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Following the heart: what does variation of resting heart rate tell about us as individuals and as a population

  • Oura Health
  • Hannu Kinnunen Innovation Oy

Abstract and Figures

Resting heart rate (RHR) and heart rate variability (HRV) reflect the autonomic control of cardiac chronotropic activity, and they associate with cardiovascular fitness, acute and chronic health status, and mental stress. Relatively low RHR and relatively high HRV are generally seen as marks of better health, performance, and recovery levels. Nevertheless, the values are highly individual and comparison between individuals is not straightforward. On the other hand, evolution of wearable devices has made it possible to follow the course of individual RHR and HRV as long-term time series, which in turn enables observation of how behavioral, societal and seasonal factors affect RHR and HRV at individual and population scale. In this article, data measured by the Oura ring is used to study how alcohol and training affect these values, and moreover, how societal and seasonal factors affect us as a population.
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Following the Heart - What Does Variation of Resting Heart Rate
Tell about Us as Individuals and as a Population
Heli Koskimäki
Hannu Kinnunen
Oura Health
Oulu, Finland
Salla Rönkä
Oulu, Finland
Benjamin Smarr
University of California, Department
of Psychology & Oura Health
Berkeley, CA, USA
Resting heart rate (RHR) and heart rate variability (HRV) reect
the autonomic control of cardiac chronotropic activity, and they as-
sociate with cardiovascular tness, acute and chronic health status,
and mental stress. Relatively low RHR and relatively high HRV are
generally seen as marks of better health, performance, and recovery
levels. Nevertheless, the values are highly individual and compari-
son between individuals is not straightforward. On the other hand,
evolution of wearable devices has made it possible to follow the
course of individual RHR and HRV as long-term time series, which
in turn enables observation of how behavioral, societal and seasonal
factors aect RHR and HRV at individual and population scale. In
this article, data measured by the Oura ring is used to study how
alcohol and training aect these values, and moreover, how societal
and seasonal factors aect us as a population.
Human-centered computing Mobile devices
studies in ubiquitous and mobile computing
Applied com-
Consumer health;Health informatics;Sociology;
puter systems organization Embedded hardware.
heart rate, heart rate variability, wearables, Oura ring, seasonal
variation, training, alcohol
ACM Reference Format:
Heli Koskimäki, Hannu Kinnunen, Salla Rönkä, and Benjamin Smarr. 2019.
Following the Heart - What Does Variation of Resting Heart Rate Tell about
Us as Individuals and as a Population. In Adjunct Proceedings of the 2019 ACM
International Joint Conference on Pervasive and Ubiquitous Computing and
the 2019 International Symposium on Wearable Computers (UbiComp/ISWC
’19 Adjunct), September 9–13, 2019, London, United Kingdom. ACM, New
York, NY, USA, 4 pages.
These authors contributed equally to this research.
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Figure 1: Age (years) composition of the Oura ring user pop-
ulation, n=57278.
Resting heart rate (RHR) and heart rate variability (HRV) are bio-
signals related to sympathetic and parasympathetic nervous system
activity, and known markers of health. For example, heart rate (HR)
has a direct role in blood pressure regulation in an interplay with
stroke volume and peripheral resistance. HR and wearable sensors
have been a natural combination since the invention of heart rate
monitors in the 1970’s. While the rst solutions were targeted to
athletes to assess their acute exercise intensity, over the past decade
trend has shifted towards optimizing your training based on your
resting HRV [6], and even evaluation of sleep and recovery [1].
HRV consists of periodic and aperiodic changes in the duration
of the cardiac cycle and provides a powerful means of observing the
interplay between the sympathetic and parasympathetic nervous
systems. The beat of a healthy heart shows signicant variation
among the time intervals between heartbeats and it is used as a
general marker of mental and physical well-being [
]. Nightly sleep
provides an excellent measurement point because external condi-
tions are typically constant and confounding factors are controlled
without extra eorts.
