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How Do Fans React When Sports Teams Are Named After Corporations?
ABSTRACT
The reaction to Red Bull naming its soccer teams after the corporation and prominently displaying the company logo on team uniforms is a mixed one among media critics and fans. Although many media observers note that trends seem to indicate that more sports teams may be named after corporations, there is still a fine line between what is seen as hip and what is taboo. Grathoff (2006), however, suggests that the idea that Major League Soccer would allow a team to be named after a corporation reinforces the league’s second-class status.
INTRODUCTION
The reaction to Red Bull naming its soccer teams after the corporation and prominently displaying the company logo on team uniforms is a mixed one among media critics and fans.
Travis (2006) criticizes the notion that sports teams should be named after corporate interests and predicts that it may not be long until other franchises are named after alcoholic drinks and other products most fans crave. He comments, “Somehow, as a sports fan, I like to think there’s something about a name that can’t be bought. Even if teams, players and stadiums can all be sold to the highest bidder, the last refuge of the fan should be the team name itself.” In contrast, Lewis (2001) argues that the owner of a franchise has the right to determine how a team should be named and marketed. Similarly, Burn (2006) comments that naming a team after a corporation may likely disturb fans more than merely placing a business name on a stadium. Burn contends that fans like to maintain the illusion that at least the team is not merely a business enterprise (as indicated when the squad is named after a corporation) but is at heart a sports organization. On the other hand, Quirk & Fort (1999) and Zimbalist (1998) correctly point out the need for additional review streams (including economic gains that may result from the naming of a team) that are needed to keep up with the exponentially growing cost of running a professional sports franchise.
Although many media observers note that trends seem to indicate that more sports teams may be named after corporations, there is still a fine line between what is seen as hip and what is taboo. Anderson (2006) and Boswell (2006) describe instances in American sport in which teams were named after corporate interests, including basketball franchises in the 1930s (e.g., the Firestone Non-Skids and the Toledo Red Man Tobaccos), soccer teams in the World War II era (e.g., Bethlehem Steel, the Akron Goodyears, and the St. Louis Central Breweries), semi-professional softball teams in the 1980s (e.g., the Coors Light Silver Bullets), and a soccer team in the 1970s (the New England Lipton Tea Men). For decades stock car racing in the USA has been prominently associated with a naming rights sponsor, first Winston and later Nextel. In a few cases, prominent American sports franchises named after corporations have gradually become accepted by most fans. For example, one of the most famous National Football League teams, the Green Bay Packers, were named after a meatpacking company, while the Detroit Pistons of the National Basketball Association were named after a manufacturer of automotive parts. Hughes (2006) and Grathoff (2006) suggest that a corporate name for a professional sports team may be more likely to be accepted by the public if it connotes an image associated with a sporting endeavor, is similar to names used by other teams (e.g., the Chicago National Basketball Association franchise and the University of South Florida use “Bulls” as their name) and is not seen to be politically incorrect. “Red Bulls” seems to meet these criteria.
On an international scale, there are examples of soccer teams named after corporations (Spangler, 2006). For example, Bayer Leverkusen in Germany is named after the firm that manufactures aspirin, while PSV Eindhoven of Holland is named for Philips Electronics. In that light, it could be argued that there is a tradition of naming soccer organizations after corporate sponsors.
Grathoff (2006), however, suggests that the idea that Major League Soccer would allow a team to be named after a corporation reinforces the league’s second-class status. Grathoff notes how the National Basketball Association, a more established and prosperous league, refused a bid by FedEx Corporation to name the Memphis franchise “The Express,” as well as a request to have a proposed Louisville team play its home games at an area to be called “the KFC Bucket.” Said Paul Swangard of the Warsaw Sports Marketing Center at the University of Oregon (quoted in Grathoff, 2006), “In the American sports landscape, we would have expected to see the Red Bull thing happen in a start-up league or a fledgling league rather than one of the mainstays. The NBA, the NFL, Major League Baseball and the NHL have been very cautious with their approach.”
HISTORY
The Birth and Marketing of Red Bull
Austrian Dieter Mateschitz created Red Bull after visiting Thailand in 1982 and learning that tired drivers in that region consumed large quantities of energy drinks. The top brand in Thailand was a mixture of caffeine, water, sugar and taurine marketed as “Water Buffalo” (referred to locally as Kratindaeng). Mateschitz created his own version of the drink, which he called Red Bull, loosely modeled after that Thai beverage. Shortly thereafter, Red Bull was introduced to Austria, Germany and other European nations. It was first marketed in America in 1997 (Gschwandter, 2004).
Sales of energy drinks like Red Bull and its competitors have increased by 75% since 2005 and totaled more than $3.5 billion in 2006. In 2006 Red Bull sold 2.5 billion cans of the drink worldwide, about 1 million of those in the United States. More than 500 varieties of energy drinks were sold in 2006, and Red Bull is one of the leading brands in the category (Rouvalis, 2006). Estimates suggest that roughly one in every three American teenagers consumed an energy drink in 2006 (Lord, 2007).
Red Bull is known as much for its unique marketing programs as for the highly caffeinated taste of the drink (Hein, 2001), which some marketing experts refer to as liquid Viagra. Van Gelder (2005) suggests that Red Bull is at the leading edge of relatively young companies that combine the best elements of creativity and strategy when building their brands. As a result, he contends, Red Bull will continue to flourish, as long as it emphasizes innovative branding strategies. McCole (2005) describes Red Bull’s branding efforts as “experiential marketing” in which target audiences are exposed to energized special events that create vivid memories. McCole argues that involving stakeholders in live action-sports events can create strong relationships between potential customers and the brand. Similarly, Dolan (2005) describes Red Bull’s promotions efforts as “guerilla marketing” relying on creative special events to bypass traditional advertising in the mass media. Ho (2006) comments that Red Bull is creating a new marketing model by actively owning teams and sports events rather than merely serving as a corporate sponsor. Gschwandter (2004) suggests that Red Bull is marketed using “alpha bees”: individuals who will enthusiastically tell others about a product they love.
Red Bull has often marketed on-site at nightclubs and extreme sports events (such as base jumping and extreme skateboarding), and motor sports events such as BMX motorcycle racing and NASCAR and Formula One automobile racing. Initially, the focus was not to market Red Bull through team sports, but instead to promote individual personalities (Lidz, 2003). Lindstrom (2004) describes Red Bull’s efforts to creatively promote and market the drink to young adults and college students; an example is the company paying people to paint their car in the company colors and place a large replica of a Red Bull can on the roof. As a result, Red Bull is consumed in large quantities on college campuses, either by itself or mixed with liquor.
Typically, Red Bull is only advertised once a target market has matured and buzz has already been created about the brand. For example, most distributors buy the drink directly from the company and sell Red Bull exclusively. According to Ho (2006) and Heinz (2001), Red Bull seeks to align itself with the lifestyle associated with action sports.
