Digital Biomarkers
Digital biomarkers enable continuous, real-world monitoring in drug development, offering patient-centered and efficient trial outcomes.
Digital health technologies are transforming the way evidence is generated in drug development. One of the most promising frontiers is the use of digital biomarkers, which are defined as objective, quantifiable physiological and behavioral data collected via digital devices such as wearables, smartphones, and sensors.1,2 Digital biomarkers enable continuous, real-world data collection, while traditional biomarkers typically rely on invasive procedures or infrequent clinic visits. The shift to digital biomarkers may create new possibilities for monitoring disease progression, evaluating therapeutic interventions, and ultimately supporting regulatory decision-making.
Traditional endpoints and validated laboratory biomarkers usually involve continued follow-ups and controlled environments. Due to this, traditional endpoints and biomarkers may not capture clinically meaningful day-to-day changes in patient function. Digital biomarkers offer an opportunity to fill this gap by providing high-frequency data that reflects a patient’s real-world experience. Digital biomarkers offer several advantages. They allow continuous monitoring, which means they capture data between clinical visits that might have otherwise been missed. They can reduce patient burden by minimizing the need for invasive procedures, and they may also enable smaller and shorter clinical trials by providing more sensitive, real-time measures of treatment effect. Since digital biomarkers reflect real-world functioning, they have the potential to capture outcomes that are clinically meaningful on a day-to-day basis.
Examples of digital biomarkers include monitors measuring gait speed or step count, smartphone applications tracking cognitive performance, voice recordings that capture subtle changes in speech patterns, or physiological sensors that continuously measure heart rate variability.2,3 The above endpoints are promising in various disease areas, including neurology, cardiology, oncology supportive care, and also rare diseases where clinical endpoints may be limited.
1. Provide continuous monitoring, which means they capture data between clinical visits that might have otherwise been missed
2. Reduce patient burden by minimizing the need for invasive procedures
3. Enable smaller and shorter clinical trials by providing more sensitive, real-time measures of treatment effect
Specific Disease States and Digital Biomarkers
Neurological diseases have been early testing grounds for digital biomarkers.4,5 For example, gait speed and step count from wearables are being evaluated as functional endpoints in Parkinson’s disease and multiple sclerosis. Speech and voice analysis are being studied for detecting cognitive decline in Alzheimer’s disease. Digital biomarkers are also being researched across motor function, eye movements, facial expressions, speech and cognition, and sleep and activity patterns. These measures offer the potential to detect subtle changes earlier than clinician-reported assessments. The goal within healthcare is to introduce earlier interventions and conduct more efficient trials.
In oncology, digital biomarkers are being explored to complement traditional tumor-based endpoints.6 Activity monitors can track fatigue and physical function, which provide real-world measures of patient performance status. Wearable devices capturing sleep disturbances, heart rate, or mobility may serve as exploratory endpoints in supportive care studies. While not yet validated as surrogates for survival, these measures can provide a more holistic picture of treatment impact, especially in immunotherapy and palliative care settings.
Table 1 includes a non-exhaustive list of digital biomarkers currently under investigation.
Table 1. Examples of Digital Biomarkers Under Development in Pharma and Biotech1,7,8,9 (Alphabetical)
Digital Biomarker | Measurement Method | Therapeutic Area(s) | Validation Stage |
Cognitive Performance (reaction time, memory tests) | Smartphone-based neurocognitive apps | Alzheimer’s, schizophrenia, ADHD | Exploratory to early validation |
ctDNA Dynamics via Digital Signal Processing | Sequencing-based signal quantification | Oncology (solid tumors) | Analytical validation ongoing; regulatory review in early stages |
Digital ECG Signals | Connected patches, wearables | Arrhythmia, heart failure, cardio-oncology monitoring | Analytical validation strong; growing clinical validation |
Gait Speed/Step Count | Wearables (accelerometer, gyroscope) | Parkinson’s disease, multiple sclerosis, ALS | Exploratory to early clinical validation |
Heart Rate Variability (HRV) | Smartwatches, chest straps | Cardiology, oncology supportive care, stress-related disorders | Analytical validation in progress; limited clinical validation |
Ocular Movement/Retinal Tracking | Smartphone or specialized sensors | Neurological diseases (Parkinson’s, Alzheimer’s) | Exploratory research phase |
Physical Activity/Energy Levels | Wearables, smartphones, actigraphy devices | Oncology supportive care, chronic fatigue syndrome | Exploratory; being studied in patient-reported outcomes integration |
Sleep Metrics (duration, REM cycles, fragmentation) | Smartwatches, EEG headbands, mattress sensors | Insomnia, depression, cancer-related fatigue | Early clinical validation; FDA discussions ongoing |
Speech and Voice Patterns | Smartphone apps, voice recording analysis (AI/ML) | Alzheimer’s disease, ALS, depression | Exploratory; pilot studies ongoing |
Voice Biomarkers of Mood | Smartphone microphone capture with sentiment analysis | Depression, bipolar disorder | Exploratory; no regulatory submissions yet |
Challenges with Digital Biomarkers
Digital biomarkers have an exciting outlook, but they pose significant challenges. Analytical validation requires proving that devices measure what they are supposed to measure reliably and accurately across populations and settings. Clinical validation requires demonstrating that changes in digital biomarker values correlate with meaningful clinical outcomes. Variability in devices used, methods of data collection, and patient adherence introduces complexity. Along with no universally agreed-upon standards, the collection of continuous, granular health data raises important questions about patient privacy, informed consent, and data ownership. Pharmaceutical companies must work closely with technology partners to ensure compliance with global regulations such as HIPAA and GDPR. Sponsors must address patient concerns regarding how patient data will be stored, shared, and used across regulatory submissions, as well as for potential secondary research or commercial purposes.
