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The IBM Hybrid Cloud group is back at it with yet one other win for design. I’m excited to publish that their design team has been awarded the 2018 red Dot: communique Design Award for IBM SPSS facts within the Interface Design class. This award is a continuation of the design achievements they maintain considered this previous year, including the A’Design Awards, IF Design Awards, and others. i'm extremely joyful to behold the challenging labor of their designers and IBM Design continue to shine and compose a change in enterprise utility.
First developed within the 1990’s, the purple Dot Award has been the revered overseas seal of brilliant design fine. Designers, businesses, and groups from forty five distinctive nations took half during this yr’s competitors, totaling over 8,600 entries that underwent a 24 member jury.
“All folks that development throughout the difficult adjudication manner to garner a pink Dot maintain each intent to exist disdainful of themselves, because the jury can provide their award simplest to creations of unreasonable design quality. This makes me the entire greater delighted to congratulate the laureates in actual fact on their hard-earned success.” — Professor Dr. Peter Zec, founder and CEO of the crimson Dot Award
Receiving this award become enormously keen for their crew and they are honored to exist among the winners. here is a tremendous success for their designers who worked on this product, and they faced a captivating and difficult event in engaged on this product.what is IBM SPSS?
IBM SPSS records is a powerful facts analysis tool that is one of the most common data purposes. for the judgement that its inception in 1968, SPSS data has been revamped and redeveloped diverse times. Now the design crew at IBM has taken on the assignment of growing a completely fresh user journey.
during this latest remodel of IBM SPSS data, they implemented design thinking concepts through working intently with their users and making bound this modernized version of SPSS facts aligns with their needs. Their most desirable purpose was to create an impressive device that is not handiest simple and intuitive to compose spend of, however that their clients can maintain fun with.Our group and Design method
The IBM SPSS design crew is a component of the IBM Design Studios in Boeblingen, Germany. The group consists of a various community, with many participants originating from diverse international locations and cultures. Some participants of the group had some tradition with data while others had been working in this box for the first time.
Following the principles of IBM Design thinking (observe > reflect > Make), their team applied a remodel that brings a far better focus of attention on clients for SPSS information. The design crew conducted intensive research on the person basis of SPSS records so as to survey how the software can more suitable meet their wants. The existing consumer basis levels from much less skilled users comparable to college students to greater expert clients such as records scientists or trade specialists. A key insight from the group’s research turned into that much less experienced users had been intimidated both by the math labor and the complexity of the utility.
the current designs focused on simplifying workflows, reducing the overall complexity of the UI and interactions, and featuring beginners a simple on-boarding to statistics and to the product. an additional crucial role in the redesign turned into a training reserve led by a personality named Simon, who serves as an in-utility ebook, helping amateur clients stand in judgement different services and obtain their desires quicker.
The crew faced some pleasing challenges in redesigning a made from such complexity, and one that has additionally been round for so decades. a tremendous success of the designers turned into making the product purchasable and tasty to current users devoid of alienating decade-long, experienced users.a glance Into the Future
The preview version of their current IBM SPSS statistics sustain was released in March 2018, and made accessible to the generic public as a visitation on the IBM account conference is Las Vegas, and on account that June 26 , the current UI is commonly purchasable to complete SPSS statistics subscribers. This preview is barely the initial step, providing the most used statistical analyses, and fundamental capabilities for facts training, for presentation and for reporting effects. Over here months the team might exist working so as to add more elements and capabilities with a purpose to meet adventure needs of complete of their person agencies.no longer just Updating — Redesigning
i'm so extremely joyful to survey yet another Hybrid Cloud design crew acquire a global award for his or her work. IBM SPSS information is yet a different illustration of how design is making an immense change within the success of their items. As they continue to spend design to create greater relatable and efficient items, they are able to provide their clients the experiences that they requisite and wish. I’m thrilled and disdainful to keep the discrepancy that their design group is making on the planet of trade software, and that i can’t wait to behold how they proceed to repercussion the lives of their users.Award Winners:
Our annual license for IBM SPSS facts has been renewed, and complete licenses bought between these days and can 1, 2015 will expire on July 31, 2015.
if you purchased a license between may additionally 1, 2014 and these days, you maintain been despatched renewal authorization codes by pass of electronic mail that may exist used to lengthen the expiration date of your utility.