There are several known behavioral and environmental factors
that aect an individual’s RHR. Acute causes include intense exer-
cise, alcohol, ambient temperature and sickness. Long-term inu-
encers include age, aerobic training and cardiovascular tness and
health. For example, a single intense or exceptionally long exercise
session can increase RHR for several hours, yet participation in
regular training will lower the baseline of RHR. In addition to these
UbiComp/ISWC ’19 Adjunct, September 9–13, 2019, London, United Kingdom Koskimäki and Kinnunen, et al.
Subject number
Resting heart rate
Figure 2: Boxplot of mean resting heart rates (RHR) over a
1-week period among 10 individual subjects. Each subject re-
ported alcohol intake during 1 day only, and the minimum
amount was 3 doses. In every subject the highest RHR value
occurred after the alcohol intake. Moreover, in 8 out of 10
subjects, the alcohol night stands out as an outlier in the
boxplot (red asterisk).
short and long term responses, woman’s RHR varies according
to menstrual cycle phase. Similar responses are also seen in HRV,
typically in the opposite direction. It has not been explored thor-
oughly how much seasonal variability there is in RHR and HRV,
yet seasonal variation in cardiovascular disease and its’ risk factors
have been reported, risks peaking in winter and lowering during
the summer [
]. Among others, environmental temperature, light
exposure, infections, bad diet and lack of physical activity are men-
tioned as risk factors that could link to seasonal variation. Distance
from the equator seems to enhance the seasonality [4].
PPG has been used for several decades to derive oxygen satura-
tion, typically with a ngertip sensor. More recently, PPG sensors
embedded into wrist bracelets and rings have been utilized to assess
HR and HRV for various applications. The accuracy seems to vary
depending on the application. The main challenge is clearly sensitiv-
ity to motions [
]. Subsequently, best accuracy has been obtained
in nocturnal measurements during sleep where wrist PPG has been
reported to reach 5-6 ms mean absolute errors (corresponding to
12-15 ms standard deviation), in inter-beat-intervals compared to
the R-R intervals derived via ECG devices [
]. Finger PPG seems
to have potential for better beat-to-beat detection accuracy than
wrist PPG [
], which is likely due to the close proximity of arteries
on the palmar side of the nger.
The Oura ring is a scientically validated, wearable sleep tracker
which estimates RHR, HRV, and sleep stages based on nocturnal
PPG (250 Hz), 3-D accelerometer (50 Hz) and skin temperature [
], [
]. From RHR and HRV accuracy point of view, a very high
between-subjects agreement in night-time average RHR and HRV
between the Oura ring and the ECG (Faros cardiac monitor) have
been reported: r
= .998 and r
= .967 for RHR and HRV, respectively,
where HRV was assessed using the time domain parameter rMSSD
(root mean square of successive dierences) [5].
Subject number
Heart rate variability
Figure 3: Boxplot of mean heart rate variability (HRV) in the
same way as with RHR on the left. In every subject the low-
est HRV value occurred after the alcohol intake. Moreover,
in 8 out of 10 subjects the alcohol night stands out as an out-
lier in the boxplot (red asterisk below). Subjects are the same
as in Figure 2.
The experimental part of this study is divided into two parts: 1) RHR
and HRV responses to reported daily choices (including alcohol in-
take and physical exercise in this study) among individual subjects,
and 2) RHR and HRV responses to lifestyle, societal and seasonal
factors across a population. In the rst part a small sub-set of Oura
ring users have been selected to demonstrate if the eect of alcohol
and training to RHR and HRV can be detected from among other
potential confounding factors. One calendar week of 10 individual
users was analyzed in each case, where seven night-time average
values of RHR and HRV were collected from each subject.
Alcohol response: a week of RHR and HRV data including
the night after a day when each user reported at least 3 doses
of alcohol
Training response: a week of data including one or two
nights following a day with at least 30 minutes of high in-
tensity exercise (jogging was considered high intensity, as
an example) with no alcohol intake during that week.
