Setting the Balance: Using Biofeedback and Neurofeedback With Gymnasts

Journal of Clinical Sport Psychology, 2012, 6, 47-66 2012 Human Kinetics, Inc. Setting the Balance: Using Biofeedback and Neurofeedback With Gymnasts Lindsay Shaw Clinical and Sport Consulting Services Leonard Zaichkowsky Vancouver Canucks Vietta Wilson York University The present paper evaluated the efficacy of a biofeedback/neurofeedback training program to create an optimal preperformance state to improve gymnasts balance beam performance in competition. Training to increase heart rate variability (HRV) and sensorimotor rhythm while inhibiting theta was provided to 11 Division I gymnasts in 10 15-min sessions. Results of this uncontrolled study indicated that competition scores and scores from an independently judged video assessment improved throughout the training, beta decreased from preto postassessment, and there were no changes in HRV, sensorimotor rhythm, or theta. The withdrawal of training resulted in a decline of competition scores. Keywords: Gymnast, biofeedback, neurofeedback, performance, sport While interest in biofeedback and neurofeedback applications in Olympic sport grows (Beauchamp & Beauchamp, 2010; Blumenstein & Orbach, 2011; Dupee & Werthner, 2011) and laboratories are being opened by professional sport teams, including A.C. Milan, Chelsea Football Club, and Vancouver Canucks (Perry, Shaw, & Zaichkowsky, 2011), with goals of giving players a competitive edge in performance and injury prevention and recovery, there is very little research on biofeedback and neurofeedback in sport domains, especially research investigating whether such interventions actually produce the desired effects. The purpose of biofeedback training is to help create awareness of internal processes that one does not typically exert conscious control over (Zaichkowsky & Fuchs, 1988). Brain biofeedback, also called neurofeedback, feeds back the electrical activity of the Lindsay Shaw is with Clinical and Sport Consulting Services in South Kingston, RI. Len Zaichkowsky is with the Vancouver Canucks in Vancouver, BC. He was formerly a Boston University professor. Vietta Wilson is an associate professor with the School of Kinesiology and Health Science at York University in Guelph, Ontario Canada. 47

48 Shaw et al. brain (EEG) that is responsible for such activities as attentional focus, relaxation, memory, and imagery and can theoretically be used to improve self-regulation. Hanin (2000) has suggested that consistent superior performance is the result of a highly self-regulated mental and physical state. In the past two decades, a number of studies have been conducted in sport identifying differences in psychophysiological patterns between experts and novices and between individuals better and worse performances (Crews & Landers, 1993; Hatfield et al., 1984; Haufler et al., 2000; Hillman et al., 2000; Landers et al., 1994; Salazar et al., 1990). Hatfield and Hillman (2001) and Hatfield, Haufler, and Spalding (2006) have presented comprehensive reviews of the EEG literature in sport that provide some theoretical support for the use of neurofeedback for sport. A simplified interpretation of their conclusions suggests that (a) the same amount of work is accomplished by experts, but with less cortical activation or effort than novices; (b) there is a reduction of activity in the left hemisphere of the brain during superior performances (interpreted as perhaps less thinking, less self talk); and (c) performance is better if the athlete learns to become more automatic rather than remaining too cognitive. To create consistent, optimal performance under competitive circumstances, a practical intervention targets the creation of a stable psychophysiological state before task execution, which presumably can be learned and replicated for optimal performance (Harkness, 2008; Singer, 2002). In the current study, the state trained was believed to include a calm and focused cortical state with synchronized respiratory and cardiovascular activity, ideal for balance beam performance (Cogan & Vidmar, 2000). The goal of an intervention program in biofeedback begins with identifying possible suboptimal physiological functioning and whether these suboptimal states occur during specific situational responses such as the stress of competition. Feedback is then provided to assist the athlete in developing strategies to self-regulate to a more optimal response. While some evidence that psychophysiological differences exist among experts/novices and good/poor performances (Crews & Landers, 1993; Haufler et al., 2000), few attempts have been made to assess if these differences or patterns can be learned to facilitate superior performance or particularly, how biofeedback affects actual competition scores. An earlier review (Zaichkowsky & Fuchs, 1988) of the efficacy of biofeedback programs using muscle biofeedback (EMG), electrodermal (EDR or SC), and temperature (T) found some success for the use of biofeedback but found a great range of variability in outcomes. Studies using heart rate variability (HRV) and/or neurofeedback training with high performing populations are summarized in Table 1. Since HRV has not previously been reviewed for performance optimization applications, it is reviewed here. Heart Rate Variability (HRV) Intervention Research Oscillations in heart rate occur normally without any specific training. Higher levels of HRV have been associated with an ability to respond, react, and adapt to changing stimuli and demands (Lehrer, Vaschillo, & Vaschillo, 2000). However, HRV training provides feedback about the timing of heart rate oscillations and is intended to teach individuals to maximize heart rate oscillations or flexibility by synchronizing their respiration with changes in their heart rate (Vaschillo et al., 2006). HRV training seeks to create an increase in heart rate during inhalation and

