Cognitive profile of young well-trained athletes with intellectual disabilities

Research in Developmental Disabilities 53 (2016) 377–390

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Research in Developmental Disabilities

Cognitive profile of young well-trained athletes with intellectual disabilities Debbie Van Biesen a,∗ , Jennifer Mactavish b , Katina McCulloch a , Laetitia Lenaerts a , Yves C. Vanlandewijck a a b

KU Leuven, Faculty of Kinesiology and Rehabilitation Sciences, Department of Rehabilitation Sciences, Belgium Yeates School of Graduate Studies, Ryerson University, Toronto, Canada

a r t i c l e

i n f o

Article history: Received 19 August 2015 Received in revised form 24 February 2016 Accepted 8 March 2016 Keywords: Sport intelligence Intellectual disability Cognitive test Paralympic Games Executive function CHC theory

a b s t r a c t Background: Previous research has shown that cognitive and motor skills are related. The precise impact of cognitive impairment on sport proficiency, however, is unknown. Aims: This study investigated group and individual differences in cognitive profiles in a large cohort of track and field athletes, basketball players, swimmers and table tennis players with (N = 468) and without (N = 162) intellectual disabilities (ID). Methods and procedures: Based on the Cattell-Horn-Carroll Theory of Cognitive abilities, eight subtests were selected for inclusion in a generic cognitive test (GCT) to assess executive functions and cognitive abilities relevant to sport, i.e., fluid reasoning, visual processing, reaction and decision speed, short-term memory and processing speed. Outcomes and results: Reliability coefficients for the subtests ranged between 0.25 and 0.88 respectively. Factor analysis revealed two clusters of subtests, i.e., a speed-based factor (simple and complex reaction time and simple and complex visual search) and a performance-based factor (Corsi Memory, Tower of London, WASI Block Design and Matrix Reasoning). After controlling for psychomotor speed, the group of ID-athletes scored significantly lower than athletes without ID on all the GCT subtests, except the complex visual search test. When cognitive profiles of individual ID- athletes were examined, some obtained higher scores than the average norm values in the reference population. Conclusions and implications: The GCT is currently administered as part of the classification process for athletes with ID who compete in the Paralympic Games. The results of this study indicate that the complex visual search and Tower of London test in the GCT should be reconsidered. © 2016 Elsevier Ltd. All rights reserved.

What this paper adds? The outcome of this study has generated new insights about the relationship between cognitive abilities and sport by spanning the boundaries of distinct research areas to produce a new body of knowledge. These insights offer the potential for resolving a primary research challenge in 21st century Paralympic sport, which is to develop evidence-based systems of

∗ Corresponding author at: KU Leuven, Faculty of Kinesiology and Rehabilitation Sciences, Department of Rehabilitation Sciences, Tervuursevest 101, 3001 Leuven, Belgium. E-mail address: [email protected] (D. Van Biesen). http://dx.doi.org/10.1016/j.ridd.2016.03.004 0891-4222/© 2016 Elsevier Ltd. All rights reserved.

