Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability

Stone, Jason D.; Ulman, Hana K.; Tran, Kaylee; Thompson, Andrew; Halter, Manuel D.; Ramadan, Jad; Stephenson, Mark; Finomore, Victor; Galster, Scott M.; Rezai, Ali R.; Hagen, Joshua A.

doi:10.3389/fspor.2021.585870

Cited by 63 publications

(71 citation statements)

References 74 publications

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“…Finding consistent viewpoints on data streams and evaluation strategies could be beneficial since it could speed up the adoption of new technology in research. The best state-of-the-art wearable technology provides access to ANS mediated signals with an unprecedented high wearing comfort and accuracy level [ 36 , 56 , 57 ]. Wearable devices also provide long-term time series of sleep and ANS mediated signals that have not been available before [ 58 ].…”

Section: Discussionmentioning

confidence: 99%

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

Altini

Kinnunen²

2021

Sensors

111

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Consumer-grade sleep trackers represent a promising tool for large scale studies and health management. However, the potential and limitations of these devices remain less well quantified. Addressing this issue, we aim at providing a comprehensive analysis of the impact of accelerometer, autonomic nervous system (ANS)-mediated peripheral signals, and circadian features for sleep stage detection on a large dataset. Four hundred and forty nights from 106 individuals, for a total of 3444 h of combined polysomnography (PSG) and physiological data from a wearable ring, were acquired. Features were extracted to investigate the relative impact of different data streams on 2-stage (sleep and wake) and 4-stage classification accuracy (light NREM sleep, deep NREM sleep, REM sleep, and wake). Machine learning models were evaluated using a 5-fold cross-validation and a standardized framework for sleep stage classification assessment. Accuracy for 2-stage detection (sleep, wake) was 94% for a simple accelerometer-based model and 96% for a full model that included ANS-derived and circadian features. Accuracy for 4-stage detection was 57% for the accelerometer-based model and 79% when including ANS-derived and circadian features. Combining the compact form factor of a finger ring, multidimensional biometric sensory streams, and machine learning, high accuracy wake-sleep detection and sleep staging can be accomplished.

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Section: Discussionmentioning

confidence: 99%

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

Altini

Kinnunen²

2021

Sensors

111

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“…Resting heart rate and HRV were collected daily using the validated HRV4Training app [ 25 , 26 , 27 ]. The HRV4Training app is a commercially available mobile phone app that allows for non-invasive measurements of resting heart rate and HRV using either the phone camera or an external sensor (see Figure 1 ).…”

Section: Methodsmentioning

confidence: 99%

What Is behind Changes in Resting Heart Rate and Heart Rate Variability? A Large-Scale Analysis of Longitudinal Measurements Acquired in Free-Living

Altini

Plews

2021

Sensors

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The aim of this study was to investigate the relationship between heart rate and heart rate variability (HRV) with respect to individual characteristics and acute stressors. In particular, the relationship between heart rate, HRV, age, sex, body mass index (BMI), and physical activity level was analyzed cross-sectionally in a large sample of 28,175 individuals. Additionally, the change in heart rate and HRV in response to common acute stressors such as training of different intensities, alcohol intake, the menstrual cycle, and sickness was analyzed longitudinally. Acute stressors were analyzed over a period of 5 years for a total of 9 million measurements (320±374 measurements per person). HRV at the population level reduced with age (p < 0.05, r = −0.35, effect size = moderate) and was weakly associated with physical activity level (p < 0.05, r = 0.21, effect size = small) and not associated with sex (p = 0.35, d = 0.02, effect size = negligible). Heart rate was moderately associated with physical activity level (p < 0.05, r = 0.30, effect size = moderate) and sex (p < 0.05, d = 0.63, effect size = moderate) but not with age (p = 0.35, r = −0.01). Similar relationships between BMI, resting heart rate (p < 0.05, r = 0.19, effect size = small), and HRV (p < 0.05, r = −0.10, effect size = small) are shown. In response to acute stressors, we report a 4.6% change in HRV (p < 0.05, d = 0.36, effect size = small) and a 1.3% change in heart rate (p < 0.05, d = 0.38, effect size = small) in response to training, a 6% increase in heart rate (p < 0.05, d = 0.97, effect size = large) and a 12% reduction in HRV (p < 0.05, d = 0.55, effect size = moderate) after high alcohol intake, a 1.6% change in heart rate (p < 0.05, d = 1.41, effect size = large) and a 3.2% change in HRV (p < 0.05, d = 0.80, effect size = large) between the follicular and luteal phases of the menstrual cycle, and a 6% increase in heart rate (p < 0.05, d = 0.97, effect size = large) and 10% reduction in HRV (p < 0.05, d = 0.47, effect size = moderate) during sickness. Acute stressors analysis revealed how HRV is a more sensitive but not specific marker of stress. In conclusion, a short resting heart rate and HRV measurement upon waking using a smartphone app can effectively be used in free-living to quantify individual stress responses across a large range of individuals and stressors.

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“…We ran the HRV4training TM to detect variations in HRV. The application is designed based on photoplethysmography (PPG) and has been validated and applied in studies to investigate physiological response [ 28 , 29 , 30 ]. The output data include the stand deviation of normal-to-normal intervals (SDNN) and the root mean square of successive R-R intervals (RMSSD) calculated based on a 60 s time frame.…”

Section: Methodsmentioning

confidence: 99%

Can Residential Greenspace Exposure Improve Pain Experience? A Comparison between Physical Visit and Image Viewing

Zhang

et al. 2021

Healthcare

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Reducing the burden of pain via greenspace exposure is a rising research topic. However, insufficient evidence has been found in relation to the environmental effect itself. Residential greenspace, as a convenient but limited natural environment for urban dwellers, has benefits and services yet to be discovered. Therefore, the current study recruited 24 young adults to evaluate the effects of physical visit to, or image viewing of, residential greenspace on pain perception and related psychophysiological outcomes, via simulated pain. Pain threshold and tolerance were recorded via the level of pain stimuli, and pain intensity was evaluated using the Visual Analog Scale (VAS). The state scale of the State–Trait Anxiety Inventory (STAI-S) and two adjective pairs were employed to measure the state anxiety and subjective stress, respectively. Meanwhile, heart rate (HR), heart rate variability (HRV), and blood pressure (BP) were measured to investigate physiological responses. Besides, Scenic Beauty Estimation (SBE) was also employed to assess participants’ preference regarding the experimental environments. The results revealed that visiting the greenspace significantly increased the pain threshold and tolerance, while no significant effect was observed for image viewing. On the other hand, no significant difference was observed in pain-related psychophysiological indices between the experimental settings, but significantly negative associations were found between the scores of SBE and subjective stress and state anxiety. In conclusion, the current study brings experimental evidence of improving pain experience via residential greenspace exposure, while the related psychophysiological benefits require further investigation.

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Assessing the Accuracy of Popular Commercial Technologies That Measure Resting Heart Rate and Heart Rate Variability

Cited by 63 publications

References 74 publications

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

The Promise of Sleep: A Multi-Sensor Approach for Accurate Sleep Stage Detection Using the Oura Ring

What Is behind Changes in Resting Heart Rate and Heart Rate Variability? A Large-Scale Analysis of Longitudinal Measurements Acquired in Free-Living

Can Residential Greenspace Exposure Improve Pain Experience? A Comparison between Physical Visit and Image Viewing

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