hinges on meaningful collaboration with regulators to ensure the scope of the clinical study is being defined and outcomes can be achieved and implemented. Guidelines such as the Clinical Trials Transformation Initiative support such collaboration through recommendations for the use of mobile technology like wearable devices. One of its first recommendations is for R&D departments to start with a clinical trial endpoint and work backward to ensure investigators select the right device.

Data collection through wearables and its interpretation reveals new horizons for clinical trials. However, consumer and medical device makers, researchers, technology companies providing data platforms, and regulators must coordinate efforts in order to realize the full potential of this technology.

Reference:

1. Wearable Devices in Clinical Trials: Hype and Hypothesis. Izmailoza, E.S., Wagner, J.A., Perakslis, E.D., Clinical Pharmacology & Therapeutics (2017).

About The Author:

Yvette Jansen is an experienced manager in Grant Thornton’s Operations Transformation – Supply Chain practice, with more than 10 years of experience specializing in healthcare and life sciences business transformation programs. She focuses on operating model design and implementation, and process and IT implementation/optimization. Jansen works with healthcare providers, government institutions, and medical device/pharmaceutical organizations on their IT strategies, redesign of processes, and implementation of new operating models. She has implemented EHR and ERP implementations for healthcare systems and medical devices organizations. Jansen previously worked for Accenture, Grant Thornton Ireland, and the healthcare sector, advising hospitals, the Department of Health, and medical device/pharma companies.