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IBMSPSSSTATL1P exam Dumps Source : IBM SPSS Statistics(R) Certification Level 1 (formerly PASW Statistics)

Test Name : IBM SPSS Statistics(R) Certification Level 1 (formerly PASW Statistics)
Vendor Name : IBM
Q&A : 70 Real Questions

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IBM IBM SPSS Statistics(R) Certification

IBM Watson Studio Spark executions using RStudio IDE | Real Questions and Pass4sure dumps

in this post i will reveal you the way to use the IBM Watson Studio Spark executions the usage of RStudio IDE, which launched from Watson Studio tasks.

RStudio IDE

RStudio is the premier built-in development environment (IDE) for R programmers. Watson Studio gives a handy manner of loading and executing R scripts.

Spark carrier

Watson Studio offers growing Spark execution environments internal projects to execute Spark courses in the cloud.

Launch RStudio IDE

Refer RStudio launch tutorial. This documentation pursuits RStudio with project and it is not yet accessible in all facts facilities.

running Spark classes from RStudio

RStudio makes use of the brand new sparklyr kit ( to join with the Spark kernel gateway on the cloud the use of Spark-as-a-service interactive APIs. The sparklyr package includes a dplyr interface to Spark facts frames as well as an R interface to Spark’s allotted computing device studying pipelines.

that you may use your latest Spark cases from RStudio. to use this feature, run right here steps:

1 Load and reveal accessible Spark circumstances

2 hook up with a Spark instance

3 Run dplyr APIs and Spark’s dispensed computer getting to know libraries

four monitor tables for Spark loaded records units

5 View logs for Spark kernel interplay

6 View Spark connect reputation and fasten or disconnect

checklist attainable Spark situations

for those who beginning RStudio two data are created in the working directory (don’t delete them!):  1) config.yml file — Lists your whole accessible Spark circumstances.  2) .Rprofile file — Configures your Spark atmosphere.

These files are created under your domestic directory, /domestic/rstudio. If the working directory is distinct from the domestic listing, you can replica the config.yml and .RProfile files to your present working directory.

you can load and display Spark instances through the use of the load_spark_kernels() and display_spark_kernels() R features . illustration:

This feature lists simplest your currently available Spark situations. if you would like one other Spark example, create it in Watson Studio Environments.

connect with the selected Spark illustration

To connect to Spark, run the spark_connect R function. for example:

sc <- spark_connect(config = kernels[1])

After this Spark context is created, all subsequent operations will be executed the use of this Spark example:

as soon as related to Spark we can see connection status like below

Run dplyr APIs and Spark’s distributed computer discovering libraries

To run dplyr services, load the dplyr package and then run the copy_to feature using the Spark context. for example:

library(dplyr) localDF <- statistics.frame(name=c("John", "Smith", "Sarah", "Mike", "Bob"), age=c(19, 23, 18, 25, 30)) sampletbl <- copy_to(sc, localDF, "sampleTbl")

This creates a Spark statistics body on the far off kernel according to a native R facts frame, and displays the local references in the Spark view:

View the desk for Spark loaded data units

Spark View indicates the entire far flung Spark data frames. that you may click on the desk icon to show pattern views of those tables.

View the log for Spark kernel interplay

you could opt for the Logs icon to view the entire calls to the Spark example.

View Spark join popularity and attach or disconnect a service

which you could view the connection status on the Spark View, and you can connect with or disconnect from a Spark provider.

connect Disconnect read mission files in Spark

RStudio gives utility feature get_project_asset_path() to simplify access to mission asset data from spark jobs. right here is the example to load mission file in spark and create spark information frame

# R interface for Apache Sparklibrary(sparklyr)


# load kernelskernels <- load_spark_kernels()

# reveal kernelsdisplay_spark_kernels()

# connect to our spark kernelsc <- spark_connect(config = kernels[1])

# create a course to Tim's check bucketpath <- get_project_asset_path("airline15krows.csv")

# read the usage of sparklyr package airline15krows_tbl <- spark_read_csv(sc,identify = "airline15krows", direction = route, delimiter="|", infer_schema = FALSE)

# listing all tablessrc_tbls(sc)head(airline15krows_tbl,four)


that you could discover the example R script information in the /ibm-sparkaas-demos folder under your home directory. These examples reveal scenarios that you could run with Spark in RStudio.