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eligibility and classification that address the impact of impairment on sport proficiency (Tweedy & Vanlandewijck, 2009). In this context, the outcomes of the present study have had a direct and positive effect by enabling the reinclusion of elite ID-athletes the world over, in a limited number of Paralympic sports. For ID-athletes, dedicated to intense training and elite performance goals, having the right to participate in Paralympic Games is highly important. The cognitive test described in this study is currently being used as one component of the process for enabling IDathletes to participate in events sanctioned by the International Paralympic Committee (IPC). Inclusion of these athletes in the Paralympic Games has provoked escalating media interest which is an important mechanism for advancing awareness of the strengths and talents of these athletes and, in turn, enhancing positive attitudes towards people with disability and diversity in general. 1. Introduction In an equal society having a disability should not be a barrier to enjoy physical activity or to excel in sport. The Paralympics Games is recognized today as the pinnacle in sporting excellence for those with a large range of disabilities (Jobling, 2012). Intellectual impairment is one group of eligible impairments that has recently been re-included in the Paralympics, but only in a limited number of sports (Kwon & Block, 2012). In order for these athletes to compete at the most elite level, evidence-based sport-specific classification systems that clearly denote the impact of impairment on performance (Tweedy & Vanlandewijck, 2009) needed to be developed. Similar to the example of a single below the elbow amputation, which has a greater affect in swimming than in marathon running, one would expect an intellectual impairment to have less affect in physically demanding events (e.g., 100 m sprint) than in those with heavy cognitive demands (e.g., decision making, pacing). The available literature (Burns, 2015), however, offers no straightforward conclusions that could substantiate these assumptions. There is a paucity of previous work in this area and, as such, a lack of clarity about the underlying principles and explanatory mechanisms for understanding the relationship between cognition and proficiency in sport. Multiple studies have highlighted the relationship between cognitive abilities and sport performance (Kasahara, Mashiko, & Niwa, 2008; Kioumourtzoglou, Derri, Tzetzis, & Theodorakis, 1998; Mann, Williams, Ward, & Janelle, 2007) but the precise nature of this relationship remains unclear. Elite athletes are found to perform significantly better than novices in various aspects of intellectual functioning, including visual-spatial awareness, memory, and response speed and accuracy (Mann et al., 2007). A recent systematic review by Van der Fels et al. (2014) showed, among young children, a strong linkage between cognitive skills and motor skills with the highest cognitive demand, i.e., fine motor skills, bilateral body coordination, and timed performance in movements. The motor tasks with limited or no connection to cognitive skills required the least amount of cognitive engagement in the tasks (e.g., strength). From a neuropsychological view, these novel findings are consistent with the notion that motor and cognitive skill functioning is mediated by the co-activation of the cerebellum (important for complex and coordinated movements) and the prefrontal cortex (important for higher-order cognitive skills). Van der Fels et al. also found evidence of a correlation between motor skills and higher-order complex cognitive skills such as fluid reasoning and visual processing. In recommendations for future research, these authors noted that to better understand the relationship between cognitive and motor skills requires moving beyond reliance on general IQ measures by focusing on relevant categories of motor and cognitive skills, which is the approach adopted in the present study. The Cattell-Horn-Carroll (CHC, Schneider & McGrew, 2012) theory of cognitive abilities is widely accepted by researchers interested in intelligence as a common nomenclature and theoretical framework for examining various aspects of human cognitive abilities. We use the CHC taxonomy as the theoretical framework for conceptualizing cognitive abilities with a focus on assessing those essential for optimal sport proficiency. According to CHC theory, there are 10 broad cognitive abilities, ranging from Gf (Fluid reasoning or the deliberate but flexible control of attention to solve novel problems that cannot be performed by relying exclusively on previously learned habits) to Gt (Reaction and decision speed, or the speed of making very simple decisions or judgments when items are presented one at a time). For the complete and detailed overview of the theory and all components we refer to McGrew (2009). From the total set of broad cognitive abilities as laid out in the CHC taxonomy five abilities with major relevance to sport performance were identified (i.e., fluid reasoning, short-term memory, processing speed, reaction and decision speed and visual processing) and this set of cognitive abilities was assessed by means of corresponding subtests. From a neuropsychological viewpoint it also was relevant to include a subtest to account for executive functions, as these are the basis for many cognitive abilities (Ardila, Pineda, & Rosselli, 2000). Further support for this approach comes from the work of Vestberg, Gustafson, Maurex, Ingvar, and Petrovic (2012) who showed that executive functioning has potential as a predictor of success in sport. The main purpose of the present study was to examine differences and similarities in the cognitive profiles of young well-trained athletes from a wide range of sports (i.e., athletics, swimming, table tennis and basketball). To fully understand the relationship between sports proficiency and cognition this study included individuals with and without intellectual impairments, with a primary focus on elite athletes with intellectual disabilities (ID-athletes). To clarify the terminology we use, Paralympic systems of athlete classification, as described in the International Paralympic Committee’s (IPC) classification code (IPC, 2007) is based on the language and concepts articulated in the International Classification of Functioning, Disability and Health (ICF) model. According to the ICF model, disability is an umbrella term used to describe the inter-relationship between impairment, activity limitations and participation restrictions; impairment as a stand-alone term describes a deficit in body function or structure. To be consistent with the IPC Classification code, throughout the remainder of the text, the

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Table 1 Cognitive test composition and subtest descriptions. Test

Description

Cognitive abilities

Method

Trials

Scoring

SRT

To react a.s.a.p. to fixed visual stimulus Similar to SRT with distracters added

Reaction and decision speed (Gt)

Keyboard

12

Mean RT in ms

Reaction and decision speed (Gt), selective attention Processing speed (Gs)

Keyboard

28

Mean RT in ms

Touchscreen

12

Mean RT in ms

Processing speed (Gs), Visual processing (Gv)

Touchscreen

12

Mean RT in ms

Short-term memory (Gsm), spatial memory span Executive functioning

Touchscreen

15

Best average score on 5 subsequent trials

Touchscreen

14 or 18

Number of correct items (max 18)

Not-computerized

max 13

Not-computerized

max 35

Score depends on correct items and speed (max 72) Number of correct items (max 35)

CRT

SVS

CVS

CMT

TOL

WBD

WMR

To react a.s.a.p. to random visual stimulus Similar to SVS with distracting field added To memorize increasing sequences of squares To replicate configurations in minimal number of moves To replicate 2D block patterns with 9 3D-cubes To complete gridded patterns (multiple choice)

Fluid reasoning (Gf)/Visual processing (Gv) Fluid reasoning (Gf, including induction, general sequential reasoning and speed of reasoning, pattern recognition)

Note. Max = maximum, RT = reaction time, SRT = simple reaction time test, CRT = complex reaction time test, SVS = simple visual search test, CVS = complex visual search test, CMT = corsi memory test, TOL = Tower of London, WBD = Block Design test, WMR = matrix reasoning test. The Block Design and Matrix Reasoning measures are from Wechsler Abbreviated Scale of Intelligence—Second Edition (Wechsler, 2011).

term ID-athletes is used to refer to the population under investigation, whereas ‘intellectual impairment’ is used to explicitly denote deficits in intellectual functioning. Psychometric properties of the test were first assessed. When 104 ID-athletes are assessed twice over a period of time (test-retest reliability), large correlations and small learning effects are expected on all subtests. The internal consistency, as assessed by means of factor analysis, is expected to be high (construct validity). The main hypothesis of this study is that there will be significant differences between well-trained ID-athletes and their comparably trained non-ID peers. Athletes with ID are expected to score lower than their counterparts in executive functioning and cognitive measures related to sport (i.e., reaction and decision time, short term memory, processing speed, fluid reasoning and visual processing). 2. Methods 2.1. Participants The data for this study were derived from 630 well-trained athletes participating in athletics (N = 191), swimming (N = 210), basketball (N = 102), and table tennis (N = 127). The total sample consisted of male (N = 424) and female (N = 206) ID-athletes (N = 468) and athletes without ID (N = 162). All ID-athletes participated in international sport events, sanctioned by the International Federation for Para-athletes with an Intellectual Disability (INAS), IPC, or ITTF (International table Tennis Federation). The participants represented 46 countries from six continents: Europe (26), Asia (9), North America (2), South America (5), Australia (2) and Africa (2). The cognitive testing was completed before or after competition as part of the classification process, with the results voluntarily approved for research purposes. All athletes were diagnosed as having an intellectual disability (ID) according to AAIDD international standards (Schalock et al., 2010): IQ ≤75, significant limitations in adaptive behavior and impairment manifested before age 18. The control group was selected based on their comparability to the ID-athletes on age, principal sport and training volume. Training volume was not systematically assessed for each ID-athlete but data on training volume (M ± SD) per sport was available from previous studies: table tennis (Van Biesen, Mactavish, & Vanlandewijck, 2014): 13 ± 5 years of experience, athletics (unpublished data): 9.6 ± 4.8 years of experience, 9.4 ± 4.0 h/week, swimming (unpublished data): 11.7 ± 7.7 years

