- Email: [email protected]
Variability of kinematic graphomotor fluency in adults with ADHD
Human Movement Science 38 (2014) 331–342
Contents lists available at ScienceDirect
Human Movement Science journal homepage: www.elsevier.com/locate/humov
Variability of kinematic graphomotor fluency in adults with ADHD Thomas A. Duda, Joseph E. Casey ⇑, Nancy McNevin University of Windsor, 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada
a r t i c l e
i n f o
Article history: Available online 19 November 2014 PsycINFO classification: 3250 Keywords: Attention deficit hyperactivity disorder (ADHD) Kinematic analysis Handwriting
a b s t r a c t Although graphomotor differences and variability of performance have been observed in children with attention deficit hyperactivity disorder (ADHD), no study has investigated whether this variability manifests in the kinematic graphomotor domain in adults with ADHD. Fourteen ADHD and 20 control participants wrote a novel grapheme and common word on a digitizing tablet 30 times each, with ADHD participants counterbalanced on and off stimulant medication. Variability of graphomotor fluency was significantly greater in ADHD versus control participants only in the novel writing task, both on, F(1, 31) = 5.988, p = .020, and off stimulant medication, F(1, 32) = 8.789, p = .006. Results suggest that motor control differences in ADHD are not limited to childhood and extend into adulthood. Given sufficient additional research, variability of kinematic graphomotor fluency may increase the sensitivity/specificity of differential diagnoses and/or represent a biomarker for ADHD. Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized primarily by symptoms of inattention and/or a combination of hyperactivity and impulsivity (Barkley, 2006). Although the etiology of ADHD is complex in nature, research implicates central nervous system differences as important determinants of pathology. Widespread reductions of ⇑ Corresponding author. Tel.: +1 519 253 3000x2220. E-mail address: [email protected] (J.E. Casey). http://dx.doi.org/10.1016/j.humov.2014.07.006 0167-9457/Ó 2014 Elsevier B.V. All rights reserved.
332
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
cortical gray matter volume, including higher-order association cortices and the prefrontal cortex, and more circumscribed reductions of subcortical nuclei (e.g., basal ganglia and anterior cingulate cortex) and cerebellar volumes have been identified in both children and adults with ADHD, (Amico, Stauber, Koutsouleris, & Frodi, 2010; Batty et al., 2010; Castellanos, Giedd, Marsh, & Hamburger, 1996; Castellanos et al., 2001; Durston, Hulshoff Pol, Schnack, Steenhuis, et al., 2004; Mackie et al., 2007; McAlonan et al., 2007; Narr et al., 2009; Romanos et al., 2010; Seidman et al., 2011; Shaw et al., 2006). Although global reduction in white matter volume has not been consistently documented within the ADHD population (Amico, Stauber, Koutsouleris, & Frodi, 2010; Durston et al., 2004; Narr et al., 2009), reduction in white matter volume in specific pathways of the cerebrum has been more consistently documented, such as reduced white matter tract volume of the corpus callosum in general (Hynd, Semrud-Clikeman, Lorys, & Novey, 1991; McAlonan et al., 2007) and the splenium of the corpus callosum in particular (Semrud-Clikeman, Filipek, Biederman, & Steingard, 1994). Other studies suggest compromised structural integrity of the superior longitudinal fasciculus and anterior corona radiata in children and adults with ADHD based on measures of fractional anisotropy (FA), mean diffusivity (MD), and apparent diffusion coefficient (ADC) (Konrad & Eickhoff, 2010; Liston, Cohen, Teslovich, Levenson, & Casey, 2011). Although relationships between ADHD symptomatology and structural abnormalities can only be inferred, functional neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), have provided additional evidence of abnormal functioning in cerebral structures thought to subserve abilities related to motor control and attention – abilities that are impaired in those with ADHD (Brossard-Racine, Majnemer, & Shevell, 2011; Seidman et al., 2006; Swanson, Castellanos, Murias, LaHoste, & Kennedy, 1998). Compared with unaffected children, children with ADHD show abnormal patterns of activation (i.e., hypoactivation) in the prefrontal cortex, basal ganglia, and cerebellum when performing tasks related to attention, inhibition, motor control, and executive function (Bush et al., 1999; Durston et al., 2003; Posner et al., 2011; Rubia et al., 1999; Teicher et al., 2000; Vaidya et al., 1998; Yeo et al., 2003). Atypical connectivity and maturation of the brain’s default mode network (see Buckner, Andrews-Hanna, & Schacter, 2008) have also been identified in youth with ADHD, suggesting that ‘‘the consolidation of this network over development may play a central role in the pathophysiology of ADHD’’ (Fair et al., 2010, p. 1088). Differences in cerebral activation also appear to persist into adulthood. Compared with neurotypical individuals, medication naive adults diagnosed with ADHD in childhood who continued to demonstrate symptomatology into adulthood were found to have reduced activation in the orbital frontal cortex, medial frontal cortex, and striatum (i.e., basal ganglia) during tasks requiring inhibition, as well as reduced activation in the lateral inferior and dorsolateral prefrontal cortices during tasks of working memory and attention (Cubillo, Halari, Giampietro, Taylor, & Rubia, 2011). Taken together and noting the highly interconnected nature of the human brain, the pattern of deficits observed in ADHD likely cannot be ascribed to any one particular structure or classification of neural tissue. Rather, dysfunction appears to arise from several functional neural networks responsible for motor control, attention, and other cognitive functions. Additional characteristics – although not diagnostic of ADHD – have been consistently documented in those with ADHD. Specific characteristics include variability of task performance and motor skill impairments, such as poor handwriting, which are often under-treated in this population (Fliers et al., 2009). Domains in which greater variability of behavioral and task performance have been demonstrated include emotional expression (i.e., emotional lability; Barkley & Fischer, 2010); qualitative and quantitative handwriting production, including writing size (Langmaid, Papadopoulos, Johnson, Phillips, & Rinehart, 2012; Rosenblum, Epsztein, & Josman, 2008); in-phase bimanual coordination (Klimkeit, Sheppard, Lee, & Bradshaw, 2004); motor force output (Pereira, Eliasson, & Forssberg, 2000); and fine motor skill movements (Pitcher, Piek, & Barrett, 2002). Although it is still unclear whether or not developmental motor milestones are generally delayed in children with ADHD (Barkley, 2006), the pervasive nature of motor difficulties that are observed in this population is highlighted by significant comorbidity with Developmental Coordination Disorder (DCD) and evidence of a shared genetic component between the two disorders (Fliers, Vermeulen, et al., 2009; Kadesjö & Gillberg, 2001; Piek, Pitcher, & Hay, 1999). Some research also suggests that motor performance is worse in children with comorbid ADHD and DCD than in children with ADHD alone (Lee, Chen, & Tsai, 2013; Pitcher et al., 2002). In contrast, research investigating comorbid
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
333
DCD in adult ADHD is limited. Regardless of the presence of DCD, it is clear that those diagnosed with ADHD demonstrate motor impairments more frequently than the general population (BrossardRacine, Majnemer, & Shevell, 2011), even in adulthood (Stray et al., 2013). Examples of motor impairments found in those diagnosed with ADHD include poor handwriting (Brossard-Racine, Majnemer, Shevell, Snider, & Belanger, 2011); decreased speed and accuracy of complex fine and tactual motor performance (Meyer & Sagvolden, 2006); and deficits in balance, manual dexterity, coordination, and fine and gross motor skills (Piek et al., 1999). ‘‘Moderate’’ and statistically significant correlations between degree of ADHD symptomatology and degree of motor sequelae have also been documented (Langmaid et al., 2012; Rommelse et al., 2009), which provide additional support for the notion that individually and in combination, both motor control dysfunction and variability in task performance could be considered primary deficits in the ADHD population (Barkley, 2006). Handwriting is an important motor skill that is also relevant to the academic success of children. The volitional control of handwriting can be thought of as a complex process involving the integration of ‘‘cognitive, psychomotor, and biophysical processes’’ (van Galen, 1991, p. 165) that is organized hierarchically and in parallel (Plamondon, 1995) to produce meaningful visual–spatial output. Using a motor program metaphor (e.g., van Galen, 1991), graphomotor processes(i.e., handwriting) are thought to begin with the retrieval of a high-level representation of the desired motor output, which is followed by a conversion of this representation into motor control ‘‘commands,’’ to finally end with the neuromuscular system responding in the desired manner (Plamondon, Yu, Stelmach, & Clement, 1991) as modified by ‘‘visual and/or kinesthetic feedback’’ (Dooijes, 1983, p. 104). Central nervous system structures likely involved in graphomotor processes include the primary motor cortex, premotor cortex, supplemental motor area, basal ganglia, cerebellum, and spinal cord (Plamondon, 1995). As outlined above, many of these same structures involved in graphomotor output have evidenced structural and functional abnormalities in those with ADHD. In a review of the literature investigating the handwriting skills of children diagnosed with ADHD, Brossard-Racine, Majnemer, Shevell, and Snider (2008) concluded that the handwriting of individuals in this population can be characterized as impaired, often illegible, and less organized than the handwriting of control children, which in turn results in low academic achievement. Poor qualitative writing observed in this population does not appear to be related to purely visual-perception, visualmotor integration, or linguistic difficulties (Adi-Japha et al., 2007; Marcotte & Stern, 1997). Rather, poor performance likely involves a combination of several processes (Brossard-Racine et al., 2008), including dysfunction in basic parameter setting, such as regulation of force, speed, and size of graphomotor movements (van Galen, 1991); visual-motor integration (Shen, Lee, & Chen, 2012); motor control; and timing aspects of handwriting (Adi-Japha et al., 2007; Schoemaker, Ketelaars, van Zonneveld, Minderaa, & Mulder, 2005). One promising method used to investigate graphomotor functioning in ADHD is kinematic analysis, which has historically involved the use of digitizing tablet technology. Kinematic analysis involves the quantification of ‘‘time changes of position, velocity, and acceleration’’ (Viviani & Terzuolo, 1982, p. 431) and allows one to make inferences about the cognitive, psychomotor, and biophysical processes underlying graphomotor function. Poor qualitative performance typically improves after taking prescribed dosages of stimulant medication (Tucha & Lange, 2001; Whalen, Henker, & Finck, 1981). Interestingly, kinematic analyses assessing process-related aspects of handwriting indicate that the handwriting produced by children diagnosed with ADHD is more dysfluent (i.e., objectively poorer) and appears less automatized when taking stimulant medication compared to when they are not, and when compared to controls (Flapper, Houwen, & Schoemaker, 2006; Tucha & Lange, 2001, 2004, 2005). This pattern of fluency and dysfluency related to medication status, however, has not been observed in adults diagnosed with ADHD under similar conditions (Tucha & Lange, 2004). Further, these studies demonstrated that while not taking stimulant medication, kinematic measures of mean graphomotor fluency in affected children were not significantly different from those of non-ADHD controls. These findings do not appear to be due to a direct effect of medication, as fluent movements can be elicited from children with ADHD taking stimulant medication (Tucha & Lange, 2004). Instead, this decreased fluency and automaticity may be the result of a secondary effect resulting from enhanced attention, greater cognitive control (Tucha & Lange, 2004; Tucha, Paul, & Lange, 2003), or possibly other cognitive, motor, or psychomotor processes influenced by stimulant medication.
