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C2090-011 | IBM SPSS Statistics Level 1 v2

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C2090-011 - IBM SPSS Statistics Level 1 v2 - braindump

Vendor IBM
Exam Number C2090-011
Exam Name IBM SPSS Statistics Level 1 v2
Questions 55 Q & A
Recent Update February 15, 2019
Free PDF Download C2090-011 Brain Dump
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C2090-011 exam Dumps Source : IBM SPSS Statistics Level 1 v2

Test Code : C2090-011
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

IBM Wins a 2018 purple Dot Design Award for SPSS information | killexams.com Real Questions and Pass4sure dumps

The IBM Hybrid Cloud team is lower back at it with yet an additional win for design. I’m excited to announce that our design crew has been awarded the 2018 pink Dot: communication Design Award for IBM SPSS facts within the Interface Design class. This award is a continuation of the design achievements we have viewed this past year, together with the A’Design Awards, IF Design Awards, and others. i am thrilled to see the tough work of our designers and IBM Design continue to shine and make a difference in business application.

First developed in the 1990’s, the crimson Dot Award has been the revered overseas seal of stunning design best. Designers, organizations, and companies from 45 distinct countries took half in this yr’s competitors, totaling over 8,600 entries that underwent a 24 member jury.

“All those that development throughout the difficult adjudication system to garner a pink Dot have each cause to be proud of themselves, because the jury grants our award best to creations of excessive design excellent. This makes me all of the extra delighted to congratulate the laureates essentially on their genuinely-earned success.” — Professor Dr. Peter Zec, founder and CEO of the pink Dot Award

Receiving this award became incredibly entertaining for our group and we are honored to be among the many winners. here's a massive success for our designers who worked on this product, and that they confronted an enchanting and difficult adventure in engaged on this product.

what is IBM SPSS?

IBM SPSS data is an impressive facts evaluation device that is one of the most common data purposes. seeing that its inception in 1968, SPSS facts has been revamped and redeveloped numerous instances. Now the design crew at IBM has taken on the project of growing a completely sparkling user adventure.

during this latest remodel of IBM SPSS data, we implemented design pondering principles by working closely with our clients and making bound this modernized edition of SPSS facts aligns with their wants. Our superior goal changed into to create an impressive device that is not simplest convenient and intuitive to make use of, but that our clients can enjoy.

Our team and Design approach

The IBM SPSS design team is part of the IBM Design Studios in Boeblingen, Germany. The team is composed of a diverse group, with many participants originating from distinctive international locations and cultures. Some members of the group had some background with information whereas others were working during this box for the primary time.

Following the ideas of IBM Design considering (study > replicate > Make), our crew implemented a redecorate that brings an improved center of attention on users for SPSS statistics. The design group conducted intensive analysis on the person base of SPSS information so as to see how the utility can more desirable meet their needs. The existing user base stages from less skilled users akin to college students to more knowledgeable clients corresponding to information scientists or enterprise experts. A key perception from the group’s research changed into that much less experienced clients have been intimidated each by the math work and the complexity of the software.

the brand new designs focused on simplifying workflows, reducing the universal complexity of the UI and interactions, and offering beginners a simple on-boarding to records and to the product. a further crucial function within the redesign turned into a practising book led through a personality named Simon, who serves as an in-application e-book, assisting novice users bear in mind different functions and achieve their goals quicker.

The group confronted some entertaining challenges in redesigning a made from such complexity, and one which has also been around for therefore a long time. a big success of the designers was making the product accessible and tasty to new clients devoid of alienating decade-long, skilled clients.

a glance Into the Future

The preview version of our new IBM SPSS data journey was released in March 2018, and made accessible to the general public as a trial on the IBM believe convention is Las Vegas, and when you consider that June 26 , the brand new UI is generally accessible to all SPSS information subscribers. This preview is just the preliminary step, providing essentially the most used statistical analyses, and fundamental capabilities for records education, for presentation and for reporting outcomes. Over the following months the team may be working to add greater points and capabilities with a view to meet adventure needs of all of our consumer groups.

now not just Updating — Redesigning

i'm so overjoyed to look an extra Hybrid Cloud design group acquire an international award for his or her work. IBM SPSS records is yet one more illustration of how design is making an important change in the success of our products. As we continue to make use of design to create more relatable and productive products, we're able to give our users the experiences that they want and need. I’m thrilled and proud to monitor the difference that our design group is making on earth of enterprise software, and i can’t wait to look how we proceed to have an effect on the lives of our clients.

Award Winners:
  • Design supervisor: Caroline law
  • Design Leads: Dirk Willuhn and Eva Cochet-Weinandt
  • Design group: 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

  • comparing the main massive information analytics utility alternatives | killexams.com Real Questions and Pass4sure dumps

    there are lots of carriers promoting items categorised as huge facts analytics application. however, it's challenging to...

    differentiate these items in line with functionality by myself, as many of the tools share similar features and capabilities. additionally, one of the tools exhibit extremely delicate alterations.

    That being said, your key differentiating components will seemingly center of attention on balancing ease of use, algorithmic sophistication and cost relating to your corporation's capability and degree of maturity in analytics.

    listed here, we investigate items from nine big facts analytics software carriers: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of those vendors provide more than one device. See the "leading vendors of massive information analytics software" sidebar beneath for extra details about their certain product offerings.

    These vendors represent diverse facets of the big data analytics market. Let's compare and distinction the ways that these items meet the company wants of consumer organizations.

