Visuomotor Performance in Collegiate Baseball Players

Test-Retest Reliability of Three Tasks on the Dynavision D2 and Visuomotor Performance in Collegiate Baseball Players

Jarad R. Vollkommer1, Jacob T. Rauch1, Domenic Sestito1, Justin Thiel1, Nicholas Cheesman1, Nicholas Miller1, Renato Barroso2, Eduardo O. De Souza1Department of Health Science and Human Performance, University of Tampa, Tampa, FL, USA

  1. College of Health Sciences, University of Tampa, Tampa, FL
  2. School of Physical Education, University of Campinas, SP, Brazil

 


Test-Retest Reliability of Three Tasks on the Dynavision and Visuomotor Performance in Collegiate Baseball Players

Abstract -The Dynavision ™ D2 Visuomotor Training Device has gained lots of popularity in its application as a vision training device for athletes. Although, this device has been deemed reliable in a recreationally active population, these studies have not been repeated in an athletic population. Therefore, the purpose of this study was to investigate the reliability of three different tasks assessing RT using the Dynavision™ D2 Visuomotor Training Device on college baseball athletes. Eighteen Division 2 baseball athletes (age: 20.83 ± 1.04 years; height: 1.83 ± 0.05 m; mass: 85.06 ± 9.96 kg; body fat %: 18.3 ± 3.75) completed five sessions with at least 48 hours between sessions. During each of the sessions, participants completed three tasks with progressive intensity and difficulty (Reaction Time, Choice A, Choice B). For the Choice Reaction Time test, multiple comparisons revealed that there were significant differences for visual reaction time (p<0.008) as sessions 4th and 5th were significantly different from 1st session. For the Mode A Test, there were significant differences for Reactive total (p<0.0001). Multiple comparisons revealed that the number of hits in sessions 3rd, 4th and 5th was significantly different from 1st session. For the Mode B Test, there were significant differences for Reactive flash total (p<0.0001). Multiple comparisons revealed that the number of hits in sessions 3rd, 4th and 5th was significantly different from 1st session. Our results show that collegiate baseball players need at least 3 sessions on the RT, Mode A, and Mode B test to obtain a stable measurement of RT. However, some of our participants seemed to increase their performance between sessions 4 and 5 on the Mode B test.  The main finding of the current study is that the Dynavision™ D2 is a reliable device for measuring RT in an athletic population


Introduction

Reaction time (RT) can be defined as the interval time between the presentation of a stimulus and initiation of the response (Jain, Bansal, Kumar, & Singh, 2015). In a majority of sports RT is a determinant of sports performance. For example, the slightest differences in RT can determine a win or a loss in sprinting events, as elite sprinters differ by only 1% in their sprint times (Behm, Bambury, Cahill & Power, 2004).

Specifically, in the sport of baseball, RT is a defining characteristic of eye-hand coordination (Clark, Ellis, Bench, Khoury, & Graman, 2012). On average, a fastball reaches home plate in approximately 0.4 seconds (Clark et al., 2012). During this time, the swing takes approximately 0.2 seconds, and it could take as long as 0.03 seconds to decide if a swing should be made or not (Clark et al. 2012). Therefore, the importance of assessing RT has been suggested to be critical for determining baseball performance (Kida, Oda, & Matsumora, 2005).

Previous literature assessed RT by using finger tapping tests, simple reaction time tests, and choice reaction time tests (Wells et al., 2014). The Dynavision™ D2 Visuomotor Training device (Dynavision LLC, West Chester, OH) was developed to assess gross motor skills, enhance neurocognitive ability, improve peripheral awareness and reaction time. Recently, the Dynavision™ D2 has been used to measure RT, and it was deemed reliable in a recreationally active population (Wells et al., 2014). However, despite previous studies addressing the test-retest reliability measuring RT with the Dynavision™ D2 (Klavora, Gaskovski & Forsyth, 1994, 1995; Wells et al., 2014), these studies have not been repeated in an athletic population. When determining the reliability of a RT assessment, it must be reproducible at different levels of visuomotor ability. To the best of our knowledge, no studies have assessed the test-retest reliability of Dynavision™ D2 in Baseball athletes. Therefore, the purpose of this study will be to investigate the reliability of three different tasks assessing RT using the Dynavision™ D2 Visuomotor Training Device on college baseball athletes.