IBM is not renewing edition 19 this 12 months, and any one the usage of that version (or earlier) will should ameliorate to a more recent version. IBM SPSS facts licenses can exist establish for purchase via OIT for $75 per laptop per yr.
The Ministry of Justice is the usage of predictive analytics expertise from SPSS, an IBM company, to assess the probability of prisoners re-offending on their liberate and to subsequently aid enhance public protection.
some of the UK's largest government departments with greater than ninety five,000 personnel and a cost purview of GBP9.2 billion, the Ministry of Justice is the spend of predictive analytics to check the facts held within its wrongdoer evaluation device (OASys). moreover, the analysis is assisting the Ministry of Justice expand treatment ambitions for prisoners complete over their sentence to cleave back the desultory they're going to relegate crimes upon their liberate.
The OASys system is used across approximately 140 prisons and complete probation areas in England and Wales and facts tips from over 3.4 million prisoner assessments. This comprises information on individual perpetrator circumstances akin to lodging, training, relationships, fiscal administration and earnings, subculture and associates, drug and alcohol misuse, emotional well-being, behaviour and attitudes.
These facts are being used for quantitative evaluation that could determine patterns inside the facts. The resulting intelligence types the basis for more desirable measurements of perpetrator possibility and need. each the OASys Violence Predictor and OASys time-honored Re-offending Predictor maintain helped to greatly ameliorate predictions about re-offending. in the case of violent crime, the prediction about re-offending has more advantageous from sixty eight per cent to 74 per cent while the prediction about re-offending when it comes to established offences more advantageous from 76 per cent to 80 per cent.With Predictive expertise from IBM SPSS, the Ministry of Justice is analysing hidden tendencies and patterns inside the facts. as an example: the technology helps establish whether offenders with specific issues akin to drug and alcohol misuse are more likely to re-offend than other prisoners.
"With just about 4 million information on file it without problems would not exist viable to trawl via this data manually in an try to identify these factors that may moreover signify a prisoner is probably going to reoffend," spoke of a spokesperson from the Ministry of Justice. "SPSS' expertise gives us constructive insight into offender records assisting us foretell who can moreover re-offend and enabling us to counsel on preventative measures, such as applicable courses addressing perpetrator behaviour before a prisoner's liberate date."
"the manner the Ministry of Justice is the usage of their technology showcases the high-level capabilities of their application in featuring analytical predictions upon which actions can moreover exist taken ," defined Ian Warner, Public Sector advisor at SPSS, an IBM company. "with the aid of deciding upon trends and patterns hidden within the records, SPSS is helping the Ministry of Justice foretell re-offending costs and win steps to meet govt ambitions to enrich public safety via cutting back the danger to the typical public and labor in opposition t a wiser executive.
"present day trade leaders should stride past intuition to insight, from gut suppose to information - as it is now viable to behold key patterns in significant quantities of records, to extract essential insights and movement to a current stage of intelligence," Warner referred to. "IBM SPSS predictive analytics is assisting executive organizations everywhere to seriously change the manner they operate through making smarter decisions and ameliorate citizen provider."
the current predictive analytics device is powered by pass of IBM SPSS statistics software, IBM SPSS Modeler facts mining workbench and IBM SPSS textual content Analytics utility.
The concurrent announcements are piece of IBM's ongoing focus of attention on assisting consumers spend their suggestions as a strategic asset through enterprise Analytics and Optimisation. IBM lately created a current enterprise Analytics & Optimisation capabilities employer, with 4,000 consultants who can champion customers arise and working with abysmal analytics capabilities and made invested more than $12 Billion in biological growth an innovation to additional construct its analytics portfolio.