334
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
1.1. Rationale and hypotheses To date, few studies have investigated the kinematic aspects of writing in adults diagnosed with ADHD. Moreover, no study has examined whether variability of performance manifests in the graphomotor fluency domain or has compared novel versus putatively automatized graphomotor processes in adults with ADHD. Using a digitizing tablet to capture kinematic aspects of handwriting, the present study sought to determine whether the variability of performance that has been observed in other domains in those diagnosed with ADHD manifests within kinematic variables of graphomotor fluency during the execution of automatized and novel graphomotor tasks. Noting that variability of task performance has been demonstrated in several areas in those diagnosed with ADHD, including motor skills, it is hypothesized that greater variability of kinematic graphomotor fluency will be observed in adults diagnosed with ADHD when compared to neurotypical adults, regardless of medication status or writing task type (i.e., automatized versus novel graphomotor output). Additionally, although no a priori hypothesis is salient with regard to the effects of novelty on variability measures in those diagnosed with ADHD, it could be speculated that an interaction will be observed in which the fluency variability of ADHD participants will be differentially affected by the novel graphomotor task compared to those without ADHD. Should statistically and practically significant differences become evident (i.e., differences of medium to large effect sizes), this would be the first study utilizing kinematic analysis to explicitly demonstrate variability of performance within the kinematic graphomotor fluency domain in adults diagnosed with ADHD. Significant results indicating greater variability in kinematic graphomotor fluency would also add to the current literature indicating that ADHD is not simply a disorder of childhood, but rather, a disorder in which specific motor control differences extend into adulthood. Further, significant findings would support conducting future research into the use of digitizing technology as an objective diagnostic and descriptive tool within the ADHD population. This in turn may enhance the specificity and/or sensitivity of current assessment techniques utilized in differential diagnoses of ADHD and potentially represent a biomarker for the disorder. 2. Method 2.1. Participants Forty-five adult participants ranging in age from 18 to 54 years were recruited through a postsecondary institution’s Student Disabilities Services office (n = 41) and the private practice of a local physician (n = 4). One control participant requested that their data be removed from the study and 10 additional control participants were selected for removal using a random number generator (RANDBETWEEN function in Microsoft Excel) to create relatively equal group sizes for data analyses, resulting in a net of 34 total participants, 20 control participants, and 14 participants diagnosed with ADHD. Although the sample was smaller than ideal based on power analysis (a = .05, [1 – b] = .80) utilizing G⁄Power software (Buchner, Erdfelder, Faul, & Lang, 2009), statistically significant findings have been demonstrated with similarly small sample sizes in prior studies investigating kinematic graphomotor functioning in those with ADHD. Participants included only those with normal or corrected to normal vision and those who did not have an existing condition that would negatively affect graphomotor performance. ADHD participants included only those who were diagnosed with ADHD by a community practitioner (psychologist or physician) and currently taking prescribed dosages of stimulant medication for the treatment of their ADHD symptoms (Adderall n = 1, Concerta n = 3, Dexedrine n = 1, Ritalin n = 2, Vyvanse n = 7). 2.2. Materials and apparatus 2.2.1. Demographics, ADHD symptomatology, and IQ Participant demographic information was collected via an in person interview. To quantify ADHD symptomatology, all participants completed the Barkley Adult ADHD Rating Scale-IV (BAARS-IV; Barkley, 2011), which is a self-report questionnaire designed to screen current and/or childhood ADHD
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
335
symptoms in adults between the ages of 18 and 81 years. Scales utilized in this study included Current Total ADHD score and Current Attention score. An estimate of IQ was derived using four subtests (Block Design, Vocabulary, Arithmetic, and Coding) of the Wechsler adult intelligence scale – fourth edition (WAIS – IV; Wechsler, 2008) based on the best combination of short form reliability and validity coefficients (.953 and .940, respectively; see Sattler & Ryan, 2009). 2.2.2. Kinematic analysis and digitizing tablet A WACOM Cintiq 21UX digitizing tablet was used to record the handwriting movements of participants. The digitizing tablet has an active display area of 1700 by 12.7500 and spatial resolution of 5080 lines per inch. A non-inking pen was used to record handwriting movements on the display. MovAlyzeR software (NeuroScript, 2009) was utilized to quantify handwriting movements with a maximum sampling rate of 200 Hz and x–y coordinates were low-pass filtered at 12 Hz. Handwriting movements were broken down by MovAlyzeR software into strokes using interpolated vertical velocity zero crossings. The per trial kinematic variable derived to indicate graphomotor fluency was Normalized Jerk (NJ). NJ is a measure of writing smoothness and fluency. High NJ scores indicate dysfluent movement and low NJ scores indicate smoother, fluent, and more automatized movement (Teulings, Contreras-Vidal, Stelmach, & Adler, 1997; Yan, Rountree, Massman, Doody, & Li, 2008). Although similar to dysfluency measures used in other studies (e.g., Flapper et al., 2006; Schoemaker et al., 2005), NJ has the advantage of allowing the comparison of words or symbols of varying size and movement durations because it is normalized (Teulings et al., 1997). The dependent variable (DV) of interest was the variability of graphomotor smoothness and fluency, which was operationalized as the standard deviation of NJ for the last 20 trials of each automatized and novel graphomotor task. NJ data from the first 10 trials of each graphomotor task were not used in the analysis to reduce the effects (error) of orienting to the task. 2.3. Procedures After obtaining informed consent, demographic information, and an estimate of IQ, each participant (1) signed their name on the digitizing tablet 10 times as a means to become familiar with the digitizing tablet and pen, (2) wrote the word ‘‘hello’’ in lower-case using cursive handwriting on the digitizing tablet 30 times (representing the automatized graphomotor task), and (3) wrote a novel symbol on the digitizing tablet 30 times (representing the novel graphomotor task). See Fig. 1 for a scaled version of the novel symbol. Samples of the word ‘‘hello’’ and novel symbol were visible to the participant on a card throughout the graphomotor task. Instructions for all tasks were given orally, with instruction provided visually on the digitizing tablet throughout both writing tasks. All data from control participants were collected in one session. Data obtained from ADHD participants were collected on two occasions, once while the participants were taking prescribed dosages of stimulant medication and a second time after abstaining from prescribed dosages of stimulant medication for 24–48 h (withdrawal of medication time-frame based on product information indicating extremely low mean drug plasma concentrations between 24 and 48 h after taking stimulant medication; U.S. Food & Drug Administration, 2007). The time-frame between test and retest was
Fig. 1. Scaled version of novel symbol.
336
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
approximately one week (M days = 6.75, SD = 0.71). The demographics questionnaire, BAARS-IV, WAIS-IV subtests, and both experimental graphomotor tasks were completed while ADHD participants were taking prescribed dosages of stimulant medication, whereas only the Current Symptoms form of the BAARS-IV questionnaire and both experimental graphomotor tasks were completed while ADHD participants were off of their prescribed stimulant medication. Task administration was counterbalanced so that half of the ADHD participants were taking stimulant medication during their first visit and half of ADHD participants were taking stimulant medication during their second visit. 3. Results All data analyses were performed using IBM SPSS Statistics, Version 21. An alpha level of .05 was used to determine statistical significance. Although Bonferroni correction is often used to manage inflated experiment wise error that occurs because of multiple comparisons, an alpha level of .05 was utilized due to the low risk involved with committing a Type I error, the exploratory nature of this research, and the lack of studies investigating graphomotor function in adults diagnosed with ADHD. Interpretations of effect sizes using x2 and x2 partial were based on Kirk’s (2003) guidelines, such that 0.010–0.058 was interpreted as a small association, 0.059–0.137 as a medium association, and 0.138 or larger as a large association. 3.1. Data analysis of assumptions Prior to hypothesis testing, the data were analyzed to identify the presence of outlier data and determine adherence to the assumptions of ANOVA, repeated measures ANOVA, and mixed design ANOVA. Data were gathered from participants in individual sessions. Combined with the general novelty of the experimental tasks utilized, lack of known organized communication between participants, and the manner in which data were gathered, it is unlikely that participant scores were systematically related based on these factors, thus satisfying the assumption of independence of observations. Data distributions on the variability of graphomotor fluency DV (i.e., standard deviation of NJ over the last 20 trials) for both controls and ADHD participants were non-normal (i.e., leptokurtic and positively skewed) and did not meet the assumption of homogeneity of variance as evidenced by statistically significant results using Levene’s test. To correct these violations and allow interpretation of the data using parametric statistical analyses, the graphomotor variability DV was transformed by calculating the square root of each observation (see Field, 2009). Data transformations and pair-wise deletion of outlier data corrected homogeneity of variance violations and largely corrected normality violations, although some control group non-normality remained. However, ANOVA is said to be robust to violations of normality and assumptions did not appear sufficiently violated to preclude the use of ANOVA. 3.2. Demographics, general intellectual functioning, and ADHD symptomatology A greater proportion of ADHD participants were left-handed (21%) than control participants (5%) and a smaller proportion of ADHD participants were women (25%) compared to control participants (90%). However, kinematic variables have not been shown to be affected by handedness or sex alone (Mergl, Tigges, Schroter, Moller, & Hegerl, 1999). No statistically significant differences were found between control and ADHD groups regarding age or estimated IQ. ADHD participants, however, reported significantly greater current total ADHD symptomatology compared to controls, F(1, 32) = 81.68, p < .001, x2 = .70. See Table 1 for descriptive statistics summarizing information regarding participant demographics, symptomatology, ADHD medication, and general intellectual functioning. 3.3. Kinematic analyses of variability Two, 2 2 factorial mixed design ANOVAs were used to compare the effects of novelty on variability of graphomotor fluency measures of healthy controls versus those diagnosed with ADHD within
337
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342 Table 1 Participant demographic information, estimated IQ, and ADHD symptomatology. ADHDa
Controls n
M
SD
n
Handedness Right Left
19 1
11 3
Sex Women Men
18 2
8 6
Race/ethnicity Asian Black/African/Caribbean Latin American Middle-Eastern White/Caucasian
1 2 1 1 15
1 1 1 1 10
ADHD subtype ADHD-C ADHD-PI ADHD-HI
-
8 4 2
Comorbid diagnosis Learning disability Anxiety disorder Depression
0 0 0
2 2 1
ADHD medication type Stimulant
0
Age (years) Age range (years) Estimated IQb BAARS-IV ADHD score
M
SD
30.80
8.65
96.64 48.93*
12.50 6.220
14 30.42
13.71
18.6–54.1
20.2–46.7 96.10 29.50*
11.11 6.134
a
ADHD = clinical participants diagnosed with ADHD. Estimated IQ = estimate of general intellectual ability using a short form of the Wechsler adult intelligence scale – fourth edition. * Statistically significant difference between groups, a = .05. b
the context of medication status. Task type (i.e., novel versus automatized graphomotor tasks) represented the within-subjects factor, whereas group membership (i.e., ADHD versus control participants) represented the between-subjects factor. A significant group main effect was observed in which ADHD participants demonstrated greater variability of graphomotor fluency overall compared with control participants both on F(1, 31) = 6.672, p = .015, x2 = .147, and off F(1, 31) = 7.258, p = .011, x2 = .159, stimulant medication, regardless of task type. There was also a statistically significant main effect for task type in which graphomotor fluency variability, irrespective of group membership, was greater in both on, F(1, 31) = 65.812, p < .001, x2 partial = .663, and off, F(1, 31) = 87.721, p < .001, x2 partial = .724, stimulant medication conditions. No significant graphomotor task by group interaction effect for variability of graphomotor fluency was observed while ADHD participants were taking stimulant medication, F(1, 31) = 3.337, NS, x2 = .066, whereas a medium and statistically significant graphomotor task by group interaction effect was observed while ADHD participants were off stimulant medication, F(1, 31) = 5.962, p = .021, x2 = .131. Taken together, these results indicate that although both groups demonstrated increased variability of graphomotor fluency when performing the novel versus the automatized writing task, while off medication, ADHD participant graphomotor fluency variability increased significantly more than that of control participant graphomotor fluency variability. Four, One-Way ANOVAs were used as follow-up analyses to identify and quantify the magnitude of potential simple effects that task type had on variability of graphomotor fluency between each group and within the context of medication status. These analyses were conducted noting that main and
338
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342 Mean Graphomotor Variability 8.000 7.000 6.000 5.000 4.000 3.000 2.000 1.000 0.000 Automatized Graphomotor Variability Controls
Novel Graphomotor Variability
ADHD On Rx
ADHD Off Rx
Fig. 2. Mean variability of graphomotor fluency for controls and ADHD participants on and off stimulant medication.