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Table 2 Participants’ characteristics. With ID

Total (N) Sport (N) Athletics Basketball Swimming Table Tennis

Without ID

Male

Female

Total

Male

Female

Total

317

151

468

107

55

162

95 62 104 56

55 0 65 31

150 62 169 87

21 30 24 32

20 10 17 8

41 40 41 40

Training volume (M years ± SD) Athletics Basketball Swimming Table Tennis

10 ± 5 12 ± 6 12 ± 8 13 ± 5

11 ± 5 12 ± 6 11 ± 5 10 ± 5

Age (M) Age (SD)

24.6 6.4

22.9 6.0

24.1 6.3

23.9 5.7

22.1 3.7

23.3 5.1

Dominant hand Right (%) Left (%)

85 15

81 19

84 16

87 13

92 8

88 12

Psychomotor speed (M ± SD) FT dominant FT non dominant

66 ± 12 61 ± 13

60 ± 13 56 ± 13

64 ± 13 59 ± 13

78 ± 8 71 ± 9

77 ± 8 70 ± 8

78 ± 8 71 ± 9

Note. FT = Finger Tapping, ID = intellectual disability.

of experience, 14.8 ± 3.1 h/week, and basketball (Pinilla, Perez-Terejo, Van Biesen, & Vanlandewijck, 2015): 12.2 ± 5.8 years of experience, 6.6 ± 5.4 h/week. The comparison sample was recruited (1) by contacting local (Belgian) sport clubs via email, phone or a personal visit, (2) by personally contacting individual athletes via e-mail, phone or a personal visit and (3) by posters in the main sport facilities of the University Sports Faculty. Informed written consent was obtained for all participants and/or their legal guardians, prior to participation in the study. The Medical Ethics Committee of the Katholieke Universiteit Leuven approved the study. 2.2. Materials To assess the sport cognitive profiles of the athletes, the generic cognitive test was administered, which is comprised of a series of seven subtests measuring broad cognitive abilities relevant in a sport context, i.e., short-term memory, visual processing, fluid reasoning, processing speed and reaction and decision speed. One subtest was added to assess executive functioning, which is related to cognitive abilities such as fluid intelligence (Salthouse, 2005) and might also be relevant with respect to predict outcomes in sport (Vestberg et al., 2012). Additionally, psychomotor speed was assessed using a finger-tapping test to control for possible influences of motor deficits. The majority of the subtests were computerized; only two tests (WASI Block Design and WASI Matrix Reasoning) were not. Table 1 provides an overview of the subtests and indicates the cognitive ability factors that were being assessed by each test and the testing method. Every subtest was preceded by a demonstration to acquaint the athlete with the test (exception: the Finger Tapping Test). All tests, except the WASI Block Design and Finger Tapping Test, were performed using the dominant hand only. 2.3. Procedure The testing was either part of the athletes’ classification process prior to competition, or part of the classification research conducted before or after competition. Testing occurred in a quiet room, free from distractions, and set up to enable optimal visibility of the screen (e.g., no sunray interference). Included in the setting were the athlete and the test instructor, and a translator if required (e.g., the coach if able to translate or a dedicated translator). The athlete was seated in front of the computer and the test leader was seated next to the athlete. The test leader made sure that the participant sat comfortably and adjusted the screen angle for maximum visibility. All subtests were administered in accordance with precise standardized instructions presented in a user’s manual, which also included guidelines for scoring and interpretation. No feedback was given to the test taker, unless this was stated explicitly in the manual (e.g., Finger Tapping). The instructions were mainly non-verbal. For every subtest, a demonstration trial was provided, and during the demonstration the instructor employed simple, clear and specific language to facilitate comprehension of the instructions. The demonstration trial continued until the test instructor was sure that the participant understood what was required, i.e., the participant correctly performed the required task. Total administration time was

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Fig. 1. Visual representation of the computerized part of the generic cognitive test. (a) Simple reaction time (SRT), (b) complex reaction time (CRT), (c) simple visual search (SVS), (d) complex visual search (CVS), (e) Corsi memory (CORSI), (f) Tower of London (TOL).

