The Effects of Video and Cognitive Imagery on Throwing Performance of Baseball Pitchers: A Single Subject Design

ABSTRACT
The purpose of this study was to examine the effects of a three-week imagery and video imagery intervention program on the throwing accuracy of individual baseball pitchers. A secondary purpose of this study was to investigate whether differences in accuracy response characterize both low- and high-ability imagers. A sample of pitchers (n=30) were asked to take the Movement Imagery Questionnaire–Revised; study participants were randomly selected from the highest and lowest 20% of the group. The participants were obtained from high school and college teams within southeastern Georgia (n= 6). Following the first week of baseline measurements, 2 high-ability and 2 low-ability imagers took part in a three-week video imagery and imagery intervention program. One participant from each group together constituted a control group, which was asked only to try their best when throwing for the study’s accuracy measurements. Results showed that 2 participants demonstrated an increase in performance, while all participants expressed a desire to continue to use imagery for its various effects. Suggestions for future research and further insight are discussed.

INTRODUCTION
Imagery has been shown to be very effective for improving accuracy in sport. Thomas and Fogarty (1997) found that imagery combined with positive self-talk improved not only putting performance, but psychological factors as well. Woolfork et al. (2005) found that positive imagery participants, in comparison to negative imagery training and control group participants, experienced significant increases in putting performance. Moreover, imagery has been shown to positively enhance free-throw shooting among collegiate basketball players. Kearns and Crossman (1992), Shambrook and Bull (1996), Templin and Vernacchia (1993, 1995),Stewart (1997), and Carboni, Burke, Joyner, Hardy, and Blom (2000) have determined imagery to be to some degree effective for most individuals at enhancing free-throw performance.

Much of the research cited above utilized a single subject design. This type of design has proved important in applied sport psychology, demonstrating improvement in individual cases that might be overlooked by traditional group analysis (Shambrook & Bull, 1996). For instance, when a multiple baseline design is used, a conclusion could be drawn that any effects were due to the specific intervention (Bryan, 1987, p. 286). The single subject design, in contrast, allows for individual analysis of the imagery implementation and a way to tailor the intervention to the individual (Stewart, 1997).

Visualization theories have not always been applied to sport performance; they began in the field of cognitive and spatial awareness research. Bess (1909) was among the first researchers of the topic and is credited with developing the measuring system for visualization. The Bess Scale addresses differences in individual imagery ability, drawing on cognitive theory of imagery and tied closely to the understanding of the term kinesthetic imagery(Schiffman, 1995).

A pitcher may be asked to imagine the ball in hand before a throw, to feel the laces and texture on the palm, maybe even to brush the dirt off, as if the ball was just grabbed from the ground. Bess notes that the image should be as clear and detailed as possible, and his Bess Scale measures the vividness of the visualizations practiced with seven classifications of vagueness and vividness. However, Wilson & Barber (1981) found that individuals can vary greatly in their ability to visualize, even when their Bess Scale scores are alike. Moreover, Stoksahl and Ascough (1998) also found that some athletes were very detailed in their imaging, while others were very vague; they concluded that the less vivid images may not be as effective for enhancing performance. Therefore, athletes with lower imagery ability may not reap full performance-enhancement benefits from imagery training. Such findings provide one more reason to investigate the effects of video imagery: Individuals who lack vivid imaging skills may find that a video re-enactment of the task allows them to see the desired performance very clearly, aiding mental preparation for an actual event or task demonstration.

Little research appears in the literature which has examined the effects on performance of internal video imagery, or video depicting an athlete’s internal perspective during performance. However, at least some research has integrated videotape modeling with imagery training. Hall and Erffmmeyer (1983) investigated female high school basketball players who were assigned to a video modeling/imagery group and a relaxation/imagery group. Results can only be attributed to a combination of psychological skills, as they were compounded within the study, but it was concluded that the video modeling/imagery group demonstrated better performance in foul shooting, compared to the relaxation/imagery group. Little research seems to exist exploring internal video imagery in other sports contexts, specifically baseball and, more specifically, pitching accuracy.

While general research on imagery is vast, this study seeks to investigate the effects of cognitive imagery and video imagery on one phenomenon: the throwing performance of baseball pitchers. A secondary purpose of this study is to see whether low-ability imagery and high-ability imagery are associated with distinct performance responses following video and cognitive imagery interventions.

METHOD
Participants
The study participants were 6 baseball pitchers from southeastern Georgia. They were selected from the region’s high schools and colleges. Four males, 2 current college athletes and 2 current high school athletes, took part in the study. The participants’ mean age was 19.8 years, with ages ranging from 16 to 22 years. Only athletes currently on pitching staffs of high school or college baseball teams were utilized. All participants had been baseball athletes for at least the previous 2 years, at either the high school or college level. All were asked to return a signed consent form before participating in the study; participants under 18 years of age were asked to return a parental consent form before participating. The consent form assured participants of confidentiality, briefed them on the study’s purpose, and listed the risks and benefits of participation. Contact was made with each institution, informing participants, parents, and coaches that athletes’ participation was completely voluntary.

Apparatus
A Samsung Sports Camcorder SC-X205L/X210L was used to record all accuracy-measurement sessions, in order to ensure that accurate points were recorded for each pitch. At no time, however, was the pitcher himself captured in these recordings. The Samsung Camcorder SC-X205L/X210L external helmet camera module, used to capture recordings of an accurate pitch from the internal perspective of the pitcher, was used in the video imagery interventions.

Instrumentation
Throwing performance was measured with an Easton© 9-square Strike Zone Target, which was placed on the plate in the visitors bullpen at Georgia Southern University, emulating an actual game. Each section of the target was assigned a point value, ranging from 1 to 10; 10 was the value for the center box, with lower values designated for squares nearer the edges of the target. Point values between the ranges of the surrounding boxes values were assigned to the dividing lines themselves. Each pitching accuracy measurement session was videotaped, allowing the researchers to review each pitch at leisure, ensuring the correct assignment of points; however, only the target and the end result of the pitch were captured on videotape during measurement sessions.

Prior to the study, an imagery ability test was given to a group of 30 high school and college baseball pitchers, to identify athletes with high- and low-ability imagery skills who might become part of the study sample. The Movement Imagery Questionnaire- Revised (MIQ-R) was used to measure the athletes’ imagery ability (see Appendix A). Hall and Martin (1997) developed the MIQ-R, a revision of Hall and Pongrac’s Movement Imagery Questionnaire, or MIQ (1983), in order to assess individuals’ capacity to generate visual imagery and also kinesthetic (or movement) imagery. The present researchers have determined the MIQ- R to be a valid and reliable revision of the original instrument: Earlier work has established significant correlations for the MIQ-R’s visual and kinesthetic scales. For the MIQ, Hall, Pongrac, and Buckholz (1985) obtained a test–retest co-efficiency score of .83; in terms of internal consistencies, a score of .89 was obtained for the visual scale and a score of .88 was obtained for the kinesthetic scale (Atienza et al., 1994).

A Post Study Imagery Questionnaire was distributed to the present study’s participants at the completion of the investigation. This questionnaire sought feedback from each pitcher as to prior experience with imagery, present attitude toward imagery, and likelihood of future imagery use. Moreover, it asked the athletes to think about effects of imagery occurring in dimensions other than performance. The questionnaire asked these questions, specifically: Did you at any time use imagery outside of this study? How do you feel about the use of imagery in general? Do you feel it helped you and how so? Do you feel there was a difference between the two types of imagery and if so what were they? Will you continue imagery use?

Procedures
The pitchers’ completed MIQ-R instruments; later on, their scores were collected and recorded by number, both to protect confidentiality and to help ensure random selection of participants. Pitchers completing the MIQ-R were also given a brief explanation of what the instrument covered and directions for providing answers. A 7-point Likert scale was employed for each question, and the points assigned each question were totaled for each participant. Using the scores obtained, 3 participants were chosen at random from the top 20% of scores, and another 3 were selected randomly from the bottom 20%; the 6 were asked to participate in the study. By omitting participants with middle-ranking scores, the study sought to secure a sample that truly represented high- and low-ability imagery skills. Participants signed a consent form or obtained written parental consent prior to participating.

Participants were asked to meet with an “observer” 5 times during the first week of the study, the period during which a stable baseline was to be established for each pitcher; after a baseline existed (which ideally required 1 week but in fact might have required more time), participant and observer were to meet 4 times during each of the next 3 weeks. The 3 weeks constituted the invention portion of the study. Prior to the intervention, each pitcher’s throwing performance was measured 5 times a week, until he had demonstrated a stable baseline, defined as an average score displaying no more than a 2-point variance in at least 3 consecutive trials. The first-week, baseline portion of the study was followed by imagery interventions beginning in the second week; each imagery intervention required 6 visits, or one and one-half weeks. Measurements were taken 4 times a week, post imagery session, during the imagery and video imagery intervention programs, until the study’s completion. Throwing-performance measurements were determined by averaging a pitcher’s scores for 10 pitches in the visitors bullpen of an NCAA Division I university. The measurement apparatus was placed in front of the bullpen home plate. During the baseline portion of the study, the Samsung Sports Camcorder SC-X205L/X210L was used to create video imagery segments for use during the intervention portion, with each pitcher wearing the “helmet-cam” module (placed aside his head, at eye level) and capturing his own internal perspective on the throwing of an accurate pitch. (At no time was any pitcher himself captured in a recording.) The module is worn comfortably on a headband, and no participate indicated discomfort during its use. The study design incorporated counterbalancing to eliminate sequence effects.

Participant 1 and Participant 4 experienced the cognitive imagery intervention during Week 1 of the intervention portion of the study, followed by video imagery intervention beginning in the middle of Week 2 (the two athletes’ seventh study session). Participant 2 and Participant 5 experienced video imagery sessions as the initial intervention during Week 1 of the study’s intervention portion. They participated in cognitive imagery intervention during Session 7 through Session 12. The throwing accuracy of Participant 3 and Participant 6 was measured 4 times a week, and they received no intervention, serving as a control group.

The university’s Mental Edge Training Facility was used for the video and cognitive imagery sessions, which were conducted individually (rather than in groups) during scheduled time slots. An imagery session was of a 10-minute (approximately) duration. During the video imagery interventions, participants were asked to watch the previously recorded 10-point pitch while imagining accompanying sensations, to include sounds, smells, tastes, and textures, in as much detail as possible. During the cognitive imagery interventions, in contrast, they were asked to imagine the 10-point pitch as vividly as they were able, again using the five senses as much as possible. At the study’s end, each participant completed the Post Study Imagery Questionnaire, providing insights into his attitudes towards imagery generally, as well as his unique responses to imagery practice, performance, or similarly related issue. The Post Study Imagery Questionnaire also attempted to determine whether and why players would continue to practice imagery techniques.

Data Analysis
Data were represented graphically to describe each participant, then reviewed for practical differences in throwing accuracy. Ocular statistics (Carboni et al., 2000) were reviewed by a group of trained researchers to determine actual changes in throwing accuracy and to provide control of the researcher’s bias. Qualitative results of the Post Study Imagery Questionnaire were collected and reported.

RESULTS
Data collected for this study were evaluated using mixed methodological procedures from ocular statistics (Carboni, et al, 200); additionally, they are explored in qualitative terms. Figures 1–6 will illustrate the participants’ throwing performance scores over the length of the study. Figures 7–12 will illustrate perfect pitch count scores over the length of the study.
Table 1 presents the participants’ throwing performance scores, with standard deviations. Table 2 presents a count of perfect pitches thrown by the participants.

Table 1
Participants’ throwing accuracy scores*

Session
Number

High- Ability Participant
1
(C.I./ V.I.)

High- Ability Participant
2
(V.I./ C.I.)

High- Ability Participant
3
(Control)

Low- Ability Participant
4
(C.I./ V.I.)

Low- Ability Participant
5
(V. I./C.I.)

Low-
Ability Participant
6
(Control)

1

2.7 (3.2) 3.6 (4.7) 3.3 (4.0) 4.3 (3.8) 3.5 (3.7) 2.0 (3.1)

2

2.5 (3.0) 1.9 (3.0) 3.2 (4.0) 2.3 (2.6) .1 (3.2) 1.8 (2.4)

3

3.4 (4.5) .9 (1.9) 4.4 (3.7) 4.6 (4.2) .8 (1.3) 3.5 (3.2)

4

2.5 (3.6) 1.1 (3.1) 3.5 (4.5) 4.5 (3.5) .6 (1.9) 3.3 (2.9)

5

3.2 (3.9) 1.0 (1.9) 3.7 (3.6) 4.0 (3.4) 1.0 (1.3) 3.4 (3.1)

6

3.2 (3.9) 1.4 (2.3) 3.4 (4.0) 3.4 (3.9) 1.3 (2.5) 3.2 (3.9)

7

3.0 (2.4) 1.6 (1.9) 3.4 (3.7) 1.8 (2.4) 1.9 (3.0) 2.6 (3.9)

8

1.4 (1.8) 1.5 (2.0) 2.5 (3.5) 2.1 (2.0) 3.3 (4.7) 2.6 (3.0)

9

3.6 (3.9) 1.9 (2.2) 3.3 (4.0) 3.1 (3.1) 4.8 (4.6) 4.4 (3.4)

10

3.9 (4.6) 1.9 (3.1) 1.0 (1.9) 3.3 (3.9) 2.5 (4.0) 1.0 (1.9)

11

3.8 (4.1) 4.1 (5.1) 4.1 (3.9) 4.5 (4.7) 1.2 (3.2) 1.5 (3.1)

12

3.5 (3.7) 5.1 (4.1) 2.3 (4.1) 4.2 (3.4) 2.5 (4.0) 1.8 (2.6)

13

3.9 (3.8) 3.6 (4.1) 1.8 (3.1) 3.8 (3.3) 2.6 (3.9) 2.2 (3.7)

14

5.3 (3.7) 3.8 (3.7) 1.0 (1.9) 4.1 (3.80 3.9 (3.3) 1.0 (1.9)

15

3.3 (3.5) 3.8 (4.0) 1.5 (1.8) 3.3 (2.4) 4.0 (4.1) 2.1 (2.8)

16

3.8 (3.8) 3.8 (3.9) 1.0 (1.1) 4.5 (4.1) 3.5 (4.6) 1.8 (3.4)

17

3.6 (3.7) 4.0 (3.7) 2.7 (4.3) 4.5 (3.5) 3.7 (3.5) 2.4 (3.6)

* Standard deviations in parentheses.