Even though it has been criticized by public health officials as being detrimental to human health and even lethal in some cases (Wilde, 2006), a few athletes, including some soccer players, tout the drink’s benefits. MLS forward Taylor Twellman of the New England Revolution endorses the product and said “Drinking Red Bull before training and matches provides me with the needed energy and focus to give me that extra edge on my opponents” (Sells, 2006). In contrast, Zeigler (2006) points out that some public health officials are concerned that the drink may lead to dehydration and that Red Bull seems to be primarily used with alcohol, so people can drink without getting tired.
Red Bull Salzburg
SV Salzburg has a rich history. The club was formed in 1933 when teams associated with the left and right wings of the political spectrum merged. In fact, the selection of violet and white as team colors was intended to suggest the new team was politically neutral (Guenther, 2006). SV Salzburg has traditionally been one of the strongest teams in Austria’s Bundesliga and won the league championship in 1994, 1995, and 1997. In 1994 the team finished as the runner-up in the UEFA Cup.
However, SV Salzburg began encountering financial difficulties around the year 2000, and Red Bull purchased the team in 2005. Robinson (2005) describes how many fans were initially supportive of Red Bull’s purchase of the team, since it would provide needed finances to recruit top-caliber players. But he notes that (fans) soon … recognized that the new management’s purpose was to destroy the old club to establish a Red Bull company club.”
Austria’s premier football association, the Bundesliga, has a history of allowing football club names to help promote private investors (Joyce, 2003). Still, Red Bull took this concept to the extreme, completely rebranding the team and replacing the traditional purple and white uniforms with the red, blue, and yellow colors used to market its drink (Plenderleith, 2007b). Red Bull also referred to the origin of the club based on when the company made the purchase (2005) rather than on the year the team was founded (1933). According to Guenther (2006), “There was a clear intention to sever any ties with the ‘old’ Austria Salzburg. Club sources went on to say that, as far as Red Bull is concerned, there is no history, no tradition” associated with the transformation of SV Salzburg to the new ownership.
When discussing the rationale for changing the color of the team’s uniforms, Red Bull CEO Dieter Mateschitz (cited in Joyce, 2003) referred to fan protests as “kindergarten stuff.” He said, “The Red Bull can’t be violet or else we couldn’t call it Red Bull. Whether you play in purple, blue, or green is irrelevant; the only thing that matters is the team being successful.”
Red Bull also instituted policies that discourage fans from showing the violet and white colors used for many years and prohibit fans from displaying in the stadium banners criticizing the new ownership. Some fans who wore the violet and white colors to Red Bull matches were harassed and assaulted with beer bottles. The end result has been that relationships between the team and many long-standing supporters were significantly damaged. In addition to claims that people who cherished the old traditions were harassed, Red Bull may have offended potential fans by providing a game-day experience that features loud rock music, a disco-style laser light show, a celebrity kick-off with the driver who leads Red Bull’s Formula One team, and fan animators who exhort the crowd to cheer when prompted (Joyce, 2003).
The divided loyalties to old and new ownership have created a group of disaffected fans calling itself “the Campaign for Violet and White” (Violett-Weiss, 2007). Some of the most important goals of this campaign are to incorporate the original team colors of violet and white into the club’s new identity; to make sure that Red Bull refers to the 1933 founding in its marketing and literature; and to improve public relations and dialog between Red Bull and fans of SV Salzburg.
Changing the Name to Red Bull New York
The New York franchise was founded at the creation of Major League Soccer in 1996. Initially, the team was named the New York/New Jersey MetroStars after another corporation, the MetroMedia Entertainment Group. In 1997 the team dropped New Jersey from its name and became known simply as the New York MetroStars.
In March 2006, Red Bull purchased the team for a reported $100 million from the Anschutz Entertainment Group (Bell, 2006). As part of negotiations that led to the purchase, Red Bull lobbied hard for permission from the league to prominently place the logo on the front of the team jersey (Weinbach, 2006). According to Red Bull CEO Dieter Mateschitz, purchasing the MetroStars made sense because it provided an opportunity to market the drink to more than 18 million Americans who play soccer, as well as to an additional 60 million fans who follow the game as spectators. Mateschitz said, “Soccer is just about to make a big breakthrough in the United States media” (Red Bull, 2006). Fatsis (2006) suggests that the investment by Red Bull is one sign that Major League Soccer has a promising future and is poised for economic growth.
The new ownership also acquired a stake in a soccer-only stadium, Red Bull Arena, now being built for the team in Harrison, New Jersey, and opening in 2008 (Thomaselli, 2006). Clark (2006) suggests that buying the club makes sense economically for Red Bull, since it allows them to promote their products using the team as a “walking billboard” in a huge media market. Clark commented that the purchase of the team by Red Bull may likely improve the team’s performance on the pitch, given the owners’ successes in Europe and the amount of capital they will invest in the team. In 2006, Red Bull New York suffered a $14 million loss, perhaps because all the branding and marketing of the energy drink lessened the participation of other corporate sponsors (Plenderleith, 2007).
Several local politicians were upset that the team will be “Red Bull New York,” even though the state of New Jersey is financing the stadium in Hudson County, New Jersey. Brendan Gilfillan, a spokesman for New Jersey Governor John Corzine, opposed dropping New Jersey from the franchise name and stated (Frankston, 2006):
Their new name may be Red Bull New York, but striking New Jersey from their name seems to be a different kind of bull altogether. This is a team that sells its products in New Jersey, draws its fan base from New Jersey, and receives funding from New Jersey.
In addition, New Jersey Senator Frank Lautenberg urged Red Bull to reconsider the decision (The Global Game, 2006). George Zoffinger, president of the New Jersey Sports and Exposition Authority which runs Meadowlands Stadium where the team now plays, said, “It is an insult to us for them to remove the name of the state,” calling the new name a “lack of respect for the state of New Jersey” (Bell, 2006). Meanwhile, Page (2006) opines that removing New Jersey from the team name disrespects the state and its residents.
The potential economic benefits of changing a team name to reflect a franchise’s association with a larger media market (i.e., the change from New Jersey to New York) are illustrated by a similar case involving the Angels Major League Baseball franchise. Nathanson (2007) and Flaccus (2006) describe how owner Arte Moreno changed the name of his team from the “Anaheim Angels” to the “Los Angeles Angels of Anaheim,” despite the fact that the team did not make a geographic move, but simply rebranded itself. According to Flaccus, Moreno “changed the name to make the most of the Angels’ location in the nation’s second-largest media market …. Using Los Angeles in the name would attract more sponsorships, advertising, and broadcast contracts.” Giulianotti & Robertson (2004) suggest that fans throughout the world often are more likely to identify a sports organization with its brand, rather than with its city or region of association.
Beyond concerns about removing New Jersey from the team name, “Red Bull” has been criticized for sending signals that Major League Soccer is not first-class. Former MetroStars public relations specialist Tony Miguel (quoted in Spangler, 2006) said:
The biggest problem (for Major League Soccer) is regarding the credibility and perception of soccer among the mainstream media. MLS is already seen by most in the mainstream media as a minor league. Red Bull New York only adds to the perception. Imagine the outcry that would occur if the New York Yankees became the New York GEICO’s. This is a desperate move by a league desperate for investors. I think in the long run this hurts MLS much more than it helps the league.
