Smartwatches in Clinical Research: A Valuable Opportunity

In this IMMEDIATE insight, Giulia Vismara, a Scientific Content Manager at Zadig srl writes about the opportunity smartwatches offer in clinical research.

In recent years, wearable technology has gained significant popularity, with smartwatches emerging as some of the most widely adopted devices in this category. These wrist-worn devices, equipped with sensors, connectivity features, and displays, offer functionalities that extend far beyond simply telling time. Their rapid growth is fuelled by their versatility, convenience, and seamless integration with smartphones and other smart devices.

Initially, smartwatches attracted attention for their fitness and activity tracking features, such as step counting, heart rate monitoring, and calorie measurement. The integration of advanced sensors, including optical heart rate monitors and accelerometers, allows users to access real-time information about their health and well-being. Over time, these devices have transitioned from niche gadgets to indispensable tools in healthcare and clinical research. Their ability to provide continuous monitoring and real-time data collection has sparked significant interest in their application to clinical studies. These devices enable researchers to gather valuable health information outside traditional medical settings, revolutionizing the way clinical data is collected, analysed, and utilised.

Smartwatches and other wearable technologies are reshaping the landscape of clinical studies by introducing innovative methods for tracking physiological and behavioural parameters. Their capacity to monitor patients’ health in natural, everyday environments provides unprecedented insights into real-world health conditions. However, while their adoption presents exciting opportunities for advancing medical research, it also introduces significant challenges that must be addressed with care.

Traditional Data vs Smartwatches Data

Traditional clinical studies primarily rely on intermittent measurements taken during scheduled medical visits. However, these periodic assessments often fail to fully capture the complexity and dynamism of a patient’s health, as they can be influenced by memory errors or the limitations of self-assessment tools. Smartwatches represent a significant breakthrough, offering the ability to collect continuous, detailed data. This approach provides a more comprehensive and accurate view of participants’ physiological and behavioural states, effectively addressing the limitations of traditional methods.

By enabling constant monitoring, smartwatches generate exceptionally rich datasets that closely reflect the daily lives of study participants. This uninterrupted data collection allows for the detection and analysis of dynamic fluctuations that might otherwise go unnoticed during occasional evaluations.

Smartwatch and Healthcare

Wearable devices can collect data 24/7 in natural environments as individuals go about their daily activities at home or work. This capability can be further enhanced by integrating digital diaries, which highlight key aspects of personal health and lifestyle. The most well-known wearable devices are commercial fitness trackers, which monitor mobility and select vital parameters. However, these devices cannot be marketed as medical devices unless their performance has been rigorously validated prior to market launch. This marks a significant advancement over traditional methods of health data collection.

Historically, basic physiological data, such as vital signs, has been collected exclusively during medical visits or clinical procedures. This approach offers only a fragmented and static view of an individual’s phenotype and physiology, relying on isolated measurements and long-term extrapolations often supplemented by the patient’s recollection of past events. Critical health decisions, including assessments of disease status and treatment plans, are typically based on comparisons of this data to population averages, which may not accurately represent individual conditions.

Additionally, replacing traditional paper diaries with electronic versions improves the quality of subjective data—such as perceived pain or functional status—through timely collection, reduced transcription errors, and a lower administrative burden. The integration of wearable device data with genomic information and other advanced technologies has the potential to create a multi-dimensional understanding of human health. This comprehensive approach not only advances precision medicine but also deepens insights into the interactions between genotype and phenotype. Such advancements pave the way for groundbreaking scientific discoveries and significant improvements in personalized treatment strategies.

The promising potential of wearable devices has garnered significant attention, leading to the initiation of experiments and partnerships between biopharmaceutical companies, Contract Research Organizations, and device manufacturers. However, the anticipated significant impact of digital technologies on biopharmaceutical research and development has yet to materialize. Key barriers include scientific, regulatory, ethical, legal, data management, infrastructure, analytical, and security challenges. Many consumer-grade wearables are marketed with claims of health and wellness benefits, often lacking robust scientific validation. Bridging this gap requires well-designed studies with clear medical objectives, rather than retrofitting existing technologies for medical applications. Furthermore, the historical divide between drug development and device engineering presents additional hurdles. Biopharmaceutical researchers may lack expertise with wearable technologies, while device engineers often have limited knowledge of the regulatory requirements for drug approval. Incorporating device engineers into drug development teams can help educate researchers, streamline processes, and facilitate adoption of these technologies.