Table 2
Perfect pitches thrown

Session
Number

High- Ability Participant 1

High- Ability Participant 2

High- Ability Participant
3

Low- Ability Participant
4

Low- Ability Participant 5

Low- Ability Participant 6

1

1 2 2 2 0 1

2

1 0 2 0 0 0

3

2 0 2 2 0 0

4

1 1 3 0 0 0

5

2 0 2 0 0 0

6

2 0 2 2 0 1

7

0 0 1 0 0 1

8

0 0 1 0 0 0

9

2 0 2 1 3 2

10

3 4 0 0 1 0

11

2 1 2 3 3 1

12

1 3 2 1 0 0

13

2 2 1 1 2 1

14

2 2 0 2 1 0

15

1 2 0 0 2 0

16

2 2 0 2 2 1

17

1 2 2 2 1 1

 

The participants provided qualitative reports concerning imagery effectiveness (with the exception of Participant 3 and Participant 6, the control group receiving no intervention).

Participant 1
Participant 1 initially received cognitive imagery intervention (Cognitive Imagery First). Participant 1 had demonstrated a 2.9 (SD= 4.5, 3.6, 3.9) throwing performance baseline during the first week of the study; with one exception, each of his throwing performance scores fell above that baseline, ranging from 3.2 (SD= 3.9) to 3.9 (SD= 4.6) (See Table 1). The exception occurred in Session 8, for which the participant’s accuracy score was 1.4 (SD= 1.8). While this score was below the participant’s baseline, it was not beyond the margins of implied change set, for this study, at .9 (See Figure 1).


Figure 1. Participant 1 (high-ability): Accuracy scores (imagery/ video Imagery)

As the participant completed subsequent video imagery interventions, the accuracy scores remained above the baseline, ranging between 3.5 (SD= 3.7) and 5.3 (SD=3.7); throwing performance scores in Session 14 reached 5.3 (SD= 3.7), exceeding the margins of implied change, set at 4.9. During the baseline portion of the study, Participant 1 recorded between 1 and 2 perfect pitches (See Table 2), a rate maintained throughout the study, except during the imagery intervention portion, Sessions 6 through 11. During the imagery intervention portion, this participant’s perfect pitch count ranged from 0 to 3 (see Figure 7).

Figure 7. Participant 1 (high-ability): Perfect pitches thrown
Post Study Imagery Questionnaire
Participant 1, who said he had never used imagery prior to entering this study, reported that he used imagery while playing in a game during the period of the study. He stated, “I would like to continue using imagery and practice it more before games. I feel like it really helps when I start rushing.” Participant 1 further reported that breathing techniques included in the relaxation portion of the imagery script helped him manage momentum and refocus his effort. Participant 1 expressed the opinion that video imagery was relatively more helpful in bringing about desired outcomes, although he found it difficult to mentally re-create accompanying sensations during the video imagery sessions.

Participant 2
Participant 2 initially received video imagery intervention (Video Imagery First). Participant 2 had established a baseline score of 1.0 (SD= 1.9) within the first week of the study; his throwing performance scores increased slowly during the first intervention, ranging from 1.4 (SD= 2.3) to 4.1(SD= 5.1) (see Table 1). Each of Participant 2’s throwing performance scores fell above the baseline; during Session 11, his score exceeded the established margins of implied change (see Figure 2).


Figure 2. Participant 2 (high-ability): Accuracy scores (video imagery/ imagery)
During Session 12 through Session 17 (the imagery intervention), all of Participant 2’s scores remained above the implied margin of change, which was set at 3.0; his scores ranged from 4.0 (SD= 3.7) to 5.1 (SD= 4.1). While establishing a baseline score during Session 2 through Session 5, Participant 2 recorded from 0 to 1 perfect pitch per session (See Table 2). During the video imagery intervention, his perfect pitch count remained at 0 until Session 10, when he threw 4 perfect pitches; in Session 11, he threw 1 perfect pitch. During the imagery intervention, which was his final intervention, Participant 2 threw either 2 or 3 perfect pitches per session (see Figure 8).

Figure 8. Participant 2 (high-ability): Perfect pitches thrown

Post Study Imagery Questionnaire
Participant 2 reported never having used imagery prior to the study, but said he is currently employing it during games because his pitching performance improved following the start of the study. He stated, “I haven’t walked anybody, it must be working. I started trying to see the ball go where I want it to before I throw the pitch, and it really seems to help.” Moreover, Participant 2 expressed a desire to continue using imagery, for its benefits both to his accuracy and his confidence.

Participant 3
Participant 3 did not participate in any intervention (No Interventions). He had established a baseline score of 3.8 (SD= 3.6) within the first week of the study. During Session 6 through Session 11, his throwing performance scores ranged from 1.0 (SD= 1.9) to 4.1 (SD= 3.9) (see Table 1). All of these scores fell below his baseline score, except the Session 11 score of 4.1 (SD= 3.9). During Session 10, the participant scored 1.0 (SD= 1.9), dropping below the margin of implied change, set at 1.8 (see Figure 3).

Figure 3. Participant 3 (high-ability): Accuracy scores (control; no intervention)

Participant 3’s throwing performance scores in Sessions 12–17 ranged from 1.0 (SD= 1.9) to 2.7 (SD= 4.3), falling below his established baseline. During Session 13 through Session 16, this participant’s scores descended to the level of the margin of implied change. During the baseline portion of the study, Participant 3 threw either 2 or 3 perfect pitches per session. Across the remainder of the study, however (Sessions 6–17), Participant 3 threw 0, 1, or 2 perfect pitches per session (see Figure 9).

Figure 9. Participant 3 (high-ability): Perfect pitches thrown

Participant 4
Participant 4 received cognitive imagery intervention first (Cognitive Imagery First). Participant 4 had established a baseline score of 4.3 (SD= 3.8) in the first week of the study, and during the first intervention, his throwing performance scores largely fell below that baseline, ranging from 1.8 (SD= 2.4) to 4.5 (SD= 4.7) (see Table 1). Session 11 comprised an exception.

Participant 4’s Session 11 score was 4.5 (SD= 4.7). During Session 7 and Session 8, the participant’s scores were 1.8 (SD= 2.4) and 2.1 (SD= 2.0), respectively, falling below the implied margin of change, which was set at 2.3. During Sessions 12–17, Participant 4 received video imagery intervention. His throwing performance scores for those sessions ranged from 3.3 (SD= 2.4) to 4.5 (SD= 3.5), and most fell below his baseline score, although his scores for Session 16 and Session 17 was 4.5 (SD= 4.1, 3.5) (see Figure 4).


Figure 4. Participant 4 (low-ability): Accuracy scores (imagery/video imagery)

During the baseline portion of the study, Participant 4 threw between 0 and 2 perfect pitches per session, a range he would go on to maintain for the duration of the study, excepting only Session 11, during which he threw 3 perfect pitches (see Figure 10).


Figure 10. Participant 4 (low-ability): Perfect pitches thrown

Post Study Imagery Questionnaire
Participant 4 reported that he had used imagery prior to this study; he furthermore reported having difficulty sustaining the vividness of imagery. He went on to express a preference for having a detailed imagery script read to him, due to such reading’s capacity to generate vivid images. Participant 4 stated, “I usually do imagery before my games that I know I’m going to be pitching in. It helps me get focused, and I want to get better at it.” Participant 4 expressed a desire to continue imagery use, but made no note of any distinction between the cognitive and video approaches.

Participant 5
Participant 5 received video imagery intervention first (Video Imagery First). Participant 5 had established a baseline score of .8 (SD= 1.3) in the first week of the study. His throwing performance scores during the first intervention ranged from 1.2 (SD= 3.2) to 4.8 (SD= 4.6), above the baseline score he had produced (see Table 1). In Session 8 and Session 9, Participant 5 recorded scores of 3.3 (SD= 4.7) and 4.8 (SD= 4.6), respectively, exceeding the margin of implied change, which was set at 2.8 (see Figure 5).

Figure 5. Participant 5 (low-ability): Accuracy scores (video imagery/ imagery)

During Session 12 through Session 17 (the imagery intervention portion of the study), Participant 5’s throwing performance scores ranged from 2.5 (SD= 4.0) to 4.0 (SD= 4.1). All scores thus fell above his baseline score, and his scores in Sessions 14–17 exceeded the margin of implied change, coming in between 3.5 (SD= 4.6) and 4.0 (SD= 4.1). During the baseline portion of the study, Participant 5 threw 0 perfect pitches. During Session 9 in the video imagery portion of the study, he threw 3 perfect pitches; in Session 10 he threw 1 perfect pitch; in Session 11 he again threw 3. Over Sessions 12–17, the participant threw anywhere from 0 to 2 perfect pitches (the 0 was recorded during Session 12; for each of the next 5 sessions, a 1 or 2 score was recorded; see Figure 11).


Figure 11. Participant 5 (low-ability): Perfect pitches thrown

Post Study Imagery Questionnaire
Participant 5 reported never having used imagery prior to the study. He reported considering adherence to use of pre-game imagery following conclusion of the research project. Participant 5 reported noticing not only improved throwing accuracy, but increased self-confidence as well. He stated, “When I stop between each pitch, take a breath and see where I want the ball to go, it helps me to refocus. Also, when I do throw a bad pitch, it doesn’t carry over as much. I don’t get caught in a bad momentum. I am more able to release the last pitch and trust the next one, because I’ve seen myself throw it where I want to put the ball (in my head) many more times before. I know I can do it.”

Participant 6
Participant 6 belonged to the control group (No Intervention) and established a baseline score of 3.4 (SD= 3.1) during the study’s first week. His throwing performance scores ranged from 1.0 to 4.4 over Session 6 though Session 11 (see Table 1). With the exception of a 4.4 (SD= 3.4) throwing performance score in Session 9, Participant 6’s subsequent scores fell below his baseline score. During Session 10, Participant 6 recorded a throwing performance score of 1.0 (SD= 1.9), below the set 1.4 margin of implied change (see Figure 6).


Figure 6. Participant 6 (low-ability): Accuracy scores (control; no intervention)

Participant 6’s throwing performance scores for Sessions 12–17 were between 1.0 (SD=1.9) and 2.4 (SD= 3.6), all falling below the baseline. Moreover, in Session 14, the participant scored a 1.0 (SD= 1.9), which fell below the margin of implied change. While establishing his baseline score for this study, Participant 6 threw 0 perfect pitches. In Sessions 6–11, he threw from 0 to 2 (in Session 9) perfect pitches per session. For the remainder of the study (Sessions 12–17), he threw 0 to 1 perfect pitch per session (see Figure 12).

Figure 12. Participant 6 (low-ability): Perfect pitches thrown

DISCUSSION
The purpose of the present study was to see whether imagery would have an effect on the throwing performance of individual baseball pitchers. Further, the present study sought to determine if individual variation in ability to “image” is associated with distinct responses to cognitive imagery interventions and video imagery interventions. By the end of Session 9, study Participants 1, 2, and 5 demonstrated higher scores (as compared to their individually established baseline scores) for throwing accuracy. This result parallels similar single subject sport-and-imagery research (Kearns & Crossman, 1992; Munroe-Chandler, Hall, Fishurne, Shannon, 2005; Shambrook & Bull, 1996; Templin & Vernacchia, 1993, 1995; Stewart, 1997, Carboni et al., 2000;). There should be further investigation into the effectiveness of brief interventions, because no research to date answers the old question of how frequent and how long intervention must be to produce the desired result (Cumming, Hall, Shambrook, 2007; Thelwell, Greenless, & Weston, 2006). The suggestion has been made that, as in the realm of physical skills, psychological-skills practice effects positive change only after an extensive investment of time (Weinberg & Williams, 2001). Thelwell, Greenless , and Weston (2006) found that combining three types of intervention—imagery, self-talk, and relaxation—produced results within a 3-day period, when 1 day of imagery training was provided and when measures were taken once weekly over a 9-match period. Murphy (1990) recommends intervention sessions of no more than 10 minutes’ duration, and Weinberg and Gould (2007) suggest providing intervention 3 to 5 times a week. Bull (1995) found that positive results ensued from a 4-week training period featuring 8 training sessions. Some researchers have examined intervention frequency and length by leaving participation to the discretion of the participant and recording objective reports; sessions as brief as 1 minute were noted (Carboni et al., 2000). Cumming, Hall, and Shambrook (2007) concluded that overall use of imagery could be increased with interventions as brief as a “workshop.” Findings from the present study indicate that, to be effective for specific tasks such as accurate pitching, imagery interventions can be as brief as 10 minutes in length, conducted 4 times weekly for 3 weeks.

The present study did not find participants to be affected distinctly by the two types of intervention (cognitive and video). The higher throwing performance score recorded for the final 6 sessions of the study are believed to reflect the lengthening period of time during which participants had practiced imagery practice, rather than to the type of intervention, since all participants receiving intervention responded similarly, whether they were in the Cognitive Imagery First group or Video Imagery First group. Gordon, Weinberg, and Jackson (1994) found similar results, investigating “internal” as opposed to “external” imagery. Future research into the effects of multiple interventions should seek to determine the relationship between effectiveness and time invested in each intervention.

Research has shown that imagery ability is a large determinant of how an individual’s physical performance will respond to imagery interventions (Hall, 1998). In the present study, however, scores for Participant 2 and Participant 5 (on both throwing accuracy and perfect pitches) improved more than they did among the other participants. That Participant 2 and Participant 5 succeeded more markedly with imagery use cannot, however, be attributable to higher-ability imagery, because Participant 2 was a high-ability imager while Participant 5 was a low-ability imager. Any individual, regardless of imagery ability, can benefit from imagery practice, although those with lower ability may continue to experience greater difficulty creating and controlling vivid imagery (Magill, 2007). Each high school level participant from the present study had a baseline score for throwing accuracy that was lower than the lowest such score established by a college level participant. Isaac and Marks (1994) and Piaget and Inhelder (1971) concur that imagery ability is developed by age 7. Moreover, Payne and Isaacs (1995) report that the highest level of cognition and abstract thinking develops at age 11–12. Participants 2 and 5 had a mean age of 17, beyond the developmental period and ranking them developmentally equal to the college level participants. The distinct intervention responses of Participants 2 and 5, then, are not due to the basic development of ability to image. Research on imagery use has found differences associated with subjects’ athletic competitive levels (Barr & Hall,1992; Salmon, Hall, & Haslam, 1994; Vadocz et al., 1997). These differences seem to be shaped by factors like years of experience, degree of motivation to play, degree of motivation to use imagery, and ability to create and control images.

