Digital Biomarkers vs. Traditional Biomarkers: A Complete Comparison
- Koneksa Health
- 4 days ago
- 8 min read
Updated: 10 hours ago
As consumer technology continues to advance, wearable or portable smart devices are becoming increasingly accessible, contributing to the rapid emergence of digital biomarkers in modern healthcare, as well as a growing demand for reliable and scalable digital biomarker solutions. Biomarkers are defined by the United States (US) Food and Drug Administration (FDA) as “a marker of a biological process which can be normal, pathological, or as a response to therapy or exposure.” These can be captured using objective measurement methods and several subclasses exist, including traditional (i.e., molecular, histological, radiographic, or physiologic) and digital biomarkers. It’s especially important to note that digital and traditional biomarkers are complementary tools, each with distinct strengths, validation pathways, and clinical applications, and that the choice between them (or the decision to use both) is fundamentally a measurement-strategy decision.
This article aims to provide a complete comparison of digital biomarkers versus traditional biomarkers in clinical research and will offer answers to the following key questions:
What are the key differences between digital biomarkers and traditional biomarkers?
Which clinical research questions are best suited to both these types of biomarkers?
What are examples of applications for digital and traditional biomarkers in clinical research?
What are the key strengths and limitations of digital vs. traditional biomarkers?
Which type of biomarker is right for me: digital, traditional, or both?
How Do Digital Biomarkers Differ from Traditional Biomarkers?
Traditional Biomarkers
In clinical development and practice, traditional biomarkers have been used to measure disease activity or progression, treatment response, or safety (Table 1), among other use cases. These biomarkers are typically derived from biological samples collected from patients, such as blood or urine, but can also be measured non-invasively. For example, body mass index (BMI) measurement is a risk biomarker used to assess the risk for metabolic diseases such as diabetes mellitus, hypertension, and dyslipidemia, and can lead to preventative health measures.
Table 1: Types of Traditional Biomarkers in Clinical Development and Practice
Categories | Examples of Traditional Biomarkers |
Disease-related | Disease susceptibility or risk biomarker, diagnostic biomarker, prognostic biomarker, monitoring biomarker |
Drug-related | Predictive biomarker, pharmacodynamics/response biomarker, safety biomarker |
Safety indicators | Measures of liver function, including serum creatinine, blood urea nitrogen, and cystatin C |
Digital Biomarkers
Digital biomarkers refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies (DHTs), including wearables, smart devices, and medical sensors. As with traditional biomarkers, these digital measures can offer detailed health insights that indicate health status, disease state, or treatment response.
To learn more about digital biomarkers, click here.
Digital vs. Traditional Biomarkers: A Comparative Overview
At first glance, traditional and digital biomarkers are similar in that they both offer unique measurement strategies for capturing clinical data; however, there are also important differences in the collection processes, nature of the data, scientific and technical maturity, and regulatory considerations. For example, as described earlier, traditional biomarkers are collected from biological samples or physical measurements, while digital biomarkers are measured through DHTs. Whereas the former only offers single, point-in-time health insights, depending on the frequency of sample collection or measurement, digital biomarker measurements can be either point-in-time or continuous, which enables real-time insights into longer term data patterns.
It is important to note that digital biomarkers are not replacements for traditional biomarkers. They simply offer a means of extending the capabilities of traditional biomarkers, allowing researchers and clinicians to capture additional information from real-world, longitudinal, behavioral, and functional signals. As detailed in Table 2 below, both biomarker types differ meaningfully in data source, sampling frequency, validation pathway, regulatory precedent, and the kinds of clinical questions they can answer.
