IoT: A Novel Method for White Coat
Effect (WCE) Detection from Cloud
for Improving Patient’s Treatments
E. S. Madhan and K. Padmanaban
Abstract White coat effect (WCE) is a major issue in medical research due to the
variation of multiple occasions of blood pressure (BP). BP is the most dynamic factor
in a clinical problem which varies from time, place, season and movements of the
body functions. BP reading is an important basic feature and initial step prediction
of hypertension, diabetes and obesity. The World Health Organization (WHO) estimate of hypertension becomes the most significant premature death in the global
world and 2025 hypertension increased nearly 1.56 billion people. The treatment of
hypertension is mainly based on occasion level of BP. Medical diagnosis is a difficult
outcome result of unstable measures from formal methods. Many researchers find the
BP variations and provide improvement suggestions for treating a patient from clinical health data and home-based reading measurements. However, existing methods
have no reliability, efficiency and accuracy in treatments. In this paper, a narrative technique is proposed as cloud computing reference model for justification of
patient treatments which follows daily update in routine manner order to evade white
coat syndrome (WCS). Additionally, the heartbeat rate, glucose monitor, pedometer
measurement performance of patient health data from a cloud are examined and also
compared the results of various scenarios. Moreover, our experimental analysis in
Hadoop sandbox reveals 85% accuracy and safety clinical prediction in healthcare
treatments. Thus, patient can prevent from an unnecessary excess of treatments and
avoid tablets intake which leads to the cause of side effects or organ damage.
Keywords Cloud computing · White coat hypertension · White coat effect ·
Sensor · Big data
E. S. Madhan (B)
School of Computing, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur,
Chengalpattu District, Tamil Nadu, India
e-mail: esmadhan@kluniversity.in
K. Padmanaban
Dept. of Computer Science and Engineering, Rajalakshmi Institute of Technology,
Chembarambakkam, Tamil Nadu, India
e-mail: padmanaban.k@kluniversity.in
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
S. Smys et al. (eds.), Computer Networks and Inventive Communication Technologies,
Lecture Notes on Data Engineering and Communications Technologies 58,
https://doi.org/10.1007/978-981-15-9647-6_74
935
936
E. S. Madhan and K. Padmanaban
1 Introduction
Health care as services (Haas) in cloud computing becomes a familiar trend of
high-performance computing [1]. According to the Forbes report, almost 83% of
healthcare services travel toward cloud computing. The rapid growth of health care
on cloud development due to large support of data storage, scalable deployment,
backup disaster recovery and lower maintenance cost also helps better analysis of
many emergency medical research issues. However, recent technological updates are
led to assist in cloud services [2, 3] as mobile health care, remote health care, sensor
health monitoring. An unstable growth of global medical data is difficult to pile up
and analyze the results in the real-time environment. Thus, cloud computing overlays the way for support of healthcare services [4]. IoT technology is an imperative
role in the healthcare services which facilitate transportation, information process
and control and communication systems [5]. IoT in healthcare services becomes
the large benefit of doctors and patient due to efficient access and interaction of a
wide variety of smart embedded devices. Cloud–IoT helps to progress the quality in
healthcare data and assist in justifying challenges of real-time clinical data. In cloud
computing, security became a complex issue for the users to store and host applications due to several possible threats in a cloud. The several security mechanisms
are used in a cloud as MacAfee software as a services (SaaS) E-mail protection and
Web protection. Some advanced levels data security are also used for special business models and healthcare applications such as Oracle ELOQUA and hybrid cloud
security. However, the optimal view of cloud security is still undergoing in research.