Thelwell, Greenless, and Weston (2006) discuss ways in which distinct levels of goal orientation affect players’ levels of investment in imagery use. Research also finds that athletes exhibiting moderate to high levels of task and ego orientation become more invested in imagery use, in turn increasing how often they practice imagery (Cumming, Hall, Gammage, & Harwook, 2002; Harwood, Cumming, & Hall, 2003). Bull (1995) examined the effects of a 4-week mental training program on varsity athletes, finding that better-motivated athletes were likelier to adhere to an imagery program, and that less-seasoned athletes were likelier to be the better motivated. It is possible that Participants 2 and 5 in our study, being at one of the earliest stages of an athletic career, were better motivated than Participants 1, 3, and 4, who were playing what they anticipated would be the final season of their careers.
Motivation can also be affected by fatigue and overtraining. The participants in the present study all were at mid-season, obligated to a vigorous training schedule as well as to the study sessions. At least one point during the study, every participant reported feeling fatigue or exhaustion, and this might have affected their concentration and performance. A perceived imbalance between demands on athletes and their response capabilities sometimes creates the negative physical and emotional state known as burnout (Creswell & Eklund, 2006). As Creswell and Eklund (2006) state, insufficient “rest and recovery periods” will also help generate negative experiences. Participants in the present study might possibly have found that study-related testing and intervention consumed the time they normally would use for recovery and rest, which could account, to some degree, for periodic “off” performance, including uncharacteristically low accuracy scores, trending down of accuracy scores, loss of interest in the study, or transfer of effort from the study to some other task. The performance of Participant 3 and Participant 6 support such an interpretation; these two athletes received no intervention and saw their performance fall off over time. “Burnout” may also help describe the expressed attitudes of Participants 1, 3, 4, and 6.

The study’s timing during the athletes’ season may help explain any shortages of focus or concentration on their part, but additional distractions should also be considered. During video imagery intervention sessions, for example, certain participants showed clear difficulty in focusing when they opened their eyes at the conclusion of the relaxation portion in order to view video. The discrepancy arose even though all of the imagery sessions took place in the university’s Mental Edge Training Facility, where each participant was assured of experiencing interventions of identical length. To better maintain focus and a relaxed state, future research might employ a different viewing method (e.g., use a dark room into which video is introduced from outside or use virtual-reality gear). Furthermore, researchers would be well advised to employ a vivid script that helps participants to incorporate as many types of sensation as possible (Thelwell, Greenless, & Weston, 2006). The script used in the present study instructed participants to “see” only the target’s center box, which perhaps explains in part why Participant 2 and Participant 5 were able to throw more perfect pitches (see Figure 8 and Figure 11).

For the present study, throwing performance was defined as a pitcher’s ability to throw a ball at a specified area deemed the target. In measuring throwing performance, the mean score for the 10 pitching efforts made each session was recorded and graphically represented. Perfect pitches were defined as those hitting the center target, and they too were recorded and graphically represented; a perfect pitch received a score of 10 points.
There are various definitions of what performance enhancement actually is. Some individuals may look for greater consistency, more pitches thrown closer to target, when seeking evidence of performance enhancement. Others may see enhanced performance in a combination of more pitches thrown at the actual target, and lower-scoring pitches. For the present study, the mean score and number of perfect pitches thrown for each session were used to measure performance response. During cognitive imagery interventions, participants were asked to envision throwing only to the center box, while during video imagery interventions, they watched tapings of pitches thrown to the center box. An increase in pitches to the center box was, for this reason, said to indicate imagery intervention’s positive effects on throwing performance.

Two limitations on the present study resulted from the data collection process. First, during the intervention sessions, participants were exposed to extraneous noise, although none directly identified this as a distraction. In future studies, areas free of extraneous noise should be employed. Second, when a participant was unable to join in throwing performance measurement or imagery intervention session during daylight hours, the researchers accommodated his schedules by conducting these activities after dark, an inconsistency which, by potentially affecting vision, perhaps also affected success. Furthermore, time of day bears on the level of concentration and fatigue.
Results obtained through the Post Study Imagery Questionnaire describe perceived positive effects imagery wields on athlete performance and confidence. This questionnaire also documents that participants’ appreciation for psychological skills training grew during the study. These findings parallel past research (Carboni et al., 2000; Kearns & Crossman, 1992; Shambrook & Bull, 1996; Templin & Vernacchia, 1993, 1995; Stewart, 1997; Thelwell et al., 2006). Participants 1, 2, and 5 expressed an outlook positive toward the imagery sessions, toward their own confidence concerning tasks, and toward anxiety-reducing effects of mentally re-creating a pitching sequence. This supports numerous findings about imagery’s possible benefits, for example improved self-confidence (Callow, Hardy, & Hall, 2001), better motivation (Callow & Hardy, 2001), improved regulation of arousal (Hecker & Kaczor, 1988), and stronger ability to modify such cognitions as self-efficacy (Feltz & Ressinger, 1990). Imagery, or mental practice, can, the research record demonstrates, be used to control anxiety and to enhance both the strategies and physical movements that will be employed in performing a skill (Magill, 2007).

Suggestions for future research include, again, the deployment of alternative methods of presenting video imagery intervention, to ensure participants’ focus is maintained. In addition, future research should examine how often and for how long interventions are best administered, in terms of performance enhancement.

The baselines established by this study’s participants did not vary more than 1 point, although the criterion we employed for defining baseline and actual change was 2 points (on a 10-point scale). Perhaps future research would benefit from more strict criteria, which would tend to identify more pronounced effects.

Psychological skills training, coaches and athletes often fear, entails a long-term commitment and many field practice hours lost. The present findings, however, imply that imagery training’s effects on at least the one position-specific task studied are observable in as little as twelve 10-minute sessions (4 per week for 3 weeks). Moreover, the study demonstrates that effective intervention may take place during the competetive season and in conjunction with rigorous physical training.

Bull (1991) identified three barriers between athletes and ongoing psychological skills training: time constraints, a disruptive home environment, and an unmet need for individually tailored training. The position-specific intervention employed in this study, together with use of a brief script, alleviate all three problems. Later, in a discussion of how best to implement psychological skills training, Shambrook and Bull (1999) emphasized the importance of time management, of structure, and of integration of psychological skills within existing training. The present study’s findings, past research focusing on workshops (Cummings, Hall, & Shambrook, 2007), and future research will complete the path around the barriers, driving home that intervention programs may be both brief and integrated within established physical training in order to reap positive returns.

Please address all correspondence to:

Dr. Daniel R. Czech, CC-AAASP

Department of Health and Kinesiology
Box 8076

Georgia Southern University

Statesboro, GA 30460-8076

Telephone (912) 681-5267

E-mail drczech@georgiasouthern.edu

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NCAA Athletes and Facebook

ABSTRACT
The use of Facebook and other social networks by a majority of National Collegiate Athletic Association (NCAA) athletes has come under intense scrutiny from college officials in recent months. The current level of monitoring by athletic departments ranges from mere advisories as to what athletes should post, to a complete ban on the use of any social networks (Brady & Libit, 2007). The findings of this study of 522 NCAA athletes representing Division I, II, and III indicate that NCAA II athletes project the least positive image on their Facebooks. Female athletes in general reported projecting a better image, while male athletes expressed the greatest resistance to being monitored.

NCAA ATHLETES AND FACEBOOK
The use of Facebook and other social networks, accessed regularly by the majority of National Collegiate Athletic Association (NCAA) athletes has come under intense scrutiny from college officials in recent months. The scrutiny has been prompted by athletic department administrators and coaches as they have become increasingly concerned about both the university image projected on Facebook and the well-being of the student-athlete. The current level of monitoring by athletic departments ranges from advisories as to what athletes should post, such as the ultimatums on type of use issued by the University of Kentucky and Florida State University, to a complete ban on the use of Facebook by athletes, as seen at Loyola University (Brady & Libit, 2007). For purposes of this article, it should be noted that the social network Facebook is a word frequently used by university administrators and athletic directors as an all-encompassing term that describes not only Facebook, but MySpace and other forums such as SportsGist.com, MyStack.com, and Badjocks.com. Such will be the case in this paper, as Facebook is the most prevalent social network utilized by college students today.

The focus of this article is to address the image and monitoring concerns associated with Facebook usage among college athletes. The secondary issue examined is the host institution’s concern over the image projected on the athlete’s social network accounts. Additionally, the battle between the university monitoring of student-athletes’ Facebook accounts and First Amendment privileges related to freedom of speech and expression will be addressed.
When a student creates a Facebook profile, he or she has the freedom to share uncensored photos and personal information with friends and other network members among the 40 million users (Lemeul, 2006). Facebook has evolved from a single-university communication tool into a public domain, accessible to anyone with a Facebook account. Facebook has certainly become a vehicle for self-expression and communication among students.

The NCAA has not taken a formal stand on how much a university can monitor or restrict accounts. Instead, the NCAA has left it up to the host institution to determine its own Facebook policies at this time. Universities have traditionally supported the 1972 Supreme Court ruling in Healy v. James wherein the Court found that state colleges and universities are not enclaves immune from the sweep of the First Amendment (Lukianoff & Creely, 2007). However, the recent trend is for universities to make specific policy statements related to expression on Facebook. The growing trend in athletic departments, as well, is to monitor the social network accounts of athletes. For example, Roper (2007) noted that the entire Catholic University lacrosse team was suspended from school after posting hazing photographs of new athletes on their personal Facebook accounts. Four female soccer players at San Diego State University were penalized for alcohol- and partying-related pictures they posted (Schrotenboer, 2006). Two athletes at the University of Colorado were issued tickets for harassment by the campus police for racially offensive messages they posted (Brady & Libit, 2006). In May of 2006, two athletes were dismissed from the Louisiana State University swim team for posting degrading comments about the swim coaches (Brady & Libit, 2006). Recent photos on Badjocks.com resulted in an increased scrutiny of athletes from Elon University and Northwestern University, as these postings projected an embarrassing image for the respective universities (Anderson, 2007). Laing Kennedy, the athletic director at Kent State University, made headlines when he forbade student-athletes there from using Facebook (Read & Young, 2006). Mr. Kennedy has recently revised his statement and simply requires all Kent State University athletes to keep their profiles private (Read & Young, 2006). Even though athletes seem to be at risk, Schrotenboer (2006) noted that coaches may be the ones who have even more to lose, as damaging pictures and statements on Facebook can hurt recruiting, team morale, and image.

Excessive access to information can put the athlete at risk by giving gamblers personal information about the injuries of marquee players, who are the most likely to be approached with point-shaving opportunities. There is also concern that a Facebook “friend” may turn out to be a professional gambler or agent and thus compromise an athlete’s eligibility by his or her affiliation with that person (Strickland, 2006). Agents or bookies may pose as a friend on a social networking site and solicit illegal contact with a student. Some students use Facebook as an alternative to a paper diary. However, these students need to be aware that any material posted on Facebook may be retained by Google’s online cache, even after the material is deleted, according to Tracy Mitrano, director of information-technology policy at Cornell University (Read & Young, 2006). A cache allows material to be viewed through a search engine, even after deletion from sites such as Facebook (Mitrano, 2006). According to Mitrano, this online cache proved deadly for one student who applied for a full-time position after graduation. Even with experience and a high grade point average, the applicant was refused employment when the employer found an inappropriate remark made by the applicant in an on-line cache. Not only can students impair themselves professionally with questionable pictures, but they can also incite legal action with libelous comments about professors or fellow classmates (Mitrano, 2006).

Ironically, at the same time many athletic departments are restricting the use of social networks, some tout the benefits; some claim that these networks may reduce the new-roommate anxiety experienced by incoming freshmen, open doors for conversation with their intended roommates even before arriving to campus, and aid in the completion of academic assignments (Farrell, 2006). Facebook can also aid students in meeting other people who share the same interests or dormitory. With the instant opening of an account, Facebook lists all of the people in a network who have a common interest. Students can also specify the networks to which they belong and even join networks based on characteristics such as metropolitan location or where they graduated from high school (Zuckerberg, 2006). Barbara Walker, senior associate athletic director at Wake Forest University, touted the positive attributes of Facebook when used innocently and suggested that administrators be careful about restrictive policies (Doughtery, 2007).

Politicians have even created accounts on social networking sites such as Facebook to spark interest among college-age voters (Vascellaro, 2006). This can be advantageous if the politician garners overwhelming support and positive comments from young voters. However, the political strategy of creating a Facebook profile can have the same negative consequences as for an athlete, when negative comments are posted on the profile of a politician.
While there are numerous benefits to creating a Facebook profile, there are concerns, not only about NCAA athletes but about the general student body, as well as in regard to projecting negative images (Read & Young, 2006). According to Pablo Malavenda, associate dean of students at Purdue University at West Lafayette, some students have little or no concern about the image they project to the public through Facebook (Read & Young, 2006). Malavenda noted that students tend to embellish profiles with exaggerated pictures of rebellion that most commonly involve underage alcohol consumption. Facebook furthermore has been used to taunt and physically threaten opponents in high school sports (Doughtery, 2007).

While student-athletes are to be treated as general college students, they are frequently subject to additional behavioral guidelines as a condition for scholarship renewal. These guidelines are presented in the form of a code of conduct, which usually requires that athletes represent the university in a positive manner. Ian McCaw, the director of athletics at Baylor University, issued a formal memo to all student-athletes and student trainers that explained that material which other students often post on Facebook pages may be inappropriate for student-athletes’ pages (Brady & Libit, 2006). Wake Forest’s athletic department instructs athletes that no comments or pictures administrators deem inappropriate may be used on Facebook (Doughtery, 2007). Furthermore, Kermit L. Hall, president at the University of Albany in New York, says that students give up some freedom and become subject to regulations when they join an athletic team.

There is concern about the impact that social networking sites might have on grade point averages. One UCLA student realized that he was spending too much time on Facebook when his grade point average dropped a point and a half (Reed & Riley, 2005). One concerned parent of a high school student noticed that his daughter’s involvement with Facebook made two hours’ worth of academic work take eight hours to complete (Duffy & August, 2006). This is likely due to the distraction that social networking sites create. Student-athletes already must be more effective time managers than the average student. The student-athlete cannot afford to spend eight hours on a two-hour assignment because of time lost to Facebook. Logistically speaking, student-athletes have little time in their schedules for the abyss of Facebook, without incurring academic consequences.