Another factor that likely increased tension about the renaming is that a small group of diehard fans may have feared that Red Bull would discard MetroStars history and traditions. However, Galarcep (2006) suggests that Red Bull learned from its mistakes with SV Salzburg and will handle the matter more sensitively. He contends that the team’s success on the pitch—not its name—will be the key to keeping existing fans and wooing new supporters.
In contrast, Red Bull officials contend that taking New Jersey from the name is not really significant. Red Bull spokesperson Patrice Redden stated that, “In the tradition of the New York Jets and the New York Giants and even the New York Cosmos, we believe that the metropolitan New York area is truly one of the most influential markets in the entire world and the New York affiliation is an excellent representation of this international culture” (Zeigler, 2006).
The French news service Agence-France Presse contends that Red Bull bought the soccer club to boost the image of its brand in the United States. Said sports marketing specialist Rainer Kress of Vienna, “American Major League Soccer … is booming and with the MetroStars deal Red Bull is pursuing a strategy built entirely around marketing” (Butler, 2006). Alexi Lalas, at the time the general manager of Red Bull New York, said renaming the team was “bold,” and “the marketplace in particular needs bold moves.” He also suggested that fans who know the history of and trends in international professional soccer should accept corporate naming. Lalas described further the significance of Red Bull’s purchase of the team (Freedman, 2006): “We are associating ourselves with a world-renowned brand that is synonymous with creative, innovative and unique marketing. All the resources of Red Bull will be brought to bear to market the Red Bulls. I’m excited.”
WHERE MIGHT THIS LEAD?
According to Chris Smith, a Dallas-based specialist in sports and event marketing, Red Bull’s example may not necessarily lead to other teams being named outright for corporations. “It will probably be more of a trickle than a flood,” he said. “While sponsors are eager to step up, they understand the emotional attachment that fans have with teams they love. There’s the potential for a strong negative backlash” (Anderson, 2006). Commented the University of Oregon’s Paul Swangard (cited in Turnbull, 2006), corporate naming is “sort of the last bastion in American sports … [American sports fans] haven’t been willing to accept it.”
On the other hand, some marketing experts contend that the corporate influence found throughout international soccer, and increased advertising in many American sports, may make corporate team names more acceptable. For example, soccer jerseys in Europe typically feature a corporate sponsor’s name prominently, while the logo of the football club may be barely noticeable. Despite the significant commercial presence, however, these teams are almost universally referred to by the name of the football club, not the sponsor. In 2007 Major League Soccer began to allow franchises to prominently display the names of corporations on the front of jerseys, although most teams do not take the name of the corporate sponsor. For example, Real Salt Lake’s uniforms prominently display the name Xanga (a natural juice drink), Chivas USA features the PEMEX logo (Mexico’s national gas company), and the jersey of the Los Angeles Galaxy is adorned with the name and logo of HerbaLife. In all these cases, the logo of the corporate sponsor is shown much larger than the team name (Weinbach, 2006).
FC Barcelona, one of the most storied football clubs in Spain, recently put a new spin on this trend when they entered into an agreement to feature the United Nations children’s charity, UNICEF, on uniforms. Even though FC Barcelona will not directly gain any revenue from this decision, featuring UNICEF’s logo is seen by marketing experts (Hughes, 2006) as a way to create an image of social responsibility on the part of the club and its supporters.
Skidmore (2006) discusses the merits of naming sports teams after corporations, writing that, “Because of mergers, bankruptcies, etc., no league wants a franchise to have a new nickname every two seasons. There is also the problem of cheering for the ‘Verizons’ or the ‘Colgates’ … [Still,] if Team Red Bull can work for MLS, it may not be much longer before we see corporate names in the big four leagues.”
Similarly, Allan Adamson, brand manager at WPP Group, warns that there may be a downside to naming a team after a corporation, especially when problems arise (cited in Bosman, 2006). “The risk is, ‘What happens to the team when a product starts selling badly?’” says Adamson. “It’s a risky strategy, especially when you choose something that’s both an energy drink and an alcoholic mixer.” He likens the permanence of a team name to a tattoo and suggests it may be more difficult to change a team than a stadium named after a corporation.
CONCLUSION
It is clear that renaming professional soccer teams after the Red Bull energy drink led to at least some level of public opposition in both the United States and Austria. However, it is important to differentiate the public outcries in each nation. In Austria, it appears that much of the anger at Red Bull was due to perceived refusal of the new owners to acknowledge and maintain traditions of the original club. Fans found it especially offensive that Red Bull Salzburg ignored the 1933 founding date, instead treating the club as a new expansion team. In a similar light, Austrian soccer fans had closely affiliated SV Salzburg with many time-honored traditions, including the violet and white colors worn for decades. Breaking that tradition was a personal affront to large numbers of fans. In contrast, fan reaction in New York and New Jersey was more localized. There was relatively little criticism in either state, largely because of the relatively low profile of Major League Soccer on the American sports landscape. Certain politicians and civic leaders were angered by the removal of New Jersey from the team name when public funds were building its stadium in New Jersey. Many local residents, however, were not especially bothered by the move: Many activities and organizations around the region refer to themselves as belonging to the “greater New York City” metropolitan area (S. Weston, personal communication, Month Day, 2006). For smaller apples, it just makes sense, from a public relations and marketing perspective, to associate oneself with the Big Apple brand.
On a broader scale, a key question to ask is the extent to which naming a team after a corporation is thought offensive. In Europe, football fans have come to expect the fronts of uniforms to be adorned with large corporate symbols. Still, few football organizations in Europe are yet named after corporations. In America, it has gradually become acceptable to embrace, for a few professional teams at least, names that stem from corporate ties (e.g., the Green Bay Packers or Detroit Pistons). In contrast, the National Basketball Association recently denied a request to name a new Memphis franchise after FedEx Corporation. Perhaps the key principle is to choose a name that is not offensive or politically incorrect and that connotes, in a broad sense, our sports traditions or sporting endeavors.
The experiences of Red Bull provide some insights into how corporate names for sports teams might meet with more public acceptance. For example, after angering Austrian fans by discarding existing club traditions, Red Bull learned how important it is to understand the passionate relationships between teams and their fanatic supporters. A wiser Red Bull then worked hard to ensure that the traditions and supporter groups of the MetroStars would be respected following that team’s acquisition. In addition, the most important factor that may influence fans’ response to a new name is the extent to which the team succeeds on the field of play. If Red Bull shows it is willing to invest in teams and facilities to boost team performance, the issue of the franchise name may become less important.
