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C2020-011 | IBM SPSS Statistics Level 1 v2

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C2020-011 - IBM SPSS Statistics Level 1 v2 - braindump

Vendor IBM
Exam Number C2020-011
Exam Name IBM SPSS Statistics Level 1 v2
Questions 55 Q & A
Recent Update February 12, 2019
Free PDF Download C2020-011 Brain Dump
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C2020-011 exam Dumps Source : IBM SPSS Statistics Level 1 v2

Test Code : C2020-011
Test Name : IBM SPSS Statistics Level 1 v2
Vendor Name : IBM
Q&A : 55 Real Questions

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IBM IBM SPSS Statistics Level

comparing the main big records analytics application options | killexams.com Real Questions and Pass4sure dumps

there are many providers selling products classified as massive facts analytics utility. however, it be difficult to...

differentiate these items in response to performance by myself, as many of the tools share similar points and capabilities. moreover, one of the vital equipment display extremely delicate modifications.

That being mentioned, your key differentiating factors will likely center of attention on balancing ease of use, algorithmic sophistication and price with regards to your company's potential and level of maturity in analytics.

listed here, we investigate items from nine huge records analytics utility companies: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of those providers provide a couple of tool. See the "main providers of large information analytics utility" sidebar beneath for extra details about their selected product choices.

These companies represent diverse sides of the massive facts analytics market. Let's compare and distinction the ways in which these products meet the enterprise wants of person organizations.

Analyst abilities and advantage 

Some data analytics tools are centered to beginner clients, some are focused to expert facts analysts and some are engineered to enchantment to each styles of clients.

products such as IBM SPSS Modeler, RapidMiner's tools, Oracle superior Analytics and the automated Analytics edition of SAP BusinessObjects Predictive Analytics are commonly designed to allow users with a confined heritage in facts or records evaluation to investigate facts, increase analytical models and design analytics workflows with little or no coding.

while each and every seller wraps its core analytics add-ons with an intuitive user interface to e book the analyst's development in records preparation, analysis, after which model design and validation, the strategy taken may additionally vary, in particular when evaluating a stand-alone product, reminiscent of RapidMiner, with one this is a component of a larger suite, such because the Oracle product.

equipment reminiscent of IBM SPSS facts, KNIME Analytics Platform, the professional Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform provide the more subtle functionality that expert users are expecting. Oracle R superior Analytics for Hadoop (ORAAH), some of the accessories within the Oracle large facts application Connectors suite, gives an R interface for manipulating Hadoop allotted File equipment facts and writing mapper and reducer services in R. this adaptability may be attractive to greater superior statistics scientists.

Alteryx and SAS enterprise Miner present performance tailored to the person's level of abilities, and almost fall into each categories. Alteryx has brought advancements to statistics profiling to support records scientists greater take note their records sources. usual, SAS enterprise Miner and IBM's SPSS equipment stand out when it comes to supporting more advanced analytical innovations and model scoring, as well as a broader array of evaluation capabilities, together with neural networks, association analysis and visualization capabilities.

Analytical diversity

reckoning on the use case and software, your organization's clients may be required to help several types of analytics capabilities in order to use particular sorts of modeling, equivalent to regression, clustering, segmentation, habits modeling and determination trees.

while this has resulted in vast help for the a considerable number of types of analytical modeling at a high degree, some providers have invested many years of labor into tweaking different models of their algorithms and adding more subtle functionality. or not it's crucial to consider which fashions are most valuable to your company complications and to evaluate the items in terms of how they most efficient serve your clients' enterprise wants.

it be important to be aware which models are most vital to your enterprise issues and to evaluate the products when it comes to how they top-rated serve your users' company needs.

The extra mature and better-end -- and, hence, bigger-priced -- tools will exhibit the optimum analytical breadth. Oracle data Miner contains an array of familiar laptop learning processes to guide clustering, predictive mining and textual content mining. each variations of IBM's SPSS product provide a diverse set of analytical suggestions and models. And SAS enterprise Miner supports many algorithms and recommendations, together with choice trees, time collection, neural networks, linear and logistic regression, sequence and internet direction analysis, market basket analysis, and hyperlink analysis.

The newer generation -- and, in some instances, decrease-priced -- products support diverse fashions, however in all probability with a narrower range of algorithmic sophistication.

The mannequin inventory in Alteryx Analytics Gallery comprises such capabilities as regression evaluation, decision bushes, association rule evaluation, classification and time sequence analysis. KNIME comprises strategies for text mining, picture mining and time series analysis, and also integrates computer gaining knowledge of algorithms from other open source initiatives, such as Weka and JFreeChart.

a further factor of analytical variety is integration with programming languages and statistical tools, comparable to R, for incorporating existing libraries, as well as person-described functionality. in reality, integration with R may well be regarded an increasingly essential differentiator.

Alteryx designer, Microsoft R, SAS business Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and support integration with R. a few of the vendors, together with IBM, Oracle, Microsoft, RapidMiner and SAP, give a transforming into library of extensions to R and Python, enabling clients to take talents of free libraries.

Scope of the information to be analyzed

There are multiple aspects of the scope of the information to be analyzed, including the problem of structured vs. unstructured suggestions, in addition to entry to regular on-premises databases and information warehouses, cloud-based mostly data sources, and facts managed in massive records platforms, comparable to Hadoop.

however, there are various levels of support for statistics managed within less-regular records lakes -- either managed within Hadoop or in one more NoSQL records administration gadget supposed to provide horizontal scaling. The components for distinguishing among the products must be in line with your firm's certain necessities for gaining access to and processing information volumes and facts range.

In consciousness of the transforming into diversity of input sources and the variety of underlying systems used to residence these data units, a further set of emerging aspects it truly is being adopted by means of these providers contains records accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all more desirable their equipment' information import, export and connectivity capabilities. These enhancements may still enable clients to access a more complete record of records sources whereas simplifying and speeding up the technique of loading records into the items.

help for scalability and high efficiency

The need for scalable performance is pushed via your firm's statistics volumes and appetite for analysis. Smaller corporations with less records may be in a position to tolerate products that do not have efficiency characteristics that scale with the accessible elements, such as the entry-stage versions of the lower-end equipment, together with RapidMiner, KNIME, Microsoft R Open and Alteryx clothier, that may run on computing device programs and don't require additional server accessories.

larger organizations usually tend to have a more suitable inventory of information sets to investigate, in addition to broader communities of users. This introduces two further necessities -- high efficiency and facilitation of collaboration. The adaptability of a product to high-efficiency architectures is a good indication of scalability, and most of the items will also be tailored to the parallelism of Hadoop or employ some other capability of attaining sooner computation.

all of the items do have some aid for Hadoop, together with IBM SPSS Modeler and SPSS facts; RapidMiner's industrial part Radoop, which connects the Studio front conclusion and Server analysis engine to statistics kept in Hadoop; Oracle's massive records Discovery and ORAAH tools; and KNIME's massive statistics Extensions and Cluster Execution add-ins.