Student-athletes are in a different situation than typical students, as they are much more visible in the public domain representing the university. Athletic departments, at their core, operate like businesses (Strickland, 2006). Athletes are the products that create the funds for the businesses. Coaches are always trying to market the athletic department and the university to prospective recruits and donors. Prospective recruits can often find mostly uncensored information about their future teammates on Facebook. Pablo Malavenda, the associate dean of students at Purdue, knows of several instances when athletes backed out of oral commitments because of what their future teammates had posted on Facebook (Read & Young, 2006). This could have devastating consequences for athletic programs. When recruiting high school athletes, college coaches spend time, energy, and money in order to sell the program to the athlete. Prospective athletes may become dissatisfied with a program after seeing Facebook pictures of their future teammates engaging in drinking, drug use, or other undesirable activities. Even though the NCAA has not developed specific policies for athletes about Facebook and other social networking sites, such sites are becoming an area of concern; the issue was on the agenda at an August 2006 NCAA meeting.
While the use of social networking sites has garnered significant media attention, research documenting student usage and image projected is sparse. The research into the motivations for social network usage by students comprises a study conducted by Michigan State University (Ellison, Steinfield, & Lampe, 2006). This study gathered data on Facebook usage through the administering of a survey to the entire student body (Ellison, Steinfield, & Lampe, 2006). One key finding from this study was that the amount of time spent on the Internet did not differ between those who were not members of Facebook versus those who were members (Ellison, Steinfield, & Lampe, 2006). Grade point averages also did not differ significantly between Facebook members and non-members (Ellison, Steinfield, & Lampe, 2006). While these results are revealing, one must remember that they are only representative of the Michigan State University student body, not of all college students. Additional research is needed on the habits of college students using social networking sites. Because of the high-visibility situations facing NCAA student-athletes, their use of social networking sites needs extensive study. As athletic departments increasingly monitor their athletes’ accounts, knowledge of usage, attitudes, and motives is of growing importance.

The research questions pursued in the present study were related to examining frequency of use, image projected, and attitude toward being monitored on Facebook, by gender and NCAA classification. The study examined NCAA athletes’ (a) responses related to perceived personal image projected; (b) responses related to the athletic department image projected by individual student-athletes on Facebook; and (c) desired level of athletic department monitoring of social network accounts. The problem of this study was to generate an updated, cross-sectional view of athletes’ stances on issues of image and responsibility related to social networks.

METHODOLOGY
This study examined college athletes’ usage of and attitudes toward Facebook. To obtain a representative sample of NCAA student-athletes, the subject pool was selected from six different NCAA Division I, II, and III universities.

The data was collected from athletes at each of the schools, with assurance of anonymity to the participants. Athletes completed the survey privately. Athletes were told that the word Facebook should be taken to mean all social networks including MySpace and Badjocks.com. The total number of subjects responding was 522. This consisted of 148 NCAA Division I athletes representing the Southeastern Conference, 146 NCAA Division II athletes from the Gulf South Conference and the Northern Sun Intercollegiate Athletic Conference, and 126 NCAA Division III athletes from the Southern Independent Athletic Conference. There were 308 male and 214 female respondents.

The data collected were analyzed using a Kruskal-Wallis test (Green & Salkind, 2003) to determine if the population mean responses differed based on gender or categorization as NCAA I, II, or III level. The respondents were asked to answer written survey questions about (a) their personal image projected on Facebook, (b) the image of their athletic department based on their personal Facebook account, and (c) the level of athletic department monitoring of Facebook they desired. The 0.05 level of confidence was set as the significance level.
The questionnaire utilized for this study was modeled after the Carnegie MellonUniversity survey of freshmen reported in 2006 (Tabreez & Pashley). Slight adaptations of the questionnaire were made to more appropriately reflect the present athlete-dominated subject pool.

RESULTS
Looking at the responses of the male NCAA athletes as contrasted to the female NCAA athletes, there was a significant difference (at the 0.05 level of confidence) for all items examined in this study, when using the Kruskal-Wallis test of significance. As seen in Table 1, these items included athlete’s perception of personal image projected on Facebook accounts, athlete’s athletic department image as influenced by personal Facebook accounts, and athlete’s recommended level of athletic department monitoring.

Table 1

NCAA Athletes Facebook Image and Recommended Level of Monitoring (N=522)
________________________________________________________________________
Gender difference NCAA I, II, III
Asymp. Sig. χ2 Asymp. Sig. χ2
________________________________________________________________________
Personal image projected .032 4.612* .045 6.221*
Athletic department image .018 5.618* .479 1.472
Monitoring level recommended .022 5.281* .133 4.036
________________________________________________________________________
Kruskal-Wallis test p* < .05.

Also presented in Table 1, NCAA classification was associated with significant differences in athlete’s personal image presented on Facebook. No significant difference was observed, however, in responses related to athletic department image projected or recommended level of Facebook monitoring, when examined by NCAA classification.

Table 2
NCAA Athletes Self-Report of Personal Image on Facebook (N=522)
_______________________________________________________________________
very positive positive neutral negative very negative
Female athletes 26.3% 58.8% 14.9% 0.9% 0.0%
Male athletes 22.3% 42.7% 30.1% 3.9% 1.0%
NCAA I athletes 14.9% 48.9% 34.0% 0.0% 0.0%
NCAA II athletes 23.8% 48.4% 23.8% 3.2% 0.8%
NCAA III athletes 38.5% 42.3% 11.5% 7.7% 0.0%

_______________________________________________________________________
female n=214, male n=308, NCAA I n=148, NCAA II n=146, NCAA III n=126

When examining aspects of personal image presented on Facebook, some significant differences were found. As Table 2 shows, 85.1% of the female athletes, as contrasted to 65% of the male athletes, leaned toward a positive image projection on Facebook. No female athletes appraised their accounts as projecting a very negative image. Also, 74.6% of the female athletes reported that their accounts projected a positive athletic department image, as contrasted to 56.1% of male athletes (Table 3). The male athletes were more likely than the female athletes to recommend “definitely” no athletic department monitoring, or monitoring on a limited basis. Additionally, as seen in Table 4, 66.8% of the male athletes recommended no monitoring or limited monitoring, as contrasted to 58.3% of female athletes.

Table 3
NCAA Athletes Self-Report of Athletic Department Image on Facebook (N=522)
_______________________________________________________________________
very positive positive neutral negative very negative
Female athletes 24.1% 50.5% 24.5% 0.0% 0.9%
 

Male athletes 16.2% 42.9% 37.5% 2.9% 0.5%

 

NCAA I athletes 21.7% 30.4% 45.7% 0.0% 2.2%

 

NCAA II athletes 17.8% 48.2% 31.2% 2.4% 0.4%

 

NCAA III athletes 24.0% 48.0% 28.0% 0.0% 0.0%

_____________________________________________________________________
female n=214, male n=308, NCAA I n=148, NCAA II n=146, NCAA III n=126

Table 4
NCAA Athletes Recommended Level of Facebook Monitoring by Athletic Department (N=522)
________________________________________________________________________
strongly limited definitely
monitor monitor unsure monitor not monitor
Female athletes 1.8% 19.5% 19.5% 38.1% 21.2%
 

Male athletes 4.4% 9.8% 19.0% 30.7% 36.1%

 

NCAA I athletes 4.3% 13.0% 34.8% 26.1% 21.7%

 

NCAA II athletes 3.2% 14.1% 17.7% 31.9% 33.1%

 

NCAA III athletes 3.8% 3.8% 7.7% 57.7% 26.9%

_______________________________________________________________________
female n=214, male n=308, NCAA I n=148, NCAA II n=146, NCAA III n=126

Among NCAA Division I athletes, no respondents indicated a perception of any negative image of their personal accounts, whereas NCAA Division II and III respondents did report negative images, at rates of 4% and 7.7%, respectively. NCAA Division I athletes were most likely to say their accounts projected a neutral image, with 34.0% choosing this response. Also, as noted in Table 2, the NCAA Division III athletes had the highest percentage for positive personal image, 80.8%. They were followed by the NCAA Division II athletes, with 72.1%, and NCAA Division I athletes, with 63.8%.

DISCUSSION
The first Harvard University Facebook, distributed annually, was initially a simple, pictorial directory of all incoming freshmen. Along with pictures, the Harvard Facebook also included the majors and hometowns of freshman students. In 2005, Harvard student Mark Zuckerberg decided to launch an online version of the Facebook (Hoover’s, 2006). It started as a simple communication tool; then, Zuckerberg decided to extend online service to the entire Harvard student body and several other universities. It was after this point that Facebook exploded into the communication vehicle currently used by over two-thirds of American college students (Schrotenboer, 2006). Unlike the original Harvard Facebook, it has become a way for students to communicate and express their individuality, creating concern in collegiate athletic departments, particularly when individuality projects a negative self-image, team image, or university image. Facebook has become a medium through which student-athletes sometimes offer less-than-desirable information to the public.

Pop culture is created when (as on Facebook) users are allowed to share information with others exactly as they wish, uncensored, without regard for image and without fear of reprisal. The current trend, however, is for athletic departments and universities to restrict this free flow of expression, even, in a growing number of cases, to ban totally athletes’ use of social networks. Currently, the courts have not charged colleges and universities with violation of the First Amendment related to censoring social networks or restricting freedom of speech in student accounts. The issue will be interesting to follow.

As the present study found, female athletes generally claim to project a more positive image on social networks than their male counterparts. This finding alone might generate increased athletic department monitoring of male athletes’ Facebook accounts. Male athletes, furthermore, expressed more resistance to athletic department monitoring than did female athletes. This is another indication of the need to monitor male athletes’ accounts, since males seem to seek relatively more opportunities to exhibit a less-than-desirable image on Facebook.

The findings of this study for the various NCAA classifications suggest that NCAA II athletes are least educated about or least aware of the implications of the image issue associated with public accounts. This might have been expected, as these schools generally have less staff to work with athletes on image issues. The greater perception of a positive image projected on Facebook reported by NCAA Division III athletes is perplexing and deserves both accolades and further study. With the relative visibility of NCAA Division I athletics, it is to be expected that these athletes’ accounts would project a positive image, and they did, according to the athletes.

After investigating the issue of image and monitoring of college athletes’ personal Facebook accounts, it appears that several related matters need investigation. These are (a) whom athletes allow to access their accounts, (b) the privacy levels or protection levels athletes use with their accounts, (c) the motives of athletes for the images projected, (d) the steps athletic departments take to educate student-athletes and monitor their accounts, and (e) female athletes’ apparently greater concern about image on Facebook accounts, as contrasted to their male counterparts. Lastly, with the growing athletics-related abuse of Facebook in high schools, as seen recently at Medfield High School in Massachusetts and McCutcheon High School in Louisiana (Doughtery, 2007), study of high school policy is also merited.

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How Do Fans React When Sports Teams Are Named After Corporations?

ABSTRACT
The reaction to Red Bull naming its soccer teams after the corporation and prominently displaying the company logo on team uniforms is a mixed one among media critics and fans. Although many media observers note that trends seem to indicate that more sports teams may be named after corporations, there is still a fine line between what is seen as hip and what is taboo. Grathoff (2006), however, suggests that the idea that Major League Soccer would allow a team to be named after a corporation reinforces the league’s second-class status.

INTRODUCTION
The reaction to Red Bull naming its soccer teams after the corporation and prominently displaying the company logo on team uniforms is a mixed one among media critics and fans.
Travis (2006) criticizes the notion that sports teams should be named after corporate interests and predicts that it may not be long until other franchises are named after alcoholic drinks and other products most fans crave. He comments, “Somehow, as a sports fan, I like to think there’s something about a name that can’t be bought. Even if teams, players and stadiums can all be sold to the highest bidder, the last refuge of the fan should be the team name itself.” In contrast, Lewis (2001) argues that the owner of a franchise has the right to determine how a team should be named and marketed. Similarly, Burn (2006) comments that naming a team after a corporation may likely disturb fans more than merely placing a business name on a stadium. Burn contends that fans like to maintain the illusion that at least the team is not merely a business enterprise (as indicated when the squad is named after a corporation) but is at heart a sports organization. On the other hand, Quirk & Fort (1999) and Zimbalist (1998) correctly point out the need for additional review streams (including economic gains that may result from the naming of a team) that are needed to keep up with the exponentially growing cost of running a professional sports franchise.

Although many media observers note that trends seem to indicate that more sports teams may be named after corporations, there is still a fine line between what is seen as hip and what is taboo. Anderson (2006) and Boswell (2006) describe instances in American sport in which teams were named after corporate interests, including basketball franchises in the 1930s (e.g., the Firestone Non-Skids and the Toledo Red Man Tobaccos), soccer teams in the World War II era (e.g., Bethlehem Steel, the Akron Goodyears, and the St. Louis Central Breweries), semi-professional softball teams in the 1980s (e.g., the Coors Light Silver Bullets), and a soccer team in the 1970s (the New England Lipton Tea Men). For decades stock car racing in the USA has been prominently associated with a naming rights sponsor, first Winston and later Nextel. In a few cases, prominent American sports franchises named after corporations have gradually become accepted by most fans. For example, one of the most famous National Football League teams, the Green Bay Packers, were named after a meatpacking company, while the Detroit Pistons of the National Basketball Association were named after a manufacturer of automotive parts. Hughes (2006) and Grathoff (2006) suggest that a corporate name for a professional sports team may be more likely to be accepted by the public if it connotes an image associated with a sporting endeavor, is similar to names used by other teams (e.g., the Chicago National Basketball Association franchise and the University of South Florida use “Bulls” as their name) and is not seen to be politically incorrect. “Red Bulls” seems to meet these criteria.

On an international scale, there are examples of soccer teams named after corporations (Spangler, 2006). For example, Bayer Leverkusen in Germany is named after the firm that manufactures aspirin, while PSV Eindhoven of Holland is named for Philips Electronics. In that light, it could be argued that there is a tradition of naming soccer organizations after corporate sponsors.

Grathoff (2006), however, suggests that the idea that Major League Soccer would allow a team to be named after a corporation reinforces the league’s second-class status. Grathoff notes how the National Basketball Association, a more established and prosperous league, refused a bid by FedEx Corporation to name the Memphis franchise “The Express,” as well as a request to have a proposed Louisville team play its home games at an area to be called “the KFC Bucket.” Said Paul Swangard of the Warsaw Sports Marketing Center at the University of Oregon (quoted in Grathoff, 2006), “In the American sports landscape, we would have expected to see the Red Bull thing happen in a start-up league or a fledgling league rather than one of the mainstays. The NBA, the NFL, Major League Baseball and the NHL have been very cautious with their approach.”