In sum, one has to ask whether Red Bull’s practice of naming sports teams after its product is a trend that will become more widespread in America and Europe. The general consensus seems to be that naming teams after corporations may be more common among teams and leagues that, like Major League Soccer, have lesser status. The top-of-the-line sports leagues in the USA seem unlikely to adopt the practice in the immediate future. In the larger cultural context of sport, one has to come to grips with the reality that corporations have been investing in and promoting sports organizations for decades, even to the extent of naming teams after themselves. Although naming an established team after a corporation may seem egregious, perhaps it is just an indication of the important role of private investors in supporting sports organizations
For more information, contact Jensen at rwjensen@ag.tamu.edu or (979) 845-8571 or (979) 574-5187. Weston can be contacted at westons@mail.montclair.edu
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Quality Control Procedure for Kinematic Analysis of Elite Seated Shot-Putters During World-Class Events
ABSTRACT
Kinematic analyses of elite shot-put throwers commonly involve shot-trajectory parameters determined under experimental conditions with an accuracy-based procedure. This can be only partially implemented within an event-constrained procedure (as opposed to experimental conditions). Event-constrained procedures, while they provide realistic information collected in an open environment, introduce several constraints that can potentially compromise accuracy measures. This study concerns a quality control procedure intended to address such constraints. The quality control procedure relies on 5 key elements aimed at reducing and reporting error and validating measures of the shot trajectory. The performance of 7 world-class shot-putters during international events was calculated using video data recorded at 50 Hz with a camera located to the side of the athlete. Accuracy was above 75% for all the attempts and above 94% during 4 attempts. This study demonstrated (a) the need to systematically implement this procedure for kinematic analyses based on event-driven recordings; (b) the value of quality indicators in making decisions concerning the instant of release; and (c) the importance of reporting this procedure’s outcomes in terms of error and percentage error.
INTRODUCTION
The performance of world elites in the shot put, measured as the distance the shot is thrown, results from the interaction between throwing technique and the design of the throwing chairs (O’Riordan & Frossard, 2006). That interaction shapes the parameters of the shot trajectory, which depends on the position, the velocity, and the angle of the shot at the instant of release ( Ariel, 1979; Dessureault, 1978; Chow, Chae, & Crawford, 2000; Linthome, 2001; Lichtenburg & Wills, 1978; McCoy, Gregor, Whiting, & Rich, 1984; Sušanka & Štepánek, 1988; Tsirakos, Bartlett, & Kollias, 1995; Zatsiorsky, Lanka, & Shalmanov, 1981). Sport scientists, classifiers, coaches, and athletes use the parameters of the shot trajectory to better understand the link between disability and performance (Higgs, Babstock, Buck, Parsons, & Brewer, 1990; McCann, 1993; Vanlandewijck & Chappel, 1996; Williamson, 1997; Chow & Mindock, 1999; Chow et al., 2000; Laveborn, 2000; Tweedy, 2002). Video recording allows for estimation of parameters, using primarily an accuracy-based procedure or event-constrained procedure, as illustrated in Figure 1.
Figure 1. Overview of the video recording (A), the data processing (B) and the outcomes (C) of the parameters of the shot’s trajectory of elite seated shot-putters. The parameters determined using an accuracy-based procedure rely on data collected during training and in laboratory,which presents the advantage of accommodating the typical experimental requirements but it provides only partially realistic information regarding the performance. The event-constrained procedure provides realistic information collected in the open environment presenting several constraints. Thus, a quality control is needed to reduce, validate, and report the errors. This will ensure that sport scientists, classifiers, coaches, and athletes have a better appreciation of the limitations of the data presented about the performance.
Accuracy-based procedure
Video recordings made during training or as part of laboratory motion analysis, whether for routine observation or for research, must accommodate typical experimental requirements for three-dimensional reconstruction, including suitable calibration volume, appropriate number of cameras, precise positioning of cameras, use of active or passive markers, and an unrestricted number of attempts. A flexible set-up of this sort enables an experimental approach employing trial and error, wherein quality control is achieved through repeat recording until the desired kinematic parameters (i.e., shot trajectories) are satisfactorily accurate. The accuracy and validity of parameters reported in research may be taken for granted, even though authors seldom report key indicators like number of frames tracked after release, or calculation of performance using parameters or using tape measure, or the difference between these two performances (Chow & Mindock, 1999; Chow et al., 2000).
Unfortunately, trajectory information obtained from non-competitive environments only partially represents the throwing technique an athlete uses while competing. Participants in a study by Chow et al. (2000) performed, on average, 15±9% below their personal best, leading the researchers to conclude that, in order to develop a data base of ideal performance characteristics, numerous quantitative data needed to be obtained, particularly those collected during leading competitions.
Event-constrained procedure
Video recordings of elite shot-putters’ throwing techniques were made on the field of play during the 2000 Paralympic Games, 2002 International Paralympic Committee World Championships, and select Australian national events (Frossard, O’Riordan, & Goodman, 2005; Frossard, O’Riordan, Goodman, & Smeathers, 2005; Frossard, Schramm, & Goodman, July 2003; O’Riordan, Goodman, & Frossard, 2004). Recording in these open environments entailed certain constraints (Frossard, O’Riordan, Goodman, & Smeathers, 2005; Frossard, Stolp, & Andrews, 2006), presented in Figure 1. Multi-purpose recording becomes necessary for capitalizing on an event’s uniqueness and for securing the distinct kinematic data sets of interest to distinct parties. Classifiers, for instance, may be interested in assessing the full range of upper-body movement (Chow et al., 2000; Tweedy, 2002). Engineers, in turn, may seek to study the deformation of the pole. Coaches’ main interest may be something as specific as hip-movement pathways during forward thrusting, or the exact position of the feet (O’Riordan, Goodman, & Frossard, 2004). Finally, the biomechanist’s interest may well be the parameters of the shot trajectory (Chow et al., 2000). Under experimental conditions, optimal accuracy often results from a focus on one data set at a time, that set obtained using optimal field of view and calibration volume. During competitive events, a compromise must be made as all parameters are observed using a single field of view. Furthermore, various technical barriers are presented on the playing field, including lack of control over the event and inevitable need to make recordings in a non-disruptive fashion. There is, in short, a one-off chance to record any attempt, with space only for one to two cameras, and despite likely obstructions of the field of view by equipment, referees, officials, TV crew, or the like.
Such constraints can be assumed to affect the accuracy of the kinematic data. Even the implementation of an accuracy-based approach within an event-constrained procedure will only partially guarantee sufficient accuracy. Nevertheless, a formal quality control procedure limited to determining shot trajectory parameters and occurring after the video recording stage could offer help to achieve highest possible accuracy.
PURPOSE
The authors’ ultimate aim is to propose a quality control procedure able to reduce error in the measurement of shot trajectory parameters and validate measured parameters, as well as to refine and standardize the format used to report measurement error. The proposed procedure relies on five key quality indicators that should influence decisions about when the moment of release occurs. The paper also has four secondary purposes. First, it comprises a detailed example of the entire procedure as it was deployed with the Class F55 male athlete who won the gold medal at the 2002 International Paralympic Committee (IPC) World Championships. Second, it tracks the procedure’s outcomes in terms of 7 elite shot-putters participating in 2 world-class events. Third, it presents possible sources of error inherent in the proposed videotaping setup. Fourth, it makes several recommendations for future on-field studies.