    Analyst talents and expertise 

    Some facts analytics tools are centered to beginner users, some are centered to skilled facts analysts and some are engineered to appeal to both forms of clients.

    products equivalent to IBM SPSS Modeler, RapidMiner's tools, Oracle advanced Analytics and the automated Analytics version of SAP BusinessObjects Predictive Analytics are frequently designed to allow clients with a limited history in statistics or records evaluation to analyze records, enhance analytical models and design analytics workflows with little or no coding.

    whereas each seller wraps its core analytics components with an intuitive consumer interface to e-book the analyst's growth in facts coaching, evaluation, after which model design and validation, the method taken may additionally fluctuate, primarily when evaluating a stand-by myself product, equivalent to RapidMiner, with one it is a part of a bigger suite, such as the Oracle product.

    equipment comparable to IBM SPSS statistics, KNIME Analytics Platform, the skilled Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform provide the more sophisticated functionality that expert users predict. Oracle R advanced Analytics for Hadoop (ORAAH), one of the most components within the Oracle massive facts software Connectors suite, offers an R interface for manipulating Hadoop allotted File system records and writing mapper and reducer capabilities in R. this flexibility could be appealing to more advanced information scientists.

    Alteryx and SAS commercial enterprise Miner offer performance tailored to the consumer's level of advantage, and essentially fall into each classes. Alteryx has added advancements to records profiling to help statistics scientists enhanced be mindful their information sources. typical, SAS enterprise Miner and IBM's SPSS tools stand out when it comes to helping greater advanced analytical innovations and model scoring, as well as a broader array of evaluation capabilities, including neural networks, affiliation analysis and visualization capabilities.

    Analytical variety

    depending on the use case and application, your firm's users will be required to help several types of analytics capabilities a good way to use certain kinds of modeling, reminiscent of regression, clustering, segmentation, behavior modeling and determination timber.

    while this has resulted in extensive help for the quite a few kinds of analytical modeling at a high stage, some providers have invested decades of labor into tweaking distinctive versions of their algorithms and adding more refined performance. it be important to be mindful which models are most important to your enterprise problems and to consider the items when it comes to how they gold standard serve your clients' business needs.

    it be critical to consider which fashions are most crucial to your company problems and to consider the items in terms of how they surest serve your clients' enterprise needs.

    The more mature and better-end -- and, for that reason, higher-priced -- equipment will demonstrate the superior analytical breadth. Oracle facts Miner includes an array of popular machine studying tactics to guide clustering, predictive mining and textual content mining. each variations of IBM's SPSS product supply a various set of analytical concepts and fashions. And SAS business Miner supports many algorithms and recommendations, together with determination bushes, time series, neural networks, linear and logistic regression, sequence and net route evaluation, market basket evaluation, and hyperlink evaluation.

    The more moderen generation -- and, in some cases, lower-priced -- items help different models, but most likely with a narrower range of algorithmic sophistication.

    The model stock in Alteryx Analytics Gallery contains such capabilities as regression evaluation, choice timber, affiliation rule evaluation, classification and time collection evaluation. KNIME contains methods for text mining, graphic mining and time sequence evaluation, and also integrates laptop discovering algorithms from other open supply tasks, comparable to Weka and JFreeChart.

    one other element of analytical variety is integration with programming languages and statistical tools, such as R, for incorporating existing libraries, as well as user-defined performance. really, integration with R may well be regarded an increasingly vital differentiator.

    Alteryx dressmaker, Microsoft R, SAS enterprise Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and guide integration with R. a few of the providers, together with IBM, Oracle, Microsoft, RapidMiner and SAP, deliver a transforming into library of extensions to R and Python, enabling users to take talents of free libraries.

    Scope of the records to be analyzed

    There are multiple aspects of the scope of the facts to be analyzed, including the subject of structured vs. unstructured suggestions, in addition to entry to normal on-premises databases and information warehouses, cloud-based mostly records sources, and facts managed in big records systems, akin to Hadoop.

    besides the fact that children, there are varying levels of guide for records managed inside less-popular information lakes -- both managed within Hadoop or in yet another NoSQL statistics administration equipment supposed to provide horizontal scaling. The elements for distinguishing among the products have to be in response to your firm's specific necessities for accessing and processing facts volumes and information variety.

    In consciousness of the turning out to be range of input sources and the range of underlying methods used to apartment those facts sets, an extra set of rising points it is being adopted with the aid of these carriers contains statistics accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all more advantageous their equipment' information import, export and connectivity capabilities. These enhancements may still enable users to access a more comprehensive list of records sources while simplifying and speeding up the method of loading records into the items.

    support for scalability and high efficiency

    The need for scalable performance is driven by your corporation's records volumes and urge for food for evaluation. Smaller companies with much less records could be in a position to tolerate products that will not have efficiency characteristics that scale with the obtainable components, such as the entry-degree models of the decrease-conclusion equipment, including RapidMiner, KNIME, Microsoft R Open and Alteryx clothier, that can run on computing device programs and do not require extra server add-ons.

    better corporations are more likely to have a stronger stock of records units to research, in addition to broader communities of users. This introduces two extra necessities -- excessive efficiency and facilitation of collaboration. The adaptability of a product to high-efficiency architectures is a very good indication of scalability, and lots of the items may also be tailored to the parallelism of Hadoop or make use of another potential of reaching faster computation.

    all the products do have some support for Hadoop, together with IBM SPSS Modeler and SPSS records; RapidMiner's commercial element Radoop, which connects the Studio entrance conclusion and Server analysis engine to statistics saved in Hadoop; Oracle's big facts Discovery and ORAAH equipment; and KNIME's big facts Extensions and Cluster Execution add-ins.

    IBM SPSS now additionally offers better aid for a few multithreaded analytical algorithms that can also velocity performance. Teradata Aster Analytics addresses excessive-performance requirements through its vastly Parallel Processing structure. SAP's skilled Analytics edition of SAP BusinessObjects Predictive Analytics can execute in-memory statistics mining for dealing with giant-volume statistics analysis effectively. Microsoft R Server leverages its ScaleR module, a complete library of large data analytics algorithms that assist parallelization. Scoring algorithms carried out the usage of SAS commercial enterprise Miner may also be deployed and executed within a Hadoop environment.

    furthermore, integration with Apache Spark seems to be of growing to be magnitude. SPSS, KNIME, Oracle, RapidMiner and SAP all supply access to Apache Spark libraries to aid analytics functions that should scale with exploding information volumes. This makes it possible for developed functions to take advantage of a excessive-performance cluster platform to distribute the workflow throughout the cluster.