Table 2: Overview of Key Differences Between Digital vs. Traditional Biomarkers
Key Dimension | Digital Biomarkers | Traditional Biomarkers |
Data source | Wearables and sensors, smartphones, passive data streams, connected devices, speech and video, digital behavior and environment signals. | Blood, urine, tissue, imaging, physiology (electrocardiogram, heart rate, blood pressure, weight), pathology, clinician assessments, lab assays. |
Collection method | Via remote or in-clinic devices, tasks or surveys in apps, passive day-to-day sensing, and telemetry. | Phlebotomy and specimen handling, imaging visits, in-clinic measurements, centralized lab processing, manual clinician scoring of disease measures. |
Sampling frequency | Often continuous or semi-continuous (seconds to hours), but may also be point-in-time. | Mostly point-in-time at visits (days to months), with limited repeated measures due to logistics and cost. |
Setting | Primarily real-world and at-home, but can include clinic-based or supervised measurements. | Primarily clinic or lab, but can also involve home collection (e.g., home visits for blood collection), but generally in controlled settings. |
Validation pathway | Analytical validation of sensors and algorithms, with validation of clinical endpoints or PROs, robust device reliability, ongoing real-time monitoring and noise management. | Well-established assay-based analytical validation, with clinical validation, reference ranges, inter-lab reproducibility, and quality systems. |
Cost and patient burdens | Costs may be lower due to less invasive collection methods and reduced travel burden.
Requires device onboarding, sufficient adherence, regular charging and connectivity, and may raise concerns with data privacy | Higher costs associated with more frequent visits, lab resources, and processing costs.
Requires greater travel/time burden and invasive sampling or radiation exposure, reducing patient comfort. |
Regulatory landscape and precedent | Ongoing growth, with variable precedents depending on device class, clinical endpoint type, and algorithm changes.
Often requires early engagement with regulatory bodies and evidence of a clear “fit-for-purpose”. | Several assays and imaging endpoints have broad regulatory precedent and approval.
Clearer pathways for qualification, labeling, and endpoint acceptance, depending on biomarker type. |
Best-suited clinical questions | Longitudinal and real-world research questions investigating patient functioning and behavior, early signal detection for diagnostic or safety purposes, symptom fluctuation, treatment response dynamics, and drug adherence. | Biological research questions investigating disease pathophysiology, therapeutic targeting, routine safety-related laboratory monitoring, diagnostic classification, and disease staging or subtyping. |
Applications of Digital Biomarkers vs. Traditional Biomarkers in Clinical Research
Traditional Biomarkers
As discussed above, traditional biomarkers are typically derived from biological samples or physiological measurements collected from patients at a single timepoint, with various applications spanning endpoints related to disease activity or progression, treatment response, safety, and more. In addition to the examples outlined in Table 1, traditional biomarkers include blood pressure, pulse rate, clinical biochemical indicators, imaging-based markers, and certain more complex laboratory tests of body fluids to evaluate various physiological systems.
More broadly, the application of traditional biomarkers in general disease diagnosis and monitoring can be categorized into the following five classes:
Antecedent biomarker types: To identify risks of disease development
Diagnostic biomarker types: To help identify any evident disorder
Screening biomarker types: To screen sub-clinical disorders
Staging biomarker types: To help categorize disease severity
Prognostic biomarker types: To help predict the prognosis or course of a disease, including its recurrence, treatment response, and monitoring of therapeutic effectiveness
Digital Biomarkers
Digital biomarkers are also extremely well suited for various applications and disease indications, particularly in therapeutic areas that heavily rely on subjective measures, such as neurology and psychiatry. In both these fields and beyond, digital outcome measurements are changing the way treatment outcomes are measured and assessed, particularly in very early stages of the disease. However, given the relative novelty of following use cases, there are few published results available, with current difficulties in assessing the overall feasibility, technology and biology underlying digital biomarkers.