METHODS
Events
Video recordings were made during two world-class events, the 2000 Paralympic Games held in Sydney, Australia (4 classes of competition), and the 2002 IPC World Championships held in Lille, France (3 classes of competition), as indicated in Table 1.
Table 1
Event and total number of athletes competing in each class included in this study (PG: Sydney 2000 Paralympic Games, WC: Lille 2002 International Paralympic Committee World Championships).
Participants
A total of 51 shot-putters were part of the present study, including 39 males and 12 females. For the competitions, each athlete had been classified according to the latest International Stoke Mandeville Wheelchair Sports Federation classification system (Laveborn, 2000). Table 1 illustrates total numbers of these athletes competing in each class, although the present analysis was limited to those who became gold medalists in four select classes (F52, F53, F54, and F55). Though not all-inclusive, the sample was deemed sufficient for illustrating the principles of the quality control procedure. (Gold medalists also typically generate greatest interest among sport scientists, coaches, and athletes.) Female athletes assigned to the F52 and F54 classes had competed jointly at the Sydney Paralympic Games, due to the small numbers of athletes in these classes, and a single gold medal was awarded. For our research, however, the performance of the event’s top competitor in each of these classes was considered. The female Class F53 shot-put event was canceled for lack of athletes.
Data processing
The sequence of the following 7 key steps used to process video data is shown in Figure 2.
Figure 2. Seven key steps of data processing, including the quality control procedure and the five associated quality indicators.
Step 1: Camera set-up
Frossard, Stolp, and Andrews (2003) have previously provided a thorough guide to the practical aspects of video camera set-up during world-class events. Therefore, this paper will limit itself to key elements of that set-up. During the 2 events included in this study, each put was recorded using 1 digital video camera (SONY Digital Handycam DCR-TRV15E), set at a sampling rate of 25 Hz. A “household” camera was chosen because it was affordable, discreet, and readily available. High-resolution cameras, by contrast, require exacting lighting conditions and are expensive and fragile. Some video cameras commercially available at the time of the events would have allowed high-speed filming, but at the cost of compromised resolution.
The SONY camera was placed approximately 1.1 m high at a distance between 8.0 m and 10.0 m, perpendicular to the length of the plate. The angle between the optical axis of the camera and the ground was approximately 90 degrees. The field of view included the full length (2.29 m) and full width (1.68 m) of the plate on the ground. The field of view was furthermore enlarged in the direction of the put, to ensure the recording of at least the first 5 frames of the shot’s aerial trajectory (see Figure 3A). Under experimental conditions, this field of view can be obtained by zooming to reduce the perspective error once the camera is positioned with respect to the plate. In this study, the camera was placed relatively close to the plate in an effort to lessen the possibility of intrusion into the field of view by equipment, referees, or TV crews. Nevertheless, the zoom was occasionally used. This camera position resulted in a pixel resolution ranging from 0.95 cm to 1.85 cm, depending on the camera’s position and the zoom setting.
Figure 3.Example of male gold medallist in the class F55 participating in the shot-put event of the 2002 IPC World Championships seated in the throwing frame (D) attached to a plate (E) using ties (C) that is facing the sector (F). Figure A provides an example of field of view of the camera with the body’s segments’ position and the shot at the instant of release (Tfinal – Frame 91). Figure B represents a stick figure of the athlete with the key instants needed to determine the parameters of the shot’s trajectory in the Global Coordinate System (GCS[O, X, Y]).
Step 2: Video recording
A total of 387 attempts, corresponding to nearly every one of the attempts made by each athlete in each class, were recorded and stored on MiniDVs. The duration of the video recording of each attempt was approximately 7 seconds. An attempt began when the referee handed the shot to the athlete and ended shortly after the shot landed on the ground. A customized calibration frame (2 m length x 1.5 m height x 1 m width) containing 43 control points placed on top of the plate was recorded at the beginning and at the end of each event.
Step 3: Video digitizing
The video recording of the calibration frame and of the best attempt in each class (the gold-medal throw) was digitized at 50 Hz using Digitiser 5.0.3.0 software, manufactured by SiliconCOACH Ltd. This sampling rate was achieved by de-interlacing the initial video frames, which affected accuracy only on the horizontal axis.
Step 4: Tracking
The Digitiser software was used to track, frame-by-frame, the center of the shot, the distal end of the middle finger, the position of the wrist, and the origin of the two-dimensional Global Coordinate System (GCS[O, X, Y]). The latter corresponded to the middle of the line of reference located in the front and at the bottom of the throwing frame, used by the referee to measure the performance, as illustrated in Figure 3. The tracking started with the back thrust and ended when the put was no longer within the field of view, which included 5 frames or more after the estimated moment of release. Tracking of the full body was obtained only for the male Class F55 gold medalist (see Figure 3B).
Step 5: Selecting instant of release
The 2 coordinates of the points tracked were imported into a customized Matlab software program (Math Works, Inc.). An operator used the software to select a combination of 2 positions of the shot, allowing calculation of the parameters of the shot’s trajectory (also see Step 6, below). The first position, (Tinitial), corresponding to the instant of release, was indicated by separation between the finger and the shot of a distance larger than the shot’s diameter. The second position, (Tfinal), corresponded to one of the 3 consecutive frames. The two-dimensional coordinates of the displacement were not smoothed or filtered to avoid end point distortions of the limited number of samples after the moment of release.
Step 6: Calculation of parameters of shot trajectory
The Matlab software implemented the classic equations from the literature (Lichtenburg & Wills, 1978; Linthome, 2001) for calculating the trajectory of the shot, allowing the landing distance to be estimated. The performance calculation was determined from the parameters of the shot at the instant of release, including (a) resultant horizontal and vertical components of the translational velocity; (b) resultant horizontal (advancement) and vertical (height) components of the position; and (c) the angle of the trajectory. The performance calculation was also corrected by the radius of the shot, as the official performance was measured from the landing mark on the ground closest to the Global Coordinate System.
Step 7: Comparison of official and measured performance
The performance calculation was compared with the official performance, which was the distance measured by the referee during the event; calculation error indicators and calculation quality indicators were employed as described below. The official performance measure was taken as the value of reference.
Quality control procedure
The quality control procedure relied on two efforts aimed at reducing and reporting error and validating measures of the shot trajectory, as presented in Figure 2. The first included the digitizing of the displacements of the shot and the operator’s subsequent selection of the best combination of Tinitial and Tfinal . Feedback on the quality of the selection was obtained from the 5 key quality indicators, as follows:
Average acceleration after release on vertical axis (Quality Indicator 1—Step 5)
In principle, the vertical velocity of the shot must be constant, and its acceleration must be equal to 9.81 m.s-2. The software therefore calculated the regression line of the vertical velocity between the frame following Tfinal and the last frame available, in order to eliminate random pointing errors. Then, it calculated the average acceleration, as illustrated in Figure 4. The average over four frames was 10.78 m.s-2 in the case of the male in Class F55.