approximately 45 min. If there were any deviations from the prescribed protocol, the test leader recorded when and why these occurred and the test results were excluded from the analyses. When athletes failed to perform one or more of the subtests (e.g., stopped before the test was finished), this was recorded as missing data. Of the 630 ID-athletes, only 30 had missing data, and none with missing data on more than 2 subtests. 2.4. Test descriptions and test instructions 2.4.1. Speed based tests In the CHC model of cognitive abilities (McGrew, 2009), there are two speed-related broad ability domains: reaction and decision speed (Gt) and processing speed (Gs). Gt is defined as “the ability to make elementary decisions and/or responses (simple reaction time) on one of several elementary decisions and/or responses (complex reaction time) at the onset of simple stimuli. These abilities are typically measured by chronometric measures of reaction time and inspection time. Gs is defined as the ability to automatically and fluently perform relatively easy or over-learned elementary cognitive tasks, especially when high mental efficiency (i.e., attention and focused concentration) is required. In our investigation these abilities are assessed by means of four computerized subtests, two reaction time and two visual search paradigms. For the Simple Reaction Time the participant rests the index finger of the dominant hand on the space bar and taps it as soon as a white circle appeared in the middle of the black screen with randomized time intervals (see Fig. 1a), the practice trials are followed by 12 test trials. The Complex Reaction Time test is similar but includes 16 distractors (negative stimuli: squares and triangles) appearing in the middle of the screen, randomly among the 12 targets (circles). The only action required was to tap when a circle appeared. The Simple Visual Search is a touch screen based subtest in which the participant sits in front of the screen and taps 12 times as fast as possible with the preferred hand in the middle of the circle when it appears on the screen, at randomized time intervals and randomly chosen spots (see Fig. 1c). The Complex Visual Search test was similar but this test involved a distracting background (computer screen composed of flickering dots) (see Fig. 1d). The appearing circle gradually becomes more intense and visible. For these four subtests the mean and standard deviations over 10 trials (after removing the best and worst score) were recorded in milliseconds and stored on the computer automatically. 2.4.2. Short term memory (Gsm) In the CHC model of cognitive abilities (McGrew, 2009) short-term memory (Gsm) is defined as the ability to comprehend and maintain awareness of a limited number of elements of information in the immediate situation (events that occurred in the last minute or so). This ability was assessed by means of a computerized visual memory span task (Fig. 1d), an adaptation of the original CORSI task (Corsi, 1972) in which the participant reproduces block-tapping sequences randomly generated with the provision that no block was repeated in any sequence. All squares turned blue when the order presentation was finished, which was the cue for the participant to start tapping blocks in the same order as they were lighting up. The sequence length started with two blocks and increased by one after correct recalls, and decreased by one block if any error was made. The computer automatically stored the length of each sequence. The final score of the athlete, i.e., the Corsi memory span was defined as the best average score of five subsequent trials (corrected by minus 0.5).

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Fig. 2. Comparison of distributions between athletes with and without intellectual disability (ID) for the four performance-based subtests: Corsi memory span, Tower of London (TOL), WASI Block Design and Matrix Reasoning. Gv = visual processing, Gf = fluid reasoning, CONTROL = athletes without ID, ID = athletes with ID.

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Table 3 Factor loadings for exploratory factor analysis with varimax rotation of cognitive ability subtests. Subtest

Speed-based

Content-based

Simple Visual Search (Gs) Complex Reaction Time (Gt) Simple Reaction Time (Gt) Complex Visual Search (Gs)

0.95 0.91 0.91 0.61

−0.12 −0.17 −0.24 −0.36

WASI Matrix Reasoning (Gf) WASI Block Design (Gv) Corsi (Gsm) TOL Executive Functioning

−0.17 −0.14 −0.28 −0.21

0.90 0.89 0.82 0.73

Note. Factor loadings >0.80 are in boldface. TOL = Tower of London. Gv = Visual processing, Gt = reaction and decision speed, Gf = Fluid reasoning, Gsm = Shortterm memory. WASI = Wechsler Abbreviated scale of Intelligence TM) (Wechsler, 2011).

2.4.3. Executive functioning For the purpose of this investigation, we used a computerized adaptation of the Tower of London (Rainville et al., 2002; Shallice, 1982) to assess executive functioning (Fig. 1f). The test ended after 14 items with a six-move problem. For participants scoring more than 90%, four additional test items were presented (seven-move problems). The emphasis in this test was accuracy rather than speed but for practical purposes an upper time limit (120 s) was set for solving each item, with two trials for each. To limit potential distress because of repeated failure, a cessation rule was applied when the participants indicated that the task was too difficult. The test score was based on the number of correctly solved items (in the least possible number of moves). The maximum score was 18.

2.4.4. Fluid reasoning (Gf) and visual processing (Gv) Two non-computerized subtests (the WASI Block Design test and the WASI Matrix Reasoning test) were administered (WASI = Wechsler Abbreviated scale of Intelligence TM) (Wechsler, 2011). Both subtests measure abilities in the CHC domains of Fluid reasoning (Gf) and Visual processing (Gv), with the highest loading for the Block Design on Gv (defined as the ability to generate, store, retrieve, and transform visual images and sensations) and for the Matrix Reasoning Test on Gf (defined as the use of deliberate and controlled mental operations such as problem solving and concept formation to solve novel problems that cannot be performed automatically). Absolute test scores were used for data analysis with a maximal obtainable test result of 72 points for Block Design and 35 points for Matrix Reasoning.

2.4.5. Control test for psychomotor speed (Gps) The Finger Tapping test was used as a control mechanism for psychomotor speed, defined as the ability to rapidly and fluently perform physical body motor movements largely independent of cognitive control (McGrew, 2009). The test involved tapping the spacebar for 10 s as fast as possible, with one finger or all the fingers together, but without holding the key down continuously nor alternating tapping with multiple fingers. This was repeated twice for the dominant and nondominant hand alternately. A real-time refreshing on-screen counter displayed the counted taps, which usually provides strong encouragement to tap as fast as possible. During this test, the test instructor also encouraged the participant verbally. The test result was the highest number of counts reached in 10 s, for dominant and non-dominant hand.

