How does pressure measured by sphygmomanometer translate directly to blood pressure?

How does pressure measured by sphygmomanometer translate directly to blood pressure?

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As per my understanding a sphygmomanometer when wrapped around the arm and inflated only measures the pressure of the air inside the cuff, doesn't it? How does that translate directly to the pressure value of blood flowing across the artery?

Korotkoff sounds!

The blood pressure measurement process is fairly cool, and goes like this.

  1. Inflate the cuff to well over plausible blood pressures (250mmHg or so).
  2. Slowly deflate the cuff while listening to the artery.
  3. When you start to hear sounds, that's when the systolic blood pressure is higher than the cuff pressure and the heart can squeeze a little blood through the cuff, which makes a little squirty noise.
  4. As cuff pressure continues to drop it stays between the systolic and diastolic blood pressures and therefore bloodflow stops and starts and creates audible turbulence.
  5. When the artery stops making noise, the cuff pressure is below the diastolic blood pressure and the cuff has no effect on the artery, so it goes back to laminar flow that doesn't make noise.

Measuring the pressure of the cuff is just part of controlling the pressure of the cuff. You could conceivably measure blood pressure by submerging their arm in water(mercury would not require a conversion, but vats of mercury are unpopular in doctor's offices) and listening for Korotkoff sounds and measuring the depths where they start and stop.

Edit: The actual question here is how does cuff pressure physically change the arterial surroundings, not how do we link the two clinically.

The body is mostly water, and water is incompressible. For small strains(amount of deformation) flesh isn't that springy and mostly "flows" like water. When you pressurize the cuff around the arm, the flesh of the arm equilibrates to the cuff pressure the way balloons equilibrate to the air pressure, only less dramatically because water is mostly incompressible. For small pressures and large cuffs, therefore, the cuff pressure is the pressure on the artery wall, and by listening for Korotkoff sounds while varying the cuff pressure the blood pressure can be roughly established.

The reason why you don't lose all blood flow to your extremities while diving underwater is because the systemic pressure on all of your body raises your internal blood pressure, making the net external pressure on any part of your body zero. (The cuff works by exerting a relative pressure difference on part of your body.) At high altitude the sphygmanometer is still accurate-ish because the cuff pressure relative to the atmosphere is still the same as the blood pressure, relative to the atmosphere. If you had air for blood(or any compressible fluid) your veins would collapse underwater. This is why your ears hurt while diving deeply if you don't equalize them: your ear is full of compressible air, and at depth the air attempts to shrink more than the flesh around it, pulling things out of place painfully.

Comparing blood pressure to fluid power pressure

Blood pressure is an interesting measurement of biological health. Most of us use the term as if it has no relation at all to fluid mechanics, and we might as well call it floxcore gleblu or some other randomly generated name (actual randomly generated word). In pneumatics, 150 psi is considered very high, and most in the hydraulic realm don’t bat an eyelash at 6000 psi, and even 10,000 psi plus has a place in the hydraulic tool industry.

Blood pressure is measured in millimeters of mercury. Most of you will recognize the archaic use of mercury for measuring vacuum. Mercury is a dense, heavy liquid with consistently low viscosity over a wide temperature range. It is used to measure pressure because it’s dense and does not evaporate easily, providing consistent and repeatable measurements. However, in the fluid power realm, we measure using inches of mercury.

I’ll save you from having to Google the conversion of millimeters to inches there are 25.4 millimeters in an inch. This means if you have a pressure gauge reading 1 inch per mercury, it can be further split up into 25.4 millimeters of mercury. One advantage of mm/hg is that it provides a high level of accuracy. A measurement of 15 mm/hg is just 0.59 in/hg, which is a rather arbitrary method to express pressure.

The second advantage of using millimeters of mercury, is that it provides a solid range of integers to represent a relatively low pressure. If you’re like me, inches of mercury is nearly as meaningless as describing distance in light-minutes. My preferred pressure unit is pounds per square inch. How does a millimeter of mercury translate into psi?

One single psi is equal to 51.7 mm/hg, and this is where it gets interesting. We can all describe blood pressure, and most of you know the range you typically fall within. Every beat of our heart creates a rise and fall of our blood pressure, and the range you see represents the maximum and minimum pressure, which is systolic and diastolic respectively.

The target blood pressure for most healthy adults is 120/80, where 120 is the systolic blood pressure during heart contraction, and 80 is the diastolic blood pressure during heart relaxation. Those of you who beat me to the calculator will have converted these numbers to psi and resulted in 2.32/1.55 as the normal pressure range. You read that right our normal blood pressure would not even be able to move the needle on a hydraulic or pneumatic pressure gauge.

If you are an unfortunate individual with “high” blood pressure, you might still find it difficult to hit 3 psi of systolic blood pressure, which equates to red-faced 155 mm/hg. That all the oxygen, nutrients, hormones, and other substrates our bodies need to live is circulated by less than 3 psi is absolutely astounding. It also gives us a clue how our bodies can be so fragile in some ways, explaining the warning that air guns should never be blown directly onto your skin. Further to this, it gives you an appreciation for the pressure we’ve been able to achieve in the fluid power industry. And the next time you’re getting your blood pressure checked, don’t complain about the tightness of the cuff … it’s only 3 psi.

How Do Automatic Blood Pressure Monitors Work?

Over time, untreated or undetected blood pressure can permanently weaken and damage your blood vessels. Having high blood pressure increases your risk for heart disease and stroke, which the Centers for Disease Control and Prevention accounts as the first and third leading causes of death in the United States.

If you are experiencing serious medical symptoms, seek emergency treatment immediately.

High blood pressure can be managed through monitoring by your physician and at home 1. The most common way you can regularly check your blood pressure at home is through the use of an automatic blood pressure cuff.


Three data points from the iPhone 6s smartphone were treated as missing values according to the iPhysioMeter SM ’s built-in algorithm detecting outliers 34 . One data point from the dedicated photoplethysmograph could not be analysed because of an intense artefact, and was removed. This unanalysable data point was overlapped with one of three data points treated as missing values in the smartphone data set.

MAP, SBP, DBP, HR, and ln mNPV values during BL and MA

The mean values of each index during baseline (BL) and mental arithmetic (MA), together with other statistics, are shown in Table 1.

HR and ln mNPV values derived using the smartphone

The agreement between HR and ln mNPV measurements derived from the iPhysioMeter SM run on an iPhone 6s and a laboratory photoplethysmograph with a 16-bit A/D converter are shown in Fig. 1.

The agreement of heart rate (HR) and natural log transformation (ln) modified normalized pulse volume (mNPV) measurements derived from an iPhone 6s (iPhone) and a laboratory photoplethysmograph (PPG). (Upper) Solid line represents the geometric mean regression line and its formula, together with r value, is shown in each scatterplot. (Lower) Corresponding Bland–Altman plots. Solid line and dashed lines represent fixed bias (M) and M ± 1 standard deviation (SD) range, respectively. Average = (iPhone + PPG)/2, Difference = PPG – iPhone. N = 49.

Multiple linear regression analyses

The results of multiple linear regression analyses using brachial ln MAP, ln SBP, and ln DBP as independent variables and using ln HR and ln mNPV derived using only the smartphone as dependent variables are summarized in Table 2(A). The same analyses replacing data from the smartphone with data from the dedicated photoplethysmograph are shown in Table 2(B).

Accuracy of MAP, SBP, and DBP estimates

Scatterplots of paired MAP, SBP, and DBP values estimated using only the smartphone and measured using a brachial cuff sphygmomanometer, together with their Bland–Altman plots, are shown in Fig. 2(A). The same analyses using those from the dedicated photoplethysmograph are shown in Fig. 2(B). The estimated MAP, SBP, and DBP from the smartphone and the dedicated photoplethysmograph were not correlated with their residuals, respectively (all |r|s < 0.03). Shapiro-Wilk tests did not detect a strong violation of normality of the distribution in the residuals (all ps > 0.026).

The accuracy of blood pressure estimation attained by the proposed method. (A) Scatterplots of mean arterial pressure (MAP: Left), systolic blood pressure (SBP: Middle), and diastolic blood pressure (DBP: Right) estimated using only the smartphone and measured using a brachial cuff sphygmomanometer (N = 49). (Upper) Solid line represents the regression line and its formula, together with r value, is shown in each scatterplot. (Lower) Corresponding Bland–Altman plots. Solid line and dashed lines represent fixed bias (M) and M ± 1 standard deviation (SD) range, respectively. Average = (estimate + brachial)/2, Difference = brachial – estimate. (B) The same analyses of (A) but using data from the dedicated photoplethysmograph (N = 51).

In this review of the literature and commentary, we examine the literature on automated blood pressure (BP) measurements in the office and clinic. Our purpose is to revisit issues as to the pros and cons of automated BP measurement published in Hypertension in June 2020 and to identify areas needing additional research. Despite initial reservations about automated BP, it is here to stay. A number of experts suggest that human error will be reduced when we move from the more complex skills required by aneroid sphygmomanometer measurement to the fewer skills and steps required by automated BP measurement. Our review indicates there is still need for reduction in errors in automated BP assessment, for example, retraining programs and monitoring of assessment procedures. We need more research on the following questions: (1) which classes of health care providers are least likely to measure BP accurately, usually by ignoring necessary steps (2) how accurate is BP assessment by affiliated health care providers for example the dental office, the optometrist and (3) why do some dedicated and well-informed health care professionals fail to follow simple directions for automated BP measurement? We offer additional solutions for improving automated BP assessment in the office and clinic.

Our first goal in this review and commentary was to provide an updated literature review of the pro and con debate on automated blood pressure measurement published in the October 20, 2019 issue of Hypertension. 1,2 In the short time since this landmark article has been published, there have been additional articles that speak to the specific points made by the authors of the pro and con opinions.

Our reading of this debate, and the articles that followed, convinced us that the accuracy of blood pressure (BP) measurement in the office and clinic was an important topic for further review and discussion. Thus, our second goal was to review the literature pertaining to human errors in BP measurement in the office and clinic setting. Data Supplement 1 summarizes the procedures employed to conduct the review.

Inaccuracy in office and clinic BP assessment can be defined as the deviation from what is considered a BP value obtained gold standard measurement procedure (eg, invasive direct measurement at the aorta), deviation from BP values obtained by an expert at BP assessment, or deviation from the patient’s typical day-time BP. We agree with these definitions, but we focus primarily on inaccuracy in BP measurement resulting from human errors in following the prescribed and approved procedures for BP assessment.

Based on our observation that measurement error is still present despite the use of automated devices, our third goal was to review existing practices and advance new suggestions for improving BP measurement by health care professionals.

Myers et al 3–5 published extensive reviews of the history of automated BP measurement, including its advantages with regard to improving the diagnostic process and reducing errors in measurement. Automated office BP measurement (AOBPM) was defined as follows: measurement with a fully automated device in the office by physicians, nurses, and other trained medical personnel (office medical staff) measurement with an automated device by the patient in a quiet room separate from office staff. Our review is constrained to the first part of the definition, measurement with a fully automated device in the office by physicians, nurses, and other trained medical personnel (office medical staff), for 2 reasons: (1) we wish to focus on the direct interaction between physicians, nurses, and other medical staff with the patient and (2) the literature on patient self-measurement is too extensive to deal with in a single review. Consequently, this review does not include office measurement by the patient in a quiet room, 24-hour ambulatory BP measurement, nor does it include white coat hypertension. These are important topics but go beyond our focus on AOBPM in the clinic. Articles by Myers and et al, 3–5 among others, provide comprehensive reviews of these topics. The pro and con arguments for AOBPM in all contexts, provides an important historic context for our comments and further review of AOBPM as we more narrowly define it. In summary, we are using AOBPM as an abbreviation of automated office blood pressure measurement (an acronym). We are simply abbreviating automated office blood pressure measurement and making it clear that AOBPM in this article is not synonymous with AOBPM as defined by Myers et al, but simply as a generic abbreviation for AOBPM.

