Digital Biomarkers: Vaccine Reactogenicity with Wearables

Vaccines are one of the most effective tools in modern medicine, but individuals often experience a wide spectrum of immune responses, ranging from mild discomfort to severe short-term symptoms. This variability, known as vaccine-associated reactogenicity, is critical to understand and manage, especially during rapid public health rollouts. In this evolving landscape, digital biomarkers, measurable physiological and behavioral data collected through digital tools, are emerging as powerful tools for personalized monitoring. By leveraging wearable sensors and digital twin models, researchers and healthcare providers can better predict, detect, and manage individual responses to vaccines. Thryve Health plays a pivotal role in this ecosystem by enabling seamless integration and harmonization of diverse health data streams from consumer and clinical-grade wearables.

This blog post explores the key components involved in building personalized digital biomarkers for vaccine reactogenicity. We cover how wearable devices capture real-time physiological changes, how digital twins model individual health baselines and simulate immune responses, and how multimodal data fusion drives the development of accurate algorithms. This all goes along with Thryve’s robust data infrastructure and the ethical considerations that guide responsible innovation in this space.

The Role of Wearable Sensors in Vaccine Monitoring

Wearables have transitioned from lifestyle gadgets to sophisticated health monitoring tools. In the context of vaccine reactogenicity, they provide continuous streams of key biometric data such as:

• Heart Rate and Heart Rate Variability (HRV)
• Skin Temperature
• Activity and Movement Patterns
• Sleep Quality and Duration

These variables offer quick, granular insights into physiological changes following vaccination. Elevated skin temperature can signal systemic inflammation; changes in heart rate or HRV can indicate immune stress; and reduced activity or disrupted sleep patterns often reflect a temporary decline in well-being. Capturing these subtle deviations is critical to identifying reactogenic events early.

Devices like the Oura Ring, Apple Watch, WHOOP, and Garmin wearables have proven effective in both clinical and consumer settings. They continuously monitor health signals that would otherwise be missed between clinic visits, enabling researchers and providers to track vaccine responses in near real time and at a population scale.

Building Personalized Models with Digital Twins

A digital twin is a sophisticated, real-time computational model that mirrors the physiological state of a person. These models are constructed using a blend of longitudinal biometric data, individual baseline readings, behavioral patterns, and contextual factors such as age, health history, and environmental exposures. In the context of vaccine monitoring, digital twins act as dynamic simulations that help anticipate how a person’s body will respond to immunization by continuously comparing live wearable sensor data with personalized and population-level benchmarks.

These models are valuable for a range of clinical and operational use cases:

• Predicting individual-level reactogenicity risks by identifying patterns in heart rate variability, skin temperature, and other physiological signals that deviate from personal norms
• Tailoring vaccine schedules and dosage intervals for individuals whose digital twins suggest heightened sensitivity or slower recovery trajectories
• Simulating outcomes for different vaccine formulations or booster strategies, aiding both personalized care and population-level health planning

As machine learning algorithms become more refined and capable of handling noisy, real-world health data, digital twins are evolving into continuously learning systems. They adapt based on incoming sensor data, enabling precision modeling that improves over time and supports more nuanced, individualized medical decisions.

Multimodal Data Fusion: Beyond Single Metrics

Effective digital biomarker development hinges on multimodal data fusion—the integration of multiple sensor-derived features into a cohesive model that can reliably capture physiological responses to vaccination. Unlike single-signal approaches, multimodal fusion leverages the interplay between different types of data to create a more holistic and nuanced picture of how the body is reacting.

This involves:

• Merging HRV, temperature, and activity data to extract composite signals that reflect systemic stress or inflammation
• Applying signal processing and filtering techniques to reduce noise, account for sensor-specific variability, and enhance signal clarity
• Detecting pattern deviations from pre-vaccine baselines by comparing longitudinal data to personal and cohort-specific norms

By aligning and synchronizing data streams across time, device, and context, researchers can isolate subtle but significant changes that may indicate an immune response or potential adverse effect. These enriched, cross-referenced datasets form the foundation for building robust training inputs for machine learning models tasked with predicting and monitoring vaccine-related reactogenicity.

From Signal to Biomarker: Developing the Algorithm

To operationalize digital biomarkers for vaccine reactogenicity, developers must transition from raw sensor outputs to validated, deployable algorithms that meet both clinical and technical performance standards. This requires not only high-quality data inputs but also outlined modeling and evaluation frameworks tailored to biomedical applications.

This process typically involves:

• Training ML models on retrospective clinical trial datasets enriched with labeled outcomes such as fever, fatigue, or heart rate spikes post-vaccination. These datasets must be representative across demographics and device types.
• Extracting features that are physiologically meaningful, such as circadian heart rate variation, thermoregulatory patterns, or deviations in step count and sleep efficiency, to serve as proxies for inflammation or systemic stress.
• Evaluating model sensitivity and specificity using gold-standard clinical measures and reference annotations. This includes tuning thresholds for early warning alerts while minimizing false positives to prevent unnecessary interventions.

Once approved through cross-validation and prospective trial data, these algorithms can be integrated into digital health apps, wearable platforms, and clinical dashboards. This enables real-time tracking of vaccine-related physiological responses and provides clinicians with actionable insights to support post-vaccination care and safety monitoring at scale.