Table 2 Summary of novel and automatized writing fluency variability results over the last 20 trials. Automatized
Controls ADHD On Rxa ADHD Off Rxb a b * **
Novel
Mean
SD
Mean
SD
1.752 2.381 2.117
0.823 1.478 1.075
4.442 6.635* 6.783**
1.814 3.337 2.798
On Rx = ADHD participants on stimulant medication. Off Rx = ADHD participants off stimulant medication. Statistically significant difference vs. controls, p < .05. Statistically significant difference vs. controls, p < .01.
interaction effects provided by the above factorial ANOVAs did not supply this specific information. No significant group differences were found related to graphomotor fluency variability between controls and ADHD participants writing the automatized grapheme, regardless of ADHD participants being on or off stimulant medication (F(1, 31) = 2.469, NS, x2 = .043 and F(1, 31) = 1.216, NS, x2 = .006, respectively). Large and statistically significant group effects were found in the novel graphomotor task in which ADHD participants demonstrated greater variability of graphomotor fluency than controls both on, F(1, 31) = 5.988, p = .020, x2 = .131, and off stimulant medication, F(1, 32) = 8.789, p = .006, x2 = .186. See Fig. 2 for a graphical representation of kinematic data and Table 2 for descriptive statistics of kinematic data.
4. Discussion Utilizing a digitizing tablet and specialized software to quantify kinematic graphomotor fluency performance during putatively automatized and novel writing tasks, the present study sought to determine, within the context of medication status, whether (1) the variability of performance that has been observed in other functional domains also manifests in kinematic variables of graphomotor fluency in adults with ADHD and (2) task novelty differentially affects variability of kinematic graphomotor fluency in this same population. Based on past research indicating greater performance variability in general and greater variability in motor performance in particular, it was hypothesized that greater graphomotor fluency variability would be observed in those diagnosed with ADHD compared to controls regardless of task type (i.e., automatized versus novel) or medication status (i.e., on versus off stimulant medication). Results of the current study demonstrated that the variability of graphomotor fluency in ADHD participants, both on and off stimulant medication, was not significantly different from that of controls
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
339
when performing an automatized graphomotor task (i.e., ‘‘hello’’). These findings suggest that ADHD participants execute well-practiced graphomotor programs with fluency that is as consistent as controls regardless of medication status. However, group differences became evident during the execution of a novel graphomotor task, in which the graphomotor fluency of ADHD participants both on and off stimulant medication was significantly more variable than that of controls. Further, a statistically significant interaction was observed when ADHD participants were off stimulant medication, indicating that despite both groups demonstrating increased variability in graphomotor fluency in the novel versus the automatized task, ADHD participants, when off stimulant medication, elicited even greater graphomotor fluency variability than controls. Although a statistically significant interaction was only observed when ADHD participants were off stimulant medication, the nearly medium effect size combined with the relatively small size of the ADHD group suggests insufficient power to detect a significant interaction when ADHD participants were taking medication. Within the context of medication status, the above significant findings that were observed only during the novel condition have several implications. First, increased graphomotor fluency variability during the execution of the novel writing task may indicate greater difficulties in learning or consolidating new graphomotor programs, noting that inconsistent performance is found in less-well learned processes in general and that putatively automatized graphomotor fluency variability was similar in both groups. This interpretation is consistent with research describing ADHD as a disorder associated with difficulties in verbal and motor learning (Adi-Japha, Fox, & Karni, 2011; Barnes, Howard, Howard, Kenealy, & Vaidya, 2010; Cutting, Koth, Mahone, & Denckla, 2003) and other research identifying associations between performance variability and motor skill development (Klotz, Johnson, Wu, Isaacs, & Gilbert, 2012). In addition, noting that stimulant medication has been shown to improve attention in adults and children with ADHD (Tucha, Mecklinger, Laufkotter, et al., 2006; Tucha, Prell, et al., 2006) and that ADHD participants in this study, as measured by the BAARS-IV Attention score, reported significant improvements in attentional functioning on (M = 19.07, SD = 6.06) compared to off (M = 27.50, SD = 6.94) stimulant medication, F(1, 13) = 20.873, p = .001, x2partial = .587, significantly increased fluency variability during the novel writing task regardless of medication status suggests the presence of deficits or inefficiencies in the motor control system that are separate from attentional functioning. Said another way, despite improved attentional functioning as a result of stimulant medication, ADHD participants continued to demonstrate significantly greater variability in graphomotor fluency when performing a novel graphomotor task. In turn, this relatively greater graphomotor fluency variability could be due to increased neuromotor noise and less effective biomechanical execution when carrying out a new graphomotor program. This interpretation is consistent with previous research demonstrating increased neuromotor noise in those rated as having poor handwriting (van Galen, Portier, Smits-Engelsman, & Schomaker, 1993) and other research suggesting persistent neuromotor noise in poor handwriters over time (Smits-Engelsman & van Galen, 1997). Despite statistically significant findings with relatively robust effect sizes, these data should be interpreted with caution and conclusions held tentatively noting the limitations of the study. First, the current study possesses low statistical power due to small sample size, which was confirmed by post hoc power analyses using G⁄Power software (Buchner et al., 2009). Beyond low statistical power, of greater concern is the study sample’s lack of representativeness based on demographic factors. That is, despite significant findings, the present study is limited in its ability to generalize to the larger adult ADHD population because of small sample size and in turn, lack of representativeness based on age, ethnicity, gender, diagnostic comorbidities (e.g., Developmental Coordination Disorder [DCD]), and ADHD subtype. Regarding the potential effects of comorbidities, this study is limited in that it was unknown if participants had a history of DCD, which is a condition known to affect motor performance. As such, future research should involve larger and more varied samples to increase both statistical power and generalizability. Further, additional studies should be designed to systematically identify the effects of a history of DCD on the graphomotor fluency performance of adults with ADHD and whether or not ADHD participants do in fact automatize graphomotor programs more slowly or demonstrate increased neuromotor noise relative to unaffected persons. Research in this area could involve participants with ADHD (with and without a history of DCD) and controls learning a novel grapheme and subsequently conducting time series or other analyses of kinematic graphomotor
340
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
fluency data to identify differences in graphomotor fluency at the beginning versus the end of learning. As these methodological limitations are addressed and additional research is conducted, consistent findings indicating that variability of graphomotor fluency performance is greater in those with ADHD may support the use of kinematic fluency measures as biomarkers in the identification of ADHD. 5. Conclusions The current study found evidence that the variability of performance observed in other domains in those with ADHD manifests within the domain of kinematic graphomotor fluency in adults with this neurodevelopmental disorder, but only when performing a novel graphomotor task. The results of this study provide (1) further evidence indicating that characteristics of ADHD are not limited to childhood but also extend into adulthood and can be quantified using kinematic analysis of graphomotor fluency and (2) indications that adults with ADHD may automatize graphomotor programs differently than those without ADHD. References Adi-Japha, E., Fox, O., & Karni, A. (2011). Atypical acquisition and atypical expression of memory consolidation gains in a motor skill in young female adults with ADHD. Research in Developmental Disabilities, 32, 1011–1020. Adi-Japha, E., Landau, Y. E., Frenkel, L., Teicher, M., Gross-Tsur, V., & Shalev, R. S. (2007). ADHD and dysgraphia: Underlying mechanisms. Cortex, 43, 700–709. Amico, F., Stauber, J., Koutsouleris, N., & Frodi, T. (2010). Anterior cingulated cortex gray matter abnormalities in adults with attention deficit hyperactivity disorder: A voxel-based morphometry study. Psychiatry Research: Neuroimaging, 191, 31–35. Barkley, R. A. (2006). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (3rd ed.). New York: Guilford Press. Barkley, R. A. (2011). Barkley adult ADHD rating scale-IV (BAARS-IV). New York: Guilford Press. Barkley, R. A., & Fischer, M. (2010). The unique contribution of emotional impulsiveness to impairment in major life activities in hyperactive children as adults. Journal of the American Academy of Child & Adolescent Psychiatry, 49, 503–513. Barnes, K. A., Howard, J. H., Howard, D. V., Kenealy, L., & Vaidya, C. J. (2010). Two forms of implicit learning in childhood ADHD. Developmental Neuropsychology, 35, 494–505. Batty, M. J., Liddle, E. B., Pitiot, A., Toro, R., Groom, M. J., Scerif, G., et al (2010). Cortical gray matter in attention-deficit/ hyperactivity disorder: A structural magnetic resonance imaging study. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 229–238. Brossard-Racine, M., Majnemer, A., & Shevell, M. I. (2011). Exploring the neural mechanisms that underlie motor difficulties in children with attention deficit hyperactivity disorder. Developmental Neurorehabilitation, 14, 101–111. Brossard-Racine, M., Majnemer, A., Shevell, M., & Snider, L. (2008). Handwriting performance in children with attention deficit hyperactivity disorder (ADHD). Journal of Child Neurology, 23, 399–406. Brossard-Racine, M., Majnemer, A., Shevell, M., Snider, L., & Belanger, S. A. (2011). Handwriting capacity in children newly diagnosed with attention deficit hyperactivity disorder. Research in Developmental Disabilities, 32, 2927–2934. Buchner, A., Erdfelder, E., Faul, F., & Lang, A. (2009). G*Power (Version 3.1.2) [Computer program].