2.5. Data analyses Statistics were performed using SPSS (version 16.2, SPSS Inc., Chicago Ill, USA) with level of significance set at p < 0.05. Eight dependent variables (one for every subtest) were defined. Three independent variables were defined, of which two were dichotomous variables, i.e., impairment (with or without ID) and gender (male and female), and one continuous variable, i.e., psychomotor speed (expressed in max number of taps/10 s). A factor analysis was first performed to determine construct validity and to gain information about the interdependencies between the subtests. The test-retest reliability was estimated with Pearson’s correlation coefficients across two test sessions for N = 115 ID-athletes who participated in two separate international championships within one year, and administered the test once at each occasion. The average time interval was 7.1 months (minimal 3 and maximum 11 months) between the first and the second test session. The mean scores and standard deviations of test-retest were compared by paired t-tests. For each of the eight subtests, a 2 × 2 analysis of variance was performed with impairment and gender as independent variables and respectively with and without psychomotor speed taken into account as a covariate in the analyses. Effect sizes (ES) were calculated as d = [mean ID-athletes − mean control group]/SD. SD was calculated as the square root of the pooled estimate of population variance. As a guide to interpreting these values, Cohen (1988) labeled an effect size ‘small’ if ES < 0.5, “moderate” if ES between 0.5 and 0.8, or “large” if ES ≥ 0.8.

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Table 4 Bivariate Correlations among the eight subtests. Subtest

1 SRT

2 CRT

3 SVS

4 CVS

5 CORSI

6 TOL

7 BLOCK

8 MR

1. Simple Reaction Time (SRT) 2. Complex Reaction Time (CRT) 3. Simple Visual Search (SVS) 4. Complex Visual Search (CVS) 5. CORSI 6. Tower Of London (TOL) 7. WASI Block (BLOCK) 8. WASI Matrix Reasoning (MR)

/ 0.85* 0.89* 0.52* −0.45* −0.35* −0.36* −0.39*

/ 0.85* 0.50* −0.36* −0.30* −0.31 −0.33*

/ 0.57* −0.36* −0.29* −0.27* −0.28*

/ −0.45* −0.36* −0.35* −0.40*

/ 0.55* 0.71* 0.73*

/ 0.54* 0.56*

/ 0.85*

/

p < 0.001, WASI = Wechsler Abbreviated scale of Intelligence TM) (Wechsler, 2011), CORSI = Corsi Block Memory Test. * p < .001. Table 5 Comparison between test and retest scores and Pearson correlations on the generic cognitive test for 114 ID-athletes. Factor

Subtest (unit)

Test 1 (M ± SD)

Test 2 (M ± SD)

Paired t

Pearson R

df

Speed

Simple Reaction Time (ms) Complex Reaction Time (ms) Simple Visual Search (ms) Complex Visual Search (s)

771.0 ± 505.6 949.8 ± 666.6 947.5 ± 823.2 10.0 ± 4.2

644.3 873.8 880.7 8.7

± ± ± ±

218.5 379.2 303.8 4.0

2.58* 1.14 0.63 3.87*

0.71* 0.66* 0.67* 0.25*

114 114 114 112

Content

CORSI (average memory span) TOL (#correct items, max 18) WASI Block (raw score, max 72) WASI Matrix (#correct, max 35)

4.5 7.5 21.7 14.1

4.7 8.2 24.2 15.0

± ± ± ±

1.4 2.8 17.5 8.4

−1.68 −2.57* −3.14* −1.65*

0.76* 0.48* 0.88* 0.81*

112 112 108 109

Control

FTD (#counts) FTND (#counts)

64.6 ± 14.3 59.3 ± 13.1

−2.6* −1.9

0.78* 0.73*

113 113

± ± ± ±

1.4 2.8 15.9 7.8

66.6 ± 14.3 60.9 ± 9.2

Note. Corsi = Corsi memory, Block = Block design, Matrix = Matrix reasoning, FTD = Finger Tapping Dominant hand, FTND = Finger Tapping non dominant hand. * p < 0.05.

3. Results An overview of participants’ characteristics is reported in Table 2. The psychomotor speed of ID-athletes as expressed in their score on the finger tapping task with dominant hand (N = 468, MFT ± SD = 64 ± 13) was significantly lower than for the athletes without ID (N = 162, MFT ± SD = 78 ± 8), F(1, 629) = 185.1, p < 0.001. The finger tapping score with non-dominant hand was also significantly different between ID and non-ID athletes, F(1, 629) = 115.0; p < 0.001. No significant differences between ID and non-ID athletes were observed for age or training volume. Gender differences were observed for age and psychomotor speed. The female athletes (N = 206, Mage ± SD = 22.6 ± 5.5) were significantly younger than the male athletes (N = 424, Mage ± SD = 24.4 ± 6.2, F(1, 629) = 12.2, p < 0.001, and their psychomotor speed score was significantly lower, F(1, 629) = 14.3, p < 0.001. 3.1. Factor analysis An initial examination of Bartlett’s sphericity test of the item response correlation matrix, ␹2 = 3803, p < 0.0001, and the Kaiser–Meyer–Olkin measure of sampling adequacy, KMO = 0.84, supported the undertaking of a factor analysis. A varimax rotation of a principal axis factor analysis revealed a two-factor solution using conventional criterion (Eigenvalue >1). Two factors were extracted, explaining 77% of the variance. The rotated factor matrices are shown in Table 3. Both factors obtained tended to have a majority of similar items. The first factor contained all the speed-based subtests (simple and complex reaction time, and simple and complex visual search), whereas the second factor contained all the performance-based subtests, i.e., the WASI matrix reasoning and block design, CORSI memory and executive functioning (TOL). All subtests load on only one factor and all factor loadings are >0.60. 3.2. Test-retest reliability An overview of the correlations and Paired T-test results for 114 II-athletes (45 female, 69 male) from three sports (49 athletics, 44 swimming, 21 table tennis) who underwent the test twice is presented in Table 5. The mean scores improved between test and retest for all subtests, indicating a learning effect. This learning effect was significant for SRT, CVS, TOL, WASI Block and WASI Matrix. As indicated in Table 5, test and retest results were positively correlated for all subtests (p < 0.001). A weak positive correlation was found on the CVS (0.25) and TOL (0.48), moderate positive correlations on the, CRT (0.66) and SVS (0.67). Strong correlations were found for the subtests WASI Block Design (0.88), WASI Matrix Reasoning

