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Original article 139
1359-5237 Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. DOI: 10.1097/MBP.0000000000000577
Blood pressure altering method affects correlation with pulse
arrival time
Sondre Heimarka,b,c, Ole Marius H. Rindald, Trine M. Seebergd,
Alexey Stepanovd, Elin S. Boysend, Kasper G. Bøtker-Rasmussend,
Nina K. Mobæke, Camilla L. Søraasb,f, Aud E. Stenehjema,
Fadl Elmula M. Fadl Elmulab,g and Bård Waldum-Grevboa
Objective Pulse arrival time (PAT) is a potential main
feature in cuff-less blood pressure (BP) monitoring.
However, the precise relationship between BP parameters
and PAT under varying conditions lacks a complete
understanding. We hypothesize that simple test protocols
fail to demonstrate the complex relationship between PAT
and both SBP and DBP. Therefore, this study aimed to
investigate the correlation between PAT and BP during two
exercise modalities with differing BP responses using an
unobtrusive wearable device.
Methods Seventy-five subjects, of which 43.7% had a
prior diagnosis of hypertension, participated in an isometric
and dynamic exercise test also including seated periods
of rest prior to, in between and after. PAT was measured
using a prototype wearable chest belt with a one-channel
electrocardiogram and a photo-plethysmography sensor.
Reference BP was measured auscultatory.
Results Mean individual correlation between PAT and
SBP was −0.82 ± 0.14 in the full protocol, −0.79 ± 0.27
during isometric exercise and −0.77 ± 0.19 during dynamic
exercise. Corresponding correlation between PAT and DBP
was 0.25 ± 0.35, −0.74 ± 0.23 and 0.39 ± 0.41.
Conclusion The results confirm PAT as a potential main
feature to track changes in SBP. The relationship between
DBP and PAT varied between exercise modalities, with the
sign of the correlation changing from negative to positive
between type of exercise modality. Thus, we hypothesize
that simple test protocols fail to demonstrate the complex
relationship between PAT and BP with emphasis on DBP.
Blood Press Monit 27: 139–146 Copyright © 2021 The
Author(s). Published by Wolters Kluwer Health, Inc.
Blood Pressure Monitoring 2022, 27:139–146
Keywords: blood pressure monitoring, pulse wave analysis,
wearable electronic devices
aDepartment of Nephrology, Oslo University Hospital, bSection for
Cardiovascular and Renal Research, Oslo University Hospital, cInstitute of
Clinical Medicine, University of Oslo, dSmart Sensor Systems, SINTEF Digital,
Oslo, eDatek Next AS, Lillestrøm, fSection for Environmental and Occupational
Medicine, Oslo University Hospital and gDepartment of Acute Medicine, Oslo
University Hospital, Oslo, Norway
Correspondence to Sondre Heimark, Department of Nephrology, Oslo University
Hospital, Kirkeveien 166, 0450 Oslo, Norway
E-mail: sondhe@ous-hf.no
Received 15 September 2021 Accepted 7 November 2021
Introduction
Many studies have conrmed that monitoring blood
pressure (BP) during a 24-hour period in ambulatory
conditions is superior to ofce BP in predicting future
disease [1]. Still, state-of-the-art 24-hour methods are
considered by many as unsatisfactory. Intermittent meas-
urements cannot capture the true hypertensive load,
which is also masked by patients being instructed to
rest during measurement as motion artifacts and non-
steady–state hemodynamic situations easily disrupt the
oscillations. Moreover, many nd the cuff measurements
painful and stressful, especially during night or if BP is
elevated, which may affect compliance to monitoring and
possibly increase the BP during measurement [2]. Thus,
innovation in BP monitoring is motivated by the aim to
improve hypertension management.