Table 1 Findings from Neurofeedback and Heart Rate Variability Training Studies With High Performance Populations Author Participants N Experience Training Type # Sessions Total Duration Landers et al., 1991 Egner & Gruzelier, 2003, Experiment 2 Archers, males and females Musicians, mean age 23 yrs, males and females Correct feedback (n = 8), incorrect feedback (n = 8) Beta (n = 9), SMR (n = 9), alpha-theta (n = 8), ex ercise (n = 16), Alexander Technique (n = 10), mental skills (n = 9) Pre-elite archers Royal College of Music students Hemispheric alpha training with correct and incorrect parameter groups Beta (Cz), SMR (C4), alpha-theta (Pz) 1 session of 45 75 min of training 10 sessions with 15-min of training 45 74 min depending on individual speed of learning 150 min over 5 weeks Performance Findings Associated with Training Correct feedback in L hemisphere significantly improves performance; incorrect feedback in R hemisphere significantly decreases performance. Alpha-theta training significantly improves performance; all other methods are not significant. (continued) 49

Table 1 (continued) Author Participants N Experience Training Type # Sessions Total Duration Raymond et al., 2005 Ros et al., 2009 Latin Ballroom Dancers, mean age 21 yrs Ophthalmic surgeons, mean age 33yrs, males and females Strack, 2003 Baseball Players, mean age 16 yrs, males alpha-theta (n = 6), HRV (n = 4), control (n = 8) alpha- theta (n = 10) SMR (n = 10), waitlist control (n = 8) Reasonant frequency breathing (n = 20), control (n = 23) Imperial College Dance Sport Team Trainees in National Health Service program Varsity caliber players Alpha-theta (Pz) SMR-theta (Cz), alphatheta (Pz) 10 sessions with 20-min of training 8 sessions with 30 minutes of training HRV 6 sessions with 20-min of training 200 min over 4 weeks 240 min over 8 12 weeks 120 min of training over 6 weeks; 2x daily of 10 min resonant frequency breathing at home Performance Findings Associated with Training Alpha-theta significantly improves timing; HRV significantly improves technique. SMR significantly improves technique and timing; alpha-theta improvements not significant. HRV significantly improves performance. 50

Biofeedback and Neurofeedback With Gymnasts 51 a decrease in heart rate during exhalation (a state also known as respiratory sinus arrhythmia). Breathing at one s resonant frequency, approximately six breaths per minute, synchronizes heart rate changes and respiration, producing maximal heart rate oscillations (Vaschillo et al., 2006). Resonant frequency breathing leads to increasing variability in heart rate and an activation of the baroreflex response (Lehrer et al., 2000), indicating flexibility in the autonomic nervous system. A commonly reported measurement of HRV uses a spectral analysis of the signal and determines the amount of low frequency (LF) activity, which is believed to be associated with a balance of sympathetic/parasympathetic functioning and representative of an optimal state for health and performance. Garet and colleagues (2004) recorded preperformance nighttime HRV measurements for 6 consecutive hours of sleep during the period of lowest heart rate for seven high school-aged elite swimmers. Better performances occurred when nighttime HRV activity was the highest the night before the swimming competition. Strack (2003) assessed performance improvements in batting in high school baseball players after HRV biofeedback training. The results showed a 60% improvement in batting performance in the HRV training group compared with a 21% improvement in the control group. These results should be cautiously interpreted, however, as the speed of the pitching machine was unintentionally set for five mph slower during posttesting as compared with pretesting. Raymond et al. (2005) trained HRV in Latin ballroom dancers and demonstrated improvement in technique compared with a control group. Lagos et al. s (2008) case study of a 14-year-old golfer showed reduced season average strokes on an 18-hole course from 91 to 76 strokes after 10 weeks of HRV training. Tanis (2008), however, found no improvements following HRV training for college female volleyball players in an uncontrolled study, even though the athletes reported positive subjective experiences. It is unknown whether the lack of improvement was due to nature of the intervention or the nature of performance assessment, which was completed by the coaches during games. Overall, while there is promise in HRV training improving sport performance, it clearly requires more research. Neurofeedback Intervention Research Neurofeedback, also known as electroencephalographic (EEG) biofeedback, is a recording of the electrical signal measured at the scalp and represents the electrical activity of billions of cortical and subcortical neurons located between and beneath the electrodes (Thompson & Thompson, 2003). In neurofeedback training, regions of the brain are selected for training, and a person is guided to increase or decrease the magnitude of specific brain wave frequencies, receiving feedback to indicate whether they are maintaining the targeted frequencies. Neurofeedback is used to assess deficits in affective and cognitive functioning, to train the individual to bring these closer to baseline, and to enhance affective experience and cognitive performance. While authors (Hatfield & Hillman, 2001; Hatfield et al., 2006) have noted that neurofeedback training can be used to teach cortical activation patterns associated with improved performance, only one study has done so in the sport domain, to date. Landers et al. (1991) experimented with neurofeedback training protocols with athletes and found that the group receiving correct feedback (shifting activity in the left hemisphere) improved shooting performance, while the group given incorrect