Big data plays an essential role in medical research and becomes a solution for
numerous use case problems in healthcare services. The reason increase uses big data
instead of conventional databases is due to minimizing in structural behavior, low
expensive and absence of roadmap. However, in medical services [6], big data helps
to find a target right health risk people. Gartner suggests that many big data analytics
projects increase to 25 billion devices connected in Internet of Things (IoT). In
healthcare services, [7] wearable devices or apps in smartphones data may send into
cloud computing. Big data analytics assist in analyzing BP, pulse rate, glucose level,
etc. Nowadays, it multiplies in the practice of sensor data in health care [8] and also
finds difficult to analyze surge data from a sensor data due to volume, velocity and
variety. So the real-time healthcare alerting is a complex thing in medical. Big data
becomes an answer for prescription analytics, predictive analytics and also genomics.
White coat effect or white coat syndrome is a major research problem in a medical
field that affects patient treatment [9] because inadequate BP registry occurs in a
physician office due to the excitement of clinical setup and doctor [10].
WCE causes:
1.
2.
2.
2.
2.
Measurement of BP in a clinic is not exactly predicted and defined.
The difference in BP readings in the clinic and home scenario.
Idiosyncratic clinic settings.
Antihypertensive treatment.
The reason for target organ damage.
IoT: A Novel Method for White Coat Effect (WCE) Detection …
937
In this paper, the causes of WCE are considered and analyzed the accuracy of
contradiction measurements of clinical BP which helps to the avoidance of negative health outcomes, namely hypertension, adverse side effects [11] and unwanted
treatments. Moreover, investigate the results of the different occurrence of patients.
The rest of this paper is organized as follows. Section 2 presents related works of
cloud and WCE. Section 3 proposes the architecture of cloud computing reference
model for preventing white coat effect. In Sect. 4, real-time WCE is analyzed.
Healthcare data from a hospital. In Sect. 5, patient’s detail live update cloud
reference model is given. In Sect. 6, Hadoop experiments are given; in Sect. 7,
measurement of accuracy and error out; in Sect. 8, limitations of research, and Sect. 9
performance results. In Sect. 10, finally, conclude and future work of the paper are
given.
2 Related Works
Health care in cloud computing becomes increasing due to scalability, flexibility,
speed and security [12]. The cloud computing helps to analyze and store large-scale
medical information and also pave the way of smart and intelligent. IoT technology
becomes a rapid development which connects different smart devices through the
Internet and depicts greater interoperability. Many recent types of research on IoT
medical applications are increased. In real-time cases, there are many medical emergency services use to collect patient data through IoT, and it supports high flexibility
[4]. Author experiments in IoT access method of emergency services that facilitates distributed environment using cloud-mobile computing. Through the analysis
acquired, better data is ubiquitous accessing method. Though better services are
present in medical research, there is still demand to improve efficiency [13] and
accuracy in patient treatments [2]. Patient interaction test bed connects to wearable
devices and cloud computing platform which help potential healthcare system.
The evolution of big data in health care becomes greater in recent trend due to surge
data from a patient and support structured, unstructured and semi-structured data in a
real-time approach [6]. Big data becomes a solution for several companies to ease in
access data from large scale to store and analyze healthcare data [15]. To access big
data in a healthcare system is an important consequence to avoid data privacy and data
security [14]. However, big data well supports confidentiality, integrity and privacy
of personalized information and helps efficiency in patient information analysis and
quick in access of medical history.
The study of BP variations is an important consideration for a patient to prevent
white coat effect [16]. The most practical and reliable methods used to prevent WCS
[17] explore a comparison of clinical BP monitoring and home BP monitoring techniques. Author investigates [18] WCH through cohort studies due to increased cardiovascular problems and organ damage. The study supports mainly on home-based BP
monitoring suggestions in Asia and Europe. The analysis report helps to determine
sustained hypertension [19] and prevention from organ damage and perfection in the
938
E. S. Madhan and K. Padmanaban
treatment of patients. Similarly, [20] investigate the difference among ethnic group
measure on different occasions to make small or complete changes in healthcare
management. [21] Note the differences in routine clinical measurement. However,
this approach is not a recovery model for preventing WCE.