For both experiments, every subject chose a week according to
their own schedules, and they gave their consent to report the day
of alcohol intake and days of exercise participation. All subjects
followed the guidance. Exact amount of alcohol, exact duration and
type of exercise were not collected: this decision was made respect-
ing subject privacy and in order to include normal-life variation in
the resulting data.
In the second part of this study the focus shifts from an individual
to a population scale. In this study all the Oura ring users were
included during a time window starting from July 2016 (n=1415)
and ending at March 2019 (n=57278). For every day, the average
nocturnal RHR is calculated, so that the contribution of a single user
to the overall variance is small especially in the latter part of the
time window. The gender composition was 36 percent female and
64 percent male, majority being between 30 and 50 years, yet 20-30-
year and 50-60-year old people were also well represented (Figure
1). Moreover, a clear majority of Oura users live in the Northern
Following the Heart UbiComp/ISWC ’19 Adjunct, September 9–13, 2019, London, United Kingdom
Subject number
Heart rate variability
Figure 4: Boxplots of resting heart rate variability among 10
individuals during a training week.
Table 1: Nightly HRV (rMSSD in ms) during a training week.
Red, bold numbers represent the nights following a subjec-
tively reported exercise day.
1 84 76 65 68 69 78 71
6 70 56 59 59 56 40
1 92 74 45 54 85 55 69
10 68 55 45 47 43 42 36
7 30 32 29 45 35 37 32
2 139 109 87 106 96 105 101
9 80 74 67 68 74 75 71
8 59 59 58 48 53 44 52
5 59 44 44 37 46 21 43
4 32 32 41 30 25 26 18
hemisphere; approximately 60 percent in US and Canada, 30 percent
in Europe and the rest in all continents including Australia and
New Zealand from the Southern hemisphere of our planet Earth.
3.1 Eect of daily choices (i.e. alcohol and
training response) at individual level
In Figures 2 and 3 the individual alcohol responses are shown. Even
though the RHR and HRV are both highly individual values - for
example in this group between-individual mean HRV values vary
from 30 to 110 ms - the eect of alcohol has similar characteristic
in nearly all individuals, manifesting as an increase of RHR and
decrease of HRV in the night with alcohol compared to the rest
nights of the week (the highest RHR and lowest HRV occur for every
subject after the alcohol intake night). To visualize the magnitude
of this eect a box plot was used; in a box plot, an observation that
falls outside normal distribution is marked as an outlier point. In
the case of alcohol intake, these nights stand out as outliers; e.g.
in Figure 2, for 8 out of 10 individuals, meaning that the eect of
alcohol exceeds the variation observed during the rest of the week.
Figure 4 and Table 1 could have capacity to reveal a response
in resting HRV to daily exercise. Nevertheless, it becomes clear
that dierent subjects respond in a dierent way to the reported
exercise days. For only 2/10 individuals the HRV after an exercise
day is considered as an outlier (Subjects 2 and 9). For some other
subjects the lowest HRV is not even the day after the training day,
suggesting that these individuals may have been accustomed to the
training mode, intensity and duration they performed during the
given week. In general, a given absolute intensity of exercise has a
bigger impact to unt or unaccustomed individuals. The usability of
the relation between training intensity and the impact in HRV as a
potential resilience or tness marker remains to be studied later in
higher detail. Moreover, HRV is susceptible to various other stress
and lifestyle factors choices, including habitual physical activity
outside structured exercise that may also positively aect HRV.