52 Shaw et al. feedback (shifting activity in the right hemisphere) experienced a decline in performance; however, it is important to state that the effects of training were not examined in competition, and total neurofeedback training was completed in one day. Neurofeedback training has been conducted in other performance domains, and improvements have generally been observed. Specifically, Egner and Gruzelier (2003) examined the effects of alpha-theta training, sensorimotor rhythm combined with beta (15 18 Hz) training, and a no-training control on scores from videotapes of musicians performance as judged by expert assessors. Neurofeedback learning indices revealed that learning success in the alpha-theta group correlated significantly with improvements on 10 of 12 musical evaluation criteria. In their second study, they examined six training groups receiving alpha-theta, beta, sensorimotor rhythm, Alexander Technique, physical exercise, or mental skills, and posttraining performance with improvements observed in only the alpha-theta group. The group receiving alpha-theta feedback significantly improved their performance in artistry in performance, but not technical aspects of performance. Sensorimotor rhythm and beta training were not associated with similar performance gains. In a study of Latin ballroom dancers on a competitive university team, Raymond, Sajid, Parkinson, and Gruzelier (2005) compared groups receiving alpha-theta and HRV training to a control group. Technique was significantly improved in the HRV group, and timing was significantly improved in the alpha-theta group. Overall execution was significantly improved in both groups compared with the control group of dancers. There was no relationship between improvements and rate of learning within neurofeedback sessions in the study, which contrasts Egner and Gruzelier s previous finding with musicians. In a 2009 study, Ros and colleagues trained ophthalmic micro surgeons using either alpha-theta or sensorimotor rhythm training, with surgical performance assessed by consulting surgeons ratings on skill execution and time to complete surgical tasks. A randomized subset of these two intervention groups comprised a wait list no-treatment control. Sensorimotor rhythm training was associated with significant improvements in overall technique, suture task, and overall time on task, which were absent in the control group. Alpha-theta training was not associated with significant effects. As can be seen, these findings are the reverse of Egner and Gruzelier s (2003) findings. The reasons for this major discrepancy are not known. Perhaps it is the nature of the tasks, as the first were large motor skills involving artistry, and the latter involved fine motor control skills executed with technical precision. To date, empirical studies examining the effects of biofeedback and neurofeedback on performance are sparse and somewhat contradictory. While some athletes and coaches report that it is of value, there is insignificant research to identify the impact, let alone the mechanisms, of biofeedback training in sport performance. The Present Study The present uncontrolled study investigated the efficacy of a biofeedback/neurofeedback training program designed to create an optimal preperformance mind-body state to improve balance beam performance in competition for gymnasts. Since differences in EEG activity have been found between practice and competition (Hatfield et al., 2009), the current study trained gymnasts both in the laboratory