According to Jackson study [22], white coat effect occurs more on older patients
and causes adverse effect due to mismatch observation of clinical and self BP readings. The author investigates and analyzes BP data collected from 257 patients that
help to study about an elevation of BP readings in clinical scenario. This study helps
for the future treatment of antihypertensive older patients.
White coat hypertension and masked hypertension [23, 24] both events cause the
treatment of cardiovascular complexity and organ damages [16, 25]. Author studies
on various patient samples from Asia and Europe and conceptualizes various dynamic
prediction of treatment.
The common problems in BP measurement are as follows:
(1)
(2)
(3)
(4)
Fault in cutoff size
Additional pressure shows in stethoscope
Improper point of measure patient arm
Auscultator gap.
However, self BP monitoring suggests high specification but in lower sensitivity
outcome of prevention WCE. Our work proposes a novel method to improve accuracy
and quick analysis of patient health care to prevent WCE by using cloud reference
model.
3 Healthcare IoT Cloud Model
Figure 1 proposes a cloud reference model for efficiency in a patient monitoring
system for avoiding white coat effect. In India, 20% of patients in clinic affect WCS
due to variants measurement results of clinical BP readings.
Our work extends to additional medical equipment comprising pedometer, heart
pulse monitoring, blood pressure monitor and noninvasive glucose monitor. The justification of accuracy in medical diagnosis is a more complex issue in clinical research.
The IoT technology is to make a communication gap between sensor devices and
cloud computing platform. The patient and doctors view from [26] cloud computing
server.
4 Analysis of Real-Time WCE from a Clinic
Figure 2 shows that BP variation is measuring analysis and evaluates from a hundred
and twenty outpatients from an LK Hospital in India. The following steps are to
piling up public healthcare data and synthesize for sustainable analysis:
IoT: A Novel Method for White Coat Effect (WCE) Detection …
Fig. 1 Healthcare data analysis in cloud for patients
Fig. 2 Measurement of
variability in treatments
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E. S. Madhan and K. Padmanaban
Table 1 Percentage levels of variability
1.
Patient unaware of treatment increase or decrease level BP
23%
2.
Patient treated and controlled
37%
3.
Patients treated but not controlled
40%
1.
2.
3.
To capture real-time from patients.
To perform provisioning data.
To analyze a Data.
After capturing data from a people, then proceed to provision analysis. Data provisioning suggests to integrate from various sources and directs to quality improvement.
Next step is analyzing data in quantitative information. The analysis is processed in
a tool called fast stats. Fast stats are one of the emerging tools in medical usage
to detect the changes in healthcare management and detect the effects of early and
later health care for patients. Table 1 suggests about the percentage levels of patient
treatment of blood pressure. Figures 3 and 4 show that readings were taken in clinic
BP and home BP.
A.
B.
Clinical health BP data
Home-based self BP data.
Fig. 3 Clinical BP health data
IoT: A Novel Method for White Coat Effect (WCE) Detection …
941
Fig. 4 Home BP health data
The study of fluctuations in a different reading suggests that it affects the clinical
treatment [27], though the correlation of both inclusive cannot predict optimal treatment of hypertensive patients. Thus, an elevation in clinical BP scenario causes
WCE.
According to World Health Organization (WHO)
(i)
(ii)
Hypertension Level-1: 140 to 159
Hypertension Level-2: 160 and higher values.
The following observations are utilized to investigate the differences of ethnic
groups between clinical BP measurement and home BP measurement. Tables 2 and
3 illustrate sample trials of patient readings at a clinic and home situations. The
mean differences are mainly used to study the behavior of changes in the future
aspect of patient treatments. The clinical BP observances are sometimes increased
Table 2 Sample BP trials
from a clinic
Sample trials
Systolic
Diastolic
1
148
92
2
155
90
3
148
95
4
150
85
5
155
88
Total
756
450
Average
151.2
90
942
Table 3 Sample BP trials
from home
E. S. Madhan and K. Padmanaban
Sample trials
Systolic
Diastolic
1
135
85
2
140
73
3
134
85
4
128
80
5
130
78
Total
667
401
Average
133.4
80.2
due to the patient’s anxiety. Thus, studies of variation in clinical and home BP trials
are especially used to the treatment of sustained hypertension patients.
