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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
: 70 actual Questions

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IBM IBM SPSS Statistics(R) Certification

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

in this post i will reveal you the way to expend 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 evolution 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 any facts facilities.

running Spark classes from RStudio

RStudio makes expend of the brand fresh sparklyr kit ( to link with the Spark kernel gateway on the cloud the expend 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 expend your latest Spark cases from RStudio. to expend this feature, dash birthright here steps:

1 Load and reveal accessible Spark circumstances

2 hook up with a Spark instance

3 dash 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 fix or disconnect

checklist attainable Spark situations

for those who nascence RStudio two data are created in the working directory (don’t delete them!):  1) config.yml file — Lists your entire 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 divorce from the domestic listing, you can replica the config.yml and .RProfile files to your present working directory.

you can load and array Spark instances through the expend 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, dash the spark_connect R function. for example:

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

After this Spark context is created, any subsequent operations will live executed the expend of this Spark example:

as soon as related to Spark they can remark connection status like below

Run dplyr APIs and Spark’s distributed computer discovering libraries

To dash dplyr services, load the dplyr package and then dash 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 corpse 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 reveal 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 link 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. birthright here is the sample 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 their 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 any tablessrc_tbls(sc)head(airline15krows_tbl,four)


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


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


loads the Popular mtcars R data frame and then generates a Spark records corpse 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. remark sparklyr — R interface for Apache Spark for more suggestions.

See too 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 evolution of quite a lot of gleaming.information elements and SparkaaS integration with RStudio. With greater than twenty years of suffer 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 simple Play On Self-service big records Prep And Analytics: wait for Smarter Valuation Entry factor | actual 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 | actual 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 custody in Spain: a multilevel analysis | actual 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]. any of these may justify disparities between studies. Despite the progressive increase in the recording of prevention activities [10], PHC screening activity remains low and can live greatly improved, especially with respect to tobacco and alcohol use. recommendation on drinking behaviour is least often provided, probably due to a reluctance to query patients about it unless there are limpid signs of risky drinking conduct [40].

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

The most prevalent screening is for hypertension and dyslipidemia, which occupy the lowest unexplained variability between PHC teams and GPs, respectively, after adjusting for individual and contextual factors. A workable 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 live 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 censorious 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 occupy a more proactive approach to screening in patients with more pathologies. Regardless of the character of screening, patients with previously identified cardiovascular risk were more likely to live screened, perhaps due to the need to obtain information to compute cardiovascular risk and determine confiscate 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]. Their 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 tall percentage of superannuated patients was negatively associated with some screening activities [12]. This may live due to the increased toil load and want 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 result was determined by the region. workable differentiating factors comprehend 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 custody policies in each region, such as economic incentives to conduct inevitable prevention activities, the rurality of the region, or sociocultural and socioeconomic aspects that move individual behaviors. With regard to financial incentives, evidence suggests that they might live efficient 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 live explained by the variable “region”, which could act as a proxy for other censorious unobserved organizational and socioeconomic variables.

Limitations and strengths

Our study has several limitations that must live 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 pleasant recording habits and the changes that occurred in the software over time could occupy 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 censorious 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 live factored in to help the property of data collection: 1) Training of basic computer skills to health professionals; 2) Training of health professionals to adequately expend and to preserve up to date with the ECR; 3) Incentives, financial and otherwise, to increase the motivation of health professionals toward achieving a better completeness and property of data. In addition, harmonization of variables and codification systems should live improved to enable information-system interoperability and data sharing for research [48].

Major strengths of the present study comprehend its big sample size and multilevel random slopes. The big 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 divorce 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 entire would modify individual-level associations, without specifying any contextual factors. Moreover, it may reveal whether contextual influences occupy a different impact on screening for inevitable 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 | actual 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 selfsame age group.1 tall blood pressure (BP) at a immature 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 tall BP in athletes is unknown, but silent athletes with BP <160/100 mm Hg are given the license to continue with sport participation if they occupy no signs of finish 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 occupy demonstrated increased left ventricular mass and increased left atrium size in athletes,12 it is workable that tall BP may live a contributing factor13 ,14 that may too 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 live 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 They 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 matter 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, they manually searched reference lists of reviews and original study articles, and their 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. They included studies of ≥100 athletes, with count 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. They excluded studies that were only presented as conference abstracts. If there were more than one publication from the selfsame group, they used the record with most participants, or the newest, if the number of participants were the same. When in doubt, they 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) manner 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 count with SD. A p<0.05 was considered statistically significant and any tests were two-tailed. The statistical analyses were conducted using SPSS (PASW Statistics 21; IBM Corporation 2013, Armonk, fresh 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 count or median age of the athletes in any studies was between 18 and 40 years, and about half of the studies had participants within this scope 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, force sports or a composite of the two (table 1). The athletes’ level of competition was described in 50 studies and ranged from participation in professional 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 selfsame studies. Among the 34 studies that reported BP, two-thirds had BP in the prehypertensive scope (SBP ≥120–139 and/or DBP ≥80–89 mm Hg).33 count SBP varied from 109±11 mm Hg (intercollegiate female college athletes (mean age 20 years))34 to 137.9±7.1 mm Hg (Italian manful force sports athletes (mean age 27.2 years)).35 count 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 force sports athletes with count corpse 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 establish more hypertension among athletes than controls. Overall, there was no significant disagreement 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 count 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 disagreement in SBP between white and black athletes. They establish 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 disagreement between American football, soccer, triathlon and long distance running (figure 4).