Mean instantaneous acceleration after release on horizontal axis (Quality Indicator 2—Step 5)
In principle, the horizontal velocity of the shot must be constant, and its acceleration must be nil. The software therefore calculated the mean instantaneous acceleration between the frame following Tfinal and the last frame available, as illustrated in Figure 4. The mean over four intervals was -0.89±0.35 m.s-2 in the case of the male in Class F55.
Calculation error (Quality Indicator 3—Step 7)
Expressed in meters and corresponding to the discrepancy between official and calculated performance measures, the calculation error suggests the general quality of the data processing. A positive error indicates a calculated performance measure that overestimates the official performance, while a negative error indicates a calculated performance measure that underestimates it.
Calculation quality (Quality Indicator 4—Step 7)
The calculation quality corresponds to the percentage of the absolute value of the error, in relation to the official performance measure (such as: Calculation quality=[100-(Abs(Error)/Official performance)*100]). This quality indicator provides an understanding of the data processing’s quality in absolute terms, but it cannot indicate the direction of error.
Sensitivity analysis of tracking of Tinitial and Tfinal (Quality Indicator 5—Step 7)
Preliminary studies showed that an error of ±2 pixels could significantly affect calculation of the performance. However, the software was able to provide a succinct sensitivity analysis of the tracking, the outcome of which is reported in Table 2. Sensitivity analysis comprised recalculation of the performance using the combination of positions from Step 6, with 2-pixel positive and negative errors on Tinitial alone, on Tfinal alone, and/or on these two combined. As needed, this feedback guided operator readjustments concerning pointing of the shot (see also Step 4 above).
Table 2
Example of sensitivity analysis of the tracking (Quality Indicator 5) for the male gold medalist in F55 class consisting on recalculating the performance using the combination of positions determined in Step 5 with positive and negative errors of two pixels (3.6 cm) either on Tinitial and Tfinal only or on both combined. The white dot corresponds to the original position; the black dot corresponds to the position with the error. X and Y represent the horizontal and vertical axes, respectively.
Figure 4. Example of feedback provided for the male gold medallist in F55 class to determine the moment of release of the shot (Step 5). Section A represents the vertical position of the shot and the finger during the complete throw until the shot is outside the field of view. The square area corresponds to the zooming on the relevant data to be used to determine the moment of release. Section B presents the selected moment of release (Tinitial = Frame 91), when the separation of the shot and the finger is greater than the diameter of the shot and the second position (Tfinal = Frame 92). Section C provides the velocity of the shot after release as well as the average acceleration (Quality indicator 1) and the mean instantaneous acceleration (Quality indicator 2).
The second of the two efforts to reduce and report error and validate measures of the shot trajectory involved our selection of software that allowed the operator to process the data over an unlimited number of iterations from Step 4 to Step 7, until discrepancies between calculated and official measures had been minimized. Each iteration represented one combination of data points as determined in Step 5.
RESULTS
Table 3
Outcome of the quality control procedure. The number of iterations corresponds to the number of attempts made by the operator during the quality control procedure to minimise the difference between the official and calculated performance. The error corresponds to the difference between the official and calculated performance (Quality indicator 3 (1)). The calculation quality corresponds to the percentage of the absolute value of the calculation error in relation to the official performance, such as: Calculation quality=[100-(Abs(Error)/Official performance)*100] (Quality indicator 4 (2)).
Table 3 presents, by competitive class, the quality control procedure’s outcomes, including number of iterations, calculation error, and calculation quality. The smallest difference between a calculated and an official performance measure was obtained from a minimum of 3 (maximum of 9) iterations. Calculation error ranged from 0.01 m to 1.33 m, with a mean of 0.54±0.46 m. The absolute calculation quality ranged from 79% to 100%, with a mean of 92±8 %.
DISCUSSION
These results overall might be considered satisfactory, since athlete performance during 4 out of 7 puts was calculated with accuracy surpassing 94%. However, accuracy surpassed only 79% for three competitive classes (F53 male, F54 male and F52 female), and the number of iterations was high. This finding indicates that, for these puts, the shot trajectory parameters were not determined with sufficient precision, the result primarily of pincushion distortion, sampling frequency, and projection of shot displacements onto the sagittal plane.
Pincushion distortion
Tracking of the shot’s displacement took place at the right top corner of the screen, outside the calibration volume with its maximum 1.5 m on the vertical, 0.5 m on the horizontal, axis. In principle, this zone is the most prone to pincushion distortion, in which straight lines appear to bow in toward the middle. While such distortion must be acknowledged, it is unlikely to have contributed strongly to the lack of accuracy.
Sampling frequency
Despite its sampling frequency of 50 Hz, the shot appears fuzzy at the instant of release because it has traveled significant distances between successive frames. This made it sometimes difficult, during Step 4, to track the exact center of the shot at the instant of release. Sampling frequency could have had impact on the estimation of the position of the shot and on the estimation of the speed of release. However, speed of release and error do not seem to be correlated here. Quality Indicator 5 assisted in determining the most accurate pointing, as illustrated in Table 2.
Projection of the displacements of the shot onto the sagittal plane
In this study, the main source of error was the positioning of the camera to the side of the athlete, which limited calculation of the speed of release to the sagittal plane alone. Visual analysis of the footage, however, showed that the throwing technique of athletes in these classes included more rotation in the transverse plane. The consequent projection of out-of-plane movement onto the sagittal plane tends to result in underestimation of speed of release and overestimation of release angle. This is reflected in our finding of a constant mean instantaneous acceleration after release on horizontal axis (Quality Indicator 2), rather than a nil mean, as was obtained for the Class F55 males. The slope of the curve corresponds, then, to the angle of the shot trajectory in the transverse plane.
In principle, the best way to alleviate these limitations would be to use a three-dimensional motion analysis system with a data acquisition rate ranging up to 100 Hz. Such a system should provide enough samples to accurately determine the shot’s position at the instant of release and to enable further smoothing of the data if required. Furthermore, with such a system the actual trajectory of the shot could be calculated in three, not two, dimensions, which would improve the accuracy of velocity and angular data
Ideally, put-throwing analysis should require at least four cameras, aligned diagonally with each corner of the plate, as well as a preferred fifth camera located above the athlete ( Allard, Stokes, & Blanchi, 1995; Marzan, 1975). Such a camera arrangement, while possible in an experimental framework, would be difficult to implement on the field during a world-class event, its invasive nature perhaps prompting organizing committees to deny researchers access. In addition, some 20 people work in the immediate throwing area alone, making it highly likely that the field of view of cameras on the floor would become obstructed or compromised as the recording of attempts progressed ( Frossard, Schramm, & Goodman, July 2003; Frossard, Stolp, & Andrews, 2003). A more feasible alternative involves using two commercially available high-speed cameras recording at 100 Hz or better, with full resolution. These cameras should be placed, at a distance, to the front and on the side of the thrower, allowing a bi-planar analysis in the sagittal and frontal planes. (Recordings made in this fashion should also accommodate three-dimensional reconstructions.) It would then become possible to estimate the rotation of the throwing upper arm in the transverse plane. Furthermore, the camera in front would provide data allowing one to determine the distance of the shot’s landing position in relation to the sagittal plane. Alternatively, the offset could be obtained from the laser pointer used by officials as they read the 3D coordinates of the shot at the point of landing. The offset could be used to correct for projection onto the sagittal plane.
