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

Submitted by: Laurent Frossard, James Smeathers ,John Evana ,Alison O’Riordan, Scott Goodman

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.

REFERENCES

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U.S. Sports Academy2016-10-12T11:13:54-05:00January 7th, 2008|Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

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

Submitted by: Courtney L. Rash, Brenda M. Malinauskas, Melani W. Duffrin, Kimberly Barber-Heidal, Reginald F. Overton

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

REFERENCES

American College of Sports Medicine, American Dietetic Association, and Dietitians of Canada. Nutrition and athletic performance. (2000). Medicine and Sports Science, 32(12), 2130–2145.

Basiotis, P. P., Carlson, A., Gerrior, S. A., Juan, W. Y., & Lino, M. The healthy eating index: 1999–2000. U.S. Department of Agriculture, Center for Nutrition Policy and Promotion. (Publication No. CNPP–12). Retrieved from http://www.usda.gov/cnpp/Pubs/HEI/HEI99-00.pdf

Clark, C, Reed, D. B., Crouse, S. F., & Armstrong, R. B. (2003). Pre- and post-season dietary intake, body composition, and performance indices of NCAA Division I female soccer players. International Journal of Sports Nutrition and Exercise Metabolism, 13, 303–319.

Food and Nutrition Board, Institute of Medicine. Dietary reference intakes for calcium, phosphorous, magnesium, vitamin D, and fluoride. Washington: National Academy of Sciences, 1997.

Food and Nutrition Board, Institute of Medicine. (2000). Dietary reference intakes for vitamin C, vitamin E, selenium, and carotenoids. Washington, DC: National Academy of Sciences.

Food and Nutrition Board, Institute of Medicine. (2001). Dietary reference intakes for vitamin A, vitamin K, arsenic, boron, chromium, copper, iodine, iron, manganese, nickel, silicon, vanadium, and zinc. Washington, DC: National Academy of Sciences.

Food and Nutrition Board, Institute of Medicine. (2003). Dietary reference intakes for energy, carbohydrate, fiber, fat, protein, and amino acids. Washington, DC: National Academy of Sciences.

Froiland, K., Koszewski, W., Hingst, J., & Kopecky, L. (2004). Nutritional supplement use among college athletes and their sources of information. International Journal of Sports Nutrition and Exercise Metabolism, 14, 104–120.

Herbold, N. H., Visconti, B. K., Frates, S, & Bandini, L. (2004). Traditional and nontraditional supplement use by collegiate female varsity athletes. International Journal of Sports Nutrition and Exercise Metabolism, 14, 586–593.

Hinton, P. S., Sanford, T. C., Davidson, M. M., Yakushko, O. F., & Beck, N. C. (2004). Nutrient intakes and dietary behaviors of male and female collegiate athletes. International Journal of Sports Nutrition and Exercise Metabolism, 14, 389–404.

Jacobson, B. H., Sobonya, C., & Ransone, J. (2001). Nutrition practices and knowledge of college varsity athletes: A follow-up. Journal of Strength and Conditioning Resistance, 15(1), 63–68.

Jonnalagadda, S. S., Rosenbloom, C. A., & Skinner, R. (2001). Dietary practices, attitudes, and physiological status of collegiate freshman football players. Journal of Strength and Conditioning Resistance, 15(4), 507–513.

Malinauskas B. M., Overton, R.F., Cucchiara, A.J., Carpenter, A.B., & Corbett, A.B. (2007). Summer league college baseball players: Do dietary intake and barriers to eating healthy differ between game and non-game days? The Sport Management and Related Topics Journal, 3(2), 23–34.

Neiper, A. (2005). Nutritional supplement practices in UK junior national track and field athletes. British Journal of Sports Medicine, 39, 645–649.

Palumbo, C. M. (2000). Case problem: Nutrition concerns related to the performance of a baseball team. Journal of the American Dietetic Association, 100, 704–705.

Rosenbloom, C. A., Jonnalagadda, S. S., & Skinner, R. (2002). Nutrition knowledge of collegiate athletes in a Division I National Collegiate Athletic Association institution. Journal of the American Dietetic Association, 103, 418–421.

Rockett, H. R., Breitenbach, M., Frazier, A. L., Witschi, J., Wolf, A. M., Field, A. E., & Colditz, G. A. (1997). Validation of a youth/adolescent food frequency questionnaire. Preventive Medicine, 26(6), 808–16.

Rockett, H. R., Wolf, A. M., & Colditz, G. A. (1995). Development and reproducibility of a food frequency questionnaire to assess diets of older children and adolescents. Journal of the American Dietetic Association, 95(3), 336–40.

Turner, L. W., and Bass, M. A. (2001). Osteoporosis knowledge, attitudes, and behaviors of female collegiate athletes. International Journal of Sports Nutrition and Exercise Metabolism, 11, 482–489.

U.S. Department of Health and Human Services & U.S. Department of Agriculture. (January 2005). Dietary Guidelines, 2005. (6th ed.). Washington, DC: U.S. Government Printing Office.

Wilta, B., Stombaugh, I., & Buch, J. (1995). Nutrition knowledge and eating practices of young female athletes. Journal of Physical Education, Research, & Dance, 66, 36–41.

Zawila, L. G., Steib, C. M., & Hoogenboom, B. (2003). The female collegiate cross-country runner: Nutritional knowledge and attitudes. Journal of Athletic Training, 38(1), 67–74.

U.S. Sports Academy2016-10-12T10:33:47-05:00January 7th, 2008|Contemporary Sports Issues, Sports Exercise Science, Sports Management, Sports Studies and Sports Psychology|Comments Off

Leisure constraints experienced by university students in Greece

Submitted by: Amalia Drakou, George Tzetzis, and Kyriaki Mamantzi

ABSTRACT
The aim of this study was (a) to investigate students’ leisure constraints; (b) to identify students’ profiles; and (c) to explore the effects of gender, residence, participation in physical activities, and health habits on the intensity of constraints experienced. Using the scale developed by Alexandris and Caroll (1997), it was observed that students’ perceived their leisure activities to be constrained by, mainly, accessibility and facilities. Analyses of variance employing constraints as the dependent variables, with (a) residence before age 18 and (b) health habits as independent variables, showed, for the dimension “lack of company,” some statistically significant differences between students born and raised in small cities and those born and raised in big cities. Furthermore, students from small cities reported significantly more constraints arising from lack of company during leisure activities. In contrast, in four of the seven constraint dimensions, students who paid much attention to their nutrition habits (i.e., who ate more healthily) perceived fewer constraints on leisure activities than did students paying no attention to nutrition. Providing leisure and sport education, inculcating positive attitudes about participation, might reduce students’ experience of leisure constraints and should be developed as a strategic marketing effort to involve both private and public sectors, since it is undeniable that an active lifestyle is healthier than a sedentary lifestyle.

