More than half of Cardio Adaptability users experienced an arrhythmic event
To better understand arrhythmias among Ultrahuman users, we investigated the incidence of cardio stress events and their type. We detected high cardio stress in 278 users, highlighting that 10.5% of the cohort potentially experienced atrial fibrillation at some point during the study period (Figure 1a). Our results also showed that mild cardio stress was detected in 1,415 users, indicating that 53.5% of the cohort potentially experienced other irregular heart rhythms at some point during the study period (Figure 1b). Additionally, we detected both high and mild cardio stress in 251 users, suggesting that 9.5% of the cohort potentially experienced both atrial fibrillation and other irregular heart rhythms at some point during the study period.
Obesity is a well-established risk factor for cardiac arrhythmias5; we aimed to understand the general metabolic health profiles of the Cardio Adaptability users’ cohort. Subsequently, we looked at metabolic health data, specifically BMI values of users who experienced high cardio stress (N = 278) as well as those who experienced mild cardio stress (N = 1,415). We observed that while mean BMI values were similar across users who experienced high cardio stress as well as those who experienced mild cardio stress (27.5 and 26.7, respectively), both groups were overweight on average.
Figure 1(a, b). Graphical representation of high and mild cardio stress incidence in Cardio Adaptability users over the study duration (N = 278 of 2,645 for high stress; N = 1,415 of 2,645 for mild stress).
There is no significant association between mild and high cardio stress events
We further investigated whether users reporting both high and mild cardio stress levels showed any correlation between an increase in major arrhythmic episodes and background minor ones. Our results displayed that although there was a weak positive association between mild and high cardio stress (β = 0.013), it did not meet the prescribed p<0.05 threshold (Table 1).
We also found notable variation between individuals (τ = 19.05), suggesting that the occurrence of high cardio stress events can vary significantly based on personal factors. While some users may experience more high cardio stress events with mild cardio stress, others may not see a similar increase (Table 1).
Table 1. Linear mixed model regression results depicting the relationship between mild and high cardio stress events in Cardio Adaptability users (N = 251).
Changes in Ring Air metrics precede mild cardio stress events
Four regular Ring AIR users shared their experiences of mild cardio stress during the study period, providing valuable context for interpreting the changes observed in their Ring AIR metrics 1 to 3 days before these events.
Prior to experiencing mild cardio stress, all four users’ sleep scores decreased substantially, by -13 to -55 points (Table 2b). During the same time, all users had accumulated sleep debt, ranging from +8 to +214 minutes, highlighting the potential role of sleep deprivation in irregular heart rhythms (Table 2b). Users also experienced large reductions in REM sleep duration, by -75 to -145 minutes (Table 2b). Beyond sleep metrics, we found changes in other physiological parameters; 3 out of 4 users experienced increased stress level, indicated by decreases in stress scores by -9 to -18 points (Table 2b). Additionally, all users experienced deviations from their baseline skin temperatures by -0.006 to +1.77 ºC in the days prior to their arrhythmic episodes (Table 2b).
User 1 is highly active and exercises for 1.5 hours per day, 6 days a week – regardless of whether he got sufficient sleep (Table 2a). After receiving mild cardio stress nudges on the Ultrahuman app, he underwent echocardiogram and electrocardiogram (ECG) tests. ECG results revealed a slight arrhythmia, where the user’s heart was skipping beats occasionally. His physician confirmed that this arrhythmia was not concerning and was likely due to irregular sleep patterns and work stress. Since then, User 1 has been trying to improve his sleeping habits and better manage his exercise load.
User 2 is moderately active, and after receiving mild cardio stress nudges on the Ultrahuman app, she underwent treadmill and ECG tests (Table 2a). ECG results revealed the presence of tachycardia, an arrhythmia where the user’s heart was beating at more than 100 beats per minute at rest. Her physician confirmed that this is likely due to her asthma medications, which are now being substituted for more appropriate ones.
User 3 is also moderately active and mentioned that he did not experience any work stressors, irregular sleep patterns, or sickness before his mild cardio stress event (Table 2a). However, around the same time, he had begun playing an outdoor sport in humid weather, which led to dehydration and potentially the arrhythmia. Since then, User 3 has been trying to employ better hydration practices during outdoor physical activity.
User 4 is moderately active and mentioned that she did not experience any work stressors, irregular sleep patterns, or physical exertion prior to her arrhythmic episode (Table 2a). However, around the same time, she was unwell and had contracted a viral flu. The fever and other symptoms brought on by the user’s sickness might have led to an irregular heart rhythm.
Table 2a. Demographics, activity levels, and types of cardio stress experienced by follow-up users (N = 4).
Table 2b. Changes in sleep, stress, and skin temperature metrics observed 1-3 days before arrhythmic events in follow-up users (N = 4).
Conclusions, Limitations and Future Directions
Cardiac rhythm changes during sleep are typically detected incidentally6 when a person wears an ambulatory ECG device, like a Holter monitor, which—while highly accurate—is cumbersome for long-term use. Wrist-worn devices that offer on-demand single-lead ECGs also require the user to be fully awake7. Ultrahuman Ring AIR, with its Cardio Adaptability PowerPlug, introduces a novel, passive monitoring approach, enabling the detection of cardiac strain in generally healthy individuals without disrupting sleep or daily routines.
While anecdotal evidence exists of missed beats and mild arrhythmias in otherwise healthy individuals, such reports are sporadic, and often lack accompanying data that could help doctors assess these episodes in relation to life events. Ultrahuman’s Cardio Adaptability PowerPlug addresses this gap, providing continuous insights into mild strain episodes and their probable triggers, as highlighted in this study. Our findings indicate that sporadic heart rhythms are more common than previously recognized. Over half of the study cohort experienced at least one arrhythmic event during the four-month period, and 10.5% of users showed potential indicators of high strain, such as possible atrial fibrillation. Interestingly, we identified a weak association (β=0.013) between the occurrence of AF and other arrhythmias, suggesting that these events may be driven by distinct physiological mechanisms or triggered by different factors.
The case studies of four users who experienced arrhythmic events highlighted several modifiable lifestyle factors that might precipitate irregular heart rhythms. All four users showed substantial deterioration in sleep metrics prior to their arrhythmic episodes, including decreased sleep scores, accumulated sleep debt, and reduced REM sleep. Additional triggers identified through user follow-ups included inadequate rest between intense exercise sessions, dehydration during outdoor activities, and acute illness. These findings suggest that lifestyle adjustments emphasising sleep hygiene, stress management, and proper exercise recovery could help reduce the risk of arrhythmic events in healthy individuals. Notably, in follow-up, these users did not experience repeat episodes after devoting more attention to their routines, reinforcing this point.
A limitation of our study was the small number of users who provided detailed follow-up information (N = 4), which limits the generalisability of our findings regarding specific triggers. Additionally, factors such as medication use, alcohol consumption, and caffeine intake were not tracked, which could have influenced the observed arrhythmic events4, 8. Future studies with larger follow-up cohorts, daytime measurements, and more comprehensive lifestyle tracking are underway to further explore the temporal relationship between various triggers and arrhythmic episodes.
The Ultrahuman Ring AIR, with its ability to continuously monitor multiple physiological parameters, proves to be a valuable tool for identifying potential arrhythmia risk factors in real-world conditions. Our results have important implications for preventive cardiology, suggesting that wearable devices could help users identify and modify lifestyle factors that may trigger irregular heart rhythms before they become clinically significant. This approach holds promise for advancing personalised health strategies and promoting proactive cardiovascular health.
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