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IBMSPSSSTATL1P - IBM SPSS Statistics(R) Certification Level 1 (formerly PASW Statistics) - Dump Information

Vendor : IBM
Exam Code : IBMSPSSSTATL1P
Exam Name : IBM SPSS Statistics(R) Certification Level 1 (formerly PASW Statistics)
Questions and Answers : 70 Q & A
Updated On : December 11, 2018
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IBMSPSSSTATL1P Questions and Answers

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IBMSPSSSTATL1P IBM SPSS Statistics(R) Certification Level 1 (formerly PASW Statistics)

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

Test Code : IBMSPSSSTATL1P
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 Wins a 2018 purple Dot Design Award for SPSS records | killexams.com Real Questions and Pass4sure dumps

The IBM Hybrid Cloud team is returned at it with yet a further win for design. I’m excited to announce that our design crew has been awarded the 2018 purple Dot: communique Design Award for IBM SPSS facts within the Interface Design class. This award is a continuation of the design achievements we have seen this past yr, including the A’Design Awards, IF Design Awards, and others. i am thrilled to look the hard work of our designers and IBM Design continue to shine and make a difference in enterprise utility.

First developed in the 1990’s, the crimson Dot Award has been the revered international seal of dazzling design first-rate. Designers, organizations, and organizations from forty five distinct countries took part during this yr’s competitors, totaling over eight,600 entries that underwent a 24 member jury.

“All folks that progress in the course of the tough adjudication method to garner a red Dot have each reason to be proud of themselves, as the jury provides our award handiest to creations of high design great. This makes me the entire greater delighted to congratulate the laureates sincerely on their well-merited success.” — Professor Dr. Peter Zec, founder and CEO of the pink Dot Award

Receiving this award became enormously entertaining for our team and we are honored to be among the winners. here's a big achievement for our designers who labored on this product, and that they faced an interesting and challenging journey in engaged on this product.

what is IBM SPSS?

IBM SPSS facts is a powerful information evaluation tool that is among the most regular information functions. on the grounds that its inception in 1968, SPSS facts has been revamped and redeveloped diverse instances. Now the design group at IBM has taken on the assignment of developing a totally fresh consumer adventure.

during this newest remodel of IBM SPSS records, we implemented design thinking principles via working closely with our users and making bound this modernized version of SPSS statistics aligns with their needs. Our most efficient aim became to create an impressive tool that is not most effective handy and intuitive to make use of, but that our clients can savour.

Our team and Design strategy

The IBM SPSS design team is part of the IBM Design Studios in Boeblingen, Germany. The team consists of a various neighborhood, with many individuals originating from different international locations and cultures. Some individuals of the crew had some history with statistics while others have been working in this box for the first time.

Following the principles of IBM Design pondering (observe > reflect > Make), our crew applied a redecorate that brings a stronger focal point on clients for SPSS information. The design crew carried out intensive research on the person base of SPSS records so as to see how the software can improved meet their wants. The current user base levels from much less skilled clients akin to students to extra knowledgeable clients such as statistics scientists or enterprise experts. A key insight from the crew’s research changed into that less skilled clients had been intimidated each by the math work and the complexity of the utility.

the brand new designs focused on simplifying workflows, cutting back the usual complexity of the UI and interactions, and offering freshmen an easy on-boarding to information and to the product. an additional essential feature in the remodel become a practicing e book led by way of a character named Simon, who serves as an in-software book, helping novice users be aware diverse capabilities and obtain their goals quicker.

The group confronted some exciting challenges in redesigning a product of such complexity, and one which has additionally been round for so decades. a large success of the designers was making the product accessible and attractive to new users with out alienating decade-lengthy, experienced clients.

a look Into the Future

The preview edition of our new IBM SPSS data journey changed into released in March 2018, and made obtainable to the public as a trial on the IBM feel conference is Las Vegas, and considering the fact that June 26 , the new UI is frequently attainable to all SPSS facts subscribers. This preview is only the initial step, offering probably the most used statistical analyses, and simple capabilities for facts practise, for presentation and for reporting results. Over here months the team will be working to add more points and capabilities with a purpose to meet event needs of all of our consumer agencies.

no longer just Updating — Redesigning

i'm so overjoyed to peer another Hybrid Cloud design group acquire an international award for his or her work. IBM SPSS statistics is yet a further instance of how design is making an important change in the success of our items. As we proceed to make use of design to create extra relatable and effective items, we're in a position to give our users the experiences that they need and need. I’m overjoyed and proud to monitor the difference that our design group is making in the world of business utility, and i can’t wait to peer how we continue to impact the lives of our users.