IBM SPSS now also provides superior guide for a couple of multithreaded analytical algorithms that may speed performance. Teradata Aster Analytics addresses high-performance requirements through its vastly Parallel Processing architecture. SAP's expert Analytics version of SAP BusinessObjects Predictive Analytics can execute in-reminiscence records mining for handling enormous-extent facts analysis successfully. Microsoft R Server leverages its ScaleR module, a comprehensive library of large statistics analytics algorithms that assist parallelization. Scoring algorithms implemented using SAS enterprise Miner will also be deployed and finished inside a Hadoop environment.

furthermore, integration with Apache Spark appears to be of growing to be importance. SPSS, KNIME, Oracle, RapidMiner and SAP all supply entry to Apache Spark libraries to assist analytics functions that need to scale with exploding data volumes. This allows developed functions to take capabilities of a high-efficiency cluster platform to distribute the workflow throughout the cluster.


As cited, the greater the company, the more likely there can be a necessity to share analyses, fashions and functions across distinct businesses and among many analysts. businesses which have many analysts disbursed across the enterprise may additionally search for extended means to share fashions and collaborate concerning the interpretation of outcomes.

IBM's SPSS Modeler Gold version gives collaboration capabilities, and RapidMiner's Server product gives guide for sharing and collaboration. Alteryx Analytics Gallery gives a mechanism for sharing refined analytics purposes within the cloud with individuals of an extended company. KNIME offers business extensions to guide group collaboration, in addition to extensions assisting operational collaboration, reminiscent of far off-scheduled execution, document era, shared facts house and a workflow repository. SAS business Miner's customer-server structure allows for company users and facts analysts to work collaboratively by way of sharing models and other work products.

Alteryx, KNIME and Teradata Aster have delivered capabilities to assist manipulate analytical workflows. also, some of the providers have begun to analyze ways to allow their equipment to combine with others that may additionally have complementary useful candy spots. for instance, Teradata Aster now has an extension to combine with KNIME that enables clients to leverage the KNIME workflow editor and comprise Aster Analytics capabilities into these workflows.

seller measurement and product integration

companies can be compared in terms of their measurement. One could examine and distinction what could be mentioned as the mega-carriers, whose huge statistics analytics equipment are only one product amongst a large portfolio of equipment. in case you work for a bigger company that usually negotiates website-huge, enterprise licenses for the complete suite of a supplier's tools from a mega-seller such as IBM, SAS, SAP or Oracle may be an affordable alternative.

The gigantic carriers sell large records analytics equipment which are part of a a lot greater tool ecosystem. presumably, the items from a mega-seller may be at the least just a little integrated and meant to work together. moreover, some americans believe more at ease with bigger carriers, with an expectation of stability and constant customer carrier. however, you may additionally handiest be able to purchase these big data analytics equipment as part of a an awful lot higher utility licensing arrangement.

Smaller providers, comparable to KNIME, Alteryx and RapidMiner, have revenues which are frequently according to licensing and support for a small number of big data analytics items. A smaller supplier may also supply nearer contact with their product management and innovation groups, and you'll be able to have an effect on the direction of the product roadmap or better functionality.

A smaller supplier may also be more flexible when it comes to price and the facets protected in the licensing association. You must understand, despite the fact, that working with a smaller vendor does existing some possibility in terms of steadiness, the components accessible for support and the probability that the enterprise may be obtained, that may affect the consumer relationship.

The bigger providers are obviously responsive to user needs for integration with other programs, although that commonly facilities on other products within every supplier's stock. as an instance, SAP Predictive Analytics has enhanced integration with SAP HANA and BusinessObjects Cloud. SAS business Miner has added nodes to execute code in a SAS open, cloud-in a position, in-memory Viya environment. Microsoft presents SQL Server R services, an R installing that runs alongside SQL Server and allows for users to integrate Microsoft R Server records with SQL Server and Microsoft's other business intelligence tools.

budget for licensing and maintenance

virtually the entire companies sell different models or variations of their products, with a range of fees for acquisition and total charge of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft sell variations at distinctive tiers, with the license cost proportional to the features, capabilities and freedom from barriers in terms of the volumes of information to be analyzed or the number of processing nodes the product can use.

KNIME and RapidMiner provide free and open source types of their products, both charging for aid functions or for variants supporting enterprise-category applications. KNIME, RapidMiner and Alteryx have fairly low licensing prices for a smaller variety of users. when you are in view that SAS or SAP, you have to contact them for pricing alternate options.

The market for massive data analytics utility may also be a perplexing vicinity, however expectantly this article has helped you take into account the benefits massive information analytics software can provide your company, and assisted you in differentiating between the selected equipment examined here.

IBM SPSS records Licenses Renewed | killexams.com Real Questions and Pass4sure dumps

Our annual license for IBM SPSS statistics has been renewed, and all licenses bought between nowadays and may 1, 2015 will expire on July 31, 2015.

if you bought a license between might also 1, 2014 and these days, you had been sent renewal authorization codes by e mail that may well be used to extend the expiration date of your utility.

IBM isn't renewing edition 19 this 12 months, and anyone the use of that edition (or past) will deserve to improve to a newer edition.  IBM SPSS information licenses are available for purchase via OIT for $seventy five per computer per 12 months.

The Ministry of Justice Selects Predictive Analytics from IBM SPSS | killexams.com Real Questions and Pass4sure dumps

The Ministry of Justice is the usage of predictive analytics know-how from SPSS, an IBM company, to assess the probability of prisoners re-offending on their release and to finally help enhance public security.

some of the UK's largest government departments with more than ninety five,000 personnel and a budget of GBP9.2 billion, the Ministry of Justice is the use of predictive analytics to determine the data held within its perpetrator assessment gadget (OASys). additionally, the analysis is assisting the Ministry of Justice advance treatment targets for prisoners all the way through their sentence to reduce the likelihood they'll commit crimes upon their unencumber.

The OASys equipment is used throughout approximately a hundred and forty prisons and all probation areas in England and Wales and information information from over 3.4 million prisoner assessments. This comprises statistics on individual perpetrator cases corresponding to accommodation, training, relationships, financial administration and salary, subculture and co-workers, drug and alcohol misuse, emotional smartly-being, behaviour and attitudes.

These records are being used for quantitative analysis that can determine patterns inside the statistics. The resulting intelligence forms the groundwork for more suitable measurements of perpetrator chance and wish. both the OASys Violence Predictor and OASys frequent Re-offending Predictor have helped to notably increase predictions about re-offending. in the case of violent crime, the prediction about re-offending has greater from sixty eight per cent to seventy four per cent while the prediction about re-offending in terms of accepted offences more suitable from seventy six per cent to eighty per cent.With Predictive technology from IBM SPSS, the Ministry of Justice is analysing hidden traits and patterns inside the records. as an example: the technology helps determine even if offenders with selected problems such as drug and alcohol misuse usually tend to re-offend than other prisoners.

"With essentially four million records on file it comfortably wouldn't be possible to trawl through this statistics manually in an try and establish those components that can also suggest a prisoner is probably going to reoffend," referred to a spokesperson from the Ministry of Justice. "SPSS' know-how offers us useful perception into culprit facts helping us predict who might also re-offend and enabling us to suggest on preventative measures, reminiscent of appropriate programs addressing wrongdoer behaviour before a prisoner's unencumber date."