5 Patient Live Update Cloud Reference Model
Figure 5illustrates sample BP trial of a patient which performs a live update from a
cloud. The sensor data send healthcare BP data to the cloud by IoT environment. The
cloud health data are stored and analyzed for future use. Table 4 shows the average
of patients readings in 12 h from a cloud which helps for the prediction of accuracy
in treatment.
Fig. 5 Live updates value of patient in cloud
IoT: A Novel Method for White Coat Effect (WCE) Detection …
Table 4 Average of sample
trials from a cloud
Time
Systolic
943
Diastolic
7.00
122
78
8.00
128
70
9.00
132
65
10.00
129
82
11.00
127
85
12.00
145
78
13.00
160
100
14.00
152
82
15.00
130
75
16.00
125
73
17.00
125
70
18.00
130
75
Total
1605
933
Average
133.75
77.75
To improve more accuracy of patient analysis for treatment predictions, Fig. 6
shows that sample trials increased to 10 days. Simultaneously, in Table 5, find an
average of 10 days patients readings. The extended readings required for investigating
guide the origination and titration in contradictive hypertensive treatments. Table 6
shows the comparison values of various scenario.
Fig. 6 Sample of 10 days BP value of a patient in cloud
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E. S. Madhan and K. Padmanaban
Table 5 Average of 10 days
sample trials from a cloud
Days
Diastolic
Day-1
132
80
Day-2
140
100
Day-3
158
80
Day-4
129
85
Day-5
127
85
Day-6
128
78
Day-7
130
85
Day-8
152
88
Day-9
130
75
Day-10
140
70
1366
819
Total
Average
Table 6 Average comparison
of different measurements
Systolic
Comparisons
136.6
81.9
Systole
Diastole
Clinic based
151.2
90
Home self based
133.4
80.2
12 h cloud data
133.75
77.75
10 days cloud data
136.6
81.9
Comparison average factor of BP:
Figure 7 shows blood glucose trials of a patient from a cloud. A graph each and
every day highlights of average peak values of blood glucose content in the blood.
Blood glucose measurement (Tables 7 and 8).
6 Hadoop Experiments
Hadoop (HDP) version-2.7 is installed in Hortonworks Sandbox platform. Hortonworks is an open source of Hadoop and consists of one node cluster run on VM
virtual machine (VM). This platform helps more helpful to ramp up, analyze and test
the live streaming data.
Steps for the analysis of sensor data in Hadoop:
(1)
To extract and load sensor data files into a Sandbox
Sensor files are present in .csv format, e.g., BP.csv. the collected data from various
patients through sensor devices. Next step is to load the sensor data into HDFS by
IoT: A Novel Method for White Coat Effect (WCE) Detection …
945
Fig. 7 Sample of 10 days trials on blood glucose measurement
Table 7 Measurement of
blood glucose levels
Blood glucose
(mm/dl)
Risk
Actions
50
Low
Seek attention
72–108
Normal
No action
120–180
Medium
Consultation required
215–280
High
Seek attention
315 and higher
Very high
Immediate action
Source healthaick.com
Table 8 Sample trials of
10 days glucose levels from a
cloud
Days in Number
Day-1
Blood glucose mg/dl
140
Day-2
130
Day-3
(220)
Day-4
180
Day-5
100
Day-6
136
Day-7
133
Day-8
(218)
Day-9
110
Day-10
160
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E. S. Madhan and K. Padmanaban
using Flume. Flume helps to insert live streaming sensor data into HDFS for storage
and analysis of the large volume of data by reducing a period of time and without a
data loss.
(2)
Apache Sqoop
Sqoop is used to shift the file, e.g., BP.csv to a structured data format in HDFS.
