As the use of smartphone sensors and wearable devices provides data on cardiovascular parameters such as HR, physicians are increasingly expected to help patients interpret the results of these readings; however, existing norms derived from controlled, clinical settings may not reflect the range of HR values occurring in real-world conditions. Our validation demonstrated that smartphone-based HR-PPG strongly correlates with HR from the gold-standard ECG. This study provides the first and largest-scale description of real-world HR values derived from smartphone HR-PPG measurements from 66,788 individuals who provided over three million data points over a 3-year period. Furthermore, we describe how demographic and medical factors affect these norms, including age, gender, race/ethnicity, anthropometric characteristics, physical activity, and disease state. These data provide reference ranges of real-world HR for patients and physicians and establish the foundation for future research, in which real-world HR might become an outcome for large-scale studies to understand the evolution of disease at an international scale.

Higher baseline HR has been shown to be an important prognostic factor, with higher HR associated with increased all-cause and cardiovascular mortality.1,2,3,4,5,10 However, previous studies have shown that HRs measured in clinical settings may not be representative of real-world HR and may be biased by the particular clinical conditions for which the ECGs were ordered.1,3 For example, a “white-coat” effect can increase HR,11 leading to false elevation. Furthermore, ambulatory, real-world HR, has been found to correlate significantly more with mortality than resting heart rate obtained in the clinical setting,12 suggesting the need to update HR norms to reflect real-world, remotely obtained values.6 The median HR-PPG of 77.6 in healthy individuals of our cohort was higher than the median HR of 68.0 bpm described by Mason et al.,3 who studied 79,743 ambulatory subjects that had a single ECG done in a clinical setting. As we averaged across multiple measurements per user (median of 60.0 measurements per user per year), our data may provide a better approximation of the average real-world HR-PPG compared with a single measurement. The NHANES study followed 20,749 Adults living in the United States and described their in-clinic resting HR over 3 years.2 Compared with this study, the corresponding levels of the 5th percentile were lower in our cohort (50–55 bpm vs 60 bpm), which demonstrates a discrepancy between HR obtained in-clinic versus in a real-world setting, whereas in clinic HR tend to be trending higher than real-world HR.2 Whereas our 95th percentile was similar among those < 40 years old (104 bpm in both cohorts), among those > 40 years old, we found a lower 95th percentile (100 bpm vs 104 bpm),2 which may be due to our repeat measurements taken outside of the clinic setting, decreasing variability, and minimizing any “white-coat” heart rate effect.11 Furthermore, we described the circadian evolution of real-world HR, which allows us to interpret these values according to the time of day.13 We observed that HR-PPG and HRV levels decline with age. Our observations suggest that the 95th percentile of real-world HR-PPG is ≤110 in individuals aged 18–45 years old, ≤100 in those aged 45–60 and ≤95 bpm in individuals >60 years old. This decrease in maximum HR-PPG and HRV as people get older is mainly owing to a sympathetic modulation decline with aging.10,14,15

A considerable number of epidemiologic studies have demonstrated a link between a higher HR and increasing burden of atherosclerosis16 and cardiovascular outcomes1,3,4,5,17,18 as well as the existence of a biological gradient between the severity of atherosclerosis and resting HR.19 Increased HR has been linked to atherosclerosis risk factors and endothelial dysfunction, plaque erosion and plaque rupture.20 Furthermore, it is acknowledged that the stress on the cardiovascular system is better investigated by real-world measurements rather than measurements obtained at rest, in a stressful clinic environment.6,12 Real-world HR is more reproducible than resting HR obtained in clinical setting.21 Therefore, it is possible that real-world measurements would better correlate than resting HR measurements with cardiovascular outcomes.12 Our study observed a higher HR-PPG for participants with hypercholesterolemia, hypertension, diabetes, MI, a prior stroke and peripheral vascular disease, all risk factors or manifestations of systemic atherosclerosis.1 Similarly, a chronic increase in sympathetic tone leading to higher HR, as was observed in our cohort, has been described in patients with COPD, sleep apnea and asthmatic patients.22,23,24,25