Historic Overview: The Pros and Cons of Automated BP Measurement

In October 2019, Hypertension published 2 articles presenting pro 1 and con 2 positions on the implementing of AOBPM in medical practice. At this time, AOBPM was gaining popularity. For example, the fully automated oscillometric sphygmomanometer was used in the SPRINT (Systolic Blood Pressure Intervention Trial), 6–8 and AOBPM for BP assessment in Candida 9 was accepted.

The Pro Side by Jones

In general, positive features of automated BP assessment include its use for BP measurement at home, thus avoiding higher BP values related to white coat hypertension and also allowing the time needed for multiple BP assessments on one occasion and multiple assessments at multiple occasions. 1

Jones 1 raised an important question: why do we give so much attention to accuracy of BP measurement when we spend considerably less time discussing accuracy in the measurement of other risk factors such as cholesterol values or fasting glucose? He offers 2 reasons: (1) there are moment-to-moment variations in BP, that is, it is fluid and (2) BP is measured indirectly. These observations are of fundamental importance because it is absolutely essential, given fluidity of BP, to sample several BP measurements on one occasion and, ideally, on 2 successive days, before the diagnosis of hypertension. 10–12 We note that this goal can be achieved in the office and clinic, but it may be more easily achieved with automated BP assessment at home if the patient is properly educated in measurement methods and invests the time and effort to take these measurements.

We agree with Jones 1 that ease of obtaining BP measurement with automated devices, as compared with manual aneroid sphygmomanometer measurement, is a compelling reason for encouraging AOBPM. BP measurement with the aneroid sphygmomanometer involves sequential sets of activities and decisions that require human judgment, motor and perceptual skills, including keen hearing. Thus, AOBPM is less demanding on human skills than measurement with the aneroid sphygmomanometer. However, we note that AOBPM does not exempt the examiner from demands on attention, knowledge, ability to follow proper directions, and the proper execution of sequential activities. Consequently, human error has not been entirely removed from the measurement process by the adoption of AOBPM devices.

The Con Side of the AOBPM by Zhang

Zhang et al 2 do not object to AOBPM. Rather, they raise a fundamental concern that the prognostic accuracy of AOBPM (as compared to manual BP measurement) has not been established. We concur, but note that there have been several studies indicating that automated BP assessment in the home and the office predicts intermediate measures of target organ damage, for example, an index of global target organ damage including retinal and renal parameters, 13 left ventricular hypertrophy and carotid intima-medial thickness. 14–17 In the study by Myers et al 16 with 3627 community dwelling residents (>65 years of age), 10 mm Hg increments in systolic and diastolic BP (defined by AOBPM) were associated with increased risk of nonfatal or fatal cardiovascular events over a follow-up period of 4.9 years for subjects free from antihypertensive drugs at baseline. The number of studies of AOBPM in relation to cardiovascular outcomes may be expected to continue to grow, and it is a matter of time until we have additional data with bearing on the prognostic value of AOBPM.

A second concern by Zhang et al 2 is the potential increased time and office space required by the need to obtain multiple AOPBM. In agreement with Vongpatanasin, 18 we argue that multiple BP measurements are essential to capture the variability described by Jones and to adequately diagnose hypertension. Among others, Figueiredo et al 19 has recommended that BP measurements be taken on at least 2 office visits in clinical practice and 2 separate occasions in research. The very basic importance of accurate BP measurement in patient care dictates that we take the time to measure it correctly.

A third concern by Zhang et al 2 is the frequent use of inadequately calibrated automated BP devices. This concern is strongly supported by the literature. 17 However, the problem is not unique to AOBPM. Cohen et al 20 have traced the evolution of BP measurement devices from what they describe as the very accurate mercury sphygmomanometer that is no longer in use due to concerns about mercury exposure. They note that the aneroid sphygmomanometer loses calibration just as do AOBPM devices. They provide an overview of the procedures for device calibration and sources for identifying valid devices.

Calibration is clearly an important aspect of accuracy of BP assessment. Ogedegbe and Pickering 21 describe United States, British, and European associations engaged in BP device validation and improved BP measurement. An international group 22 of experts in BP measurement has created a nonprofit organization, STRIDE (Science and Technology for Regional Innovation and Development in Europe), to further the effort of lowering BP worldwide through the correct diagnosis and management of hypertension ( Even well-calibrated devices may be used incorrectly if measurement is not conducted properly by the user (human error).

Stergiou et al, 23 on behalf of the Association for the Advancement of Medical Instrumentation (The European Society of Hypertension) provide a comprehensive history of the efforts to obtain standardization and validation of BP measuring devices. They list the many associations involved in the task of standardization and validation. These investigators conclude that this organization will provide universal standards for validation of BP measuring devices. 23

Failure to calibrate one’s BP measuring device or being unaware of the need to calibrate is among the errors in human BP measurement. The wrist-cuff device has been a special concern with regard to validation. Questions have been raised as to its accuracy 21,24–28 and solutions suggested for improving accuracy, including arm-heart positioning sensors, have been advanced. 24 In summary, the consensus of opinion from the literature is that, for patients without physical disabilities or with circumstances preventing its use, the arm-cuff is to be preferred in the clinic and for opportunistic screening.

AOBP Related to Specific Patient Needs

Use of the wrong device is a human error in BP monitoring. Melville et al 29 have discussed the fact that the wrist-cuff device may cause discomfort in patients who are fragile or obese or for various reasons cannot obtain the correct posture necessary for measurement with the arm-cuff. Moreover, there is a literature indicating that automated BP monitoring is inaccurate in patients with atrial fibrillation because of the high variability of heart rate and stroke volume. 30 Pagonas et al, 30 employing intraarterial BP measurement, reported that atrial fibrillation slightly increased the intraindividual variability of oscillometric measurement however, this phenomenon did not impair the accuracy of oscillatory devices after 3 consecutive measurements. Following a review and meta-analysis of this literature, Stergiou et al 31 concluded the AOBPM monitors are accurate in measuring systolic BP but not diastolic BP in patients with atrial fibrillation.

Human Error in BP Assessment: A Fundamental Problem

Errors in AOBPM follow in the wake of concern for errors associated with manual BP assessment. One of the most recent manuals dealing with the proper measurement of BP includes a section on AOBPM 32 and the American Heart Association has published an advisory piece on how to measure BP with the AOBPM device. 33,34 Thus, concerns about automated BP assessment are essentially concerns about BP measurement in the office and clinic in general, including manual BP measurement. Vongpatanasin 18 characterizes current BP measurement practices in general as sloppy and attributes this phenomenon to bad habits learned early in medical training. Vongpatanasin’s 18 opinion was based on a now classic article by Rakotz et al, 35 in which these investigators describe their study on a simulated patient encounter for BP assessment. Rakotz et al 35 sampled BP measurement skills in 159 first-to fourth-year medical students attending a meeting of the American Medical Association. Only one of the 159 students met all of 11 criteria for the proper measurement of BP. The mean number of skills performed properly out of the 11 examined was 4.1. Importantly, Rakotz et al 35 reported their unrelenting efforts to teach the proper methods of manual BP measurement in medical school, and his concern that good techniques were often replaced by bad techniques learned from practicing physicians, as well as internship and practicum experiences. We note that in many countries practical training in medicine begins earlier than in the United States, thus allowing more time for good and bad habits to be learned and reinforced.

The failure to adhere to proper BP measurement protocol is well documented in research by Woolsey et al. 36 These investigators examined hypertension diagnosis practices in Utah primary health clinics using United States Preventive Task Force recommendations for the accurate diagnosis of hypertension. They employed a survey sent by internet to 321 primary care clinics. Thirty-eight percent of the clinics completed the questionnaires. Estimated adherence to the recommendation for proper BP measurement methods ranged from 57.5% to 93.5%. Percentages of accuracy for each BP measurement technique are shown in Table 1. The overall recommendation was that BP measurement could be improved by the use of automated devices.

Table 1. Accuracy of BP Measurement Techniques Used in Study by Woolsey 36

ABPM indicates automated office blood pressure measurement and BP, blood pressure.

Does AOBPM Eliminate Errors?

Myers’ 37 work indicates that AOBPM performed properly reduces errors in measurement compared with manual BP assessment. This finding makes good logical sense because more skill and sensory and psychomotor abilities are needed for proper measurement with the manual aneroid sphygmomanometer. In a comprehensive meta-analysis, Roerecke et al 38 found that AOBPM was more accurate than other methods of BP assessment typically employed in the office and recommend that AOBPM be the preferred method of measuring BP in clinical practice. We do not disagree with this conclusion. Our argument, based on our review of the literature that follows, is that AOBPM has not entirely eliminated measurement error.

Table 2 lists the most common mistakes made with AOBPM devices as described in a study by Hwang et al. 39 The authors employed data from 54 unique patient encounters at 6 adult primary care centers located in and around Houston, Texas. 39 All revealed common errors. 32 The Hwang et al 39 study involved an important feature: the investigators asked health care providers why they reported (self-report) failure to follow instructions. We paraphrase and summarize the major responses to the questionnaires in Table 3. It is clear that a number of these issues relate to work loads, time constraints, and the right equipment being available at the right time.

Table 2. Most Frequent BP Assessment Errors in 6 Clinics 39

BP indicates blood pressure.

Table 3. Seven of the Most Common Reasons for Deviation From BP Measurement Criteria 39

BP indicates blood pressure.

Who Makes Errors in BP Assessment?

If we could identify classes of health care professionals who are more likely to err in AOBPM, we would have a step up in designing remedies. For example, do we focus on physicians and nurses or other medical specialists who are called into service for BP assessment? Our review of this potential literature revealed more opinion by experts than actual data.

There is indirect evidence relative to nurses and physicians and other medical specialists which must be viewed with caution but may be helpful in hypothesis generation. Nurses obtain lower BP values than doctors in general, regardless of the measurement procedure. 40,41 This phenomenon is often attributed to less white coat reactivity to the nurse, but could also be related to factors such as taking more time to do it correctly and better educational programs for nurses. 42 At least one study indicates that BP values obtained by nurses are better predictors of hypertension-related target damage 41 thus providing indirect support for the hypothesis that nurses are measuring BP more accurately than physicians. Nursing associations are acutely aware of the errors in measurement made by nurses, and a number of studies of educational interventions have been successful in reducing error in measurement. 43

It is clear that persons at the highest professional levels, physicians, and nurses, err in BP assessment. It is estimated that up to 27 of 29 potential sources of error in BP have been identified in the measurement of BP by trained clinicians, 44 but these studies do not further classify trained clinicians by job specialty. We very much need more data on this topic. This may be a difficult task because there are many classifications of nurses and physicians in terms of education and job responsibilities.

Opportunistic Screening Outside of the Medical Clinic

Hot Spots for Inaccurate Measurement

The emphasis on opportunistic screening for hypertension has created a class of BP examiners employed in health affiliated practices, for example, dentists, optometrists, ophthalmologists, and podiatrists. Elias and Goodell 45 argue that it is very possible that measurement with AOBPM devices is done less well in these contexts, but emphasize that more data are needed to confirm this hypothesis. There is much in the way of anecdotal data from patients suggesting that issues such as lack of awareness of directions, use of a wrist-cuff device and ignoring of posture, including legs crossed and feet off the floor (eg, measurement in the dental chair) are recurring issues. We very much need data to confirm these reports of poor measurement practices.

