Resting Heart Rate Reshapes Underwriting

Resting heart rate emerges as a powerful, underused mortality predictor that may outperform traditional underwriting metrics like cholesterol and BMI.

Kishan Bakrania

July 21, 2026

In an era defined by expanding data streams and increasingly sophisticated underwriting tools, it is often assumed that better decisions require more complex inputs. However, recent RGA research challenges that assumption by highlighting the value of a familiar, easily measured metric: resting heart rate.

Resting heart rate (RHR) – the number of heartbeats per minute when an individual is at rest – provides a real-time snapshot of cardiovascular efficiency and overall physiological health.

Despite its simplicity, RHR has historically been underused in underwriting frameworks. That gap is now narrowing.

A growing body of evidence points to RHR as a strong independent predictor of all-cause mortality. Recent research has gone so far as to describe it as a "forgotten risk factor," with findings indicating that elevated RHR can outperform traditional measures, such as hypertension, in predicting mortality risk.

Evidence that challenges underwriting conventions

RGA's research reinforces these findings with compelling consistency across populations and datasets. Analysis of UK Biobank data shows a clear and measurable relationship between RHR and mortality outcomes. For example, individuals with higher resting heart rates face significantly elevated mortality risk compared with peers with lower rates, even after adjusting for conventional underwriting factors.

What makes this relationship particularly relevant to insurers is its stability across different segments. The predictive power of RHR holds across:

Age groups
Sexes
Standard and substandard risk classes
Individuals with chronic conditions

This consistency positions RHR as more than a supplementary data point. It is a robust, independent factor that captures dimensions of risk not fully reflected in traditional metrics.

In fact, RGA findings suggest that RHR can outperform total cholesterol as a predictor of mortality and may substitute for BMI without meaningful loss of predictive accuracy. These findings challenge long-standing underwriting hierarchies and open the door to recalibrated risk models.

Why RHR captures what traditional metrics may miss

From a physiological perspective, RHR acts as a proxy for several underlying health dimensions, including cardiovascular fitness, autonomic nervous system function, and overall metabolic health. These attributes are not always fully captured by standard underwriting variables.

As a result, RHR provides incremental insight, helping underwriters identify hidden or emerging risks that might otherwise remain undetected.

Equally important is its practicality. RHR is:

Non-invasive and easy to measure
Routinely recorded in electronic health records
Available through medical exams and ECGs
Increasingly accessible through wearable devices and smartphones

Advances in photoplethysmography technology have further supported its reliability, demonstrating strong agreement with clinical-grade measurements.

Together, these characteristics make RHR predictive and operationally viable at scale.

From research to real-world application

The shift from academic insight to underwriting practice is already underway. As insurers integrate digital health data into workflows, attention is shifting toward maximizing the value of existing information rather than simply expanding data volume.

Within this context, RHR offers a compelling use case. RGA has begun incorporating RHR into underwriting decisions, including its integration into a facultative underwriting platform designed for complex cases.

This integration enables underwriters to apply evidence-based insights in real time, enhancing consistency and decision quality without introducing additional complexity.

Practical applications of RHR in underwriting include:

Refining preferred versus standard classifications
Identifying favorable risk within traditionally substandard cases
Supporting more proportionate evidence requirements

For example, an applicant flagged as higher risk based on conventional metrics may demonstrate a more favorable RHR profile, enabling a more nuanced – and potentially more competitive – offer.

Strategic implications for insurers

The emergence of RHR as a key underwriting variable has broader implications at the enterprise level.

It reinforces the importance of reexamining existing data. In many cases, valuable signals are already available but underused. Unlocking their potential can lead to meaningful improvements in risk selection without significant operational investment.
It highlights the growing convergence between underwriting and digital health ecosystems. As wearable devices and remote monitoring tools become more prevalent, the availability of continuous biometric data will only increase. RHR is well positioned to serve as a foundational metric in this evolving landscape.
It underscores the need for adaptive underwriting frameworks. Static guidelines may fail to capture emerging sources of insight. Incorporating dynamic, evidence-based metrics such as RHR can help ensure that underwriting remains aligned with real-world risk.

Conclusion: Keeping the industry's finger on the pulse

The case for integrating resting heart rate into underwriting is no longer theoretical. The evidence is robust, the data is accessible, and the operational pathways are clear.

As underwriting continues to evolve, the ability to extract deeper insight from simple metrics will become increasingly valuable. RHR exemplifies this shift, offering a blend of predictive strength, practical accessibility, and strategic relevance.

For insurers, the opportunity is twofold: improve mortality risk assessment while enhancing efficiency and customer experience.

By leveraging RHR more effectively, organizations can move toward more precise pricing, better risk differentiation, and ultimately stronger portfolio performance.