Participants’ Experience and Device Choice

The acceptability of wearable devices among study participants is critical for successful employment. Technical features such as size, ease of use, battery life, and impact on daily activities must be carefully considered. Direct patient involvement before the study begins may be necessary to ensure the adoption of the technology. If user acceptability is uncertain, conducting a pilot study can provide valuable insights, as participant acceptance significantly affects compliance.

The practical experience of clinical researchers involved in study design and execution has proven highly beneficial. It accelerates device adoption by clinical teams and enables researchers to quickly rule out devices unsuitable for participants or incapable of generating interpretable data. Devices are typically managed by site staff trained to provide participants with information and support. In addition to training participants and staff, the data flow should be mapped out prior to the study to assess its impact on participants and other clinical procedures. For example, this planning might include determining whether a mobile phone is required for data synchronization, ensuring app compatibility with various phone models, addressing translation needs, setting synchronization frequency, and identifying compatible computer models for docking devices. Participant compliance with data collection should be actively monitored, and interventions such as reminders should be implemented in cases of non-compliance. Decisions regarding when to process data into secondary derivatives and when to review it must be made in advance. For real-time data review, pre-established and thoroughly tested workflows for data processing, analysis, and visualization should be in place before the study begins. Retrospective data processing, on the other hand, is more suitable for exploratory endpoints, offering greater flexibility for iterative raw data processing and visualization. The use of data must be clearly defined in the study protocol, specifying whether it influences patient care or other study procedures. The protocol should also include plans for managing participants who experience allergic reactions or other adverse effects related to wearable device components. Depending on how the data is utilized, participants with known sensitivities may be excluded from the study or permitted to participate in other study procedures if their involvement in the wearable-related portion is optional and does not compromise overall data integrity.

Both consumer-grade and medical-grade devices can be used in clinical studies. Medical-grade devices require less preliminary work, as their performance is often already established for regulatory approval purposes. However, consumer-grade devices require analytical and clinical validation studies to ensure they are fit for purpose. The availability of raw and derived data must be carefully evaluated, as consumer-grade devices often provide only summary data, which may lack a complete audit trail. Including wearable devices in clinical studies involves multidimensional considerations. Research, development, and healthcare organizations must overcome several challenges to make wearable technology implementation routine. The ongoing development of analytical and clinical validation methodologies and the widespread adoption of devices based on fitness-for-purpose principles will remain critical for future success.

Infrastructure Challenges, Ethical and Legal Challenges

Clinical teams often lack experience managing the vast amounts of continuous data collected by wearables. Sensor data is complex, with multiple layers including raw, filtered, secondary, and final interpreted data. Issues such as data ownership, audit trails, and reporting outcomes remain unresolved. Additionally, the processing, analysis, and visualization of large datasets are significant challenges. Machine learning methods have shown promise for automated signal processing but require standardized approaches for data organization and integration.

Ethical and legal concerns focus on data ownership, consent requirements, privacy, and security, with geographical differences in regulatory approaches. In the EU, the General Data Protection Regulation (GDPR) applies to all data generated by wearables in medical contexts, requiring explicit consent for data use and allowing patients to withdraw consent at any time. Ensuring data security requires proactive measures beyond compliance-focused, retrospective strategies.

Final Consideration

Wearable technologies hold significant promise and have the potential to revolutionize healthcare and drug development by transforming how health data is collected, processed, and visualized. Their potential applications are diverse, with utility across multiple therapeutic areas, and they are likely to evolve rapidly. The ultimate goal should be a better understanding of disease variability and treatment responses, alongside reducing healthcare costs and increasing efficiency in conducting clinical trials. Furthermore, adopting new methods for remote data collection could lead to more accessible treatments and care management for all patients in need.

The scientific community would benefit from frequent information sharing to exchange findings and learning experiences. Such collaboration would facilitate the development and adoption of best practices for implementing technology, as well as for data collection, analysis, and interpretation. While the field is currently marked by enthusiasm, more data from rigorously designed studies is required to move beyond the initial excitement and establish robust scientific methodologies for generating and testing hypotheses. A deeper dialogue between the biopharmaceutical industry and device manufacturers is essential to develop methodological approaches and a shared understanding of experimental requirements to meet clinical and analytical validation standards.

Reference

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