Intuos 4

Visuospatial capacity and task-switching ability were assessed by means of the Trail Making Test (TMT) part B (Tombaugh, 2004 (link)), in which subjects had to connect 13 numbers and letters in alternating and ascending order (1, A, 2, B, 3, C, …) as quickly as possible. The TMT was performed with a standardized task sheet fixed on a Wacom Intuos IV digitizing tablet (Wacom Co., Saitama, Japan), which captured trial duration.
The forward and backward parts of the Wechsler Digit Span Test (WDST; Psychological Corporation, San Antonio, USA) were used to assess short-term and working memory. Participants were asked to repeat a series of number strands that increased in length, first in forward and then in reverse order. The sum of correctly reproduced strands from both conditions served as outcome parameter.
Response inhibition, complex attention, and processing speed were examined by means of the Simon Task (Simon and Rudell, 1967 (link)). A green or red square was displayed on either the left or right side of a computer screen using Presentation software (Neurobehavioral Systems).² Subjects were instructed to press the left “Alt” key each time a green square appeared and the right “Alt” key each time a red square appeared, irrespective of the location of the square. For congruent (C) trials, the response key was on the same side as the stimulus, whereas for incongruent (IC) trials, the response key was on the opposite side of the stimulus. Athletes had to respond as quickly and accurately as possible to a total of 80 stimuli (40 C and 40 IC in randomized order). The difference in mean reaction times between C and IC trials (Simon Effect) as well as the proportion of correct responses across trials (Accuracy) were used as outcome variables.
A computerized version of the Stroop Test (Stroop, 1935 (link)) was administered to assess response inhibition, complex attention, and processing speed. Two types of color stimuli were presented on a computer screen and the subjects’ task was to respond to the color (blue, green, red, or yellow) in which each stimulus was printed by pressing the corresponding color-coded key on a computer keyboard. Neutral stimuli (N) were a series of “x” letters (“xxxxx”), whereas incongruent stimuli (IC) consisted of the words blue, green, red, or yellow, each printed in a different color compared to their semantic meaning. Participants were instructed to respond as quickly and accurately as possible to a total of 48 N and 48 IC stimuli that were presented in randomized order. The difference in mean reaction times between N and IC trials (Stroop Effect) as well as the proportion of correct responses across trials (Accuracy) were used as outcome parameters.