Creates elementary R information frames and generates far flung Spark facts frames in response to the native R statistics frames. also runs some basic filters and DBI queries.


loads the popular mtcars R data frame and then generates a Spark records body for the mtcars records body. It then does transformations to create a working towards records set and runs a linear mannequin on the working towards statistics set.


hundreds some higher statistics sets, creates ggplot for prolong and runs windows features. See sparklyr — R interface for Apache Spark for more suggestions.

See also sparklyr Examples for more examples.

Mahesh Kurapati is an Advisory software Engineer with the IBM Analytics team. Mahesh’s simple focus is on the construction of a lot of micro-capabilities for IBM data Science experience. Mahesh is worried within the development of quite a lot of gleaming.information elements and SparkaaS integration with RStudio. With greater than twenty years of experience in utility construction, Mahesh has contributed key functionalities to IBM products including SPSS facts, SPSS Modeler, and SPSS Analytics Server.

firstly published at on September 28, 2016.

A Pure Play On Self-service large records Prep And Analytics: wait for Smarter Valuation Entry factor | Real Questions and Pass4sure dumps

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IBM SPSS Statistics(R) Certification Level 1 (formerly PASW Statistics)

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Reliability of Telemedicine in the Assessment of Seriously Ill Children | real questions and Pass4sure dumps

Reliability of Telemedicine in the Assessment of Seriously Ill Children | Articles | Pediatrics

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Variability in screening prevention activities in primary care in Spain: a multilevel analysis | real questions and Pass4sure dumps

The recorded screening ranges from 36.6% for tobacco to 64.4% for dyslipidaemia, with major differences between the 2 Spanish regions studied. These results are similar to other studies based on electronic databases [21,35]. Nonetheless, they are lower than results based on self-reports by health professionals [14,36] and patients [37]. Some of the problems of electronic databases are well known: underreporting during the first years of implementation, variability resulting from heterogeneity in coding, using open-text fields to record activity without linking it to a diagnosis, etc. [18,35,38,39]. All of these may explain disparities between studies. Despite the progressive increase in the recording of prevention activities [10], PHC screening activity remains low and can be greatly improved, especially with respect to tobacco and alcohol use. Advice on drinking behaviour is least often provided, probably due to a reluctance to ask patients about it unless there are clear signs of risky drinking behavior [40].

Our results agree in part with other studies in which practitioners from large urban areas reported more prevention services involving alcohol and drugs, while respondents in rural areas reported fewer screening procedures [15]. The studied PHC teams in Catalonia were more urban and their patients had a higher prevalence of all screenings, but especially for tobacco and alcohol use, than those in Navarre.

The most prevalent screening is for hypertension and dyslipidemia, which have the lowest unexplained variability between PHC teams and GPs, respectively, after adjusting for individual and contextual factors. A possible explanation is that these screenings, primarily related to the prescription of medications, are easier and preferred over lifestyle modification activities by some GPs [41]. On the other hand, tobacco and alcohol screening had the highest variability between PHC teams and GPs, respectively, that could not be explained by the contextual factors studied.

Overall, the odds of being screened were higher for women, older patients, those with more comorbidities, more cardiovascular risk factors, and more frequent office visits, and those assigned to a female GP, a GP with a lower patient load, or a PHC team with a lower percentage of patients older than 65 years. Region was the most important contextual factor at the PHC team level.

Morbidity was positively related to screening for hypertension, dyslipidemia, and obesity, as in other studies [12,17], showing that GPs take a more proactive approach to screening in patients with more pathologies. Regardless of the type of screening, patients with previously identified cardiovascular risk were more likely to be screened, perhaps due to the need to obtain information to calculate cardiovascular risk and determine appropriate treatment. In the case of at-risk drinkers, the only associations observed were with screening for dyslipidemia and for tobacco use, reflecting the approach to preventing consumption of addictive substances.

At the GP level, female GPs were more likely to screen for dyslipidemia levels and tobacco use, as in other studies of prevention activities [14-16,42,43]. Our study showed that increased patient coverage is associated with less screening, specifically hypertension and tobacco, as in other studies [12]. Similarly, at the PHC team level, having a high percentage of elderly patients was negatively associated with some screening activities [12]. This may be due to the increased work load and lack of time for carrying out preventive services that is perceived by PHC professionals [44].