HISTORY
The Birth and Marketing of Red Bull
Austrian Dieter Mateschitz created Red Bull after visiting Thailand in 1982 and learning that tired drivers in that region consumed large quantities of energy drinks. The top brand in Thailand was a mixture of caffeine, water, sugar and taurine marketed as “Water Buffalo” (referred to locally as Kratindaeng). Mateschitz created his own version of the drink, which he called Red Bull, loosely modeled after that Thai beverage. Shortly thereafter, Red Bull was introduced to Austria, Germany and other European nations. It was first marketed in America in 1997 (Gschwandter, 2004).

Sales of energy drinks like Red Bull and its competitors have increased by 75% since 2005 and totaled more than $3.5 billion in 2006. In 2006 Red Bull sold 2.5 billion cans of the drink worldwide, about 1 million of those in the United States. More than 500 varieties of energy drinks were sold in 2006, and Red Bull is one of the leading brands in the category (Rouvalis, 2006). Estimates suggest that roughly one in every three American teenagers consumed an energy drink in 2006 (Lord, 2007).

Red Bull is known as much for its unique marketing programs as for the highly caffeinated taste of the drink (Hein, 2001), which some marketing experts refer to as liquid Viagra. Van Gelder (2005) suggests that Red Bull is at the leading edge of relatively young companies that combine the best elements of creativity and strategy when building their brands. As a result, he contends, Red Bull will continue to flourish, as long as it emphasizes innovative branding strategies. McCole (2005) describes Red Bull’s branding efforts as “experiential marketing” in which target audiences are exposed to energized special events that create vivid memories. McCole argues that involving stakeholders in live action-sports events can create strong relationships between potential customers and the brand. Similarly, Dolan (2005) describes Red Bull’s promotions efforts as “guerilla marketing” relying on creative special events to bypass traditional advertising in the mass media. Ho (2006) comments that Red Bull is creating a new marketing model by actively owning teams and sports events rather than merely serving as a corporate sponsor. Gschwandter (2004) suggests that Red Bull is marketed using “alpha bees”: individuals who will enthusiastically tell others about a product they love.

Red Bull has often marketed on-site at nightclubs and extreme sports events (such as base jumping and extreme skateboarding), and motor sports events such as BMX motorcycle racing and NASCAR and Formula One automobile racing. Initially, the focus was not to market Red Bull through team sports, but instead to promote individual personalities (Lidz, 2003). Lindstrom (2004) describes Red Bull’s efforts to creatively promote and market the drink to young adults and college students; an example is the company paying people to paint their car in the company colors and place a large replica of a Red Bull can on the roof. As a result, Red Bull is consumed in large quantities on college campuses, either by itself or mixed with liquor.

Typically, Red Bull is only advertised once a target market has matured and buzz has already been created about the brand. For example, most distributors buy the drink directly from the company and sell Red Bull exclusively. According to Ho (2006) and Heinz (2001), Red Bull seeks to align itself with the lifestyle associated with action sports.

Even though it has been criticized by public health officials as being detrimental to human health and even lethal in some cases (Wilde, 2006), a few athletes, including some soccer players, tout the drink’s benefits. MLS forward Taylor Twellman of the New England Revolution endorses the product and said “Drinking Red Bull before training and matches provides me with the needed energy and focus to give me that extra edge on my opponents” (Sells, 2006). In contrast, Zeigler (2006) points out that some public health officials are concerned that the drink may lead to dehydration and that Red Bull seems to be primarily used with alcohol, so people can drink without getting tired.

Red Bull Salzburg
SV Salzburg has a rich history. The club was formed in 1933 when teams associated with the left and right wings of the political spectrum merged. In fact, the selection of violet and white as team colors was intended to suggest the new team was politically neutral (Guenther, 2006). SV Salzburg has traditionally been one of the strongest teams in Austria’s Bundesliga and won the league championship in 1994, 1995, and 1997. In 1994 the team finished as the runner-up in the UEFA Cup.

However, SV Salzburg began encountering financial difficulties around the year 2000, and Red Bull purchased the team in 2005. Robinson (2005) describes how many fans were initially supportive of Red Bull’s purchase of the team, since it would provide needed finances to recruit top-caliber players. But he notes that (fans) soon … recognized that the new management’s purpose was to destroy the old club to establish a Red Bull company club.”
Austria’s premier football association, the Bundesliga, has a history of allowing football club names to help promote private investors (Joyce, 2003). Still, Red Bull took this concept to the extreme, completely rebranding the team and replacing the traditional purple and white uniforms with the red, blue, and yellow colors used to market its drink (Plenderleith, 2007b). Red Bull also referred to the origin of the club based on when the company made the purchase (2005) rather than on the year the team was founded (1933). According to Guenther (2006), “There was a clear intention to sever any ties with the ‘old’ Austria Salzburg. Club sources went on to say that, as far as Red Bull is concerned, there is no history, no tradition” associated with the transformation of SV Salzburg to the new ownership.

When discussing the rationale for changing the color of the team’s uniforms, Red Bull CEO Dieter Mateschitz (cited in Joyce, 2003) referred to fan protests as “kindergarten stuff.” He said, “The Red Bull can’t be violet or else we couldn’t call it Red Bull. Whether you play in purple, blue, or green is irrelevant; the only thing that matters is the team being successful.”

Red Bull also instituted policies that discourage fans from showing the violet and white colors used for many years and prohibit fans from displaying in the stadium banners criticizing the new ownership. Some fans who wore the violet and white colors to Red Bull matches were harassed and assaulted with beer bottles. The end result has been that relationships between the team and many long-standing supporters were significantly damaged. In addition to claims that people who cherished the old traditions were harassed, Red Bull may have offended potential fans by providing a game-day experience that features loud rock music, a disco-style laser light show, a celebrity kick-off with the driver who leads Red Bull’s Formula One team, and fan animators who exhort the crowd to cheer when prompted (Joyce, 2003).

The divided loyalties to old and new ownership have created a group of disaffected fans calling itself “the Campaign for Violet and White” (Violett-Weiss, 2007). Some of the most important goals of this campaign are to incorporate the original team colors of violet and white into the club’s new identity; to make sure that Red Bull refers to the 1933 founding in its marketing and literature; and to improve public relations and dialog between Red Bull and fans of SV Salzburg.

Changing the Name to Red Bull New York
The New York franchise was founded at the creation of Major League Soccer in 1996. Initially, the team was named the New York/New Jersey MetroStars after another corporation, the MetroMedia Entertainment Group. In 1997 the team dropped New Jersey from its name and became known simply as the New York MetroStars.

In March 2006, Red Bull purchased the team for a reported $100 million from the Anschutz Entertainment Group (Bell, 2006). As part of negotiations that led to the purchase, Red Bull lobbied hard for permission from the league to prominently place the logo on the front of the team jersey (Weinbach, 2006). According to Red Bull CEO Dieter Mateschitz, purchasing the MetroStars made sense because it provided an opportunity to market the drink to more than 18 million Americans who play soccer, as well as to an additional 60 million fans who follow the game as spectators. Mateschitz said, “Soccer is just about to make a big breakthrough in the United States media” (Red Bull, 2006). Fatsis (2006) suggests that the investment by Red Bull is one sign that Major League Soccer has a promising future and is poised for economic growth.

The new ownership also acquired a stake in a soccer-only stadium, Red Bull Arena, now being built for the team in Harrison, New Jersey, and opening in 2008 (Thomaselli, 2006). Clark (2006) suggests that buying the club makes sense economically for Red Bull, since it allows them to promote their products using the team as a “walking billboard” in a huge media market. Clark commented that the purchase of the team by Red Bull may likely improve the team’s performance on the pitch, given the owners’ successes in Europe and the amount of capital they will invest in the team. In 2006, Red Bull New York suffered a $14 million loss, perhaps because all the branding and marketing of the energy drink lessened the participation of other corporate sponsors (Plenderleith, 2007).

Several local politicians were upset that the team will be “Red Bull New York,” even though the state of New Jersey is financing the stadium in Hudson County, New Jersey. Brendan Gilfillan, a spokesman for New Jersey Governor John Corzine, opposed dropping New Jersey from the franchise name and stated (Frankston, 2006):

Their new name may be Red Bull New York, but striking New Jersey from their name seems to be a different kind of bull altogether. This is a team that sells its products in New Jersey, draws its fan base from New Jersey, and receives funding from New Jersey.

In addition, New Jersey Senator Frank Lautenberg urged Red Bull to reconsider the decision (The Global Game, 2006). George Zoffinger, president of the New Jersey Sports and Exposition Authority which runs Meadowlands Stadium where the team now plays, said, “It is an insult to us for them to remove the name of the state,” calling the new name a “lack of respect for the state of New Jersey” (Bell, 2006). Meanwhile, Page (2006) opines that removing New Jersey from the team name disrespects the state and its residents.

The potential economic benefits of changing a team name to reflect a franchise’s association with a larger media market (i.e., the change from New Jersey to New York) are illustrated by a similar case involving the Angels Major League Baseball franchise. Nathanson (2007) and Flaccus (2006) describe how owner Arte Moreno changed the name of his team from the “Anaheim Angels” to the “Los Angeles Angels of Anaheim,” despite the fact that the team did not make a geographic move, but simply rebranded itself. According to Flaccus, Moreno “changed the name to make the most of the Angels’ location in the nation’s second-largest media market …. Using Los Angeles in the name would attract more sponsorships, advertising, and broadcast contracts.” Giulianotti & Robertson (2004) suggest that fans throughout the world often are more likely to identify a sports organization with its brand, rather than with its city or region of association.

Beyond concerns about removing New Jersey from the team name, “Red Bull” has been criticized for sending signals that Major League Soccer is not first-class. Former MetroStars public relations specialist Tony Miguel (quoted in Spangler, 2006) said:
The biggest problem (for Major League Soccer) is regarding the credibility and perception of soccer among the mainstream media. MLS is already seen by most in the mainstream media as a minor league. Red Bull New York only adds to the perception. Imagine the outcry that would occur if the New York Yankees became the New York GEICO’s. This is a desperate move by a league desperate for investors. I think in the long run this hurts MLS much more than it helps the league.

Another factor that likely increased tension about the renaming is that a small group of diehard fans may have feared that Red Bull would discard MetroStars history and traditions. However, Galarcep (2006) suggests that Red Bull learned from its mistakes with SV Salzburg and will handle the matter more sensitively. He contends that the team’s success on the pitch—not its name—will be the key to keeping existing fans and wooing new supporters.
In contrast, Red Bull officials contend that taking New Jersey from the name is not really significant. Red Bull spokesperson Patrice Redden stated that, “In the tradition of the New York Jets and the New York Giants and even the New York Cosmos, we believe that the metropolitan New York area is truly one of the most influential markets in the entire world and the New York affiliation is an excellent representation of this international culture” (Zeigler, 2006).

The French news service Agence-France Presse contends that Red Bull bought the soccer club to boost the image of its brand in the United States. Said sports marketing specialist Rainer Kress of Vienna, “American Major League Soccer … is booming and with the MetroStars deal Red Bull is pursuing a strategy built entirely around marketing” (Butler, 2006). Alexi Lalas, at the time the general manager of Red Bull New York, said renaming the team was “bold,” and “the marketplace in particular needs bold moves.” He also suggested that fans who know the history of and trends in international professional soccer should accept corporate naming. Lalas described further the significance of Red Bull’s purchase of the team (Freedman, 2006): “We are associating ourselves with a world-renowned brand that is synonymous with creative, innovative and unique marketing. All the resources of Red Bull will be brought to bear to market the Red Bulls. I’m excited.”

WHERE MIGHT THIS LEAD?
According to Chris Smith, a Dallas-based specialist in sports and event marketing, Red Bull’s example may not necessarily lead to other teams being named outright for corporations. “It will probably be more of a trickle than a flood,” he said. “While sponsors are eager to step up, they understand the emotional attachment that fans have with teams they love. There’s the potential for a strong negative backlash” (Anderson, 2006). Commented the University of Oregon’s Paul Swangard (cited in Turnbull, 2006), corporate naming is “sort of the last bastion in American sports … [American sports fans] haven’t been willing to accept it.”

On the other hand, some marketing experts contend that the corporate influence found throughout international soccer, and increased advertising in many American sports, may make corporate team names more acceptable. For example, soccer jerseys in Europe typically feature a corporate sponsor’s name prominently, while the logo of the football club may be barely noticeable. Despite the significant commercial presence, however, these teams are almost universally referred to by the name of the football club, not the sponsor. In 2007 Major League Soccer began to allow franchises to prominently display the names of corporations on the front of jerseys, although most teams do not take the name of the corporate sponsor. For example, Real Salt Lake’s uniforms prominently display the name Xanga (a natural juice drink), Chivas USA features the PEMEX logo (Mexico’s national gas company), and the jersey of the Los Angeles Galaxy is adorned with the name and logo of HerbaLife. In all these cases, the logo of the corporate sponsor is shown much larger than the team name (Weinbach, 2006).

FC Barcelona, one of the most storied football clubs in Spain, recently put a new spin on this trend when they entered into an agreement to feature the United Nations children’s charity, UNICEF, on uniforms. Even though FC Barcelona will not directly gain any revenue from this decision, featuring UNICEF’s logo is seen by marketing experts (Hughes, 2006) as a way to create an image of social responsibility on the part of the club and its supporters.
Skidmore (2006) discusses the merits of naming sports teams after corporations, writing that, “Because of mergers, bankruptcies, etc., no league wants a franchise to have a new nickname every two seasons. There is also the problem of cheering for the ‘Verizons’ or the ‘Colgates’ … [Still,] if Team Red Bull can work for MLS, it may not be much longer before we see corporate names in the big four leagues.”

Similarly, Allan Adamson, brand manager at WPP Group, warns that there may be a downside to naming a team after a corporation, especially when problems arise (cited in Bosman, 2006). “The risk is, ‘What happens to the team when a product starts selling badly?’” says Adamson. “It’s a risky strategy, especially when you choose something that’s both an energy drink and an alcoholic mixer.” He likens the permanence of a team name to a tattoo and suggests it may be more difficult to change a team than a stadium named after a corporation.