    Collaboration

    As noted, the larger the company, the more possible there could be a necessity to share analyses, fashions and applications across distinctive groups and among many analysts. groups that have many analysts disbursed across the commercial enterprise may also search for increased capacity to share models and collaborate concerning the interpretation of consequences.

    IBM's SPSS Modeler Gold edition gives collaboration capabilities, and RapidMiner's Server product provides help for sharing and collaboration. Alteryx Analytics Gallery offers a mechanism for sharing subtle analytics applications within the cloud with participants of a long corporation. KNIME offers industrial extensions to aid crew collaboration, as well as extensions aiding operational collaboration, equivalent to faraway-scheduled execution, file technology, shared data house and a workflow repository. SAS commercial enterprise Miner's client-server architecture permits enterprise clients and information analysts to work collaboratively by sharing fashions and other work items.

    Alteryx, KNIME and Teradata Aster have brought capabilities to support manage analytical workflows. additionally, one of the crucial carriers have begun to study how you can enable their tools to integrate with others that might also have complementary practical sweet spots. as an example, Teradata Aster now has an extension to integrate with KNIME that allows for clients to leverage the KNIME workflow editor and incorporate Aster Analytics capabilities into these workflows.

    dealer dimension and product integration

    companies will also be compared when it comes to their dimension. One could examine and distinction what could be observed because the mega-providers, whose huge facts analytics tools are only one product amongst a massive portfolio of equipment. if you work for a bigger corporation that usually negotiates web site-vast, enterprise licenses for the full suite of a supplier's equipment from a mega-vendor such as IBM, SAS, SAP or Oracle may be an affordable alternative.

    The big carriers promote huge statistics analytics tools that are part of a lots higher device ecosystem. possibly, the items from a mega-supplier may be as a minimum just a little built-in and meant to work collectively. furthermore, some americans believe more relaxed with bigger companies, with an expectation of stability and consistent client service. nonetheless, you may additionally simplest be in a position to purchase these big data analytics equipment as part of a a good deal greater software licensing association.

    Smaller companies, comparable to KNIME, Alteryx and RapidMiner, have revenues that are frequently in keeping with licensing and guide for a small variety of big facts analytics items. A smaller dealer may additionally give closer contact with their product administration and innovation groups, and you can be capable of have an effect on the route of the product roadmap or more suitable performance.

    A smaller supplier could even be greater bendy in terms of fee and the aspects protected in the licensing arrangement. You ought to realize, despite the fact, that working with a smaller seller does latest some chance when it comes to stability, the supplies available for assist and the chance that the enterprise could be bought, that could influence the client relationship.

    The better companies are certainly responsive to person needs for integration with different techniques, despite the fact that often centers on different products inside each supplier's stock. as an instance, SAP Predictive Analytics has more desirable integration with SAP HANA and BusinessObjects Cloud. SAS commercial enterprise Miner has brought nodes to execute code in a SAS open, cloud-competent, in-memory Viya ambiance. Microsoft offers SQL Server R capabilities, an R installation that runs alongside SQL Server and makes it possible for clients to integrate Microsoft R Server facts with SQL Server and Microsoft's different enterprise intelligence equipment.

    price range for licensing and upkeep

    practically all the vendors sell diverse versions or versions of their items, with a range of fees for acquisition and complete can charge of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft promote versions at distinct tiers, with the license cost proportional to the facets, capabilities and freedom from boundaries in terms of the volumes of information to be analyzed or the variety of processing nodes the product can use.

    KNIME and RapidMiner provide free and open supply types of their products, both charging for assist functions or for versions assisting enterprise-class functions. KNIME, RapidMiner and Alteryx have fantastically low licensing prices for a smaller number of users. if you're considering that SAS or SAP, you ought to contact them for pricing alternatives.

    The market for huge facts analytics software will also be a perplexing vicinity, but confidently this text has helped you bear in mind the advantages massive statistics analytics software can supply your company, and assisted you in differentiating between the specific equipment examined here.


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

    Our annual license for IBM SPSS facts has been renewed, and all licenses bought between these days and can 1, 2015 will expire on July 31, 2015.

    in case you purchased a license between can also 1, 2014 and nowadays, you were despatched renewal authorization codes through electronic mail that could be used to prolong the expiration date of your application.

    IBM isn't renewing version 19 this 12 months, and any one using that version (or past) will deserve to upgrade to a newer version.  IBM SPSS data licenses can be found for buy via OIT for $75 per laptop per year.


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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 1.2.2.1/2.1/3.1/4.5) and PAST-U (modified version of PAST, TASST taxon 1.2.2.1/2.1/3.1/4.5) 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 1.2.2.1/2.2/3.1/4.3, 1.2.2.1/NA/NA/NA, 1.2.2.1/2.4/3.1/4.3) 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.

    Implications

    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.


    Associations of ADL and IADL disability with physical and mental dimensions of quality of life in people aged 75 years and older | killexams.com real questions and Pass4sure dumps

    Introduction

    Quality of life has been defined by the World Health Organization Quality of Life Group as “an individual’s perception of their position in life in the context of the culture and value system in which they live and in relation to their goals, expectations, standards and concerns” (World Health Organization Quality of Life Group, 1995, p. 1405). Quality of life in community-dwelling older people predicts the adverse outcomes of institutionalization and premature death, even after controlling for disability and frailty (Bilotta et al., 2011). To support independent living in older people, both health care and social care professionals may need to carry out preventive interventions focused on aspects related to quality of life, with the aim of delaying institutionalization and avoiding premature death. Determining the influence of disability on quality of life in older people is important to developing early detection of problems and conducting preventive interventions.