"the way the Ministry of Justice is the use of our know-how showcases the excessive-level capabilities of our software in featuring analytical predictions upon which moves can also be taken ," explained Ian Warner, Public Sector advisor at SPSS, an IBM company. "by means of selecting developments and patterns hidden inside the facts, SPSS is assisting the Ministry of Justice predict re-offending costs and take steps to fulfill govt pursuits to enrich public defense by cutting back the danger to the usual public and work in opposition t a smarter executive.

"state-of-the-art business leaders should stream beyond instinct to perception, from intestine consider to data - as it is now possible to see key patterns in immense quantities of statistics, to extract critical insights and flow to a brand new degree of intelligence," Warner mentioned. "IBM SPSS predictive analytics is helping executive groups all over the world to seriously change the style they operate by using making smarter selections and enrich citizen carrier."

the new predictive analytics gadget is powered with the aid of IBM SPSS records software, IBM SPSS Modeler facts mining workbench and IBM SPSS textual content Analytics utility.

The fresh bulletins are a part of IBM's ongoing center of attention on helping customers use their assistance as a strategic asset through enterprise Analytics and Optimisation. IBM these days created a new business Analytics & Optimisation capabilities agency, with four,000 consultants who can support customers rise up and operating with deep analytics capabilities and made invested more than $12 Billion in organic growth an innovation to further construct its analytics portfolio.

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IBM SPSS Statistics Level 1 v2

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Technology Infrastructure, Graphics and Visualization, and Adaptive Technologies | killexams.com real questions and Pass4sure dumps

Technology Infrastructure, Graphics and Visualization, and Adaptive Technologies

Technology Infrastructure: Servers

Acer Altos Servers

Designed for workgroup networking, the Altos server series supports systems for file management, a department, or a LAN or WAN. Features include multiple processor support, large memory and cache possibilities, hot-swappable power supplies and storage modules, and support for multiple operating systems, including Windows NT, Novell Netware, or SCO OpenServer environments. A broad selection of scalable configurations, from basic add-in cards to key-activated Internet, Intranet, or RAID solutions, is also available. Contact: Acer America, San Jose, CA; (800) SEE-ACER; www.acer.com.

Dell PowerEdge Servers

The PowerEdge Server line has three different models, the PE300, PE2400, and PE4400. The PE300 has up to two Pentium III 800MHz processors and up to 1GB of ECC SDRAM. The PE2400 has up to two Pentium III 1GHz processor, 2 GB of ECC SDRAM and 144 GB of Hot Swap internal disk capacity. The PE 4400 has up to two Pentium III 1GHz processors, 4GB of PC133 SDRAM and 252GB of hot plug ultra-3SCSI internal disk capacity. Contact: Dell, Round Rock, TX; (888) 560-8324; www.dell.com.

Gateway Ultra-Thin Server

Gateway offers a full-featured server in a compact design, for companies with growing server requirements but limited physical space. The 7450-R supports Intel's two latest processors, 4CG of RAM and three hot-plug SCSI drives, optional RAID configurations, and two full-length 64-bit PCI slots. The components are designed for durability. Two high-powered blowers control the unit's temperature, and a tool-free chassis makes servicing convenient. Contact: Gateway, North Sioux City, IA; (800) 846-2000; www.gateway.com.

IBM RS/6000 Model 43P-140

The 43P-140 is an entry-level desktop/deskside system that provides a range of performance options, from drafting, design, and software development to high-definition 3D graphics and technical simulations. It includes the choice of processor speed, storage devices, and communications features, allowing users to configure the system for particular needs. With the selection of 2D or 3D graphics accelerators or 3D graphics input devices, the 43P-140 provides the necessary capability for demanding 2D or high-function 3D applications. Contact: International Business Machines, Inc., Armonk, NY; (914) 499-1900; www.ibm.com.

Informix Dynamic Server 2000

The Dynamic Server 2000 delivers a transaction engine for mission-critical applications while providing an upgrade path to the Internet. Capable of supporting thousands of concurrent users, it is scalable to power even the largest transaction processing systems. Features include enhanced Virtual Table Interface (VTI), which provides the ability to integrate and view legacy data from a variety of disparate systems, databases, and formats, and easy migration from previous Informix database products. Contact: Informix Software, Menlo Park, CA; (650) 926-6300; http://www-3.ibm.com/software/data/informix/ids/.

Criterion validity and test-retest reliability of SED-GIH, a single item question for assessment of daily sitting time | killexams.com real questions and Pass4sure dumps

The aim of the current study was to investigate the criterion validity and test-retest reliability of the SED-GIH question using activPAL3 micro as the criterion measure. The main findings were a moderate correlation (r = 0.31, CI = 0.20–0.41) and a poor agreement (weighted Kappa 0.12, CI = 0.05–0.18) between SED-GIH and activPAL derived sitting time (activPAL-SIT). Significant differences in activPAL-SIT existed between individuals in the different categorical answer options of SED-GIH. The reliability of SED-GIH was excellent (ICC = 0.86, CI = 0.79–0.90) with a substantial agreement (weighted Kappa 0.77, CI = 0.68–0.86).

The TASST framework was developed to gain an overview of tools used for assessing sedentary behaviour, and categorized them into four domains: type of assessment, recall period, temporal unit and assessment period. According to TASST, SED-GIH is defined as a single item direct measure of sitting, for an unanchored recall period with a temporal unit of a day, and an non-defined assessment period (taxon 1.1.1/2.4/3.1/4.5) [14]. The moderate correlation between sitting time measured objectively with activPAL and sitting time measured subjectively using the SED-GIH question is in line with other questionnaires. IPAQ (International Physical Activity Questionnaire, (TASST taxon 1.1.1/2.2/3.1/4.3) contains three specific sitting items, which have been validated using activPAL. For sitting time during weekdays, including transportation, correlation was low (r = 0.16, ICC = 0.15) and non-significant (p = 0.2) between the two methods. Here, IPAQ underestimated sitting time by 2.2 h per day [19]. PAST (Past-day Adults Sedentary Time, TASST taxon and PAST-U (modified version of PAST, TASST taxon asks participants to report their time spent sitting or lying during the previous day. When using activPAL (version 3) as criterion measure, the validity for PAST was assessed to be r = 0.57 [20], and PAST-U ICC = 0.64 [21]. When Busschaert and co-workers tested the validity of three different questionnaires measuring context-specific sedentary behaviour (TASST taxon,, they found weak to acceptable validity for adults (r = 0.06–0.52) and older adults (r = 0.38–0.50) [22]. This implies that the SED-GIH has stronger associations with objective sitting than other single item questionnaires, such as IPAQ, when compared to direct measurement. However, these associations are not as strong as the time-specified PAST and PAST-U, which collect information on sitting during the previous day only.