INTRODUCTION
Leisure constraints research focuses on investigating factors that inhibit or prohibit participation and enjoyment in leisure (Jackson, 2000). As a scientific field, it belongs to the broader field of leisure studies, and only in the last two decades has it arrived as a distinct field, thanks to systematic research (Jackson, 2005). Studying leisure constraints might lead to both humanitarian and managerial outcomes. From a humanitarian point of view, it would be valuable to understand the reasons underlying the final decision to participate in activities, since participation, even in soft forms of physical activity, has been found to offer various benefits to participants, such as a high level of self-esteem, freedom from some diseases, a high quality of life, and improvement of cardiac health (Strauss, 2000). From a managerial point of view, probing the source of leisure constraints may ultimately help in better organizing and promoting leisure activities. Research may also become valuable in the development of focused leisure policies and strategies for every institute or company that provides organized leisure activities.

Furthermore, a thorough understanding of what keeps people away from physical activities is essential for the identification of appropriate points of intervention to promote active lifestyles and the health benefits they offer (Davison & Lawson, 2006). Additionally, as Larson (2000) noted, leisure is a crucial developmental context for young people and adolescents. From this point of view, investigating leisure constraints among the specific age-based category of young students is vital. Knowledge gained could improve the implementation of leisure services for youth—the future, hopefully healthier, society.

It is valuable to investigate leisure constraints, since they seem to determine to a great degree actual participation in activities (Alecandris, Tsorbatzoudis & Grouios, 2002). Moreover, identifying the strongest constraints may provide information helpful in creating strategies to promote leisure and sport activities. Understanding differences in perceived constraints associated with gender, age, participation, and nutrition habits, should be useful for planning, promoting, and managing organized leisure sport activities.

The present study aimed to (a) identify the leisure constraints experienced by students in Thessaloniki in northern Greece; (b) depict students’ profiles in terms of their health habits; (c) identify the hierarchy of intensity of the experience of constraints; and (d) investigate differences in constraints experienced, by gender, residence, participation in physical activities, and nutrition habits.

LITERATURE REVIEW
Leisure constraints began to be systematically investigated in the 1980s. At that time, they were closely related to participation, presenting “barriers” that existed between a person’s desire to participate actively in a leisure activity and his/her actual participation. (Jackson, 2005) The optic angle changed greatly throughout the 1980s and 1990s (Jackson & Scott, 1999), as the variety of constraints acknowledged to wield an influence grew. This was the outcome of such new methodological approaches as factor analysis and cluster analysis (Hawkins & Freeman, 1993; Norman, 1996; Norman, 1995; Stodolska, 1998).

Constraints, however, are no longer considered the only factors that influence participation. In other words, a person’s experience of constraints does not necessarily lead to non-participation (Jackson, 2005). Crawford and Godbey (1987) distinguished three categories of leisure constraints: (a) intrapersonal constraints, including negative individual psychological states and/or other characteristics of an individual that interact with personal preferences (e.g., self-esteem and perceived physical skills); (b) interpersonal constraints, stemming from interactions and relationships among individuals (e.g., access to partners’ or friends’ company for leisure activities); and (c) structural constraints, which intervene between leisure preferences and participation (e.g., costs of participating and problems with facilities). Crawford and Godbey’s classification of leisure constraints (1987) reflects the dimensionality underlying leisure constraints and has been well supported by subsequent research (Backman, 1991; Henderson, Stalnaker, & Taylor, 1998; Hultsman, 1995; Jackson, 1991).

The hierarchical model by Crawford, Jackson and Godbey (1991), which was based on earlier work by Scott (1991), assigns intrapersonal and interpersonal constraints the strongest influence on formation of leisure habits, relegating structural constraints to a role of least importance. Individuals experience the three types of constraint hierarchically, according to the model, through the participation decision-making process; constraints interact with motivations and preferences and shape the level of participation. Individuals may, however, negotiate their way through constraints, finding ways to participate in the face of them.

Time- and cost-related constraints rank among the most frequent and powerful constraints on leisure activities generally (Jackson, 2005). Walker and Virden (2004) noted that constraints on time are the strongest ones, and the ones most common in relevant studies.

Leisure constraints and gender
Most of the relevant studies (Alexandris & Carroll, 1997; Jackson, 2005; Horna, 1989; Jackson & Henderson, 1995; Rocklynn, 1998) have come to the common conclusion that women face more intense leisure constraints than men, and this results mainly from lack of time. They tend to suggest that women’s place within society, women’s roles and responsibilities, often limit women’s freedom of choice. Furthermore, lack of technical skills, of private transportation, and of financial resources are also experienced by women more intensely than men (Harahoussou, 1996; Harrington & Dawson, 1995).
Leisure constraints, educational level, age and marital status

Leisure constraints have also been found to be related to demographic data other than gender, such as education, age, and marital status (Alexandris & Carroll, 1997; Jackson & Henderson, 1995; Witt & Goodale, 1981). People with more education have been found to experience a lower level of constraints, while older people report greater time constraints and married people report more constraints related to family responsibilities.
Leisure constraints and residence

The direct relationship between leisure constraints and residence has not previously been investigated. However, in a national survey in the United States (Klepeis et al., 1996) concerning energy expenditure for leisure-time physical activity, differences were reported among the country’s regions. Inhabitants of the Pacific region (California, Nevada, Arizona, and Hawaii) were more physically active than those of the Central region (Nebraska, Kansas, Iowa, and Missouri), for example.

Leisure constraints and participation in leisure activities
During the process of deciding to participate in leisure activities, experienced constraints may affect individuals’ preferences, interests, and enjoyment derived from participation. Alexandris, Tsorbatzoudis, and Grouios (2002) found that leisure constraints may affect frequency of participation in activities, sometimes leading even to complete non-participation. However, studies exist flatly countering that conclusion (Kay & Jackson, 1991; Scott, 1991). This discrepancy between findings makes the present investigation of leisure constraints and frequency of participation of some importance.
Leisure constraints and nutrition habits

Many studies demonstrate that regular participation in physical activity is part of a healthy lifestyle (U.S. Department of Health and Human Services, 2000). Physical activity may prevent those diseases fostered by the under-mobility characterizing everyday life; they may also enhance quality of life more generally (Berlin & Colditz, 1998; Blair & Morrow, 1998; Corbin, Lindsey & Welk, 2000). It is also undeniable that healthy nutrition habits are important for good health (Twisk, Van Mechelen, Kemper & Post, 1997; U.S. Department of Health and Human Services, 1999).