    In addition to lower quality of life, disability is a relevant health outcome for older persons. There are several ways of defining disability. The most widely used is: experiencing difficulty in carrying out activities that are essential to independent living - difficulties in performing activities of daily living (ADL), and/or instrumental activities of daily living (IADL) (Tas et al., 2007a; Tas et al., 2007b). ADL functions are essential for an individual’s self- care (e.g., wash and dry your whole body and get on and off the toilet), whereas IADL functions are more concerned with self-reliant functioning in a given environment (e.g., prepare dinner and do the shopping). ADL disability represents a more severe and later form of disability than IADL disability (Hardy et al., 2005; Wong et al., 2010), resulting in a lower proportion of persons with ADL disability than IADL disability (Akosile et al., 2018; Chatterji et al., 2015).

    Disability is associated with increased health care utilization and related costs (Fried et al., 2004), and premature death (Manton, 1988; Mor et al., 1994; Walter et al., 2001). In addition, disability is associated with impaired quality of life in older people (Akosile et al., 2018; Den Ouden et al., 2013; Gureje et al., 2006; Soósová, 2016). However, the “disability paradox” (Albrecht & Devlieger, 1999) suggests that persons with severe impairments may nevertheless report high quality of life (Watson, 2002), although this paradox seems to dissolve when contextual factors (i.e., personal and environmental situation) are considered (Fellinghauer et al., 2012). Disability has a dynamic nature, so persons can move in and out of disability, with transitions between states of disability (Hardy et al., 2005; Nikolova et al., 2011; Van Houwelingen et al., 2014). Transitions to greater disability were more common than improvements in disability in people aged ≥85 years (Van Houwelingen et al., 2014). In particular, people with more than one chronic disease, depressive symptoms, and cognitive impairment had the highest risk of deteriorating; however, a small number of very old people are able to improve in their disability status (Van Houwelingen et al., 2014).

    The aim of the present cross-sectional study was to determine the influence of both ADL and IADL disability on quality of life, incorporating a physical and a mental dimension, in people aged 75 years and older. In contrast to previous research (Akosile et al., 2018; Den Ouden et al., 2013; Gureje et al., 2006; Soósová, 2016) the main focus here is on the associations between ADL and IADL items and quality of life. Items are more concrete than the types of disability (ADL, IADL) and thereby provide health care professionals (e.g., nurses, general practitioners, physiotherapists) and professionals working in the social domain (e.g., social workers, domestic help) specific targets to enhance quality of life in older people. To enhance quality of life of older people, it is relevant to know which items of ADL and IADL are associated with lower quality of life because the interventions will be carried out by different professionals. For example, if an older person can no longer wash and undress themselves, a district nurse can provide support (e.g., in the Netherlands) and if an older person has difficulties performing household activities then domestic help can provide the necessary support.

    Methods Study population and data collection

    The Senioren Barometer is a web-based questionnaire used to assess the opinion of a panel of Dutch older people (aged 50 years and older) about different aspects of life. This questionnaire has been used in previous studies (Gobbens, Luijkx & Van Assen, 2013; Gobbens, Van Assen & Schalk, 2014).

    In the period from December 2009 to January 2010 1,492 respondents completed at least part of the questionnaire, of whom 1,031 filled out the section on background characteristics, quality of life, and disability. Because disability is associated with greater age (Tas et al., 2007a; Tas et al., 2007b) the author selected only people aged 75 years and older (n = 377). As described in previous studies using the Senioren Barometer, older people can volunteer, and participation is always without obligation. The sample was invited to participate in the study in different ways and through multiple sources. First, people could indicate through the website (http://www.seniorenbarometer.nl) that they wanted to complete the questionnaire. Second, organizations for older people in the Netherlands were approached and asked to issue an announcement of the study on their websites so that their members who were interested could register. Third, a major source of participants was persons who attended computer training courses for older persons given by a large training and educational institute in the Netherlands.

    Medical ethics approval was not necessary as particular treatments or interventions were not offered or withheld from respondents. The integrity of respondents was not encroached upon as a consequence of participating in the study, which is the main criterion in medical-ethical procedures in the Netherlands (Central Committee on Research inv. Human Subjects, 2010). Informed consent in relation to detailing the study and maintaining confidentiality was observed.

    Measures Quality of life

    The author used the Short-Form Health Survey (SF-12) for measuring quality of life (Ware Jr, Kosinski & Keller, 1996). The SF-12 is a shorter version of the SF-36 (Ware Jr & Sherbourne, 1992) that uses only 12 questions. The SF-12 is developed to replicate the SF-36 with the aim to minimalize respondent burden. The 12 items are used to derive two summary quality of life measures, the physical dimension (six items) and the mental dimension (six items); their scores range from 0 to 100. Higher scores refer to better quality of life. Several studies have reported the validity and reliability of the SF-12 as a measure of quality of life in the general population, including older people (Bentur & King, 2010; Cernin et al., 2010; Jakobsson et al., 2012; Kim et al., 2014; Kontodimopoulos et al., 2007). In the present study, the (unstandardized) Cronbach’s alpha was .82 for the physical dimension and .73 for the mental dimension; an adequate value of the Cronbach’s alpha is between .70 and .90 (Cortina, 1993).