Biofeedback and Neurofeedback With Gymnasts 53 and gymnasium in an attempt to facilitate the transfer of skill. Assessment included measures of psychophysiological baseline states, an independent judge s evaluation, balance beam performances during competition, and the gymnasts subjective experiences. Participants Method Eleven female gymnasts (N = 11) from a Division I university on the East Coast of the United States completed consent forms approved by the Board of Review, Department of Athletics, and the gymnastics Head Coach. Because the team had previous experience with traditional sport psychology interventions, the coach presented the biofeedback intervention as another aspect of their ongoing sport psychology training. As college gymnasts can be event specialists, and not all members train the balance beam, only those athletes who regularly practiced beam were selected. The mean age of the gymnasts was 19.9 (SD = 0.39) years, with 14.83 (SD = 0.6) years of experience in their sport. Measures Balance Beam Performances. Balance beam performances were assessed by a nationally certified, independent judge who was hired for the study. The judge knew that a research study was being conducted but was blind as to the nature of the study. The judge was provided with videotaped routines from one preseason and two regular season competitions for the competitive line-up. The chronological and competitor order was randomized, and the judge was asked to serve as an independent assessor adhering to the Code of Points as strictly as possible. This procedure helped created a measure of independent reliability across longitudinal data collection. Competition scores can be influenced by who is judging, the location of the competition, and the order in which events are performed. Second, the independent judge provided subscale scores that are not available at official competitions. In addition, scores from the four competitions during which the biofeedback/neurofeedback training was conducted (January), were examined in relation to scores earned in the following four competitions when there was no biofeedback/neurofeedback training (February). While all 11 participants trained to compete on the balance beam, NCAA regulations permit only six gymnasts to compete on each event, with the top five individual scores counted toward the team score. Perception/Subjective State. A repeated assessment of participants preperformance state on the beam was used to examine changes in self-reported levels of calm, focus, confidence, or energy. As regulations prevent contact with athletes during competition time, the athletes who competed on the balance beam were contacted within one hour of the competition ending and asked to report, on a 5-point Likert scale ranging from 1 (low) to 5 (high), how calm, focused, confident, and energized they felt in the time just before beginning their beam routine. While a postevent self-report assessment may introduce factors that were not actually present during the preperformance state (i.e., whether they performed

54 Shaw et al. well, others comments), ethical considerations do not allow for communication with the athletes preperformance, nor for interventions that may potentially negatively impact the athlete, such as a focus on a preperformance inventory. Participants were also asked to report their experience during biofeedback training after 5 and 10 sessions. As this was an exploratory study, participants were asked if they had noticed anything on balance beam that they thought might be related to the biofeedback and neurofeedback training was having on their balance beam performance in practice or competitions; however, we understand that the athletes may not be able to truly know this information. Psychophysiological Measures. HRV and EEG data collection were completed using Thought Technology (TT) Procomp Infiniti, through a USB interface unit into a 16-inch laptop. Biograph Infiniti software (TT) was used. HRV assessment and training used a blood volume pulse sensor on the underside of the left index finger and a respiration belt secured around the lower ribcage. The decision to place the belt on the ribcage of the gymnasts, rather than the traditional abdominal location, was made as gymnasts typically keep their stomach still and tend to breathe more into their ribcage. HRV was calculated by examining the standard deviation of normal-to-normal beats (SDNN) and the interbeat-interval data for use in a spectral analysis of frequencies (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). A fast Fourier analysis within the CardioPro software program (Thought Technology) was used to separate heart rate variability into spectral bands of high (HF), low (LF), and very low frequency (VLF) activity, which were reported as a percentage of total spectral power. Low frequency (LF) activity is increased when changes in respiration and heart rate are synchronized. The SDNN, as measured for 60-s recordings, reported in milliseconds (ms), was recorded to determine variations in heart rate that occurred but were not synchronous with respiration and thus would not be captured in changes in frequency activity. Six breaths per minute was chosen as the breathing rate for training in the current study with female participants based on recommendations regarding adaptation for training accounting for gender and height (Vaschillo et al., 2006). While Lehrer et al. (2000) has recommended that individual resonant frequency be determined before the start of HRV training, strong therapeutic effects have been found in studies that have not individualized training (Herbs et al., 1993; McCraty et al., 2003). For neurofeedback assessment and training, the scalp was prepared with NuPrep, and Ten20 conductive paste was used to attach electrodes to the head and ears. During pre- and postmeasures, active electrodes were placed at T3 and Cz (International 10 20 System; Jasper, 1958), referenced to linked ears, using silver-silver chloride sensors. During neurofeedback training, an active electrode was placed at Cz, referenced to the right ear lobe and grounded to the left ear lobe using gold sensors. Impedance was checked before the start of each measurement or training session, with all readings under 10 kω. Cz has been used as a training site in previous performance enhancement protocols, as it is believed to provide a measurement of the activity in both hemispheres and in the frontal lobes (Thompson & Thompson, 2003). Training sensorimotor rhythm at Cz is associated with training a calm state of reflection, which is believed to precede action (Thompson & Thompson); a previous study examining surgeons