At the PHC team level, contextual variables better explained major variability (more than 80% in the case of hypertension and alcohol), compared to the GP-level variables. The larger contextual PHC team-level effect was determined by the region. Possible differentiating factors include the software used by each region, because software design can determine what health professionals record [18,45], and organizational aspects inherent to the different health care policies in each region, such as economic incentives to conduct certain prevention activities, the rurality of the region, or sociocultural and socioeconomic aspects that affect individual behaviors. With regard to financial incentives, evidence suggests that they might be effective in changing the practice of healthcare professionals [46]. However, a lower level of screening was recorded in PHC teams from Navarre, where they had more incentives related to the studied screenings. This discrepancy may be explained by the variable “region”, which could act as a proxy for other important unobserved organizational and socioeconomic variables.

Limitations and strengths

Our study has several limitations that must be acknowledged. It was based on a registry of daily clinical activity at the point when computerization of PHC health records had just begun to mature. The acquisition of good recording habits and the changes that occurred in the software over time could have affected the recording of clinical activity [39,47]. Finally, available programs did not allow adequate recording of the activities conducted by nursing professionals, despite their important role in prevention [10].

Due to differences in the implementation of electronic health records and the availability of data only 2 regions of Spain were included in the study. Future studies, with more regions, are needed to estimate the association between region-specific characteristics and screening. Other factors should be factored in to improve the quality of data collection: 1) Training of basic computer skills to health professionals; 2) Training of health professionals to adequately use and to keep up to date with the ECR; 3) Incentives, financial and otherwise, to increase the motivation of health professionals toward achieving a better completeness and quality of data. In addition, harmonization of variables and codification systems should be improved to enable information-system interoperability and data sharing for research [48].

Major strengths of the present study include its large sample size and multilevel random slopes. The large sample size drawn from REGIPREV, a database specífically focused on prevention activities, provided a broad view of PHC screening implementation. A multilevel approach allows us to separate the potential sources of variability (individual, GP and PHC team) and to control for clustering effects. The random slopes analysis contributes to examining whether the PHC team or GP environment as a whole would modify individual-level associations, without specifying any contextual factors. Moreover, it may show whether contextual influences have a different impact on screening for certain groups of individuals [32].

Variation remained statistically significant at the PHC team and GP level, even after accounting for individual and contextual factors. Future research should explore whether other individual factors (e.g., variables specific to each screening) and contextual features (such as factors linked to PHCT organization, changes in the software, nurses assigned to the patient, reminder alerts or feedback to GPs concerning prevention activities, etc.) may account for variation in the screening registry. Moreover, the random slopes analysis would allow the examination of contextual effects that pertain to specific groups of people and of cross-level interactions to establish PHC team-individual or GP-individual causal pathways.

Blood pressure and hypertension in athletes: a systematic review | real questions and Pass4sure dumps


In Western countries, the prevalence of hypertension has been reported as 14.4% and 21.2% in men aged 20–29 and 30–39 years, respectively, and as 6.2% and 9.9% in women in the same age group.1 High blood pressure (BP) at a young age predicts cardiovascular mortality and morbidity decades later.2 ,3

High BP is the most common abnormal finding during preparticipation cardiac screening of athletes.4–8 The prognostic significance of high BP in athletes is unknown, but still athletes with BP <160/100 mm Hg are given the license to continue with sport participation if they have no signs of end organ damage, such as pathological left ventricular hypertrophy.9 Increased left ventricular mass is considered as subclinical organ damage in people with hypertension.10 ,11 As several studies have demonstrated increased left ventricular mass and increased left atrium size in athletes,12 it is possible that high BP may be a contributing factor13 ,14 that may also link to the increased risk of atrial fibrillation in endurance athletes.15–17 Hence, there is increasing interest in BP in athletes.14 ,18 ,19 BP measurement during preparticipation screening of athletes should be performed according to ‘best clinical care’,20 as outlined in the European Society of Cardiology's guidelines, with hypertension defined as systolic BP (SBP) ≥140 mm Hg and/or diastolic BP (DBP) ≥90 mm Hg after repeated measurements.21 We aimed to review BP and prevalence of hypertension in different athletes, and study the association between increasing BP and left ventricular hypertrophy.