In our cohort, women had a higher HR than those of men by 6 bpm, which extends prior observations to the real-world setting.2,14,26,27,28,29 It has been speculated to be owing to women having, on average, smaller stroke volumes.1,26,28,30 Differences in resting HR-PPG by race has also been previously reported in smaller studies of fewer than 170 participants.22,23 Bathula et al.31 demonstrated that on average, South Asians have 5 bpm higher HR-PPG than Europeans, findings that seemed genetically driven and were not related to other risk factors. Our cohort extends prior literature, by demonstrating within a larger sample size that African Americans had the highest HR-PPG. These racial differences may be explained by distinct genetic phenotypes, leading to a different neural control of HR-PPG in African Americans compared with Non-Hispanic Whites.14 We also observed an increase in HR-PPG and a reduction of HRV with increasing BMI, where individuals with a BMI ≥ 30 had a higher HR-PPG compared with their “normal weight” counterparts. These data reveal that obesity is associated with higher HRs, suggesting that weight loss may lead to lower HR and better overall health.18,32 Large-scale epidemiological studies involving 13,761 adults, demonstrated the link between an activation of the sympathetic nervous system, increased HR, and pulse pressure and BMI.32 Furthermore, we observed a “U-shaped” relationship with BMI and HR, where both underweight and overweight participants demonstrate an increase in HR compared with their ‘normal weights’ counterparts, complementing prior findings from the literature.33 We detected a reduction in HR with height, whereas the taller the person, the lower the heart rate was, extending prior findings from the literature.34 Our large sample enabled us to describe the real-world HR-PPG distribution according to daily step count strata. We observed that individuals with a higher activity level as measured by step counts had a lower HR-PPG and a higher HRV, which is consistent with prior studies.35,36,37 We also showed that for an increase of 5000 steps, the average resting HR-PPG decreased by 1 bpm, up to ~8000 steps/day. However, step count was not a significant predictor of reduced HR-PPG after multivariable adjustment, suggesting that the benefits of increased step counts might be difficult to disentangle from the effects of age, gender or racial differences. Our findings extend prior findings by being the largest cohort of real-world HR measures to date, reinforcing the notion that individual characteristics such as age, gender, ethnicity, step counts, and BMI should be taken into account when interpreting HR values in the clinical setting. Using repeated, real-world, HR-PPG data obtained from wearables or apps data could enable physicians to provide personalized HR goals to a level that was before unattainable.9

In this study, we have shown that HR-PPG measurements are valid, and our nomograms of HR-PPG measurements obtained by patients remotely can now be interpreted by physicians, across a wide variety of patient phenotypes. These data can inform patients about physical fitness and could help providers offer counseling on lifestyle changes or provide overall encouragement and support based on these real-world HR norms.9

Our study has several important limitations. Our enrollment of individuals who downloaded the Instant Heart Rate app may be associated with higher socioeconomic status, technological awareness, and knowledge of elevated cardiovascular risk factors. Our validation cohort comprised of consecutive patients referred to the cardiovascular clinic differing from the general population, which could limit generalizability. However, our validation was purposefully designed to look at a broader spectrum of people who might use the app-based PPG for HR measurements, including more people with abnormal ECGs and cardiovascular disease in whom PPG might be expected to be less accurate. Despite this, we demonstrated a high validity of these measurements, in line with previously published literature.

The PPG in our data was obtained using a specific app and accuracy of measurement may vary based on different user interfaces to ensure adequate contact and signal processing algorithms that may occur in different PPG approaches. In addition, as users recorded HR-PPG measurements on demand, rather than being passively monitored, available HR-PPG do not reflect all possible real-world HR and our nomograms might not generalize to HR values measured passively by wearables. In addition, we did not have the context around the measurements (i.e., food intake, post exercise, palpitations, etc.), which may have influenced the HR values. For example, in the “known resting HR data set”, we observed an average HR 2.8 bpm higher than in our full HR data set. One plausible explanation for this finding is that patients might be measuring their HR at rest, while having palpitations, leading to a higher upper boundary of HR in this data set and dragging the average HR higher. However, our high number of measurements collected per user in the full HR data set, combined with our large cohort size was able to describe the variability of HR according to age, gender, race, or BMI. Although the relationship between HR-PPG and step count confirms prior literature, our absolute values of step count may be underestimated owing to non-carrying time of the smartphone.36 Therefore, our findings should be interpreted with caution, especially in those >8000 average daily step counts, which represent a very small subset of participants in our study. The Health eHeart Study population is less racially, ethnically and geographically diverse and of a higher socioeconomic status than the average United States population, so care must be taken in applying these results to other populations with different characteristics.18,33 However, our population is likely representative of participants who are most likely to use this kind of technology. Owing to the cross-sectional nature of our study design, we were unable to investigate incident disease states and its relationship with HR-PPG and this should be examined in future studies. In addition, although self-reports of medical diagnoses in the HeH study is reliable,37 it may suffer from recall bias and social desirability biases.

Using a unique, real-world cohort that is the largest of its kind, we were able to describe the distribution of real-world HR-PPG among patients by means of remotely measured, smartphone-based PPG measurements. Our findings add granularity to the distribution of HR in specific subgroups not previously described and may assist physicians to interpret remotely obtained, real-world, on-demand, HR-PPG values measured by patients across a wide variety of patient phenotypes and medical conditions.