(3)
Hive scripts for fine-tuning sensor data
To load a data into hive in two different ways, namely internal table and external
table
(a)
(b)
Internal Table:
(i) HDFS: hive > load data local in path <file path> into table <table
name>
(ii) Local File System: hive > load data in path <file path> into table
<table name>
External Table: hive > create external table patient (pressure float, id int)
> row format delimited
> fields terminated by ‘\t’
> location “/src/rack”;
(4)
Apache Zeppelin or MS Excel to access elegant data and visualize data.
(i)
(ii)
(iii)
ODBC driver for report analysis in MS Excel
MS Excel for visualization of limited data > From Microsoft Query
Apache Zeppelin for visualization of unlimited data.
7 Measurement Accuracy and Error Out
In this section, to evaluate the accuracy of patient measurement which helps for future
treatments is discussed to find out the error between the estimated value and measured
value of a patient in a satisfactory range. Since the absolute predicted readings cannot
be measured by any device, therefore, an accuracy of predicted value using the error
is between a measured value of a patient and estimated value.
Directions to better accuracy in measurement:
1.
2.
3.
4.
To measure the highest level of precision by using a device.
To avoid parallax errors applying a proper method of measuring device.
To repeat identical measurements, several times until average value is got.
To assist a measurement below control conditions
Because measurement will depend on the same conditions in certain situations
due to variation upon the patient’s age.
IoT: A Novel Method for White Coat Effect (WCE) Detection …
(a)
947
Absolute error:
Find actual large error
|Vmeasured − Vestimated |
Vmeasure
Error =
(b)
Relative error:
Find large error in relative to the right value.
Error =
|Vmeasured − Vactual |
Vactual
The same set of experiments should test on 15 or more than in times for avoiding
random fluctuation.
n
1 |Vmeasured − Vestimated |
Error =
n j=1
Vmeasure
(c)
Stability of evaluation metric
=
(d)
EVAL stability = Variance (Error)
EVAL stability = Variance
Percentage of error (%):
=
|Vmeasured − Vactual |
· 100%
Vactual
Thus, equation helps to the evaluation methods for the estimation of predicted
values for patient treatments.
A sample model for calculating accuracy:
Source
Systole
Diastole
Cloud reference
152
90
Clinical reference
136
82
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E. S. Madhan and K. Padmanaban
Table 9 Error in percentage of cloud and clinical reference values
% Error
Systole
Diastole
Absolute
10.5
8.8
Relative
11.7
9.7
(a)
Absolute error:
Cloud Reference − Clinical Reference
Cloud Reference
152−136
= 0.105
152
90−82
90 = 0.088
Systole:
Diastole:
(b) Relative error:
Cloud Reference − Clinical Reference
Clinical Reference
Systole:
Diastole:
152−136
= 0.117
136
90−82
82 = 0.097
Table 9 shows the percentage error present in cloud reference and clinical reference
for better treatments.
8 Limitations of Research
While the research has been delivered and achieved the goal of accuracy in WCE
treatments, there was some limitation. Our experiments and analysis of healthcare
details are in India, but in global scenario presents various ethnic groups results that
may differ on various old age and young age peoples and also peoples who affect
from any other disease.
9 Performance Results
Healthcare informatics wants to visualize the clear picture of the patient analysis
report. Figure 8 shows the sample of five patient systoles and diastoles of average
peak points of BP in a day from a cloud. The graphical visualization can be viewed
IoT: A Novel Method for White Coat Effect (WCE) Detection …
949
200
150
100
SYSTOLE
50
DIASTOLE
0
Fig. 8 Average peak values of BP in a day
400
300
200
100
0
Blood Glucose
Blood Glucose
Fig. 9 Average peak values of blood glucose in a day
through the Zeppelin. Similarly, Fig. 9 illustrates the samples of patient blood glucose
level average peak values in a day from a cloud. The noninvasive glucose meter is fast
and has high efficiency in testing and carries out a reading within 10 s and forwards
to cloud server.