Biofeedback and Neurofeedback With Gymnasts 55 skill execution associates sensorimotor rhythm training with improved performance (Ros et al., 2009). T3 was selected as an assessment site for comparison with previous studies that have reported changes with athletes performance at this site (e.g., Deeny et al., 2003; Haufler et al., 2000). The left temporal lobe, T3, is associated with auditory processing and may play a role in the emotional valance of thoughts (Thompson & Thompson, 2003). The training goals for each neurofeedback session were to decrease theta and increase sensorimotor rhythm. Theta waves (4 7 Hz) are associated with a distracted state, and reducing the production of these waves is believed to result in a more focused state. Sensorimotor rhythm (13 15 Hz) is produced along the sensorimotor strip and is believed to be associated with a calm, ready state for action. During training to reward sensorimotor rhythm and inhibit theta frequencies, data were collected and analyzed for changes in all bands from 1 Hz up to 40 Hz. In all sessions, slow wave activity (1 4 Hz) and fast wave activity (52 58 Hz, associated with EMG noise) were inhibited. Pre- and postmeasures and all training sessions were conducted with eyes-open. After the second session, theta inhibit thresholds were adjusted for those participants with elevated theta at rest. All participants began with the theta threshold at 10 microvolts (μv), and three participants had their threshold settings refined to 12.5 μv. These thresholds remained the same for the duration of the training. Sensorimotor rhythm reward thresholds were set at 2.5 μv. These settings mean that a participant receives the reward meeting both criteria, producing less than 10 or 12.5 μv of theta and more than 2.5 μv of sensorimotor rhythm. Procedure All participants were assessed on pre- and postbiofeedback/neurofeedback measures and received training. Ten sessions of HRV and theta/sensorimotor rhythm training were conducted two times per week for 5 weeks. Training was conducted in the following format: three cycles of 90 s of HRV training, a 5-s break, followed by 90 s of theta/sensorimotor rhythm training. A 10-s break was given between the first and second and second and third cycles. The 90-s HRV and theta/sensorimotor rhythm training duration were chosen, as this is the length of a balance beam routine. Keeping with the Wingate five-step approach to mental training (Blumenstein et al., 1997), the instruction was designed to be transferred from the laboratory to a field setting. Sessions one through five of biofeedback/neurofeedback training were conducted in a classroom in the university s athletic facility. Sessions six through 10 were conducted in the gymnastics training facility with gymnasts standing near the balance beam. The laptop was placed on stacked mats to be at chest height at a distance from the balance beam similar to where they would stand while preparing to do their routine during competition. Training sessions one through five were conducted without audio feedback during HRV or theta/sensorimotor rhythm screens, and sessions six through 10 were conducted with audio feedback, which was used as a reward. After the sixth session of training, participants were encouraged to look away from the screen periodically to see if they could keep their respiration and heart rate synchronized without looking during HRV training. The athletes were also encouraged to identify the state that was associated with decreased theta and increased sensorimotor rhythm by listening to the audio feedback while glancing away from the screen. The timeline for data collections is shown in Table 2.

56 Shaw et al. Table 2 Data Collection Timeline Date Activity Dec 12, 13 Pretraining Videotape Performance Assessment A at Intersquad meet Biofeedback/Neurofeedback Premeasures Dec 13- Jan 8 5 training sessions Jan 9 Videotape Performance Assessment B at competition 1 Jan 11 25 5 training Competition scores 2, 3 sessions Jan 26 30 Posttraining Videotape Performance Assessment C at competition 4, Biofeedback/Neurofeedback Postmeasures Feb 6 27 No training Competition scores 5, 6, 7, 8 Analyses Artifacts from all EEG data from T3 and Cz were removed using visual inspection and auto-rejection settings in the Biograph Infiniti software. Artifacts from HRV data were removed and session segments were normalized using the CardioPro software program before data analysis. Two separate analyses were run. Multiple paired t tests were run on a number of measures taken pre and posttest, and significance levels were corrected using a Bonferroni adjustment. Tests are reported as significant if they reached a p value of less than 0.05/10 = 0.005 for scores given by the independent judge, and 0.05/8 = 0.006 for values related to HRV and EEG. A separate repeated measures analysis examined how competitors scores changed over time on the balance beam. Statistical analysis included 11 participants for EEG and HRV measures pre and post, seven for the hired judge scoring pre and post, six for competitive scores, and five for subjective states, as one of the six competitors did not respond after the researcher withdrew training. Scores Results Paired t tests of the independent judge s assessments from pre- to poststudy showed significant improvement in the level of deductions for the seven gymnasts who competed in the meets (six official competitors with one exhibition). These results can be seen in Table 3. Final scores improved from the first to the third assessment, and subscale scores for Artistry (variation in rhythm, sureness of performance, and additional deductions) and Execution (concentration pause and extended preparation) were improved, as shown by significant reductions in deductions. Individual beam score average was 9.64 (SD = 0.2) in January when biofeedback/neurofeedback training was conducted, and dropped to 9.43 (SD = 0.37) in February, when the training was withdrawn. In March, the average returned to 9.6 (SD = 0.17). Mean individual beam scores from competitions in January and