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The flat 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 indigent 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 respond 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: sort of assessment, recall period, temporal unit and assessment period. According to TASST, SED-GIH is defined as a single particular 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) . 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 maintain 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 . PAST (Past-day Adults Sedentary Time, TASST taxon 18.104.22.168/2.1/3.1/4.5) and PAST-U (modified version of PAST, TASST taxon 22.214.171.124/2.1/3.1/4.5) 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 exist r = 0.57 , and PAST-U ICC = 0.64 . When Busschaert and co-workers tested the validity of three different questionnaires measuring context-specific sedentary behaviour (TASST taxon 126.96.36.199/2.2/3.1/4.3, 188.8.131.52/NA/NA/NA, 184.108.40.206/2.4/3.1/4.3) they establish fragile to acceptable validity for adults (r = 0.06–0.52) and older adults (r = 0.38–0.50) . This implies that the SED-GIH has stronger associations with objective sitting than other single particular questionnaires, such as IPAQ, when compared to direct measurement. However, these associations are not as tough 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, complete underestimated their sitting time as compared to activPAL-SIT (see Table 3). Furthermore, participants who estimated their sitting as ≥13 h almost complete 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 elevated levels of sitting . 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) , 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 particular questionnaires are more likely to underestimate sitting time, while questionnaires assessing sitting during a sum of sedentary behaviours using a composite of several items attend to overestimate sitting time. Questionnaires assessing sitting during a sum of sedentary behaviours over an unanchored or longer period of time attend to report larger underestimations . According to this study, the reasons for sitting time underestimations by the SED-GIH question can exist explained by it being based on a single-item question during an unanchored period of time.
The original seven SED-GIH respond categories were collapsed into five, since there were very few participants choosing “Virtually complete day” or “Never”. The intent of including complete seven answering options was that “Virtually complete day” and “Never” might exist easier to relate to instead of < 1 h and > 15 h. They moreover provide the respond options with some anchorage. When the five categories were analysed, the signify values (displayed in Table 1) of sitting time measured with activPAL did not vary much between the categorical respond options of SED-GIH (varying from 8.7 to 10.3 h per day, signify 9.7 h per day). Thus, the objectively measured tolerable 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 exist useful for identifying hazardous sitters (≥ 10 h per day). Objective measurements may exist 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 just to marvelous reliability (ICC = 0.50), and three different questionnaires measuring context-specific sedentary behaviour had marvelous reliability for adults (ICC = 0.73–0.77) and older adults (ICC = 0.68–0.80) [20, 22]. However, SED-GIH is a single particular questionnaire, whereas PAST and the three different questionnaires measuring context-specific sedentary consist of several questions, which can impress test-retest reliability. With a tool consisting of a single particular question, it might exist easier to respond the very question twice compared to tools consisting of several questions. Thus, SED-GIH has marvelous repeatability and generates trustworthy 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 province needs further research.
Limitations to the current study maintain been observed in the methods and the processing of the data. Participants may maintain become more conscious about their habits regarding sitting time when they answered the web questionnaire prior to the objective measures, which may maintain 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 maintain affected the agreement. One repercussion on internal validity is the accuracy of the participants’ dedication to fill in the diary log correctly, which can impress the whole dataset. In the validity study, participants were employees with an office-based job, which is not representative of a generic population. SED-GIH should exist validated in other contexts and with different populations. In the reliability piece of the current study, complete participants were elderly. This may maintain an consequence on the results since some aged persons can maintain reduced reminiscence role compared to younger adults.Implications
SED-GIH may exist useful as a tool when identifying sitting time as a determinant for health risks on a population level, but would not in itself exist sufficiently informative for screening for unhealthy sitting habits in primary care. More studies performed on a broader population are needed.
Quality of life has been defined by the World Health Organization trait 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). trait 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 champion independent live in older people, both health supervision and convivial supervision professionals may requisite to carry out preventive interventions focused on aspects related to trait of life, with the flat of delaying institutionalization and avoiding premature death. Determining the influence of disability on trait of life in older people is principal to developing early detection of problems and conducting preventive interventions.