Award Winners:
  • Design supervisor: Caroline legislations
  • Design Leads: Dirk Willuhn and Eva Cochet-Weinandt
  • Design team: Christian Fritsche, Dimitri Hoffmann, Jaehee (Chloe) Lee, Oleksandr Sabov, Stephan Feger
  • because of these contributing designers: Katrin Ellice Heintze, Leila Johannesen, Marion Bruells, Phil Brucker, Robin Auer, Sammy Schuckert, Stefan Schwarz
  • Design interns: Mengzhu Deng, Nathalie Mader, Ting-Hao (Howard) Huang, Vanessa Ng

  • A Pure Play On Self-service big statistics Prep And Analytics: look ahead to Smarter Valuation Entry point | killexams.com Real Questions and Pass4sure dumps

    No outcomes discovered, are attempting new key phrase!Many information scientists emerge from bigger education classes with skills in R, Python, SQL, IBM SPSS information, as an example ... facets however we didn't see collaborative points, information certification/go...

    What exactly Is IBM Doing in another way With computing device getting to know? | killexams.com Real Questions and Pass4sure dumps

    computing device gaining knowledge of itself isn't new. The ideas had been around for decades, and a lot of agencies had been building ML fashions and doing predictive analytics for a long time. So what exactly is IBM doing during this space?

    i would like to say, "IBM takes an commercial enterprise strategy to ML," but that sounds too indistinct. This submit details IBM's commitment to computer researching and the way our method goes beyond hype and hand-waving to present the systems, equipment, and procedures our enterprise consumers need.

    i need to focal point on three things:

  • Democratizing laptop learning.
  • Operationalizing computing device discovering.
  • Hybrid computing device learning with healthy-for-purpose deployment options.
  • Let's analyze each one.

    Democratizing ML

    One point of "democratizing" is increasing the entry to computer studying with open-source code, open languages, and open frameworks. Languages like Python, Scala, and R; frameworks like Spark and TensorFlow; tools like Apache Zeppelin™, Jupyter, and RStudio. What about agencies that have been the usage of proprietary ML equipment? With assist for importing PMML, SPSS and different proprietary environments, we can work with present fashions — proprietary or open supply — during this ambiance. presenting that diversity is our manner of meeting users the place they are, with the expertise they already have. machine gaining knowledge of is moving too quickly to drive groups to burn time ramping up on whatever thing unfamiliar.

    The second point of "democratizing" is reducing the barrier for individuals who do not have the deep math or information background it truly is been integral in the past. I are looking to be careful about this. I do not wish to imply that the equipment cast off the need to have in mind what's going on (removed from it!), but visible, guided interfaces for building and testing fashions deliver a level of productiveness — assuming that you've got some simple hold close of the ideas and domain. An illustration is the Auto records training alternative (see determine 1), which does a fundamental degree of feature engineering immediately. As one more instance, the Cognitive advice for records Science (CADS) function selects the most appropriate algorithm for you according to the statistics set and class of algorithms you want to analyze.

    Image title

    determine 1: Auto data guidance for feature engineering

    a 3rd factor of "democratizing" is the capability to collaborate. information science is a crew sport, involving information engineers, records scientists, business analysts, application builders, and other personas within the business. These personas can engage and collaborate, giving your total team a collaborative workbench to consider facts sources, share statistics exploration in notebooks, information sets, and fashions — from both visual and programmatic mannequin-building equipment — with access manage and governance in location.

    Operationalizing ML

    as the phrase suggests, here's about taking computer getting to know from the laptops of the facts science group and weaving it into the specific operations of the commercial enterprise. How do you put in force version manage? How are you able to deploy fashions quickly? How can application developers invoke models quite simply? How do you make sure the models live current as time goes by means of? Most agencies presently doing ML have faced this challenge. a brand new mannequin that has just been proficient and evaluated performs staggering on day one; although, the predictions are not that superb after a couple of weeks, and the mannequin has develop into fully pointless in a couple of months. data patterns have changed, and the model performance has degraded. How do you alert stakeholders when the mannequin does not work that smartly anymore? How do you manage the assignment of fresh models with new practising records?

    those are the questions that compelled us to create a framework for managing the whole statistics science lifecycle: ingest, put together, construct, install, video display, and continuously enhance. Integration with GitHub and GitHub business provides the capacity to merge and control code types. An built-in self-service catalog provides a "store for statistics" adventure. The ability to install models with just a few clicks, permitting utility developers the ease of consuming with a rest call, and so on. are also examples of this lifecycle. Of specific relevance is a remarks loop that ensures the predictive efficiency of your models stays robust — without the normal time-sink of babysitting them.

    Image title

    figure 2: Specifying efficiency metrics for a classification model

    further and further, we had been seeing statistics science groups pulled away from their true work of brainstorming, function engineering, and mannequin design — and as an alternative forced to manipulate pursuits initiatives that could be computerized. So we automated them.