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
341
Flapper, B. T., Houwen, S., & Schoemaker, M. M. (2006). Fine motor skills and effects of methylphenidate in children with attention-deficit–hyperactivity disorder and developmental coordination disorder. Developmental Medicine & Child Neurology, 48, 165–169. Fliers, E., Franke, B., Lambregts-Rommelse, N. N., Altink, M. E., Buschgens, C. J., Nijhuis-van der Sanden, M., et al (2009). Undertreatment of motor problems in children with ADHD. Child Adolescent Mental Health, 15, 85–90. Fliers, E., Vermeulen, S., Rijsdijk, F., Altink, M., Buschgens, C., Rommelse, N., et al (2009). ADHD and poor motor performance from a family genetic perspective. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 25–34. Hynd, G. W., Semrud-Clikeman, M., Lorys, A. R., & Novey, E. S. (1991). Corpus callosum morphology in attention deficit– hyperactivity disorder: Morphometric analysis of MRI. Journal of Learning Disabilities, 24, 141–146. Kadesjö, B., & Gillberg, C. (2001). The comorbidity of ADHD in the general population of Swedish school-age children. Journal of Child Psychology and Psychiatry, 42, 487–492. Kirk, R. E. (2003). The importance of effect magnitude. In S. F. Davis (Ed.), Handbook of research methods in experimental psychology (pp. 88–105). Malden, MA: Blackwell Publishing. Klimkeit, E. I., Sheppard, D. M., Lee, P., & Bradshaw, J. L. (2004). Bimanual coordination deficits in attention deficit/hyperactivity disorder (ADHD). Journal of Clinical & Experimental Neuropsychology, 26, 999–1010. Klotz, J. M., Johnson, M. D., Wu, S. W., Isaacs, K. M., & Gilbert, D. L. (2012). Relationship between reaction time variability and motor skill development in ADHD. Child Neuropsychology, 18, 576–585. Konrad, K., & Eickhoff, S. B. (2010). Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Human Brain Mapping, 31, 904–916. Langmaid, R. A., Papadopoulos, N., Johnson, B. P., Phillips, J. G., & Rinehart, N. J. (2012). Handwriting in children with ADHD. Journal of Attention Disorders, 1–7. Lee, I. C., Chen, Y. J., & Tsai, C. L. (2013). Kinematic performance of fine motor control in attention-deficit/hyperactivity disorder: The effects of comorbid developmental coordination disorder and core symptoms. Pediatrics International, 55, 24–29. Liston, C., Cohen, M. M., Teslovich, T., Levenson, D., & Casey, B. J. (2011). Atypical prefrontal connectivity in attention-deficit/ hyperactivity disorder: Pathway to disease or pathological end point? Biological Psychiatry, 69, 1168–1177. Mackie, S., Shaw, P., Lenroot, R., Pierson, R., Greenstein, D. K., Nugent, T., et al (2007). Cerebellar development and clinical outcome in attention deficit hyperactivity disorder. The American Journal of Psychiatry, 164, 647–655. Marcotte, A. C., & Stern, C. (1997). Qualitative analysis of graphomotor output in children with attentional disorders. Child Neuropsychology, 3, 147–153. McAlonan, G. M., Cheung, V., Cheung, C., Chua, S. E., Murphy, D. G. M., Suckling, J., et al (2007). Mapping brain structure in attention deficit–hyperactivity disorder: A voxel-based MRI study of regional gray and white matter volume. Psychiatry Research: Neuroimaging, 154, 171–180. Mergl, R., Tigges, P., Schroter, A., Moller, H.-J., & Hegerl, U. (1999). Digitized analysis of handwriting and drawing movements in healthy subjects: Methods, results, and perspectives. Journal of Neuroscience Methods, 90, 157–169. Meyer, A., & Sagvolden, T. (2006). Fine motor skills in South African children with symptoms of ADHD: Influence of subtype, gender, age, and hand dominance. Behavioral and Brain Functions, 2, 33. NeuroScript. (2009). MovAlyzeR. Tempe, AZ: NeuroScript, LLC. Narr, K. L., Woods, R. P., Lin, J., Kim, J., Phillips, O. R., Del’Homme, M., et al (2009). Widespread cortical thinning is a robust anatomical marker for attention deficit/hyperactivity disorder (ADHD). Journal of the American Academy Of Child & Adolescent Psychiatry, 48, 1014–1022. Pereira, H. S., Eliasson, A.-C., & Forssberg, H. (2000). Detrimental neural control of precision grip lifts in children with ADHD. Developmental Medicine & Child Neurology, 42, 545–553. Piek, J. P., Pitcher, T. M., & Hay, D. A. (1999). Motor coordination and kinaesthesis in boys with attention deficit–hyperactivity disorder. Developmental Medicine & Child Neurology, 41, 159–165. Pitcher, T. M., Piek, J. P., & Barrett, N. C. (2002). Timing and force control in boys with attention deficit hyperactivity disorder: Subtype differences and the effect of comorbid developmental coordination disorder. Human Movement Science, 21, 919–945. Plamondon, R. (1995). A kinematic theory of rapid human movements. Part I: Movement representation and generation. Biological Cybernetics, 72, 295–307. Plamondon, R., Yu, L., Stelmach, G. E., & Clement, B. (1991). On the automatic extraction of biomechanical information from handwriting signals. Systems, Man, and Cybernetics, 21, 90–101. Posner, J., Maia, T. V., Fair, D., Peterson, B. S., Sonuga-Barke, E. J., & Nagel, B. J. (2011). The attenuation of dysfunctional emotional processing with stimulant medication: An fMRI study of adolescents with ADHD. Psychiatry Research: Neuroimaging, 193, 151–160. Romanos, M., Weise, D., Schliesser, M., Schecklmann, M., Loffler, J., Warnke, A., et al (2010). Structural abnormality of the substantia nigra in children with attention-deficit hyperactivity disorder. Journal of Psychiatry Neuroscience, 35, 55–58. Rommelse, N. J., Altink, M. E., Fliers, E. A., Martin, N. C., Buschgens, C. M., Hartman, C., et al (2009). Comorbid problems in ADHD: Degree of association, shared endophenotypes, and formation of distinct subtypes. Implications for a future DSM. Journal of Abnormal Child Psychology: An Official Publication of the International Society for Research in Child and Adolescent Psychopathology, 37, 793–804. Rosenblum, S., Epsztein, L., & Josman, N. (2008). Handwriting performance of children with Attention Deficit Hyperactive Disorders: A pilot study. Physical & Occupational Therapy in Pediatrics, 28, 219–234. Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S. R., Simmons, A., et al (1999). Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: A study with functional MRI. The American Journal of Psychiatry, 156, 891–896. Sattler, J. M., & Ryan, J. J. (2009). Assessment with the WAIS-IV. La Mesa, CA: Sattler. Schoemaker, M. M., Ketelaars, C. J., van Zonneveld, M., Minderaa, R. B., & Mulder, T. (2005). Deficits in motor control processes involved in production of graphic movements of children with attention-deficit–hyperactivity disorder. Developmental Medicine & Child Neurology, 47, 390–395.
342
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
Seidman, L. J., Biederman, J., Liang, L., Valera, E. M., Monuteaux, M. C., Brown, A., et al (2011). Gray matter alternations in adults with attention-deficit/hyperactivity disorder identified by voxel based morphometry. Biological Psychiatry, 69, 857–866. Seidman, L. J., Valera, E. M., Makris, N., Monuteaux, M. C., Boriel, D. L., Kelkar, K., et al (2006). Dorsolateral prefrontal and anterior cingulated cortex volumetric abnormalities in adults with attention-deficit/hyperactivity disorder identified by magnetic resonance imaging. Biological Psychiatry, 60, 1071–1080. Semrud-Clikeman, M., Filipek, P. A., Biederman, J., & Steingard, R. (1994). Attention-deficit hyperactivity disorder: Magnetic resonance imaging morphometric analysis of the corpus callosum. Journal of the American Academy of Child & Adolescent Psychiatry, 33, 875–881. Shaw, P., Lerch, J., Greenstein, D., Sharp, W., Clasen, L., Evans, A., et al (2006). Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Archives of General Psychiatry, 63, 540–549. Shen, I.-H., Lee, T.-Y., & Chen, C.-L. (2012). Handwriting performance and underlying factors in children with attention deficit hyperactivity disorder. Research in Developmental Disabilities, 33, 1301–1309. Smits-Engelsman, B. C. M., & van Galen, G. P. (1997). Dysgraphia in children: Lasting psychomotor deficiency or transient developmental delay? Journal of Experimental Child Psychology, 67, 164–184. Stray, L. L., Kristensen, O., Lomeland, M., Skorstad, M., Stray, T., & Tonnessen, F. E. (2013). Motor regulation problems and pain in adults diagnosed with ADHD. Behavioral and Brain Functions, 8, 1–10. Swanson, J., Castellanos, F. X., Murias, M., LaHoste, G., & Kennedy, J. (1998). Cognitive neuroscience of attention deficit hyperactivity disorder and hyperkinetic disorder. Current Opinion in Neurobiology, 8, 263–271. Teicher, M. H., Anderson, C. M., Polcari, A., Glod, C. A., Maas, L. C., & Renshaw, P. F. (2000). Functional deficits in basal ganglia of children with attention-deficit/hyperactivity disorder shown with functional magnetic resonance imaging relaxometry. Nature Medicine, 6, 470–473. Teulings, H.-L., Contreras-Vidal, J. L., Stelmach, G. E., & Adler, C. H. (1997). Parkinsonism reduces coordination of fingers, wrist, and arm in fine motor control. Experimental Neurology, 146, 159–170. Tucha, O., & Lange, K. W. (2001). Effects of methylphenidate on kinematic aspects of handwriting in hyperactive boys. Journal of Abnormal Child Psychology, 29, 351–356. Tucha, O., & Lange, K. (2004). Handwriting and attention in children and adults with attention deficit hyperactivity disorder. Motor Control, 8, 461–471. Tucha, O., & Lange, K. (2005). The effect of conscious control on handwriting in children with attention deficit hyperactivity disorder. Journal of Attention Disorders, 9, 323–332. Tucha, O., Mecklinger, L., Laufkotter, R., Klein, H. E., Walitza, S., & Lange, K. W. (2006). Methylphenidate-induced improvements of various measures of attention in adults with attention deficit hyperactivity disorder. Journal of Neural Transmission, 113, 1575–1592. Tucha, O., Mecklinger, L., Walitza, S., & Lange, K. W. (2006). Attention and movement execution during handwriting. Human Movement Science, 25, 536–552. Tucha, O., Paul, G. M., & Lange, K. W. (2003). Attentional control of handwriting movements. The International Journal of Forensic Document Examiners, 6, 1–4. Tucha, O., Prell, S., Mecklinger, L., Bormann-Kischkel, C., Kubber, S., Linder, M., et al (2006). Effects of methylphenidate on multiple components of attention in children with attention deficit hyperactivity disorder. Psychopharmacology (Berl), 185, 315–326. U.S. Food and Drug Administration. FDA asks attention-deficit hyperactivity disorder (ADHD) drug manufacturers to develop patient medication guides. Retrieved from. Vaidya, C. J., Austin, G., Kirkorian, G., Ridlehuber, H. W., Desmond, J. E., Glover, G. H., et al (1998). Selective effects of methylphenidate in attention deficit hyperactivity disorder: A functional magnetic resonance study. Proceedings of the National Academy of Sciences of the United States of America, 95, 14494–14499. van Galen, G. P. (1991). Handwriting: Issues for a psychomotor theory. Human Movement Science, 10, 165–191. van Galen, G. P., Portier, S. J., Smits-Engelsman, B. C. M., & Schomaker, L. R. B. (1993). Neuromotor noise and poor handwriting in children. Acta Psychologica, 82, 161–178. Viviani, P., & Terzuolo, C. (1982). Trajectory determines movement dynamics. Neuroscience, 7, 431–437. Wechsler, D. (2008). Wechsler adult intelligence scale – fourth edition. San Antonio, TX: Pearson. Whalen, C. K., Henker, B., & Finck, D. (1981). Medication effects in the classroom: Three naturalistic indicators. Journal of Abnormal Child Psychology: An Official Publication of the International Society for Research in Child and Adolescent Psychopathology, 9, 419–433. Yan, J. H., Rountree, S., Massman, P., Doody, R. S., & Li, H. (2008). Alzheimer’s disease and mild cognitive impairment deteriorate fine movement control. Journal of Psychiatric Research, 42, 1203–1212. Yeo, R. A., Hill, D. E., Campbell, R. A., Vigil, J., Petropoulos, H., Hart, B., et al (2003). Proton magnetic resonance spectroscopy investigation of the right frontal lobe in children with attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 42, 303–310.