CONCLUSION
It is clear that renaming professional soccer teams after the Red Bull energy drink led to at least some level of public opposition in both the United States and Austria. However, it is important to differentiate the public outcries in each nation. In Austria, it appears that much of the anger at Red Bull was due to perceived refusal of the new owners to acknowledge and maintain traditions of the original club. Fans found it especially offensive that Red Bull Salzburg ignored the 1933 founding date, instead treating the club as a new expansion team. In a similar light, Austrian soccer fans had closely affiliated SV Salzburg with many time-honored traditions, including the violet and white colors worn for decades. Breaking that tradition was a personal affront to large numbers of fans. In contrast, fan reaction in New York and New Jersey was more localized. There was relatively little criticism in either state, largely because of the relatively low profile of Major League Soccer on the American sports landscape. Certain politicians and civic leaders were angered by the removal of New Jersey from the team name when public funds were building its stadium in New Jersey. Many local residents, however, were not especially bothered by the move: Many activities and organizations around the region refer to themselves as belonging to the “greater New York City” metropolitan area (S. Weston, personal communication, Month Day, 2006). For smaller apples, it just makes sense, from a public relations and marketing perspective, to associate oneself with the Big Apple brand.
On a broader scale, a key question to ask is the extent to which naming a team after a corporation is thought offensive. In Europe, football fans have come to expect the fronts of uniforms to be adorned with large corporate symbols. Still, few football organizations in Europe are yet named after corporations. In America, it has gradually become acceptable to embrace, for a few professional teams at least, names that stem from corporate ties (e.g., the Green Bay Packers or Detroit Pistons). In contrast, the National Basketball Association recently denied a request to name a new Memphis franchise after FedEx Corporation. Perhaps the key principle is to choose a name that is not offensive or politically incorrect and that connotes, in a broad sense, our sports traditions or sporting endeavors.

The experiences of Red Bull provide some insights into how corporate names for sports teams might meet with more public acceptance. For example, after angering Austrian fans by discarding existing club traditions, Red Bull learned how important it is to understand the passionate relationships between teams and their fanatic supporters. A wiser Red Bull then worked hard to ensure that the traditions and supporter groups of the MetroStars would be respected following that team’s acquisition. In addition, the most important factor that may influence fans’ response to a new name is the extent to which the team succeeds on the field of play. If Red Bull shows it is willing to invest in teams and facilities to boost team performance, the issue of the franchise name may become less important.

In sum, one has to ask whether Red Bull’s practice of naming sports teams after its product is a trend that will become more widespread in America and Europe. The general consensus seems to be that naming teams after corporations may be more common among teams and leagues that, like Major League Soccer, have lesser status. The top-of-the-line sports leagues in the USA seem unlikely to adopt the practice in the immediate future. In the larger cultural context of sport, one has to come to grips with the reality that corporations have been investing in and promoting sports organizations for decades, even to the extent of naming teams after themselves. Although naming an established team after a corporation may seem egregious, perhaps it is just an indication of the important role of private investors in supporting sports organizations

For more information, contact Jensen at rwjensen@ag.tamu.edu or (979) 845-8571 or (979) 574-5187. Weston can be contacted at westons@mail.montclair.edu

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Quality Control Procedure for Kinematic Analysis of Elite Seated Shot-Putters During World-Class Events

ABSTRACT
Kinematic analyses of elite shot-put throwers commonly involve shot-trajectory parameters determined under experimental conditions with an accuracy-based procedure. This can be only partially implemented within an event-constrained procedure (as opposed to experimental conditions). Event-constrained procedures, while they provide realistic information collected in an open environment, introduce several constraints that can potentially compromise accuracy measures. This study concerns a quality control procedure intended to address such constraints. The quality control procedure relies on 5 key elements aimed at reducing and reporting error and validating measures of the shot trajectory. The performance of 7 world-class shot-putters during international events was calculated using video data recorded at 50 Hz with a camera located to the side of the athlete. Accuracy was above 75% for all the attempts and above 94% during 4 attempts. This study demonstrated (a) the need to systematically implement this procedure for kinematic analyses based on event-driven recordings; (b) the value of quality indicators in making decisions concerning the instant of release; and (c) the importance of reporting this procedure’s outcomes in terms of error and percentage error.

INTRODUCTION
The performance of world elites in the shot put, measured as the distance the shot is thrown, results from the interaction between throwing technique and the design of the throwing chairs (O’Riordan & Frossard, 2006). That interaction shapes the parameters of the shot trajectory, which depends on the position, the velocity, and the angle of the shot at the instant of release ( Ariel, 1979; Dessureault, 1978; Chow, Chae, & Crawford, 2000; Linthome, 2001; Lichtenburg & Wills, 1978; McCoy, Gregor, Whiting, & Rich, 1984; Sušanka & Štepánek, 1988; Tsirakos, Bartlett, & Kollias, 1995; Zatsiorsky, Lanka, & Shalmanov, 1981). Sport scientists, classifiers, coaches, and athletes use the parameters of the shot trajectory to better understand the link between disability and performance (Higgs, Babstock, Buck, Parsons, & Brewer, 1990; McCann, 1993; Vanlandewijck & Chappel, 1996; Williamson, 1997; Chow & Mindock, 1999; Chow et al., 2000; Laveborn, 2000; Tweedy, 2002). Video recording allows for estimation of parameters, using primarily an accuracy-based procedure or event-constrained procedure, as illustrated in Figure 1.


Figure 1. Overview of the video recording (A), the data processing (B) and the outcomes (C) of the parameters of the shot’s trajectory of elite seated shot-putters. The parameters determined using an accuracy-based procedure rely on data collected during training and in laboratory,which presents the advantage of accommodating the typical experimental requirements but it provides only partially realistic information regarding the performance. The event-constrained procedure provides realistic information collected in the open environment presenting several constraints. Thus, a quality control is needed to reduce, validate, and report the errors. This will ensure that sport scientists, classifiers, coaches, and athletes have a better appreciation of the limitations of the data presented about the performance.

Accuracy-based procedure

Video recordings made during training or as part of laboratory motion analysis, whether for routine observation or for research, must accommodate typical experimental requirements for three-dimensional reconstruction, including suitable calibration volume, appropriate number of cameras, precise positioning of cameras, use of active or passive markers, and an unrestricted number of attempts. A flexible set-up of this sort enables an experimental approach employing trial and error, wherein quality control is achieved through repeat recording until the desired kinematic parameters (i.e., shot trajectories) are satisfactorily accurate. The accuracy and validity of parameters reported in research may be taken for granted, even though authors seldom report key indicators like number of frames tracked after release, or calculation of performance using parameters or using tape measure, or the difference between these two performances (Chow & Mindock, 1999; Chow et al., 2000).

Unfortunately, trajectory information obtained from non-competitive environments only partially represents the throwing technique an athlete uses while competing. Participants in a study by Chow et al. (2000) performed, on average, 15±9% below their personal best, leading the researchers to conclude that, in order to develop a data base of ideal performance characteristics, numerous quantitative data needed to be obtained, particularly those collected during leading competitions.
Event-constrained procedure

Video recordings of elite shot-putters’ throwing techniques were made on the field of play during the 2000 Paralympic Games, 2002 International Paralympic Committee World Championships, and select Australian national events (Frossard, O’Riordan, & Goodman, 2005; Frossard, O’Riordan, Goodman, & Smeathers, 2005; Frossard, Schramm, & Goodman, July 2003; O’Riordan, Goodman, & Frossard, 2004). Recording in these open environments entailed certain constraints (Frossard, O’Riordan, Goodman, & Smeathers, 2005; Frossard, Stolp, & Andrews, 2006), presented in Figure 1. Multi-purpose recording becomes necessary for capitalizing on an event’s uniqueness and for securing the distinct kinematic data sets of interest to distinct parties. Classifiers, for instance, may be interested in assessing the full range of upper-body movement (Chow et al., 2000; Tweedy, 2002). Engineers, in turn, may seek to study the deformation of the pole. Coaches’ main interest may be something as specific as hip-movement pathways during forward thrusting, or the exact position of the feet (O’Riordan, Goodman, & Frossard, 2004). Finally, the biomechanist’s interest may well be the parameters of the shot trajectory (Chow et al., 2000). Under experimental conditions, optimal accuracy often results from a focus on one data set at a time, that set obtained using optimal field of view and calibration volume. During competitive events, a compromise must be made as all parameters are observed using a single field of view. Furthermore, various technical barriers are presented on the playing field, including lack of control over the event and inevitable need to make recordings in a non-disruptive fashion. There is, in short, a one-off chance to record any attempt, with space only for one to two cameras, and despite likely obstructions of the field of view by equipment, referees, officials, TV crew, or the like.

Such constraints can be assumed to affect the accuracy of the kinematic data. Even the implementation of an accuracy-based approach within an event-constrained procedure will only partially guarantee sufficient accuracy. Nevertheless, a formal quality control procedure limited to determining shot trajectory parameters and occurring after the video recording stage could offer help to achieve highest possible accuracy.

PURPOSE

The authors’ ultimate aim is to propose a quality control procedure able to reduce error in the measurement of shot trajectory parameters and validate measured parameters, as well as to refine and standardize the format used to report measurement error. The proposed procedure relies on five key quality indicators that should influence decisions about when the moment of release occurs. The paper also has four secondary purposes. First, it comprises a detailed example of the entire procedure as it was deployed with the Class F55 male athlete who won the gold medal at the 2002 International Paralympic Committee (IPC) World Championships. Second, it tracks the procedure’s outcomes in terms of 7 elite shot-putters participating in 2 world-class events. Third, it presents possible sources of error inherent in the proposed videotaping setup. Fourth, it makes several recommendations for future on-field studies.

METHODS

Events
Video recordings were made during two world-class events, the 2000 Paralympic Games held in Sydney, Australia (4 classes of competition), and the 2002 IPC World Championships held in Lille, France (3 classes of competition), as indicated in Table 1.

Table 1
Event and total number of athletes competing in each class included in this study (PG: Sydney 2000 Paralympic Games, WC: Lille 2002 International Paralympic Committee World Championships).

Participants
A total of 51 shot-putters were part of the present study, including 39 males and 12 females. For the competitions, each athlete had been classified according to the latest International Stoke Mandeville Wheelchair Sports Federation classification system (Laveborn, 2000). Table 1 illustrates total numbers of these athletes competing in each class, although the present analysis was limited to those who became gold medalists in four select classes (F52, F53, F54, and F55). Though not all-inclusive, the sample was deemed sufficient for illustrating the principles of the quality control procedure. (Gold medalists also typically generate greatest interest among sport scientists, coaches, and athletes.) Female athletes assigned to the F52 and F54 classes had competed jointly at the Sydney Paralympic Games, due to the small numbers of athletes in these classes, and a single gold medal was awarded. For our research, however, the performance of the event’s top competitor in each of these classes was considered. The female Class F53 shot-put event was canceled for lack of athletes.

Data processing

The sequence of the following 7 key steps used to process video data is shown in Figure 2.


Figure 2. Seven key steps of data processing, including the quality control procedure and the five associated quality indicators.

Step 1: Camera set-up

Frossard, Stolp, and Andrews (2003) have previously provided a thorough guide to the practical aspects of video camera set-up during world-class events. Therefore, this paper will limit itself to key elements of that set-up. During the 2 events included in this study, each put was recorded using 1 digital video camera (SONY Digital Handycam DCR-TRV15E), set at a sampling rate of 25 Hz. A “household” camera was chosen because it was affordable, discreet, and readily available. High-resolution cameras, by contrast, require exacting lighting conditions and are expensive and fragile. Some video cameras commercially available at the time of the events would have allowed high-speed filming, but at the cost of compromised resolution.
The SONY camera was placed approximately 1.1 m high at a distance between 8.0 m and 10.0 m, perpendicular to the length of the plate. The angle between the optical axis of the camera and the ground was approximately 90 degrees. The field of view included the full length (2.29 m) and full width (1.68 m) of the plate on the ground. The field of view was furthermore enlarged in the direction of the put, to ensure the recording of at least the first 5 frames of the shot’s aerial trajectory (see Figure 3A). Under experimental conditions, this field of view can be obtained by zooming to reduce the perspective error once the camera is positioned with respect to the plate. In this study, the camera was placed relatively close to the plate in an effort to lessen the possibility of intrusion into the field of view by equipment, referees, or TV crews. Nevertheless, the zoom was occasionally used. This camera position resulted in a pixel resolution ranging from 0.95 cm to 1.85 cm, depending on the camera’s position and the zoom setting.

Figure 3.Example of male gold medallist in the class F55 participating in the shot-put event of the 2002 IPC World Championships seated in the throwing frame (D) attached to a plate (E) using ties (C) that is facing the sector (F). Figure A provides an example of field of view of the camera with the body’s segments’ position and the shot at the instant of release (Tfinal – Frame 91). Figure B represents a stick figure of the athlete with the key instants needed to determine the parameters of the shot’s trajectory in the Global Coordinate System (GCS[O, X, Y]).

Step 2: Video recording
A total of 387 attempts, corresponding to nearly every one of the attempts made by each athlete in each class, were recorded and stored on MiniDVs. The duration of the video recording of each attempt was approximately 7 seconds. An attempt began when the referee handed the shot to the athlete and ended shortly after the shot landed on the ground. A customized calibration frame (2 m length x 1.5 m height x 1 m width) containing 43 control points placed on top of the plate was recorded at the beginning and at the end of each event.

Step 3: Video digitizing
The video recording of the calibration frame and of the best attempt in each class (the gold-medal throw) was digitized at 50 Hz using Digitiser 5.0.3.0 software, manufactured by SiliconCOACH Ltd. This sampling rate was achieved by de-interlacing the initial video frames, which affected accuracy only on the horizontal axis.

Step 4: Tracking
The Digitiser software was used to track, frame-by-frame, the center of the shot, the distal end of the middle finger, the position of the wrist, and the origin of the two-dimensional Global Coordinate System (GCS[O, X, Y]). The latter corresponded to the middle of the line of reference located in the front and at the bottom of the throwing frame, used by the referee to measure the performance, as illustrated in Figure 3. The tracking started with the back thrust and ended when the put was no longer within the field of view, which included 5 frames or more after the estimated moment of release. Tracking of the full body was obtained only for the male Class F55 gold medalist (see Figure 3B).
Step 5: Selecting instant of release
The 2 coordinates of the points tracked were imported into a customized Matlab software program (Math Works, Inc.). An operator used the software to select a combination of 2 positions of the shot, allowing calculation of the parameters of the shot’s trajectory (also see Step 6, below). The first position, (Tinitial), corresponding to the instant of release, was indicated by separation between the finger and the shot of a distance larger than the shot’s diameter. The second position, (Tfinal), corresponded to one of the 3 consecutive frames. The two-dimensional coordinates of the displacement were not smoothed or filtered to avoid end point distortions of the limited number of samples after the moment of release.
Step 6: Calculation of parameters of shot trajectory

The Matlab software implemented the classic equations from the literature (Lichtenburg & Wills, 1978; Linthome, 2001) for calculating the trajectory of the shot, allowing the landing distance to be estimated. The performance calculation was determined from the parameters of the shot at the instant of release, including (a) resultant horizontal and vertical components of the translational velocity; (b) resultant horizontal (advancement) and vertical (height) components of the position; and (c) the angle of the trajectory. The performance calculation was also corrected by the radius of the shot, as the official performance was measured from the landing mark on the ground closest to the Global Coordinate System.
Step 7: Comparison of official and measured performance

The performance calculation was compared with the official performance, which was the distance measured by the referee during the event; calculation error indicators and calculation quality indicators were employed as described below. The official performance measure was taken as the value of reference.