Participants who estimated their sitting as ≤3 h using SED-GIH, all underestimated their sitting time as compared to activPAL-SIT (see Table 3). Furthermore, participants who estimated their sitting as ≥13 h almost all overestimated their sitting time. These results are in line with comparisons between PAST and activPAL (version 3) derived sitting times. PAST underestimated sitting times at low levels of sitting, and overestimated sitting time at high levels of sitting [20]. However, a Bland Altman between IPAQ and activPAL indicated that IPAQ underestimated sitting time by up to 2.2 h per day (during a total week including transportation) [19], and both PAST-U and the three different questionnaires measuring context-specific sedentary behaviour overestimated sedentary time, with activPAL as the criterion measure [21, 22]. Dall and colleagues concluded that most sitting questionnaires underestimate sitting time by 2–4 h per day. Single item questionnaires are more likely to underestimate sitting time, while questionnaires assessing sitting during a sum of sedentary behaviours using a composite of several items tend to overestimate sitting time. Questionnaires assessing sitting during a sum of sedentary behaviours over an unanchored or longer period of time tend to report larger underestimations [14]. According to this study, the reasons for sitting time underestimations by the SED-GIH question can be explained by it being based on a single-item question during an unanchored period of time.

The original seven SED-GIH answer categories were collapsed into five, since there were very few participants choosing “Virtually all day” or “Never”. The intention of including all seven answering options was that “Virtually all day” and “Never” might be easier to relate to instead of < 1 h and > 15 h. They also provide the answer options with some anchorage. When the five categories were analysed, the mean values (displayed in Table 1) of sitting time measured with activPAL did not differ much between the categorical answer options of SED-GIH (varying from 8.7 to 10.3 h per day, mean 9.7 h per day). Thus, the objectively measured average sitting time per day had a narrow distribution, even though the participants subjectively estimated their sitting time with SED-GIH in a wider range. However, the accuracy of SED-GIH changed when only two categories were used (more or less than 10 h of sitting per day). The majority of the participants who rated themselves as sitting for 10 h or more, actually sat for more than 10 h (56.3%). The low sensitivity and specificity of SED-GIH indicates that it would not be useful for identifying hazardous sitters (≥ 10 h per day). Objective measurements may be more useful in detecting sedentary behaviour, possibly in combination with PAST or similar questionnaires. More research is thus needed to develop questionnaires assessing sedentary behaviour and provide better outcomes together with objective methods.

Test-retest reliability of SED-GIH was excellent (ICC = 0.86, CI = 0.79–0.90), which is better than other reliability tested questionnaires. PAST had fair to good reliability (ICC = 0.50), and three different questionnaires measuring context-specific sedentary behaviour had good reliability for adults (ICC = 0.73–0.77) and older adults (ICC = 0.68–0.80) [20, 22]. However, SED-GIH is a single item questionnaire, whereas PAST and the three different questionnaires measuring context-specific sedentary consist of several questions, which can affect test-retest reliability. With a tool consisting of a single item question, it might be easier to answer the same question twice compared to tools consisting of several questions. Thus, SED-GIH has good repeatability and generates reliable answers among older adults. However, it is not known whether SED-GIH can detect changes of sedentary behaviour over time, such as before and after a behavioural change intervention period. This field needs further research.

Limitations to the current study have been observed in the methods and the processing of the data. Participants may have become more conscious about their habits regarding sitting time when they answered the web questionnaire prior to the objective measures, which may have affected their sitting habits during the week of measurement with activPAL. Additionally, the measurement period between answering SED-GIH and wearing the activPAL varied (mean 16 days ±14 days), which may have affected the agreement. One impact on internal validity is the accuracy of the participants’ dedication to fill in the diary log correctly, which can affect the whole dataset. In the validity study, participants were employees with an office-based job, which is not representative of a general population. SED-GIH should be validated in other contexts and with different populations. In the reliability part of the current study, all participants were elderly. This may have an effect on the results since some elderly persons can have reduced memory function compared to younger adults.


SED-GIH may be useful as a tool when identifying sitting time as a determinant for health risks on a population level, but would not in itself be sufficiently informative for screening for unhealthy sitting habits in primary care. More studies performed on a broader population are needed.

Intraoperative Noise Increases Perceived Task Load and Fatigue in Anesthesiology Residents: A Simulation-Based Study | killexams.com real questions and Pass4sure dumps

During the past 5 decades, the operating room (OR) has acquired the unenviable distinction of being one of the noisiest of clinical environments, with sound pressure levels increasing an average 0.4 dB per year.1 Commonly performed surgeries, such as orthopedic procedures, can have sustained sound levels exceeding 100 dB for 40% of the time,2 far exceeding the limit recommended by the Environmental Protection Agency of 45 dB (day-night average sound level) to avoid annoyance and maintain 100% speech intelligibility3 and the World Health Organization guidelines of ≤55 dB (equivalent sound level during daylight [16] hours).4 There has been an increasing appreciation of the harmful effects noise pollution has on caregiver health, cognition, and performance.5,6

Two of the noisiest periods during surgery coincide with anesthetic induction and emergence.7 Hypothetically, less frequent but potentially catastrophic intraoperative emergencies such as anaphylaxis, pneumothorax, and hemorrhage also would be associated with elevated noise levels, although this has not been studied. Therefore, anesthesiologists may be particularly susceptible to noise exposure, with 84% subjectively reporting that noise has a negative impact on their work.8 Recently, 2 laboratory studies reported a negative effect of noise on accuracy and response times for detecting audible changes in oxygen saturation9 and on anesthesiology resident performance on mental efficiency and short-term memory tests.10

OR noise pollution is therefore a significant clinical problem impacting caregiver well-being, and by extension, patient safety. It is difficult, however, to conduct clinical trials to investigate rigorously this problem because of difficulty in controlling for the real-world complexity present in operating suites and because of concern with testing interventions in real patient-care settings. An important study recently reported a lower incidence of postoperative complications after intraoperative noise levels were decreased. However, that study was not randomized nor blinded and was conducted in a pediatric cardiac OR, a specialized perioperative environment.11

Medical simulators offer a safer and more controlled venue for performing randomized controlled studies investigating the noise problem and the effect of interventions, but the degree to which findings are extrapolatable to the clinical arena depends on simulation realism. The clinical auditory environment (soundscape) is an important simulation component with respect to clinical noise. However, current OR simulators lack the native capability to simulate realistic clinical soundscapes beyond rendering the pulse oximeter auditory display and annunciating medical device alarm sounds.

Recently, we sought to address this gap by retrofitting our fully functional replica of an OR with a high-fidelity audio reproductive system to add immersive, auditory realism to the simulation experience. We refer to our simulator as NOISE (Noisy OR Immersive Simulation Environment). In a separate study, we investigated the acoustical environments in NOISE and several ORs at our institution. The NOISE has a shorter reverberation time (benefiting intelligibility) relative to the ORs (565 vs 700 milliseconds) likely because the total space (room volume) in NOISE is smaller and construction materials are different between the 2 rooms. However, the equivalent continuous noise levels are similar between the NOISE (76.5 dB) and a typical OR at our institution (76.0 dB).11a

The primary objective of the current study was to perform randomized and controlled simulation experiments in our NOISE to test the hypothesis that OR noise increases perceived task load and fatigue, which are contributors to workplace stress. A secondary objective of this study was to propose and test the plausibility of a new psychometric model that combines psychometric indicators of task load and fatigue into an instrument for measuring perceived stress. Development of new techniques for measuring the psychological variables in an experimental setting will help increase our basic understanding of the underlying psychological constructs at the interface between environment and caregiver and will augment the effort to characterize and mitigate the harmful effects of clinical noise.