Methods Literature search

We performed a systematic review of studies reporting BP in athletes by using a comprehensive search strategy developed for PubMed and EMBASE (see online supplementary material). The medical subject headings and text words were: ‘Athlete’, ‘Sport and Professional’, ‘Exercise Test’ and ‘Sudden Death’, combined with ‘Blood Pressure’ or ‘Hypertension’. The electronic search was restricted to studies published before 6 April 2014. In addition, we manually searched reference lists of reviews and original study articles, and our own archive.

Inclusion and exclusion criteria

We searched for studies of athletes that reported BP or prevalence of hypertension, using the studies’ own definitions of hypertension. We included studies of ≥100 athletes, with mean or median age between 18 and 40 years, of any epidemiological design (with or without follow-up, and with or without controls), and reported in English language. We excluded studies that were only presented as conference abstracts. If there were more than one publication from the same group, we used the record with most participants, or the newest, if the number of participants were the same. When in doubt, we contacted the corresponding authors.

Outcome variables

The primary outcome variable was BP or prevalence of hypertension in different categories of athletes (defined by gender, ethnicity, sports discipline or level of athletic activity). Secondary outcome variables were (1) method for measurement of BP and (2) association between BP and left ventricular hypertrophy (determined by left ventricular mass or relative wall thickness on echocardiography or by voltage criteria on ECG).

Extraction of data

All data were extracted by one reviewer (CBI) and checked by another reviewer (HMB), using a standardised data extraction sheet.

Statistical analysis

Differences between subgroups of athletes were analysed using t tests for continuous variables. Data are presented as mean with SD. A p<0.05 was considered statistically significant and all tests were two-tailed. The statistical analyses were conducted using SPSS (PASW Statistics 21; IBM Corporation 2013, Armonk, New York, USA).

Results Study selection

The searches retrieved a total of 4433 records (figure 1). After addition of studies from other sources and removal of duplicates, 3723 records remained. Screening of titles and abstracts excluded 2896 and 361, respectively. Another 404 studies did not meet the inclusion criteria, 9 were duplicate reports, and 2 studies were not available. The remaining 51 studies were included in the review.

Figure 1

Flow chart illustrating search strategy.

Study characteristics

Table 1 shows characteristics of the 51 studies, including a total of 138 390 athletes, with a median number of 434 athletes (range 10022–42 386 athletes23). Sixteen studies included non-athletes as controls and the median number of controls was 176 (range 26–9997). The mean or median age of the athletes in all studies was between 18 and 40 years, and about half of the studies had participants within this range only; however, several studies included participants with an age outside this range. Twenty studies included males only and across the 31 studies of both genders, 72.5% were males.

Table 1

Study characteristics

Most studies (28) included athletes from different sports disciplines, but 16 included athletes from only one discipline, eg, soccer (4),13 ,24–26 American football (3),27–29 triathlon (2)30 ,31 and long distance running (2);16 ,32 other studies classified sports disciplines as either endurance sports, strength sports or a mixture of the two (table 1). The athletes’ level of competition was described in 50 studies and ranged from participation in amateur sport to the Olympic Games. Hours of training per week or previous years of vigorous training were given in 24 studies, and ranged from 4 to 28 h a week and from 2 to 30 years, respectively.

BP in athletes

Table 2 shows BP and prevalence of hypertension in the same studies. Among the 34 studies that reported BP, two-thirds had BP in the prehypertensive range (SBP ≥120–139 and/or DBP ≥80–89 mm Hg).33 Mean SBP varied from 109±11 mm Hg (intercollegiate female college athletes (mean age 20 years))34 to 137.9±7.1 mm Hg (Italian male strength sports athletes (mean age 27.2 years)).35 Mean DBP ranged from 56.9±11.5 mm Hg (young college level athletes in the USA (mean age 18.4 years))36 to 92.2±9.6 mm Hg (male Chinese strength sports athletes with mean body weight 130 kg (mean age 21.7 years)).34 No studies reported ambulatory BP measurements.

Table 2

Blood pressure and prevalence of hypertension

Among the 16 studies that included non-athletes as controls, BP was lower in athletes than in controls in 9 studies and higher in athletes in 7 studies (figure 2). Only 3 of the 16 studies reported prevalence of hypertension in controls and 2 studies found more hypertension among athletes than controls. Overall, there was no significant difference in BP between athletes and controls.