Figure 10 shows the samples of pedometer patient average peak value in a day,
the number of calories burned by counting steps per day and the calculation results
based on the classification of a walk and run mode.
10 Conclusion and Future Work
In the Indian population, WCE is a primary important issue in contrast to clinical treatments. The degree of treatment increases with the growth of hypertension
due to WCE, therefore, masked hypertension arises due to the false impression of
BP measurements in a clinic that may lead to consuming more dosage tablets and
medicine which may cause organ damage and cardiovascular risk. Accuracy in treatment for a patient is more important due to the deflecting of adverse side effects. Thus,
our experiment results reinforce the accuracy with prediction guide of patient diagnosis which helps to suggest appropriate treatments to avoid unwanted prescription
drugs intake by the patients.
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E. S. Madhan and K. Padmanaban
Fig. 10 Average values of pedometer of a patient
Acknowledgements This research work was partially affirmed by Bio-products Healthcare
Company and also helps financially support the improvement of the paper.
References
1. John N, Shenoy S (2014) Health cloud—healthcare as a service (HaaS). In: Proceedings of the
2014 international conference on advances in computing, communications and informatics,
ICACCI 2014, pp 1963–1966. http://doi.org/10.1109/ICACCI.2014.6968627
2. Misra S, Chao H-C, Tirkey R, Mondal A, Bera S, Chattopadhyay S (2014) Optimal gateway
selection in sensor—cloud framework for health monitoring. IET Wirel Sens Syst 4(2):61–68.
https://doi.org/10.1049/iet-wss.2013.0073
3. Sawand A, Djahel S, Zhang Z, Naït-abdesselam F (n.d.) Toward energy-efficient and
trustworthy eHealth monitoring system, pp 46–65
4. Xu B, Da Xu L, Cai H, Xie C, Hu J, Bu F (2014) Ubiquitous data accessing method in IoT-based
information system for emergency medical services. IEEE Trans Industr Inf 10(2):1578–1586.
https://doi.org/10.1109/TII.2014.2306382
5. Hu HAN, Wen Y, Member S, Chua T (2014) Toward scalable systems for big data analytics :
a technology tutorial-2
6. Yu C-M, Chen C-Y, Chao H-C (2015) Verifiable, privacy-assured, and accurate signal collection
for cloud-assisted wireless sensor networks. IEEE Commun Mag 53(8):48–53. https://doi.org/
10.1109/MCOM.2015.7180507
7. Rao S, Suma SN, Sunitha M (2015) Security solutions for big data analytics in healthcare. In: 2015 second international conference on advances in computing and communication
engineering, pp 510–514. https://doi.org/10.1109/ICACCE.2015.83
8. Pohn B, Horauer M (2014) A cloud-based integrated development environment for embedded
systems
IoT: A Novel Method for White Coat Effect (WCE) Detection …
951
9. Tomicic M, Petric D, Rumboldt M, Carevic V, Rumboldt Z (2015) Deep breathing: a simple
test for white coat effect detection in primary care. Blood Press 24(1651–1999 (Electronic),
pp 158–163. http://doi.org/10.3109/08037051.2014.997102
10. http://news.health.com/2014/03/26/doctors-really-do-raise-your-blood-pressure/
11. Krahn T, Eichelberg M, Gudenkauf S, Erturkmen GBL, Appelrath H-J (2014) Adverse drug
event notification system: reusing clinical patient data for semi-automatic ADE detection. In:
Proceedings of the 27th IEEE international symposium on computer-based medical systems
(CBMS 2014)—to be published, pp 251–256. http://doi.org/10.1109/CBMS.2014.49
12. Parane KA, Patil NC, Poojara SR, Kamble TS (2014) Cloud based intelligent healthcare
monitoring system. In: 2014 international conference on issues and challenges in intelligent
computing techniques (ICICT), pp 697–701. https://doi.org/10.1109/ICICICT.2014.6781365
13. Cobos B, Haskard-Zolnierek K, Howard K (2015) White coat hypertension: improving the
patient-health care practitioner relationship. Psychol Res Behav Manag 8:133–41. https://doi.