3.2 Lifestyle, society and seasonality eect at
population level
As shown and discussed in the previous section, a mixture of daily
lifestyle factors aects our health and recovery, and some of their
eects are highly individual by nature. In order to study the health
related outcomes broadly, such as the impacts of lifestyle, societal
impositions, or natural phenomena including seasonality, long term
data and large number of people will be a pre-requisite. However,
clean individual long-term data can be dicult to achieve without
a burdensome requirement for subjects to respond to queries and
keep diary - solutions which are subjective by nature and do not
lend themselves to scale. By contrast, wearable device data can be
easily scaled into the population level, see for example Figure 5
which gives an example outcome from a data collection that did
not require any additional eort from individual users. This kind of
solutions illustrate a huge potential awaiting exploration of natural
human dynamics, as well as culturally or regionally specic public
health investigations.
3.2.1 Distinct dates (New Year). Looking at the RHR curve pre-
sented in Figure 5, there are three distinct upward peaks where an
abrupt increase in RHR is observed. A closer look at the date axis
reveals that those peaks are exactly one year apart from each other,
and they are all from New Year’s Eves, a public holiday associated
with staying up late and drinking across the Western society. These
two lifestyle factors behind this particular increase in RHR are quite
obvious, and as indicated by the big response in RHR, the negative
health impact at the population level can be huge. It would be in-
teresting to study more closely the relationship between adverse
health outcomes and RHR.
3.2.2 Seasonal highs and lows. Another interesting observation is
the yearly uctuation in RHR with a peak in winter and a trough at
the end of the summer season. The magnitude of seasonal variation
can be observed to be roughly 2 bpm: In 2018, August averages
1.6 bpm lower than March 2018, and December averages 2.1 bpm
higher than August. The reasons behind these uctuations may
be of biological, societal, and lifestyle origin. On one hand, the
role of biological factors, such as seasonal variation in Sun light
and ambient temperature are supported by the observation that in
people living at the Northern hemisphere, RHR start to decline in
April and reach its minimum in August, before autumnal equinox.
Interestingly, looking at subjects from New Zealand and Australia
separately, exactly opposite phase with regards to the length of the
day was observed. Since the response in RHR is delayed in respect
to change of day length at both hemispheres, ambient temperature
UbiComp/ISWC ’19 Adjunct, September 9–13, 2019, London, United Kingdom Koskimäki and Kinnunen, et al.
Jan 2017 Jul 2017 Jan 2018 Jul 2018 Jan 2019
Resting heart rate
Duration of Night (h)
Jan 2017 Jul 2017 Jan 2018 Jul 2018 Jan 2019
Resting heart rate
Duration of Night (h)
Figure 5: Nocturnal mean resting heart rate among users of Oura ring for people from the Northern (above, n = 1400 - 56
000) and Southern hemisphere (below, n = 120 - 1500). Duration from sunset to sunrise in New York (above) and Melbourne,
Australia (below) plotted with red line to mark the seasonal variation in daylight.
could really be another, potentially complementing input. Seasonal
variations can also be linked to time spent outside, amount of phys-
ical activity, and general health status including u. However, some
clear observations emphasize the role of lifestyle eects. For ex-
ample, the rate of increase in RHR in December exceeds the speed
expected from the change in the day length, leading us to specu-
late whether elevated RHR is also linked to holiday season stress.
There is also a drop in RHR from December to January when a new,
healthier life is started by people at a large scale. If we consider
the societal inputs further, summer holiday season should decrease
work related stress. All in all, it seems obvious that the annual
trends are due to both seasonality and societal factors, which fur-
ther experiments can help to identify. Distance from the equator
could not be assessed in our data in detail. With reference to the
reported seasonality in cardiovascular disease and observed links
to the distance from equator [
], it could be a topic of future study.
3.2.3 Weekly rhythm. Figure 5 also illustrate a regular weekly
rhythm where HR peaks at weekends. Quantifying the magnitude
of this weekly rhythm, night between Saturday and Sunday aver-
ages 1.6 bpm higher than the night between Monday and Tuesday
in our data. It is a well known social pattern that sleep timing is
delayed during weekends: the phenomenon is also called social
jetlag as it results from sleep debt accumulated during the week
when social factors make people wake up earlier than their biologi-
cally preferred wake-up time. Delayed sleep during the weekends
increases RHR because sleep is misaligned from the time when
body metabolism is the lowest due to circadian regulation. Alcohol
consumption across the weekdays also peaks during Friday and
Saturday nights, and a portion of population apparently increase
their physical activity levels at weekends as well.