Cost of Poor Measurement to the Patient

With the lowering of systolic and diastolic BP values (mm Hg) for the diagnosis of hypertension 11 and the use of AOBPM in clinical trials, 8,32,46 the demand for accuracy in BP assessment has increased, albeit has always been important to the patient. Woolsey et al 36 point out that a false positive diagnosis of hypertension exposes the patient to unnecessary costs of medication, side effects from medication administered unnecessarily or prematurely, and adverse psychological effects of being diagnosed as hypertensive. These adverse psychological phenomena related to being labeled as hypertensive include anxiety, depression, and adopting a sick role. 47,48 Clearly the person with hypertension has to be informed and treated, but it is important to get it right on the diagnosis and that information needs to be conveyed in a manner that avoids unnecessary stress to the patient. 49

Elias and Goodell 45 argue that a false diagnosis of hypertension can precipitate further false diagnoses if the patient becomes sensitized to the BP assessment procedure. They describe a scenario in which the patient, via classical conditioning (an elementary form of learning), may develop a learned BP elevation, so that one sees a higher than normal BP in the presence of the cuff, the examiner, and the examining room. Regrettably, we could find no studies on the proportion of patients who consult their physicians with the suspicion that they have been given a false diagnosis of hypertension or how many health affiliates doing screening actually follow-up with the patient or the patient’s physician.

From Lament to Action

Despite its promise, measurement of BP with the AOBPM devices does not seem to yet live up to its potential for better measurement. Initial training appears to be offset by the learning of poor BP measurement practices on the job. We see a literature characterized by years of lamenting poor BP measurement practices, including traditional measurement with the manual aneroid sphygmomanometer, but little evidence of success in remediation of the issues. Thus, in the following section, we recommend several approaches to alleviate this problem.

Bundling (Multi-Modal Approaches)

One promising response to the recognition of poor BP measuring practices is referred to as bundling. Boonyasai et al 50 uses it to describe programs that use AOBPM in the context of a redesign of the office workflow by using human factors and ergonomics principles. The approach can be applied to the health care practitioners’ reasons for not measuring BP accurately as discussed earlier in this article, that is, not enough time, unsuitable equipment, multitasking, etc. Rather than attempt to address these issues separately, bundling approaches them all in a systematic way and is sometimes referred to as a multidimensional approach.

Umscheid and Townsend 51 have reviewed the literature indicating that bundling has been employed successfully in the treatment of infections, and they argue that it could be used to elicit better BP assessment with automated devices.

The study of bundling by Boonyasai et al 50 provides an illustration of the application of the bundling approach to the assessment of BP. The study was designed to improve BP measurement at 6 primary care centers over a 6-month period. It was conducted as part of Project Reducing Disparities and Controlling Hypertension in Primary Care at Johns Hopkins Hospital. The study sample was representative of racial and economic diversity. The components of the program are shown in Table 4. Adherence to correct BP assessment protocol was evaluated by unannounced audits and electronic medical records.

Table 4. Components of the ReDCHiP Improvement in BP Measurement Program 50

BP indicates blood pressure and ReDCHiP, Reducing Disparities and Controlling Hypertension in Primary Care.

Overall adherence (percent of staff adhering) to the proper BP protocol at the 6 sites was 71.6%, varying from 84.6% at the best site to 19.6% at the least adherent site, with a median of 74.4%. The investigative team described the response to the program as robust, but imperfect.

Unannounced audits indicated that 5 of the 6 clinics used the automated devices and followed the rules at least 75% of the time. The need to repeat measurements by primary care personnel who received the reports decreased by 15.5% by the end of the program.

The investigators did not provide estimates as to the total cost of the bundling program. The costs of the devices (Omron HEM-907XL) used in the program can be estimated at a minimum of $530 per each from the cost data provided. This amount did not include the cost of multiple machines, follow-up training or support for the clinics involved.

The study team reached 3 major conclusions: (1) bundling improves BP assessment in the real world clinic setting (2) improvement in the workflow was a critical intervention and (3) isolated interventions are likely to have little value in the clinic setting.

Bundling is a logical and arguably successful approach to improving BP assessment. Below, we suggest some obvious micro-solutions that are very likely more appropriate for the small office or clinic setting.

Posting Instructions in the Examining Room

Hyman, 52 in a news article, points out that medical devices used in clinics and hospitals must come with an instruction manual called instructions for use, but that instructions for use are not routinely read and may not even get to the intended recipient, that is, the health care provider who is using the device. Our expertise, albeit anecdotal, is consistent with this argument. However, we could find no formal studies addressing this hypothesis. Instructions posted in the examining room serve as a reminder of appropriate measurement methods but also serve as a basis for patients to become aware of proper measurement steps and procedures. Straightforward instructions on Monitoring Your Blood Pressure at Home have been provided by the American Heart Association 32 and can be adapted for this purpose.

Proper Furniture in the Examining Room

Automated BP measurement requires a suitable chair with an armrest or a suitable table and chair. The drawing shown in Figure 1 is representative of the illustrations used in AOBPM manufacturer instruction manuals. We have not been able to find a literature in which the presence of these simple, but essential, equipment items are mentioned and no relevant surveys. However, the medical architecture literature does indicate a significant concern with improving the ergonomic design of the clinic and the examining room. 53 None of the elaborate plans in the Freihoefer et al 53 publication deal with proper furniture to measure BP. It is clear from Figure 1 (and almost all illustrations of how to measure AOBPM properly), that it requires a table and chair or a chair with armrest. These need not be high-tech furniture items. A high-end examination chair and kiosks designed for BP measurement are available, but at considerable cost. 54 However, proper measurement methods can be accomplished far more economically. Proper furniture for BP measurements should permit the patient to sit erect, with back and arm support, with the instrument cuff at the level of the heart, and there must be allowance for an adjustment of chair height so that the other criteria can be met (eg, feet flat on the floor).

Figure. Image of best practices for automated office blood pressure measurement. Drawing reprinted with permission courtesy of OMRON Healthcare, Inc.

Improved Oversight: Contributions From the Patient

Improvement in BP measurement is not likely to result from monitoring by outside regulatory forces, for example, government or state agencies, because people change their behavior when they are observed. But the first line of observation is the patient and it seems evident that health care providers do not change their behavior despite being observed by patients.

One would expect behavior in a positive direction to be affected by the fact that the patient is the captive observer of BP measurement methods. Patients complain via blog exchanges 55 that they are ignored when they question measurement procedures and these complaints are consistent with our own personal experience. Unfortunately, we could find no formal data speaking to health care providers’ responses to patient complaints or the prevalence of such complaints. This leads to a further question. Do health care providers presume that their patients are not competent or are not entitled to raise questions about the measurement procedure? This is a reasonable question, but once more, we could find no studies that speak to this issue in the context of BP measurement.

There is a literature indicating that patients fear speaking up because they will be labeled uncooperative 56 and beyond this concern, it is obviously awkward if not embarrassing to confront an expert with criticism of a manifestation of their expertise, that is, properly measuring BP.

The issue of patient empowerment to speak up was addressed by Lastinger et al 57 in the hand washing study. This investigation was conducted to determine the best ways to get patients to speak up and to evaluate health care providers’ reactions to patient speaking up behavior. Hospitalized children’s parents and adult patients were study participants (N=222). Anonymous email surveys were administered to the parents, residents, and attending physicians in the Departments of Internal Medicine, Pediatrics, and Family Medicine. Patients were provided the opportunity to use signs instead of confronting the patient care person with words. Both the patient participants and their health care providers preferred direct verbal communication to signs. Speaking up resulted in improvement in hand washing behavior. By virtue of the element of patient empowerment to speak up, this study may be a model for studies in relation to speaking up with regard to proper steps in BP assessment. We learned several important things: (1) speaking up helps (2) health care providers do not like it and (3) providers prefer to be spoken to directly with concerns.

This patient empowerment approach could possibly work if we can assume that there are informed patients. BP measurement at home, with premeasurement and follow-up training, has very likely contributed to an informed patient. Moreover, health care providers and researchers are themselves patients and are thus not lacking in training as to BP assessment. In general, we need to encourage policies and procedures that allow the patient to express concerns about BP measurement procedures. No patient should leave the clinic or office without the opportunity to comment on the BP measurement process, and commentary should be encouraged. We do not presume that patients are always or even mostly correct our call is for an opportunity for the patients to express concerns and begin an important dialogue.

To our knowledge, no studies have been done to support the micro-approaches we suggest, nor have there been cost estimates investigated. These are suggested as possibilities for implementation and research as to their effectiveness in reducing errors in AOBPM.

Limits of the Review

We do not include patient-measured BP or ambulatory BP assessment in this review. We do cite reviews by Myer et al 3–5 on this topic. We also do not discuss home BP assessment or the role of telemetry in home BP assessment given our focus on BP assessment in the office and clinic by health care professionals. See reviews of these topics by Niiranen et al, 58 Parati et al, 59 and Zullig et al, 60 among others.

Need for Fundamental Research

Aside from the various descriptive studies, it comes to a fundamental question: Why do health care providers who are devoted to their profession, their work and their patients (and sometimes perform complex procedures routinely), ignore the relatively simple directions for AOBPM? Neither the social psychological literature nor the behavioral medicine literature offers the most commonly reported popular explanation, that is, the instructions are unnecessary, irrelevant, or take too much time. It would seem that following the instructions is relevant and necessary to reliable AOBPM.


Despite initial concerns about AOBPM it is likely to be here to stay. The assumption, or hope, that the introduction of automated BP monitoring devices eliminates human error in BP measurement is not supported by the literature. In this review, we illuminate errors in measurement and argue that we need more data on which health care professionals are most likely to make errors in BP assessment and thus benefit from retraining and we summarize studies that have evaluated accuracy of automated BP measurement in the office and clinic and offer some straightforward and potentially inexpensive approaches to facilitate better measurement practices with automated BP measurements devices.

Conclusions and Perspectives

BP measurement with the automated device is here to stay in routine clinical practice 3,37,61,62 and in observational studies and clinical trials. 6,8 It holds promise for reducing human error in BP assessment, but this promise has not yet been fulfilled. Two major conclusions from our review are that AOBPM does not solve the problems of inaccuracy of BP assessment by health care providers, and that while there is general agreement that obvious errors in BP measurement are made, we have very limited data as to which health care providers are most likely to make errors. We do suspect that poor BP assessment is more likely in the context of screening programs offered by affiliate health care providers, for example, dentists and optometrists, than in the medical office or clinic. However, we have no substantial body of evidence confirming or refuting this hypothesis. This is an important future research topic because poor measurement defeats the positive goal of screening for hypertension outside the medical clinic or office, just as it defeats the goal of accurately diagnosing hypertension in the clinic, the office and research studies where accurate diagnosis is critical.

While multidimensional approaches have shown some success in improving BP assessment, we advance 3 simple suggestions, among others, where cost is a consideration: (1) make sure the health care provider gets the manufacturer’s instructions and that these instructions are posted (2) provide the simple furniture required for AOBPM in the examining room and (3) create a patient advocacy or empowerment system in the office and clinic so that informed patients can make a positive contribution to the supervision of AOBPM techniques.