In addition to lower trait of life, disability is a relevant health outcome for older persons. There are several ways of defining disability. The most widely used is: experiencing rigor in carrying out activities that are essential to independent live - difficulties in performing activities of daily live (ADL), and/or instrumental activities of daily live (IADL) (Tas et al., 2007a; Tas et al., 2007b). ADL functions are essential for an individual’s self- supervision (e.g., wash and parch your whole corpse and gain on and off the toilet), whereas IADL functions are more concerned with self-reliant functioning in a given environment (e.g., prepare dinner and accomplish the shopping). ADL disability represents a more strict and later form of disability than IADL disability (Hardy et al., 2005; Wong et al., 2010), resulting in a lower symmetry of persons with ADL disability than IADL disability (Akosile et al., 2018; Chatterji et al., 2015).
Disability is associated with increased health supervision 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 trait 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 strict impairments may nevertheless report elevated trait 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 stride 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 inveterate disease, depressive symptoms, and cognitive impairment had the highest risk of deteriorating; however, a diminutive number of very former people are able to ameliorate in their disability status (Van Houwelingen et al., 2014).
The flat of the present cross-sectional study was to determine the influence of both ADL and IADL disability on trait 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 trait of life. Items are more concrete than the types of disability (ADL, IADL) and thereby provide health supervision professionals (e.g., nurses, generic practitioners, physiotherapists) and professionals working in the convivial domain (e.g., social workers, domestic help) specific targets to enhance trait of life in older people. To enhance trait of life of older people, it is relevant to know which items of ADL and IADL are associated with lower trait of life because the interventions will exist carried out by different professionals. For example, if an older person can no longer wash and undress themselves, a district nurse can provide champion (e.g., in the Netherlands) and if an older person has difficulties performing household activities then domestic champion can provide the necessary support.Methods Study population and data collection
The Senioren Barometer is a web-based questionnaire used to assess the feeling 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 piece of the questionnaire, of whom 1,031 filled out the section on background characteristics, trait 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 testify 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 big 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 trait 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 flat to minimalize respondent burden. The 12 items are used to derive two summary trait of life measures, the physical dimension (six items) and the mental dimension (six items); their scores purview from 0 to 100. Higher scores advert to better trait of life. Several studies maintain reported the validity and reliability of the SF-12 as a measure of trait of life in the generic 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 particular has four response options: (1) able to achieve the activity without any difficulty, (2) able to achieve the activity with some difficulty, (3) able to achieve the activity with noteworthy difficulty, and (4) unable to achieve the activity independently. A distinction can then exist 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 big sample of older people (Kempen et al., 1996b). The scores for the ADL and IADL subscales purview from 11 to 44 and 7 to 28, respectively, with higher scores indicating greater disability; cut-points for these subscales accomplish not exist. The GARS has shown marvelous 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. survey Table 1 for a detailed description of the respond categories. Multimorbidity was assessed by asking the respondents, “Do you maintain two or more diseases and/or inveterate disorders?” (yes/no).Analysis strategies
First, the author determined the characteristics of the sample using descriptive statistics. Second, the trait of life dimensions (physical, mental) scores for non-disabled and disabled participants were compared using student’s t–tests assuming unequal population variances. consequence size was assessed with Cohen’s d, assuming equal population variances; .2, .5, .8 corresponding to small, medium, big consequence size, respectively (Cohen, 1988). Correlations of ADL and IADL disability with the physical and the mental dimensions of the SF-12 were moreover examined. According to Cohen, correlations were considered as small, medium, or big with coefficients of .1, .3, or .5, respectively (Cohen, 1988).Table 1:
Participant characteristics (N = 377).Characteristic n(%) Age, signify ± 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, signify ± 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 visage and hands 6 (1.6) Wash and parch 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 supervision of your feet and toenails 183 (48.5) IADL disability, signify ± 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 trait of life, signify ± SD, range 66.9 ± 25.6, 0–100 Mental dimension of trait of life, signify ± 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 flat of education were incorporated into the analyses (Gobbens, Luijkx & Van Assen, 2013). Bivariate associations between one background variable or disability particular on the one hand and one trait 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 complete the other variables in the model. The simplest model only assessed the effects of complete background variables together. One model moreover included complete 11 ADL disability items, whereas another model moreover included the seven IADL items together with the background variables. The most involved model included complete 24 items. The meet (explained variance) of complete 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 consequence of Cohen’s f2 = .056 which is a diminutive to medium consequence size (Cohen, 1988).