Table 3 Means and Significance From an ANOVA for Video Assessments Significant with Bonferroni Correction at Premeasure Postmeasure Significance P Value 0.005 Start Value 9.73 10 F(2,5) = 7.3, p <.01 0.01029 No Artistry Deductions Insufficient variation in rhythm 0.1 0.008 F(2,5) = 22.14, p <.01 0.00001 Yes Sureness of performance 0.14 0.01 F(2,5) = 9.85, p <.01 0.00201 Yes Additional artistry deduction 0.08 0.008 F(2,5) = 7.3, p <.01 0.0041 Yes Execution Deductions Poor rhythm in connections 0.12 0.03 F(2,5) = 5.9, p <.05 0.0096 No Extended concentration pause 0.25 0.07 F(2,5) = 26.4, p <.01 0.00182 Yes Extended preparation into dance element 0.06 0.008 F(2,5) = 3.49, p >.05 0.0041 Yes Additional move to maintain balance 0.125 0.17 F(2,5) = 7.55, p <.01 0.77237 No Additional execution deductions 1.023 0.33 F(2,5) = 5.82, p <.05 0.0225 No Final Score 7.56 9.37 F(2,5) = 20.2, p <.01 0.00115 Yes Note. Improvements can be seen in reduced deductions in artistry subscales and some execution subscales, and higher final scores across the three assessments; Summed deductions across the 7 participants who performed routines in the pre and postmeasures and the p values given by two-tailed, paired sample t tests; Bonferoni Correction threshold is 0.05/10 = 0.005. 57

58 Shaw et al. Figure 1 Mean scores from the six members of the competitive line up across 8 competitions. Solid bars, competitions 1 4, are from January when biofeedback/neurofeedback training was taking place. Patterned bars, competitions 5 8, are from February when biofeedback/neurofeedback training was withdrawn. February are shown in Figure 1. A repeated measures analysis was run, and a cubic trend was revealed for beam scores, F(1,5) = 16.234, p = 0.01, ηp 2 = 0.765). The fact that scores decreased in February after the intervention ended was an unexpected finding, and a comparison was made to the previous seasons scores to see if this was a typical response. This decrease in individual scores did not exist in the 2009 season, but rather, there was a significant increase in scores t 19 = 2.01, p <.05. In fact, when comparing the team s four close competitors performances in January and February in the past three seasons, only once was there a drop in average beam score over time for one team, by 0.05. The decrease in score on balance beam was not reflected in the intervention teams other events: from January to February, there was an increase of 0.17 on vault, an increase of 0.18 on bars, and a slight decrease in floor by 0.06. This suggests that a drop in score is unusual. Subjective State A repeated measures analysis was run, and a cubic trend was revealed for reported levels of energy, F(1,4) = 15.961, p = 0.01, ηp 2 = 0.8). 1 There were no changes in reported levels of calm, confidence, or focus from January to February. Comments from the athletes regarding their experiences were positive and included statements indicating a perception of an increase in awareness, mastery of the self-regulation skills, and use of skills before and during beam routines (see below). Readers interested in full disclosure of comments are referred to Shaw (2010).

Biofeedback and Neurofeedback With Gymnasts 59 I notice my breathing now and when it gets out of control and I try to control it. I take deep breaths when I m nervous to calm myself down. I ve been much calmer and much more able to solely focus on a routine when necessary. Before routines, I start the slower breathing and I find it helps me relax. I have been able to stay more calm before/during my routines. I try to do the same thing on beam as I would in the brain training, like only focus on looking at the beam. I have learned to focus on the task at hand. From doing the brain training games, I have learned that it is easier to focus on just one thing instead of over thinking. Applying my pre-beam state to my actual performance- I ve gotten better at doing one thing at a time. HRV No significant changes were observed in HRV measures of SDNN, %LF, %HF, and %VLF, from preto postmeasure. EEG No changes were seen in sensorimotor rhythm or theta from pre- to postmeasures at either Cz or T3; however, changes were seen in the fast beta band, which was outside of the training parameters. A repeated measures analysis reveals that activity in the 23 35 Hz band, associated with distractions and negative ruminations (referred to as the busy brain band), was reduced, F(1,10) = 14.82, p = 0.003, ηp 2 = 0.597). Using pre to post two-tailed t tests for pre- to postbaseline measures for assessment sites showed reductions in the intensity band (19 22 Hz, associated with intense emotions, often anxiety or excessive effort) at T3 and the busy brain band (23 35 Hz, associated with distraction and negative ruminations) at both Cz and T3. The busy brain/sensorimotor rhythm ratio was also reduced at Cz. These differences are shown in Table 4. In summary, the independent judge reported improvements in artistry and execution across the study. The balance beam scores improved in competition across the course of biofeedback/neurofeedback training and then declined when training was withdrawn. The self-reported experience of the athletes during training was positive, and the gymnasts found the training moving to the gymnasium as being useful/relevant. Changes in reported subjective state when training was withdrawn show a decline for energy, which is consistent with the decline in scores earned in competition; however, no change in reported states of calm, focus, or confidence were seen at the group level. Pre- to postmeasures showed no change in HRV; additionally, while there were no prepost changes in theta or sensorimotor rhythm, there was a reduction in beta, 23 35 Hz, at Cz, and the left hemisphere, as assessed at T3, showed a reduction in two beta bands, 19 22 Hz and 23 35 Hz.

