Digital biomarkers are revolutionizing the way healthcare professionals and researchers monitor, diagnose, and treat diseases. Enabled by digital technologies, these biomarkers provide objective, quantifiable physiological and behavioral data collected through connected devices such as smartphones, wearables, and sensors. Unlike traditional biomarkers, which rely on laboratory tests or imaging, digital biomarkers enable continuous, real-world monitoring, offering unprecedented insights into disease progression, treatment response, and patient well-being. As healthcare shifts toward personalized medicine and continuous monitoring, digital biomarkers are becoming an essential tool for capturing real-world data (RWD) and driving clinical decision-making.
This article explores their definition, types, applications, challenges, and future potential.
What Are Digital Biomarkers?
A biomarker is a measurable indicator of a biological process, disease state, or response to treatment. Digital biomarkers are a subset that leverage wearable sensors, smartphones, mobile apps, and other connected devices to track physiological or behavioral parameters in real time.
How Are Digital Biomarkers Collected?
Digital biomarkers are collected through various technologies that enable continuous and objective health monitoring. Wearables and smart devices, such as smartwatches, fitness trackers, and continuous glucose monitors, play a crucial role in gathering physiological data. Mobile applications further enhance data collection by assessing cognitive function, monitoring mental health, and tracking medication adherence. Remote sensors and Internet of Things (IoT) devices provide home-based monitoring solutions for conditions like heart failure or Parkinson’s disease, allowing for real-time health assessments. Additionally, artificial intelligence (AI) and machine learning (ML) are used to process and analyze large-scale, high-frequency data, transforming raw information into meaningful insights for clinical and research applications..
Types of Digital Biomarkers
Digital biomarkers can be classified into different categories based on their data source and intended use.
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1. Physiological Biomarkers
These biomarkers track vital signs and biological functions and are captured via wearables or remote sensors.
- Heart rate variability (HRV) – Captured via ECG or smartwatches
- Blood oxygen saturation (SpO2) – Monitored using pulse oximeters
- Respiratory rate– Measured using wearable sensors
- Blood glucose levels – Tracked with continuous glucose monitors (CGMs)
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2 .Behavioral Biomarkers
Collected through digital interactions and movement tracking, behavioral biomarkers provide insights into cognitive and mental health. They are derived from speech, typing, gait, sleep, or smartphone usage patterns, providing insights into neurological and mental health conditions.
- Speech patterns– Analyzed for early detection of neurodegenerative diseases
- Typing speed and keystroke dynamics– Potential indicators of cognitive decline
- Physical activity levels – Measured through accelerometers in wearables
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3. Digital Imaging Biomarkers
Advanced AI and ML algorithms analyze medical images to extract insights.
- Retinal scans– Used for early diagnosis of diabetic retinopathy
- Skin lesion imaging– Helps detect melanoma
- MRI and CT scan analysis– AI-powered analysis for tumor progression
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4. Passive or Active biomarkers
Digital biomarkers are also classified as passive or active depending whether are passively collected (e.g., step count, HRV), or whether they require active engagement (e.g., perform cognitive tasks in an app).
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Applications of Digital Biomarkers
Digital biomarkers have broad applications across various therapeutic areas and clinical research:
- Disease Prevention and Early Diagnosis
- Wearable ECGs can detect atrial fibrillation (AFib) early, reducing stroke risk.
- Smartphone-based cognitive assessments can help identify Alzheimer’s disease in early stages.
- AI-driven voice analysis can predict mental health conditions such as depression and anxiety.
- Remote Patient Monitoring
- Continuous glucose monitoring can enable better diabetes management.
- Smart inhalers track medication adherence in asthma and COPD patients.
- Sleep tracking devices help diagnose sleep disorders like sleep apnea.
- Drug Development and Clinical Trials
- Digital biomarkers provide real-time patient data, improving endpoint assessment.