Data were processed using SPSS version 24.0 (IBM Corporation, Armonk, NY, USA). complete 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 signify age of the participants was 79.8 (SD = 3.7), 69.2% were male, and 64.8% were married or cohabiting. The tolerable scores on trait 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 supervision 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 exist celebrated 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 trait 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 trait of life dimensions (p-values < 0.001), with very big consequence sizes, d = 1.30 for the mental dimension and d = 1.82 for the physical dimension.Table 2:
Comparison of trait 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 trait of life 76.19 (19.29) 39.61 (22.27) t(144.95) = 14.29 < 0.001 d = 1.82 Mental dimension of trait of life 79.85 (14.59) 58.68 (20.46) t(128.05) = 9.31 < 0.001 d = 1.30 Correlations between disability and trait of life
Table 3 shows the correlations between ADL disability, IADL disability, physical trait of life, and mental trait of life. Most correlations were tough (>5); only the correlation between ADL disability and mental trait of life could exist considered as medium (.483) (all p-values < 0.001).Table 3:
Correlations between ADL disability, IADL disability, physical and mental dimensions of trait of life.IADL disability Physical trait of life Mental trait of life ADL disability 0.702 −0.683 −0.483 IADL disability −0.676 −0.541 Physical trait of life 0.734 Regression analyses: effects of ADL and IADL disability items on trait of life
Table 4 presents the results of the bivariate and sequential linear regression analyses on the physical and mental trait 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 trait 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, complete were associated with physical trait of life and 10 were associated with mental trait of life. The exception was the particular “feed yourself” (p = 0.058). complete seven IADL disability items were associated with both trait of life dimensions.Table 4:
Effect of background characteristics, ADL and IADL disability items on the physical and mental dimensions of trait of life.Physical dimension of trait of life Mental dimension of trait 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 visage 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 parch 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 supervision 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 trait of life dimensions were explained by complete the predictors together, respectively. After controlling for the background variables (sociodemographic characteristics, multimorbidity), disability (ADL and IADL items together) explained 35.0% of physical trait of life and 28.2% of mental trait 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 complete background characteristics and IADL disability items, representing a medium to big consequence size (f2 = .20) and a diminutive to medium consequence size (f2 = .08), respectively. The IADL disability items together explained a significant piece of both trait of life dimensions after controlling for background characteristics and ADL items, with increases in explained variance of 10.8% (physical; f2 = .38, big consequence size) and 13.5% (mental; f2 = .24, medium to big consequence 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 trait of life. The columns five and 11 exhibit the regression coefficients with corresponding criterion 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 complete 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 trait of life, both physical and mental. None of the other background characteristics were associated with trait of life, after controlling for complete the other variables in the model.
Of the 11 ADL disability items only four were significantly associated with trait of life. The ADL particular “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 trait of life. Of the seven IADL disability items, three were associated with trait of life. The two IADL items (do “heavy” household activities, accomplish the shopping) were negatively associated with both the physical and mental dimensions of trait of life and “prepare breakfast or lunch” was positively associated with the physical dimension. complete consequence sizes (f2) of the individual ADL and IADL disability items on physical as well as mental trait of life were <.15, representing diminutive consequence sizes. Of complete ADL disability items, “go up and down the stairs” and “stand up from sitting in a chair” had the largest consequence sizes on the physical and mental trait of life dimensions, f2 = .042 and f2 = .016, respectively. Of complete IADL disability items, the particular with the largest consequence sizes on the physical as well as the mental dimension of trait 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 trait 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 trait 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 trait of life. In addition, no previous study paid attention to the predictive value of the individual ADL and IADL disability items on trait of life.