    Disability

    The author used the Groningen Activity Restriction Scale (GARS) for assessing disability (Kempen & Suurmeijer, 1990). The GARS is a self-report questionnaire consisting of two subscales. The first subscale measures ADL (11 items) and the second subscale relates to IADL (seven items). Each item has four response options: (1) able to perform the activity without any difficulty, (2) able to perform the activity with some difficulty, (3) able to perform the activity with great difficulty, and (4) unable to perform the activity independently. A distinction can then be made in two categories, complete independence and dependency (more or less). The disability total score ranges from 18 (no disability) to 72 (maximum disability). Following Ormel et al. (2002) the cut-point of 29 has been chosen for the disabled group because this cut-point corresponds with the 85th percentile of the GARS in a large sample of older people (Kempen et al., 1996b). The scores for the ADL and IADL subscales range from 11 to 44 and 7 to 28, respectively, with higher scores indicating greater disability; cut-points for these subscales do not exist. The GARS has shown good psychometric properties for assessing disability in older people (Kempen et al., 1996a). In this study, the (unstandardized) Cronbach’s alpha’s for ADL and IADL disability were .82 and .80, respectively, representing adequate values (Cortina, 1993)

    Background characteristics: sociodemographic and multimorbidity

    The sociodemographic background characteristics considered were age, sex, marital status, education level, and net household income. See Table 1 for a detailed description of the answer categories. Multimorbidity was assessed by asking the respondents, “Do you have two or more diseases and/or chronic disorders?” (yes/no).

    Analysis strategies

    First, the author determined the characteristics of the sample using descriptive statistics. Second, the quality of life dimensions (physical, mental) scores for non-disabled and disabled participants were compared using student’s t–tests assuming unequal population variances. Effect size was assessed with Cohen’s d, assuming equal population variances; .2, .5, .8 corresponding to small, medium, large effect size, respectively (Cohen, 1988). Correlations of ADL and IADL disability with the physical and the mental dimensions of the SF-12 were also examined. According to Cohen, correlations were considered as small, medium, or large with coefficients of .1, .3, or .5, respectively (Cohen, 1988).

    Table 1:

    Participant characteristics (N = 377).

    Characteristic n(%) Age, mean ± SD, range 79.8 ± 3.7, 75–95 Sex, % of men 261 (69.2) Marital status Married or cohabiting 244 (64.8) Single 36 (9.5) Divorced 11 (2.9) Living apart together 3 (0.8) Widowed 83 (22.0) Education None 30 (8.0) Primary 34 (9.0) Secondary 160 (42.4) Polytechnics and higher vocational training 113 (30.0) University 40 (10.6) Incomea €999 - or less 7 (2.1) €1,000–€1,499 44 (13.2) €1,500-€1,999 54 (16.1) €2,000–€2,499 90 (27.0) €2,500–€2,999,- 54 (16.1) €3,000–€3,499,- 38 (11.4) €3,500–€3,999,- 25 (7.5) €4,000–€4,499,- 11 (3.3) €5,000 or more 11 (3.3) Multimorbidity, % yes 166 (44.0) GARS Total disability 95 (25.2) ADL disability, mean ± SD, range 13.6 ± 3.8,11–33 Dress yourself 55 (14.6) Get in and out of bed 31 (8.2) Stand up from sitting in a chair 53 (14.1) Wash your face and hands 6 (1.6) Wash and dry your whole body 57 (15.1) Get on and off the toilet 12 (3.2) Feed yourself 4 (1.1) Get around in the house (if necessary, with a cane) 18 (4.8) Go up and down the stairs 134 (35.5) Walk outdoors (if necessary, with a cane) 75 (19.9) Take care of your feet and toenails 183 (48.5) IADL disability, mean ± SD, range 11.2 ± 4.5, 7–28 Prepare breakfast or lunch 14 (3.7) Prepare dinner 88 (23.3) Do “light” household activities 69 (18.3) Do “heavy” household activities 212 (56.2) Wash and iron your clothes 169 (44.8) Make the beds 185 (49.1) Do the shopping 86 (22.8) SF-12 Physical dimension of quality of life, mean ± SD, range 66.9 ± 25.6, 0–100 Mental dimension of quality of life, mean ± SD, range 74.5 ± 18.7, 10–100

    Before carrying out regression analyses some sociodemographic variables were coded for analysis. As in a previous study, the author created dummies for sex (“1” woman, “0” man), marital status (“1” married or cohabiting, “0” rest) and multimorbidity (“1” yes, “0” no), and linear effects of age and level of education were incorporated into the analyses (Gobbens, Luijkx & Van Assen, 2013). Bivariate associations between one background variable or disability item on the one hand and one quality of life dimension (physical, mental) on the other hand were tested using regression analyses. Subsequently, the author examined the effects of each variable (background variables, disability items) on the physical and mental dimensions in four multiple linear regression analyses, controlling for all the other variables in the model. The simplest model only assessed the effects of all background variables together. One model also included all 11 ADL disability items, whereas another model also included the seven IADL items together with the background variables. The most complex model included all 24 items. The fit (explained variance) of all four models was tested (R2) and compared (delta R2). Power analyses using GPower 3.1.0 (Faul et al., 2007) showed that the sequential linear regression analyses on 377 participants had a power of at least 80% to detect an effect of Cohen’s f2 = .056 which is a small to medium effect size (Cohen, 1988).

    Data were processed using SPSS version 24.0 (IBM Corporation, Armonk, NY, USA). All reported p-values are two-tailed. A p-value <0.05 was considered statistically significant.

    Results Participant characteristics

    See Table 1 for an overview of the descriptive statistics of the participant characteristics. The mean age of the participants was 79.8 (SD = 3.7), 69.2% were male, and 64.8% were married or cohabiting. The average scores on quality of life for the physical and mental dimensions were 66.9 (SD 25.6) and 74.5 (SD 18.7), respectively. Using the cut-point of 29 on the GARS, 25.2% of the participants were totally disabled, including both the ADL and the IADL subscale. In addition, 54.6% and 67.4% of the participants had at least one ADL disability and IADL disability, respectively. Of the 11 ADL disability items, participants experienced the greatest dependency in relation to taking care of their feet and toenails (48.5%). Of the 7 IADL disability items, participants experienced the greatest dependency in relation to doing “heavy” household activities (56.2%). In general, it should be noted that the percentages of the IADL disability items are higher than the percentages of the ADL disability items; five IADL disability items scored higher than 20% versus two ADL disability items (see Table 1).