60 Shaw et al. Table 4 EEG Changes From Pre- to Postmeasures (for Cz and T3 Sites) EEG Frequency Change at Cz Change at T3 Significant with Bonferroni Correction at 0.006 Theta: 4 8 Hz Mean No No No SMR: 12 15 Hz Mean (only for Cz) No No Intensity: 19 22 Hz Mean Busy Brain: 23 35 Hz Mean Decrease: 4.13 μv to 3.83 μv, t10 = 2.38, p = 0.0383 Decrease: 5.94 μv to 5.12 μv, t10 = 3.85, p = 0.003 Decrease: 4.89 μv to 3.66 µv, t10 = 3.72, p = 0.0039 Decrease: 3.57 μv to 2.36 μv, t10 = 4.88, p = 0.0006 No change at Cz, decrease at T3 Decrease at both Busy Brain/SMR ratio Mean (only for Cz) *p = 0.006 Decrease at Cz Note. Bonferroni correction 0.05/8 = 0.006. * p value is significant with the Bonferroni correction. Discussion The objective of this preliminary uncontrolled study was to assess a biofeedback/ neurofeedback protocol on improving balance beam performance in competition. This was done by teaching the gymnasts to increase their HRV by synchronizing respiration and heart rate to maximize functioning of the autonomic nervous system. In addition, the gymnasts were taught to increase sensorimotor rhythm at Cz while inhibiting theta, in an effort to maintain a calm and focused state before balance beam performance. Score While the scores from the independent judge s evaluation and competition scores improved from the beginning of competition to the end of the biofeedback/ neurofeedback training in January, it was not anticipated that the scores would drop after the withdrawal of the training. A similar drop did not occur in other events for the team, as can be seen in Figure 2. The source of the cubic trend noted in beam score means across January and February is unknown; it may possibly be related to energy levels, home versus away competitions, or some other unidentified source. While it could be hypothesized that biofeedback/neurofeedback training actually had a long term negative effect, beam scores in March (9.61) approached January (9.68) levels and were consistent to the previous season s March average (9.63), suggesting a short term response. Given that no known study examining the effects of biofeedback/neurofeedback training has collected data beyond the end of training, this finding of decreased scores in February is interesting and awaits further study.

Biofeedback and Neurofeedback With Gymnasts 61 Figure 2 Team score averages for four events in January and February. The unexpected finding of poorer performance only on balance beam perhaps may be due to the withdrawal of the training and/or the withdrawal of the researcher. This was perhaps corroborated by the coach calling the researcher and requesting that training be resumed. In addition, some of the gymnasts reported being frustrated that training had ended and stated that they wanted to continue. Due to the study design and administrative constraints, the researcher could not continue the training. Much of the purported success of biofeedback/neurofeedback training during active training periods is thought to be due to its integration into sport demands (Wilson, Thompson, Thompson, & Peper, 2011). Herein, cycles within sessions were periodized to match those of balance beam performance, and images included on the training screen were used to approximate what is seen in competition (a balance beam, a judge) to create a bridge for skill transferability. Even if the biofeedback/neurofeedback skills taught were fully learned, biofeedback operates under the principles of operant conditioning, and it is likely that after some time, additional booster sessions would have been required to retain the skills (Lubar, 2003). Subjective Data The subjective state comparison yielded an interesting result of a decrease in reported energy in February. It is unknown if this is a result of the academic demands with the beginning of classes at the end of January, the withdrawal of biofeedback/ neurofeedback training, or another source. In addition, given that gymnasts were asked directly about biofeedback/neurofeedback training for qualitative comments regarding changes they might have noticed on beam, it is possible that the responses are biased.