Nutrition habits have been studied in relation to exercise habits (Pitsavos et al., 2005; Rimal, 2002; Schnohr et al., 2004), establishing that physically active people have healthier nutrition habits than those who are less physically active. However, nutrition habits have not previously been investigated in terms of their relation to leisure constraints. The authors of the present study asked whether constraints experienced on healthful leisure activities might have a negative association with healthy nutrition habits, in a context of a healthy modus vivendi.

Leisure constraints, smoking, and alcohol use
Smoking has also been studied in relation to participation in leisure activities (Schnohr et al., 2000; Theodorakis & Hassandra, 2005). Study results suggest in common that physically active people are less likely to smoke than inactive people, and there are similar findings concerning alcohol use (Krick & Sobal, 1990; Schnohr et al. 2000), in that physically inactive people were found to be relatively likelier to drink heavily. The present study’s direct exploration of a relationship between leisure constraints and smoking and drinking should pinpoint these habits’ roles in decisions about participating (the negotiation process) in activities.

METHOD
Participants and procedure
The present research was conducted among university students in Greece. Self-report questionnaires were distributed at student clubs and in teaching classrooms, between December 2005 and February 2006. Of 380 questionnaires distributed, 320 were returned (a response rate of 84%).

Instrument
Alexandris and Caroll’s scale (1997), which was developed and standardized for the general adult population in Greece, was used to measure experienced (or perceived) constraints. The scale comprises 39 statements, classified in seven dimensions, or constraint categories, about students’ current participation in leisure activities. The seven-point Likert-type scale offers responses ranging from “very important” (1) to “not important” (7). Questions about demographic details followed.

RESULTS
Of the surveyed students, 57.2% were women and 42.8% were men. The mean age was 21.60 years (S.D. = 2.11). As to residence, 33.8% had grown up in one of the two biggest urban centers in Greece, Athens and Thessaloniki, while 18.8% came from cities of no more than 200,000 inhabitants; 18.4% came from cities of no more than 50,000 inhabitants; 17.5% from cities of 25,000 or fewer inhabitants; and 11.6% from cities of 15,000 or fewer inhabitants. Students were asked for information about their nutrition, alcohol consumption, smoking, and drug use. The results are shown in table 1.

Table 1
Health habits

Nutrition	Alcohol	Smoking	Drugs
Always consume healthy food 10.3%	Never drink 17.8%	Non-smoker 71.9%	Never used 90%
Mostly healthy food 34.7%	1 time per month 21.9%	1-3 per day 5.6%	<1 time per month 6.6%
Sometimes healthy food 41.6%	1 time per week 42.2%	4-10 per day 6.9%	1-3 times per month 1.3%
Do not consume healthy food 13.4%	>1 per week 18.1%	11-20 per day 9.7%	1 time per week 2.1%
		>20 per day 5.9%

Students were also asked about their behavior concerning physical activity. More precisely, they were asked how often weekly they visited private gyms, whether they considered themselves to be athletes, how often they participated in university sport programs, and how often they practiced individually. All these questions were referred to weekly participation.

Table 2

Participation in physical activities (hourly totals per week)

	Not at all	1-2 hours	3-4 hours	5-6 hours	>7 hours	Total
Private gyms	76.3%	8.4%	6.6%	4.1%	4.6%	100%
Sport clubs	83.4%	4.1%	4.4%	2.8%	5.3%	100%
University	81.9%	5.3%	5.9%	3.1%	3.8%	100%
Individual	41.9%	37.2%	15.9%	2.5%	2.5%	100%

Descriptive statistics derived from the leisure constraints scale are contained in Table 3, which also presents the results (alpha scores) of reliability testing of each dimension’s measure.
Table 3
Descriptive statistics from scale, including reliability

Dimensions	M	SD	Alpha
Lack of access	3.59	1.76	.77
Lack of facilities	3.92	1.49	.81
Lack of company	4.37	1.50	.78
Lack of time	4.54	1.09	.60
Lack of knowledge	5.00	1.71	.84
Lack of interest	5.33	1.40	.85
Psychological dimension	5.72	1.13	.89

The dimension “lack of access” is perceived as the most important constraint, followed by “lack of facilities” and “lack of company.” The reliability of the dimensions ranges from .60 to .89.

Anova
Students’ residence prior to age 18
The ANOVA revealed statistically significant differences (F4,313=2.52, p<.05) in the dimension “lack of company” based on place of residence before age 18; the post hoc Scheffe test showed that students who had lived in cities of 15,000 citizens (M=3.90) found lack of company to be a more important constraint than did students from the biggest cities (M2 = 4.60).
Nutrition habits

The ANOVA revealed statistically significant differences related to students’ nutrition habits in four out of seven constraint dimensions. The dimensions in which there were significant differences were: (a) lack of time (F3,316 = 4.58, p<.05); (b) psychological dimension (F3,316=6.33, p<.05); (c) lack of company (F3,314=4.69, p<.05); and (d) lack of interest (F3,314=5.44, p<.05). The post hoc Scheffe test revealed that (a) for students who did not pay attention to nutrition and did not consume healthy food (M=4.29), time was a more important constraint than for students who paid much attention to nutrition and consumed healthy food (M=5.08); (b) for students who did not pay attention to nutrition and did not consume healthy food (M1=5.21), the psychological dimension was a more important constraint than for students who paid attention to nutrition and consumed healthy food (M2=6.29); (c) for students who did not pay attention to nutrition and did not consume healthy food (M1=3.74), “lack of company” was a more important constraint than for students who paid attention to nutrition and consumed healthy food (M2=4.76); and (d) for students who did not pay attention to nutrition and did not consume healthy food (M1=4.79), “lack of interest” was a more important constraint than for students who paid attention to nutrition and consumed healthy food (M2=5.71). No statistically significant differences were seen according to gender or to weekly sport participation.

DISCUSSION

Students’ profile
The majority of the students in the sample were undergraduate men beginning the third decade of life. Most were born and had grown up in cities of more than 200,000 inhabitants; they were largely non-smokers and mainly social drinkers. They tended to give little or no attention to nutrition habits. As far as participation in physical activities, the majority did not participate in university leisure or sports programs, nor were they active athletes at sport clubs. However, almost one-third of them did regularly visit private gyms, and most spent from one to seven or more hours per week in individually organized physical activities. These results seem to be in accord with similar studies (Pitsavos et al., 2005; Rimal, 2002; Schnohr et al., 2004), in that physically active people have previously been found to have more healthy nutrition habits than physically inactive people.