In this paper we have shown that data generated from the Oura
Ring sensor platform can be used to identify health and lifestyle
related eects both for individuals and across large populations. We
provide evidence that individuals interested in regulating their RHR
or HRV can identify acute impacts from alcohol consumption and
from exercise, as well as monitor long term changes, as in changing
resilience with rising tness after sustained training. At the same
time, because of the large population contributing anonymized
data, the Oura Ring is also providing insights into population scale
phenomena. For instance, here we quantify the eect of New Years
on RHR, and also provide the highest resolution of human physi-
ological response to seasons of which we are aware. Many more
avenues suggest themselves for exploration, including other acute
and longitudinal eects for individuals, and social cost explorations
and basic human naturalism at the population level. We can even
imagine experiments in quantifying social policy impacts, as in
changing work schedules on RHR by region.
Massimiliano de Zambotti, Leonardo Rosas, Ian M Colrain, and Fiona C Baker.
2019. The sleep of the ring: comparison of the
OURA sleep tracker against
polysomnography. Behavioral sleep medicine 17, 2 (2019), 124–136.
J. Harju, Tarniceriu A, Parak J, Vehkaoja A, Yli-Hankala A, and Korhonen I. 2018.
Monitoring of heart rate and inter-beat intervals with wrist plethysmography in
patients with atrial brillation. Physiol. Meas. 39 (2018). Issue 065007.
Parak J, Tarniceriu A, Renevey P, Bertschi M, Delgado-Gonzalo R, and Korhonen
I. 2015. Evaluation of the beat-to-beat detection accuracy of PulseOn wearable
optical heart rate monitor. Conf Proc Annu Int Conf IEEE Eng Med Biol Soc (2015),
Pell JP and Cobbe SM. 1999. Seasonal variations in coronary heart disease. QJM
Int J Med. 92 (1999), 689–96. Issue 12.
HO Kinnunen and H Koskimäki. 2018. 0312 The HRV Of The Ring-Comparison
Of Nocturnal HR And HRV Between A Commercially Available Wearable Ring
And ECG. Sleep 41 (2018), A120.
Antti M Kiviniemi, Arto J Hautala, Hannu Kinnunen, and Mikko P Tulppo.
2007. Endurance training guided individually by daily heart rate variability
measurements. European journal of applied physiology 101, 6 (2007), 743–751.
Heli Koskimäki, Hannu Kinnunen, Teemu Kurppa, and Juha Röning. 2018. How
Do We Sleep: a Case Study of Sleep Duration and Quality Using Data from Oura
Ring. In Proceedings of the 2018 ACM International Joint Conference and 2018
International Symposium on Pervasive and Ubiquitous Computing and Wearable
Computers. ACM, 714–717.
U. Rajendra Acharya, K. Paul Joseph, N. Kannathal, C. M. Lim, and J. S. Suri. 2006.
Heart rate variability: a review. Med. Biol. Eng. Comput. 44 (2006), 1031–1051.
Stewart S, Keates AK, Redfern A, and McMurray JJV. 2017. Seasonal variations
in cardiovascular disease. Nat Rev Cardiol. 14 (2017), 654–64. Issue 11.
A. Schäfer and J. Vagedes. 2013. How accurate is pulse rate variability as an
estimate of heart rate variability? A review on studies comparing photoplethys-
mographic technology with an electrocardiogram. Int. J. Cardiol. 166 (2013),
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We spend almost one third of our life in sleep. Sleep is one of the main contributors to our life and wellbeing. Sleep disorders are know to have adverse health effects but studies have also shown that too little or too much sleep is correlated with a greater risk of death. Sleep trackers have introduced new tools for sleep related studies by providing detailed, long-term sleep data. In this study, data collected with a wearable wellness device, Oura ring, is used to reveal how people sleep from the point of view of duration, consistency and timing. It is shown that, on average, Oura users sleep approximately 7 hours per night and that following a consistent sleep schedule is associated with more efficient sleep.