Contents lists available at ScienceDirect
Human Movement Science journal homepage: www.elsevier.com/locate/humov
Variability of kinematic graphomotor fluency in adults with ADHD Thomas A. Duda, Joseph E. Casey ⇑, Nancy McNevin University of Windsor, 401 Sunset Avenue, Windsor, Ontario N9B 3P4, Canada
a r t i c l e
i n f o
Article history: Available online 19 November 2014 PsycINFO classification: 3250 Keywords: Attention deficit hyperactivity disorder (ADHD) Kinematic analysis Handwriting
a b s t r a c t Although graphomotor differences and variability of performance have been observed in children with attention deficit hyperactivity disorder (ADHD), no study has investigated whether this variability manifests in the kinematic graphomotor domain in adults with ADHD. Fourteen ADHD and 20 control participants wrote a novel grapheme and common word on a digitizing tablet 30 times each, with ADHD participants counterbalanced on and off stimulant medication. Variability of graphomotor fluency was significantly greater in ADHD versus control participants only in the novel writing task, both on, F(1, 31) = 5.988, p = .020, and off stimulant medication, F(1, 32) = 8.789, p = .006. Results suggest that motor control differences in ADHD are not limited to childhood and extend into adulthood. Given sufficient additional research, variability of kinematic graphomotor fluency may increase the sensitivity/specificity of differential diagnoses and/or represent a biomarker for ADHD. Ó 2014 Elsevier B.V. All rights reserved.
1. Introduction Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized primarily by symptoms of inattention and/or a combination of hyperactivity and impulsivity (Barkley, 2006). Although the etiology of ADHD is complex in nature, research implicates central nervous system differences as important determinants of pathology. Widespread reductions of ⇑ Corresponding author. Tel.: +1 519 253 3000x2220. E-mail address: [email protected] (J.E. Casey). http://dx.doi.org/10.1016/j.humov.2014.07.006 0167-9457/Ó 2014 Elsevier B.V. All rights reserved.
332
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
cortical gray matter volume, including higher-order association cortices and the prefrontal cortex, and more circumscribed reductions of subcortical nuclei (e.g., basal ganglia and anterior cingulate cortex) and cerebellar volumes have been identified in both children and adults with ADHD, (Amico, Stauber, Koutsouleris, & Frodi, 2010; Batty et al., 2010; Castellanos, Giedd, Marsh, & Hamburger, 1996; Castellanos et al., 2001; Durston, Hulshoff Pol, Schnack, Steenhuis, et al., 2004; Mackie et al., 2007; McAlonan et al., 2007; Narr et al., 2009; Romanos et al., 2010; Seidman et al., 2011; Shaw et al., 2006). Although global reduction in white matter volume has not been consistently documented within the ADHD population (Amico, Stauber, Koutsouleris, & Frodi, 2010; Durston et al., 2004; Narr et al., 2009), reduction in white matter volume in specific pathways of the cerebrum has been more consistently documented, such as reduced white matter tract volume of the corpus callosum in general (Hynd, Semrud-Clikeman, Lorys, & Novey, 1991; McAlonan et al., 2007) and the splenium of the corpus callosum in particular (Semrud-Clikeman, Filipek, Biederman, & Steingard, 1994). Other studies suggest compromised structural integrity of the superior longitudinal fasciculus and anterior corona radiata in children and adults with ADHD based on measures of fractional anisotropy (FA), mean diffusivity (MD), and apparent diffusion coefficient (ADC) (Konrad & Eickhoff, 2010; Liston, Cohen, Teslovich, Levenson, & Casey, 2011). Although relationships between ADHD symptomatology and structural abnormalities can only be inferred, functional neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), have provided additional evidence of abnormal functioning in cerebral structures thought to subserve abilities related to motor control and attention – abilities that are impaired in those with ADHD (Brossard-Racine, Majnemer, & Shevell, 2011; Seidman et al., 2006; Swanson, Castellanos, Murias, LaHoste, & Kennedy, 1998). Compared with unaffected children, children with ADHD show abnormal patterns of activation (i.e., hypoactivation) in the prefrontal cortex, basal ganglia, and cerebellum when performing tasks related to attention, inhibition, motor control, and executive function (Bush et al., 1999; Durston et al., 2003; Posner et al., 2011; Rubia et al., 1999; Teicher et al., 2000; Vaidya et al., 1998; Yeo et al., 2003). Atypical connectivity and maturation of the brain’s default mode network (see Buckner, Andrews-Hanna, & Schacter, 2008) have also been identified in youth with ADHD, suggesting that ‘‘the consolidation of this network over development may play a central role in the pathophysiology of ADHD’’ (Fair et al., 2010, p. 1088). Differences in cerebral activation also appear to persist into adulthood. Compared with neurotypical individuals, medication naive adults diagnosed with ADHD in childhood who continued to demonstrate symptomatology into adulthood were found to have reduced activation in the orbital frontal cortex, medial frontal cortex, and striatum (i.e., basal ganglia) during tasks requiring inhibition, as well as reduced activation in the lateral inferior and dorsolateral prefrontal cortices during tasks of working memory and attention (Cubillo, Halari, Giampietro, Taylor, & Rubia, 2011). Taken together and noting the highly interconnected nature of the human brain, the pattern of deficits observed in ADHD likely cannot be ascribed to any one particular structure or classification of neural tissue. Rather, dysfunction appears to arise from several functional neural networks responsible for motor control, attention, and other cognitive functions. Additional characteristics – although not diagnostic of ADHD – have been consistently documented in those with ADHD. Specific characteristics include variability of task performance and motor skill impairments, such as poor handwriting, which are often under-treated in this population (Fliers et al., 2009). Domains in which greater variability of behavioral and task performance have been demonstrated include emotional expression (i.e., emotional lability; Barkley & Fischer, 2010); qualitative and quantitative handwriting production, including writing size (Langmaid, Papadopoulos, Johnson, Phillips, & Rinehart, 2012; Rosenblum, Epsztein, & Josman, 2008); in-phase bimanual coordination (Klimkeit, Sheppard, Lee, & Bradshaw, 2004); motor force output (Pereira, Eliasson, & Forssberg, 2000); and fine motor skill movements (Pitcher, Piek, & Barrett, 2002). Although it is still unclear whether or not developmental motor milestones are generally delayed in children with ADHD (Barkley, 2006), the pervasive nature of motor difficulties that are observed in this population is highlighted by significant comorbidity with Developmental Coordination Disorder (DCD) and evidence of a shared genetic component between the two disorders (Fliers, Vermeulen, et al., 2009; Kadesjö & Gillberg, 2001; Piek, Pitcher, & Hay, 1999). Some research also suggests that motor performance is worse in children with comorbid ADHD and DCD than in children with ADHD alone (Lee, Chen, & Tsai, 2013; Pitcher et al., 2002). In contrast, research investigating comorbid
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
333
DCD in adult ADHD is limited. Regardless of the presence of DCD, it is clear that those diagnosed with ADHD demonstrate motor impairments more frequently than the general population (BrossardRacine, Majnemer, & Shevell, 2011), even in adulthood (Stray et al., 2013). Examples of motor impairments found in those diagnosed with ADHD include poor handwriting (Brossard-Racine, Majnemer, Shevell, Snider, & Belanger, 2011); decreased speed and accuracy of complex fine and tactual motor performance (Meyer & Sagvolden, 2006); and deficits in balance, manual dexterity, coordination, and fine and gross motor skills (Piek et al., 1999). ‘‘Moderate’’ and statistically significant correlations between degree of ADHD symptomatology and degree of motor sequelae have also been documented (Langmaid et al., 2012; Rommelse et al., 2009), which provide additional support for the notion that individually and in combination, both motor control dysfunction and variability in task performance could be considered primary deficits in the ADHD population (Barkley, 2006). Handwriting is an important motor skill that is also relevant to the academic success of children. The volitional control of handwriting can be thought of as a complex process involving the integration of ‘‘cognitive, psychomotor, and biophysical processes’’ (van Galen, 1991, p. 165) that is organized hierarchically and in parallel (Plamondon, 1995) to produce meaningful visual–spatial output. Using a motor program metaphor (e.g., van Galen, 1991), graphomotor processes(i.e., handwriting) are thought to begin with the retrieval of a high-level representation of the desired motor output, which is followed by a conversion of this representation into motor control ‘‘commands,’’ to finally end with the neuromuscular system responding in the desired manner (Plamondon, Yu, Stelmach, & Clement, 1991) as modified by ‘‘visual and/or kinesthetic feedback’’ (Dooijes, 1983, p. 104). Central nervous system structures likely involved in graphomotor processes include the primary motor cortex, premotor cortex, supplemental motor area, basal ganglia, cerebellum, and spinal cord (Plamondon, 1995). As outlined above, many of these same structures involved in graphomotor output have evidenced structural and functional abnormalities in those with ADHD. In a review of the literature investigating the handwriting skills of children diagnosed with ADHD, Brossard-Racine, Majnemer, Shevell, and Snider (2008) concluded that the handwriting of individuals in this population can be characterized as impaired, often illegible, and less organized than the handwriting of control children, which in turn results in low academic achievement. Poor qualitative writing observed in this population does not appear to be related to purely visual-perception, visualmotor integration, or linguistic difficulties (Adi-Japha et al., 2007; Marcotte & Stern, 1997). Rather, poor performance likely involves a combination of several processes (Brossard-Racine et al., 2008), including dysfunction in basic parameter setting, such as regulation of force, speed, and size of graphomotor movements (van Galen, 1991); visual-motor integration (Shen, Lee, & Chen, 2012); motor control; and timing aspects of handwriting (Adi-Japha et al., 2007; Schoemaker, Ketelaars, van Zonneveld, Minderaa, & Mulder, 2005). One promising method used to investigate graphomotor functioning in ADHD is kinematic analysis, which has historically involved the use of digitizing tablet technology. Kinematic analysis involves the quantification of ‘‘time changes of position, velocity, and acceleration’’ (Viviani & Terzuolo, 1982, p. 431) and allows one to make inferences about the cognitive, psychomotor, and biophysical processes underlying graphomotor function. Poor qualitative performance typically improves after taking prescribed dosages of stimulant medication (Tucha & Lange, 2001; Whalen, Henker, & Finck, 1981). Interestingly, kinematic analyses assessing process-related aspects of handwriting indicate that the handwriting produced by children diagnosed with ADHD is more dysfluent (i.e., objectively poorer) and appears less automatized when taking stimulant medication compared to when they are not, and when compared to controls (Flapper, Houwen, & Schoemaker, 2006; Tucha & Lange, 2001, 2004, 2005). This pattern of fluency and dysfluency related to medication status, however, has not been observed in adults diagnosed with ADHD under similar conditions (Tucha & Lange, 2004). Further, these studies demonstrated that while not taking stimulant medication, kinematic measures of mean graphomotor fluency in affected children were not significantly different from those of non-ADHD controls. These findings do not appear to be due to a direct effect of medication, as fluent movements can be elicited from children with ADHD taking stimulant medication (Tucha & Lange, 2004). Instead, this decreased fluency and automaticity may be the result of a secondary effect resulting from enhanced attention, greater cognitive control (Tucha & Lange, 2004; Tucha, Paul, & Lange, 2003), or possibly other cognitive, motor, or psychomotor processes influenced by stimulant medication.