The bivariate regression analyses showed that the following factors were associated with physical trait of life as well as mental trait 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 particular “feed yourself” was not associated with the mental dimension. However, the sequential linear regression analyses revealed that only multimorbidity, ADL particular “stand up from sitting in a chair”, and IADL items “do ‘heavy’ household activities” and “do the shopping” were significantly associated with both trait of life dimensions, after controlling for complete the variables in the model.
The finding that multimorbidity is associated with lower trait 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, trait 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 signify and severity of inveterate conditions (Brettschneider et al., 2013). In Canada, 238 people completed the SF-36 (Ware Jr & Sherbourne, 1992) for assessing trait of life, and multimorbidity was measured by counting the number of inveterate diseases and with the Cumulative Illness Rating Scale (CIRS) (Linn, Linn & Gurel, 1968); this study showed that the physical health dimension of trait of life deteriorated more than the mental health dimension of trait of life with increasing multimorbidity (Fortin et al., 2006). A study among Spanish people (≥50 years) moreover demonstrated that the number of inveterate diseases was associated with lower trait of life (Garin et al., 2014), assessed with the WHOQOL-AGE (Caballero et al., 2013). Finally, a longitudinal study conducted in China showed that sever multimorbidity patterns had various impacts on different dimensions of trait of life among community-dwelling older people (Gu et al., 2018). These findings are principal 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 repercussion of multimorbidity patterns on trait of life in other countries. These studies should focus in particular on effects of combinations of common inveterate diseases on trait of life, thereby providing direction to (preventive) interventions.
All ADL disability items combined explained a significant piece of the variance of both the physical dimension and the mental dimension of trait of life. Another study showed that maintaining independence in ADL had a positive consequence on four domains of the WHOQOL-OLD (sensory abilities; autonomy; past, present, and future activities; convivial participation) (Power, Quinn & Schmidt, 2005), and one domain of the WHOQOL-BREF (physical health) (The WHOQOL Group, 1998; Soósová, 2016)). trait 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 moreover establish that trait 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 supervision professionals (e.g., district nurses, physiotherapists, generic practitioners, occupational therapists) should identify (potential) limitations in performing ADL at an early stage in order to maintain or expand trait of life in older people. Based on the present study, special attention is needed to address problems people maintain when standing from sitting, because this activity is associated with lower physical and mental trait of life.
All IADL disability items combined explained a larger piece of the variance of both the physical and the mental dimension of trait of life compared with complete ADL disability items together, 10.8% versus 5.8% and 13.5% versus 4.5%, respectively. Two studies referred to above moreover establish that IADL disability was associated with trait 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 involved and appeals more to cognitive function. In Nigeria the prevalence design of IADL disability was 39.3% versus ADL disability 32.5% (Akosile et al., 2018). Among Dutch older people, with an tolerable 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 ultimate particular are principal because the present study showed that these two items were associated with the physical as well as the mental dimension of trait of life in older people. These findings maintain not been available to date. Conducting interventions on problems that older people can sustain with performing hefty household activities and shopping could champion them reach a higher trait of life. Domestic champion may meet these needs or additionally reablement or restorative supervision services may exist of benefit. These are short term services aimed at improving the independence of older people so they can hopefully stride back to live independently without ongoing assistance.