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Table 6 Multivariate analysis of variance with psychomotor speed as covariate. Subtest (unit)

With ID (N = 440) M ± SD

Speed-based subtests Simple Reaction Time (ms) Complex Reaction Time (ms) Simple Visual Search (ms) Complex Visual Search (s)

705.0 902.2 855.3 9.8

± ± ± ±

Content-based subtests 4.3 ± CORSI (average memory span) 7.8 ± TOL (#correct items, max 18) WASI Block (raw score, max 72) 21.7 ± WASI Matrix (#correct, max 35) 13.6 ±

MIN, MAX

Without ID (N = 162) 95% CI

M ± SD

F

SkewnessKurtosisES Cohen d

MIN, MAX 95% CI

331.5 331.2, 2625.3[673.4, 736.6] 382.5 362.3 448.6, 3212.3[867.7, 936.7] 500.8 344.9 450.2, 2790.7[860.3, 1025.5] 539.4 3.5 6.7, 30.1 [9.2, 9.9] 7.6

± ± ± ±

45.7 293.7, 557.8[375.4, 389.6] 93.9* 58.3 389.1, 657.9[491.7, 509.8]142.3* 70.0 417.1, 767.3[528.6, 550.3] 70.3* 0.4 6.4, 9.0 [7.5, 7.7] 0.9

1.3 2.7 15.8 7.4

± ± ± ±

0.9 3.1 9.7 3.6

0.0, 9.9 0, 15 0, 71 0, 32

[4.2, 4.4] [7.6, 8.1] [20.2, 23.2] [12.9, 14.3]

6.7 11.8 58.8 29.1

4.7, 8.9 3, 18 30, 71 10, 35

[6.5, 6.8] [11.3, 12.2] [57.3, 60.3] [28.4, 29.6]

2.4 2.4 3.5 2.5

* 189.9−0.46 122.5* 0.12 495.3* 0.34 348.7* 0.1

6.9 8.6 19.5 9.9

0.9 0.7 0.6 0.8

−0.44 0.12 1.3 1.2

2.8 1.4 2.9 2.7

Note. ID = intellectual disability, CI = confidence interval, TOL = Tower of London, CORSI = Corsi Block Memory Test, ES = Cohen d Effect Size. * p < 0.05.

(0.81), Corsi (0.76) and SRT (0.71). On the control test for psychomotor speed strong positive correlations were found: FTD (0.78), FTND (0.73). 3.3. Generic cognitive test Appropriate data screening techniques were used to confirm the assumptions underlying the statistical analyses used. Not all the normality assumptions were met, i.e., no homogeneity of variance for any of the subtests, skewness and kurtosis >2 for the speed-based measures. Therefore, data transformation was done using log 10 transformations for the speed-based measures, resulting in acceptable values for asymmetry and kurtosis in order to prove normal univariate distribution (George & Mallery, 2010). A 2 × 2 way MANCOVA was used to examine the effect of impairment (with or without ID) and gender (male or female) on the eight cognitive subtests, taking psychomotor speed (finger tapping with dominant hand) into account. For the speedbased subtests (SRT, CRT, SVS and CVS), log 10-transformations were computed before entering the variables in the model. The analysis revealed no main effects of gender, F(1, 588) = 0.74, p = 0.66 and no interaction effects, F(1, 588) = 0.98, p = 0.17. A significant main effect for impairment was found, and the univariate analysis revealed significant differences for all subtests (except complex visual search), with overall better scores for the athletes without ID. The results are shown in Table 6. Medium Cohen d Effect Sizes were found for Complex Reaction time (0.6) and Simple Visual Search (0.7). Large Effect Sizes (≥0.8) were found for all other subtests. The distributions for all subtests are visually presented by means of boxplot diagrams in Fig. 2 (Performance-based subtests) and Fig. 3 (Speed-based subtests). Whereas clear group differences were observed for all subtests, Fig. 2 and Fig. 3 indicate that on individual level, the distribution range is large, mostly for the ID-athletes. On every subtest there are some individual ID-athletes outperforming the mean values of athletes without ID, and even reaching maximum scores on some tests. 4. Discussion The purpose of this study was to assess the cognitive abilities of ID-athletes and to compare their cognitive profile with a group of well-trained athletes without ID, comparable in terms of training volume. As was expected, the cognitive abilities of athletes with ID were much lower than those of the non-ID group across the full spectrum of abilities assessed, and this remained unchanged when accounting for psychomotor speed. There were, however, large inter-individual differences among the ID-athletes, with some exceeding the group average for athletes without ID. 4.1. Psychometrics (reliability and validity) Psychometric properties of the test battery were first assessed. The results of the test-retest analysis indicated a positive correlation between the first and second testing occasions on all subtests. In general, tests need to have high test-retest reliability (TRR) coefficients to be suitable for testing, preferably above 0.8. The results we found in our investigation for Matrix Reasoning (0.81) and for Block Design (0.88) are in line with these norms and indicate high TRR. The TRR coefficients on the subtests SRT, CRT, SVS and Corsi ranged between 0.66 and 0.76. These were deemed acceptable as it is recognized that a number of many factors related to the nature of intellectual impairment could influence the TRR. For example, it is known that stress and/or anxiety affects performance on completely new tasks conducted in unfamiliar environments (Roy, Retzer, & Sikabofori, 2013). The low reliability results for the complex visual search test (0.25) and the TOL (0.48) in our study indicate that they should be removed or replaced. The TOL results are not surprising as tests of executive functioning often have poor to moderate TRR (Lemay et al., 2004). The assessment of these abilities, such as planning, often involves

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Fig. 3. Comparison of distributions between athletes with and without intellectual disability (ID) for the four speed-based subtests: simple reaction time, complex reaction time, simple and complex visual search. CONTROL = athletes without ID, ID = athletes with ID.