If we assume that we have the issue of unreliable BP measurement solved by virtue of using AOBPM, we will experience what American icon and baseball catcher Yogi Berra described as deja vu all over again. We need to get it right this time.


Whelton PK, Carey RM, Aronow WS, Casey DE, Collins KJ, Dennison Himmelfarb C, DePalma SM, Gidding S, Jamerson KA, Jones DW, MacLaughlin EJ, Muntner P, Ovbiagele B, Smith SC, Spencer CC, Stafford RS, Taler SJ, Thomas RJ, Williams KA, Williamson JD, Wright JT

Peacock J, Diaz KM, Viera AJ, Schwartz JE, Shimbo D

Shimbo D, Abdalla M, Falzon L, Townsend RR, Muntner P

Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, Jones DW, Kurtz T, Sheps SG, Roccella EJ

Lewington S, Clarke R, Qizilbash N, Peto R, Collins R

Rapsomaniki E, Timmis A, George J, Pujades-Rodriguez M, Shah AD, Denaxas S, White IR, Caulfield MJ, Deanfield JE, Smeeth L, Williams B, Hingorani A, Hemingway H

Rutan GH, Kuller LH, Neaton JD, Wentworth DN, McDonald RH, Smith WM

Sesso HD, Stampfer MJ, Rosner B, Hennekens CH, Gaziano JM, Manson JE, Glynn RJ

Stamler J, Stamler R, Neaton JD

Benetos A, Thomas F, Bean K, Gautier S, Smulyan H, Guize L

Franklin SS, Khan SA, Wong ND, Larson MG, Levy D

Mancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, Böhm M, Christiaens T, Cifkova R, De Backer G, Dominiczak A, Galderisi M, Grobbee DE, Jaarsma T, Kirchhof P, Kjeldsen SE, Laurent S, Manolis AJ, Nilsson PM, Ruilope LM, Schmieder RE, Sirnes PA, Sleight P, Viigimaa M, Waeber B, Zannad F, Redon J, Dominiczak A, Narkiewicz K, Nilsson PM, Burnier M, Viigimaa M, Ambrosioni E, Caufield M, Coca A, Olsen MH, Schmieder RE, Tsioufis C, van de Borne P, Zamorano JL, Achenbach S, Baumgartner H, Bax JJ, Bueno H, Dean V, Deaton C, Erol C, Fagard R, Ferrari R, Hasdai D, Hoes AW, Kirchhof P, Knuuti J, Kolh P, Lancellotti P, Linhart A, Nihoyannopoulos P, Piepoli MF, Ponikowski P, Sirnes PA, Tamargo JL, Tendera M, Torbicki A, Wijns W, Windecker S, Clement DL, Coca A, Gillebert TC, Tendera M, Rosei EA, Ambrosioni E, Anker SD, Bauersachs J, Hitij JB, Caulfield M, De Buyzere M, De Geest S, Derumeaux GA, Erdine S, Farsang C, Funck-Brentano C, Gerc V, Germano G, Gielen S, Haller H, Hoes AW, Jordan J, Kahan T, Komajda M, Lovic D, Mahrholdt H, Olsen MH, Ostergren J, Parati G, Perk J, Polonia J, Popescu BA, Reiner Z, Rydén L, Sirenko Y, Stanton A, Struijker-Boudier H, Tsioufis C, van de Borne P, Vlachopoulos C, Volpe M, Wood DA

Williams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, Clement DL, Coca A, de Simone G, Dominiczak A, Kahan T, Mahfoud F, Redon J, Ruilope L, Zanchetti A, Kerins M, Kjeldsen SE, Kreutz R, Laurent S, Lip GYH, McManus R, Narkiewicz K, Ruschitzka F, Schmieder RE, Shlyakhto E, Tsioufis C, Aboyans V, Desormais I

Forouzanfar M, Dajani HR, Groza VZ, Bolic M, Rajan S, Batkin I

Kallioinen N, Hill A, Horswill MS, Ward HE, Watson MO

Manning DM, Kuchirka C, Kaminski J

Tolonen H, Koponen P, Naska A, Männistö S, Broda G, Palosaari T, Kuulasmaa K

Pan F, Zheng D, He P, Murray A

Liu C, Griffiths C, Murray A, Zheng D

Stergiou GS, Alpert B, Mieke S, Asmar R, Atkins N, Eckert S, Frick G, Friedman B, Graßl T, Ichikawa T, Ioannidis JP, Lacy P, McManus R, Murray A, Myers M, Palatini P, Parati G, Quinn D, Sarkis J, Shennan A, Usuda T, Wang J, Wu CO, O’Brien E

Benmira A, Perez-Martin A, Schuster I, Aichoun I, Coudray S, Bereksi-Reguig F, Dauzat M

Chio SS, Urbina EM, Lapointe J, Tsai J, Berenson GS

Shennan A, Gupta M, Halligan A, Taylor DJ, de Swiet M

Conroy RM, Atkins N, Mee F, O’Brien E, O’Malley K

O’Brien E, Mee F, Atkins N, O’Malley K

Kronmal RA, Rutan GH, Manolio TA, Borhani NO

Alpert BS, Quinn D, Gallick D

Turner MJ, Speechly C, Bignell N

Stergiou GS, Karpettas N, Kollias A, Destounis A, Tzamouranis D

Tasker F, De Greeff A, Shennan AH

Benmira AM, Perez-Martin A, Coudray S, Schuster I, Aichoun I, Laurent J, Bereski-Reguig F, Dauzat M

O’Brien E, Atkins N, Stergiou G, Karpettas N, Parati G, Asmar R, Imai Y, Wang J, Mengden T, Shennan A

Dahlöf B, Sever PS, Poulter NR, Wedel H, Beevers DG, Caulfield M, Collins R, Kjeldsen SE, Kristinsson A, McInnes GT, Mehlsen J, Nieminen M, O’Brien E, Ostergren J

Jones CR, Taylor K, Poston L, Shennan AH

Armstrong D, Matangi M, Brouillard D, Myers MG

Myers MG, Valdivieso M, Kiss A

Lamarre-Cliché M, Cheong NN, Larochelle P

Myers MG, Valdivieso M, Kiss A, Tobe SW

Myers MG, Godwin M, Dawes M, Kiss A, Tobe SW, Grant FC, Kaczorowski J

Myers MG, Valdivieso M, Kiss A

Myers MG, Valdivieso M, Kiss A

Godwin M, Birtwhistle R, Delva D, Lam M, Casson I, MacDonald S, Seguin R

Myers MG, Valdivieso M, Chessman M, Kiss A

Andreadis EA, Agaliotis GD, Angelopoulos ET, Tsakanikas AP, Chaveles IA, Mousoulis GP

Padwal RS, Townsend RR, Trudeau L, Hamilton PG, Gelfer M

Ringrose JS, Cena J, Ip S, Morales F, Hamilton P, Padwal R

Myers MG, Oh PI, Reeves RA, Joyner CD

Campbell NR, McKay DW, Conradson H, Lonn E, Title LM, Anderson T

Kaczorowski J, Chambers LW, Dolovich L, Paterson JM, Karwalajtys T, Gierman T, Farrell B, McDonough B, Thabane L, Tu K, Zagorski B, Goeree R, Levitt CA, Hogg W, Laryea S, Carter MA, Cross D, Sabaldt RJ

Myers MG, Kaczorowski J, Paterson JM, Dolovich L, Tu K

Myers MG, Kaczorowski J, Dolovich L, Tu K, Paterson JM

Leung AA, Daskalopoulou SS, Dasgupta K, McBrien K, Butalia S, Zarnke KB, Nerenberg K, Harris KC, Nakhla M, Cloutier L, Gelfer M, Lamarre-Cliche M, Milot A, Bolli P, Tremblay G, McLean D, Tran KC, Tobe SW, Ruzicka M, Burns KD, Vallée M, Prasad GVR, Gryn SE, Feldman RD, Selby P, Pipe A, Schiffrin EL, McFarlane PA, Oh P, Hegele RA, Khara M, Wilson TW, Penner SB, Burgess E, Sivapalan P, Herman RJ, Bacon SL, Rabkin SW, Gilbert RE, Campbell TS, Grover S, Honos G, Lindsay P, Hill MD, Coutts SB, Gubitz G, Campbell NRC, Moe GW, Howlett JG, Boulanger JM, Prebtani A, Kline G, Leiter LA, Jones C, Côté AM, Woo V, Kaczorowski J, Trudeau L, Tsuyuki RT, Hiremath S, Drouin D, Lavoie KL, Hamet P, Grégoire JC, Lewanczuk R, Dresser GK, Sharma M, Reid D, Lear SA, Moullec G, Gupta M, Magee LA, Logan AG, Dionne J, Fournier A, Benoit G, Feber J, Poirier L, Padwal RS, Rabi DM

Kaczorowski J, Myers MG, Gelfer M, Dawes M, Mang EJ, Berg A, Grande CD, Kljujic D

Mancia G, Parati G, Pomidossi G, Grassi G, Casadei R, Zanchetti A

Bauer F, Seibert FS, Rohn B, Bauer KAR, Rolshoven E, Babel N, Westhoff TH

Johnson KC, Whelton PK, Cushman WC, Cutler JA, Evans GW, Snyder JK , Ambrosius WT, Beddhu S, Cheung AK, Fine LJ, Lewis CE, Rahman M, Reboussin DM, Rocco MV, Oparil S, Wright JT

Stergiou G, Kollias A, Parati G, O’Brien E

Powers BJ, Olsen MK, Smith VA, Woolson RF, Bosworth HB, Oddone EZ

Clark CE, Taylor RS, Shore AC, Campbell JL

Eguchi K, Yacoub M, Jhalani J, Gerin W, Schwartz JE, Pickering TG

Weinberg I, Gona P, O’Donnell CJ, Jaff MR, Murabito JM

Clark CE, Taylor RS, Butcher I, Stewart MC, Price J, Fowkes FG, Shore AC, Campbell JL

Wright JT, Williamson JD, Whelton PK, Snyder JK, Sink KM, Rocco MV, Reboussin DM, Rahman M, Oparil S, Lewis CE, Kimmel PL, Johnson KC, Goff DC, Fine LJ, Cutler JA, Cushman WC, Cheung AK, Ambrosius WT

Stergiou GS, Baibas NM, Gantzarou AP, Skeva II, Kalkana CB, Roussias LG, Mountokalakis TD

Freestone S, Silas JH, Ramsay LE

Sakuma M, Imai Y, Nagai K, Watanabe N, Sakuma H, Minami N, Satoh H, Abe K

Espinosa R, Spruill TM, Zawadzki MJ, Vandekar L, Garcia-Vera MP, Sanz J, Pickering TG, Linden WL, Gerin W

Roubsanthisuk W, Wongsurin U, Saravich S, Buranakitjaroen P

Muntner P, Carey RM, Gidding S, Jones DW, Taler SJ, Wright JT, Whelton PK

. Ambulatory monitoring of blood pressure: an overview of devices, analyses, and clinical utility.