Assessment of fine motor control was carried out using a Writing Analysis on a Wacom Intuos IV digitizing tablet and a pressure-sensitive stylus. The first subtask comprised three trials, across which subjects had to write the German sentence “Die Wellen schlagen hoch” (“The waves are surging high”) on a blank sheet of paper fixed upon the tablet. In the second subtask, participants were asked to produce superimposed circles as fast as possible for three seconds across three trials. Although the analysis of handwriting, to date, has not been commonly used to assess sensorimotor deficits following RHI or concussion, writing deficits have been previously reported after brain injury (Faddy et al., 2008 (link); Titchener et al., 2018 (link)). Moreover, we opted for the inclusion of these two subtasks, as handwriting generally constitutes a fine motor skill with a high degree of automatization and both subtasks have been previously identified as highly sensitive measures for the assessment of sensorimotor deficits (Hermsdörfer et al., 2011 (link)). For both subtasks, the average number of up and down strokes per second (Freq) and the mean pressure (Press) exerted onto the tablet by the tip of the stylus were used as performance parameters. Within this context, studies showed that both parameters are very sensitive to detect subtle changes in the fine motor control of handwriting (Garre-Olmo et al., 2017 (link)). Moreover, negative alterations in pen pressure have been reported in individuals suffering from parkinsonism (Saini and Kaur, 2019 (link))—a clinical syndrome commonly linked to RHI in retired contact sport athletes, including soccer players (Mackay et al., 2019 (link)).
Grip Force Control was assessed across two subtasks using a custom-built grip force sensor (71 mm × 57 mm × 22 mm; 180 g). Participants were asked to grasp the device with the tips of the thumb and three fingers of their dominant hand (index-, middle- and ring finger) in opposition. In both subtasks, the grip force exerted on the manipulandum was represented as a vertical bar on a computer screen in front of the subjects. In a visuomotor tracking subtask, athletes had to align the top of the bar to a randomly vertically moving horizontal line by adjusting their grip force accordingly. Five 20-s trials were recorded for which the average root mean square error (RMS) was used as a measure of deviation between actual and target force. In a force change subtask, two stationary horizontal lines (4 N and 8 N) were displayed on the monitor and the task was to move the vertical bar in between these two target lines by increasing and reducing grip force as fast as possible. Participants completed three eight-second trials, whereby the emphasis of the instruction was on speed rather than accuracy. The average frequency of force change (FCFreq) served as outcome parameter.
Subjects’ performance in the 9-Hole-Peg Test (9HPT) was used as a measure of manual dexterity (Mathiowetz et al., 1985 (link)). A Rolyan 9-Hole-Peg Test Kit was centered in front of the participants with the shallow dish on their dominant hand side. Across three trials, the subjects’ task was to place the pegs, one at a time, as quickly as possible into the peg board and subsequently remove them, again one by one, to put them back into the dish. The mean duration across trials was used as outcome measure.
Postural Control was assessed across three different conditions using a Bertec triaxial force plate (Bertec Corp, Columbus, USA) sampling at 600 Hz. Each condition comprised three 30-s trials, in which subjects performed a tandem stance with their dominant leg in front. The three conditions were: (a) eyes closed on a firm surface; (b) eyes open on an Airex^® Balance-Pad (Airex AG, Sins, Switzerland); and (c) eyes open on a firm surface while simultaneously performing a visual letter variant of the 2Back Task. While the first two conditions constitute integral parts of the Balance Error Scoring System (BESS), a commonly administered multi-step test to assess postural control following sport-related head impacts (Azad et al., 2016 (link)), dual-task protocols (c) have been suggested to serve as particularly sensitive means to detect balance deficits as a result of concussion (Kleiner et al., 2018 (link)). Using custom MATLAB routines (MATLAB R2021a, The MathWorks, Natick, USA) center of pressure (CoP) data were processed using a 4th order low-pass Butterworth filter (cut-off: 10 Hz). Across all trials of each condition, the mean displacement velocity of the CoP (CoPV) and the mean distance travelled by the CoP (CoPDist) were calculated to quantify balance performance (Raymakers et al., 2005 (link)). In the dual-task condition (c), players stood on the force plate and were presented with letters on a 17″ computer screen, one at a time, and were instructed to respond to the displayed letter if it matched the one shown two trials previously by clicking the left button of a wireless computer mouse. Twenty letters (four targets, 16 distractors) were displayed to match the duration of the balance assessment. Performance parameters for the 2Back Task were the mean reaction time for target stimuli (2Back RT) and the average proportion of correct responses (Accuracy) across trials (Table 2). Due to technical problems, baseline postural control data was corrupted and could not be used for analysis. Therefore, soccer players’ balance performances during the actual re-test served as baseline values.

Participants completed two test sessions, one before treatment commenced and one after eight weeks of treatment. Participants in the MPH group were first tested just before starting treatment with methylphenidate and a second time on methylphenidate after week 8 of treatment. GM were registered by a digitizing tablet (WACOM INTUOS4, Wacom, Neuss, Germany) with a specific pen containing no ink refill. The current position of the pen on the tablet, velocity, acceleration and axial pen pressure were measured continuously during the writing process. Localization of the tip of the pen was possible with an accuracy of 0.25 mm in both directions (x/y) at a frequency of 200 Hz. For data processing, a commercial software for the analysis of GM was utilized (CSWin [32 ]. Writing conditions were nearly natural because the tablet was constructed to resemble a common desk pad. Children were not instructed to write neatly, accurately, or legibly.
The procedure and each task was explained verbally to the child by the experimenter, and the child was given a single sheet of paper with the instructions for each task lying on the tablet surface to write on it. Children were not able to see the recordings of their drawings/writings in real time.
GM testing comprised six tasks (see Fig. 2). Two tasks involved everyday handwriting. The remaining four tasks involved basic drawing movements in different levels of visual control (eyes open or closed) and automation (dominant or non-dominant hand).