Methods

Experimental approach to the problem

In spite of current suggestion for using Dynavision™ D2 Visuomotor Training Device for athletic population, data on test-retest reliability in baseball athletes are still lacking. In order to assess reliability of the Dynavision™ D2 Visuomotor Training Device on three different tasks, participants reported to the Human Performance Laboratory on five separate occasions with at least 48 hours between sessions. During each of the sessions, participants completed three tasks with progressive intensity and difficulty. Reaction time was measured using the Dynavision™ D2 Visuomotor Training Device (D2; Dynavision International LLC, West Chester, OH). The Dynavision, as well as the tachistoscope, brock string, eyeport, rotary, strobe glasses, and saccades have been used in previous research (Clark et al., 2012). Participants were instructed to stay in an athletic stance with their hands out in front of them. The Dynavision was then raised to the height of the participant so that the LCD screen was set at eye level, and the outer-most ring of lights was within arms reach. After the verbal instruction of the protocol and performance tests, participant completed a few practice trials of each test. Once the practice round was completed, each participant was taken through the first session of testing.

 

Participants

Eighteen male college baseball players volunteered to participate in this study (n = 18; age: 20.83 ± 1.04 years; height: 1.83 ± 0.05 m; mass: 85.06 ± 9.96 kg; body fat %: 18.3 ± 3.75). The research protocol was approved by the University of Tampa Institutional Review Board. Each participant gave their informed consent after reading the details on procedures, risks, and benefits included in the study. All participants were healthy individuals and reported having no vision problems, with the exception of corrective eyewear. Participants were also verbally instructed before each session not to consume any caffeine 5 hours prior to testing.

 

Reaction Time Testing

The first test (Choice Reaction Test; CRT) measured the participant’s visual and motor reaction time (RT) to a visual stimulus with the dominant hand. The test began when the participant placed his hand on the illuminated red “home” button. A single button, the visual stimulus, would light up in one of four locations across the same horizontal plane as the home button. Once the participant saw the visual stimulus, they were instructed to leave the home button and strike the stimulus with the dominant hand and then return back to the home button. Visual RT was measured as the amount of time it took for the participant to identify the stimulus and let go of the home button. Motor RT was measured as the amount of time it took the participant to then strike the stimulus following the initial recognition of the stimulus. Time was measured to the nearest hundredth of a second. Participants were instructed to strike the light as fast as possible. However, if a stimulus was missed, the test was repeated until there was an error free trial. The average visual RT and motor RT were used for statistical analysis.

The second test (Reactive; Mode A) measured the participant’s ability to react to a stimulus as it changed positions on the board. The test began after an initial stimulus would present itself on a random position on the board. The stimulus remained illuminated until the participant struck the stimulus. The stimulus would then appear at another random location on the board. The participant was instructed to successfully identify and strike as many visual stimuli as possible within a 60-second period with both hands. Participants were instructed to keep their eyes directly at the LCD screen throughout the duration of the test to use their peripheral vision, keep their hands raised in front of them and avoid crossing hands over their body. The number of hits and average reaction time per hit were recorded for each participant.

The third test (Reactive with cognitive stress; Mode B) was similar to Mode A in that the participants were required to react to a visual stimulus with both hands as it changed positions on the board. The only difference between Modes A and B was that participants were asked to successfully recite a five-digit number that was presented on the LCD screen. The participants were instructed to identify and strike each stimulus before it changed its position and obtain as many strikes as possible during the test. The visual stimulus remained illuminated for one second before changing its location, requiring the participant to be more reactive to the stimulus. Similar to Mode A, participants were instructed to use their peripheral vision, keep their hands raised in front of them, and avoid crossing hands over the body. The number of hits and average reaction time per hit were recorded for each participant.

 

Statistical Analysis

Data normality and variance homogeneity were confirmed through Shapiro-Wilk and Levine’s tests, respectively. Statistically significant differences across the five sessions were calculated by one-way repeated-measures ANOVA. Whenever a significant F value was obtained, a Bonferroni’s post hoc test adjustment was performed for pairwise comparison purposes. In addition, we presented the mean, upper and lower limits values of the 95% confidence intervals of the absolute differences between sessions (CIdiff). Following, Bland-Altman plots were obtained having on the X-axis the average value between different sessions (i.e. 1st vs 2nd), and on the Y-axis the difference between these same values. In addition, we have presented the bias values (i.e. mean differences between sessions) Finally, the coefficient of variation [(standard deviation/mean) *100] of the respective comparison was presented. The significance level was previously set p < 0.05. All data are presented as means ± standard deviation.