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Introduction The beat of a healthy heart shows significant variation among the time intervals between heartbeats. Heart rate variability (HRV) consists of periodic and aperiodic changes in the duration of the cardiac “QRS-wave” cycle. Greater nocturnal HRV has been linked with better sleep quality in healthy and clinical populations. Measurement of HRV during sleep might therefore have applications for sleep related maladies, but to collect long term data there is a strong need for comfortable measurement devices that do not disturb rest. Here we assess the Oura ring’s (Ring) ability to fill this need. Methods We measured nocturnal photopletysmogram (PPG) based inter-beat interval (IBI) data with the Ring (Oura Health Ltd, Oulu, Finland) and simultaneous R-R interval data with Faros 360 electrocardiogram (ECG) device (Mega Electronics, Kuopio, Finland) in 10 healthy individuals (3 female, 7 male). All subjects had the Ring on both hands, resulting in 20 nightly recordings for analysis. Root mean square of successive differences (rMSSD) was used as the HRV measurement. We determined heart rate HR and HRV as nightly averages for the Ring using only the normal IBI values (HRRing and rMSSDRing) and for ECG using Kubios software with automatic filter having medium setting (HRECG and rMSSDECG). The agreement between the methods was assessed by correlation analysis using Matlab software. Results High correlation was observed between HRRing and HRECG (r² = .998) with a bias of -0.53 bpm (p<.001). Moreover, high correlation was found between rMSSDRing and rMSSDECG (r² = .967) with a bias of -0.47 (p<.001). The observed HR range was 48 to 66 bpm, and rMSSD range was 21 to 78 ms. Conclusion As a ring provides high wearing comfort for the measurement of nocturnal HR and HRV, the present confirmation about the reliability of the HR and HRV numbers is welcome for sleep and recovery related long-term studies. High correlation observed in rMSSD encourage comparing other parameters of HRV, too. Support (If Any) This work was supported by Oura Health Ltd.
Conference Paper
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Heart rate variability (HRV) provides significant information about the health status of an individual. Optical heart rate monitoring is a comfortable alternative to ECG based heart rate monitoring. However, most available optical heart rate monitoring devices do not supply beat-to-beat detection accuracy required by proper HRV analysis. We evaluate the beat-to-beat detection accuracy of a recent wrist-worn optical heart rate monitoring device, PulseOn (PO). Ten subjects (8 male and 2 female; 35.9±10.3 years old) participated in the study. HRV was recorded with PO and Firstbeat Bodyguard 2 (BG2) device, which was used as an ECG based reference. HRV was recorded during sleep. As compared to BG2, PO detected on average 99.57% of the heartbeats (0.43% of beats missed) and had 0.72% extra beat detection rate, with 5.94 ms mean absolute error (MAE) in beat-to-beat intervals (RRI) as compared to the ECG based RRI BG2. Mean RMSSD difference between PO and BG2 derived HRV was 3.1 ms. Therefore, PO provides an accurate method for long term HRV monitoring during sleep.
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Heart rate variability (HRV) is a reliable reflection of the many physiological factors modulating the normal rhythm of the heart. In fact, they provide a powerful means of observing the interplay between the sympathetic and parasympathetic nervous systems. It shows that the structure generating the signal is not only simply linear, but also involves nonlinear contributions. Heart rate (HR) is a nonstationary signal; its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. Therefore, the HRV signal parameters, extracted and analyzed using computers, are highly useful in diagnostics. In this paper, we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.