    Hybrid ML With healthy-for-intention Deployment alternatives

    We realize that for many corporations the longer term is multi-cloud. A majority of our customers are now managing data and workloads in dissimilar environments — on distinctive public clouds, at the back of the firewall in a private cloud, on mainframes, on really expert hardware and home equipment, or spanning these. it's crucial to conform to the principles of data gravity (carry analytics to the information; try to avoid moving information around) and assembly efficiency or protection SLAs that certain platforms supply.

    it could make feel to educate on one platform and deploy on another. as an example, you could need to coach and build an ML mannequin on an x86 cluster on-premises using sensitive information, and deploy the informed mannequin to an off-premises cloud. (See Figures 3 and 4.)

    Image title

    figure 3: construct and teach a mannequin on-premises, however put up to cloud

    Image title

    determine 4: ML model built on proficient on-premises turns into attainable within the cloud

    In an identical approach, consider you are engaged on a complex model for fraud detection. it will make experience to dramatically speed up working towards on energy machines leveraging GPUs and the excessive-velocity NVLink connections (see right here), however then you definately could need to set up the mannequin right into a CICS in-transaction call on the mainframe for sub-millisecond scoring of a credit card transaction. These use instances want maximum interoperability and a unified consumer adventure across diverse environments.

    fit-for-aim ML is a tall order, but definite core principles practice, including bringing computer studying capabilities native to a number of platforms and systems. that you would be able to deploy your desktop discovering models where it makes probably the most experience, no matter if this is within the public cloud, at the back of the firewall, within the optimized IBM built-in Analytics equipment, or on the mainframe, simply to name a number of. extra, our Kubernetes based ML ambiance will also be stood up on infrastructure from plenty of normal Cloud carriers, giving you the potential to installation to IBM and non-IBM cloud environments and enabling a multi-cloud approach.

    This keeps purchasers from working against data gravity: We allow them to convey the tools, intelligence, and analytics to the facts instead of forcing them to drag information slowly — and insecurely — from vicinity to area.

    An commercial enterprise Platform for ML

    We introduced all these capabilities collectively into statistics Science adventure (DSX), IBM's enterprise platform for ML.

    perhaps it goes without announcing, but the suggestions and capabilities described above don't stand alone. Working with IBM ability the potential to combine with the broader IBM portfolio, which levels from master data administration to governance to determination optimization to Watson Explorer to Watson Cognitive APIs to PowerAI and greater. Combining these units of capabilities does not just create incidental efficiencies; collectively they could basically seriously change your company. for instance use case, Watson image focus produces a group of points that feed into downstream customized laptop learning models in DSX that predict constructing power efficiency. In a 2nd use case, integration between choice Optimization with CPLEX and DSX brings advanced save team of workers scheduling, going past linear programming to quadratic services. In a 3rd use case, Watson Speech-to-textual content and superior NLP capabilities in Watson Explorer (WEX) integrate with DSX to combine unsupervised and supervised thoughts for knowing deep intent in client calls into the call middle.

    And additionally...

    we have integration no longer simply with the IBM portfolio but also with a prosperous ecosystem of partners like H2O, HortonWorks and many others. as an instance, integration with the HortonWorks records Platform lets you leverage native capabilities including searching for statistics in the Hadoop cluster and authentication the usage of Apache Knox.

    a further electricity is a gentle move of innovation coming in from IBM analysis, ranging from computerized characteristic engineering to cognitive databases to distributed deep researching for scaling acceleration and extra.

    however in the course of the entire points and tools that IBM is proposing to force facts science for the enterprise, it may also be easy to forget the deeper reason our purchasers appear to IBM within the first region. They seem to IBM as a result of they be aware of that our a long time of precise-world experience throughout each possible business skill we convey an unmatched depth of talents about how organizations run and what makes them thrive.

    As potent as desktop researching is, it's a fantasy to feel that organizations can throw facts into a black box and get back actionable effects. Doing characteristic engineering and building fashions and infusing them into the purposes and tactics of a enterprise needs an intimate expertise of the business and its dynamics — each on the excessive level of the trade sector and on the low degree of transaction stream, law, and so on. that's what differentiates IBM. Our business-leading strategy to laptop learning is just the latest manifestation of a protracted lifestyle of assisting valued clientele locate the longer term.


    IBMSPSSSTATL1P IBM SPSS Statistics(R) Certification Level 1 (formerly PASW Statistics)

    Study Guide Prepared by Killexams.com IBM Dumps Experts


    Killexams.com IBMSPSSSTATL1P Dumps and Real Questions

    100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



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

    Test Code : IBMSPSSSTATL1P
    Test Name : IBM SPSS Statistics(R) Certification Level 1 (formerly PASW Statistics)
    Vendor Name : IBM
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    Reliability of Telemedicine in the Assessment of Seriously Ill Children | killexams.com 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 | killexams.com 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 | killexams.com real questions and Pass4sure dumps

    Introduction

    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

    Discussion

    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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