Perloff D, Sokolow M, Cowan R

Shimbo D, Kent ST, Diaz KM, Huang L, Viera AJ, Kilgore M, Oparil S, Muntner P

Pickering TG, Gerin W, Schwartz JE, Spruill TM, Davidson KW

. Validation and reliability testing of blood pressure monitors.

di Rienzo M, Grassi G, Pedotti A, Mancia G

Parati G, Stergiou G, O’Brien E, Asmar R, Beilin L, Bilo G, Clement D, de la Sierra A, de Leeuw P, Dolan E, Fagard R, Graves J, Head GA, Imai Y, Kario K, Lurbe E, Mallion JM, Mancia G, Mengden T, Myers M, Ogedegbe G, Ohkubo T, Omboni S, Palatini P, Redon J, Ruilope LM, Shennan A, Staessen JA, vanMontfrans G, Verdecchia P, Waeber B, Wang J, Zanchetti A, Zhang Y

O’Brien E, Parati G, Stergiou G, Asmar R, Beilin L, Bilo G, Clement D, de la Sierra A, de Leeuw P, Dolan E, Fagard R, Graves J, Head GA, Imai Y, Kario K, Lurbe E, Mallion JM, Mancia G, Mengden T, Myers M, Ogedegbe G, Ohkubo T, Omboni S, Palatini P, Redon J, Ruilope LM, Shennan A, Staessen JA, vanMontfrans G, Verdecchia P, Waeber B, Wang J, Zanchetti A, Zhang Y

de la Sierra A, Redon J, Banegas JR, Segura J, Parati G, Gorostidi M, de la Cruz JJ, Sobrino J, Llisterri JL, Alonso J, Vinyoles E, Pallarés V, Sarría A, Aranda P, Ruilope LM

Banegas JR, Ruilope LM, de la Sierra A, Vinyoles E, Gorostidi M, de la Cruz JJ, Ruiz-Hurtado G, Segura J, Rodríguez-Artalejo F, Williams B

Boggia J, Li Y, Thijs L, Hansen TW, Kikuya M, Björklund-Bodegård K, Richart T, Ohkubo T, Kuznetsova T, Torp-Pedersen C, Lind L, Ibsen H, Imai Y, Wang J, Sandoya E, O’Brien E, Staessen JA

Thomas SJ, Booth JN, Bromfield SG, Seals SR, Spruill TM, Ogedegbe G, Kidambi S, Shimbo D, Calhoun D, Muntner P

Li Y, Staessen JA, Lu L, Li LH, Wang GL, Wang JG

Stergiou GS, Malakos JS, Zourbaki AS, Achimastos AD, Mountokalakis TD

Booth JN, Muntner P, Abdalla M, Diaz KM, Viera AJ, Reynolds K, Schwartz JE, Shimbo D

Pickering TG, James GD, Boddie C, Harshfield GA, Blank S, Laragh JH

Franklin SS, Thijs L, Hansen TW, O’Brien E, Staessen JA

Omboni S, Aristizabal D, De la Sierra A, Dolan E, Head G, Kahan T, Kantola I, Kario K, Kawecka-Jaszcz K, Malan L, Narkiewicz K, Octavio JA, Ohkubo T, Palatini P, Siègelovà J, Silva E, Stergiou G, Zhang Y, Mancia G, Parati G

Brown MA, Buddle ML, Martin A

de la Sierra A, Segura J, Banegas JR, Gorostidi M, de la Cruz JJ, Armario P, Oliveras A, Ruilope LM

Muntner P, Booth JN, Shimbo D, Schwartz JE

Asayama K, Thijs L, Li Y, Gu YM, Hara A, Liu YP, Zhang Z, Wei FF, Lujambio I, Mena LJ, Boggia J, Hansen TW, Björklund-Bodegård K, Nomura K, Ohkubo T, Jeppesen J, Torp-Pedersen C, Dolan E, Stolarz-Skrzypek K, Malyutina S, Casiglia E, Nikitin Y, Lind L, Luzardo L, Kawecka-Jaszcz K, Sandoya E, Filipovský J, Maestre GE, Wang J, Imai Y, Franklin SS, O’Brien E, Staessen JA

de la Sierra A, Vinyoles E, Banegas JR, Segura J, Gorostidi M, de la Cruz JJ, Ruilope LM

Franklin SS, Thijs L, Asayama K, Li Y, Hansen TW, Boggia J, Jacobs L, Zhang Z, Kikuya M, Björklund-Bodegård K, Ohkubo T, Yang WY, Jeppesen J, Dolan E, Kuznetsova T, Stolarz-Skrzypek K, Tikhonoff V, Malyutina S, Casiglia E, Nikitin Y, Lind L, Sandoya E, Kawecka-Jaszcz K, Filipovský J, Imai Y, Wang JG, O’Brien E, Staessen JA

Fagard RH, Staessen JA, Thijs L, Gasowski J, Bulpitt CJ, Clement D, de Leeuw PW, Dobovisek J, Jääskivi M, Leonetti G, O’Brien E, Palatini P, Parati G, Rodicio JL, Vanhanen H, Webster J

Mancia G, Bombelli M, Facchetti R, Madotto F, Quarti-Trevano F, Polo Friz H, Grassi G, Sega R

Piper MA, Evans CV, Burda BU, Margolis KL, O’Connor E, Whitlock EP

Pickering TG, Davidson K, Gerin W, Schwartz JE

Wang YC, Shimbo D, Muntner P, Moran AE, Krakoff LR, Schwartz JE

Booth JN, Diaz KM, Seals SR, Sims M, Ravenell J, Muntner P, Shimbo D

Shimbo D, Newman JD, Schwartz JE

Viera AJ, Lin FC, Tuttle LA, Shimbo D, Diaz KM, Olsson E, Stankevitz K, Hinderliter AL

Schwartz JE, Burg MM, Shimbo D, Broderick JE, Stone AA, Ishikawa J, Sloan R, Yurgel T, Grossman S, Pickering TG

Franklin SS, Thijs L, Li Y, Hansen TW, Boggia J, Liu Y, Asayama K, Björklund-Bodegård K, Ohkubo T, Jeppesen J, Torp-Pedersen C, Dolan E, Kuznetsova T, Stolarz-Skrzypek K, Tikhonoff V, Malyutina S, Casiglia E, Nikitin Y, Lind L, Sandoya E, Kawecka-Jaszcz K, Filipovsky J, Imai Y, Wang J, Ibsen H, O’Brien E, Staessen JA

Gorostidi M, Sarafidis PA, de la Sierra A, Segura J, de la Cruz JJ, Banegas JR, Ruilope LM

Baguet JP, Lévy P, Barone-Rochette G, Tamisier R, Pierre H, Peeters M, Mallion JM, Pépin JL

Pogue V, Rahman M, Lipkowitz M, Toto R, Miller E, Faulkner M, Rostand S, Hiremath L, Sika M, Kendrick C, Hu B, Greene T, Appel L, Phillips RA

Sega R, Trocino G, Lanzarotti A, Carugo S, Cesana G, Schiavina R, Valagussa F, Bombelli M, Giannattasio C, Zanchetti A, Mancia G

Liu JE, Roman MJ, Pini R, Schwartz JE, Pickering TG, Devereux RB

Pierdomenico SD, Cuccurullo F

Ohkubo T, Kikuya M, Metoki H, Asayama K, Obara T, Hashimoto J, Totsune K, Hoshi H, Satoh H, Imai Y

Tomiyama M, Horio T, Yoshii M, Takiuchi S, Kamide K, Nakamura S, Yoshihara F, Nakahama H, Inenaga T, Kawano Y

Husain A, Lin FC, Tuttle LA, Olsson E, Viera AJ

Dolan E, Stanton A, Thijs L, Hinedi K, Atkins N, McClory S, Den Hond E, McCormack P, Staessen JA, O’Brien E

O’Brien E, Sheridan J, O’Malley K

Roush GC, Fagard RH, Salles GF, Pierdomenico SD, Reboldi G, Verdecchia P, Eguchi K, Kario K, Hoshide S, Polonia J, de la Sierra A, Hermida RC, Dolan E, Zamalloa H

Hansen TW, Li Y, Boggia J, Thijs L, Richart T, Staessen JA

Fan HQ, Li Y, Thijs L, Hansen TW, Boggia J, Kikuya M, Björklund-Bodegård K, Richart T, Ohkubo T, Jeppesen J, Torp-Pedersen C, Dolan E, Kuznetsova T, Stolarz-Skrzypek K, Tikhonoff V, Malyutina S, Casiglia E, Nikitin Y, Lind L, Sandoya E, Kawecka-Jaszcz K, Imai Y, Ibsen H, O’Brien E, Wang J, Staessen JA

Hermida RC, Ayala DE, Mojón A, Fernández JR

Rahman M, Greene T, Phillips RA, Agodoa LY, Bakris GL, Charleston J, Contreras G, Gabbai F, Hiremath L, Jamerson K, Kendrick C, Kusek JW, Lash JP, Lea J, Miller ER, Rostand S, Toto R, Wang X, Wright JT, Appel LJ

Portaluppi F, Tiseo R, Smolensky MH, Hermida RC, Ayala DE, Fabbian F

Muntner P, Lewis CE, Diaz KM, Carson AP, Kim Y, Calhoun D, Yano Y, Viera AJ, Shimbo D

Fagard RH, Thijs L, Staessen JA, Clement DL, De Buyzere ML, De Bacquer DA

Tsioufis C, Andrikou I, Thomopoulos C, Syrseloudis D, Stergiou G, Stefanadis C

Ohkubo T, Hozawa A, Yamaguchi J, Kikuya M, Ohmori K, Michimata M, Matsubara M, Hashimoto J, Hoshi H, Araki T, Tsuji I, Satoh H, Hisamichi S, Imai Y

Kario K, Pickering TG, Matsuo T, Hoshide S, Schwartz JE, Shimada K

Muller JE, Stone PH, Turi ZG, Rutherford JD, Czeisler CA, Parker C, Poole WK, Passamani E, Roberts R, Robertson T

Willich SN, Levy D, Rocco MB, Tofler GH, Stone PH, Muller JE

Kario K, Pickering TG, Umeda Y, Hoshide S, Hoshide Y, Morinari M, Murata M, Kuroda T, Schwartz JE, Shimada K

Li Y, Thijs L, Hansen TW, Kikuya M, Boggia J, Richart T, Metoki H, Ohkubo T, Torp-Pedersen C, Kuznetsova T, Stolarz-Skrzypek K, Tikhonoff V, Malyutina S, Casiglia E, Nikitin Y, Sandoya E, Kawecka-Jaszcz K, Ibsen H, Imai Y, Wang J, Staessen JA

Eguchi K, Hoshide S, Hoshide Y, Ishikawa S, Shimada K, Kario K

Musso NR, Vergassola C, Barone C, Lotti G

Hinderliter AL, Routledge FS, Blumenthal JA, Koch G, Hussey MA, Wohlgemuth WK, Sherwood A

van der Steen MS, Lenders JW, Graafsma SJ, den Arend J, Thien T

Abdalla M, Goldsmith J, Muntner P, Diaz KM, Reynolds K, Schwartz JE, Shimbo D

Viera AJ, Lin FC, Tuttle LA, Olsson E, Stankevitz K, Girdler SS, Klein JL, Hinderliter AL

de la Sierra A, Vinyoles E, Banegas JR, Parati G, de la Cruz JJ, Gorostidi M, Segura J, Ruilope LM

Weber MA, Schiffrin EL, White WB, Mann S, Lindholm LH, Kenerson JG, Flack JM, Carter BL, Materson BJ, Ram CV, Cohen DL, Cadet JC, Jean-Charles RR, Taler S, Kountz D, Townsend RR, Chalmers J, Ramirez AJ, Bakris GL, Wang J, Schutte AE, Bisognano JD, Touyz RM, Sica D, Harrap SB