Cuff-less BP assessment has received increasing
research attention in the past decade [3–5]. Pulse wave
propagation times such as pulse arrival time (PAT) and
pulse transit time (PTT) are commonly used surrogate
measurements. The theoretical basis behind PAT as a
BP surrogate marker is described in the arterial wall and
pulse wave propagation models [6]. In short, if the pres-
sure within a vessel increases, the pulse waves travel
faster. This is detectable as a decrease in the measured
pulse wave propagation time. PAT, dened as the time
interval from an R-wave in an electrocardiogram (ECG)
signal to a ducial point in a peripheral photo-plethys-
mography (PPG) waveform, is particularly popular due
to measurement simplicity, only requiring a simple
ECG signal as a proximal timing reference and a sec-
ond continuous bio-signal such as PPG as a distal timing
This is an open-access article distributed under the terms of the Creative
Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-
NC-ND), where it is permissible to download and share the work provided it is
properly cited. The work cannot be changed in any way or used commercially
without permission from the journal.
140 Blood Pressure Monitoring 2022, Vol 27 No 2
reference. However, it includes the pre-ejection period
(PEP), dened as the time delay from the electrical
onset of systole to the mechanical onset of the pulse
wave transit time initiated by aortic valve opening.
Whether or not exclusion of PEP is necessary for satis-
factory accuracy in estimation of BP remains unknown.
While some studies argue that simple PAT measure-
ments are inaccurate due to PEP variability [7] others
have demonstrated better accuracy of PAT compared to
PTT [8]. Extensive research on PAT has demonstrated
its relative dependency on BP changes [4,5,9,10]. Still,
a key challenge is the transformation of PAT as a single
parameter to both SBP and DBP. Most studies investi-
gated PAT and its ability to predict or track both SBP
and DBP in experimental protocols where both BP
parameters change in the same directions [4]. Thus, test
protocols with simple BP altering methods have poten-
tial pitfalls. BP regulation and its variations are complex.
SBP and DBP may not always covary, for example dur-
ing different exercise states [11] or in individuals with
increased arterial stiffness where pulse pressure (PP)
amplication more easily causes isolated rises in sys-
tolic pressure [12]. Current evidence indicates a strong
correlation between SBP changes and PAT [4]. On the
contrary, there is insufcient knowledge on the associa-
tion between PAT and DBP and on how PAT is affected
when SBP and DBP do not change in the same direc-
tion [7,13–15].
A differing BP response in isometric compared to
dynamic exercise is well known [11]. Isometric exercise
generally produces a ‘pressor effect’ causing both SBP
and DBP to increase. Dynamic exercise generally intro-
duces a large PP, where SBP increases markedly while
DBP is less affected.
Thus, as a step to enable continuous, cuff-less SBP and
DBP measurements, the aim of the present study was to
utilize the differential BP response in isometric versus
dynamic exercise to investigate the effects of differing
BP alterations on PAT on an individual level.
Methods
Subjects and recruitment
This study included subjects reecting the general adult
population with a broad range of age as well as inclu-
sion BPs. Subjects with atrial brillation, pregnancy or
any contraindication to standard cardiac stress testing
were excluded [16]. From December 2019 to September
2020, 80 subjects 18–79 years of age were recruited
among volunteers and from a local hypertension registry
after approval from its steering committee. Five subjects
were excluded from the test protocol for the following
reasons; inaudible or difcult to auscultate Korotkoff
sounds during exercise (n = 2), poor signal quality (n =
1), baseline SBP >180mmHg (n = 1) and vasovagal reac-
tion (n = 1).
Test device and estimation of pulse arrival time
The test device is a fully wearable and easy-to-use chest
belt with three standard electrodes for ECG and a PPG
sensor with potential for seamless integration with clinical
applications. Technical details on the device have been
published previously [17,18], and an upgraded version
(new casing, a higher sampling rate of 1 kHz, new PPG
sensor) was used in the present study. PAT was calculated
for each cardiac cycle from the R-peak in the ECG to
the foot in the PPG waveform. Corresponding PAT meas-
urements for each reference SBP and DBP measurement
were calculated by nding the median PAT value from
10 valid cardiac cycles before and after. The PAT values
were ltered using a gliding lter with a window size of 30
cycles, only keeping the cycles where the PAT value was
within a 20% difference from the median value within
the window. Subjects were tted with the test device and
an appropriately sized cuff on the non-dominant arm for
reference auscultatory BP measurements.
Study protocol
The test protocol (Fig.1) consisted of an isometric leg
exercise, an incremental cycle ergometer test and seated
periods of rest before, in-between and after. Prior to the
isometric exercise, subjects were instructed to adjust
the ankle, knee and hip angle to endure for 3 minutes.