Leisure constraints
In the present study, “lack of access” was the dimension deemed their most important constraint by the students. Perceived “lack of facilities” was the second most important constraint, and “lack of company” was the third. This finding accords with findings of previous studies, throughout which these three dimensions usually constitute the most important factors preventing people from participating in leisure activities (Alexandris & Carroll, 1997; Alexandris & Carroll, 1999).

A possible explanation for the importance of “lack of access” is that students lack opportunity to participate in physical activities close to home, since most live in the center of a city. Transportation often demands time, with traffic jams a daily problem in, for example, Thessaloniki. In addition, students, especially those living in Thessaloniki on a temporary basis, to study, typically do not own cars. By its unpunctuality, furthermore, public transportation apparently discourages students from using it.

The finding concerning lack of facilities may reflect the low quality of some sport and leisure facilities, including overcrowding. Studies conducted in Greek environments have showed that leisure services, especially in public and municipal facilities, are not satisfying, mainly due to insufficient promotion of sport and leisure activities for all (Alexandris & Carroll, 1999). As Alexandris (1998) noted, insufficient sport facilities and limited opportunities in leisure programs are often responsible for low participation.

Facilities-related problems also give an idea of how students feel about university facilities and programs. One statement from the instrument, “I do not like activities that are offered in organized programs,” was indicated by the students to be a significant constraint; they report preferring individual activities in high-quality facilities, according to the descriptive statistics.

Finally, “lack of company,” the third most important dimension of constraint in this study, may be explained by the generic phenomenon of isolation, which seems stronger in big cities. However, the finding may also reflect the fact that, after all, young people prefer other kinds of activities in their free time, despite declaring that they would participate in physical activities if accompanied by a companion. As Aittasalo, Miilunpalo, and Suni (2003) pointed out, in technologically developed countries, a sedentary lifestyle is adopted by more and more people.

The dimension “lack of time,” which is characterized as the most common and strongest constraint by Jackson (2005), in this study ranks only fourth in the hierarchy of intensity. In other words, one might argue that students do not experience time as a strong constraint on their leisure activities. A reason may be that students’ daytime programs comprise studying and attending lectures only some of which are compulsory. Therefore, students have more free time than those adults who are already in the labor market.

Regarding residence before age 18, students from towns of no more than 15,000 inhabitants experienced the constraint “lack of company” more intensely than did students who came from the two biggest cities in Greece. In other words, it was more common among students born and raised in small communities to feel a lack of friends or partners for leisure activity companionship. This is straightforward. People from small communities have more opportunity to develop friendly relations and interactions with people than do city dwellers. When they move to a bigger city (as many students in the sample had, in order to attend college), such people experience “lack of company” comparatively intensely.

Regarding students’ nutrition habits, the statistically significant differences that were observed distinguished “students who paid much attention to their nutrition by always consuming healthy food” from “students who did not pay any attention at all to their nutrition habits.” More precisely, students who paid attention to nutrition experienced leisure constraints at a lower level than students unconcerned with the food they consumed. It seems, then, that students who take care of themselves in terms of diet do the same in terms of physical activity, their approach counterbalancing any constraints experienced. As Twisk et al. (1997) pointed out, physical activity and diet are two important components of contemporary life. Healthy food and regular participation in leisure activities, or physical activities of soft form, seem to play an important part in good health. While nutrition habits have previously been studied in relation to participation in physical activities (Pitsavos et al., 2005; Schnohr et al., 2004; Rimal, 2002), the results of the present study represent a more sensitive approach and lead to the conclusion that people with healthy nutrition habits feel less constrained in their leisure physical activities than do people unmindful of their nutrition habits.

The portion of this study examining smoking and drinking in a context of leisure constraints showed no statistically significant differences between smokers/drinkers and non-smokers/non-drinkers. However, it has been found that smoking and drinking can affect leisure participation (Krick & Sobal, 1990; Schnohr et al., 2000; Theodorakis & Hassandra, 2005). The “bad” habits of smoking and alcohol use do not seem strong enough to affect constraints; they affect actual participation, but not the beginning of decision making, where negotiation plays a part.

The novelty of the current study lies in the fact that it directly links leisure constraints to nutrition habits. So far, nutrition habits have been examined for their relevance to actual participation. One could argue that this finding highlights even more clearly the important role that healthy nutrition habits can play in a balanced, high-quality life.

The fact that most of the students did not participate in university leisure and sport programs should, first of all, put university leisure and sport program providers on alert. Students experienced problems with facilities; overcrowding might mean facilities were inadequate to cover students’ needs, or perhaps that there were some very popular activities. University leisure providers should pinpoint student needs and preferences, then redesign their programs as necessary. This could be achieved with such marketing tools as SWOT analysis, which focuses on gathering data about potential participants and describing their needs.

Of course, students’ characteristic preference for individually organized activities might be another indication of the social alienation that people experience and/or prefer in big cities. This is an important issue, though one beyond the authors’ scope. Access to sport facilities seems to be another constraint for students. It is in part an issue of urban planning concerning local authorities and public transportation officials; but as far as universities are concerned, student buses could be provided to transport students from a department or other central point on campus, to exercise facilities or sites for outdoor recreation.

In conclusion, providing leisure and sport education and fostering positive attitudes towards lifelong fitness could prevent the experience of leisure constraints. Such education should not be approached, however, as an effort to be made only by individual leisure and sport providers. It should be developed as a strategic marketing plan involving the private and the public sector, since it is undeniable that participating in leisure and sport activities promotes health.

Lead author: Amalia Drakou
1, Alexandrou Svolou Street
546 22 Thessaloniki
Greece
Email: adrakou@phed.auth.gr

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U.S. Sports Academy2013-11-27T19:26:11-06:00January 7th, 2008|Contemporary Sports Issues, Sports Exercise Science, Sports Studies and Sports Psychology|Comments Off

Impact of Media Coverage of the 42nd World Archery Championships on Audience Attendance and Purchases

Submitted by: Ping-Kun Chiu and Chiung-Hsia Wang

ABSTRACT
Sports and the media, two of the most prevalent elements in contemporary society, rely on each other to prosper and have been deeply ingrained in our daily lives. While studies have been conducted on the influence of media on the consumption of major spectator sports (Bernstein & Blain, 2003; Donnelly, 1996; Real & Mechikoff, 1992; Schultz, 2002; Verveer, 2001;), to date no one has studied how media coverage influences an audience’s attendance at and involvement in archery events. The purpose of this study was to explore the relationship between media coverage and spectator attendance at the 42nd World Archery Championships in New York City. The variables studied were two: (a) media coverage, including TV, radio, sports pages of newspapers, and professional archery magazines; and (b) audience demographic characteristics, including gender, income, education, occupation, and marriage. After evaluating 250 usable responses, results indicate that radio coverage of the event and Internet communication were the primary media that influenced attendance at the event. In addition, TV advertisements, an archery Web site, and viewing the televised event also influenced attendance at the World Archery Championships.