- Enables hybrid or fully decentralized clinical trials (DCTs), reducing patient burden and site visits.
- Reduces reliance on subjective patient-reported outcomes (PROs).
- Novel biomarkers validated as surrogate endpoints for regulatory approval.
- Personalized Medicine and Treatment Optimization
- AI-driven heart rate (HR) monitoring can tailor exercise programs for cardiac patients.
- Movement tracking in Parkinson’s disease aids in medication adjustments.
- Speech and voice analysis can personalize mental health interventions.
- Real-World Evidence (RWE) and Post-Market Surveillance
- Digital biomarkers generate longitudinal RWD that support post-approval commitments and label expansions.
- Continuous tracking of adverse events (AEs) and treatment adherence improves pharmacovigilance.
Clinical Applications of Digital Biomarkers
Digital biomarkers enhance early diagnosis, treatment decisions, and remote patient monitoring in a number of different therapeutic areas. Some example are:
- Neurological Disorders: Parkinson’s Disease & Alzheimer’s
- Gait analysis, speech tremors, and typing speed variations can help detect early signs of Parkinson’s disease.
- Digital cognitive assessments on smartphones can monitor early Alzheimer’s disease progression.
- Cardiology & Metabolic Health: Cardiovascular Disease & Diabetes
- Wearables (e.g., Apple Watch, Garmin, Fitbit) track HRV and detect AFib.
- Continuous glucose monitors (CGMs) provide real-time blood sugar monitoring for diabetes management.
- Psychiatry & Mental Health: Depression, Anxiety, and Bipolar Disorder
- Smartphone usage patterns, speech analysis, and sleep tracking can indicate shifts in mental health conditions.
- AI-driven models use social media activity and voice tone to predict mood changes.
- Oncology & Treatment Response: Cancer Therapy Monitoring
- Wearable sensors can track fatigue levels, heart rate, and mobility, providing insights into how cancer patients respond to treatment.
- Digital biomarkers help personalize chemotherapy dosing by monitoring side effects in real time.
- Pulmonary & Infectious Diseases: Respiratory Conditions
- Cough analysis apps and oxygen saturation (SpO2) sensors support early detection of respiratory infections.
- Wearables tracking respiration rate and body temperature can detect fever trends in pandemics.
Advantages of Digital Biomarkers
Continuous monitoring offers real-time and longitudinal health tracking, providing a more comprehensive view of disease progression compared to traditional one-time measurements. Non-invasive data collection relies on connected devices to gather health information without disrupting daily life. Real-world data collection captures insights outside of clinical settings, enhancing patient-centered care and making healthcare more personalized. Early detection and prevention become possible through continuous data collection, allowing for proactive disease intervention. The scalability, remote capabilities, and cost-effectiveness of digital biomarkers reduce the need for frequent hospital visits, lowering healthcare costs and improving accessibility for diverse populations. By providing objective and quantifiable measurements, these technologies minimize reliance on subjective patient reporting, ensuring more accurate and reliable health assessments.
Regulatory Landscape & Industry Adoption
Digital biomarkers are increasingly being recognized by regulatory agencies, shaping their integration into healthcare and drug development. The FDA’s Digital Health Pre-Cert Program is designed to accelerate the approval process for digital biomarkers, ensuring their timely adoption in clinical practice. The European Medicines Agency (EMA) and the UK’s Medicines and Healthcare products Regulatory Agency (MHRA) have issued guidance on digital endpoints, highlighting the role of digital biomarkers in drug development and regulatory decision-making. Industry collaborations, such as those led by the Digital Medicine Society (DiMe) and the Critical Path Institute (C-Path), are working to standardize the validation of digital biomarkers, promoting their reliability and widespread use in research and clinical applications.
Challenges in Digital Biomarker Adoption
Despite their potential, several challenges must be addressed for widespread adoption.
- Data Quality, Validation and Standardization
- Digital biomarkers must be rigorously validated to ensure accuracy and reliability.