The model including complete the prediction variables explained a big piece 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 trait of life compared with people experiencing individual conditions (Lutomski et al., 2014). It is viable that the explained variances in the scores of the trait of life dimensions were moreover greater if depression as a predictive variable was included in the model; a review, including 74 studies, establish an association between depression and lower trait of life in older people, independent of how trait 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 trait 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 involve performance-based measures. A combination of both measures may exist the best pass to fully capture the picture of disability in ADL and IADL. However, in a sample of oldest former (≥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 maintain led to selection bias. In this context, it should exist celebrated 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 trait of life in older people. Therefore, it is principal for health supervision professionals to carry out interventions aimed at preventing and diminishing disability or its adverse outcomes, such as a lower trait of life. Promising interventions are multidisciplinary and multifactorial in nature, should exist preceded by an individualized assessment, and should involve case management and long-term supervene up (Daniëls et al., 2010). Lifestyle interventions targeting physical exercise, nutrition, and cognition loom to exist efficient against disability in ADL and IADL; in order to exist actually effective, these interventions should exist inexpensive, feasible, and simple 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 maintain the greatest repercussion on trait 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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ACT [1 Certification Exam(s) ]
Admission-Tests [13 Certification Exam(s) ]
ADOBE [93 Certification Exam(s) ]
AFP [1 Certification Exam(s) ]
AICPA [2 Certification Exam(s) ]
AIIM [1 Certification Exam(s) ]
Alcatel-Lucent [13 Certification Exam(s) ]
Alfresco [1 Certification Exam(s) ]
Altiris [3 Certification Exam(s) ]
Amazon [2 Certification Exam(s) ]
American-College [2 Certification Exam(s) ]
Android [4 Certification Exam(s) ]
APA [1 Certification Exam(s) ]
APC [2 Certification Exam(s) ]
APICS [2 Certification Exam(s) ]
Apple [69 Certification Exam(s) ]
AppSense [1 Certification Exam(s) ]
APTUSC [1 Certification Exam(s) ]
Arizona-Education [1 Certification Exam(s) ]
ARM [1 Certification Exam(s) ]
Aruba [6 Certification Exam(s) ]
ASIS [2 Certification Exam(s) ]
ASQ [3 Certification Exam(s) ]
ASTQB [8 Certification Exam(s) ]
Autodesk [2 Certification Exam(s) ]
Avaya [96 Certification Exam(s) ]
AXELOS [1 Certification Exam(s) ]
Axis [1 Certification Exam(s) ]
Banking [1 Certification Exam(s) ]
BEA [5 Certification Exam(s) ]
BICSI [2 Certification Exam(s) ]
BlackBerry [17 Certification Exam(s) ]
BlueCoat [2 Certification Exam(s) ]
Brocade [4 Certification Exam(s) ]
Business-Objects [11 Certification Exam(s) ]
Business-Tests [4 Certification Exam(s) ]
CA-Technologies [21 Certification Exam(s) ]
Certification-Board [10 Certification Exam(s) ]
Certiport [3 Certification Exam(s) ]
CheckPoint [41 Certification Exam(s) ]
CIDQ [1 Certification Exam(s) ]
CIPS [4 Certification Exam(s) ]
Cisco [318 Certification Exam(s) ]
Citrix [48 Certification Exam(s) ]
CIW [18 Certification Exam(s) ]
Cloudera [10 Certification Exam(s) ]
Cognos [19 Certification Exam(s) ]
College-Board [2 Certification Exam(s) ]
CompTIA [76 Certification Exam(s) ]
ComputerAssociates [6 Certification Exam(s) ]
Consultant [2 Certification Exam(s) ]
Counselor [4 Certification Exam(s) ]
CPP-Institue [2 Certification Exam(s) ]
CPP-Institute [1 Certification Exam(s) ]
CSP [1 Certification Exam(s) ]
CWNA [1 Certification Exam(s) ]
CWNP [13 Certification Exam(s) ]
Dassault [2 Certification Exam(s) ]
DELL [9 Certification Exam(s) ]
DMI [1 Certification Exam(s) ]
DRI [1 Certification Exam(s) ]
ECCouncil [21 Certification Exam(s) ]
ECDL [1 Certification Exam(s) ]
EMC [129 Certification Exam(s) ]
Enterasys [13 Certification Exam(s) ]
Ericsson [5 Certification Exam(s) ]