The cycle ergometer test was performed on a standard
cardiac stress test ergometer cycle (Ergoline Ergoselect
200, GmbH, Bitz, Germany) and consisted of four incre-
ments lasting 4 minutes each. The three rst increments
had stepwise increasing workload and the fourth was a
recovery period with equal workload to the rst incre-
ment. The cycle ergometer test aimed for submaximal
exertion during the third increment. Standardization of
cycle workload was achieved by each subject determin-
ing their tness level by the rating of perceived capacity
tool, which rates maximum exercise capacity for 30 min-
utes based on metabolic equivalents [19]. Subsequently,
the maximum workload during the third increment was
calculated to two to three metabolic equivalents below
the rating of perceived capacity.
A trained physician measured reference auscultatory BP
every 1 to 1½ minute throughout the protocol with an
aneroid sphygmomanometer (Maxi-Stabil 3; Welch Allyn,
Skaneateles Falls, New York, USA). Korotkoff I deter-
mined systolic pressure and Korotkoff V diastolic pres-
sure. In case of inaudible Korotkoff V during exercise,
Korotkoff IV was used. Reference BP was measured 43
times in each subject; seven measurements during the
rst rest period, three measurements during the isomet-
ric exercise, seven reference measurements during the
second rest period, 12 measurements during the dynamic
exercise and 10 measurements during the third rest
period. In addition, standing measurements were taken
prior to and after the isometric exercise, and seated on
the cycle prior to and after the dynamic exercise. PAT
Blood pressure and pulse arrival time Heimark et al. 141
measurements from the test device were obtained con-
tinuously throughout the test protocol. Because SBP and
DBP from the reference BP measurements were sepa-
rated in time and not from the same cardiac cycle, each
SBP and DBP was noted to the nearest second to allow
for PAT calculations from 10 cardiac cycles before and
after the exact time when SBP or DBP was measured.
Five subjects with corrupted test-device signals detected
in the ofine analysis were invited back for re-test to dif-
ferentiate between subtypes of observable waveforms in
the PPG and ECG signals and noise and were included
in the analysis with data from the second attempt.
Data and statistical analyses
All analyses were performed ofine using the Python
programming language using the following packages:
NumPy (1.18.2), SciPy (1.4.1), NeuroKit2 (0.0.40),
Pandas (1.0.3) and Plotly (4.7.1) [20–24]. Continuous
variables were evaluated for normality by visual inspec-
tion of histograms and the Shapiro-Wilk test. The
strength of the association between BP variables and
PAT was investigated in each subject using Pearson’s
correlation. Since it was not possible to measure more
than three reference BP measurements during the iso-
metric exercise, two measurement pairs taken standing
prior to and after exercise were included for increased
statistical power. However, a control analysis including
only the three measurements during active isometric
exercise was performed, and showed a non-signicant
reduction in the correlation coefcients and still a sig-
nicant difference between the regression coefcients
for PAT and SBP between the two exercise modalities
(data not shown). Correlation coefcients were classied
in strength in the following way; r = 0–0.19 was consid-
ered very weak, 0.2–0.39 as weak, 0.4–0.59 as moderate,
0.6–0.79 as strong and 0.8–1 as very strong [25]. Further
analysis of the relationship between PAT and BP param-
eters was performed with simple linear regression per
individual with PAT as the dependent variable and BP
parameters as independent variables. Mean of both indi-
vidual Pearson’s correlation coefcients and regression
coefcients were compared between exercise modalities
with Wilcoxon Sign Rank Test after assessing for test
assumptions. Unless otherwise specied, all continuous
variables are presented as mean ± SD, while categorical
values are presented as absolute numbers with percent-
age in parentheses and P < 0.05 was chosen as signi-
cance level.
Results
General characteristics and group average change in
measured variables during exercise
General characteristics of the test subjects are presented
in Table1. Group average change from baseline (dened
as the average of the two last measurements during rest
period 1) to maximum or minimum for all parameters
Fig. 1
Illustration of the test protocol with isometric exercise, dynamic exercise and rest periods. The dynamic exercise consisted of four 4-minute incre-
ments with increasing workload from the first through the third and a fourth recovery increment.