Krause T, Lovibond K, Caulfield M, McCormack T, Williams B

Dasgupta K, Quinn RR, Zarnke KB, Rabi DM, Ravani P, Daskalopoulou SS, Rabkin SW, Trudeau L, Feldman RD, Cloutier L, Prebtani A, Herman RJ, Bacon SL, Gilbert RE, Ruzicka M, McKay DW, Campbell TS, Grover S, Honos G, Schiffrin EL, Bolli P, Wilson TW, Lindsay P, Hill MD, Coutts SB, Gubitz G, Gelfer M, Vallée M, Prasad GV, Lebel M, McLean D, Arnold JM, Moe GW, Howlett JG, Boulanger JM, Larochelle P, Leiter LA, Jones C, Ogilvie RI, Woo V, Kaczorowski J, Burns KD, Petrella RJ, Hiremath S, Milot A, Stone JA, Drouin D, Lavoie KL, Lamarre-Cliche M, Tremblay G, Hamet P, Fodor G, Carruthers SG, Pylypchuk GB, Burgess E, Lewanczuk R, Dresser GK, Penner SB, Hegele RA, McFarlane PA, Khara M, Pipe A, Oh P, Selby P, Sharma M, Reid DJ, Tobe SW, Padwal RS, Poirier L

Aronow WS, Fleg JL, Pepine CJ, Artinian NT, Bakris G, Brown AS, Ferdinand KC, Ann Forciea M, Frishman WH, Jaigobin C, Kostis JB, Mancia G, Oparil S, Ortiz E, Reisin E, Rich MW, Schocken DD, Weber MA, Wesley DJ

Lovibond K, Jowett S, Barton P, Caulfield M, Heneghan C, Hobbs FD, Hodgkinson J, Mant J, Martin U, Williams B, Wonderling D, McManus RJ

McCormack T, Krause T, O’Flynn N

Fagard RH, Van Den Broeke C, De Cort P

Pickering TG, Miller NH, Ogedegbe G, Krakoff LR, Artinian NT, Goff D

Ward AM, Takahashi O, Stevens R, Heneghan C

Ohkubo T, Imai Y, Tsuji I, Nagai K, Kato J, Kikuchi N, Nishiyama A, Aihara A, Sekino M, Kikuya M, Ito S, Satoh H, Hisamichi S

Niiranen TJ, Hänninen MR, Johansson J, Reunanen A, Jula AM

Stergiou GS, Nasothimiou EG, Destounis A, Poulidakis E, Evagelou I, Tzamouranis D

Green BB, Cook AJ, Ralston JD, Fishman PA, Catz SL, Carlson J, Carrell D, Tyll L, Larson EB, Thompson RS

Agarwal R, Bills JE, Hecht TJ, Light RP

Pawloski PA, Asche SE, Trower NK, Bergdall AR, Dehmer SP, Maciosek MV, Nyboer RA, O’Connor PJ, Sperl-Hillen JM, Green BB, Margolis KL

Parati G, Stergiou GS, Asmar R, Bilo G, de Leeuw P, Imai Y, Kario K, Lurbe E, Manolis A, Mengden T, O'Brien E, Ohkubo T, Padfield P, Palatini P, Pickering T, Redon J, Revera M, Ruilope LM, Shennan A, Staessen JA, Tisler A, Waeber B, Zanchetti A, Mancia G

Stergiou GS, Skeva II, Zourbaki AS, Mountokalakis TD

Stergiou GS, Nasothimiou EG, Kalogeropoulos PG, Pantazis N, Baibas NM

Parati G, Stergiou GS, Asmar R, Bilo G, de Leeuw P, Imai Y, Kario K, Lurbe E, Manolis A, Mengden T, O’Brien E, Ohkubo T, Padfield P, Palatini P, Pickering TG, Redon J, Revera M, Ruilope LM, Shennan A, Staessen JA, Tisler A, Waeber B, Zanchetti A, Mancia G

Stergiou GS, Asayama K, Thijs L, Kollias A, Niiranen TJ, Hozawa A, Boggia J, Johansson JK, Ohkubo T, Tsuji I, Jula AM, Imai Y, Staessen JA

Calvo-Vargas C, Padilla Rios V, Troyo-Sanromán R, Grover-Paez F

Scisney-Matlock M, Grand A, Steigerwalt SP, Normolle D

Tucker KL, Sheppard JP, Stevens R, Bosworth HB, Bove A, Bray EP, Earle K, George J, Godwin M, Green BB, Hebert P, Hobbs FDR, Kantola I, Kerry SM, Leiva A, Magid DJ, Mant J, Margolis KL, McKinstry B, McLaughlin MA, Omboni S, Ogedegbe O, Parati G, Qamar N, Tabaei BP, Varis J, Verberk WJ, Wakefield BJ, McManus RJ

Uhlig K, Patel K, Ip S, Kitsios GD, Balk EM

Magid DJ, Olson KL, Billups SJ, Wagner NM, Lyons EE, Kroner BA

Kerby TJ, Asche SE, Maciosek MV, O’Connor PJ, Sperl-Hillen JM, Margolis KL

Ralston JD, Cook AJ, Anderson ML, Catz SL, Fishman PA, Carlson J, Johnson R, Green BB

Bosworth HB, Powers BJ, Olsen MK, McCant F, Grubber J, Smith V, Gentry PW, Rose C, Van Houtven C, Wang V, Goldstein MK, Oddone EZ

McKinstry B, Hanley J, Wild S, Pagliari C, Paterson M, Lewis S, Sheikh A, Krishan A, Stoddart A, Padfield P

Ogedegbe G, Schoenthaler A

Fletcher BR, Hartmann-Boyce J, Hinton L, McManus RJ

Shimbo D, Abdalla M, Falzon L, Townsend RR, Muntner P

Kronish IM, Kent S, Moise N, Shimbo D, Safford MM, Kynerd RE, O’Beirne R, Sullivan A, Muntner P

Kent ST, Shimbo D, Huang L, Diaz KM, Viera AJ, Kilgore M, Oparil S, Muntner P

Viera AJ, Lingley K, Hinderliter AL

van der Steen MS, Lenders JW, Thien T

Johnson KA, Partsch DJ, Rippole LL, McVey DM

Logan AG, Dunai A, McIsaac WJ, Irvine MJ, Tisler A

Tislér A, Dunai A, Keszei A, Fekete B, Othmane Tel H, Torzsa P, Logan AG

Cheng C, Studdiford JS, Diamond JJ, Chambers CV

Staessen J, Bulpitt CJ, Fagard R, Mancia G, O’Brien ET, Thijs L, Vyncke G, Amery A

Staessen JA, O’Brien ET, Amery AK, Atkins N, Baumgart P, De Cort P, Degaute JP, Dolenc P, De Gaudemaris R, Enström I

Head GA, Mihailidou AS, Duggan KA, Beilin LJ, Berry N, Brown MA, Bune AJ, Cowley D, Chalmers JP, Howe PR, Hodgson J, Ludbrook J, Mangoni AA, McGrath BP, Nelson MR, Sharman JE, Stowasser M

Mancia G, Sega R, Bravi C, De Vito G, Valagussa F, Cesana G, Zanchetti A

Kikuya M, Hansen TW, Thijs L, Björklund-Bodegård K, Kuznetsova T, Ohkubo T, Richart T, Torp-Pedersen C, Lind L, Ibsen H, Imai Y, Staessen JA

Ohkubo T, Imai Y, Tsuji I, Nagai K, Ito S, Satoh H, Hisamichi S

Ravenell J, Shimbo D, Booth JN, Sarpong DF, Agyemang C, Beatty Moody DL, Abdalla M, Spruill TM, Shallcross AJ, Bress AP, Muntner P, Ogedegbe G

Niiranen TJ, Asayama K, Thijs L, Johansson JK, Ohkubo T, Kikuya M, Boggia J, Hozawa A, Sandoya E, Stergiou GS, Tsuji I, Jula AM, Imai Y, Staessen JA

Staessen JA, Thijs L, Ohkubo T, Kikuya M, Richart T, Boggia J, Adiyaman A, Dechering DG, Kuznetsova T, Thien T, de Leeuw P, Imai Y, O’brien E, Parati G

Cloutier L, Daskalopoulou SS, Padwal RS, Lamarre-Cliche M, Bolli P, McLean D, Milot A, Tobe SW, Tremblay G, McKay DW, Townsend R, Campbell N, Gelfer M

Head GA, McGrath BP, Mihailidou AS, Nelson MR, Schlaich MP, Stowasser M, Mangoni AA, Cowley D, Brown MA, Ruta LA, Wilson A

Gerhard-Herman MD, Gornik HL, Barrett C, Barshes NR, Corriere MA, Drachman DE, Fleisher LA, Fowkes FG, Hamburg NM, Kinlay S, Lookstein R, Misra S, Mureebe L, Olin JW, Patel RA, Regensteiner JG, Schanzer A, Shishehbor MH, Stewart KJ, Treat-Jacobson D, Walsh ME

van Egmond J, Hasenbos M, Crul JF

Parati G, Casadei R, Groppelli A, Di Rienzo M, Mancia G

Altunkan S, Iliman N, Altunkan E

Casiglia E, Tikhonoff V, Albertini F, Palatini P

Thomas SS, Nathan V, Zong C, Soundarapandian K, Shi X, Jafari R

Deutsch C, Krüger R, Saito K, Yamashita S, Sawanoi Y, Beime B, Bramlage P

Davies JH, Kenkre J, Williams EM

Harju J, Vehkaoja A, Kumpulainen P, Campadello S, Lindroos V, Yli-Hankala A, Oksala N

Stergiou GS, Lourida P, Tzamouranis D

Beulen BW, Bijnens N, Koutsouridis GG, Brands PJ, Rutten MC, van de Vosse FN

Idzenga T, Reesink KD, van Swelm Y, Hansen HH, Holewijn S, de Korte CL

Nelson MR, Stepanek J, Cevette M, Covalciuc M, Hurst RT, Tajik AJ

Fischer MO, Avram R, Cârjaliu I, Massetti M, Gérard JL, Hanouz JL, Fellahi JL

Kumar N, Khunger M, Gupta A, Garg N

Cortez NG, Cohen IG, Kesselheim AS

Bruining N, Caiani E, Chronaki C, Guzik P, van der Velde E

Woo SH, Choi YY, Kim DJ, Bien F, Kim JJ

Chandrasekaran V, Dantu R, Jonnada S, Thiyagaraja S, Subbu KP

Nwankwo T, Yoon SS, Burt V, Gu Q

Dobson RT, Taylor JG, Henry CJ, Lachaine J, Zello GA, Keegan DL, Forbes DA

Sabater-Hernández D, Sánchez-Villegas P, Lacampa P, Artiles-Campelo A, Jorge-Rodríguez ME, Faus MJ

Santschi V, Chiolero A, Burnand B, Colosimo AL, Paradis G

Santschi V, Chiolero A, Colosimo AL, Platt RW, Taffé P, Burnier M, Burnand B, Paradis G

Albasri A, O’Sullivan JW, Roberts NW, Prinjha S, McManus RJ, Sheppard JP

Sendra-Lillo J, Sabater-Hernández D, Sendra-Ortolá Á, Martínez-Martínez F

Sabater-Hernández D, de la Sierra A, Sánchez-Villegas P, Baena MI, Amariles P, Faus MJ

Sabater-Hernández D, Sánchez-Villegas P, García-Corpas JP, Amariles P, Sendra-Lillo J, Faus MJ

Sendra-Lillo J, Sabater-Hernández D, Sendra-Ortolá A, Martínez-Martínez F

Campbell NR, Niebylski ML, Redburn K, Lisheng L, Nilsson P, Zhang XH, Lackland DT

Ostchega Y, Hughes JP, Zhang G, Nwankwo T, Chiappa MM

Alpert BS, Dart RA, Sica DA

Al Hamarneh YN, Houle SK, Chatterley P, Tsuyuki RT

Chemla D, Teboul JL, Richard C

Flynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, de Ferranti SD, Dionne JM, Falkner B, Flinn SK, Gidding SS, Goodwin C, Leu MG, Powers ME, Rea C, Samuels J, Simasek M, Thaker VV, Urbina EM

Stergiou GS, Boubouchairopoulou N, Kollias A

Dionne JM, Abitbol CL, Flynn JT

Zaheer S, Watson L, Webb NJ

Veiga EV, Arcuri EA, Cloutier L, Santos JL

Thomas M, Radford T, Dasgupta I

Muhamed PK, Olsen MH, Holm JC, Ibsen H, Hvidt KN

Stergiou GS, Yiannes NG, Rarra VC, Panagiotakos DB

. Methodology and applicability of home blood pressure monitoring in children and adolescents.