org/10.2147/PRBM.S61192
14. Nepal S, Ranjan R, Choo K-KR (2015) Trustworthy processing of healthcare big data in hybrid
clouds. IEEE Cloud Comput 2(2):78–84. https://doi.org/10.1109/MCC.2015.36
15. Through N (2015) AIWAC: an effective interaction through wearable computing and cloud
technology, pp 20–27
16. Fukuhara M, Arima H, Ninomiya T, Hata J, Hirakawa Y, Doi Y, Kiyohara Y (2013) White-coat
and masked hypertension are associated with carotid atherosclerosis in a general population: the
Hisayama study. Stroke 44(6):1512–1517. http://doi.org/10.1161/STROKEAHA.111.000704
17. http://www.huffingtonpost.com/entry/white-coat-syndrome-blood-pressure-hypertension_us5
641204ee4b0411d3072366e?section=india
18. Tientcheu D, Ayers C, Das SR, McGuire DK, De Lemos JA, Khera A, Vongpatanasin W
et al (2015) Target organ complications and cardiovascular events associated with masked
hypertension and white-coat hypertension: analysis from the Dallas heart study. J Am Coll
Cardiol 66(20):2159–2169. https://doi.org/10.1016/j.jacc.2015.09.007
19. http://www.hypertension-bloodpressure-center.com/white-coat-syndrome.html
20. Martin U, Haque MS, Wood S, Greenfield SM, Gill PS, Mant J, McManus RJ et al (2015)
Ethnicity and differences between clinic and ambulatory blood pressure measurements. Am J
Hypertens 28(6):729–738. https://doi.org/10.1093/ajh/hpu211
21. Ps PS (2015) Posters’ session reference values of pulse pressure, heart rate, load and variability
of blood pressure in normotensive patients blood pressure measurement and variability (2015)
22. Tanner RM, Shimbo D, Seals SR, Reynolds K, Bowling CB, Ogedegbe G, Muntner P (2016)
White-coat effect among older adults: data from the Jackson heart study. J Clin Hypertension
18(2):139–145. https://doi.org/10.1111/jch.12644
23. Banegas JR, Ruilope LM, De La Sierra A, De La Cruz JJ, Gorostidi M, Segura J, Williams
B (2014) High prevalence of masked uncontrolled hypertension in people with treated
hypertension. Eur Heart J 35(46):3304–3312. https://doi.org/10.1093/eurheartj/ehu016
24. Verdecchia P, Angeli F, Reboldi G (2015) Masked and white-coat hypertension: moving to
African Americans. J Am Coll Cardiol 66(20):2170–2172. https://doi.org/10.1016/j.jacc.2015.
09.008
25. Measurement BP (2015) Oral Sessıon 1b 24 Hour Modulatıon Of Perıpherand Central Stıffness
In Heart Transplant Hypertensıve Indıvıduals Central Versus Perıpheral Blood Pressure In
Predıctıng Target-Organ Damage: A Systematıc Factors For Progressıon To Sustaıned, 5–6
26. Dzombeta S, Stantchev V, Berlin SRHH (2014) Cloud computing services for the life sciences
27. Messerli FH, Cotiga D (2005) Masked hypertension and white-coat hypertension: therapeutic
navigation between scylla and charybdis. J Am Coll Cardiol 46(3):516–517. https://doi.org/
10.1016/j.jacc.2005.05.001
28. Thomas O, Shipman KE, Day K, Thomas M, Martin U, Dasgupta I (2015) Prevalence and
determinants of white coat effect in a large UK hypertension clinic population. J Human
Hypertension 1–6. http://doi.org/10.1038/jhh.2015.95
29. http://www.medscape.com/viewarticle/482891