Figure 2

Mean systolic blood pressure (SBP; continuous line) and diastolic blood pressure (DBP; dotted line) in athletes (black squares) and controls (grey circles).

Figure 3 shows the mean BP in different categories of athletes. Males had significantly higher BP than females (121.2±4.5/75.1±2.9 vs 113.5±2.9/71.9±2.6 mm Hg, p<0.05), but there was no significant difference in SBP between white and black athletes. We found that strength-trained athletes had higher BP than endurance-trained athletes (131.3±5.3/77.3±1.4 vs 118.6±2.8/71.8±1.2 mm Hg, p<0.05), while there was a trend towards higher BP in athletes training ≥10 h/week compared with those training <10 h/week (121.8±3.8/73.8±2.5 vs 117.6±3.3/66.8±6.9 mm Hg, p=0.058). There was no major difference between American football, soccer, triathlon and long distance running (figure 4).

Figure 3

Blood pressure (BP) in relation to gender, ethnicity, type of training and hours of training per week.

Figure 4

Blood pressure (BP) in different sports disciplines.

Prevalence of hypertension in athletes

Hypertension was defined in 11 different ways in the 25 studies presenting a definition (table 2). The most often used criteria for hypertension ranged from SBP ≥140 or DBP ≥90 mm Hg to BP>140/90 mm Hg. The lowest cut-off value for hypertension was BP ≥130/85 mm Hg37 and the highest cut-off value was ≥160/95 mm Hg.27 Three studies also used antihypertensive medication to define hypertension,18 ,29 ,34 one accepted self-reported hypertension18 and one only included participants with BP≤120/80 mm Hg.38

The prevalence of hypertension varied from 83%34 to 0% (table 2).39 ,40 The prevalence of hypertension was lower in studies that were restricted to athletes within the age range 18–40 years and six studies excluded patients with high BP, mostly >140/90 mm Hg.38–43

Method of measurement of BP in athletes

Some descriptions of measurement methods were present in 21 studies (figure 5; see online supplementary figure S3). BP was measured in the sitting position in 10 studies and in a supine position in 6 studies. At least 5 min of rest prior to BP recordings was required in 11 studies, while only 4 informed about time from physical activity to BP measurement.6 ,13 ,44 ,45 Athletes abstained from caffeine and/or smoking prior to BP recordings in two studies6 ,44 and no studies informed about the physical environment where the BP measurements took place. Only eight studies reported whether an appropriate cuff size was used. In the eight studies using a ‘standard’ mercury sphygmomanometer, the method of measurement performance was reported in three studies.45–47 Only the three studies that used an automated BP device reported the device type and manufacturer.13 ,26 ,29 A single measurement was used in five studies, but repeated in three of these if BP was high. The lowest of these values was registered in two studies18 ,48 and the highest in one.27 BP was recorded two and three times in six and four studies, respectively, and there was a significant difference in SBP between one and two BP recordings (127±4.7 vs 118±4.0 mm Hg, p<0.05). Choice of arm for measurement was presented in five studies and no study measured BP in both arms. Three studies recommended repeated BP recordings on a separate occasion if the BP was elevated. Only one study referred athletes with elevated office BP to ambulatory BP measurement.49

Figure 5

Number of studies describing each of the recommended elements in blood pressure measurements.

Association between BP and left ventricular hypertrophy

Three of the four studies relating high BP to left ventricular hypertrophy showed a significant positive linear association, either between BP and indexed left ventricular mass,13 between resting SBP and left ventricle mass and left ventricle wall thickness,49 or between SBP and the RaVL lead in ECG.29 One study found no association between SBP and relative wall thickness.30


The most striking finding in this review was that the methods of BP measurement in athletes were poorly standardised and varied widely. The Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure states that at least two measurements shall be made and the averaged recorded,33 and the European guidelines state that BP shall be measured three times after 5–10 min rest in the sitting position and the mean of the last two measurements shall be registered.21 The IOC has recommended BP recordings from both arms during preparticipation cardiac screening.50 All guidelines also recommend use of ambulatory BP measurements, but nearly all studies in our review were negligent to all these recommendations, as was a study among general practitioners in the UK.51

Given this background, it was difficult to give an estimate of BP or prevalence of hypertension in athletes. Naturally, the prevalence will be dependent on the definition of hypertension and varied from 0% to 83% in a subgroup of heavy weightlifters. The study with the lowest cut-off value (≥130/85 mm Hg) also had the highest overall prevalence of hypertension (45.1%, compared with 17% in an age-matched and gender-matched control group).37 The study with the highest cut-off value for hypertension (≥160/90 mm Hg) still reported a prevalence of 9.5%,27 but this study included athletes with the highest age (36.4±4.5 years), and selectively reported the highest of the measured BPs.

