Mark Matson, Senior Director, Sensor Cloud at Medidata
Clinical trials are the foundation of drug development, generating life-saving medicines for the betterment of society. But trials can be arduous, lengthy, and expensive for patients, sites, and sponsors, particularly in neurological disease trials, where phase III trials cost up to $370 million, last 7.9 years, and face a 95% failure rate among drug candidates 1,2,3. Moreover, traditional site-based data collection methods do not capture the complete picture of how patients are affected by their disease and how they respond to treatment. To expand our understanding of disease processes and therapeutic effects, clinical trial sponsors are embracing sensor technology, which provides an opportunity to not only enhance clinical evidence, we can reduce the time and costs of clinical research while benefitting all stakeholders.
According to Silvia Piai, an expert in the medical devices industry and a senior research manager at International Data Corporation (IDC), “remote, sensor-driven monitoring is transforming clinical trials by reducing patient burden and improving data quality. Passive, real-time data collection eliminates the need for frequent clinic visits and reliance on self-reported symptoms, enhancing convenience and accessibility, especially for patients in remote or underserved areas. This approach improves recruitment and retention while generating more granular, accurate data for faster, more reliable decision-making. As IDC highlights, integrating IoT and AI enables early detection of adverse events and better patient compliance, ultimately boosting safety, efficiency, and cost-effectiveness across the clinical research process.”
As these innovations are becoming more deeply embedded in clinical research, therapeutic areas like central nervous system (CNS), metabolic, and cardiovascular disorders are readily seeing benefits. Sensors collect valuable physiological data that better measures disease manifestations, such as sub-second movement features of gait or tremor, accelerating trial timelines by reducing the number of patients needed to reach statistical significance (due to the higher-resolution measures), helping bring therapeutics to market faster with more objective evidence about the interventions. We can also leverage sensors and AI to address increasing clinical trial complexity, ensuring more efficient and effective study designs.
From Episodic Snapshots to Real-World, Continuous Monitoring
All clinical trials rely heavily on data collected during site visits, e.g., blood pressure measured once every few weeks, symptom checklists filled out from memory, and lab assessments evaluated centrally. But this approach creates blind spots, missing the full story of how patients respond to interventions throughout their daily lives, both good days and bad days.
Sensor-driven digital measures have been shown to reduce participant burden—patients no longer need to visit clinics as frequently or rely on memory to report symptoms. That lower burden can lead to faster recruitment, retention, and higher-quality data with less bias and fewer errors.
Transforming CNS Trials Through Sensor-Based Insights
Sensor technology, in particular, benefits CNS research. Neurological diseases, which affect more than 3 billion people globally, are notoriously difficult and expensive to study. Symptoms can be highly variable between individuals and even within the same individual across time and are subject to placebo effects, making sensitive, continuous monitoring crucial to understand the complete picture.
Sensor technology is playing a pivotal role in overcoming these challenges. For example, in multiple sclerosis (MS) trials, sensors can track changes in gait, balance, and overall activity level between site visits. These passive data streams allow researchers to detect meaningful changes in patient status earlier, sometimes even before a patient is aware of them. In a healthcare setting, these data would be able to determine when a patient would benefit from the assistance of a cane or walker prior to the onset of falls which plague this population.
This granularity also helps evaluate treatment efficacy with greater precision, especially when subtle improvements are critical endpoints. Integrating electronic clinical outcome assessment (eCOA) tools further enhances this ecosystem by streamlining how patient-reported outcomes are collected and managed, making the trial process more efficient for both sites and participants. Composite measures combining eCOA and digital health technologies (DHTs) also provide great promise for remote and longitudinal monitoring moving forward.
Broader Applications Across Therapeutic Areas
While CNS trials may offer the most immediate and direct examples of impact, the benefits of sensor technology extend across all therapeutic areas. In cardiovascular research, continuous heart rate and activity monitoring allow for early detection of arrhythmias or post-exercise stress markers, helping researchers understand how therapies perform in real-world conditions. New digital biomarkers such as Cardiac Effort can be collected passively at home, producing data on cardiac function that correlates with echocardiograms, MUGA, and cardiac MRIs.4
In addition, site visits, invasive sample methods (including blood draws and biopsies), and laboratory testing are commonly used in the diagnosis, detection, and treatment of metabolic diseases. These approaches increase site and patient burden. Wearable and remote monitoring technologies that allow for continuous, real-time assessment of metabolic indicators in daily life are being used by researchers more and more in an effort to overcome these obstacles. These technologies provide more frequent and sensitive information to help investigators understand the extent of disease and response to treatment in their patients.