Quality control procedure

The quality control procedure relied on two efforts aimed at reducing and reporting error and validating measures of the shot trajectory, as presented in Figure 2. The first included the digitizing of the displacements of the shot and the operator’s subsequent selection of the best combination of Tinitial and Tfinal . Feedback on the quality of the selection was obtained from the 5 key quality indicators, as follows:

Average acceleration after release on vertical axis (Quality Indicator 1—Step 5)

In principle, the vertical velocity of the shot must be constant, and its acceleration must be equal to 9.81 m.s-2. The software therefore calculated the regression line of the vertical velocity between the frame following Tfinal and the last frame available, in order to eliminate random pointing errors. Then, it calculated the average acceleration, as illustrated in Figure 4. The average over four frames was 10.78 m.s-2 in the case of the male in Class F55.

Mean instantaneous acceleration after release on horizontal axis (Quality Indicator 2—Step 5)

In principle, the horizontal velocity of the shot must be constant, and its acceleration must be nil. The software therefore calculated the mean instantaneous acceleration between the frame following Tfinal and the last frame available, as illustrated in Figure 4. The mean over four intervals was -0.89±0.35 m.s-2 in the case of the male in Class F55.
Calculation error (Quality Indicator 3—Step 7)

Expressed in meters and corresponding to the discrepancy between official and calculated performance measures, the calculation error suggests the general quality of the data processing. A positive error indicates a calculated performance measure that overestimates the official performance, while a negative error indicates a calculated performance measure that underestimates it.

Calculation quality (Quality Indicator 4—Step 7)

The calculation quality corresponds to the percentage of the absolute value of the error, in relation to the official performance measure (such as: Calculation quality=[100-(Abs(Error)/Official performance)*100]). This quality indicator provides an understanding of the data processing’s quality in absolute terms, but it cannot indicate the direction of error.
Sensitivity analysis of tracking of Tinitial and Tfinal (Quality Indicator 5—Step 7)

Preliminary studies showed that an error of ±2 pixels could significantly affect calculation of the performance. However, the software was able to provide a succinct sensitivity analysis of the tracking, the outcome of which is reported in Table 2. Sensitivity analysis comprised recalculation of the performance using the combination of positions from Step 6, with 2-pixel positive and negative errors on Tinitial alone, on Tfinal alone, and/or on these two combined. As needed, this feedback guided operator readjustments concerning pointing of the shot (see also Step 4 above).

Table 2

Example of sensitivity analysis of the tracking (Quality Indicator 5) for the male gold medalist in F55 class consisting on recalculating the performance using the combination of positions determined in Step 5 with positive and negative errors of two pixels (3.6 cm) either on Tinitial and Tfinal only or on both combined. The white dot corresponds to the original position; the black dot corresponds to the position with the error. X and Y represent the horizontal and vertical axes, respectively.


Figure 4. Example of feedback provided for the male gold medallist in F55 class to determine the moment of release of the shot (Step 5). Section A represents the vertical position of the shot and the finger during the complete throw until the shot is outside the field of view. The square area corresponds to the zooming on the relevant data to be used to determine the moment of release. Section B presents the selected moment of release (Tinitial = Frame 91), when the separation of the shot and the finger is greater than the diameter of the shot and the second position (Tfinal = Frame 92). Section C provides the velocity of the shot after release as well as the average acceleration (Quality indicator 1) and the mean instantaneous acceleration (Quality indicator 2).
The second of the two efforts to reduce and report error and validate measures of the shot trajectory involved our selection of software that allowed the operator to process the data over an unlimited number of iterations from Step 4 to Step 7, until discrepancies between calculated and official measures had been minimized. Each iteration represented one combination of data points as determined in Step 5.

RESULTS

Table 3
Outcome of the quality control procedure. The number of iterations corresponds to the number of attempts made by the operator during the quality control procedure to minimise the difference between the official and calculated performance. The error corresponds to the difference between the official and calculated performance (Quality indicator 3 (1)). The calculation quality corresponds to the percentage of the absolute value of the calculation error in relation to the official performance, such as: Calculation quality=[100-(Abs(Error)/Official performance)*100] (Quality indicator 4 (2)).

Table 3 presents, by competitive class, the quality control procedure’s outcomes, including number of iterations, calculation error, and calculation quality. The smallest difference between a calculated and an official performance measure was obtained from a minimum of 3 (maximum of 9) iterations. Calculation error ranged from 0.01 m to 1.33 m, with a mean of 0.54±0.46 m. The absolute calculation quality ranged from 79% to 100%, with a mean of 92±8 %.

DISCUSSION

These results overall might be considered satisfactory, since athlete performance during 4 out of 7 puts was calculated with accuracy surpassing 94%. However, accuracy surpassed only 79% for three competitive classes (F53 male, F54 male and F52 female), and the number of iterations was high. This finding indicates that, for these puts, the shot trajectory parameters were not determined with sufficient precision, the result primarily of pincushion distortion, sampling frequency, and projection of shot displacements onto the sagittal plane.
Pincushion distortion

Tracking of the shot’s displacement took place at the right top corner of the screen, outside the calibration volume with its maximum 1.5 m on the vertical, 0.5 m on the horizontal, axis. In principle, this zone is the most prone to pincushion distortion, in which straight lines appear to bow in toward the middle. While such distortion must be acknowledged, it is unlikely to have contributed strongly to the lack of accuracy.
Sampling frequency

Despite its sampling frequency of 50 Hz, the shot appears fuzzy at the instant of release because it has traveled significant distances between successive frames. This made it sometimes difficult, during Step 4, to track the exact center of the shot at the instant of release. Sampling frequency could have had impact on the estimation of the position of the shot and on the estimation of the speed of release. However, speed of release and error do not seem to be correlated here. Quality Indicator 5 assisted in determining the most accurate pointing, as illustrated in Table 2.
Projection of the displacements of the shot onto the sagittal plane

In this study, the main source of error was the positioning of the camera to the side of the athlete, which limited calculation of the speed of release to the sagittal plane alone. Visual analysis of the footage, however, showed that the throwing technique of athletes in these classes included more rotation in the transverse plane. The consequent projection of out-of-plane movement onto the sagittal plane tends to result in underestimation of speed of release and overestimation of release angle. This is reflected in our finding of a constant mean instantaneous acceleration after release on horizontal axis (Quality Indicator 2), rather than a nil mean, as was obtained for the Class F55 males. The slope of the curve corresponds, then, to the angle of the shot trajectory in the transverse plane.

In principle, the best way to alleviate these limitations would be to use a three-dimensional motion analysis system with a data acquisition rate ranging up to 100 Hz. Such a system should provide enough samples to accurately determine the shot’s position at the instant of release and to enable further smoothing of the data if required. Furthermore, with such a system the actual trajectory of the shot could be calculated in three, not two, dimensions, which would improve the accuracy of velocity and angular data

Ideally, put-throwing analysis should require at least four cameras, aligned diagonally with each corner of the plate, as well as a preferred fifth camera located above the athlete ( Allard, Stokes, & Blanchi, 1995; Marzan, 1975). Such a camera arrangement, while possible in an experimental framework, would be difficult to implement on the field during a world-class event, its invasive nature perhaps prompting organizing committees to deny researchers access. In addition, some 20 people work in the immediate throwing area alone, making it highly likely that the field of view of cameras on the floor would become obstructed or compromised as the recording of attempts progressed ( Frossard, Schramm, & Goodman, July 2003; Frossard, Stolp, & Andrews, 2003). A more feasible alternative involves using two commercially available high-speed cameras recording at 100 Hz or better, with full resolution. These cameras should be placed, at a distance, to the front and on the side of the thrower, allowing a bi-planar analysis in the sagittal and frontal planes. (Recordings made in this fashion should also accommodate three-dimensional reconstructions.) It would then become possible to estimate the rotation of the throwing upper arm in the transverse plane. Furthermore, the camera in front would provide data allowing one to determine the distance of the shot’s landing position in relation to the sagittal plane. Alternatively, the offset could be obtained from the laser pointer used by officials as they read the 3D coordinates of the shot at the point of landing. The offset could be used to correct for projection onto the sagittal plane.

CONCLUSION
A quality control procedure for video-recording elite male and female shot-putters during world-class events has been developed whose outcome is the calculation, with reasonable accuracy, of performances at outdoor competitive events. The developers of the quality control procedure acknowledge that diminished accuracy results mainly from limited sampling frequency supplied by the selected SONY video camera and from significant out-of-plane movement. The point is made that kinematic analyses of shot-putters at this level would be more beneficial if they were three-dimensional, rather than two-dimensional, even though most throwing action occurs in the sagittal plane. Because use of a three-dimensional motion analysis system is precluded on the field of play for logistical reasons, practical compromises must be made.

The present study made three majors contributions by demonstrating (a) the need to systematically implement a quality control procedure when conducting kinematic analyses of event-constrained recordings; (b) the benefits of using quality indicators to support decisions about tracking and determining instants of release; and (c) the need to report quality control outcomes in terms of both error and calculation quality. Equipped with data of this type, sport scientists, classifiers, coaches, and athletes will have a better feel for the level of accuracy truly obtainable during competitive events. A better appreciation of such data’s limitations should serve them all well. The quality control procedure that has been proposed can be implemented within an accuracy-based effort.

Recommendations from this study would be particularly important to future studies focusing predominantly on from-the-field data. It is further anticipated that this study will provide key information to sport scientists, coaches, and elite shot-put athletes trying to fully grasp the correlation between shot trajectory parameters and either classification or performance.

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Nutrition-related knowledge, attitude, and dietary intake of college track athletes

ABSTRACT
Although it is recognized that athletic performance is enhanced by optimal nutrition, nutrition-related knowledge deficits and dietary inadequacies continue to persist among many college athletes. The purpose of this study of college track athletes was to measure nutrition knowledge, attitude regarding healthy eating and athletic performance, and dietary intake, identifying relationships among these parameters. A self-administered nutrition knowledge and attitudes survey and the youth/adolescent semi-quantitative food frequency questionnaire were used to measure nutrition knowledge and nutrition attitude and to assess diet quality, employing a convenience sample of 113 track athletes from two NCAA Division I schools. Mean knowledge was fair, with highest component scores attained for carbohydrate, vitamins and minerals, and protein. Low scores were found for vitamins E and C. Mean attitude scores were high and similar by sex. Overall mean diet quality was 84 ± 10 (M ± SD) of 110 possible. High mean dietary intake scores were found for vitamins C and A, cholesterol, saturated fat, calcium, and magnesium; low mean dietary intake scores were found for vitamin E, fiber, sodium, and potassium. Weak correlations existed between nutrition knowledge and attitude versus diet quality. In summary, we identified adequate intake and knowledge (carbohydrates), poor intake and knowledge (vitamin E), and adequate intake and lack of knowledge (vitamin C and protein). Future research should explore factors other than knowledge and attitude that may have primary influence on dietary intake among college athletes.

INTRODUCTION

It is well recognized that athletic performance is enhanced by optimal nutrition (American College of Sports Medicine, American Dietetic Association, and Dietitians of Canada, 2000). However, college athletes encounter numerous barriers that can hinder healthy eating, including lack of time to prepare healthy foods (due to rigorous academic and training schedules), insufficient financial resources to purchase healthy foods, limited meal planning and preparation skills, and travel schedules necessitating “eating on the road”(Malinauskas, Overton, Cucchiara, Carpenter, & Corbett, 2007; Palumbo, 2000). Research has demonstrated that athletes are interested in nutrition information, and that sport nutrition information is increasingly available (Froiland, Koszewski, Hingst, & Kopecky, 2004; Jonnalagadda, Rosenbloom, & Skinner, 2001; Zawila, Steib, & Hoogenboom, 2003).

Nevertheless, nutrition-related knowledge deficits and dietary inadequacies persist among many college athletes (Jacobson, Sobonya, & Ransone, 2001; Rosenbloom, Jonnalagadda, & Skinner, 2002; Malinauskas, Overton, Cucchiara, Carpenter, & Corbett, 2007; Zawila, Steib, & Hoogenboom, 2003). College athletes exhibit a lack of knowledge about the roles of protein, vitamins, and minerals in the body and also about supplementation with these nutrients (Jacobson, Sobonya, & Ransone, 2001; Rosenbloom, Jonnalagadda, & Skinner, 2002; Zawila, Steib, & Hoogenboom, 2003). For example, Jacobson and colleagues (2001) reported that male athletes are likely to believe that protein provides immediate energy and that high-protein diets increase muscle mass. Zawila and colleagues (2003) reported nutrition knowledge deficits among female cross-country runners.

Nutrition can play a key role in optimizing physical performance and recovery from strenuous exercise (American College of Sports Medicine, American Dietetic Association, and Dietitians of Canada, 2000). However, many college athletes have diets that warrant change to promote health and support performance (Malinauskas, Overton, Cucchiara, Carpenter, & Corbett, 2007). Specifically, diets that are low in fruits, vegetables, and whole grains and high in fat and processed foods are common among college athletes (Clark, Reed, Crouse, & Armstrong, 2003; Hinton, Sanford, Davidson, Yakushko, & Beck, 2004). To improve dietary intake among college athletes, further research is warranted identifying dietary inadequacies as well as factors influencing the dietary intake of athletes (Hinton, et al, 2004; Turner & Bass, 2001).

