Accuracy Assessment of Oura Ring Nocturnal Heart Rate and Heart Rate Variability in Comparison With Electrocardiography in Time and Frequency Domains: Comprehensive Analysis

Abstract

Background Photoplethysmography is a noninvasive and low-cost method to remotely and continuously track vital signs. The Oura Ring is a compact photoplethysmography-based smart ring, which has recently drawn attention to remote health monitoring and wellness applications. The ring is used to acquire nocturnal heart rate (HR) and HR variability (HRV) parameters ubiquitously. However, these parameters are highly susceptible to motion artifacts and environmental noise. Therefore, a validity assessment of the parameters is required in everyday settings. Objective This study aims to evaluate the accuracy of HR and time domain and frequency domain HRV parameters collected by the Oura Ring against a medical grade chest electrocardiogram monitor. Methods We conducted overnight home-based monitoring using an Oura Ring and a Shimmer3 electrocardiogram device. The nocturnal HR and HRV parameters of 35 healthy individuals were collected and assessed. We evaluated the parameters within 2 tests, that is, values collected from 5-minute recordings (ie, short-term HRV analysis) and the average values per night sleep. A linear regression method, the Pearson correlation coefficient, and the Bland–Altman plot were used to compare the measurements of the 2 devices. Results Our findings showed low mean biases of the HR and HRV parameters collected by the Oura Ring in both the 5-minute and average-per-night tests. In the 5-minute test, the error variances of the parameters were different. The parameters provided by the Oura Ring dashboard (ie, HR and root mean square of successive differences [RMSSD]) showed relatively low error variance compared with the HRV parameters extracted from the normal interbeat interval signals. The Pearson correlation coefficient tests (P<.001) indicated that HR, RMSSD, average of normal heart beat intervals (AVNN), and percentage of successive normal beat-to-beat intervals that differ by more than 50 ms (pNN50) had high positive correlations with the baseline values; SD of normal beat-to-beat intervals (SDNN) and high frequency (HF) had moderate positive correlations, and low frequency (LF) and LF:HF ratio had low positive correlations. The HR, RMSSD, AVNN, and pNN50 had narrow 95% CIs; however, SDNN, LF, HF, and LF:HF ratio had relatively wider 95% CIs. In contrast, the average-per-night test showed that the HR, RMSSD, SDNN, AVNN, pNN50, LF, and HF had high positive relationships (P<.001), and the LF:HF ratio had a moderate positive relationship (P<.001). The average-per-night test also indicated considerably lower error variances than the 5-minute test for the parameters. Conclusions The Oura Ring could accurately measure nocturnal HR and RMSSD in both the 5-minute and average-per-night tests. It provided acceptable nocturnal AVNN, pNN50, HF, and SDNN accuracy in the average-per-night test but not in the 5-minute test. In contrast, the LF and LF:HF ratio of the ring had high error rates in both tests.

Prenatal stress assessment using heart rate variability and salivary cortisol: A machine learning-based approach

“…These different modes of activation may contribute to the observed differences in fluctuations of these bio signals under stress. HR and HRV parameters can be measured easily using a chest band, a smartwatch, or a ring [28] in everyday life or in laboratory settings making it a feasible approach for most research studies. However, our results suggest that among pregnant women, HRV parameters alone may not be adequate as an acute stress assessment tool but may benefit from other bio signals to help distinguish between stress and non-stress states more accurately.…”

“…Further details about the heart rate and HRV features calculation have been previously described [4]. The abnormal IBI and HRV values generated by motion artifacts were removed before proceeding with the analysis according to the removal criteria described in another study utilizing HRV measures [28]. The removal criteria are based on the normal range of HR and IBI values.…”

Wearable-Measured Sleep and Resting Heart Rate Variability as an Outcome of and Predictor for Subjective Stress Measures: A Multiple N-of-1 Observational Study

“…The consumer-available ring contains 2 infrared Light-Emitting Diode (LED) sensors, 2 Negative Temperature Coefficient (NTC) thermistor sensors, a tri-axial accelerometer, and a gyroscope. Although the algorithms that are used by the Oura ring to classify sleep and HRV based on the outputs of these sensors are proprietary, the ring (generation 2) has been confirmed to provide valid measurements of TST [ 48 , 49 ] and HRV [ 25 , 50 , 51 ] in independent research. Participants used a ring-size kit to determine their correct ring size to optimize fit for both user comfort and measurement accuracy and were allowed to choose a ring color of their preference.…”

“Leave your smartphone out of bed”: quantitative analysis of smartphone use effect on sleep quality

“…Validation studies indicated generally good accuracy of sleep-wake detection for the Oura ring, with small absolute error for total sleep time estimation compared to polysomnography (PSG; 87.8% of nights within 30-min error) [63], ambulatory electroencephalography (7.39% mean absolute percentage error) [64], and 94% accuracy for 2-stage detection (sleep, wake) compared to polysomnography for a simple accelerometerbased model and 96% for a full model using machine learning algorithm [62]. It could accurately measure nocturnal heart rate (HR) and average heart rate variability (RMSSD) in both the 5-min and average-per-night tests compared to an electrocardiogram device [65]. It also outperforms other wearable devices such as smartwatches [66].…”