Applications in Personalized Vaccination and Remote Monitoring

The true potential of digital biomarkers lies in their real-world applications, where passive data collection meets personalized clinical insight. We have already covered how useful passive data can be for health organisations. However, now we are emphasizing that these tools allow for more accurate and timely responses to vaccination by analyzing physiological signals in context and in real time. Rather than relying solely on scheduled check-ins or self-reported symptoms, healthcare providers can use continuous digital biomarkers to detect subtle signs of immune reactivity, fatigue, or inflammation well before a patient recognizes them.

Personalized digital biomarkers can inform:

• Tailored vaccine dosing intervals, adapting timing based on each individual’s baseline health data, rate of recovery, and biometric response to prior doses
• Remote patient monitoring protocols that alert clinicians to abnormal patterns, enabling early interventions before symptoms escalate
• Automated clinical alerts for physiological anomalies such as prolonged fever, irregular heart rate, or deteriorating sleep, facilitating timely follow-up

This is particularly valuable for high-risk populations, such as the elderly, immunocompromised, or individuals with chronic conditions, where undetected complications could escalate rapidly. These capabilities not only improve patient safety but also optimize resource allocation in telehealth and public health settings.

Enabling Public Health Insights and Trial Innovation

Beyond individual monitoring, aggregated digital biomarker data holds immense potential for reshaping public health strategies and population-level vaccine planning. By compiling anonymized physiological data across diverse cohorts, health authorities and researchers can generate highly granular insights into how various demographics respond to specific vaccine types, dosing intervals, and booster regimens.

Key benefits include:

• Tracking population-level reactogenicity trends over time, segmented by age, sex, comorbidity profiles, and even regional factors, to better understand variability in vaccine responses
• Comparing vaccine tolerability across demographics, identifying patterns of adverse event likelihood that inform targeted education, outreach, and clinical protocols
• Informing booster rollout strategies and communication campaigns by quantifying the window of reduced reactogenicity and tailoring messaging to minimize hesitancy and maximize compliance

Moreover, integrating wearable sensor data into clinical trial designs transforms how studies are conducted. For example, a couple of posts ago, we deep dived into how wearables transform STI Diagnostics. It was another proof that continuous biometric monitoring supports more objective and scalable endpoints, accelerates participant recruitment by reducing in-clinic burden, and enhances trial quality by minimizing self-report bias. This approach enables sponsors to capture more meaningful outcomes and supports regulatory decision-making with higher fidelity real-world evidence.

Ethical Considerations: Privacy, Bias, and Equity

As digital biomarkers and wearable-driven monitoring solutions become more prevalent in vaccine research and clinical care, the importance of strong ethical governance cannot be overstated. Building trust with patients and users depends on robust privacy protections, transparent communication, and equitable data practices.

• User consent and data transparency must be built into every stage of the digital biomarker lifecycle—from data capture and transmission to algorithm training and output. Users should have access to clear, accessible information about what data is collected, how it is used, and their rights to opt out or control usage.
• GDPR and HIPAA-aligned data handling is essential to ensuring compliance with international data privacy standards. This includes encryption during transmission, secure data storage, access controls, and audit trails to prevent misuse and maintain data integrity.
• Algorithmic fairness must be continuously assessed to ensure digital twin models do not replicate or amplify systemic health disparities. Inputs like race, age, gender, and socioeconomic status must be evaluated for bias, and training datasets should be diverse and representative to avoid marginalizing underrepresented populations.

Furthermore, inclusive design and accessibility should be embedded in every stage of product development. This means ensuring that digital biomarkers are not only accurate across diverse physiological profiles but also usable by individuals with varying levels of digital literacy, connectivity, and device access. Ethical innovation demands that the benefits of wearable-driven healthcare extend to all communities, not just the digitally privileged.

The Future of Vaccine Monitoring is Personalized

Digital biomarkers offer a powerful opportunity to reimagine vaccine monitoring—from reactive reporting to proactive, individualized care. With wearable sensors capturing real-time physiology and digital twins contextualizing those signals, healthcare stakeholders can optimize outcomes for both individuals and populations. Thryve stands at the forefront of this transformation, equipping healthcare innovators with the tools to unify wearable data, build robust digital biomarkers, and scale insights into clinical and public health practice. With us, you benefit from:

• Seamless Device Integration: Easily connect Oura Ring and over 500 other health monitoring devices to your platform via a single API, eliminating the need for multiple integrations.
• Standardized Biometric Models: Automatically harmonize biometric data streams, including heart rate, sleep metrics, skin temperature, activity levels, and HRV, making the data actionable and consistent across devices.
• GDPR-Compliant Infrastructure: Ensure full compliance with international privacy and security standards, including GDPR and HIPAA. All data is securely encrypted and managed according to the highest privacy requirements.
• Customizable Dashboards and Alerts: Create tailored dashboards for healthcare providers and patients to visualize pregnancy-related trends, receive real-time alerts for abnormal patterns, and track recovery and readiness metrics post-delivery.

With Thryve, vaccine monitoring organizations and clinical trial sponsors can launch digital biomarker programs without the overhead of building custom integrations.

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