Results

Choice reaction time

Anova for repeated measures results are presented in the figure 1A and 1B. There were significant differences for visual reaction time (p<0.008). Multiple comparisons revealed that sessions 4th and 5th were significantly different from 1st session. (4th vs. 1st – CIdiff: mean -0.036 s, -0.071 to -0.002 s; 5th vs. 1st – CIdiff: mean -0.029 s, -0.060 to -0.001 s). There were no significant differences for motor reaction time (p>0.05).

Mode A

Anova for repeated measures results are presented in the figure 1C and 1D. There were significant differences for Reactive total (p<0.0001). Multiple comparisons revealed that the number of hits in sessions 3rd, 4th and 5th was significantly different from 1st session (3th vs 1st – CIdiff: mean 11.28, 5.19 to 17.36; 4th vs. 1st – CIdiff: mean 10.33, 3.04 to 17.62; 5th vs. 1st – CIdiff: mean 15.00, 6.88 to 23.12). There were significant differences for Reactive average reaction time (p<0.0001). Multiple comparisons revealed that sessions 3rd, 4th and 5th were significantly different from 1st session (3th vs 1st – CIdiff: mean -0.102 s, -0.162 to – 0.041 s; 4th vs. 1st – CIdiff: mean -0.097 s, -0.163 to -0.031 s; 5th vs. 1st – CIdiff: mean -0.128 s, -0.198 to -0.059 s).

Mode B

Anova for repeated measures results are presented in the figure 1E and 1F. There were significant differences for Reactive flash total (p<0.0001). Multiple comparisons revealed that the number of hits in sessions 3rd, 4th and 5th was significantly different from 1st session. (3th vs. 1st – CIdiff: mean 13.06, 1.170 to 24.94; 4th vs. 1st – CIdiff: mean 20.78, 9.766 to 31.79; 5th vs. 1st – CIdiff: mean 25.61, 16.78 to 34.44). There were significant differences for Reactive flash average reaction time (p<0.0001). Multiple comparisons revealed that sessions 2nd, 4th and 5th were significantly different from 1st session. (2th vs. 1st – CIdiff: mean -0.03 s, -0.07101 to -9.979e-5 s; 4th vs. 1st – CIdiff: mean -0.062 s, -0.09790 to -0.02654 s; 5th vs. 1st – CIdiff: mean -0.081 s, -0.118 to -0.045 s). However, there was a strong tendency toward statistical significance  (p=0.051) 3th vs. 1st.

Bland-Altman plots and coefficient of variation are displayed on figure (X). The differences between test sessions decreased across the sessions. In addition, the 95% limits of agreements (i.e. dashed lines) did not become smaller for all the tests, probably due to few for some tests. However, the bias which represents the value determined by one session minus the value determined by the following session decreased considerably when it was compared 1st vs. 2nd against 4th vs. 5th session (Table X)

Variable Comparison Bias C.V
RT visual (s) 1st vs. 2nd 0.017 2.75%
  4th vs. 5th -0.006 1.58%
RT motor (s) 1st vs. 2nd 0.011 3.06%
  4th vs. 5th -0.008 4.08%
Proactive total (no of hits) 1st vs. 2nd -5.66 4.50%
  4th vs. 5th -4.66 3.57%
Proactive AVR RT (s) 1st vs. 2nd 0.04 4.69%
  4th vs. 5th 0.03 3.35%
Reactive flash total (no of hits) 1st vs. 2nd -8.05 8.15%
  4th vs. 5th -4.83 3.83%
Reactive flash AVG time (s) 1st vs. 2nd 0.035 4.05%
  4th vs. 5th 0.019 2.40%

Figure 1 — Reaction time, successful hits on each testing session (m ± sd). ap < 0.05 different from the first session, b p < 0.05 different from second session, c p < 0.05 different from the third session.

Figure 2. Bland-Altman plots of the differences in reaction time and successful hits between the previous and the following testing days (i.e. session 1 – session 2, session 2 – session 3, session 3 – session 4, session 4 – session 5). Dashed lines represent the 95% limits of agreement between the two different days.


Discussion

The main finding of the current study is that the Dynavision™ D2 is a reliable device for measuring RT in an athletic population. Multiple comparisons revealed that individuals needed at least three sessions on the device to get a stable measure of RT on the RT test. These results are in disagreement with previous research (Wells et al., 2014). Wells et al. (2014) demonstrated that a recreationally active population only needed one session to obtain a stable measurement on the same assessment. On the Mode A test, participants needed at least three sessions on the Dynavision™ D2 to get a stable measure in RT and amount of lights hit. These results agree with Wells et al. (2014); however, they found a significant difference between sessions 3 and 4, whereas we did not. While Wells et al. (2014) found a difference between sessions 3 and 4 on the Mode A test, we found a different result in the Mode B test: the final session was greater than sessions 3 and 4. We attribute these results to the complexity and inherent randomness of the test. It is theorized that an individual increases his/her concentration to the task as it becomes more complex (Welford, 1980).