This study was approved by IRBs at the University of Miami-Miller School of Medicine and the Jackson Health System. Written informed consent was obtained from all subjects. This study was funded by the Anesthesia Patient Safety Foundation. The funder had no role in the study design, study conduct, or writing of the manuscript.

The NOISE Simulation Setup

Our OR simulator at the University of Miami-Jackson Memorial Hospital Center for Patient Safety contains an METI human patient simulator (Medical Education Technologies, Inc., Sarasota, FL), an anesthesia workstation (Datex-Ohmeda, GE Healthcare, Little Shalfont, UK), and associated medical alarm equipment. For this study, we installed 4 corner speakers powered by an audio interface (MOTU Traveler, MOTU, Cambridge, MA). Then, quadriphonic soundscapes (Supplemental Digital Content 1, Video 1, http://links.lww.com/AA/B302) were composed by the use of recordings of typical sounds obtained in our clinical ORs (e.g., telephone ringing, suctioning, door closing, shoe skidding, and stepstool and instrument clanging). The desired effect was to create a sound field in which discrete sound sources would be perceived by subjects as coming from distinct areas of the simulator room, similar to where they usually originate in our clinical ORs (Fig. 1). Two 30-minute soundscapes were composed in this fashion with the use of open-source audio editing software (http://audacity.sourceforge.net/).

A custom multimedia graphical user interface (GUI) was designed with the use of MATLAB® R2010a (The Mathworks, Inc., Natick, MA) to run on a PC laptop (Dell® Inspiron) inconspicuously located on top of the anesthesia workstation (Supplemental Digital Content 1 and 2, Video 1 and 2, http://links.lww.com/AA/B302, http://links.lww.com/AA/B303). The GUI served 3 functions. First, it was capable of displaying simulated patient vital signs and ventilator variables on a 15-inch liquid crystal display (i.e., LCD) monitor connected to the laptop and readily visible to subjects. The GUI continuously updates screen variables by reading XML files pertaining to a custom 30-minute simulation script. Second, the GUI was responsible for rendering the pulse oximeter auditory display and any triggered audible alarms (based on typically used alarm thresholds) to the laptop speaker, which has similar specifications to the workstation speaker. The audible alarms were designed to comply with the International Electrotechnical Commission standard medical audible alarm sounds (60601-1-8). Third, the GUI had a text input/logging feature that allows subjects to enter responses relevant to simulated patient status and answers to distractor task questions via a standard keyboard and mouse. The distractor task questions consisting of a set of 100 questions that were variations of 20 distinct questions related to the practice of anesthesiology were menial and tedious, usually requiring simple calculations to be performed to arrive at the answer (Appendix 1).

Although our custom GUI logged text entry content and response times relating to simulated patient care and the answering of distractor questions, these variables were not treated as dependent variables under the null hypothesis of the current experiment. We currently are using these preliminary data to guide the development of psychometric instruments for assessing performance in screen-based simulation (Richard R. McNeer, MD, PhD, Roman Dudaryk, MD, Nicholas B. Nedeff, MD, Christopher L. Bennett, MD, unpublished data, 2015).

The simulated clinical soundscapes consisted of combinations of the quadraphonic soundscapes and the GUI sonification of the script-responsive pulse oximeter display and alarm sounds. Specifically, the noise condition was achieved by combining GUI output with an accompanying quadraphonic soundscape played through the 4 corner speakers. To achieve the Quiet condition, the pulse oximeter and alarm sounds were rendered without an accompanying soundscape. From a position in front of the anesthesia workstation at approximately head level while seated, the sound levels (equivalent [peak]) for the noise and quiet conditions were 76.5 (93.0) and 72 (84), respectively. These levels are comparable with sound levels present in our ORs.11a When we account for the logarithmic basis of the decibel unit, the noise soundscape is 3 times and 8 times louder than the quiet soundscape in terms of equivalent and peak levels, respectively.

Experimental Procedure

The experiment was designed to investigate the impact of noise on subject perception of task load and fatigue while subjects cared for a simulated patient and simultaneously answered a set of distractor questions in a simulated OR. There were 2 sessions, spaced approximately 1 week apart (Fig. 2). On the morning of the first scheduled session, each subject was given a sheet with an example of the 20 types of questions to be used as a distractor task (Appendix 1). The purpose of the distractor task was to decrease the likelihood that subject attention would be directed solely and continuously to the GUI. The subject was given enough time to determine that he or she knew how to answer each question type. Questions that could not be answered were explained to the subject by the investigator.

Each session was composed of 2 consecutive simulated lunch breaks (Fig. 2A). The first lunch break followed a script that was uneventful during which minor fluctuations in vital signs and machine variables occurred, whereas the second lunch break followed an eventful script with 3 intraoperative emergency scenarios. The order of sessions and comprising lunch breaks were constant for all subjects. On the day of the first session, subjects were randomized into 1 of the 2 Soundscape groups (Fig. 2B). Group 1 experienced the quiet condition during the first session and was then crossed-over to the noise condition during the second session 1 week later. Group 2 was exposed to the Soundscape levels in reverse. Both sessions followed the same process flow.

Sessions began between 12 PM and 1 PM. Each subject was instructed to have eaten lunch, could not have worked the previous night, and had to be on a rotation involving active clinical duty. At the start of each session, subjects completed the Perceived Stress Scale (PSS) 14-item instrument12 (0–100) so their baseline fatigue level could be assessed. The subject also was instructed to use the restroom if needed before the start of each session. To gather preliminary data for another study, a portable, wireless, 4-lead biosensor was attached to the subject to record the electrocardiogram. The electrocardiogram data were recorded throughout all phases of the simulation experiments to obtain preliminary physiologic data not related to the null hypothesis being tested. We are using these data to develop novel methodology to measure physiologic responses including heart rate variability, and the results will be presented in a future manuscript.13,14

A 15-minute rest period occurred during which the subject was instructed to sit comfortably in a quiet room and to relax by clearing the mind without falling asleep. Next, the subject was brought to the simulated OR, asked to sit at the head of the bed as shown in Figure 1, and to adjust the height of the seat to ensure that the GUI display on the anesthesia workstation could be viewed comfortably. He or she was asked to familiarize himself or herself with the GUI layout and with the location of the text entry box. The subject was instructed that he or she was to be the lunch person and was given 2 simulated lunch breaks and that 2 tasks were to be accomplished: (1) The questions (supplied on a handout) needed to be answered and entered into the GUI; (2) The simulated patient was to be monitored for any changes in patient vital signs or ventilator variables. The subject was instructed to document via the text entry box when problems with patient care were detected, then a differential would need to be generated, and an action or plan for intervention or therapy would need to be formulated. Subjects were instructed to enter the information pertaining to detection (e.g., tachycardia, decreased end-tidal CO2), differential (e.g., hypovolemia), and intervention (e.g., give phenylephrine) into the GUI, pressing the return key between each thought or item. For instance, if the differential consisted of 2 items, each would be entered separately.