334
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
1.1. Rationale and hypotheses To date, few studies have investigated the kinematic aspects of writing in adults diagnosed with ADHD. Moreover, no study has examined whether variability of performance manifests in the graphomotor fluency domain or has compared novel versus putatively automatized graphomotor processes in adults with ADHD. Using a digitizing tablet to capture kinematic aspects of handwriting, the present study sought to determine whether the variability of performance that has been observed in other domains in those diagnosed with ADHD manifests within kinematic variables of graphomotor fluency during the execution of automatized and novel graphomotor tasks. Noting that variability of task performance has been demonstrated in several areas in those diagnosed with ADHD, including motor skills, it is hypothesized that greater variability of kinematic graphomotor fluency will be observed in adults diagnosed with ADHD when compared to neurotypical adults, regardless of medication status or writing task type (i.e., automatized versus novel graphomotor output). Additionally, although no a priori hypothesis is salient with regard to the effects of novelty on variability measures in those diagnosed with ADHD, it could be speculated that an interaction will be observed in which the fluency variability of ADHD participants will be differentially affected by the novel graphomotor task compared to those without ADHD. Should statistically and practically significant differences become evident (i.e., differences of medium to large effect sizes), this would be the first study utilizing kinematic analysis to explicitly demonstrate variability of performance within the kinematic graphomotor fluency domain in adults diagnosed with ADHD. Significant results indicating greater variability in kinematic graphomotor fluency would also add to the current literature indicating that ADHD is not simply a disorder of childhood, but rather, a disorder in which specific motor control differences extend into adulthood. Further, significant findings would support conducting future research into the use of digitizing technology as an objective diagnostic and descriptive tool within the ADHD population. This in turn may enhance the specificity and/or sensitivity of current assessment techniques utilized in differential diagnoses of ADHD and potentially represent a biomarker for the disorder. 2. Method 2.1. Participants Forty-five adult participants ranging in age from 18 to 54 years were recruited through a postsecondary institution’s Student Disabilities Services office (n = 41) and the private practice of a local physician (n = 4). One control participant requested that their data be removed from the study and 10 additional control participants were selected for removal using a random number generator (RANDBETWEEN function in Microsoft Excel) to create relatively equal group sizes for data analyses, resulting in a net of 34 total participants, 20 control participants, and 14 participants diagnosed with ADHD. Although the sample was smaller than ideal based on power analysis (a = .05, [1 – b] = .80) utilizing G⁄Power software (Buchner, Erdfelder, Faul, & Lang, 2009), statistically significant findings have been demonstrated with similarly small sample sizes in prior studies investigating kinematic graphomotor functioning in those with ADHD. Participants included only those with normal or corrected to normal vision and those who did not have an existing condition that would negatively affect graphomotor performance. ADHD participants included only those who were diagnosed with ADHD by a community practitioner (psychologist or physician) and currently taking prescribed dosages of stimulant medication for the treatment of their ADHD symptoms (Adderall n = 1, Concerta n = 3, Dexedrine n = 1, Ritalin n = 2, Vyvanse n = 7). 2.2. Materials and apparatus 2.2.1. Demographics, ADHD symptomatology, and IQ Participant demographic information was collected via an in person interview. To quantify ADHD symptomatology, all participants completed the Barkley Adult ADHD Rating Scale-IV (BAARS-IV; Barkley, 2011), which is a self-report questionnaire designed to screen current and/or childhood ADHD
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
335
symptoms in adults between the ages of 18 and 81 years. Scales utilized in this study included Current Total ADHD score and Current Attention score. An estimate of IQ was derived using four subtests (Block Design, Vocabulary, Arithmetic, and Coding) of the Wechsler adult intelligence scale – fourth edition (WAIS – IV; Wechsler, 2008) based on the best combination of short form reliability and validity coefficients (.953 and .940, respectively; see Sattler & Ryan, 2009). 2.2.2. Kinematic analysis and digitizing tablet A WACOM Cintiq 21UX digitizing tablet was used to record the handwriting movements of participants. The digitizing tablet has an active display area of 1700 by 12.7500 and spatial resolution of 5080 lines per inch. A non-inking pen was used to record handwriting movements on the display. MovAlyzeR software (NeuroScript, 2009) was utilized to quantify handwriting movements with a maximum sampling rate of 200 Hz and x–y coordinates were low-pass filtered at 12 Hz. Handwriting movements were broken down by MovAlyzeR software into strokes using interpolated vertical velocity zero crossings. The per trial kinematic variable derived to indicate graphomotor fluency was Normalized Jerk (NJ). NJ is a measure of writing smoothness and fluency. High NJ scores indicate dysfluent movement and low NJ scores indicate smoother, fluent, and more automatized movement (Teulings, Contreras-Vidal, Stelmach, & Adler, 1997; Yan, Rountree, Massman, Doody, & Li, 2008). Although similar to dysfluency measures used in other studies (e.g., Flapper et al., 2006; Schoemaker et al., 2005), NJ has the advantage of allowing the comparison of words or symbols of varying size and movement durations because it is normalized (Teulings et al., 1997). The dependent variable (DV) of interest was the variability of graphomotor smoothness and fluency, which was operationalized as the standard deviation of NJ for the last 20 trials of each automatized and novel graphomotor task. NJ data from the first 10 trials of each graphomotor task were not used in the analysis to reduce the effects (error) of orienting to the task. 2.3. Procedures After obtaining informed consent, demographic information, and an estimate of IQ, each participant (1) signed their name on the digitizing tablet 10 times as a means to become familiar with the digitizing tablet and pen, (2) wrote the word ‘‘hello’’ in lower-case using cursive handwriting on the digitizing tablet 30 times (representing the automatized graphomotor task), and (3) wrote a novel symbol on the digitizing tablet 30 times (representing the novel graphomotor task). See Fig. 1 for a scaled version of the novel symbol. Samples of the word ‘‘hello’’ and novel symbol were visible to the participant on a card throughout the graphomotor task. Instructions for all tasks were given orally, with instruction provided visually on the digitizing tablet throughout both writing tasks. All data from control participants were collected in one session. Data obtained from ADHD participants were collected on two occasions, once while the participants were taking prescribed dosages of stimulant medication and a second time after abstaining from prescribed dosages of stimulant medication for 24–48 h (withdrawal of medication time-frame based on product information indicating extremely low mean drug plasma concentrations between 24 and 48 h after taking stimulant medication; U.S. Food & Drug Administration, 2007). The time-frame between test and retest was
Fig. 1. Scaled version of novel symbol.
336
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
approximately one week (M days = 6.75, SD = 0.71). The demographics questionnaire, BAARS-IV, WAIS-IV subtests, and both experimental graphomotor tasks were completed while ADHD participants were taking prescribed dosages of stimulant medication, whereas only the Current Symptoms form of the BAARS-IV questionnaire and both experimental graphomotor tasks were completed while ADHD participants were off of their prescribed stimulant medication. Task administration was counterbalanced so that half of the ADHD participants were taking stimulant medication during their first visit and half of ADHD participants were taking stimulant medication during their second visit. 3. Results All data analyses were performed using IBM SPSS Statistics, Version 21. An alpha level of .05 was used to determine statistical significance. Although Bonferroni correction is often used to manage inflated experiment wise error that occurs because of multiple comparisons, an alpha level of .05 was utilized due to the low risk involved with committing a Type I error, the exploratory nature of this research, and the lack of studies investigating graphomotor function in adults diagnosed with ADHD. Interpretations of effect sizes using x2 and x2 partial were based on Kirk’s (2003) guidelines, such that 0.010–0.058 was interpreted as a small association, 0.059–0.137 as a medium association, and 0.138 or larger as a large association. 3.1. Data analysis of assumptions Prior to hypothesis testing, the data were analyzed to identify the presence of outlier data and determine adherence to the assumptions of ANOVA, repeated measures ANOVA, and mixed design ANOVA. Data were gathered from participants in individual sessions. Combined with the general novelty of the experimental tasks utilized, lack of known organized communication between participants, and the manner in which data were gathered, it is unlikely that participant scores were systematically related based on these factors, thus satisfying the assumption of independence of observations. Data distributions on the variability of graphomotor fluency DV (i.e., standard deviation of NJ over the last 20 trials) for both controls and ADHD participants were non-normal (i.e., leptokurtic and positively skewed) and did not meet the assumption of homogeneity of variance as evidenced by statistically significant results using Levene’s test. To correct these violations and allow interpretation of the data using parametric statistical analyses, the graphomotor variability DV was transformed by calculating the square root of each observation (see Field, 2009). Data transformations and pair-wise deletion of outlier data corrected homogeneity of variance violations and largely corrected normality violations, although some control group non-normality remained. However, ANOVA is said to be robust to violations of normality and assumptions did not appear sufficiently violated to preclude the use of ANOVA. 3.2. Demographics, general intellectual functioning, and ADHD symptomatology A greater proportion of ADHD participants were left-handed (21%) than control participants (5%) and a smaller proportion of ADHD participants were women (25%) compared to control participants (90%). However, kinematic variables have not been shown to be affected by handedness or sex alone (Mergl, Tigges, Schroter, Moller, & Hegerl, 1999). No statistically significant differences were found between control and ADHD groups regarding age or estimated IQ. ADHD participants, however, reported significantly greater current total ADHD symptomatology compared to controls, F(1, 32) = 81.68, p < .001, x2 = .70. See Table 1 for descriptive statistics summarizing information regarding participant demographics, symptomatology, ADHD medication, and general intellectual functioning. 3.3. Kinematic analyses of variability Two, 2 2 factorial mixed design ANOVAs were used to compare the effects of novelty on variability of graphomotor fluency measures of healthy controls versus those diagnosed with ADHD within
337
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342 Table 1 Participant demographic information, estimated IQ, and ADHD symptomatology. ADHDa
Controls n
M
SD
n
Handedness Right Left
19 1
11 3
Sex Women Men
18 2
8 6
Race/ethnicity Asian Black/African/Caribbean Latin American Middle-Eastern White/Caucasian
1 2 1 1 15
1 1 1 1 10
ADHD subtype ADHD-C ADHD-PI ADHD-HI
-
8 4 2
Comorbid diagnosis Learning disability Anxiety disorder Depression
0 0 0
2 2 1
ADHD medication type Stimulant
0
Age (years) Age range (years) Estimated IQb BAARS-IV ADHD score
M
SD
30.80
8.65
96.64 48.93*
12.50 6.220
14 30.42
13.71
18.6–54.1
20.2–46.7 96.10 29.50*
11.11 6.134
a
ADHD = clinical participants diagnosed with ADHD. Estimated IQ = estimate of general intellectual ability using a short form of the Wechsler adult intelligence scale – fourth edition. * Statistically significant difference between groups, a = .05. b
the context of medication status. Task type (i.e., novel versus automatized graphomotor tasks) represented the within-subjects factor, whereas group membership (i.e., ADHD versus control participants) represented the between-subjects factor. A significant group main effect was observed in which ADHD participants demonstrated greater variability of graphomotor fluency overall compared with control participants both on F(1, 31) = 6.672, p = .015, x2 = .147, and off F(1, 31) = 7.258, p = .011, x2 = .159, stimulant medication, regardless of task type. There was also a statistically significant main effect for task type in which graphomotor fluency variability, irrespective of group membership, was greater in both on, F(1, 31) = 65.812, p < .001, x2 partial = .663, and off, F(1, 31) = 87.721, p < .001, x2 partial = .724, stimulant medication conditions. No significant graphomotor task by group interaction effect for variability of graphomotor fluency was observed while ADHD participants were taking stimulant medication, F(1, 31) = 3.337, NS, x2 = .066, whereas a medium and statistically significant graphomotor task by group interaction effect was observed while ADHD participants were off stimulant medication, F(1, 31) = 5.962, p = .021, x2 = .131. Taken together, these results indicate that although both groups demonstrated increased variability of graphomotor fluency when performing the novel versus the automatized writing task, while off medication, ADHD participant graphomotor fluency variability increased significantly more than that of control participant graphomotor fluency variability. Four, One-Way ANOVAs were used as follow-up analyses to identify and quantify the magnitude of potential simple effects that task type had on variability of graphomotor fluency between each group and within the context of medication status. These analyses were conducted noting that main and
338
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342 Mean Graphomotor Variability 8.000 7.000 6.000 5.000 4.000 3.000 2.000 1.000 0.000 Automatized Graphomotor Variability Controls
Novel Graphomotor Variability
ADHD On Rx
ADHD Off Rx
Fig. 2. Mean variability of graphomotor fluency for controls and ADHD participants on and off stimulant medication.