Flynn JT, Ingelfinger JR, Redwine KM

Sorof JM, Turner J, Franco K, Portman RJ

Roberts CL, Ford JB, Algert CS, Antonsen S, Chalmers J, Cnattingius S, Gokhale M, Kotelchuck M, Melve KK, Langridge A, Morris C, Morris JM, Nassar N, Norman JE, Norrie J, Sørensen HT, Walker R, Weir CJ

Tranquilli AL, Dekker G, Magee L, Roberts J, Sibai BM, Steyn W, Zeeman GG, Brown MA

Magee LA, Pels A, Helewa M, Rey E, von Dadelszen P

Pomini F, Scavo M, Ferrazzani S, De Carolis S, Caruso A, Mancuso S

Almeida FA, Pavan MV, Rodrigues CI

Poon LC, Kametas N, Strobl I, Pachoumi C, Nicolaides KH

Ishikuro M, Obara T, Metoki H, Ohkubo T, Yamamoto M, Akutsu K, Sakurai K, Iwama N, Katagiri M, Yagihashi K, Yaegashi N, Mori S, Suzuki M, Kuriyama S, Imai Y

Bello NA, Woolley JJ, Cleary KL, Falzon L, Alpert BS, Oparil S, Cutter G, Wapner R, Muntner P, Tita AT, Shimbo D

Pierin AM, Alavarce DC, Gusmão JL, Halpern A, Mion D

Graves JW, Bailey KR, Sheps SG

Masiero S, Saladini F, Benetti E, Palatini P

Irving G, Holden J, Stevens R, McManus RJ

Bonso E, Saladini F, Zanier A, Benetti E, Dorigatti F, Palatini P

Umana E, Ahmed W, Fraley MA, Alpert MA

Masaki KH, Schatz IJ, Burchfiel CM, Sharp DS, Chiu D, Foley D, Curb JD

Kario K, Eguchi K, Hoshide S, Hoshide Y, Umeda Y, Mitsuhashi T, Shimada K

Lahrmann H, Cortelli P, Hilz M, Mathias CJ, Struhal W, Tassinari M

Juraschek SP, Daya N, Rawlings AM, Appel LJ, Miller ER, Windham BG, Griswold ME, Heiss G, Selvin E

White WB, Wolfson L, Wakefield DB, Hall CB, Campbell P, Moscufo N, Schmidt J, Kaplan RF, Pearlson G, Guttmann CR

Kleman M, Dhanyamraju S, DiFilippo W

Pathak RK, Middeldorp ME, Lau DH, Mehta AB, Mahajan R, Twomey D, Alasady M, Hanley L, Antic NA, McEvoy RD, Kalman JM, Abhayaratna WP, Sanders P

Stergiou GS, Kollias A, Destounis A, Tzamouranis D

Verberk WJ, Omboni S, Kollias A, Stergiou GS

Chan PH, Wong CK, Pun L, Wong YF, Wong MM, Chu DW, Siu CW

Hafner F, Froehlich H, Gary T, Tiesenhausen K, Scarpatetti M, Brodmann M

Bennett MK, Roberts CA, Dordunoo D, Shah A, Russell SD

Martina JR, Westerhof BE, Van Goudoever J, De Jonge N, Van Lieshout JJ, Lahpor JR, De Mol BA

Parati G, Ochoa JE, Lombardi C, Bilo G

Stevens SL, Wood S, Koshiaris C, Law K, Glasziou P, Stevens RJ, McManus RJ

Hansen TW, Thijs L, Li Y, Boggia J, Kikuya M, Björklund-Bodegård K, Richart T, Ohkubo T, Jeppesen J, Torp-Pedersen C, Dolan E, Kuznetsova T, Stolarz-Skrzypek K, Tikhonoff V, Malyutina S, Casiglia E, Nikitin Y, Lind L, Sandoya E, Kawecka-Jaszcz K, Imai Y, Wang J, Ibsen H, O’Brien E, Staessen JA

Muntner P, Levitan EB, Reynolds K, Mann DM, Tonelli M, Oparil S, Shimbo D

Schutte R, Thijs L, Liu YP, Asayama K, Jin Y, Odili A, Gu YM, Kuznetsova T, Jacobs L, Staessen JA

Rothwell PM, Howard SC, Dolan E, O’Brien E, Dobson JE, Dahlöf B, Sever PS, Poulter NR

Muntner P, Whittle J, Lynch AI, Colantonio LD, Simpson LM, Einhorn PT, Levitan EB, Whelton PK, Cushman WC, Louis GT, Davis BR, Oparil S

Whittle J, Lynch AI, Tanner RM, Simpson LM, Davis BR, Rahman M, Whelton PK, Oparil S, Muntner P

Diaz KM, Tanner RM, Falzon L, Levitan EB, Reynolds K, Shimbo D, Muntner P

Levitan EB, Kaciroti N, Oparil S, Julius S, Muntner P

Muntner P, Levitan EB, Lynch AI, Simpson LM, Whittle J, Davis BR, Kostis JB, Whelton PK, Oparil S

Webb AJ, Fischer U, Mehta Z, Rothwell PM

Muntner P, Levitan EB, Joyce C, Holt E, Mann D, Oparil S, Krousel-Wood M

Kronish IM, Lynch AI, Oparil S, Whittle J, Davis BR, Simpson LM, Krousel-Wood M, Cushman WC, Chang TI, Muntner P

Muntner P, Shimbo D, Tonelli M, Reynolds K, Arnett DK, Oparil S

Hodgkinson JA, Sheppard JP, Heneghan C, Martin U, Mant J, Roberts N, McManus RJ

O’Brien E, Petrie J, Littler W, de Swiet M, Padfield PL, Altman DG, Bland M, Coats A, Atkins N

Tholl U, Anlauf M, Lichtblau U, Dammer R, Roggenbuck U

Beime B, Deutsch C, Gomez T, Zwingers T, Mengden T, Bramlage P

Eguchi K, Kuruvilla S, Ishikawa J, Schwartz JE, Pickering TG

Stone NJ, Robinson JG, Lichtenstein AH, Bairey Merz CN, Blum CB, Eckel RH, Goldberg AC, Gordon D, Levy D, Lloyd-Jones DM, McBride P, Schwartz JS, Shero ST, Smith SC, Watson K, Wilson PWF

Blood pressure

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blood pressure, force originating in the pumping action of the heart, exerted by the blood against the walls of the blood vessels the stretching of the vessels in response to this force and their subsequent contraction are important in maintaining blood flow through the vascular system.

In humans, blood pressure is usually measured indirectly with a special cuff over the brachial artery (in the arm) or the femoral artery (in the leg). There are two pressures measured: (1) the systolic pressure (the higher pressure and the first number recorded), which is the force that blood exerts on the artery walls as the heart contracts to pump the blood to the peripheral organs and tissues, and (2) the diastolic pressure (the lower pressure and the second number recorded), which is residual pressure exerted on the arteries as the heart relaxes between beats. In healthy individuals, systolic pressure is normally between 90 and 120 millimetres of mercury (mmHg). Diastolic pressure is normally between 60 and 80 mmHg. Hence, in general, a reading of 110/70 mmHg would be considered healthy, whereas 80/50 mmHg would be low and 160/100 mmHg would be high.

Studies have shown that there are stark contrasts in the blood pressure of vessels of different sizes. For example, blood pressure in the capillaries is usually about 20 to 30 mmHg, whereas the pressure in the large veins may become negative (lower than atmospheric pressure [760 mmHg at sea level] technically, measurements of blood pressure are relative to atmospheric pressure, which represents the “zero reference point” for blood pressure readings).

Arterial blood pressure varies among individuals and in the same individual from time to time. It is lower in children than in adults and increases gradually with age. It tends to be higher in those who are overweight. During sleep it decreases, and during exercise and emotional excitement it increases. Abnormally high blood pressure, when sustained above healthy levels at rest, is known as hypertension when blood pressure remains below normal levels, the condition is called hypotension. Hypertension is associated with an increased risk of various forms of cardiovascular disease hypotension can be caused by a sudden loss of blood or decrease in blood volume and may result in dizziness and fainting.


The challenge of creating a satisfactory theoretical treatment of the genesis and interpretation of cuff pressure oscillations has attracted a diverse community of thinkers [4, 5, 7–10, 16]. Nevertheless, specifying a valid method for extracting systolic and diastolic pressures from the envelope of cuff pressure oscillations remains an open problem. Here is presented a mathematical model incorporating anatomy, physiology, and biomechanics of arteries that predicts cuff pressure oscillations produced during noninvasive measurements of blood pressure using the oscillometric method. Understanding of the underlying mechanisms leads to a model-based algorithm for deducing systolic and diastolic pressures accurately from cuff pressure oscillations in the presence of varying arterial stiffness or varying pulse pressure.

The shape of the oscillation amplitude envelope dictates the stiffness parameters for the artery during both compression and distension. Semilog regression procedures give good estimates of the artery stiffness parameters that characterize each individual cuff deflation sequence. Using these parameters one can create and exercise an individualized cuff-arm-artery model for a wide range of possible systolic and diastolic pressures. The pair of systolic and diastolic pressures that best reproduces the observed oscillation envelope according to a least squares criterion constitutes the output of the algorithm.

When applied to amplitude normalized oscillation data the algorithm is insensitive to variations among subjects in zero pressure artery volume, Va0 , or initial cuff volume V0, since these terms are constants that are eliminated by the normalization procedure. Compression of the entire length of artery underlying the cuff is not necessary. Incomplete coupling of cuff pressure to the artery near the ends of the cuff merely decreases the ratio Va0 / V0 without effecting the extracted systolic and diastolic pressures.

The cuff-arm-artery model can be used as well to test the validity of the algorithm for over a wide range of possible conditions by generating trial cuff pressure data for known arterial pressure waveforms. A stress test for the algorithm can be done by comparing systolic and diastolic pressure levels extracted from synthesized cuff pressure oscillations with the arterial pressure that generated the synthesized oscillations over a wide range of test conditions. These conditions may include extreme cases that are hard to reproduce experimentally, contamination with excessive noise, any conceivable blood pressure waveforms, cardiac arrhythmias such as atrial fibrillation, etc. Such computational experiments, in addition to future animal and clinical studies, can boost confidence in the reliability of the oscillometric method and can suggest further refinements.