INTRODUCTION
Global impact of sports
Sports influence our daily lives, playing a key role in our socialization and entertainment. The Summer Olympic Games and Winter Olympic Games, hosted every four years, attract billions of viewers who enjoy the competitions through the global media. In 1996, the Centennial Olympic Games, which were hosted by Atlanta, Georgia, attracted almost a quarter million people and media representatives to the city to enjoy the gala. It was estimated that an additional 1.5 billion people watched the games through network and cable television (Marketing Matters, 1996). Verveer (2001) stated that the Sydney Olympics were broadcast to 220 countries and territories, making them the most-watched television sports event in history. In Dayan and Katz’s view (1995), the hallmark of media events is their rarity and, therefore, their ability to interrupt our daily lives; media events are live and unfolding, and both broadcasters and audiences adjust their schedules in order to attend them (1995).
Importance of media coverage

The growth of modern sports is considered to provide an interesting example of globalization. Sports not only provide an attraction to bring people together, they also work to attract media involvement. A comparative study of television coverage in the context of sports (Bernstein & Blain, 2003) reported that the opening ceremony at the Barcelona Olympics drew 28 broadcasters from around the world. The media includes not only broadcasters but newspapers, magazines, books, movies, and the Internet. The media often serve the interests of people who have power and wealth, usually emphasizing images and messages consistent with dominant ideologies. The impact of global processes on sports may emphasize either globalization or processes such as Americanization, modernization and post-modernization, as well as cultural imperialism and cultural dominance (Donnelly, 1996).

Through television and the other media, we can appreciate the outstanding performances of elite athletes. This process will get more people involved in sports, bringing more media participation, creating a positive circle. The more sports broadcasts, the larger the audience involved in sports. According to the Web site Tour de France á la Voile 2002, during 2001, 1,027 programs about the Tour de France were broadcast. The advertising value of the 2001 Tour de France television coverage has been estimated at 42 million francs (we141.lerelaisinternet.com). Do sports depend on the media? Do the media depend on sports? In reality, they have a reciprocal relationship, depending on each other. Sports produce a unique form of news and entertainment. Media coverage of sports enhances enjoyment of daily life. However, keep in mind that mass media do not shape sports, but rather intensify and extend the process and effects of commercialization of sports. They bring us information, interpret it for us, and entertain us. This process “re-presents” reality. As Real and Mechikoff (1992) state, specific media technology and commercial advertising provide the structure through which the public accesses media sports. Sporting events are becoming more common in society because of media that provide a connection between sports audiences and favorite teams and athletes. Sports have many dimensions, not just the shape presented by the media. And there is much more to the media than sports. In newspapers, sports sections provide more daily coverage of sports than any other single topic receives elsewhere in the edition. Televised sports events, a major part of programming, have continued to gain advertising revenue. A number of channels are now exclusively dedicated to sports and sports events, focused media packages satisfying people’s demonstrated needs.

PURPOSE OF THE STUDY
This study may be the first one of how media coverage influences audience attendance at archery events. Undoubtedly, without mass media and adequate audiences participating in archery events, sponsorship and the general awareness of archery would not grow; archery as part of major competitions could even be terminated.

The International Archery Federation (FITA) Congress held in Helsinki in 1955 introduced the “FITA Round,” which to today’s audiences likely would seem a very boring competition format. From 1955 until 1985, world championships were to be determined in a “Double FITA Round,” comprising a similarly dull format. It was, in short, a style poorly suited to the modern broadcasting style because it lacked excitement. Therefore, in 1988 FITA introduced the “Grand FITA Round,” which later became today’s “Olympic Round.” The new formats were meant to enhance interest in archery within the media.

With the FITA revisions in mind, the main purpose of this study was to explore the relationship between media coverage and a major archery event held in New York City. It is important to consider this relationship, because our ideas about sports are formed by the images and messages throughout sports media. The study included the following aims:

To identify the relationship between media coverage and the archery event.
To explore the demographic characteristics of audiences involved in archery.
To investigate the media sources used by persons in deciding to attend the World Archery Championships.
To analyze the relationship between the audience’s involvement in FITA and the purchase of merchandise at or related to archery events.

REVIEW OF LITERATURE

Relationship of sports and the media

Sports and the media are no doubt two of the most prevalent elements in contemporary society. As we know, mass media play an important part in American industry, and not just in relation to sports. On the other hand, sports themselves, at all levels, are approached as a business (some as multimillion-dollar businesses); they rank 11th among America’s industries, by size (Meek, 1997). The value of media coverage generated by a sports event is often built into estimates of that event’s economic effect (Dwyer et al., 2000; Higham, 1999). How do the two giant industries sports and media establish and cement their symbiotic relationship in order to benefit each other? During leisure time, people have such choices as to watch television, read magazines, or play sports; mass media and sports, in this aspect, fall in the same dimension, but in direct competition with each other. A North American folklore has developed involving watching sports on television (Wenner & Gantz, 1998). However, mass media have in fact done much more for the development of sports than most people imagine (P.E. Centre Web site).

Sports and mass media clearly rely on each other to prosper. The mass media profit from offering a valuable commodity, sports information, which the public seems to want; sports, in turn, gains popularity and wealth by offering broadcast rights (Smith & Blackman, 1982). Heinemann (n.d.) describes the mutual interests of sports and mass media as follows:

Sport has become an essential part of the entertainment program of the mass media; simultaneously there is another advantage for sport: the widespread coverage of sport via the mass media contributed to its popularization. Interest in a particular sport rises considerably when its television coverage is extensive. (¶ 5).

Mass media’s role in this particularly reciprocal relationship centers on the huge injection of money it provides to sports; this creates an ever-ascending spiral that has meant better media coverage of sports, better sports equipment and facilities, larger sports audiences, additional sponsorship opportunities, and larger athlete and staff salaries. Mass media benefits, on the other hand, from using sports as a powerful promotion outlet attracting advertising contracts and the viewing public’s attention, thanks to exclusive sports information. The symbiotic relationship between sports and mass media creates nothing less than a win-win strategy.