62 Shaw et al. Physiology HRV Baseline. There were no changes from pre- to postmeasures for HRV. It is possible that the current study did not include sufficient training to produce increases in %LF HRV activity at baseline. While the parameters were similar, the amount of training was approximately 1/3 of total training time reported in other studies (Lagos et al., 2008; Strack, 2003; Vaschillo et al., 2006). EEG Baseline. The failure to see changes in sensorimotor rhythm from training may again have been due to insufficient time to train the brain, which is believed to require about 40 sessions. It also appears to not have been enough time to create a feeling of a state that could be recalled without feedback when the training finished. It is also possible that the training parameters were insufficient to bring about learning required for performance enhancement once the training was removed. With a larger sample size and if possible, a matched control group, the type of training and number of sessions could establish whether the state was learned. Thompson and Thompson (2003) and Lubar (2003) have noted that the functioning of neural networks of the brain can mean training in a specific band in one location can yield results in another area or a different frequency. This may have occurred in the current study. The reduction in beta seen at baseline in intensity (19 22 Hz) at T3, and busy brain (25 35 Hz) at both T3 and Cz, suggests that the participants learned to reduce fast wave activity associated with anxiety and negative ruminations or distractions. The finding of left hemisphere reduction in beta and increased scores from the independent judge are consistent with previous studies associating a reduction of left hemisphere activity before performance and improved motor skill execution. Previous research (Hatfield et al., 2009) showed that excessive levels of beta interfere with the execution of efficient action, and engaging in excessive cortico-cortical communication before skill execution can result in poorer performance in shooting tasks (Haufler et al., 2000). In Egner and Gruzelier s study (2003) with musicians, the beta training group showed significant within-group reductions in anxiety. Ros et al. s (2009) study with surgeons associated sensorimotor rhythm training with a reduction in trait anxiety. The busy brain/sensorimotor rhythm ratio is used to assess information processing and was included as a measure in the pre- and postassessments to observe if the athletes were experiencing a possible nonproductive busy brain. An elevated ratio suggests that the brain is active when it should be calm and highly focused on the single task-at-hand (Thompson & Thompson, 2006). Calm athletes typically have lower values than athletes who report engaging in a lot of self-evaluation, judgment, rumination, or over-thinking in their sports, especially under the stress of competition. The reduction for the gymnasts seen in this ratio from baseline to postmeasures suggests that the athletes may have been able to more effectively quiet their minds or focus when necessary; however, since this is an uncontrolled study, causality cannot be determined. The reduction in beta in this study may be more important than an increase in sensorimotor rhythm for the gymnasts competing on the balance beam. Clinicians in attention deficient training (Thompson & Thompson, 2003) have reported that it takes longer to change sensorimotor rhythm than beta, and yet training in sensorimotor rhythm changes other functions of the brain, such as beta. Perhaps more sessions are needed to see this sensorimotor rhythm change. On the other hand, if

Biofeedback and Neurofeedback With Gymnasts 63 the athlete learns to decrease faster beta activity, perhaps the calming, as suggested by sensorimotor enhancement, is not necessary for optimal performance in many sports. This, however, remains to be seen, as EEG data were not collected in February to determine if increased busy brain was associated with poorer performance. Limitations and Future Research There are a number of important limitations to consider. Due to the uncontrolled nature of this study, the small sample size, and the female-only sample, this study should be considered exploratory. Without a control group, the results observed could be not be ascribed to the HRV training, neurofeedback training, or mere attention from/association with the researcher. A future study should be conducted with a larger sample, discreet training interventions, and an appropriate control group from which to draw causality. A weakness in the present design was the lack of individualized training. Tailoring of EEG threshold settings and breathing rates to individual needs would be done when training athletes in a nonresearch setting. Future research could investigate if and how individualized training maximizes performance outcomes. Future research should also address the impact of biofeedback training at various points in athletic development. The participants in this study had an average of 15 years of experience in their sport. It is not known if similar effects would be seen in developing gymnasts, males, or those from other sports. Conclusion It has been theorized that athletes who have developed appropriate awareness and self-regulation of arousal levels can come closer to replicating the states that lead to their best performance, compared with those athletes who do not have awareness or self-regulation strategies (Ravizza, 2006). The biofeedback/neurofeedback training intervention presented herein was designed to teach the gymnasts to transfer the practice of self-regulation from the classroom to the gymnasium. The improved balance beam performance during the duration of the biofeedback/neurofeedback training suggests that participants did experience some benefits associated with the training. Yet, when training was withdrawn, this benefit was not continued. The present uncontrolled intervention was designed as a sport specific biofeedback/neurofeedback training protocol for balance beam performance in competition. Positive athlete feedback as well as improvements shown in biofeedback data should encourage further investigation of the application of biofeedback training in a competitive setting. The unexpected finding of the withdrawal of the biofeedback training (or the withdrawal of the researcher) having such a negative effect on the athletes mood and performance was unexpected and suggests that this research model may not be applicable to active competition. A newer model allowing for more research control within the context of the actual training/competition setting needs to be implemented in future studies of biofeedback. End Note 1 The statistical analysis assumes that the subjective state scores are interval level.

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