Table 2 Summary of novel and automatized writing fluency variability results over the last 20 trials. Automatized
Controls ADHD On Rxa ADHD Off Rxb a b * **
Novel
Mean
SD
Mean
SD
1.752 2.381 2.117
0.823 1.478 1.075
4.442 6.635* 6.783**
1.814 3.337 2.798
On Rx = ADHD participants on stimulant medication. Off Rx = ADHD participants off stimulant medication. Statistically significant difference vs. controls, p < .05. Statistically significant difference vs. controls, p < .01.
interaction effects provided by the above factorial ANOVAs did not supply this specific information. No significant group differences were found related to graphomotor fluency variability between controls and ADHD participants writing the automatized grapheme, regardless of ADHD participants being on or off stimulant medication (F(1, 31) = 2.469, NS, x2 = .043 and F(1, 31) = 1.216, NS, x2 = .006, respectively). Large and statistically significant group effects were found in the novel graphomotor task in which ADHD participants demonstrated greater variability of graphomotor fluency than controls both on, F(1, 31) = 5.988, p = .020, x2 = .131, and off stimulant medication, F(1, 32) = 8.789, p = .006, x2 = .186. See Fig. 2 for a graphical representation of kinematic data and Table 2 for descriptive statistics of kinematic data.
4. Discussion Utilizing a digitizing tablet and specialized software to quantify kinematic graphomotor fluency performance during putatively automatized and novel writing tasks, the present study sought to determine, within the context of medication status, whether (1) the variability of performance that has been observed in other functional domains also manifests in kinematic variables of graphomotor fluency in adults with ADHD and (2) task novelty differentially affects variability of kinematic graphomotor fluency in this same population. Based on past research indicating greater performance variability in general and greater variability in motor performance in particular, it was hypothesized that greater graphomotor fluency variability would be observed in those diagnosed with ADHD compared to controls regardless of task type (i.e., automatized versus novel) or medication status (i.e., on versus off stimulant medication). Results of the current study demonstrated that the variability of graphomotor fluency in ADHD participants, both on and off stimulant medication, was not significantly different from that of controls
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
339
when performing an automatized graphomotor task (i.e., ‘‘hello’’). These findings suggest that ADHD participants execute well-practiced graphomotor programs with fluency that is as consistent as controls regardless of medication status. However, group differences became evident during the execution of a novel graphomotor task, in which the graphomotor fluency of ADHD participants both on and off stimulant medication was significantly more variable than that of controls. Further, a statistically significant interaction was observed when ADHD participants were off stimulant medication, indicating that despite both groups demonstrating increased variability in graphomotor fluency in the novel versus the automatized task, ADHD participants, when off stimulant medication, elicited even greater graphomotor fluency variability than controls. Although a statistically significant interaction was only observed when ADHD participants were off stimulant medication, the nearly medium effect size combined with the relatively small size of the ADHD group suggests insufficient power to detect a significant interaction when ADHD participants were taking medication. Within the context of medication status, the above significant findings that were observed only during the novel condition have several implications. First, increased graphomotor fluency variability during the execution of the novel writing task may indicate greater difficulties in learning or consolidating new graphomotor programs, noting that inconsistent performance is found in less-well learned processes in general and that putatively automatized graphomotor fluency variability was similar in both groups. This interpretation is consistent with research describing ADHD as a disorder associated with difficulties in verbal and motor learning (Adi-Japha, Fox, & Karni, 2011; Barnes, Howard, Howard, Kenealy, & Vaidya, 2010; Cutting, Koth, Mahone, & Denckla, 2003) and other research identifying associations between performance variability and motor skill development (Klotz, Johnson, Wu, Isaacs, & Gilbert, 2012). In addition, noting that stimulant medication has been shown to improve attention in adults and children with ADHD (Tucha, Mecklinger, Laufkotter, et al., 2006; Tucha, Prell, et al., 2006) and that ADHD participants in this study, as measured by the BAARS-IV Attention score, reported significant improvements in attentional functioning on (M = 19.07, SD = 6.06) compared to off (M = 27.50, SD = 6.94) stimulant medication, F(1, 13) = 20.873, p = .001, x2partial = .587, significantly increased fluency variability during the novel writing task regardless of medication status suggests the presence of deficits or inefficiencies in the motor control system that are separate from attentional functioning. Said another way, despite improved attentional functioning as a result of stimulant medication, ADHD participants continued to demonstrate significantly greater variability in graphomotor fluency when performing a novel graphomotor task. In turn, this relatively greater graphomotor fluency variability could be due to increased neuromotor noise and less effective biomechanical execution when carrying out a new graphomotor program. This interpretation is consistent with previous research demonstrating increased neuromotor noise in those rated as having poor handwriting (van Galen, Portier, Smits-Engelsman, & Schomaker, 1993) and other research suggesting persistent neuromotor noise in poor handwriters over time (Smits-Engelsman & van Galen, 1997). Despite statistically significant findings with relatively robust effect sizes, these data should be interpreted with caution and conclusions held tentatively noting the limitations of the study. First, the current study possesses low statistical power due to small sample size, which was confirmed by post hoc power analyses using G⁄Power software (Buchner et al., 2009). Beyond low statistical power, of greater concern is the study sample’s lack of representativeness based on demographic factors. That is, despite significant findings, the present study is limited in its ability to generalize to the larger adult ADHD population because of small sample size and in turn, lack of representativeness based on age, ethnicity, gender, diagnostic comorbidities (e.g., Developmental Coordination Disorder [DCD]), and ADHD subtype. Regarding the potential effects of comorbidities, this study is limited in that it was unknown if participants had a history of DCD, which is a condition known to affect motor performance. As such, future research should involve larger and more varied samples to increase both statistical power and generalizability. Further, additional studies should be designed to systematically identify the effects of a history of DCD on the graphomotor fluency performance of adults with ADHD and whether or not ADHD participants do in fact automatize graphomotor programs more slowly or demonstrate increased neuromotor noise relative to unaffected persons. Research in this area could involve participants with ADHD (with and without a history of DCD) and controls learning a novel grapheme and subsequently conducting time series or other analyses of kinematic graphomotor
340
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
fluency data to identify differences in graphomotor fluency at the beginning versus the end of learning. As these methodological limitations are addressed and additional research is conducted, consistent findings indicating that variability of graphomotor fluency performance is greater in those with ADHD may support the use of kinematic fluency measures as biomarkers in the identification of ADHD. 5. Conclusions The current study found evidence that the variability of performance observed in other domains in those with ADHD manifests within the domain of kinematic graphomotor fluency in adults with this neurodevelopmental disorder, but only when performing a novel graphomotor task. The results of this study provide (1) further evidence indicating that characteristics of ADHD are not limited to childhood but also extend into adulthood and can be quantified using kinematic analysis of graphomotor fluency and (2) indications that adults with ADHD may automatize graphomotor programs differently than those without ADHD. References Adi-Japha, E., Fox, O., & Karni, A. (2011). Atypical acquisition and atypical expression of memory consolidation gains in a motor skill in young female adults with ADHD. Research in Developmental Disabilities, 32, 1011–1020. Adi-Japha, E., Landau, Y. E., Frenkel, L., Teicher, M., Gross-Tsur, V., & Shalev, R. S. (2007). ADHD and dysgraphia: Underlying mechanisms. Cortex, 43, 700–709. Amico, F., Stauber, J., Koutsouleris, N., & Frodi, T. (2010). Anterior cingulated cortex gray matter abnormalities in adults with attention deficit hyperactivity disorder: A voxel-based morphometry study. Psychiatry Research: Neuroimaging, 191, 31–35. Barkley, R. A. (2006). Attention-deficit hyperactivity disorder: A handbook for diagnosis and treatment (3rd ed.). New York: Guilford Press. Barkley, R. A. (2011). Barkley adult ADHD rating scale-IV (BAARS-IV). New York: Guilford Press. Barkley, R. A., & Fischer, M. (2010). The unique contribution of emotional impulsiveness to impairment in major life activities in hyperactive children as adults. Journal of the American Academy of Child & Adolescent Psychiatry, 49, 503–513. Barnes, K. A., Howard, J. H., Howard, D. V., Kenealy, L., & Vaidya, C. J. (2010). Two forms of implicit learning in childhood ADHD. Developmental Neuropsychology, 35, 494–505. Batty, M. J., Liddle, E. B., Pitiot, A., Toro, R., Groom, M. J., Scerif, G., et al (2010). Cortical gray matter in attention-deficit/ hyperactivity disorder: A structural magnetic resonance imaging study. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 229–238. Brossard-Racine, M., Majnemer, A., & Shevell, M. I. (2011). Exploring the neural mechanisms that underlie motor difficulties in children with attention deficit hyperactivity disorder. Developmental Neurorehabilitation, 14, 101–111. Brossard-Racine, M., Majnemer, A., Shevell, M., & Snider, L. (2008). Handwriting performance in children with attention deficit hyperactivity disorder (ADHD). Journal of Child Neurology, 23, 399–406. Brossard-Racine, M., Majnemer, A., Shevell, M., Snider, L., & Belanger, S. A. (2011). Handwriting capacity in children newly diagnosed with attention deficit hyperactivity disorder. Research in Developmental Disabilities, 32, 2927–2934. Buchner, A., Erdfelder, E., Faul, F., & Lang, A. (2009). G*Power (Version 3.1.2) [Computer program].