The importance of media coverage

The mass media are becoming steadily more dependent on sports, which can be seen in all media coverage. USA Today, presently the widest circulating daily newspaper in the nation, has a sports section occupying more than 25% of the editorial space for each issue (National Register Publishing, 1993). The all-sports television networks (e.g., ESPN) are exclusively devoted to sports coverage and serve at least 95 million households worldwide (Baker & Boyd, 1997). The powerful Web site Yahoo Sports delivered full coverage of the 1998 Olympic Winter Games in seven different languages, giving 1.5 million global users a quick and easy experience of events in Nagano (Yahoo.com, 1998). These examples demonstrate that mass media rely on sports and use sports’ worldwide popularity to their great advantage.

The effects of media coverage on the sports industry have also become particularly apparent over the last few years. According to an informal survey ranking coverage, conducted by Latelinenews.com, sports news in June 2003 was fifth in importance out of all news programming and related hits (Latelinenews.com, 2003). Furthermore, a survey of spending habits conducted by Outsports.com showed that 79% of the site’s readers attend at least three fee-for-admission sporting events annually and buy an average of some 4.5 sports-related articles of clothing every year (Outsports.com, 2002). With the prevalence of personal computers and Internet access, online sports services, with their strong consumer base, have become big business, with purchases reaching $3 billion in 2003 (Schultz, 2002). Such evidence shows that sports today are not simply competition or even entertainment; they are also an essential part of our daily life, one of the most important variables within the “consumer black box.”

METHOD
The purpose of this research was to fully explore the relationship between media coverage and the sport of archery, by analyzing the audience at an archery event, the 42nd World Archery Championships. (The event marked the 100th anniversary of FITA world-championship competition and was held in New York City.) The predictor variables were (a) media coverage (that is, TV, radio, newspapersports pages, and professional archery magazines); and (b) audience demographic characteristics including gender, income, education, occupation, and marital status.

Concerning sampling strategy, Rea and Parker (1997) state that “a crucial question at the outset of a survey research project is how many observations are needed in a sample so that the generalizations can be made about the entire population” (p. 114). The present researchers distributed questionnaires at the 42nd World Archery Championships, outside the entrance to the archery field. They later collected the completed surveys from audience members at the same place. This procedure generated a response of 169 completed surveys.

The instrument used for data collection was a four-part survey questionnaire (Appendix A) designed by Shih (1998). Each part of the instrument had 25 questions pertaining to TV, radio, newspaper, magazine, and Internet sports coverage. Respondents were asked to indicate how much they had been influenced by the particular media in terms of their decision to become involved in the event. As recommended by Ary et al. (1996), Babbie (1989), and Rea and Parker (1997), a 5-point response scale was used, with responses ranging from “low” to “high,” plus the option “not available” for respondents not having access to a particular media source. To assure reliability and validity of the instrument, the questionnaire was drawn from Shih’s published instrument from his “Study of the Relationship between Media Coverage, Audience Behavior, and Sporting Events” (1998). Using the split-half technique with the questionnaire’s reliability coefficients, a measure of 0.86 was found for media coverage. The present researcher modified the questionnaire for application to the archery event, testing the factor analysis to determine the involvement factor.

Version 11.0 of the SPSS program for Windows was utilized to analyze the data from the questionnaires. First, the frequencies and percentages of demographic characteristics were analyzed in terms of the structure and distribution of the subjects. Second, the raw score, the mean, and the standard deviation for each question were measured by the SPSS program. Third, the questionnaire was tested with the Cronbach’s alpha tool (which provides reliability oefficients); furthermore, statistical t-testing, one-way ANOVA, regression, and logistic regression were used in seeking significant factors influencing individual decisions to become involved in the 42nd World Archery Championships.

Each question’s use of a 5-point scale meant all answers constituted categorical data, not continuous data; hence, all answers were ordinal in nature rather than interval or ratio data. Logistic regression analysis, in such a case, can pinpoint the best-fitted, most reasonable model describing the relationship between the criterion and predictor variables (Hosmer & Lemeshow, 1989). The odds ratio, which is the outcome of logistic regression, provides a fairly comprehensive view of results interpreting their relationship. Hosmer and Lemeshow (1989) furthermore state that “the odds ratio is defined as the ratio of the odds for predictor variables equal to one (likely) to the odds for predictor variables equal to zero (unlikely).” Therefore, an odds ratio obtained via logistic regression was the key to the present interpretation of results concerning the surveyed audience’s purchase of merchandise related to the archery event.

RESULTS
The purpose of this study was to explore the relationship between media coverage of and audience involvement at a major archery event. The data were collected from 169 subjects attending the 42nd World Archery Championships in New York City as spectators. Study results are presented in two sections: (a) a description of the population and demographic data; and (b) statistical analyses including factor analysis, reliability analysis, t-testing, one-way ANOVA, regression, and logistic regression measuring how much influence media had in determining the audience for this world championship event.

A total of 250 questionnaires were distributed to spectators entering the archery field area; 169 valid questionnaires were returned, for an overall response rate of 67.6% (Tables 1 and 2).

Factor Analysis

Factor analysis of the event attend items allowed for one factor to be extracted; therefore, we combined all the involvement items and used the combined score for the dependent variable in the subsequent analysis. The eigenvalues from the “greater than 1 criterion” are shown in Table 3.

Reliability Analysis
Cronbach’s reliability alpha showed that the involvement items’ internal consistency reached alpha = .9407; all items were highly intercorrelated, and the average item-total correlation was .8256.

T-test

For event attend items, the paired t-test of gender was tenable, at .05 level of significance, t(167) = .944, p = .347, as shown in Table 4. Hence, no evidence from the sample suggests that males and females had differential degrees of involvement in these world archery championships.

For event attend items, the paired t-test of marital status was also tenable, at .05 level of significance, t(167) = -1.114, p = .27, as shown in Table 5. Hence, no evidence from the sample suggests that single and married participants had differential degrees of involvement in the championships.

ANOVA
For event attend items, the one-way ANOVA for income level proved statistically significant, F(2, 167) = 4.789, p＜.05, as shown in Table 7. As to post hoc comparisons, only the income categories “US $35,000–$65,000” and “above US $65,000” reached the specified .05 significance level, t(167) = 1.831, p＜.05, as shown in Table 6. We therefore concluded there was sufficient evidence from the sample to suggest that participants with different incomes, particularly the groups with annual income of $35,000 or above demonstrated statistically distinct degrees of involvement in these world archery championships.