    Differences between non-disabled and disabled participants on quality of life

    Table 2 presents the results of comparing disabled and non-disabled people on the physical and the mental dimensions of the SF-12. Disabled participants scored lower on both quality of life dimensions (p-values < 0.001), with very large effect sizes, d = 1.30 for the mental dimension and d = 1.82 for the physical dimension.

    Table 2:

    Comparison of quality of life dimensions between disabled and non-disabled participants.

    Non-disabled n = 279 M (SD) Disabled n = 95 M (SD) Results t-testa Effect size Cohen’s db Physical dimension of quality of life 76.19 (19.29) 39.61 (22.27) t(144.95) = 14.29 < 0.001 d = 1.82 Mental dimension of quality of life 79.85 (14.59) 58.68 (20.46) t(128.05) = 9.31 < 0.001 d = 1.30 Correlations between disability and quality of life

    Table 3 shows the correlations between ADL disability, IADL disability, physical quality of life, and mental quality of life. Most correlations were strong (>5); only the correlation between ADL disability and mental quality of life could be considered as medium (.483) (all p-values < 0.001).

    Table 3:

    Correlations between ADL disability, IADL disability, physical and mental dimensions of quality of life.

    IADL disability Physical quality of life Mental quality of life ADL disability 0.702 −0.683 −0.483 IADL disability −0.676 −0.541 Physical quality of life 0.734 Regression analyses: effects of ADL and IADL disability items on quality of life

    Table 4 presents the results of the bivariate and sequential linear regression analyses on the physical and mental quality of life dimensions of the SF-12. The table shows the effects of six background characteristics, 11 ADL disability items, and seven IADL items on the two dimensions of quality of life (physical, mental). Columns 2–4 and 8–10 present the bivariate regressions. Being a man, younger age, married or cohabiting, higher education, higher income, and no multimorbidity were associated with higher scores on both the physical and mental dimensions. Of the 11 ADL disability items, all were associated with physical quality of life and 10 were associated with mental quality of life. The exception was the item “feed yourself” (p = 0.058). All seven IADL disability items were associated with both quality of life dimensions.

    Table 4:

    Effect of background characteristics, ADL and IADL disability items on the physical and mental dimensions of quality of life.

    Physical dimension of quality of life Mental dimension of quality of life Bivariate Multiple Bivariate Multiple B SE p B SE p B SE p B SE p Background characteristics Sex (women) −11.92 2.81 <0.001 −2.81 2.15 0.192 −4.89 2.08 0.019 2.26 2.10 0.283 Age −0.73 0.36 0.040 0.27 0.23 0.251 −0.81 0.26 0.002 −0.06 0.23 0.801 Marital status (married) 8.48 2.74 0.002 −0.83 2.08 0.692 5.18 2.01 0.010 0.99 2.04 0.626 Education 4.86 1.26 <0.001 0.62 0.88 0.481 2.86 0.92 0.002 0.22 0.86 0.795 Income 3.60 0.74 <0.001 0.33 0.52 0.524 1.92 0.52 <0.001 0.35 0.51 0.496 Multimorbidity −29.13 2.21 <0.001 −13.35 1.82 <0.001 −13.50 1.82 <0.001 −4.03 1.78 0.024 ΔR2 0.364 <0.001 0.162 <0.001 ADL disability items Dress yourself −27.20 2.45 <0.001 −6.98 3.09 0.024 −12.63 1.96 <0.001 −0.12 3.02 0.967 Get in and out of bed −32.04 3.72 <0.001 −6.95 3.42 0.043 −17.72 2.82 <0.001 −6.57 3.34 0.050 Stand up from sitting in a chair 26.94 2.90 <0.001 −5.68 2.59 0.029 −16.63 2.19 <0.001 −5.55 2.53 0.029 Wash your face and hands −27.89 5.92 <0.001 2.07 5.50 0.707 −12.96 4.39 0.003 2.70 5.37 0.615 Wash and dry your whole body −24.35 2.19 <0.001 2.01 3.10 0.516 −12.28 1.73 <0.001 0.95 3.03 0.754 Get on and off the toilet −37.37 7.27 <0.001 0.99 4.94 0.841 −25.07 5.33 <0.001 −4.28 4.82 0.375 Feed yourself −40.77 12.72 0.001 −11.28 9.22 0.222 −17.79 9.36 0.058 −3.12 9.00 0.729 Get around in the house (if necessary, with a cane) −24.78 4.24 <0.001 6.43 3.37 0.057 −13.93 3.15 <0.001 1.33 3.29 0.686 Go up and down the stairs −21.00 1.23 <0.001 −5.78 1.63 <0.001 −10.62 1.07 <0.001 −0.84 1.59 0.597 Walk outdoors (if necessary, with a cane) −20.33 1.60 <0.001 0.03 1.82 0.985 −11.03 1.28 <0.001 0.93 1.77 0.601 Take care of your feet and toenails −10.80 0.88 <0.001 1.41 0.86 0.101 −5.29 0.71 <0.001 1.35 0.83 0.108 ΔR2 0.058 <0.001 0.045 0.012 IADL disability items Prepare breakfast or lunch −14.34 4.11 0.001 6.83 3.34 0.042 −7.97 3.01 0.009 2.07 3.26 0.526 Prepare dinner −5.89 1.42 <0.001 0.74 1.15 0.517 −3.29 1.05 0.002 0.003 1.12 0.998 Do “light” household activities −19.29 1.74 <0.001 −1.52 1.68 0.368 −9.83 1.37 <0.001 0.83 1.64 0.613 Do “heavy” household activities −14.27 0.75 <0.001 −6.57 1.05 <0.001 −8.64 0.62 <0.001 −4.55 1.03 <0.001 Wash and iron your clothes −7.49 1.05 <0.001 −1.03 0.91 0.260 −4.03 0.79 <0.001 0.58 0.89 0.516 Make the beds −13.05 0.88 <0.001 −1.23 1.07 0.254 −7.90 0.70 <0.001 −1.83 1.05 0.083 Do the shopping −18.06 1.37 <0.001 −5.41 1.42 <0.001 −11.13 1.06 <0.001 −5.74 1.39 <0.001 ΔR2 0.108 <0.001 0.135 <0.001 ΔR2 ADL and IADL 0.350 <0.001 0.282 <0.001 R2 total 0.714 <0.001 0.444 <0.001