- Device-to-device variability can affect consistency of measurements.
- Regulatory, Privacy and Ethical Considerations
- Regulatory frameworks (FDA, EMA) are evolving but not yet standardized for digital biomarkers.
- Ethical concerns around data privacy, security, and informed consent require clear guidelines.
- Data Integration & Interoperability
- Standardizing digital biomarkers across different platforms and healthcare systems remains complex.
- Lack of interoperability between medical devices and electronic health records (EHRs) is a barrier.
- Accessibility, Digital Divide and Patient Compliance
- Adoption of digital health tools varies based on socioeconomic factors.
- Digital literacy and access to technology can impact patient engagement.
- Encouraging consistent device usage for high-quality data collection
In conlusion, digital biomarkers are revolutionizing healthcare by enabling continuous, real-world monitoring of diseases and treatment effects. As technology advances, these biomarkers will become increasingly integral to clinical trials, remote patient monitoring, and precision medicine, ultimately improving early detection, intervention, and patient outcomes. Ongoing advancements in AI, sensor technology, and regulatory frameworks will further integrate digital biomarkers into clinical practice, improving patient outcomes and research efficiency.
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References:
Barac M, Scaletty S, Hassett LC, Stillwell A, Croarkin PE, Chauhan M, Chesak S, Bobo WV, Athreya AP, Dyrbye LN. Wearable Technologies for Detecting Burnout and Well-Being in Health Care Professionals: Scoping Review. J Med Internet Res. 2024 Jun 25;26:e50253. doi: 10.2196/50253. PMID: 38916948; PMCID: PMC11234055.
-
Borson S, Au R, Chodos AH, Gandy S, Jain H, Alagor A, Cohn K, Kerwin DR, Mintzer J, Monroe S, Robinson D, Mielke MM, Wilcock DM. Opportunities to encourage adoption of a biomarker-enabled care pathway for Alzheimer's in primary care. Alzheimers Dement (Amst). 2025 Mar 11;17(1):e70095. doi: 10.1002/dad2.70095. PMID: 40071061; PMCID: PMC11895972.
-
Cay G, Sada YH, Dehghan Rouzi M, Uddin Atique MM, Rodriguez N, Azarian M, Finco MG, Yellapragada S, Najafi B. Harnessing physical activity monitoring and digital biomarkers of frailty from pendant based wearables to predict chemotherapy resilience in veterans with cancer. Sci Rep. 2024 Jan 31;14(1):2612. doi: 10.1038/s41598-024-53025-z. PMID: 38297103; PMCID: PMC10831115.
-
Foong YC, Merlo D, Gresle M, Zhu C, Buzzard K, Lechner-Scott J, Barnett M, Wang C, Taylor BV, Kalincik T, Kilpatrick T, Darby D, Dobay P, van Beek J, Hyde R, Simpson-Yap S, Butzkueven H, van der Walt A. Longitudinal Trajectories of Digital Cognitive Biomarkers for Multiple Sclerosis. Ann Clin Transl Neurol. 2025 Feb 25. doi: 10.1002/acn3.70015. Epub ahead of print. PMID: 40007145.
-
Gandhewar R, Guimaraes T, Sen S, Pontikos N, Moghul I, Empeslidis T, Michaelides M, Balaskas K. Imaging biomarkers and artificial intelligence for diagnosis, prediction, and therapy of macular fibrosis in age-related macular degeneration: Narrative review and future directions. Graefes Arch Clin Exp Ophthalmol. 2025 Mar 10. doi: 10.1007/s00417-025-06790-0. Epub ahead of print. PMID: 40059223.
-
Perna G, Spiti A, Torti T, Daccò S, Caldirola D. Biomarker-Guided Tailored Therapy in Major Depression. Adv Exp Med Biol. 2024;1456:379-400. doi: 10.1007/978-981-97-4402-2_19. PMID: 39261439.