Instructions were reiterated that both completion of the anesthesia-related questions and monitoring/care of the patient needed to be accomplished. If a resident asked which task was more important, he or she was told to take care of the patient and answer the questions. At the start of a lunch break, sign out was brief and the subject was told that “this is an ASA physical status I 20-year-old man who came from home for an elective left inguinal hernia repair under general anesthesia. He has no allergies and good IV access. We are currently in the maintenance period, and everything has been going fine.” The simulation was then started for the first lunch break that lasted 30 minutes. The second lunch break immediately followed, and sign out was similar to the first except that the procedure was a right inguinal hernia repair. At the conclusion of the second lunch break simulation, the NASA Task Load Index (NASA-TLX)15 for assessing perceived task load and the Swedish Occupational Fatigue Inventory (SOFI)16 for assessing fatigue were administered. These validated psychometric instruments are detailed in the next section.

Study Design and Statistical Methods

The experiments followed a repeated-measures counterbalanced (mixed) design (Fig. 2). The Soundscape condition consisted of 2 levels (Quiet and Noise) and was treated as the within-subjects variable because each subject was exposed to both levels. Each subject initially was exposed randomly to either Quiet or Noise conditions in the first session and then was crossed-over to the other Soundscape condition in the second session approximately 1 week later. The order of exposure (Order), therefore, was treated as the between-subjects factor. The dependent variables consisted of subject responses from the NASA-TLX and SOFI instruments. Subjects were instructed to complete the instruments separately, NASA-TLX instrument first followed by the SOFI instrument. In addition, subjects were instructed not to discuss instrument responses or any other experimental details with other participants.

The NASA-TLX instrument (Appendix 2) is composed of 6 items (Mental Demand, Physical Demand, Temporal Demand, Performance, Effort, and Frustration), which subjects respond to on a continuous scale from 0 (very low) to 100 (very high). These items are then individually weighted and combined into an index (total score) that also ranges from 0 to 100. The SOFI instrument (Appendix 3) is composed of 5 items (Lack of Energy, Lack of Motivation, Physical Exertion, Physical Discomfort, and Sleepiness). Subjects select responses from a 7-point Likert scale (0 = not at all and 6 = to a very high degree). Therefore, there were a total of 12 dependent variables: NASA-TLX total score; 6 NASA-TLX items; 5 SOFI items.

Power Analysis

The required sample size for this study was calculated with an online power and sample size calculator for general linear multivariate models (GLIMMPSE v2.1.0).17 The set of parameters used for this study is in the form of a JavaScript Object Notation (i.e., JSON) file, which can be uploaded to the GLIMMPSE Web site and easily reviewed (http://fore.miami.edu/downloads/NASA_SOFI_PA.zip). To summarize, the model chosen for power analysis was repeated-measures design; however, after we specified the main effect as Soundscape condition (within-subjects) and predictor as Order (between-subjects), the model is by definition a mixed-design. Because there were 12 response variables, in anticipation of adjusting the significance level because of multiple pairwise comparisons, the type I error was entered as 0.004 (Bonferroni correction for 12 comparisons [0.05/12]).

Attempts were made to estimate response means and effect size from literature reports for NASA-TLX18 and SOFI.16,19 The effect of Soundscape was predicted to be 15% of predicted mean. In addition to this main effect based on Soundscape grouping, we predicted that subjects would perceive greater stress during the first session relative to the second because of anticipatory cognitive appraisal20,21; we estimated this effect to be 5% of the predicted means. We estimated that within-subject correlation would be 0.6 with a decay rate of 0.05. The within-instrument item correlations for both the NASA-TLX and the SOFI instruments were estimated to be 0.7, whereas between-instrument item correlation (i.e., between the NASA-TLX items and the SOFI items) would be 0.3. With these input parameters and a desired power of 0.90, calculation results indicated that 18 subjects would be required (actual power of 0.935). We decided to enroll 20 subjects for this study to allow for 1 or 2 subject dropouts or exclusions.

Hypothesis Testing and Strategy to Reduce the Risk of Type I Error

All statistical analyses were performed using IBM SPSS software suite (version 22, IBM Corp., Armonk, NY). Normality of subject responses to instrument items was determined with the Shapiro-Wilk test, by assessing the degree of skewness and kurtosis and by observing histograms and normal Q-Q and detrended normal Q-Q plots. The Levine test for homogeneity of variance was used to test the assumption that response variance was similar across experimental conditions. For hypothesis testing, a general linear model in the form of a mixed-design (split-plot) analysis was performed to determine main effects of Soundscape condition and any interaction effects between Soundscape and Order (i.e., Soundscape × Order). In addition to lowering the sensitivity level (σ = 0.004) because of multiple pairwise comparisons, a multivariate analysis of variance (MANOVA) was performed on the 12 response variables, and when MANOVA is significant (P < 0.05), likelihood of type 1 error because of multiple comparisons is considered to be decreased.22

In addition to calculating P values for the univariate comparisons, point estimation was performed, and 95% confidence intervals (CIs) are reported. Effect size is reported as absolute mean difference along each respective psychometric instrument scale. In addition, standardized effect size is reported using eta-squared (η2),23 which ranges from 0 to 1 and can be categorized into small effect (0.02–0.13), medium effect (0.13–0.26), and large effect (>0.26).24 Two additional effect size parameters (partial η2 and generalized η2) are reported in the Supplemental Digital Content section (refer to Results section). The formulas used to calculate the standardized effect size parameters are listed in Appendix 4.

Presentation of Psychometric Data

Figure 3 illustrates the format used in this manuscript for presenting all 11 psychometric instrument item responses and the NASA-TLX total score. The within-subjects Soundscape effect is depicted by the use of parallel plots of individual subject responses with point estimates for means and 95% CIs (Fig. 3A). To the right of each parallel plot is a floating axis (green in color), which shows the mean differences and 95% CIs. Interaction plots (Fig. 3B) were used to show Soundscape × Order interactions, and point estimates of the mean differences from the interaction plots are presented in a separate plot (Fig. 3C).

Development and Feasibility Testing of a Psychometric Model of Stress

All factor analyses were performed using SPSS software suite (IBM). To determine whether the NASA-TLX and SOFI instruments measured their respective latent constructs (i.e., task load and fatigue), internal consistency was evaluated with the Cronbach α. To test the plausibility of the proposed psychometric model, a partial confirmatory factor analysis (pCFA) was performed.25 Traditionally, psychometric models are proposed based on empirical evidence obtained from exploratory (unrestricted) factor analysis (EFA), wherein the indicators (e.g., NASA-TLX and SOFI items) are allowed to freely load onto extracted factors. Subsequent model confirmation is accomplished by performing a confirmatory (restricted) factor analysis (CFA) on newly acquired data. In contrast to the EFA, the indicators in a CFA are forced to load zero (i.e., not to load) onto some of the latent variables (formerly, the extracted factors from the EFA). A figure highlighting these points can be found in the Supplemental Digital Content section (Supplemental Digital Content 4, http://links.lww.com/AA/B305). A pCFA has been suggested as an intermediary process lying somewhere between EFA and CFA on the pathway to model confirmation.25 Although by itself not confirmatory, a pCFA can supply useful information regarding model fit and help the researcher gauge the likelihood that a future CFA will be successful. For this study, factors were extracted with conventional data reduction techniques that rely partly on calculation of factor eigenvalues and parallel analysis.26,27

Parallel analysis was performed using an SPSS syntax script available from an online source (https://people.ok.ubc.ca/brioconn/nfactors/nfactors.html). Several indices were calculated to evaluate model fit as part of the pCFA25 and are reported. They are the Bentler-Bonett Normed Fit Index, the Tucker-Lewis Index, the Bentler Comparative Fit Index, root mean square error of approximation, and the standardized root mean square residual. In addition, the pattern matrix nonsalient loadings were used to calculate nonsalient loading distribution. Normality of the nonsalient loading distribution which is another parameter of model fit was assessed by reviewing histograms, Q-Q normal plots, and detrended normal plots, and by performing a Shapiro-Wilk test in SPSS.