Table 1 General characteristics
Characteristic Quantity
Sex, male (%) 35 (46.7)
Age, years (range) 47.9 ± 15.5 (18–79)
BMI (kg/m2) 25.6 ± 5.2
Hypertension diagnosis 32 (43.7)
Antihypertensive medication 31 (41.3)
Baseline SBP (range) (mmHg) 124.4 ± 15.5 (92.5–168)
Baseline DBP (range) (mmHg) 75.9 ± 9.6 (55–104)
Baseline PP (mmHg) 50.0 ± 11.8
Baseline PAT (ms) 180.8 ± 23.2
SBP distribution at baseline (%)
≤100 mmHg 3 (4.0)
≥160 mmHg 1 (1.3)
≥140 mmHg 17 (22.7)
DBP distribution at baseline (%)
≤60 mmHg 4 (5.3)
≥100 mmHg 2 (2.7)
≥85 mmHg 12 (16.0)
Values are presented as absolute numbers with percentages in parentheses or
mean ± SD. Baseline values were defined by averaging the two last measure-
ments during the first rest period.
PAT, pulse arrival time (ms); PP, pulse pressure (mmHg).
142 Blood Pressure Monitoring 2022, Vol 27 No 2
during the two exercise modalities are presented in Fig.2.
SBP, DBP, HR and PP increased while PAT decreased
during isometric exercise. During dynamic exercise SBP,
HR and PP increased while DBP decreased slightly and
PAT decreased.
Correlation between pulse arrival time and blood
pressure
Based on one typical subject, Fig.3 illustrates how the
measured physiological variables varied throughout the
experimental protocol (Fig.3a) and visualizes the correla-
tion analysis for the full protocol (Fig.3b), isometric exer-
cise (Fig.3c) and during dynamic exercise (Fig.3d). The
correlation analyses and univariate linear regression were
performed separately for the full protocol, the isometric
exercise period and the dynamic exercise period. The
results of the correlation analyses are presented in Fig.4.
Differences in the pulse arrival time/blood pressure
relationship between exercise modalities
Simple linear regression was performed to determine the
equation with the best t between PAT and BP param-
eters for each subject for the full protocol and in the
isometric and dynamic exercise periods. The results are
presented in Fig.5 as the mean of the individual regres-
sion coefcients to allow for a visual representation of
the change in PAT per one-unit change in BP as well
as comparisons of the regression coefcients between
exercise modalities. The mean of individual regression
coefcients between PAT and SBP were signicantly
different when comparing the isometric and dynamic
exercise periods (−0.55 ± 0.29 ms/mmHg versus −0.79 ±
0.34 ms/mmHg, P < 0.001).
Discussion
As a step to enable continuous, cuff-less SBP and DBP
measurements, the present study investigated the effects
on PAT from distinctly different BP changes during iso-
metric and dynamic exercise. Included subjects repre-
sented the general population with broad ranges of age
and baseline BPs. The study presents two main nd-
ings. First, the lack of a clear association between PAT
and DBP was demonstrated by the inconsistent correla-
tion between the parameters in the two exercise modal-
ities. Second, the PAT/SBP slope differed signicantly
between exercise modalities. A secondary nding was the
conrmation of previously known very strong individual
correlation between PAT and SBP. To our knowledge, this
is the rst study to clearly demonstrate the uncertainty of
using PAT alone as a surrogate DBP measurement in the
same cohort.
Our results demonstrated a strong negative correlation
between PAT and DBP in isometric exercise, a weak pos-
itive correlation in dynamic exercise and consequently a
weak positive overall correlation. A clear demonstration
of this discrepancy in a comparable cohort, is previously
unreported. A weak association between PAT and DBP
has previously been reported [7,14,15], but stand in
Fig. 2
Group average change from baseline in measured physiological variables during the two exercise modalities. Values are presented as mean ± SD.
HR, heart rate (bpm); PAT, pulse arrival time (ms); PP, pulse pressure (mmHg).