It is unclear if college athletes’ nutrition knowledge and attitudes about nutrition have an association with their dietary intake. Wilta and colleagues (1995) found that greater nutrition knowledge was associated with healthier dietary practices among runners, whereas Turner and colleagues (2001) reported no significant correlate relationships between knowledge and dietary intake among female athletes. These conflicting findings suggest that further research is needed to learn whether knowledge and attitude are primary factors impacting college athletes’ dietary intake. The purpose of the present study was to assess the nutrition knowledge, nutrition-related attitudes, and dietary intake of college track athletes. Specific research objectives were (a) to measure nutrition knowledge in regard to carbohydrate, protein, vitamins and minerals in general, and selected antioxidant vitamins; (b) to assess attitude regarding healthy eating and athletic performance; (c) to evaluate dietary intake; and (d) to identify if, for college track athletes, relationships exist among nutrition knowledge, attitude, and dietary intake.

METHODS

Approval to conduct the study was secured from the appropriate Institutional Review Board prior to data collection. Written consent was obtained from each participant. All data collection was performed by a single researcher.
Nutrition knowledge and attitude survey

A registered dietitian constructed a nutrition knowledge and attitude pilot survey (Jonnalagadda, et al, 2001; Zawila, et al, 2003). The knowledge section included five subject areas (carbohydrates, protein, vitamins and minerals in general, vitamin C, vitamin E) with 2–5 true/false statements per subject area. The attitude section included five statements of belief that healthy eating supports athletic performance. Participants used a 5-point Likert scale (1 = strongly disagree, 3 = neither agree nor disagree, 5 = strongly agree) to indicate level of agreement with each statement. The survey was reviewed for content validity by a second registered dietitian and for content clarity by a person in a profession other than health care. To pilot test the survey, 47 track athletes (26 males, 21 females) from a NCAA Division I program in the Piedmont region of the United States completed the self-administered survey. Only minor syntax modifications were necessary based on participant responses.

Assessing diet quality
The semi-quantitative youth/adolescent food frequency questionnaire (YAQ) assesses dietary intake over the 12 preceding months. The YAQ has demonstrated reproducibility and validity in youth and has been used to measure nutrient intakes among college athletes (Hinton, et al, 2004; Rockett, Wolf, & Colditz, 1995; Rockett et al., 1997). In the present study, data obtained with the YAQ were used to calculate diet quality scores. The total score was the sum of 11 “nutrient component scores,” including nutrients of concern (fiber, calcium, potassium, magnesium, and vitamins A, E, and C) and nutrients promoting metabolic dysregulation (saturated fat, cholesterol, added sugar, and salt) as indicated in the 2005 Dietary Guidelines for Americans (U.S. Department of Health and Human Services [USDHH] & U.S. Department of Agriculture [USDA], 2005). Under a framework provided by the Healthy Eating Index, each nutrient component score was 10 at maximum and 0 at minimum (Basiotis, Carlson, Gerrior, Juan, & Lino, 1999). A component score of 10 was assigned for a nutrient when intake met or exceeded the Dietary Reference Intake. Proportionately lower scores were assigned to nutrients when was intake less than recommended (Food and Nutrition Board, Institute of Medicine [FNBIM], 1997, 2000, 2001). Cholesterol, saturated fat, sodium, and fiber recommendations were based on 2005 Dietary Guidelines, while sugar recommendations were based on Recommended Dietary Allowances (USDHH & USDA, 2005; Food and Nutrition Board, Institute of Medicine, 2003). To obtain the maximum score of 10, criteria to be met included intakes of < 300 mg cholesterol, < 10% calories from saturated fat or sugar, < 2300 mg sodium, and > 14 g fiber/1,000 calories. To obtain the minimum score of 0, criteria to be met included intakes of > 15% calories from saturated fat or sugar, > 450 mg cholesterol, and > 4600 mg sodium (USDHH & USDA, 2005; Food and Nutrition Board, Institute of Medicine, 2003). Values between the maximum and minimum criteria were scored proportionately (Basiotis, et al, 1999).

Survey administration
A convenience sample of track athletes (N = 113) from two NCAA Division I track programs in the southeastern United States participated in the study during the fall of 2006.

Statistical analysis
All statistical analysis was conducted using SPSS 13.0. Descriptive statistics include means, standard deviations, 95% confidence intervals, and frequency distributions. Independent t-tests were used to compare mean knowledge and diet quality scores by sex. Simple linear regression was used to examine relationships between knowledge, attitude, and diet quality. An alpha level of .05 was used for all statistical tests.

RESULTS

A total of 118 participants completed the study. Data from 5 were excluded due either to incompleteness (n = 2), to a respondent’s age being less than 18 years (n = 1), or to a respondent’s competing only in field events (n = 2). The final sample size was 113 (61 males, 52 females), and the overall participation rate was 71%. Demographic characteristics of participants are reported in Table 1. The majority (67%) of participants were freshmen and sophomores. The participants’ reported event specialties were sprinting (45%), middle-distance (27%), and long-distance (29%). YOU ARE HERE
Table 1

Demographic Characteristics of College Track Athletes

Parameter (M ± SD) Males (n = 61) Females (n = 52)
Age (in years) 19.3 ± 1.2 19.1 ± 1.1

n % n %

Academic classification
Freshman 22 36 20 39
Sophomore 19 32 17 33
Junior 13 21 8 15
Senior 5 8 7 13
5th-year senior 2 3
Ethnic origin
American Indian 1 2 1 2
African American 21 35 19 37
Hispanic 1 2
Caucasian 30 49 26 50
Asian 1 2

Other 7 11 5 9
Not reported 1 1
Event specialty
Sprinting 25 41 24 46
Middle-distance running 12 20 4 8
Long-distance running 14 23 16 31
Not reported 10 16 8 15

Note. An athlete was described as a sprinting specialist if he or she reported primary competition events shorter than 800 m; as a middle-distance specialist if he or she reported primary competition events 800 m to 1500 m; and as a long-distance specialist if he or she reported primary competition events longer than 1500 m.

Mean nutrition knowledge and attitude scores are reported in Table 2. The mean knowledge score for all participants was 58% ± 13% (M ± SD), which did not differ significantly by sex. Although mean knowledge component scores were similar for males and females, by subject area the rate of correct responses ranged widely, from 26% to 76%. The highest mean knowledge scores were for carbohydrate, vitamins and minerals, and protein. Mean scores of less than 50% were found for vitamin E and vitamin C. Mean attitude scores were high and were similar for males and females.

Table 2
Nutrient Knowledge* and Attitude† Scores of College Track Athletes

Parameter (M ± SD) Males (n = 61) Females (n = 52) 95% CI

Nutrition knowledge 58.7 ± 1.6 57.8 ± 1.8 (55.9, 60.9)

Carbohydrate 76.1 ± 20.9 74.6 ± 17.3 (17.2, 33.3)
Protein 55.1 ± 19.9 54.2 ± 16.0 (0.2, 6.1)
Vitamins and minerals 63.0 ± 20.6 62.3 ± 20.0 (-6.9, 8.2)
Vitamin C 26.2 ± 34.9 33.7 ± 36.7 (7.8, 20.8)
Vitamin E 43.0 ± 30.7 47.1 ± 33.8 (5.2, 16.7)

Nutrition attitudes 80.4 ± 14.0 77.6 ± 12.4 (19.2, 20.4)

*Percent correct.
†Percent agreement that healthy eating supports athletic performance.

Mean diet quality scores are reported in Table 3. Overall mean diet quality for all participants was 83.6 ± 9.8. There were no significant differences in diet quality between the sexes. High mean dietary component scores were found for vitamin C, vitamin A, cholesterol, saturated fat, calcium, and magnesium, while low mean dietary component scores were found for vitamin E, fiber, sodium, and potassium. Mean fiber, cholesterol, and magnesium scores were significantly greater for females than males.

Table 3
Diet Quality Scores of College Track Athletes

Parameter (M ± SD) Males (n = 61) Females (n = 52) 95% CI_

Diet quality 82.6 ± 8.8 84.8 ± 10.8 (-5.8, 1.6)
Vitamin E 5.6 ± 2.1 5.3 ± 2.4 (-0.6, 1.2)
Vitamin C 9.4 ± 1.5 9.6 ± 1.2 (-0.7, 0.4)
Vitamin A 8.4 ± 2.3 8.5 ± 2.2 (-1.0, 0.7)
Fiber 6.1 ± 1.6 6.8 ± 1.7* (-1.3, -0.1)
Cholesterol 7.6 ± 3.5 8.6 ± 2.9* (-2.2, .2)
Saturated fat 8.0 ± 2.7 8.3 ± 2.6 (-1.3, 0.7)
Sucrose 7.8 ± 3.1 7.5 ± 3.2 (-0.9, 1.5)
Sodium 6.9 ± 3.1 7.1 ± 3.3 (-1.4, 1.0)
Potassium 6.8 ± 2.1 6.2 ± 2.3 (-0.3, 1.4)
Calcium 8.5 ± 1.7 8.4 ± 2.1 (-0.6, 0.9)

Magnesium 7.7 ± 1.9 8.5 ± 2.1* (-1.5, 0.1)

Note. Dietary intake was assessed using the youth/adolescent food frequency questionnaire (Rockett, Wolf, & Colditz, 1995). With this instrument, dietary quality is represented as the sum of the 11 nutrient component scores. Each component score ranged from 0 (minimum) to 10 (maximum), based on actual dietary intake as compared to recommended intakes (FNBIM, 1997, 2000, 2001, 2003; USDHH & U.S. Department of Agriculture, 2005). Higher scores indicate nutrient intakes relatively close to recommended levels.
*p < .05

There were very weak correlations for diet quality and attitude (r = 0.048) and diet quality and knowledge (r = 0.001). There was little correlation between knowledge scores for specific nutrients and corresponding dietary intake: carbohydrate (r = 0.011), protein (r = -0.009), vitamin C (r = -0.004), and vitamin E (r = -0.005).

DISCUSSION

The purpose of this study was to assess nutrition knowledge, attitude, and dietary intake of college track athletes. Specifically, we asked if knowledge and attitude were related to dietary intake. This research is novel because we examined relationships between knowledge about specific nutrients (carbohydrate, protein, and vitamins C and E) and actual intakes of these nutrients. Further, there is a lack of research on college athletes’ knowledge concerning antioxidant vitamins, despite the fact that many of them do supplement their diets with antioxidants (Froiland, Koszewski, Hingst, & Kopecky, 2004; Herbold, Visconti, Frates, & Bandini, 2004).

Among the college track athletes participating in this study, knowledge about carbohydrate and general knowledge of the roles of vitamins and minerals in exercise was fair. These athletes lacked knowledge, however, about the roles of protein, vitamin C, and vitamin E. For example, 82% (n = 93) of the athletes believed that vegetarian athletes require protein supplements to meet their protein needs, and 40% (n = 45) believed that the body relies on protein for immediate energy. Previous studies have similarly indicated a lack of knowledge of the specified nutrients among college athletes. Rosenbloom and colleagues (2002) found that 46% of athletes believed protein is the main energy source for the muscle and 34% believed athletes require protein supplementation.

Indeed, athletes may be tempted to use supplements to gain a competitive edge. Primary reasons athletes give for nutrient supplementation include increasing strength and energy and improving athletic performance (Froiland, Koszewski, Hingst, & Kopecky, 2004; Herbold, Visconti, Frates, & Bandini, 2004). In the present study, a majority (67%, n = 76) of the athletes believed athletes must take a multivitamin each day and 56% (n = 66) believed vitamins and minerals supply energy. Other studies, as well, have reported many athletes believing vitamins and minerals can increase energy (Jonnalagadda, et al, 2001; Rosenbloom, Jonnalagadda, & Skinner, 2002).

Furthermore, misconceptions about antioxidant vitamins characterized the majority of athletes in our study. For example, 53% (n = 60) believed it was necessary for an athlete to supplement with vitamin C to boost immune functioning, and 56% (n = 63) believed that vitamin E supplementation was necessary to protect red blood cells from oxidative damage and to promote oxygen transport to muscles. Other researchers have reported athletes supplementing with vitamins C and E to enhance their immune system and prevent illness (Froiland, Koszewski, Hingst, & Kopecky, 2004; Neiper, 2005). Overall, the nutrition knowledge deficits identified in the present study confirm that many college athletes lack understanding of the roles of protein, vitamins, and minerals in the body, and thus lack the ability to assess whether their dietary intake of nutrients warrants use of a supplement. Education strategies for sports professionals and athletes should focus on the roles of selected nutrients in exercise, how to obtain adequate dietary intake of the nutrients, and how to evaluate need for nutrient supplementation.

The mean nutrition attitude score was high for both sexes. Seventy-one percent (n = 80) strongly agreed that “Eating healthy foods will improve my athletic performance.” Our findings about positive nutrition-related attitudes are consistent with those of Zawila and colleagues (2003), who reported that runners exhibited positive attitudes regarding nutrition education. College athletes may be receptive to learning how to improve their dietary intake to correct nutrient inadequacies that can impact their sport performance.

The mean diet quality for both males and females was greater than 80%, indicating an overall healthy diet among those surveyed. In regard to mean component scores, males and females alike had high scores (greater than 8) for vitamin A, vitamin C, and calcium. In contrast, mean scores for intake of vitamin E, potassium, fiber, and sodium were low, indicating a need for nutrition education moving dietary intake of these nutrients into line with dietary recommendations.

We found that neither nutrition knowledge nor attitude correlated with dietary intake; knowledge was less than 1% predictive of dietary intake. Conflicting results have been reported for athletes regarding relationships between nutrition knowledge and dietary intake. Wilta and colleagues (1995) found that dietary intake was 27% predictive of nutrition knowledge among runners and thus concluded that runners with greater nutrition knowledge make better food choices. On the other hand, Turner and colleagues (2001) reported that osteoporosis knowledge was only 3% predictive of dairy intake among athletes and thus concluded that, among college athletes, there was no significant correlation between knowledge of osteoporosis and intake of dairy products. In the present study, nutrition-related attitude was only 5% predictive of dietary intake, indicating that attitude about eating to support performance was not the primary influence on dietary intake. In addition, no significant correlations were found between knowledge of specific nutrients and actual dietary intake of the nutrients. While examining these relationships, we identified adequate intake with adequate knowledge (carbohydrate), poor intake with lack of knowledge (vitamin E), and adequate intake with lack of knowledge (protein and vitamin C). As a result of this study’s findings, we suggest that future research should explore factors other than nutrition knowledge and attitude that influence dietary intake among college athletes, since knowledge and attitude were not found here to be primary factors impacting dietary intake.

Address correspondence to: B. Malinauskas, Ph.D., R.D., Assistant Professor, Department of Nutrition and Dietetics, East Carolina University,
Greenville, NC 27858-4353, malinauskasb@ecu.edu

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