RESULTS Subject Randomization and Demographics

Twenty Clinical Anesthesia year 1 (CA-1) residents (12 male/8 female) participated in the study. There were an equal number of subjects in the quiet first (n = 10, 5 male/5 female) and noise first (n = 10, 7 male/3 female) groups. NASA-TLX and SOFI data for 1 female subject were excluded from the data set because of observed irregularities while completing the subjective instruments. All subjects were instructed to complete the NASA-TLX instrument before starting the SOFI instrument; however, this subject was observed cross-referencing her responses for the 2 instruments.

Psychometric Instrument Results

Baseline perceived fatigue level was assessed with the 14-item PSS before each session. There was no statistical difference in baseline fatigue levels at the starts of session 1 (33.00 [0.69] and session 2 (32.45 [0.40]), as measured by the PSS instrument.

The NASA-TLX items, NASA-TLX total score, and the SOFI items were all approximately normally distributed and satisfied the Levene test for homogeneity of variance. The MANOVA of the 12-item set (6 NASA-TLX items, NASA-TLX total score, and 5 SOFI items) reached significance (P = 0.003), suggesting that subsequent univariate analysis of variance of individual items are less susceptible to type I error.22

The NASA-TLX total score was greater in noise than in quiet (Fig. 3A; Table 1) on a scale from 0 to 100 by a mean difference of 13.3 (SE = 4.0, P = 0.004). The standardized effect size η2 was 0.36, indicating that 36% of the variance in the NASA-TLX total score was attributable to Soundscape grouping. A Soundscape × Order interaction was observed, suggesting that the effect of Soundscape was larger in the subjects who were exposed to the Noise condition first (Fig. 3, B and C); however, this interaction did not reach significance (P = 0.131).

Subject responses to the 5 NASA-TLX items were greater in Noise than in Quiet (Table 1), with Temporal Demand reaching the criterion for significance after Bonferroni adjustment of sensitivity level to 0.004. The largest effect size was observed in Temporal Demand whereas Performance had the smallest (η2 = 0.53 and 0.07, respectively). Except for the Physical Demand item, mean differences based on Soundscape were larger for subjects in the Noise First group, with the largest Soundscape × Order interaction observed for the Mental Demand item (P = 0.045; Fig. 4, A and B; Table 1). Parallel plots and additional calculations of standardized effect size parameters pertaining to the NASA-TLX items can be found in the Supplemental Digital Content section (Supplemental Digital Content 5, http://links.lww.com/AA/B306; Supplemental Digital Content 6, http://links.lww.com/AA/B307).

Figure 4

Figure 4

Table 2

Table 2

Figure 5

Figure 5

Of the 5 SOFI items, Lack of Energy, Lack of Motivation, and Sleepiness showed an effect of Soundscape, with subjects reporting greater levels in Noise than in Quiet (Table 2). Of these, Lack of Energy reached significance (P =0.001, η2 = 0.467). Modest Soundscape × Order interactions between were observed but did not reach significance (Fig. 5, A and B; Table 2). Parallel plots and additional calculations of standardized effect size parameters pertaining to the SOFI items can be found in the Supplemental Digital Content section (Supplemental Digital Content 7, http://links.lww.com/AA/B308; Supplemental Digital Content 8, http://links.lww.com/AA/B309).

Proposed Psychometric Model of Stress

The Cronbach α, performed on the 6-item NASA-TLX, yielded a good internal consistency (α = 0.766), which supports the assumption that the NASA-TLX items measured the same construct (i.e., Task Load). Internal consistency increases substantially after the Performance item is removed from the analysis (Table 3). The 5-item SOFI instrument also had good internal consistency (α = 0.768), which supports the assumption that the SOFI instrument measured the same construct (i.e., Fatigue). Internal consistency increases if either the Physical Exertion or Physical Discomfort items are removed from the analysis (Table 4). When both are removed, the Cronbach α increases to 0.848.

Table 3

Table 3

Table 4

Table 4

Partial CFA of the NASA-TLX and SOFI item responses yielded 4 factors with eigenvalues >1, a measure used routinely to indicate factor significance26,27 (Table 5; Supplemental Digital Content 9, http://links.lww.com/AA/B310). Significance of the extracted factors was further verified by performing a Parallel analysis/Monte Carlo simulation. Factor 1 was loaded with the Lack of Energy, Lack of Motivation, and Sleepiness items from the SOFI instrument, whereas factor 2 was loaded with Mental Demand, Temporal Demand, Effort, and Frustration items from the NASA-TLX instrument, although there was cross-loading of the Frustration item with other factors (Table 5). Factor 3 loaded with the NASA-TLX Performance and Physical Demand items and the SOFI Physical Discomfort item. Factor 4 loaded with the SOFI Physical Exertion item. The correlations between factors ranged from negligible between factors 2 and 4 and between factors 3 and 4, to moderate between factors 1 and 2, to strong between factors 2 and 3 (Table 6).

Table 5

Table 5

Table 6

Table 6

Table 7

Table 7

Figure 6

Figure 6

On the basis of these results, a model was proposed to explain the relationship between the psychometric instrument items (observed measures or indicators) and extracted factors (latent variables or constructs; Fig. 6). Global goodness of fit indices were calculated as part of the pCFA (Table 7; Supplemental Digital Content 9, http://links.lww.com/AA/B310). All but one of the fit indices was consistent with good fit (the Normed Fit Index was <0.95). Taken together, the results of the pCFA indicate that the proposed model is plausible, and a future CFA on a new set of data is likely to be successful.


In this simulation-based study, we observed that intraoperative noise increased the perception of task load and fatigue, which likely contributed to an increase in the stress experienced by the CA-1 resident subjects. This finding is consistent with the previously reported finding that anesthesiologists consider OR noise to have a negative impact on their job.8 Our counterbalanced experimental design controlled for other sources of stress, which could manifest differentially between, for example, sessions 1 and 2. For example, we anticipated and subsequently observed a greater effect of Soundscape on observed stress in residents exposed to noise first and quiet second relative to residents exposed to the conditions in reverse. We attribute this observation to anticipatory cognitive stress appraisal,20 which emphasizes the importance of counterbalancing to control for this effect.