Blood pressure and pulse arrival time Heimark et al. 143
contrast to the strong correlations reported by the major-
ity of research [4]. In Wibmer et al. [14], a weak associa-
tion between PAT and DBP was found in patients with
an indication of cardiopulmonary exercise testing. Only
dynamic exercise was investigated and similarly to us
they observed small uctuations in DBP during dynamic
exercise and still a very strong correlation between PAT
and SBP. In Marie et al. [15], isometric and dynamic exer-
cise-induced BP changes were studied in the same proto-
col in ve healthy young male subjects with an invasive
BP reference. A strong correlation between PAT and DBP
was observed during isometric exercise and a moderate
correlation during dynamic exercise. Similar to our nd-
ings, PAT correlated strongly with SBP changes across
all interventions. The results are not directly compara-
ble because exercise intensities and BP changes were of
much lower magnitude in Marie et al. [15], and isometric
handgrip exercise was performed during the last minute
of cycling. Thus, we hypothesize that simple test pro-
tocols fail to capture the complex relationship between
PAT and BP. This nding is important for ongoing and
future research on new methods for BP measurements
based on PAT.
The linear relationship between PAT and SBP differed
signicantly between exercise modalities, suggesting
that PAT is dependent on the characteristic of the BP
change or other physiological changes. One previous
study also indicated that the PAT/BP slope is altered
across different BP changes in the same subject [26].
The inclusion of the PEP, a known source of error in
PAT measurements [7] shown to decrease more in
dynamic exercise compared to isometric exercise [27],
is one possible explanation. Furthermore, in our study
PP demonstrated signicantly stronger correlation with
PAT compared to SBP for both the full protocol and
dynamic exercise, indicating that the maximum exerted
pressure on the arterial wall is more important compared
to an increase in both SBP and DBP. However, this con-
trasts with previous hypotheses stating that PAT is more
dependent on the mean arterial pressure [28]. The role
of PP changes on PAT is scarcely researched, but showed
superior correlation compared to SBP in one study from
a large bio-signal database [8]. Lastly, there is evidence
of a BP independent effect of HR on pulse wave veloc-
ity (PWV), where increasing HR increases PWV [29,30].
The effect of HR on PWV is difcult to investigate
Fig. 3
Measurements during the experimental protocol and correlation analysis for one typical subject. (a) All measured physiological variables through-
out the test protocol. PAT is inverted on the Y-axis for illustrative purposes. Darker blue background indicates the isometric exercise period and
green background indicates the dynamic exercise period. (b) Scatter plot and Pearson’s correlation coefficients of PAT and BP during the full
protocol in the same subject as in (a). (c) Scatter plot and Pearson’s correlation coefficients of PAT and BP during the isometric exercise in the
same subject as in (a). (d) Scatter plot and Pearson’s correlation coefficients of PAT and BP during dynamic exercise in the same subject as in (a).
HR, heart rate (beats per minute); PAT, pulse arrival time (ms); PP, pulse pressure (mmHg).
144 Blood Pressure Monitoring 2022, Vol 27 No 2
Fig. 4
Mean ± SD of individual Pearson’s correlation coefficients between PAT/SBP, PAT/DBP and PAT/PP. Analyses were performed for the full proto-
col and then separately for the isometric exercise period and dynamic exercise period. PAT, pulse arrival time (ms); PP, pulse pressure (mmHg).
Fig. 5
Mean ± SD of the individual regression coefficients between PAT as the dependent variable and SBP and DBP as the independent variable. The
analysis was performed for the full protocol and then separately for the isometric exercise period and dynamic exercise period. The presented
numerical data in the graph represents change in PAT per one-unit change in BP (ms/mmHg). PAT, pulse arrival time (ms).
Blood pressure and pulse arrival time Heimark et al. 145
because HR and BP often change in the same direc-
tion and existing research also show conicting results
[29]. Future research needs to investigate the above-dis-
cussed physiological parameters and the implications on
PAT accuracy.
Regarding SBP, our results are consistent with estab-
lished evidence of a very strong negative correlation with
PAT with most studies reporting correlation coefcients
between −0.8 and −0.9 [4]. These ndings indicate that
PAT is a potential main feature in surrogate measure-
ment of SBP in ambulatory monitoring. All associations
between PAT and BP variables discussed in this article
represent individual associations between PAT and BP.
The current use of PAT as a BP surrogate requires cali-
bration with a cuff measurement [4] to adjust for individ-
ual offsets [4,6].
