- Physical activity data from wearables
- Sleep duration, resting heart rate and grip strength
- Physical measurement alternatives to BMI, including waist-to-height ratio
The first paper examines physical activity data from wearables:
- Step count: Individuals walking at least 7,000 steps per day experience significantly lower mortality risk, regardless of BMI, age, or smoking status:
- Smokers with a daily step count of at least 7,000 have better mortality than non-smokers with fewer than 5,000 daily steps.
- Obese participants with at least 7,000 daily steps have 40% lower mortality than “normal” BMI participants with a daily step count below 5,000.
- Pre-diabetics with at least 7,000 daily steps have a 60% lower mortality risk than individuals with normal A1C who walk fewer than 5,000 daily steps.
- Other activity: Even modest increases in light-intensity activities such as household chores or walking deliver significant benefits, particularly for older adults:
- For individuals 60 and older, going from less than 3 hours to at least 4 hours of daily light physical activity results in a 50% reduction in mortality risk.
Paper two focuses on novel attributes for segmenting mortality risk including sleep duration, resting heart rate and grip strength:
- Sleep: Seven hours of sleep per night is associated with the lowest mortality risk, while five hours or less increases mortality by 50%, underscoring the critical importance of sufficient sleep.
- Heart Rate: Lower resting heart rates (RHR) correspond to reduced mortality risk. Notably, those in the lowest RHR group also had the highest average daily activity levels.
- Grip strength: As an indicator of overall physical strength and health, grip strength can effectively segment mortality risk across age and gender.
The third paper goes beyond BMI to assess mortality risk:
- Waist-to-height ratio: There is strong evidence that waist-to-height ratio can effectively segment mortality risk on top of BMI and can be used to identify high-risk individuals across all BMI categories. This is also true for other measurements such as waist-to-hip and waist circumference. Within the ‘healthy’ BMI range (18.5-25), individuals with a waist-to-height ratio of 0.6+ face nearly double the mortality risk of those with a ratio under 0.5.
“Through the combination of Klarity’s predictive models and the ubiquity of smartphones and wearable devices, insurers are now able to better stratify risk and enhance underwriting precision,” said Will Cooper, Founder and CEO of Klarity. “Our models are trained on a wide array of health data sources that go beyond conventional metrics, enabling us to integrate new data streams alongside cutting-edge predictive analytics, resulting in enhanced precision.”
“This research demonstrates the potential of using data from wearables to segment risk and potentially expand insurability,” said Dr. Gina Guzman, Chief Medical Officer at Munich Re Life US. “Wearable data can be a window into the real-time health and lifestyle habits of applicants and may allow insurers to create more accurate and inclusive underwriting while simultaneously encouraging healthier behaviors among policyholders.”
Bridging Innovation and Consumer Trust
Klarity’s AI-powered models offer transparent, explainable insights that empower both insurers and policyholders to understand the factors driving mortality risk predictions.
“The reality is, consumers already choose to share their wearable data with apps and fitness platforms every day,” said Cooper. “Our goal is to help insurers use this data responsibly—offering more accurate underwriting that reflects real-world health habits. Doing so will open up new opportunities to engage customers, refine risk segmentation, and expand access to life insurance for millions of people.”
The Future of Wearable Data in Life Insurance
As insurers navigate the next evolution of underwriting, Klarity and Munich Re’s collaboration provides a blueprint for leveraging AI and wearable data at scale. Factors like physical activity, sleep, resting heart rate, grip strength and waist-to-height ratio can further improve the predictive accuracy of the mortality model for assessing long-term health risks.
“Carriers looking to incorporate third-party or wearables data will first want to conduct a pilot to assess participation rates and estimate baseline activity patterns,” added Guzman. “We aim to provide tailored support and guidance to carriers considering using these new data sources in their underwriting programs.”
Read the papers here. Media inquiries can be directed to [email protected] and [email protected].
About Klarity
Klarity is a UK-based predictive analytics company providing AI-driven solutions for mortality and morbidity risk prediction. Its proprietary models leverage advanced data sources—including wearables, electronic health records, and biomarkers—to help insurers improve risk stratification, expand insurability, and develop next-generation underwriting models. Learn more at klarity.health.
About Munich Re Life US
Munich Re Life US, a subsidiary of Munich Re Group, is a leading US reinsurer with a significant market presence and extensive technical depth in all areas of life and disability reinsurance. Beyond its vast reinsurance capacity and unrivaled risk expertise, the company is recognized as an innovator in digital transformation and aims to guide carriers through the changing industry landscape with dynamic solutions insightfully designed to grow and support their business. Learn more at https://www.munichre.com/us-life/en.html.
Media Contact
Rachel Van Dolsen, Klarity, 1 9142604636, [email protected]
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