Two-thirds of the studies reported mean BP in the prehypertensive range. There are several possible explanations for this. First, in many of these studies, BP was measured only once and one recording is often higher than the mean of two recordings. Second, the cuff size might have been too small for the muscular upper arms of athletes, which means that BP is measured falsely too high since only a minority of studies reported if they had used an appropriate cuff size. Third, the environment was probably not quiet in most studies and the athletes had no rest prior to the BP recordings. Fourth, the BP was recorded postseason. In a recent study of 132 professional American-style football participants, both SBP and DBP increased significantly from before to after the season.14 There is also the possibility that many athletes do have BP in the prehypertensive range, as suggested by several studies in this review and as supported by the trend towards a higher BP in athletes training ≥10 h/week than in those training <10 h/week. Physical activity has a well-known BP-lowering effect in the general population33 and studies of ‘exercise as medicine’ report a decrease in BP of 4–9 mm Hg33; but the amount of physical activity in these studies is often limited to 30 min most days a week. In our review, most athletes were ‘elite’ or ‘professional’, training on average 14.4 h per week or had been training vigorously for an average of 8.8 years, and it may well be that BP reacts differently to such amounts of training than to more moderate amounts of physical activity. There can be many biological reasons for high BP in athletes. First, the mental stress associated with competition at a high level might increase the athletes’ BP, as indicated by the finding of a larger difference in SBP between professional athletes and controls (4.4 mm Hg, p=0.350), than that between non-professional athletes and controls (0.2 mm Hg, p=0.916, data not shown). Second, BP might be increased due to ‘spurious systolic hypertension’ when BP is measured in the upper arm in athletes.52 Third, some athletes might use BP-increasing drugs, as shown in several studies,18 ,53 which is an argument for collecting information about use of medication during preparticipation screening of athletes.33

We also found interesting differences between subgroups of athletes. For example, male athletes had significantly higher BP than female athletes; this was also found in a small study of 15 pairs in sports dancing, which showed that male dancers had significantly higher BP than their female counterparts, despite similar levels of training.54 We also found higher BP and a higher prevalence of hypertension in strength-trained athletes than in endurance-trained athletes, in accordance with the ‘Morganroth hypothesis’.55 The highest prevalence of hypertension, of 83.0%, was found in professional male Chinese strength athletes, predominantly weightlifters, in the unlimited maximum body weight class.33

There is increasing concern about the effects of vigorous, long-term athletic training on cardiovascular health56–58 and it is possible that some of the harmful effects may be mediated through high BP. High BP in adulthood increases risk of cardiovascular disease in the general population2 ,3; we and others have found an association between high BP and left ventricular hypertrophy in athletes.13 ,54 ,59 Whether this is a benign physiological adaptation to high BP or a beginning of pathological remodelling is not known. It may also be that left ventricular hypertrophy provokes hypertension or that other factors confound the association between high BP and left ventricular hypertrophy. High BP may also be a part of the explanation for the fivefold increased risk of atrial fibrillation in endurance athletes15 ,60–62 and exercise-induced arrhythmogenic right ventricular cardiomyopathy56 through repeated bouts of high BP on myocyte junctions in the atria and the ventricles.

Clinical impact and conclusions

BP and prevalence of hypertension in athletes varies considerably partly because of variations in measurement methods, but type and intensity of training seem to play a role. Strength-trained athletes have significantly higher BP than endurance-trained athletes and vigorous physical activity does not seem to reduce BP in athletes compared with controls. Some studies found an association between high BP and left ventricular hypertrophy, but the clinical impact of high BP in athletes is not known. Future studies should adhere more rigorously to the recommendations for measurement of BP and should be designed to determine more precisely the prevalence, determinants and prognostic significance of hypertension in athletes.

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