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Purpose of this study was to test utility of heart rate variability (HRV) in daily endurance exercise prescriptions. Twenty-six healthy, moderately fit males were randomized into predefined training group (TRA, n = 8), HRV-guided training group (HRV, n = 9), and control group (n = 9). Four-week training period consisted of running sessions lasting 40 min each at either low- or high-intensity level. TRA group trained on 6 days a week, with two sessions at low and four at high intensity. Individual training program for HRV group was based on individual changes in high-frequency R-R interval oscillations measured every morning. Increase or no change in HRV resulted in high-intensity training on that day. If there was significant decrease in HRV (below reference value [10-day mean-SD] or decreasing trend for 2 days), low-intensity training or rest was prescribed. Peak oxygen consumption (VO(2peak)) and maximal running velocity (Load(max)) were measured in maximal treadmill test before and after the training. In TRA group, Load(max) increased from 15.1 +/- 1.3 to 15.7 +/- 1.2 km h(-1) (P = 0.004), whereas VO(2peak) did not change significantly (54 +/- 4 pre and 55 +/- 3 ml kg(-1) min(-1) post, P = 0.224). In HRV group, significant increases were observed in both Load(max) (from 15.5 +/- 1.0 to 16.4 +/- 1.0 km h(-1), P < 0.001) and VO(2peak) (from 56 +/- 4 to 60 +/- 5 ml kg(-1) min(-1), P = 0.002). The change in Load(max) was significantly greater in HRV group compared to TRA group (0.5 +/- 0.4 vs. 0.9 +/- 0.2 km h(-1), P = 0.048, adjusted for baseline values). No significant differences were observed in the changes of VO(2peak) between the groups. We concluded that cardiorespiratory fitness can be improved effectively by using HRV for daily training prescription.
Atrial fibrillation (AF) causes marked risk for patients, while silent fibrillation may remain unnoticed if not suspected and screened. Development of comfortable yet accurate beat-to-beat heart rate (HR) monitoring with good AF detection sensitivity would facilitate screening and improve treatment. The purpose of this study was to evaluate whether a wrist-worn photoplethysmography (PPG) device can be used to monitor beat-to-beat HR accurately during postoperative treatment in patients suffering from AF and whether wrist-PPG can be used to distinguish AF from sinus rhythm (SR). 29 patients (14 with AF, 15 with SR, mean age 71.5y) with multiple comorbidities were monitored during routine postoperative treatment. The monitoring included standard ECG, finger PPG monitoring and a wrist-worn PPG monitor with green and infrared light sources. The HR from PPG sensors was compared against ECG derived HR. The wrist PPG technology had very good HR and beat detection accuracy when using green light. For the SR group, the mean absolute error (MAE) for HR was 1.50 bpm, and for the inter-beat-intervals (IBI), the MAE was 7.64 ms. For the AF group, the MAE for HR was 4.28 bpm and for IBI, the MAE was 14.67 ms. Accuracy for the infrared (IR) channel was worse. Finger PPG provided similar accuracy for HR and better accuracy for the IBI. AF detection sensitivity using green light was 99.0% and the specificity was 93.0%. Performance can be improved by discarding unreliable IBI periods. Results suggest that wrist PPG measurement allows accurate HR and beat-to-beat HR monitoring also in AF patients, and could be used for differentiating between SR and AF with very good sensitivity. &#13.
Cardiovascular disease (CVD) follows a seasonal pattern in many populations. Broadly defined winter peaks and clusters of all subtypes of CVD after 'cold snaps' are consistently described, with corollary peaks linked to heat waves. Individuals living in milder climates might be more vulnerable to seasonality. Although seasonal variation in CVD is largely driven by predictable changes in weather conditions, a complex interaction between ambient environmental conditions and the individual is evident. Behavioural and physiological responses to seasonal change modulate susceptibility to cardiovascular seasonality. The heterogeneity in environmental conditions and population dynamics across the globe means that a definitive study of this complex phenomenon is unlikely. However, given the size of the problem and a range of possible targets to reduce seasonal provocation of CVD in vulnerable individuals, scope exists for both greater recognition of the problem and application of multifaceted interventions to attenuate its effects. In this Review, we identify the physiological and environmental factors that contribute to seasonality in nearly all forms of CVD, highlight findings from large-scale population studies of this phenomenon across the globe, and describe the potential strategies that might attenuate peaks in cardiovascular events during cold and hot periods of the year.