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
341
Flapper, B. T., Houwen, S., & Schoemaker, M. M. (2006). Fine motor skills and effects of methylphenidate in children with attention-deficit–hyperactivity disorder and developmental coordination disorder. Developmental Medicine & Child Neurology, 48, 165–169. Fliers, E., Franke, B., Lambregts-Rommelse, N. N., Altink, M. E., Buschgens, C. J., Nijhuis-van der Sanden, M., et al (2009). Undertreatment of motor problems in children with ADHD. Child Adolescent Mental Health, 15, 85–90. Fliers, E., Vermeulen, S., Rijsdijk, F., Altink, M., Buschgens, C., Rommelse, N., et al (2009). ADHD and poor motor performance from a family genetic perspective. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 25–34. Hynd, G. W., Semrud-Clikeman, M., Lorys, A. R., & Novey, E. S. (1991). Corpus callosum morphology in attention deficit– hyperactivity disorder: Morphometric analysis of MRI. Journal of Learning Disabilities, 24, 141–146. Kadesjö, B., & Gillberg, C. (2001). The comorbidity of ADHD in the general population of Swedish school-age children. Journal of Child Psychology and Psychiatry, 42, 487–492. Kirk, R. E. (2003). The importance of effect magnitude. In S. F. Davis (Ed.), Handbook of research methods in experimental psychology (pp. 88–105). Malden, MA: Blackwell Publishing. Klimkeit, E. I., Sheppard, D. M., Lee, P., & Bradshaw, J. L. (2004). Bimanual coordination deficits in attention deficit/hyperactivity disorder (ADHD). Journal of Clinical & Experimental Neuropsychology, 26, 999–1010. Klotz, J. M., Johnson, M. D., Wu, S. W., Isaacs, K. M., & Gilbert, D. L. (2012). Relationship between reaction time variability and motor skill development in ADHD. Child Neuropsychology, 18, 576–585. Konrad, K., & Eickhoff, S. B. (2010). Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Human Brain Mapping, 31, 904–916. Langmaid, R. A., Papadopoulos, N., Johnson, B. P., Phillips, J. G., & Rinehart, N. J. (2012). Handwriting in children with ADHD. Journal of Attention Disorders, 1–7. Lee, I. C., Chen, Y. J., & Tsai, C. L. (2013). Kinematic performance of fine motor control in attention-deficit/hyperactivity disorder: The effects of comorbid developmental coordination disorder and core symptoms. Pediatrics International, 55, 24–29. Liston, C., Cohen, M. M., Teslovich, T., Levenson, D., & Casey, B. J. (2011). Atypical prefrontal connectivity in attention-deficit/ hyperactivity disorder: Pathway to disease or pathological end point? Biological Psychiatry, 69, 1168–1177. Mackie, S., Shaw, P., Lenroot, R., Pierson, R., Greenstein, D. K., Nugent, T., et al (2007). Cerebellar development and clinical outcome in attention deficit hyperactivity disorder. The American Journal of Psychiatry, 164, 647–655. Marcotte, A. C., & Stern, C. (1997). Qualitative analysis of graphomotor output in children with attentional disorders. Child Neuropsychology, 3, 147–153. McAlonan, G. M., Cheung, V., Cheung, C., Chua, S. E., Murphy, D. G. M., Suckling, J., et al (2007). Mapping brain structure in attention deficit–hyperactivity disorder: A voxel-based MRI study of regional gray and white matter volume. Psychiatry Research: Neuroimaging, 154, 171–180. Mergl, R., Tigges, P., Schroter, A., Moller, H.-J., & Hegerl, U. (1999). Digitized analysis of handwriting and drawing movements in healthy subjects: Methods, results, and perspectives. Journal of Neuroscience Methods, 90, 157–169. Meyer, A., & Sagvolden, T. (2006). Fine motor skills in South African children with symptoms of ADHD: Influence of subtype, gender, age, and hand dominance. Behavioral and Brain Functions, 2, 33. NeuroScript. (2009). MovAlyzeR. Tempe, AZ: NeuroScript, LLC. Narr, K. L., Woods, R. P., Lin, J., Kim, J., Phillips, O. R., Del’Homme, M., et al (2009). Widespread cortical thinning is a robust anatomical marker for attention deficit/hyperactivity disorder (ADHD). Journal of the American Academy Of Child & Adolescent Psychiatry, 48, 1014–1022. Pereira, H. S., Eliasson, A.-C., & Forssberg, H. (2000). Detrimental neural control of precision grip lifts in children with ADHD. Developmental Medicine & Child Neurology, 42, 545–553. Piek, J. P., Pitcher, T. M., & Hay, D. A. (1999). Motor coordination and kinaesthesis in boys with attention deficit–hyperactivity disorder. Developmental Medicine & Child Neurology, 41, 159–165. Pitcher, T. M., Piek, J. P., & Barrett, N. C. (2002). Timing and force control in boys with attention deficit hyperactivity disorder: Subtype differences and the effect of comorbid developmental coordination disorder. Human Movement Science, 21, 919–945. Plamondon, R. (1995). A kinematic theory of rapid human movements. Part I: Movement representation and generation. Biological Cybernetics, 72, 295–307. Plamondon, R., Yu, L., Stelmach, G. E., & Clement, B. (1991). On the automatic extraction of biomechanical information from handwriting signals. Systems, Man, and Cybernetics, 21, 90–101. Posner, J., Maia, T. V., Fair, D., Peterson, B. S., Sonuga-Barke, E. J., & Nagel, B. J. (2011). The attenuation of dysfunctional emotional processing with stimulant medication: An fMRI study of adolescents with ADHD. Psychiatry Research: Neuroimaging, 193, 151–160. Romanos, M., Weise, D., Schliesser, M., Schecklmann, M., Loffler, J., Warnke, A., et al (2010). Structural abnormality of the substantia nigra in children with attention-deficit hyperactivity disorder. Journal of Psychiatry Neuroscience, 35, 55–58. Rommelse, N. J., Altink, M. E., Fliers, E. A., Martin, N. C., Buschgens, C. M., Hartman, C., et al (2009). Comorbid problems in ADHD: Degree of association, shared endophenotypes, and formation of distinct subtypes. Implications for a future DSM. Journal of Abnormal Child Psychology: An Official Publication of the International Society for Research in Child and Adolescent Psychopathology, 37, 793–804. Rosenblum, S., Epsztein, L., & Josman, N. (2008). Handwriting performance of children with Attention Deficit Hyperactive Disorders: A pilot study. Physical & Occupational Therapy in Pediatrics, 28, 219–234. Rubia, K., Overmeyer, S., Taylor, E., Brammer, M., Williams, S. R., Simmons, A., et al (1999). Hypofrontality in attention deficit hyperactivity disorder during higher-order motor control: A study with functional MRI. The American Journal of Psychiatry, 156, 891–896. Sattler, J. M., & Ryan, J. J. (2009). Assessment with the WAIS-IV. La Mesa, CA: Sattler. Schoemaker, M. M., Ketelaars, C. J., van Zonneveld, M., Minderaa, R. B., & Mulder, T. (2005). Deficits in motor control processes involved in production of graphic movements of children with attention-deficit–hyperactivity disorder. Developmental Medicine & Child Neurology, 47, 390–395.
342
T.A. Duda et al. / Human Movement Science 38 (2014) 331–342
Seidman, L. J., Biederman, J., Liang, L., Valera, E. M., Monuteaux, M. C., Brown, A., et al (2011). Gray matter alternations in adults with attention-deficit/hyperactivity disorder identified by voxel based morphometry. Biological Psychiatry, 69, 857–866. Seidman, L. J., Valera, E. M., Makris, N., Monuteaux, M. C., Boriel, D. L., Kelkar, K., et al (2006). Dorsolateral prefrontal and anterior cingulated cortex volumetric abnormalities in adults with attention-deficit/hyperactivity disorder identified by magnetic resonance imaging. Biological Psychiatry, 60, 1071–1080. Semrud-Clikeman, M., Filipek, P. A., Biederman, J., & Steingard, R. (1994). Attention-deficit hyperactivity disorder: Magnetic resonance imaging morphometric analysis of the corpus callosum. Journal of the American Academy of Child & Adolescent Psychiatry, 33, 875–881. Shaw, P., Lerch, J., Greenstein, D., Sharp, W., Clasen, L., Evans, A., et al (2006). Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. Archives of General Psychiatry, 63, 540–549. Shen, I.-H., Lee, T.-Y., & Chen, C.-L. (2012). Handwriting performance and underlying factors in children with attention deficit hyperactivity disorder. Research in Developmental Disabilities, 33, 1301–1309. Smits-Engelsman, B. C. M., & van Galen, G. P. (1997). Dysgraphia in children: Lasting psychomotor deficiency or transient developmental delay? Journal of Experimental Child Psychology, 67, 164–184. Stray, L. L., Kristensen, O., Lomeland, M., Skorstad, M., Stray, T., & Tonnessen, F. E. (2013). Motor regulation problems and pain in adults diagnosed with ADHD. Behavioral and Brain Functions, 8, 1–10. Swanson, J., Castellanos, F. X., Murias, M., LaHoste, G., & Kennedy, J. (1998). Cognitive neuroscience of attention deficit hyperactivity disorder and hyperkinetic disorder. Current Opinion in Neurobiology, 8, 263–271. Teicher, M. H., Anderson, C. M., Polcari, A., Glod, C. A., Maas, L. C., & Renshaw, P. F. (2000). Functional deficits in basal ganglia of children with attention-deficit/hyperactivity disorder shown with functional magnetic resonance imaging relaxometry. Nature Medicine, 6, 470–473. Teulings, H.-L., Contreras-Vidal, J. L., Stelmach, G. E., & Adler, C. H. (1997). Parkinsonism reduces coordination of fingers, wrist, and arm in fine motor control. Experimental Neurology, 146, 159–170. Tucha, O., & Lange, K. W. (2001). Effects of methylphenidate on kinematic aspects of handwriting in hyperactive boys. Journal of Abnormal Child Psychology, 29, 351–356. Tucha, O., & Lange, K. (2004). Handwriting and attention in children and adults with attention deficit hyperactivity disorder. Motor Control, 8, 461–471. Tucha, O., & Lange, K. (2005). The effect of conscious control on handwriting in children with attention deficit hyperactivity disorder. Journal of Attention Disorders, 9, 323–332. Tucha, O., Mecklinger, L., Laufkotter, R., Klein, H. E., Walitza, S., & Lange, K. W. (2006). Methylphenidate-induced improvements of various measures of attention in adults with attention deficit hyperactivity disorder. Journal of Neural Transmission, 113, 1575–1592. Tucha, O., Mecklinger, L., Walitza, S., & Lange, K. W. (2006). Attention and movement execution during handwriting. Human Movement Science, 25, 536–552. Tucha, O., Paul, G. M., & Lange, K. W. (2003). Attentional control of handwriting movements. The International Journal of Forensic Document Examiners, 6, 1–4. Tucha, O., Prell, S., Mecklinger, L., Bormann-Kischkel, C., Kubber, S., Linder, M., et al (2006). Effects of methylphenidate on multiple components of attention in children with attention deficit hyperactivity disorder. Psychopharmacology (Berl), 185, 315–326. U.S. Food and Drug Administration. FDA asks attention-deficit hyperactivity disorder (ADHD) drug manufacturers to develop patient medication guides. Retrieved from