The authors of the first report investigating noise in the OR compared the problem with air and water pollution.28 Despite the initial lack of understanding and evidence bases for the exact roles of these pollutions in surgery, efforts to provide aseptic surgical environments were implemented early and have been in practice for well over a century. It can be argued that it is time for noise, the third pollution, to be addressed in our ORs with similar urgency, especially considering that numerous, minimally disruptive measures are available such as behavioral modification,11 and use of plastic materials when possible in lieu of clangy metal. The scientific rigor required in modern clinical research is difficult to attain in hard-to-control clinical settings, and findings obtained in approximately realistic clinical simulations like the current study may not be considered completely extrapolatable to the real-world. However, the simulation-based findings reported here should be weighed accordingly using a balance between the desire for definitive scientific results and the need to expeditiously address the clinical noise problem.

For our secondary objective, we performed factor analyses of the data. The NASA-TLX and SOFI are validated instruments, and reliability analysis of our results indicate that each performed with good internal consistency, supporting the assumption that both Task Load and Fatigue constructs (latent variables) were reliably measured in our experiments. To assess the underlying relationships between the individual NASA-TLX and SOFI items (measured variables) and the Task Load and Fatigue constructs, we performed a pCFA that gives an indication of the likelihood or plausibility that a future CFA (with a new data set) will be successful based on model fit. Our expectation was that pCFA would yield 2 extracted factors from a combined data set of the NASA-TLX and SOFI items, each factor corresponding to the Task Load or Fatigue. Instead, 4 factors were extracted (Fig. 6). The first 2 factors make logical sense by corresponding to the psychological-based NASA-TLX and SOFI items, and we refer to them as Psychological Task Load and Psychological Fatigue constructs. The Physical Exertion item that is described on the SOFI instrument with acute and symptomatic terminology (i.e., palpitations, sweaty, out of breath, and breathing heavily) loaded to the fourth factor and we refer to this construct as Acute Physical Load. The SOFI Physical Discomfort item is defined in more chronic terms (tense muscles, numbness, stiff joints, and aching), whereas the NASA-TLX Physical Demand is defined with the question “How physically demanding was the task?” In our experiment, the task referred to both lunch breaks (over an hour) in a session. The fact that NASA-TLX Performance and Physical Demand items and SOFI Physical Discomfort item loaded to the same factor are therefore understandable because self-appraisal of how one performed in an essentially mental task may correlate with more chronic physical symptoms evoked over the course of an hour in this case. We refer to this construct as Self-Appraisal/Chronic Physical Load.

Importantly, the pCFA indicates a reasonably good fit of our data with the model presented in Figure 6 and suggests that performance of a future CFA with larger sample size is likely to be successful yielding measures of construct (convergent and discriminant) validity. The model would then represent a new psychometric instrument for specific use in investigations of the noise problem.

There are several limitations inherent in this study. First, it is simulation-based and uses a screen interface loosely based on existing monitor displays. It is, therefore, difficult to quantify the extent to which these results extrapolate to a real clinical OR. Second, the results were obtained in residents (CA-1s) at our institution because it was logistically easier to enroll and obtain clinical coverage for this group during the conduct of experiments. In addition, restricting enrollment to a single class of anesthesia residents helped ensure homogeneity with regard to subject clinical experience and past simulation exposure. We do not know whether the findings would be reproducible at other institutions or be observed in residents at different levels of training or in fully trained anesthesiologists, anesthesia assistants, nurse anesthetists, surgeons, and other OR staff members.

A major limitation of the experimental setup is that resident visual attention was directed solely to the GUI, which was part of the anesthesia workstation. A more realistic setup would have resident visual attention necessarily divided (by 90°) between the patient mannequin and GUI. Our NOISE simulator uses a highly realistic OR soundscape, and this immersive condition was crucial in providing the independent variable in these studies. However, because the NOISE soundscape was composed of a heterogeneous group of sound sources that included beepers, equipment noise, clangs, and music, it is not possible to determine which soundscape components were responsible for increasing task load and fatigue levels in our residents.

Another possible limitation of this study is that it was not adequately powered. Given that it was designed primarily to test the hypothesis that noise increases perceived task load and fatigue, the risk of type 1 error is unlikely for the following reasons. We used an online tool (Glimmpse), which to our knowledge is the most comprehensive resource currently available for calculating power and sample size in mixed-design experiments. Previous reports on the NASA-TLX18 and SOFI16,19 instruments allowed us to input estimates of effect sizes and SDs. The response means, SDs, and effect sizes observed in our data are comparable with those previously published. Socioemotional stress has been shown to increase perceived task load as assessed by NASA-TLX in paramedics while administering advanced life support in a simulated setting; mental demand increased from 39 (18) to 57 (25), temporal demand increased from 25 (21) to 33 (22), effort increased from 40 (26) to 54 (22), and frustration increased from 19 (17) to 42 (25).18 Noise was one factor used to induce socioemotional stress, and the similarity of that study to ours helps to put our NASA-TLX data into context. For example, we observed an increase in NASA-TLX total score from 47.5 (10.2) in quiet to 60.8 (13.5) in noise, and we contend that this statistically significant difference is likely to be clinically relevant. In anticipation of performing Bonferroni corrections, we entered an adjusted type 1 error rate of 0.004 into the calculation. In addition, MANOVA of the data set reached significance, suggesting a decreased risk of type 1 error in the pairwise item comparisons.22 Furthermore, by using a repeated-measures, counterbalanced design, the sample size requirement to achieve a power of 0.90 was greatly reduced. Nonetheless, we cannot completely rule out the possibility of type 1 error or that other unknown and uncontrolled factors influenced our results.

In summary, we demonstrated that noise increases perceived levels of task load and fatigue in anesthesia CA-1 residents while being given lunch breaks during simulated surgeries. Our NOISE simulator, which faithfully reproduces the auditory environment characteristic in our clinical ORs, was a crucial component in our experimental design. In addition, we used validated psychometric instruments for assessing perceived task load and fatigue. We believe the current findings add significantly to the growing mass of evidence, implicating the negative impact noise has on caregivers and patient safety. In addition, we proposed a psychometric model for stress that combines items from the task load and fatigue instruments. A preliminary pCFA of this model supports further validation with a CFA.

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    Interaction between within-subjects and between-subjects factors

    = Generalized eta-squared,

    = Partial eta-squared,

    = Eta-squared,

    SSA = Between factor type III sum of squares,

    SSP = Within factor type III sum of squares,

    SSPA = Within × between factor type III sum of squares,

    SSs/A = Between-subjects type III sum of squares error,

    SSPs/A = Within × between factor type III sum of squares error.


    The authors acknowledge the Anesthesia Patient Safety Foundation (Masimo Foundation Research Award) for funding this study.


    Name: Richard R. McNeer, MD, PhD.

    Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

    Attestation: Richard R. McNeer has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

    Name: Christopher L. Bennett, PhD.

    Contribution: This author helped design the study and conduct the study.

    Attestation: Christopher L. Bennett has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

    Name: Roman Dudaryk, MD.

    Contribution: This author helped conduct the study and write the manuscript.

    Attestation: Roman Dudaryk has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

    This manuscript was handled by: Franklin Dexter, MD, PhD.

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    Anesthesia & Analgesia122(2):512-525, February 2016.

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