For additional examples of how digital biomarkers are being applied in other disease areas, such as diabetes, cardiovascular disease, respiratory disorders, and cancer
Applications In Neurology:
Parkinson’s disease:
An app that measures active and passive Parkinson’s disease tests and assesses disease severity
Multiple sclerosis:
Digital monitoring to understand physical activity measurements
Alzheimer’s disease:
Remote digital measures of cognitive ability to assess cognition and disease progression
Applications In Psychiatry:
Bipolar disorder:
“Digital behavioral biomarkers,” such as phone usage patterns, have shown promising insights into behaviors and associated disease activity
Major depressive and bipolar disorders: Digital biomarkers used as predictive measures for the diagnosis and prognosis of symptom severity
Current Challenges of Digital and Traditional Biomarkers in Clinical Settings
Traditional Biomarkers
Although traditional biomarkers are often well embedded into clinical practice and research, offering less analytical complexity and greater biological specificity, they can be expensive and invasive to collect. For example, researchers must be mindful of the frequency of blood draws required over the duration of a clinical trial to ensure patient comfort and safety. Therefore, due to the limited number of measurements that can be collected over time, also known as the “snapshot” problem, traditional biomarkers present an incomplete view of disease activity, safety, or treatment response. This problem is particularly challenging for diseases that are complex and dynamic in nature, highlighting the need for a different type of biomarker that provides easier and cheaper access to continuous and longitudinal measurements.
Digital Biomarkers
Alternatively, digital biomarkers are usually less or non-invasive, modular, and often cheaper to measure both quantitative and qualitative data. However, because these are still fairly new and have not yet been widely implemented, limiting their regulatory precedent, there are unique challenges to consider. For example, digital biomarkers often produce large, complex data that pose difficulties for data analytics due to the noise and lack of guidance for data validation, storage, and privacy (Table 3).
Table 3: Key Challenges Currently Associated with Digital Biomarkers in Clinical Research
Challenge | Description |
Data standardization | Large number of heterogeneous data sources, formats, scales |
Data privacy: protection of user data, anonymization | Regulatory guidelines on privacy, right to opt out, informed consent, data ownership is not fully developed, continuously changing, and different between countries |
Data storage | Large volumes of complex data are being generated but there are few guidelines on how they should be stored |
Identification of relevant data and interpretation | Identification of pertinent data is important for analysis, and how to correctly analyze the data and use accurate baselines is still unclear; this process is often not transparent due to proprietary issues |
Regulatory approval: data analytics, algorithm and tool validation | Regulatory standards lag behind rapid innovation in tool, analytics, and algorithm development |
Which Biomarker is the Right Choice to Optimize Your Clinical Outcomes?
Choosing the “right” biomarker depends less on whether it is digital or traditional and more on whether it answers your clinical question of interest with an appropriate, fit-for-purpose measurement strategy. Importantly, biomarker selection should also not be determined solely by the ease and convenience of the collection method. As noted in Table 2, digital biomarkers may be better suited to help answer clinical research questions related to longitudinal and real-world patient functioning and outcomes, while traditional biomarkers are appropriate for research questions investigating disease biology or therapeutic targeting. However, the ideal approach is often to integrate both types of biomarkers to ensure optimally-designed measurement strategies that produce clearer signals and enable faster decisions with lower development risk.
This integration generally enables the use of composite endpoints in multi-modal trials, which helps produce a clearer, more complete picture of a patient’s health state than either type alone. Digital biomarkers now offer novel ways of measuring health status that were unavailable before and are complementary to biological insights from traditional biomarkers. Therefore, the combination of continuous longitudinal measurements with detailed molecular and physiological assessments of health outcomes often offers the most robust approach when collecting data to inform clinical disease diagnosis, monitoring, treatment, and clinical decision-making across trials.
Ready to choose the right biomarker strategy for your next trial?
Koneksa is a measurement-science partner supporting clinical development through biologically grounded measurement strategy and high-reliability multi-measure data collection for digital biomarkers across multiple therapeutic areas, including oncology and Parkinson's disease. If you’re weighing digital versus traditional biomarkers for an upcoming study, or another therapeutic area, connect with Koneksa to discuss your optimal measurement strategy.
Get expert input on when digital biomarkers add value and when traditional is the better approach
Plan a validation pathway that matches your intended clinical use and investigational questions
Combine composite endpoints and multi-modal data to clarify treatment effects and support more confident trial decisions
To discuss your measurement strategy, or explore our Advanced Modeling and Analysis Services to see how we can help your team build comprehensive biomarkers that deliver clearer signals in clinical trials connect with Koneksa