Executive Interest in Digital Biomarkers
Regulatory bodies such as the FDA and EMA have expressed increasing interest in digital biomarkers. Initiatives such as the FDA’s Digital Health Center of Excellence and EMA’s Big Data Task Force illustrate a willingness from regulatory bodies to engage with pharmaceutical companies in developing evidence-based standards for digital measures. While digital biomarkers offer the excitement of new health technology, regulators stress the importance of analytical and clinical validation before they can be accepted as a clinical or surrogate endpoint in drug development. A major hurdle in digital biomarker development is ensuring data quality and harmonization. Unlike traditional assays, digital data streams may involve terabytes of data from various sources that require preprocessing, cleaning, and algorithm development to compile, store, and analyze in real time. Without standardization, results may not be reproducible across studies or devices. Industry organizations such as the Digital Medicine Society (DiMe) and Critical Path Institute are working to define frameworks for data integrity and interoperability.
For digital biomarkers to become accepted clinical endpoints, sponsors must follow a structured validation pathway: analytical validation proving device reliability, clinical validation proving a correlation with meaningful healthcare outcomes, and context of use definition proving clinically meaningful outcomes in specific patient populations, disease stages, and clinical interventions. Regulatory agencies continue to encourage early engagement with sponsors to discuss digital endpoint strategies for drug development tools.
Closing Remarks
Overall, digital biomarkers hold potential to reshape drug development by providing patient-focused measures of treatment benefit. However, their adoption as clinical endpoints will depend on robust validation, standardization of data harmonization, and clear regulatory guidance. For pharmaceutical and biotech companies, investment in digital biomarker development represents both a scientific opportunity and a strategic differentiator. Sponsors that can successfully integrate digital endpoints into their clinical programs may accelerate drug approvals, enhance patient centricity, and gain a competitive advantage in an increasingly technological and data-driven healthcare landscape.
Resources:
- Kumari A. Digital Biomarkers: The Smart Devices That Could Save Your Life. BCC Research. 2025.
- Macias Alonso AK, Hirt J, et al. Definitions of Digital Biomarkers: A Systematic Mapping of the Biomedical Literature. BMJ Health Care Inform. 2024; 31(1):e100914.
- Urso D, Van Wamelen DJ, et al. Chapter Three - Digital Biomarkers in Movement Disorders. Int Review of Mvmt Disorders. 2023; 3(5):49-70.
- Erickson CM, Wexler A, Largent EA. Digital Biomarkers for Neurodegenerative Disease. JAMA Neurol. 2025; 82(1):5–6.
- Powell D, Adams SA, et al. Exploring the Potential of Digital Biomarkers as a Measure of Brain Health ‘Capital’. 2025. 334(8).
- Oakley-Girvan I, Yunis R. Digital Biomarkers to Improve Quality of Life in Cancer Patients: Moonshot-Funded Decentralized Clinical Research Aims to Better Capture Effects of Cancer Therapy. DIA October 2021 Global Forum. 2021.
- Abid M, Péntek M, et al. Digital Biomarker-Based Studies: Scoping Review of Systematic Reviews. JMIR Mhealth Uhealth. 2022; 10(10):e35722.
- Delve Health. Integrating Digital Biomarkers into Clinical Trials for Enhanced Patient Monitoring. 2025.
- Ghaffari R. Bridging Computer Science and Biomedicine. Digit Biomark. 2025; 1(9).
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