Previous literature has assessed RT by using finger tapping tests, simple reaction time tests, and choice reaction time tests (Wells et al., 2014). However, these assessments were not found to be reliable in measuring RT (Wells et al., 2014). Our results show that collegiate baseball players need more sessions to see stable measures in performance variables in comparison to a recreationally active population. This may be due to differences in skill level and the cognitive complexity (an attribute of sport success) between athletes and the average population. For example, the participants from Wells et al. (2014) had an average visual/motor RT of 0.35/0.21s on the RT test. Our participants had an average visual/motor RT of 0.31/0.17s faster) on the RT test. . For example, the anticipation of ball location and flight path is a key factor in baseball batting success, as well as perceiving the time the ball will reach home plate (Nakamoto & Mori, 2008). Therefore, elite baseball players, having more on-field experience, have a faster RT to baseball-specific stimuli as compared to novice baseball players (Nakamoto & Mori, 2008). Elite baseball players may also see an increase in batting average when training for RT. When players were matched at bats, Classe et al. (1997) showed that RT significantly correlated with batting average. Previous research has also shown that visuomotor training with the Dynavision, as well as other modalities, resulted in an increase in batting average and slugging percentage (Clark et al., 2012).


 Conclusion

Our results show that collegiate baseball players need at least 3 sessions on the RT, Mode A, and Mode B test to obtain a stable measurement of RT. However. Overall, these results show that RT is specific to the nature of the sport of activity that the individual participates in.


Practical Application

Collegiate baseball players need at least three sessions on all three tests (RT, Mode A, Mode B) on the Dynavision™ D2 to obtain a stable measurement of RT. Strength and conditioning coaches must be aware of the athlete’s cognitive level and previous workload in the weight room as this may affect the reliability of the RT assessment (Levitt and Gutin, 2013).


References

Behm, D.G., Bambury, A., Cahill, F., and Power, K. (2004). Effect of Acute Static Stretching on Force, Balance, Reaction Time, and Movement Time. Med. Sci. Sports Exerc., Vol. 36, No. 8, pp. 1397–1402

Clark, J.F., Ellis, J.K., Bench, J., Khoury, J., and Graman, P. (2012). High performance vision training improves batting statistics for University of Cincinnati baseball players. PLoS ONE 7(1),

Classe, J.G., Semes, L.P., Daum, K.M., Nowakowski, R., Alexander, L.J., Wisniewski, J., Beisel, J.A., Mann, K., Rustein, R., Smith, M., Bartolucci, A. (1997). Association between visual reaction time and batting, fielding, and earned run averages among players of the Southern Baseball League. Journal of the American Optometric Association 68(1), 43-49.

Jain, A., Bansal, R., Kumar, A., & Singh, K. (2015). A comparative study of visual and auditory reaction times on the basis of gender and physical activity levels of medical first year students. International Journal of Applied and Basic Medical Research, 5(2), 124–127.

Kida, N., Oda, S., & Matsumura, M. (2005). Intensive baseball practice improves the Go/Nogo reaction time, but not the simple reaction time. Cognitive Brain Research 22(2), 257-264.

Klavora, P., Gaskovski, P., and Forsyth, R. (1994). Test-retest reliability of the Dynavision apparatus. Perceptual and Motor Skills 79, 448-450.

Levitt, S. & Gutin, B. (2013). Multiple choice reaction time and movement time during physical exertion. Research Quarterly for Exercise and Sport 42(4), 405-410.

Nakamoto, H. and Mori., S. (2008). Sport-specific decision-making in a go/no go reaction task: difference among nonathletes and baseball and basketball players. Perceptual and Motor Skills106(1), 163-171

Welford, A. T. (1980). Relationships between reaction time and fatigue, stress, age and sex. Reaction times, 321-354.

Wells, A.J., Hoffman, J.R., Beyer, K.S., Jajtner, A.R., Gonzalez, A.M., Townsend, J.R., Mangine, G.T., Robinson IV, E.H., McCormack, W.P., Fragala, M.S., and Stout, J.R. (2014). Reliability of the Dynavision D2 for assessing reaction time performance. Journal of Science and Sports Medicine 13, 145-150.


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