Objective/background: To evaluate the performance of a multisensor sleep-tracker (ŌURA ring) against polysomnography (PSG) in measuring sleep and sleep stages. Participants: Forty-one healthy adolescents and young adults (13 females; Age: 17.2 ± 2.4 years). Methods: Sleep data were recorded using the ŌURA ring and standard PSG on a single laboratory overnight. Metrics were compared using Bland-Altman plots and epoch-by-epoch (EBE) analysis. Results: Summary variables for sleep onset latency (SOL), total sleep time (TST), and wake after sleep onset (WASO) were not different between ŌURA ring and PSG. PSG-ŌURA discrepancies for WASO were greater in participants with more PSG-defined WASO (p < .001). Compared with PSG, ŌURA ring underestimated PSG N3 (~20 min) and overestimated PSG REM (~17 min; p < .05). PSG-ŌURA differences for TST and WASO lay within the ≤ 30 min a-priori-set clinically satisfactory ranges for 87.8% and 85.4% of the sample, respectively. From EBE analysis, ŌURA ring had a 96% sensitivity to detect sleep, and agreement of 65%, 51%, and 61%, in detecting "light sleep" (N1), "deep sleep" (N2 + N3), and REM sleep, respectively. Specificity in detecting wake was 48%. Similarly to PSG-N3 (p < .001), "deep sleep" detected with the ŌURA ring was negatively correlated with advancing age (p = .001). ŌURA ring correctly categorized 90.9%, 81.3%, and 92.9% into PSG-defined TST ranges of < 6 hr, 6-7 hr, > 7 hr, respectively. Conclusions: Multisensor sleep trackers, such as the ŌURA ring have the potential for detecting outcomes beyond binary sleep-wake using sources of information in addition to motion. While these first results could be viewed as promising, future development and validation are needed.
Background: The usefulness of heart rate variability (HRV) as a clinical research and diagnostic tool has been verified in numerous studies. The gold standard technique comprises analyzing time series of RR intervals from an electrocardiographic signal. However, some authors have used pulse cycle intervals instead of RR intervals, as they can be determined from a pulse wave (e.g. a photoplethysmographic) signal. This option is often called pulse rate variability (PRV), and utilizing it could expand the serviceability of pulse oximeters or simplify ambulatory monitoring of HRV. Methods: We review studies investigating the accuracy of PRV as an estimate of HRV, regardless of the underlying technology (photoplethysmography, continuous blood pressure monitoring or Finapresi, impedance plethysmography). Results/conclusions: Results speak in favor of sufficient accuracy when subjects are at rest, although many studies suggest that short-term variability is somewhat overestimated by PRV, which reflects coupling effects between respiration and the cardiovascular system. Physical activity and some mental stressors seem to impair the agreement of PRV and HRV, often to an inacceptable extent. Findings regarding the position of the sensor or the detection algorithm are not conclusive. Generally, quantitative conclusions are impeded by the fact that results of different studies are mostly incommensurable due to diverse experimental settings and/or methods of analysis.
Coronary heart disease exhibits a winter peak and summer trough in incidence and mortality, in countries both north and south of the equator. In England and Wales, the winter peak accounts for an additional 20,000 deaths per annum. It is likely that this reflects seasonal variations in risk factors. Seasonal variations have been demonstrated in a number of lifestyle risk factors such a physical activity and diet. However, a number of studies have also suggested a direct effect of environmental temperature on physiological and rheological factors. We review the available evidence on seasonal variations in coronary heart disease and possible explanations for them.