CONCLUSION
A quality control procedure for video-recording elite male and female shot-putters during world-class events has been developed whose outcome is the calculation, with reasonable accuracy, of performances at outdoor competitive events. The developers of the quality control procedure acknowledge that diminished accuracy results mainly from limited sampling frequency supplied by the selected SONY video camera and from significant out-of-plane movement. The point is made that kinematic analyses of shot-putters at this level would be more beneficial if they were three-dimensional, rather than two-dimensional, even though most throwing action occurs in the sagittal plane. Because use of a three-dimensional motion analysis system is precluded on the field of play for logistical reasons, practical compromises must be made.
The present study made three majors contributions by demonstrating (a) the need to systematically implement a quality control procedure when conducting kinematic analyses of event-constrained recordings; (b) the benefits of using quality indicators to support decisions about tracking and determining instants of release; and (c) the need to report quality control outcomes in terms of both error and calculation quality. Equipped with data of this type, sport scientists, classifiers, coaches, and athletes will have a better feel for the level of accuracy truly obtainable during competitive events. A better appreciation of such data’s limitations should serve them all well. The quality control procedure that has been proposed can be implemented within an accuracy-based effort.
Recommendations from this study would be particularly important to future studies focusing predominantly on from-the-field data. It is further anticipated that this study will provide key information to sport scientists, coaches, and elite shot-put athletes trying to fully grasp the correlation between shot trajectory parameters and either classification or performance.
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Nutrition-related knowledge, attitude, and dietary intake of college track athletes
ABSTRACT
Although it is recognized that athletic performance is enhanced by optimal nutrition, nutrition-related knowledge deficits and dietary inadequacies continue to persist among many college athletes. The purpose of this study of college track athletes was to measure nutrition knowledge, attitude regarding healthy eating and athletic performance, and dietary intake, identifying relationships among these parameters. A self-administered nutrition knowledge and attitudes survey and the youth/adolescent semi-quantitative food frequency questionnaire were used to measure nutrition knowledge and nutrition attitude and to assess diet quality, employing a convenience sample of 113 track athletes from two NCAA Division I schools. Mean knowledge was fair, with highest component scores attained for carbohydrate, vitamins and minerals, and protein. Low scores were found for vitamins E and C. Mean attitude scores were high and similar by sex. Overall mean diet quality was 84 ± 10 (M ± SD) of 110 possible. High mean dietary intake scores were found for vitamins C and A, cholesterol, saturated fat, calcium, and magnesium; low mean dietary intake scores were found for vitamin E, fiber, sodium, and potassium. Weak correlations existed between nutrition knowledge and attitude versus diet quality. In summary, we identified adequate intake and knowledge (carbohydrates), poor intake and knowledge (vitamin E), and adequate intake and lack of knowledge (vitamin C and protein). Future research should explore factors other than knowledge and attitude that may have primary influence on dietary intake among college athletes.
INTRODUCTION
It is well recognized that athletic performance is enhanced by optimal nutrition (American College of Sports Medicine, American Dietetic Association, and Dietitians of Canada, 2000). However, college athletes encounter numerous barriers that can hinder healthy eating, including lack of time to prepare healthy foods (due to rigorous academic and training schedules), insufficient financial resources to purchase healthy foods, limited meal planning and preparation skills, and travel schedules necessitating “eating on the road”(Malinauskas, Overton, Cucchiara, Carpenter, & Corbett, 2007; Palumbo, 2000). Research has demonstrated that athletes are interested in nutrition information, and that sport nutrition information is increasingly available (Froiland, Koszewski, Hingst, & Kopecky, 2004; Jonnalagadda, Rosenbloom, & Skinner, 2001; Zawila, Steib, & Hoogenboom, 2003).
Nevertheless, nutrition-related knowledge deficits and dietary inadequacies persist among many college athletes (Jacobson, Sobonya, & Ransone, 2001; Rosenbloom, Jonnalagadda, & Skinner, 2002; Malinauskas, Overton, Cucchiara, Carpenter, & Corbett, 2007; Zawila, Steib, & Hoogenboom, 2003). College athletes exhibit a lack of knowledge about the roles of protein, vitamins, and minerals in the body and also about supplementation with these nutrients (Jacobson, Sobonya, & Ransone, 2001; Rosenbloom, Jonnalagadda, & Skinner, 2002; Zawila, Steib, & Hoogenboom, 2003). For example, Jacobson and colleagues (2001) reported that male athletes are likely to believe that protein provides immediate energy and that high-protein diets increase muscle mass. Zawila and colleagues (2003) reported nutrition knowledge deficits among female cross-country runners.
Nutrition can play a key role in optimizing physical performance and recovery from strenuous exercise (American College of Sports Medicine, American Dietetic Association, and Dietitians of Canada, 2000). However, many college athletes have diets that warrant change to promote health and support performance (Malinauskas, Overton, Cucchiara, Carpenter, & Corbett, 2007). Specifically, diets that are low in fruits, vegetables, and whole grains and high in fat and processed foods are common among college athletes (Clark, Reed, Crouse, & Armstrong, 2003; Hinton, Sanford, Davidson, Yakushko, & Beck, 2004). To improve dietary intake among college athletes, further research is warranted identifying dietary inadequacies as well as factors influencing the dietary intake of athletes (Hinton, et al, 2004; Turner & Bass, 2001).
It is unclear if college athletes’ nutrition knowledge and attitudes about nutrition have an association with their dietary intake. Wilta and colleagues (1995) found that greater nutrition knowledge was associated with healthier dietary practices among runners, whereas Turner and colleagues (2001) reported no significant correlate relationships between knowledge and dietary intake among female athletes. These conflicting findings suggest that further research is needed to learn whether knowledge and attitude are primary factors impacting college athletes’ dietary intake. The purpose of the present study was to assess the nutrition knowledge, nutrition-related attitudes, and dietary intake of college track athletes. Specific research objectives were (a) to measure nutrition knowledge in regard to carbohydrate, protein, vitamins and minerals in general, and selected antioxidant vitamins; (b) to assess attitude regarding healthy eating and athletic performance; (c) to evaluate dietary intake; and (d) to identify if, for college track athletes, relationships exist among nutrition knowledge, attitude, and dietary intake.
METHODS
Approval to conduct the study was secured from the appropriate Institutional Review Board prior to data collection. Written consent was obtained from each participant. All data collection was performed by a single researcher.