A strong negative correlation between PAT and DBP
shown during isometric exercise is similar to ndings in
previous studies [4,15,31], as well as studies applying a
BP changing method where SBP and DBP change in the
same directions, such as the cold pressor test [32], mental
arithmetic stress test [32] or the Valsalva maneuver [33].
This suggests that DBP can be predicted from PAT dur-
ing specic conditions, however, it is unlikely to be able to
capture all DBP variations during ambulatory conditions.
The present study measured PAT and BP during active
exercise to investigate the effects of BP on PAT dur-
ing large BP uctuations. Previous comparable stud-
ies have investigated dynamic exercise-induced BP
changes, most commonly cycle ergometry or treadmill
running. However, BP measurements were mainly reg-
istered post-exercise or when exercise was intermittently
stopped [34–36]. As BP changes rapidly towards ‘normal’
level immediately after stopping the exercise [11,37],
the actual BP during the active exercise may have been
masked. This may in part explain why strong negative
DBP correlations have been previously reported from
dynamic exercise-induced BP changes.
In this study, PAT was measured from a vascular pathway
different from the brachial artery reference cuff meas-
urement site. PAT measured at chest level detects pulse
waves that propagate from the aorta to the skin vascula-
ture via a mixture of central elastic arteries and the mus-
cular internal thoracic arteries, and it is not known if this
PAT reects central BP rather than brachial BP.
PPG as well as ECG signals are susceptible to corruption
by artifacts from noise. After retrospect visual inspection
of seven outliers with a correlation between SBP and PAT
less than −0.70, it is likely that this is a result of motion
artifacts and noise in the PPG and ECG waveforms. Still,
we did not omit them from the analysis as algorithms
that could identify all artifacts are currently not available.
These ndings emphasize the importance of signal pro-
cessing and robust methods to detect corrupt waveforms.
Limitations
The BP measurement method is the major limitation
in all studies with protocols involving exercise and is
a matter of debate and conicting evidence regarding
accuracy and appropriate noninvasive method [38–40].
Invasive measurements are generally considered as the
gold standard during exercise but were not an available
alternative in this study due to ethical considerations.
Particularly DBP is difcult to measure during exer-
cise and is known to either increase slightly, decrease
slightly or remain unchanged during dynamic exercise
[11]. The magnitude and direction of DBP change dur-
ing dynamic exercise differ depending on study popu-
lation, exercise modality and body position as well as
workload intensity [37,39,41]. In one study, auscultatory
measurements during dynamic exercise compared to an
invasive reference showed a –5 ± 7mmHg difference
[42]. On the contrary, the auscultatory method is con-
sidered acceptable during exercise [38]. Although we
acknowledge that high precision noninvasive BP meas-
urements during exercise are not possible, it is unlikely
that the uncertainty from the BP measurement method
would have affected the study conclusions; that PAT is
not consistently and strongly correlated to DBP changes
across various hemodynamic states. The correlation and
regression analyses were performed for each individual
subject. With only three measurements during isometric
exercise, a standing measurement immediately prior to
and after was included to increase statistical power.
Conclusion
The present study demonstrated the lack of a clear associ-
ation between PAT and DBP, enabled by an experimental
protocol that included two different BP-altering exercise
interventions. In addition, the change in PAT per unit
change in SBP differed signicantly between exercise
modality. Thus, we raise concern regarding PAT alone as
a surrogate BP measurement across various hemodynamic
settings and argue that simple test protocols may fail to
capture the complex relationship between PAT as a single
parameter and both SBP and DBP. Future research should
focus on additional parameters to improve the robustness
of cuff-less BP estimation and include various BP alter-
ing methods. Despite this, our study showed consistent
very strong negative correlations on an individual basis
between PAT and SBP, suggesting that PAT is a potential
main feature in cuff-less BP measurements.
Acknowledgements
This work was supported by the HyperSension project
(project number 282039), a research project in the BIA
program nanced by the Norwegian Research Council.
Conflicts of interest
N.K.M. is with Datek Next AS, a project partner involved
in the development of the device prototype. For the
remaining authors, there are no conicts of interest.
146 Blood Pressure Monitoring 2022, Vol 27 No 2
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