    Columns 5–7 and 11–13 summarize the results of the sequential linear regression analyses. R2 total indicates that 71.4% and 44.4% of the physical and mental quality of life dimensions were explained by all the predictors together, respectively. After controlling for the background variables (sociodemographic characteristics, multimorbidity), disability (ADL and IADL items together) explained 35.0% of physical quality of life and 28.2% of mental quality of life, with both p-values <0.001. The ADL disability items together explained 5.8% and 4.5% of the physical and mental dimension, with p-values <0.001 and 0.012, respectively, after controlling for all background characteristics and IADL disability items, representing a medium to large effect size (f2 = .20) and a small to medium effect size (f2 = .08), respectively. The IADL disability items together explained a significant part of both quality of life dimensions after controlling for background characteristics and ADL items, with increases in explained variance of 10.8% (physical; f2 = .38, large effect size) and 13.5% (mental; f2 = .24, medium to large effect size) (both p-values <  0.001).

    In addition, Table 4 presents the effects of each of the background characteristics and individual ADL and IADL items on physical and mental quality of life. The columns five and 11 show the regression coefficients with corresponding standard errors (columns six and 12) and p-values (columns seven and 13).

    Before interpreting the effects of individual items after controlling for the other variables, the author checked for multicollinearity. As the variance inflation factors (VIF) for all items were smaller than 5, which is below the threshold of 10 (Yu, Jiang & Land, 2015), the author relied on his estimates as they are not strongly affected by multicollinearity.

    Of the background variables, only multimorbidity was negatively associated with quality of life, both physical and mental. None of the other background characteristics were associated with quality of life, after controlling for all the other variables in the model.

    Of the 11 ADL disability items only four were significantly associated with quality of life. The ADL item “stand up from sitting in a chair” was negatively associated with both dimensions (physical, mental). The ADL items “dress yourself”, “get in and out of bed”, and “go up and down the stairs” were only negatively associated with the physical dimension of quality of life. Of the seven IADL disability items, three were associated with quality of life. The two IADL items (do “heavy” household activities, do the shopping) were negatively associated with both the physical and mental dimensions of quality of life and “prepare breakfast or lunch” was positively associated with the physical dimension. All effect sizes (f2) of the individual ADL and IADL disability items on physical as well as mental quality of life were <.15, representing small effect sizes. Of all ADL disability items, “go up and down the stairs” and “stand up from sitting in a chair” had the largest effect sizes on the physical and mental quality of life dimensions, f2 = .042 and f2 = .016, respectively. Of all IADL disability items, the item with the largest effect sizes on the physical as well as the mental dimension of quality of life was “do “heavy” household activities”, with f2 = .13 and .065, respectively.

    Discussion

    In this study the author determined the associations between ADL and IADL disability items and quality of life in a sample consisting of 377 Dutch people aged 75 years or older. The author used two validated questionnaires, the GARS for assessing disability and the SF-12 for assessing quality of life, containing a physical and a mental dimension. To the best of my knowledge, the present study was the first using the GARS and the SF-12 to determine the associations between disability and quality of life. In addition, no previous study paid attention to the predictive value of the individual ADL and IADL disability items on quality of life.

    The bivariate regression analyses showed that the following factors were associated with physical quality of life as well as mental quality of life: being a man, younger age, married or cohabiting, higher education, higher income, no multimorbidity, ten ADL disability items, and seven IADL disability items. The ADL disability item “feed yourself” was not associated with the mental dimension. However, the sequential linear regression analyses revealed that only multimorbidity, ADL item “stand up from sitting in a chair”, and IADL items “do ‘heavy’ household activities” and “do the shopping” were significantly associated with both quality of life dimensions, after controlling for all the variables in the model.

    The finding that multimorbidity is associated with lower quality of life in older people is supported by previous studies in several countries using different measurement instruments (Brettschneider et al., 2013; Fortin et al., 2006; Garin et al., 2014; Gu et al., 2018). In Germany, quality of life of multimorbid people aged 65 to 85, assessed with the EQ-5D and the EQ-5D visual analogue scale (EQ-VAS) (Rabin & De Charro, 2001), decreased with an increasing count and severity of chronic conditions (Brettschneider et al., 2013). In Canada, 238 people completed the SF-36 (Ware Jr & Sherbourne, 1992) for assessing quality of life, and multimorbidity was measured by counting the number of chronic diseases and with the Cumulative Illness Rating Scale (CIRS) (Linn, Linn & Gurel, 1968); this study showed that the physical health dimension of quality of life deteriorated more than the mental health dimension of quality of life with increasing multimorbidity (Fortin et al., 2006). A study among Spanish people (≥50 years) also demonstrated that the number of chronic diseases was associated with lower quality of life (Garin et al., 2014), assessed with the WHOQOL-AGE (Caballero et al., 2013). Finally, a longitudinal study conducted in China showed that distinct multimorbidity patterns had various impacts on different dimensions of quality of life among community-dwelling older people (Gu et al., 2018). These findings are important because multimorbidity is frequently present in older people; in the age group 75–84 years the prevalence is 71.7% (Abad-Diez et al., 2014). The author recommends more studies focusing on the impact of multimorbidity patterns on quality of life in other countries. These studies should focus in particular on effects of combinations of common chronic diseases on quality of life, thereby providing direction to (preventive) interventions.