Here for convenience we have used the bi-exponential model to generate cuff pressure oscillations for algorithm testing. However, the regression algorithm does not “know” where the sample data came from. It tries to extract constants a and b from the head and tail portions of the semi-log plot of oscillation amplitude versus cuff pressure. The resulting best fit values of a and b will still work for non-ideal or noise contaminated data to produce a model envelope that can be matched to the actual data. An extremely stiff artery with a linear pressure volume curve is easily accommodated by this process, since e x ≈ 1 + x for small values of x. In this limiting case the exponential pressure-volume curve becomes linear. An exceptionally flabby artery, rather like dialysis tubing, is well described by larger values of a and b and a larger ratio a/b. Thus the family of bi-exponential models is very inclusive of a wide range of arterial mechanical properties, as suggested in Figure 3.

Classically the oscillometric method has been relatively well validated as a measure of mean arterial pressure, which is indicated by the peak of the oscillation amplitude envelope [4]. Automated oscillometric pressure monitors have found use in hospitals for critical care monitoring in which the goal is to detect any worrisome trend in blood pressure more so than the exact absolute value. Out of hospital use of the oscillometric method in screening for high blood pressure is more problematic, because heretofore the accuracy of systolic and diastolic end points has been questioned and doubted. For example Stork and Jilek [17] studied two published algorithms, differing in detail and based on cuff oscillation ratios of either 50% for systolic and 80% for diastolic or 40% for systolic and 55% for diastolic. Compared to a reference pressure of 122/78 mmHg the algorithmic methods applied to oscillometric data gave pressures of 135/88 and 144/81 mmHg, respectively. An advisory statement from the Council for High Blood Pressure Research, American Heart Association [18] stressed the need for caution in the selection of all instruments used for blood pressure determination and the need for continuing studies to validate their the safety and reliability.

Accurate measurements of blood pressure in routine clinic and office settings are important because systemic arterial hypertension is a major cause of serious complications, including accelerated atherosclerosis, heart attacks, strokes, kidney disease, and death. These serious complications increase smoothly with every point above the nominal 120/80 mmHg, hence even small increases in blood pressure are important to detect. In screening for hypertension systematic bias or inaccuracy in blood pressure readings of a few mmHg can be significant, since the difference between high normal (85 diastolic) and abnormal (90 diastolic) is only a few mmHg. A recent 1 million-patient meta-analysis suggests that a 3–4 mmHg increase in systolic blood pressure would translate into 20% higher stroke mortality and a 12% higher mortality from ischemic heart disease [19].

False negative readings would be problematic because untreated high blood pressure can lead to strokes, blindness, kidney failure, and lethal heart attacks. False positive readings would be undesirable because the usual drugs for hypertension must be taken every day for life and can be expensive. They also have side effects. Hence accurate readings are essential. Given a reliable algorithm for extracting systolic and diastolic pressures, an automatic oscillometric device could provide screening for high blood pressure that is performed in the same way each time without inter-observer variation. The present research could lead to a wider role for oscillometric blood pressure monitors in physicians’ offices and clinics.

How Is Blood Pressure Measured and What Do the Numbers Mean?

Learn what your blood pressure numbers mean, and how they are measured.

When you visit your health care provider, a blood pressure measurement is one of the most important routine tests you’ll have.

Blood pressure is the force exerted by your blood against your arteries. As your heart pumps, it forces blood out through arteries that carry the blood throughout your body. The arteries keep tapering off in size until they become tiny vessels, called capillaries. At the capillary level, oxygen and nutrients are released from your blood and delivered to the organs.

Types of Blood Pressure

There are two types of blood pressure: Systolic blood pressure refers to the pressure inside your arteries when your heart is pumping diastolic pressure is the pressure inside your arteries when your heart is resting between beats.

When your arteries are healthy and dilated, blood flows easily and your heart doesn't have to work too hard. But when your arteries are too narrow or stiff, blood pressure rises, the heart gets overworked, and arteries can become damaged.

Measuring Blood Pressure

Blood pressure is measured with an instrument called a sphygmomanometer. First, a cuff is placed around your arm and inflated with a pump until the circulation is cut off. A small valve slowly deflates the cuff, and the doctor measuring blood pressure uses a stethoscope, placed over your arm, to listen for the sound of blood pulsing through the arteries. That first sound of rushing blood refers to the systolic blood pressure once the sound fades, the second number indicates the diastolic pressure, the blood pressure of your heart at rest.

Blood pressure is measured in millimeters of mercury (mm Hg) and recorded with the systolic number first, followed by the diastolic number. For example, a normal blood pressure would be recorded as something under 120/80 mm Hg.

Blood pressure readings can be affected by factors like:

  • Smoking
  • Coffee or other caffeinated drinks
  • A full bladder
  • Recent physical activity

Blood pressure is also affected by your emotional state and the time of day. Since so many factors can affect blood pressure readings, you should have your blood pressure taken several times to get an accurate measurement.

What Is Normal Blood Pressure?

Experts consider normal blood pressure to be less than 120/80 mm Hg. Based on population data, about 42 percent of American adults have normal blood pressure. At one point, blood pressure at or above 120/80 and less than 140/90 was considered normal to high these numbers are now considered pre-hypertensive. Blood pressure consistently at or above 140/90 is considered high blood pressure or hypertension.

Blood pressure normally rises as you age and grow. Normal blood pressure readings for children are lower than for adults, while blood pressure measurements for adults and older teenagers are similar.

Blood pressure can also be too low, a condition called hypotension. Hypotension refers to blood pressure lower than 90/60. Symptoms of hypotension include dizziness, fainting, and sometimes shock.

Checking Blood Pressure at Home

Many people can learn to check their blood pressure at home. You can buy blood pressure kits that use the cuffs or electronic digital technology at your pharmacy, a medical supply store, or an online retailer.

Since high blood pressure can exist without any symptoms, it is important to know your numbers. High blood pressure can cause stroke, heart attack, heart failure, and kidney failure.

Getting your blood pressure checked is quick, painless, and one of the most important things you can do to better your health.


In this multicentre randomised controlled trial, we found that a 3-month yoga intervention reduces systolic and diastolic blood pressure among hypertensive patients. This implies that yoga programmes can be promoted through primary care settings as an effective non-pharmacological therapy to treat hypertension.

Our findings are consistent with a recent systematic review that found an average reduction of SBP by 7.9 mmHg and DBP by 4.3 mmHg among the participants who received a yoga intervention including breathing techniques and meditation [15]. In another review, Cramer et al. found that yoga interventions lasting eight weeks or more, reduced SBP on average by − 9.65 mmHg [32]. The pooled effect from the Cramer et al. meta-analysis may seem somewhat higher than the average effect found in our study. However, due to a relatively small pooled sample size and large heterogeneity between individual studies included in the meta-analysis, the confidence interval of the pooled effect from Cramer et al. [32] study was very wide, and it largely overlaps with our narrower confidence interval for the respective effect in our study. A smaller blood pressure-lowering effect in our study compared to the Cramer et al. [32] meta-analysis might be because of the attenuation of the intervention effect due to its implementation in a real-world clinical setting. Likewise, the implementation of yoga intervention in our study was done by health workers. It might be that the effect of yoga on blood pressure reduction would be higher, if the intervention was implemented by certified yoga instructors or kinesiologists.

Studies have investigated several possible underlying mechanisms for clinical effects of yoga on hypertension [33,34,35]. One of the hypothesized mechanisms is that yoga affects the autonomic nervous system by stimulating activity of parasympathetic and reducing activity of sympathetic nervous system [33]. It is also postulated that yoga increases bioavailability and blood levels of nitric oxide and promotes vasodilation [33]. Additionally, participation in yoga as a “mind-body” activity has been associated with improved physiological markers, reduced symptoms of stress, and better mood [36, 37]. Pascoe et al. [36] in their systematic review concluded that mindfulness-based activities, including yoga, lead to decreased cortisol level, a stress hormone that has been linked to high blood pressure. Thoroughly investigating the mechanism of the effect of yoga on blood pressure was beyond the scope of this study. Nevertheless, we considered the possible mediating effect of the change in BMI and resting heart rate between baseline and follow-up, and we found no strong indication either of these would constitute the underlying mechanism. Given that yoga is a complex activity, it might be challenging to determine a single mechanism that would explain antihypertensive effects of all components of yoga. Therefore, to illuminate the underlying causal pathways, future studies will need to assess in detail different physiological, biomedical and stress biomarkers in relation to specific yoga components.

The main strength of the current study was that the intervention was evaluated in a real-world clinical setting. The number of such studies is generally limited. Moreover, to the best of our knowledge, this was the first study that investigated the effects of a primary health care staff-led yoga intervention on high blood pressure among the patients attending public health centres in a low-income country. One of the benefits of conducting the trial in a real-world setting is that the study could have good external validity and it could enhance the likelihood that it is translated into practice [18, 19]. The current study has the potential to be scaled up nationwide in Nepal, as the remaining AHCs are also equipped with both physical and human resources to implement yoga intervention. The situation is likely to be similar in many other LMICs. In Nepal, the national policy and mechanisms of using yoga as a health promotion tool are also already in place. The Multisectoral Action Plan for Prevention and Control of NCDs (2014–2020) and Urban Health Policy (2015) integrated yoga as a strategy for NCD prevention and control. Similarly, the Department of Ayurveda and Alternative Medicines have launched yoga-based interventions such as ‘Swatha Jiwan karyakram’(informal translation: Healthy Life Program) and ‘Vidhaylaya yoga shiskya karyakram’ (informal translation: School Yoga Education Program) in 75 districts of Nepal to promote health and wellbeing of elderly and school children. The current intervention could also be an economically viable approach, as it can utilize existing resources and can also be integrated into the ongoing program that has similar modalities, such as ‘Swatha Jiwan karyakram’. However, further studies are required to test the cost-effectiveness of upscaling the program. Furthermore, the current study also had well-structured intervention packages comprising different components of yoga, including postures, breathing exercises, relaxation and meditation. Previous evidence showed that these components in combination were likely to have a better positive impact on health than individual components [15, 28]. Likewise, the session timing (i.e. 30 min) and frequency (i.e. five sessions per week) were selected to be in line with the World Health Organization physical activity guidelines (i.e. 150 or more minutes a week of moderate-to-vigorous physical activity). This study had a shorter session timing compared to previous studies in which the average session time was 59.2 min [15]. This might have positively affected participant compliance. Lastly, as this trial was conducted in several centres, representing large geographical areas of Nepal, the findings could be generalized beyond the trial participants and centres.

The current study has some limitations. Firstly, hypertension was diagnosed based on blood pressure measured on two occasions only that were 1–2 weeks apart. Although most participants were previously diagnosed hypertensive patients, it might be that we misclassified some of the newly diagnosed participants. We did not manage to collect information on r adherence to the study protocol from all participants. Evidence shows that the effect of yoga may vary depending on the frequency and duration of yoga practice [15]. Future studies on the effects of yoga on blood pressure should aim to collect such data, to enable conducting per-protocol analysis. Furthermore, the post-intervention blood pressure measurements were not done on the same day for all participants, as this was not feasible. It was measured between the 85th and the 95th day of the intervention, as not all participants were available for the follow-up measurement on the 90th day. Besides, the variation in the level of yoga competency of the health workers who provided training to the participants might have also influenced the study outcomes. Likewise, the pre and post-intervention data were collected by the same persons and they used aneroid blood pressure machines to assess blood pressure. That might have introduced rater bias. We did not assess long-term effects of the intervention. It might be that the intervention would not be as efficient and sustainable over a longer period, as participant compliance to the protocol would likely reduce over time. Lastly, as the study included only first-stage hypertensive patients, study findings cannot be generalised to patients with higher stages of hypertension.

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