ESPA [1 Certification Exam(s) ]
Esri [2 Certification Exam(s) ]
ExamExpress [15 Certification Exam(s) ]
Exin [40 Certification Exam(s) ]
ExtremeNetworks [3 Certification Exam(s) ]
F5-Networks [20 Certification Exam(s) ]
FCTC [2 Certification Exam(s) ]
Filemaker [9 Certification Exam(s) ]
Financial [36 Certification Exam(s) ]
Food [4 Certification Exam(s) ]
Fortinet [13 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
FSMTB [1 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [9 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
GIAC [15 Certification Exam(s) ]
Google [4 Certification Exam(s) ]
GuidanceSoftware [2 Certification Exam(s) ]
H3C [1 Certification Exam(s) ]
HDI [9 Certification Exam(s) ]
Healthcare [3 Certification Exam(s) ]
HIPAA [2 Certification Exam(s) ]
Hitachi [30 Certification Exam(s) ]
Hortonworks [4 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [750 Certification Exam(s) ]
HR [4 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [21 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IAAP [1 Certification Exam(s) ]
IAHCSMM [1 Certification Exam(s) ]
IBM [1532 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICAI [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIA [3 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISA [1 Certification Exam(s) ]
ISACA [4 Certification Exam(s) ]
ISC2 [6 Certification Exam(s) ]
ISEB [24 Certification Exam(s) ]
Isilon [4 Certification Exam(s) ]
ISM [6 Certification Exam(s) ]
iSQI [7 Certification Exam(s) ]
ITEC [1 Certification Exam(s) ]
Juniper [64 Certification Exam(s) ]
LEED [1 Certification Exam(s) ]
Legato [5 Certification Exam(s) ]
Liferay [1 Certification Exam(s) ]
Logical-Operations [1 Certification Exam(s) ]
Lotus [66 Certification Exam(s) ]
LPI [24 Certification Exam(s) ]
LSI [3 Certification Exam(s) ]
Magento [3 Certification Exam(s) ]
Maintenance [2 Certification Exam(s) ]
McAfee [8 Certification Exam(s) ]
McData [3 Certification Exam(s) ]
Medical [69 Certification Exam(s) ]
Microsoft [374 Certification Exam(s) ]
Mile2 [3 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Misc [1 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
NBSTSA [1 Certification Exam(s) ]
NCEES [2 Certification Exam(s) ]
NCIDQ [1 Certification Exam(s) ]
NCLEX [2 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [39 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
NIELIT [1 Certification Exam(s) ]
Nokia [6 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [37 Certification Exam(s) ]
OMG [10 Certification Exam(s) ]
Oracle [279 Certification Exam(s) ]
P&C [2 Certification Exam(s) ]
Palo-Alto [4 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
Pegasystems [12 Certification Exam(s) ]
PEOPLECERT [4 Certification Exam(s) ]
PMI [15 Certification Exam(s) ]
Polycom [2 Certification Exam(s) ]
PostgreSQL-CE [1 Certification Exam(s) ]
Prince2 [6 Certification Exam(s) ]
PRMIA [1 Certification Exam(s) ]
PsychCorp [1 Certification Exam(s) ]
PTCB [2 Certification Exam(s) ]
QAI [1 Certification Exam(s) ]
QlikView [1 Certification Exam(s) ]
Quality-Assurance [7 Certification Exam(s) ]
RACC [1 Certification Exam(s) ]
Real-Estate [1 Certification Exam(s) ]
RedHat [8 Certification Exam(s) ]
RES [5 Certification Exam(s) ]
Riverbed [8 Certification Exam(s) ]
RSA [15 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [5 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [98 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [1 Certification Exam(s) ]
SCO [10 Certification Exam(s) ]
SCP [6 Certification Exam(s) ]
SDI [3 Certification Exam(s) ]
See-Beyond [1 Certification Exam(s) ]
Siemens [1 Certification Exam(s) ]
Snia [7 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [4 Certification Exam(s) ]
SpringSource [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [134 Certification Exam(s) ]
Teacher-Certification [4 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trainers [3 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [6 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [33 Certification Exam(s) ]
Vmware [58 Certification Exam(s) ]
Wonderlic [2 Certification Exam(s) ]
Worldatwork [2 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [6 Certification Exam(s) ]
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