For the event attend items, the one-way ANOVA for education level was tenable, at the .05 level of significance, F(2, 167) = .315, p＝.73, as shown in Table 8. Hence, there is no evidence from this sample suggesting that participants of different education levels had differential degrees of involvement in the championships. Also in terms of the event attend items, the one-way ANOVA for number of children was tenable, at the .05 level of significance, F(2, 167)=.529, p=.59, as shown in Table 9. No evidence from this sample suggests that involvement in the archery event varied with the number of one’s children. For event attend items again, the one-way ANOVA for age was tenable, at the .05 level of significance, F(2, 167) = .472, p＝.625, as shown in Table 10. Again, no evidence from this sample suggests that participants of different ages had different degrees of involvement in the championships.

For the event attend items, the one-way ANOVA for years of participation was tenable, at the .05 level of significance, F(2, 167) = .862, p＝.424, as shown in Table 11. Hence, no evidence was obtained from the sample to suggest that likelihood of involvement in the 42nd World Archery Championships varied with the number of years one had been involved in the sport of archery.

Regression Analysis

To develop a scale of enjoyment, we used Internet column, on-site commentator, newspaper sports-page column, TV commentator, professional archery magazine editor, and radio commentator as the predictor variables, with involvement in the 42nd World Archery Championships as the criterion variable. Multiple regression analysis was used to test the amount of influence the six predictor variables wielded in terms of enjoyment derived from event participation. The multiple regression analysis yielded a significant result: R²＝.58, F (6,162 )＝36.48 , p＜.05, shown in Table 12. The R-squared value indicates that about 58% of the variance in involvement in the event is explained by the six predictor variables. During post-test procedures, only radio commentator and Internet column reached the specified .05 significance level, t(1) = 3.166, p＜.05 and t(1) = 1.559, p＜.05 respectively, as shown in Table 12. Hence, we concluded there is enough evidence from the sample to suggest that media coverage, particularly radio comment and Internet postings, have a significant, positive influence on enjoyment associated with event participation.

As to the scale of attendance activities, we took into account the same six predictor variables and criterion variable as used for the enjoyment factor. From the multiple regression analysis a significant result was obtained, R²＝.599, F (6,162 ) ＝ 39.86 , p＜.05, as shown in Table 13; the R-squared value indicates that about 60% of the variance in involvement in the event is explained by the six predictor variables. During the post-test procedures, TV advertisement, archery Web site, and televising of the event showed significant influence on attendance activities, with respective findings of t(1) = 4.122, p＜.05, t(1) = 2.406, p＜.05, and t(1) = 2.169, p＜.05, as shown in Table 13. There is enough evidence, we therefore concluded, present in the sample to suggest that media coverage—particularly TV advertising, archery Internet sites, and televising of events—had a significant, positive influence on attendance at the 42nd World Archery Championships.

In our evaluation of purchases of archery merchandise, an extremely skewed outcome from our Number 8 demographic question prompted us to merge the original 11 categories within two groups, under US $100 and above US $100. Logistic regression was first used to test the relationship between the set of predictor variables and the criterion variable and then to detect which predictor variables, if more than one, were effective predictors of archery merchandise purchase.

Results of logistic regression were significant in that the obtained likelihood ratio showed at least one predictor variable contributing significantly to archery merchandise purchase, χ² (6, N = 169) = 36.92 , p＜.05, as shown in Table 14. As for post-test procedures, on-site display appears to be the only effective media coverage prompting purchases of merchandise (χ² (1, N = 169) = 36.05 , p＜.05). The value of the odds ratio indicates that purchase of archery merchandise was 1.29 higher among participants who had viewed an on-site display than among those not viewing an on-site display; on-site display, then, can be regarded as an effective predictor of archery merchandise purchase.

DISCUSSION AND CONCLUSIONS
This study was designed to analyze both an audience at and media coverage of the 42nd World Archery Championships in New York City. (It was during this global archery event that New York City made its enthusiastic resolution to host the 2012 Olympic Games.) In Central Park on-site media such as a movable large-screen TV “wall,” experienced broadcasters and announcers, and attention-grabbing televised entertainment drew busy New Yorkers to consider the archery event being staged in their city and even to become involved in it. In addition, leading sports publications and broadcasts had joined the professional archery magazines in publicizing, to varying degrees, this biennial event.

A quantitative methodology was used to collect data from persons attending the archery event as audience members. The topic of media influence on audience involvement at an archery event had not previously been much explored. The present study described the demographics of an archery event audience and explored factors influencing attendance by this audience along with its enjoyment of the event. The demographic variables were chosen to aid understanding of the characteristics of the sample; the logistic regression method was subsequently used to search for the factors which statistically influenced attendance and enjoyment. A total of 250 questionnaires were distributed, 169 of which were returned and found valid. The overall response rate, then, was 67.6%.

Demographically speaking, this study found that two income groups (“US $35,001–$65,000” and “above US $65,000”) were most likely to attend the archery event. In terms of media influence, involvement in the archery event was most likely to occur in the presence of radio and Internet publicity about the event, according to the results. Other media predictors of attendance found by the study are TV advertising, archery Web sites, and televising of an event. Purchases of archery-related merchandise were influenced most strongly by on-site displays, the main predictor of such purchases.

In general, most of the respondents reported enjoying the 42nd World Archery Championships, and many of them said they were willing to participate in similar events in the future. Involvement with archery in the future questionnaire items drew a positive response in 83.4% of cases. An even larger 97.6% of the audience reported satisfaction with the event. In sum, the study showed that both the International Archery Federation and the New York City Organizing Committee performed to an excellent standard and contributed to creating potential archers and archery audiences for the future.

Contact Information:
Ping-Kun Chiu
250 Wenhua 1st Rd., Kweishan
Taoyuan, 33333
Taiwan

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U.S. Sports Academy2017-08-07T15:41:45-05:00January 7th, 2008|Contemporary Sports Issues, Sports Management, Sports Studies and Sports Psychology|Comments Off

Letter to the Editor – The Sport Journal Pierre de Coubertin, arts administrator

Submitted by: Raymond T. Grant

Ed:

During the preparation of this issue of the Sport Journal, we received a piece sent to us by Mr. Raymond Grant, the artistic director of the 2002 Olympic Art Festival, reflecting on the historic and modern cultural aspects of the Olympic Games. Although the article does not fall within the normal editorial plan of the Sports Journal, it is very insightful and we felt, as such, it would be of interest to the readership

With the permission of the author, we are reprinting the piece titled “Contrast, Culture, and Courage: A Cultural Administrator’s Tribute to Pierre de Coubertin” in the form of a letter to the editor. We trust the readership will find as much value in reading the piece as we did.

As Beijing, Vancouver, and London prepare to host future
Olympic Games, it seems fitting to remind readers of The Sport Journal
of the value of cultural programs within the Olympic Movement and the
connection between artists and athletes. That value, and the corresponding
cultural development surrounding the successful hosting of the Olympic
Games, has deep roots within the Olympic Movement thanks to the vision
of Baron Pierre de Coubertin. de Coubertin was both a sports and arts
administrator.