    All ADL disability items combined explained a significant part of the variance of both the physical dimension and the mental dimension of quality of life. Another study showed that maintaining independence in ADL had a positive effect on four domains of the WHOQOL-OLD (sensory abilities; autonomy; past, present, and future activities; social participation) (Power, Quinn & Schmidt, 2005), and one domain of the WHOQOL-BREF (physical health) (The WHOQOL Group, 1998; Soósová, 2016)). Quality of life, assessed with the WHOQOL-OLD (Power, Quinn & Schmidt, 2005) and the WHOQOL-BREF (The WHOQOL Group, 1998), were significantly associated with ADL disability in two samples of Nigerian older people aged 65 years and older (Akosile et al., 2018; Gureje et al., 2006). A Dutch study including a total of 537 middle-aged and older persons also found that quality of life, assessed with the SF-36 (Ware Jr & Sherbourne, 1992), was associated with ADL disability, measured with the Katz-questionnaire (Katz & Akpom, 1976; Den Ouden et al., 2013). In particular, health care professionals (e.g., district nurses, physiotherapists, general practitioners, occupational therapists) should identify (potential) limitations in performing ADL at an early stage in order to maintain or increase quality of life in older people. Based on the present study, special attention is needed to address problems people have when standing from sitting, because this activity is associated with lower physical and mental quality of life.

    All IADL disability items combined explained a larger part of the variance of both the physical and the mental dimension of quality of life compared with all ADL disability items together, 10.8% versus 5.8% and 13.5% versus 4.5%, respectively. Two studies referred to above also found that IADL disability was associated with quality of life (Akosile et al., 2018; Gureje et al., 2006). The finding that IADL disability items were more prevalent than ADL disability is supported by other studies (Akosile et al., 2018; Bleijenberg et al., 2017; Hu et al., 2012) and contributes to the evidence that IADL disability occurs earlier than ADL disability; probably because IADL is more complex and appeals more to cognitive function. In Nigeria the prevalence figure of IADL disability was 39.3% versus ADL disability 32.5% (Akosile et al., 2018). Among Dutch older people, with an average age of 74.6 years, carrying out household tasks was the most frequent problem (44.8%), followed by travelling (26.9%), and grocery shopping (23.0%) (Bleijenberg et al., 2017). In particular, the first and the last item are important because the present study showed that these two items were associated with the physical as well as the mental dimension of quality of life in older people. These findings have not been available to date. Conducting interventions on problems that older people can experience with performing heavy household activities and shopping could help them reach a higher quality of life. Domestic help may meet these needs or additionally reablement or restorative care services may be of benefit. These are short term services aimed at improving the independence of older people so they can hopefully go back to living independently without ongoing assistance.

    The model including all the prediction variables explained a large part of the variance in scores of the physical and mental dimensions of the SF-12, 71.4% and 44.4%, respectively. In a sample of community-dwelling older Dutch people (n = 8,928) it was shown that people experiencing disability, multimorbidity, and frailty scored lower on quality of life compared with people experiencing individual conditions (Lutomski et al., 2014). It is possible that the explained variances in the scores of the quality of life dimensions were also greater if depression as a predictive variable was included in the model; a review, including 74 studies, found an association between depression and lower quality of life in older people, independent of how quality of life was assessed (Sivertsen et al., 2015).

    This study has some limitations. First, the cross-sectional nature of this study does not allow strict cause–effect interpretations of the associations between the ADL and IADL disability items and quality of life. A longitudinal study is recommended to establish such associations. Second, disability was assessed by the GARS, a self-report measure, that does not include performance-based measures. A combination of both measures may be the best way to fully capture the picture of disability in ADL and IADL. However, in a sample of oldest old (≥80 years) it was demonstrated that self-assessments for disability in ADL and IADL reliably reflect direct assessment in performance (Bravell, Zarit & Johansson, 2011). Third, the author used the Senioren Barometer for data collection. This is a web-based questionnaire, so access to Internet was necessary for participating in the present study; this may have led to selection bias. In this context, it should be noted that in the study sample 69.2% were men, while in the Dutch population aged 75 years and older, only 37.9% are men, as established January 1, 2010 (Statistics Netherlands, 2017).

    Conclusions

    In this study the author showed that disability in ADL and IADL is negatively associated with quality of life in older people. Therefore, it is important for health care professionals to carry out interventions aimed at preventing and diminishing disability or its adverse outcomes, such as a lower quality of life. Promising interventions are multidisciplinary and multifactorial in nature, should be preceded by an individualized assessment, and should involve case management and long-term follow up (Daniëls et al., 2010). Lifestyle interventions targeting physical exercise, nutrition, and cognition appear to be effective against disability in ADL and IADL; in order to be actually effective, these interventions should be inexpensive, feasible, and easy to implement (Fougère et al., 2018). In line with the findings of the present study, it is recommended to first focus on the disability items that have the greatest impact on quality of life of older people (“stand up from sitting in a chair”, “do ‘heavy’ household activities” and “do the shopping”) to achieve the best outcome.

    Supplemental Information Raw data exported from the Seniorenbarometer 2009 applied for data analysis and preparation for Tables 1– 4


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    weSRCH : https://www.wesrch.com/business/prpdfBU1HWO000YXRO
    Scribd : https://www.scribd.com/document/356765972/Pass4sure-C2090-011-Braindumps-and-Practice-Tests-with-Real-Questions
    Dropmark-Text : http://killexams.dropmark.com/367904/12024124
    Youtube : https://youtu.be/RPYA-dTaxAw
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    Box.net : https://app.box.com/s/gb4z49tcb4thajkhxl9lt1oq9hgm0k59
    zoho.com : https://docs.zoho.com/file/2q0x29bbd4e81a09d4ed59a4b4259b0488f6b






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