This paper is the second in our series examining the potential of third-party data sources to enhance life insurance underwriting.

Background and objective

This publication is a continuation of the collaboration between Klarity, a UK-based health data analytics firm, and Munich Re Life US to analyze the UK Biobank data.¹ While the first whitepaper in this series examined activity data attributes in the Biobank dataset, this second paper shifts the focus to other novel variables in the dataset that are not typically considered in life insurance underwriting but provide evidence of protective value. We found evidence that resting heart rate, sleep duration, and grip strength are effective in stratifying mortality risk in a simulated insurable population. 

This study examines the UK Biobank data to accomplish the following objectives: 

• Compare findings to previous Munich Re studies on resting heart rate (RHR) and sleep duration for mortality risk segmentation. 

• Assess a different dimension of health not typically used in underwriting, muscular strength, by studying the relationship between dominant hand grip strength and mortality risk.  

Like the earlier publications in this series, our analysis in this study focuses on the UK Biobank data that Klarity extracted. Variables relevant to this study include: 

• Demographic information: age, gender, income, index of multiple deprivation (IMD, a UK measurement of poverty and access to services within a small geographic area²)  
• Physical measurements/vitals: weight, height, waist circumference, hip circumference, BMI, blood pressure, resting heart rate 
• Self-disclosed lifestyle attributes: smoking, alcohol use, sleep  

Note that some variables, such as grip strength, were recorded in a well-controlled setting that may not be easily replicable in a life insurance underwriting setting. Also, the Biobank study participants were not underwritten for life insurance, so any anti-selection associated with applying for life insurance was not relevant in this setting.

Like the previous studies, the analysis and results discussed onwards are based on the filtered “insurable” block of individuals, unless stated otherwise. 

We confirm that sleep duration and resting heart rate are significant predictors of mortality, consistent with findings from past Munich Re Life US studies. Optimal sleep duration of seven hours is associated with the lowest mortality risk, while shorter sleep durations, particularly five or fewer hours, show a sharp increase in mortality risk. Resting heart rate analysis reveals that lower heart rates, often indicative of better cardiovascular fitness, correspond to reduced mortality risk. In addition, we find compelling evidence that low grip strength is indicative of adverse mortality for older age groups.

However, it is important to note that some attributes, such as grip strength, require controlled conditions for accurate measurement, making their widespread use in life insurance underwriting more challenging. Even so, these findings highlight the potential for carriers to enhance their underwriting processes by incorporating such novel measures. On the other hand, resting heart rate and sleep duration are often recorded by wearable devices, enabling easier and more scalable implementation in a life insurance context. The increasing prevalence of wearable technology provides an opportunity for carriers to access real-time, continuous data on these attributes, further enhancing their predictive power for mortality risk.