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tasks that rely on assessing people in a new context or non-routine situations, which if done repeatedly reduces novelty and test sensitivity. TRR must be distinguished from the practice effect, whereby repetition leads to improved performance. Thus, a test score can show a large practice effect, and nevertheless have a high TRR if performance remains consistent across time, as the results of our study indicate. In our study, a significant learning effect was found for five subtests (SRT, CVS, TOL, Block and Matrix). This is not uncommon as results on many neurobehavioral tests change on subsequent administration because of practice, maturation, or other intervening effects that take place between test and retest. 4.2. Speed based subtests Reaction times are a widespread, important and informative tool in the study of cognitive ability (Nissan, Liewald, & Deary, 2013). In general, associations between reaction times and cognitive ability test scores have been consistently reported in the literature (Deary, Der, & Ford, 2001), with varying estimates of the strength of these associations (Der & Deary, 2003). In the present investigation four subtests (i.e., simple reaction time, complex reaction time, simple visual search and complex visual search) loaded on the speed-based factor. According to the CHC model of cognitive abilities these four subtests cover the broad abilities: Reaction and Decision Speed (Gt), Processing Speed (Gs) and Visual Processing (Gv). Individuals with ID are known to have slower and more variable reaction times (Klotz, Johnson, Wu, Isaacs, & Gilbert, 2011; Kosinski, 2013), which corresponds to the finding in our study that ID-athletes were slower on the speed measures than their non-ID peers. In a study of individuals without ID, Lee and Chabris (2013) found that superior ability to respond to stimuli presented in quick succession is a function of brain processing speed and not faster muscle/motor response. Schweitzer (2001) also found that the speed advantage of more intelligent people is greatest in tests requiring complex responses. The reaction time subtests used in the present study were pure speed measures that did not require substantial cognitive effort. Even the more complex versions of the tests (choice reaction time and complex visual search), used distractions that did not introduce greater information processing or response demands. These factors may explain why we did not find significant differences in cognitive speed assessed with the complex visual search measure when controlling for psychomotor speed. 4.3. Performance-based subtests A consistent finding in intelligence literature is the existence of a general intelligence factor (g) that results in positive correlations between a diverse range of cognitive tests with differing content (Kaufman, DeYoung, Reis, & Gray, 2011). This was also found in our investigation with the four subtests (i.e., Corsi, Tower of London, WASI Matrix Reasoning and WASI Block Design) loading on the same performance-based factor. Three of these subtests specifically assess cognitive abilities: Short Term Memory (Gsm), Fluid Reasoning (Gf) and Visual Processing (Gv) whereas the Tower of London is an extensively used measure of executive functioning (Rainville et al., 2002). Although executive functioning and cognitive ability are different concepts, the factor analysis and correlation matrix (Table 4) reveal that all of the abilities included in this factor are clearly related. This finding corresponds with literature from studies of non-ID adults (Lezak, 1995; Salthouse, 2005) that shows that the many variables purported to assess executive functioning are in fact closely related to cognitive abilities such as reasoning and perceptual skills. As expected, as a group, the ID-athletes scored significantly lower than well-trained athletes without impairments on the four performance-based subtests; however the inter-individual variability was large. 4.3.1. Short-term memory (Corsi) Studies dating back to the 1970s indicated that elite athletes are superior to novices in recognizing and recalling sport specific information (Allard, Graham, & Paarsalu, 1980; Beilock, Wierenga, & Carr, 2003; Chase & Simon, 1973), and that these cognitive abilities are not directly transferable to contexts outside of the sport-specific expertise of the athlete. Cognitive abilities as examined in the present investigation were done in a generic way, i.e., making use of abstract, not culturally specific or sport-specific visual information. Our purpose was to gain insights about the generic (i.e., non-sport-specific) memory capacity of athletes, excluding the possible impact of training or sport-specific expertise. The results confirm that generic memory capacity is low among athletes with ID (average sequence length of 4.3 blocks compared to 6.7 for athletes without ID), which is consistent with Henry’s (2001) comparison of spatial memory span of non-athlete samples with mild-ID (spatial span: 4.4 ± 1.1) and a control group without ID (6.1 ± 1.0). A few individuals with ID in our sample appeared to have excellent short-term memory skills (with recall of 9.9 digits which is far better than average). What is not clear is whether superior memory is an artifact of sport-specific training or conversely, that people with better memory are more inclined to participate in high level sports. As the ‘limited transfer hypothesis’ (Charbris & Simons, 2010) is commonly accepted in literature, indicating that cognitive abilities such as spatial memory are not transferable to contexts outside the specific expertise of the athlete the latter explanation is more likely. 4.3.2. Fluid Reasoning and visual processing (WASI Block Design and Matrix Reasoning) The two subtests of the WASI utilized in the present study tap various facets of intelligence, including: visual information processing, spatial ability and fluid reasoning. Fluid reasoning refers to the use of mental operations to solve novel problems, for example, extrapolating, transforming and classifying (Primi, Ferrao, & Almeida, 2010). In sport, these cognitive abilities are considered essential to optimal performance (Voss, Kramer, Basak, Prakash, & Roberts, 2010). A study by Kasahara et al.