With respect to the respiratory therapeutic area, commonly used methods like spirometry, pneumography, plethysmography, or capnography are typically invasive and often inconvenient for patients. In order to lessen patient burden, teams are focusing their efforts on developing wearable and remote technologies that can continuously monitor respiratory parameters in a variety of settings to reduce patient burden.
Streamlining Study Timelines and Costs
Sensor technology enhances the clinical evidence package, while also improving the quality of data and increasing trial efficiency. High-fidelity, continuous data makes it possible to adapt protocols in ongoing trials, stratify patients based on real-time biomarkers, and reduce the number of participants needed to achieve study goals. Medidata’s own research suggests that sensor-enabled studies can accelerate trial timelines by four to five months and reduce Phase III costs by as much as $27 to $48 million.
This potential to shorten trials and bring effective therapies to market sooner is especially valuable in high-cost, high-failure areas like Alzheimer’s disease, where traditional models are both slow and uncertain.
Ensuring Data Privacy and Integration
As with any health data innovation, safeguarding patient information remains a top priority. While wearables create large volumes of data, sponsors, sites, and device / platform providers are proactively protecting it. Role-based access controls, encryption, and audit trails are widely implemented to ensure data security and compliance with global standards.
Clear, accessible communication with patients is also essential. When participants understand what data is being collected, how it will be used, and what safeguards are in place, trust is strengthened, supporting both compliance and engagement.
Ensuring high-quality data also requires addressing technical complexity. Device compatibility, data integration into trial platforms, and scalable infrastructure are key elements that must be in place. Training, intuitive interfaces, and responsive support can help smooth adoption for both patients and study teams.
Another option to ensure data privacy is pooling an individual’s data with many other patient’s data to help establish normative ranges for physiologic measures. This approach would be especially useful in placebo arms of trials, helping researchers understand how the complexities of disease manifest throughout the patient journey.
A New Era of Clinical Trials
Sensor technology is rapidly redefining clinical trials. By shifting from episodic site-based assessments to continuous, real-world monitoring, researchers gain deeper, more actionable insights while easing the burden on participants. Whether identifying early signs of disease progression in CNS disorders or tailoring treatments in cardiovascular or metabolic conditions, sensors enable more precise, responsive, and inclusive clinical trials.
As the industry continues to embrace these tools, the ultimate beneficiaries are the patients, receiving care that is more informed, more personalized, and more timely than ever before.
About Mark Matson
Mark Matson is a seasoned pharmaceutical research and development professional with over 30 years of experience in drug development, sales leadership, licensing, and program management. He currently serves as Managing Partner of Patient Cloud at Medidata Solutions, where he leads initiatives to integrate digital health technologies into clinical trials. Matson holds a Bachelor of Science in Molecular and Cellular Biology and a Master’s in Business, and has held positions in research, manufacturing, portfolio management, and business development across large pharmaceutical companies, biotech firms, contract research organizations, and technology companies.
References
- Boxer AL, Sperling R. Accelerating Alzheimer’s therapeutic development: The past and future of clinical trials. Cell. 2023;186(22):4757-4772. doi:10.1016/j.cell.2023.09.023
- Tampi RR, Ghosh S, Mena E, et al. Disease-modifying therapies in Alzheimer’s disease: a review of the evidence. Alzheimer’s & Dementia: Translational Research & Clinical Interventions. 2018;4(1):8-14. doi:10.1016/j.trci.2018.03.009
- Cummings JL, Lee G, Ritter A, et al. Alzheimer’s disease drug development pipeline: 2023. Alzheimer’s & Dementia: Translational Research & Clinical Interventions. 2023;9(1):1-10. doi:10.1002/trc2.12388.
- Lachant, D., Kennedy, E., Derenze, B., Light, A., Lachant, M., & White, R. J. (n.d.). Cardiac Effort to Compare Clinic and Remote 6-Minute Walk Testing in Pulmonary Arterial Hypertension. CHEST Journal. https://pubmed.ncbi.nlm.nih.gov/35777448/
