Multiple studies encompassing over 1mm individuals have concluded that resting heart rate is an independent predictor of all-cause and cardiovascular mortality in the general population. The effectiveness of resting heart rate in stratifying mortality is referenced numerous times in research. As consumer expectations and the current market landscape call for the use of alternative data sources in risk selection, information captured through wearables may present a solution for insurance carriers looking for new opportunities to segment health and mortality outcomes. Through a series of papers, Munich Re has concluded that physical activity in the form of steps, hours of sleep, and resting heart rate effectively segment mortality, particularly when used in combination. Mortality segmentation by resting heart rate follows a characteristic J-curve where abnormally low resting heart rates present elevated mortality risk, but the highest mortality risk occurs in individuals with abnormally high resting heart rate. Resting heart rate effectively segments mortality across age, gender and BMI, even when factoring in number of steps and hours slept. The overall A/E using the HMD basis is 73%, consistent with expectations that an insurable population is healthier than the general population. population mortality split by age, gender and calendar year. Life Tables without any mortality improvement, which represents U.S. To confirm the reasonability of the filters applied to identify insurable individuals in the survey dataset, we also applied the expected mortality basis taken from the Human Mortality Database (HMD) U.S. 2015 VBT actual-to-expected ratios are presented relative to a 100% fit for the entire simulated population. The expected mortality basis shown in the following plots was taken from the 2015 Valuation Basic Table (VBT) primary select and ultimate Age Last Birthday (ALB) tables split by age, gender and smoker status, without any mortality improvement. We performed a classical actuarial actual-to-expected (A/E) mortality analysis on the simulated insurable population dataset. population at the midpoint of the survey period. Each life is weighted by the interview weights provided by NHANES so that the final dataset is roughly equivalent to an insured population drawn from the U.S. The simulated insurable population consists of 26,406 lives and 1,672 deaths. 4,5 Munich Re concludes that physical activity in the form of steps, hours of sleep, and resting heart rate effectively segment mortality, particularly when used in combination. We see this as a companion piece to our prior papers which explore the potential of using information captured by wearables in the life insurance process. insured population simulated from National Center for Health Statistics (NCHS) survey data. In this study, we assess the effectiveness of resting heart rate in stratifying the mortality risk profile of a U.S. Wearable technologies today commonly track steps, sleep and heart rate, and have expanded to incorporate information like electrocardiogram, breathing rate, and breathing volume. The growth of wearables and their user base opens opportunities to understand how wearable measurements relate to health and mortality outcomes. 1 In 2019, 1 in every 5 American adults were already regularly using a wearable. Wearables have consistently placed as the No. With the largest technology companies including Amazon, Google, Samsung, and Apple all offering wearables in the market, it is clear that wearables continue to headline in the health technology space.
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