Figure 3

Blood pressure (BP) in relation to gender, ethnicity, character 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 too 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 scope 18–40 years and six studies excluded patients with tall 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; remark 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 comfort 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 confiscate cuff size was used. In the eight studies using a ‘standard’ mercury sphygmomanometer, the manner of measurement performance was reported in three studies.45–47 Only the three studies that used an automated BP device reported the device character and manufacturer.13 ,26 ,29 A unique 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 disagreement in SBP between one and two BP recordings (127±4.7 vs 118±4.0 mm Hg, p<0.05). option 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 divorce 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 tall 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 establish 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 tall Blood Pressure states that at least two measurements shall live made and the averaged recorded,33 and the European guidelines status that BP shall live measured three times after 5–10 min comfort in the sitting position and the count of the eventual two measurements shall live registered.21 The IOC has recommended BP recordings from both arms during preparticipation cardiac screening.50 any guidelines too recommend expend of ambulatory BP measurements, but nearly any studies in their review were neglectful to any these recommendations, as was a study among universal 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 live matter on the definition of hypertension and varied from 0% to 83% in a subgroup of ponderous weightlifters. The study with the lowest cut-off value (≥130/85 mm Hg) too 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) silent 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 count BP in the prehypertensive range. There are several workable explanations for this. First, in many of these studies, BP was measured only once and one recording is often higher than the count of two recordings. Second, the cuff size might occupy been too petite for the brawny upper arms of athletes, which means that BP is measured falsely too tall since only a minority of studies reported if they had used an confiscate cuff size. Third, the environment was probably not tightlipped in most studies and the athletes had no comfort 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 too the possibility that many athletes Do occupy 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 result in the universal population33 and studies of ‘exercise as medicine’ report a reduce 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 their review, most athletes were ‘elite’ or ‘professional’, training on indifferent 14.4 h per week or had been training vigorously for an indifferent of 8.8 years, and it may well live that BP reacts differently to such amounts of training than to more temper amounts of physical activity. There can live many biological reasons for tall BP in athletes. First, the mental stress associated with competition at a tall level might increase the athletes’ BP, as indicated by the finding of a larger disagreement 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 live increased due to ‘spurious systolic hypertension’ when BP is measured in the upper arm in athletes.52 Third, some athletes might expend BP-increasing drugs, as shown in several studies,18 ,53 which is an dispute for collecting information about expend of medication during preparticipation screening of athletes.33

We too establish absorbing differences between subgroups of athletes. For example, manful athletes had significantly higher BP than female athletes; this was too establish in a petite study of 15 pairs in sports dancing, which showed that manful dancers had significantly higher BP than their female counterparts, despite similar levels of training.54 They too establish 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 establish in professional manful Chinese force athletes, predominantly weightlifters, in the unlimited maximum corpse weight class.33

There is increasing concern about the effects of vigorous, long-term athletic training on cardiovascular health56–58 and it is workable that some of the harmful effects may live mediated through tall BP. tall BP in adulthood increases risk of cardiovascular disease in the universal population2 ,3; they and others occupy establish an association between tall BP and left ventricular hypertrophy in athletes.13 ,54 ,59 Whether this is a benign physiological adaptation to tall BP or a nascence of pathological remodelling is not known. It may too live that left ventricular hypertrophy provokes hypertension or that other factors confound the association between tall BP and left ventricular hypertrophy. tall BP may too live a partake of the explanation for the fivefold increased risk of atrial fibrillation in endurance athletes15 ,60–62 and exercise-induced arrhythmogenic birthright ventricular cardiomyopathy56 through repeated bouts of tall 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 character and intensity of training appear to play a role. Strength-trained athletes occupy significantly higher BP than endurance-trained athletes and vigorous physical activity does not appear to reduce BP in athletes compared with controls. Some studies establish an association between tall BP and left ventricular hypertrophy, but the clinical impact of tall BP in athletes is not known. Future studies should adhere more rigorously to the recommendations for measurement of BP and should live designed to determine more precisely the prevalence, determinants and prognostic significance of hypertension in athletes.

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