Current applications of wearable biosensors, activity trackers, and mobile apps are only touching the surface of the potential of connected devices and their roles in the data collection and patient journey. Advances in connected devices can inspire innovative clinical trial design including novel ways to collect data, transform and streamline clinical operations and improve the patient experience.

Since the early 2000s, connected digital device use has grown with a compound annual growth rate of approximately 32% through 2017, according to a report by Harvard Business School. Further, the study showed that more than 1,000 trials that launched in 2017 used at least one connected digital device tool, representing a more than a ten-fold increase over the same count in the early 2000s. Many therapeutic areas have utilised this growing trend, including cardiovascular, oncology, CNS, women’s health, neurology, general surgery and orthopaedics.

These connected devices within the healthcare industry, also known as the Internet of Medical Things (IoMT), are being applied across all phases of clinical research, including trials sponsored by pharmaceutical, diagnostic, and medical device companies, government bodies and non-profit organisations. As more technologies coming onto the market everyday, industry stakeholders are recognising its potential to create a more patient-centric trial, while streamlining clinical operations.

New ways to collect data

Connected devices and apps allow for continuous data monitoring, making trials safer, reducing site visits, improving study oversight, and ensuring patient compliance, leading to shortened study timelines and reduced costs.

The real world evidence (RWE) being produced by the IoMT supports trial safety, early decision making and adaptive changes, further accelerating development. As 43% of med tech companies are using RWE to drive business decisions, the IoMT will impact business models providing new revenue sources and related services.

Harnessing IoMT in clinical trials allows biosensor, wearable and mobile app data to be collected and transmitted into cloud databases, where artificial intelligence (AI)-driven platforms analyse the data in real-time to file clinically relevant signals. Gathering real-time behavioural and physiological data from patients can form the basis for robust data collection and allows for the creation of digital biomarkers. For example, a digital biomarker could help to explain or predict clinical outcomes.

Also, providing real-time data allows research personnel and sponsors to address any issues promptly, including making product and or trial design adjustments prior to pre-market and commercial claims and approvals. A digital platform provides automatic collection of consistent, unbiased data and remote monitoring for data analysis, and can help accelerate time to market by producing high-quality insights. Further, insights into the safety and efficacy of an experimental drug in a real-world environment could accelerate regulatory approval.

The exponential growth in data volume from various sources — such as from imaging, lab tests, genomics, and social media — can cause fragmented collection. To bridge any data silos, AI-enabled software can allow for easier access to data and to facilitate analytics.

Transforming clinical ops

Incorporating electronic health records (EHR) for study design provides participants with greater access to trials. Matching the right patients to the right trials can result in the need for fewer participants and accelerated study start up. Also, EHRs can potentially lower the number of amendments to trials, often resulting in increased efficiency. Further, EHR data can inform feasibility and study design, help create better protocols, and enable the development of combined-stage adaptive studies.

In addition, automated data collection and monitoring can generate RWE, serving as a tool to inform and supplement clinical trials in protocol feasibility assessment, site/physician identification and historical controls. This data can also be used in simulations and synthetic control arms as an aid to predicting safety and efficacy of proposed therapies for future trials and informing go/no-go decisions. Using data for these purposes can reduce or eliminate the need to enroll control participants, increasing efficiency, reducing delays, lowering trial costs and speeding therapies to market.

While integrating the IoMT into clinical trials can be costly and requires standardisation, adopting a partner with expertise in data science, management and analytics could provide the skills for success.

The patient experience

Incorporating IoMT within clinical trial design has the potential to improve the participant experience, ultimately bringing new treatments to patients much faster. Patient-centric technologies directly touch the patient, such as electronic informed consent, at home digital monitoring and virtual visits.

For example, using electronic informed consent facilitates patient education for understanding the implications and commitments of joining a study, offering greater patient protection. When patients have realistic expectations of participation in a clinical trial, they can feel empowered to make informed decisions, and have more ownership of the consent discussion. This structure also increases a patient’s trust in his or her clinician and the study.

Real-time information collected from sensors and wearable devices supports patient-centricity by informing protocol adherence and helping patients manage medications, perform structured tests and report symptoms. Respondents of a survey conducted by The Clinical Trials Transformation Initiative (CTTI) cited easier daily compliance with trial-related procedures and the ability to see their data and track their own health as reasons they would prefer to participate in a mobile versus traditional clinical trial.

Moreover, mobile applications can enhance convenience for patients when reporting electronic diaries and outcomes. Transmitting data to a secure cloud infrastructure for storage and analysis allows for machine learning and other AI methods to assess and quantify a therapy’s impact, such as drug dose response or device efficacy. Using digital monitoring help identify any safety or dosage issues, and allows the sponsor to adapt protocols to mitigate risks or even terminate a trial early.

The connectivity and interoperability of the IoMT can help to facilitate remote patient monitoring, enabling a decentralised clinical research model. In this instance, trial participants no longer need to live in close proximity or travel to a research site. When patients only visit sites when necessary and use virtual visits in other instances, trial participants experience less disruption in their daily lives, increasing study retention and patient adherence. This is evident from the CTTI survey, where nearly half of respondents preferred to see the trial doctor only at the beginning and end of a decentralised trial using mHealth technology.

Further, ongoing monitoring for post-market surveillance can capture and report any adverse events. Monitoring the safety and efficacy of medical devices and drugs ensures quality of the final product and positive outcomes for patients receiving the therapy or product.


As momentum builds for incorporating IoMT into clinical trials, early adopters can reap the benefits. The integration of connected digital tools and their growing use in research holds great promise for improving the study experience for patients and researchers, while expanding the nature and quality of data collected in medical studies.

David Novotny is general manager and global head, Medical Device & Diagnostic Research, and Marie McCarthy senior director of Product Innovation, Information Technology, both at ICON.