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(2008) demonstrated that elite rugby players score higher on Block Design task than non-athletes. As would be expected because of their impairment, the majority of athletes with ID in our investigation, performed in the lowest ranges of fluid reasoning measures. High correlations between fluid reasoning and the g factor have been commonly reported (Barkl, Porter, & Ginns, 2012). Apparently the underlying impairment accounts for more to the variance in fluid reasoning ability than the sport expertise of the individual. 4.3.3. Executive functioning (Tower of London) Vestberg et al. (2012) recently showed that executive functioning (EF) has the potential to predict success in soccer. More specifically, they used the subtest design fluency, a standardized test that measures multiprocessing such as creativity, response inhibition, and cognitive flexibility. Vestberg et al. used design fluency for their main analysis, because they believed it was the major EF factor relevant in soccer (fast creativity and problem solving ability) and at the same time it did not contain a verbal aspect that may be affected by education/schooling. For the purpose of our investigation, exactly the same line of reasoning was applied for incorporating a test of EF. We used a computerized version of the Tower of London for this purpose because it is a relatively simple, non-verbal task. The TOL subtest requires the ability to conceptualize change (anticipate or look ahead), generate and select alternatives, and sustain attention (Culbertson & Zillmer, 1998). This subtest simulates the executive chain of decision making in a way that applies in a live sport situation. Only a few previous studies have conducted TOL tests in the population of persons with ID. Shallice (1982) examined the validity of an adapted TOL version and the results indicated it was a valid test to use in this population. Bishop, Aamodt-Leeper, Creswell, McGurk, and Skuse (2001) found impaired functioning on Tower tasks in various clinical groups, including people with ID. As such, TOL is generally described as higher-order planning task because successful completion requires the participant to look ahead and solve the problem cognitively before actually moving the balls or disks. Persons with ID generally do not demonstrate overt behaviors that are considered ‘planful’ (i.e., implementing systematic move sequences) but deploy more of a trial and error approach (Hartman, Houwen, Scherder, & Visscher, 2010). 4.4. Strengths and limitations Our study is one of the first of its kind situated in a sport context and a major strength is the unique sample of 468 elite athletes with intellectual impairments from around the world. Given the field based nature of the work, the study is not without its limitations. While standardized to the fullest extent possible, testing conditions were not identical because the athletes were tested at various sanctioned competition events, in different locations, and by different testers. Detailed data on training history and volume data were not available for all ID-athletes and as such, status as elite athletes was attributed to their inclusion in international competition (e.g., world championship or INAS Global Games), which is the preeminent stage for these athletes. From previous sport-specific investigations we retrieved average data on training history and training volume from the ID-athletes coaches. The comparison sample was selected according to these ranges on training volume. This was necessary as ID-athletes and sport opportunities are not presently on the same level as, world class non-ID standards—although since reinclusion of ID-athletes in the 2012 Paralympic Games, the opportunities and performance levels have improved significantly (Einarsson, Cisic, Van Biesen, & Daly, 2015; Van Biesen, Mactavish, Pattyn, & Vanlandewijck, 2012). Although the subtests selected for this study were chosen on the basis of their relevance to sport, they assess cognitive abilities in a generic (=non sport-specific) way and are, therefore, not specifically trainable. Assuming that elite athletes require specific cognitive abilities to make it to the top, the subtests used in this study might have the potential to help IDathletes to find a sport they can excel in. For the speed-based abilities such as reaction time, the question remains whether the fast reaction times of some ID-athletes is a product of their experience or an artifact of individuals with naturally high processing speed being attracted to, and excelling in sport. The final drawback of this study (and many other studies involving large samples of individuals with ID) is the large inter-individual variability causing the violation of the assumption of homogeneity of variance. It is important to take this into account when interpreting the results. 4.5. Future perspectives The outcomes of this study provided new insights about the cognitive profiles of well-trained athletes with ID, how they compare to a non-ID reference group, and the usefulness of the cognitive test. Future research is needed to enhance understanding of variations in performance and the reasons for those. Longitudinal data on the trainability of cognitive abilities within a sport context also is needed. Finally, integrating the cognitive data, sport specific proficiency data, and a more comprehensive assessment of training volume is required as we seek greater clarity about the impact of impaired cognitive functioning on sport specific proficiency. 5. Conclusion This study examined the cognitive profiles of young well-trained athletes with and without ID with the intention of identifying the relevant cognitive determinants of sport proficiency. Clearly evident from the results, ID-athletes score

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significantly below their non-ID counterparts on cognitive ability measures relevant to sport (e.g., Fluid Reasoning, Shortterm Memory, Reaction and Decision Speed, Visual Processing) and executive functioning. As cognitive abilities and executive functioning contribute to elite sport proficiency, the performance of ID-athletes will likely remain affected by their underlying impairment, no matter how hard they train. Coaches need clear guidelines on how to effectively navigate these disadvantages to optimize athlete training and development. The findings of this study also substantiate that the ID class fits within the IPC Classification Code, and the Paralympic Games. Acknowledgements The authors thank INAS and IPC for their support to conduct this study, and all members of the INAS-IPC Research Group (Prof. Jan Burns from the University of Canterbury, Dr. Peter Van de Vliet, medical and scientific director of IPC, and Nick Parr, executive INAS member) for their valuable input. 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