The recently completed Turin Olympic Winter Games and Athens Olympic
Games warrant reflection brought about by the cultural legacy of Pierre
de Coubertin. The very public challenges surrounding the hosting of the
Olympic Games, the reforms of the IOC, and the successful return of the
Summer Games to Athens suggests that this contemporary period in the Olympic
Movement has elements of the historic.

The on-going research of Norbert Muller, Manfred Messing, and Research
Team Olympia of the University of Mainz (Germany) in their new publication
From Chamonix to Turin, holds significant value in the study
of cultural programs within the context of the Olympic Games. In their
research on the meaning of the cultural program for spectators in Salt
Lake in 2002, the authors found that 84% of respondents agreed with the
statement that “The Olympic idea combines sport and art.”
This significantly high response compares with 72% for the Olympic Games
in Sydney 2000, 23% for Atlanta 1996, and 40% for Barcelona 1992. Can
this be a trend in the growth of awareness and significance of Cultural
Olympiads and Olympic Arts Festivals? If so, as the communities of Beijing,
Vancouver, and London prepare to host upcoming Olympic Games, much can
be celebrated and learned by engaging artists and encouraging their role
in community development and the creative economy.

The magic of the Olympic Movement – its power, if you will, is
in how individual communities who are invited to host the Games reinvigorate
the Movement. And, local participation is a defining element of this reinvigoration.
In her article More Than a Game. The Value of Arts Programming to
Increase Local Participation, author and Olympic researcher Beatriz
Garcia points to “ways in which some of the less known – but
more meaningful – dimensions of the Games could place participation
back at the centre of the [Olympic] celebration.”

The arts were always at the center of Pierre de Coubertin’s vision
for the Olympic Movement. In the years of preparation required to deliver
a credible Olympic Cultural program, I have found that de Coubertin’s
unflagging belief in the power of music, dance, and words was sustaining.

In Dr. Norbert Muller’s opus Olympism, we have the wonderful benefit
of the selected writings of Pierre de Coubertin. To any cultural administrator
of the Games, the historical event of the Olympic Movement in Paris in
May of 1906 is singularly defining. The festivities in the great amphitheater
of the Sorbonne, which ended the 1906 Advisory Conference in Paris (the
Conference itself was held in the historic foyer of the Comedie Francaise)
on the inclusion of the arts and humanities in the modern Olympics, is,
for all intents and purposes, the birth right for those of us who use
the arts to help define the atmosphere of the Modern Games.

In a circular letter to the International Olympic Committee (IOC) dated
April 2, 1906, de Coubertin invites members to an Advisory Conference
to determine “to what extent and in what form the arts and literature
can participate in the celebration of the modern Olympiads.” Thanks
to the vision of de Coubertin, his question is as applicable today for
the organizing committees of Beijing, Vancouver, and London, as it was
for the nascent Olympic Movement of 1906.

The announcement of the 1906 Advisory Conference was attached to the
invitation to IOC members to attend the Games in Athens. As completely
as de Coubertin believed in the merger of sport and art, the summoning
of this “Consultative Conference on Art, Letters, and Sport”
was not completely altruistic. In his Olympic Memoirs, de Coubertin said
“I would be able to use this (the conference) as an excuse for not
going to Athens, a journey I particularly wished to avoid.”

Excuses aside, de Coubertin, I believe, understood that artists provide
communities with a sense of place and the Olympic Movement of 1906 was
missing a vital link to this sense of place. A distinct challenge remains
today as arts and culture programs within the context of host organizing
committees fight for survival, respect, resources, and presence. de Coubertin’s
vision of Olympism – what the Olympic Movement aspires to be –
is inextricably linked to the arts and humanities “harmoniously
joined with sports.”

Celebrating the achievements of athletes alongside the accomplishments
of artists became the vision of the 2002 Olympic Arts Festival.

In an article I wrote for The Olympic Review entitled Contrast, Culture,
and Courage, I reflected on the cultural legacy of de Coubertin citing
the seminal meetings he convened. In that article, I said ‘I will
leave it to greater minds to decide if the 2002 Olympic Arts Festival,
in any substantive way, realized this broad de Coubertin vision’.

Now, I am especially encouraged by the results of the studies conducted
by Research Team Olympia in 2002 and just released in which the researchers
(Muller, Messing, and Preub) say, “It can be concluded that the
Salt Lake 2002 Olympic Arts Festival was a relatively successful one.
Although not all of the projects could be realized, the understanding
of the inner connection of Olympic sport and art was higher than at three
former (Summer) Olympic Games and the biathlon spectators were more involved
in visits of the Cultural Program. It seems that the Arts Festival in
Salt Lake 2002 has set a benchmark for Winter Games which needs further
study to measure the achievements of cultural programs in the future.”
Hopefully, the sports and arts administrators of the Games of Beijing,
Vancouver, and London, can engage in, commission, and contribute to this
Olympic research area.

Participation is the key to promoting the role culture plays in great
social gatherings. And, the Olympic Movement stands as the great social
gathering of our time.

I posit that the Olympic Movement is furthered, as well, by the perspective
and point of view of artists, for it has been said that “only artists
find the uncommon in the commonplace.” I, for one, look forward
to the role that gifted artists, poets, playwrights, and essayists will
play in future Games. If history is any judge, they will leave a cultural
legacy for the Games and the communities which host them.

Twenty-five years after the 1906 Advisory Conference, de Coubertin reflected:

I have already repeated – so often that I am a trifle ashamed
of doing so once again, but so many people still do not seem to have
understood – that the Olympic Games are not just ordinary world
championships but a four-year festival of universal youth, “the
spring of mankind”, a festival of supreme efforts, multiple ambitions
and all forms of youthful activity celebrated by each succeeding generation
as it arrives on the threshold of life. It was no mere matter of chance
that in ancient times, writers and artists gathered together at Olympia
to celebrate the Games, thus creating the inestimable prestige the Games
have enjoyed for so long.

Today, the Olympic Games have as compelling an obligation and opportunity
to gather writers and artists together as they did in 1906.

If “this was how the reunion of the muscles and the mind, once
divorced, was celebrated in the year of grace 1906,” let us look
toward years of grace in 2008 in Beijing; 2010 in Vancouver; and 2012
in London.

U.S. Sports Academy2015-03-27T14:13:02-05:00September 8th, 2006|Contemporary Sports Issues, Sports Facilities, Sports Management, Sports Studies and Sports Psychology|Comments Off