Nutrition knowledge and attitude survey
A registered dietitian constructed a nutrition knowledge and attitude pilot survey (Jonnalagadda, et al, 2001; Zawila, et al, 2003). The knowledge section included five subject areas (carbohydrates, protein, vitamins and minerals in general, vitamin C, vitamin E) with 2–5 true/false statements per subject area. The attitude section included five statements of belief that healthy eating supports athletic performance. Participants used a 5-point Likert scale (1 = strongly disagree, 3 = neither agree nor disagree, 5 = strongly agree) to indicate level of agreement with each statement. The survey was reviewed for content validity by a second registered dietitian and for content clarity by a person in a profession other than health care. To pilot test the survey, 47 track athletes (26 males, 21 females) from a NCAA Division I program in the Piedmont region of the United States completed the self-administered survey. Only minor syntax modifications were necessary based on participant responses.
Assessing diet quality
The semi-quantitative youth/adolescent food frequency questionnaire (YAQ) assesses dietary intake over the 12 preceding months. The YAQ has demonstrated reproducibility and validity in youth and has been used to measure nutrient intakes among college athletes (Hinton, et al, 2004; Rockett, Wolf, & Colditz, 1995; Rockett et al., 1997). In the present study, data obtained with the YAQ were used to calculate diet quality scores. The total score was the sum of 11 “nutrient component scores,” including nutrients of concern (fiber, calcium, potassium, magnesium, and vitamins A, E, and C) and nutrients promoting metabolic dysregulation (saturated fat, cholesterol, added sugar, and salt) as indicated in the 2005 Dietary Guidelines for Americans (U.S. Department of Health and Human Services [USDHH] & U.S. Department of Agriculture [USDA], 2005). Under a framework provided by the Healthy Eating Index, each nutrient component score was 10 at maximum and 0 at minimum (Basiotis, Carlson, Gerrior, Juan, & Lino, 1999). A component score of 10 was assigned for a nutrient when intake met or exceeded the Dietary Reference Intake. Proportionately lower scores were assigned to nutrients when was intake less than recommended (Food and Nutrition Board, Institute of Medicine [FNBIM], 1997, 2000, 2001). Cholesterol, saturated fat, sodium, and fiber recommendations were based on 2005 Dietary Guidelines, while sugar recommendations were based on Recommended Dietary Allowances (USDHH & USDA, 2005; Food and Nutrition Board, Institute of Medicine, 2003). To obtain the maximum score of 10, criteria to be met included intakes of < 300 mg cholesterol, < 10% calories from saturated fat or sugar, < 2300 mg sodium, and > 14 g fiber/1,000 calories. To obtain the minimum score of 0, criteria to be met included intakes of > 15% calories from saturated fat or sugar, > 450 mg cholesterol, and > 4600 mg sodium (USDHH & USDA, 2005; Food and Nutrition Board, Institute of Medicine, 2003). Values between the maximum and minimum criteria were scored proportionately (Basiotis, et al, 1999).
Survey administration
A convenience sample of track athletes (N = 113) from two NCAA Division I track programs in the southeastern United States participated in the study during the fall of 2006.
Statistical analysis
All statistical analysis was conducted using SPSS 13.0. Descriptive statistics include means, standard deviations, 95% confidence intervals, and frequency distributions. Independent t-tests were used to compare mean knowledge and diet quality scores by sex. Simple linear regression was used to examine relationships between knowledge, attitude, and diet quality. An alpha level of .05 was used for all statistical tests.
RESULTS
A total of 118 participants completed the study. Data from 5 were excluded due either to incompleteness (n = 2), to a respondent’s age being less than 18 years (n = 1), or to a respondent’s competing only in field events (n = 2). The final sample size was 113 (61 males, 52 females), and the overall participation rate was 71%. Demographic characteristics of participants are reported in Table 1. The majority (67%) of participants were freshmen and sophomores. The participants’ reported event specialties were sprinting (45%), middle-distance (27%), and long-distance (29%). YOU ARE HERE
Table 1
Demographic Characteristics of College Track Athletes
Parameter (M ± SD) Males (n = 61) Females (n = 52)
Age (in years) 19.3 ± 1.2 19.1 ± 1.1
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
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.
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)
*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)
Note. Dietary intake was assessed using the youth/adolescent food frequency questionnaire (Rockett, Wolf, & Colditz, 1995). With this instrument, dietary quality is represented as the sum of the 11 nutrient component scores. Each component score ranged from 0 (minimum) to 10 (maximum), based on actual dietary intake as compared to recommended intakes (FNBIM, 1997, 2000, 2001, 2003; USDHH & U.S. Department of Agriculture, 2005). Higher scores indicate nutrient intakes relatively close to recommended levels.
*p < .05
There were very weak correlations for diet quality and attitude (r = 0.048) and diet quality and knowledge (r = 0.001). There was little correlation between knowledge scores for specific nutrients and corresponding dietary intake: carbohydrate (r = 0.011), protein (r = -0.009), vitamin C (r = -0.004), and vitamin E (r = -0.005).
DISCUSSION
The purpose of this study was to assess nutrition knowledge, attitude, and dietary intake of college track athletes. Specifically, we asked if knowledge and attitude were related to dietary intake. This research is novel because we examined relationships between knowledge about specific nutrients (carbohydrate, protein, and vitamins C and E) and actual intakes of these nutrients. Further, there is a lack of research on college athletes’ knowledge concerning antioxidant vitamins, despite the fact that many of them do supplement their diets with antioxidants (Froiland, Koszewski, Hingst, & Kopecky, 2004; Herbold, Visconti, Frates, & Bandini, 2004).
Among the college track athletes participating in this study, knowledge about carbohydrate and general knowledge of the roles of vitamins and minerals in exercise was fair. These athletes lacked knowledge, however, about the roles of protein, vitamin C, and vitamin E. For example, 82% (n = 93) of the athletes believed that vegetarian athletes require protein supplements to meet their protein needs, and 40% (n = 45) believed that the body relies on protein for immediate energy. Previous studies have similarly indicated a lack of knowledge of the specified nutrients among college athletes. Rosenbloom and colleagues (2002) found that 46% of athletes believed protein is the main energy source for the muscle and 34% believed athletes require protein supplementation.
Indeed, athletes may be tempted to use supplements to gain a competitive edge. Primary reasons athletes give for nutrient supplementation include increasing strength and energy and improving athletic performance (Froiland, Koszewski, Hingst, & Kopecky, 2004; Herbold, Visconti, Frates, & Bandini, 2004). In the present study, a majority (67%, n = 76) of the athletes believed athletes must take a multivitamin each day and 56% (n = 66) believed vitamins and minerals supply energy. Other studies, as well, have reported many athletes believing vitamins and minerals can increase energy (Jonnalagadda, et al, 2001; Rosenbloom, Jonnalagadda, & Skinner, 2002).
Furthermore, misconceptions about antioxidant vitamins characterized the majority of athletes in our study. For example, 53% (n = 60) believed it was necessary for an athlete to supplement with vitamin C to boost immune functioning, and 56% (n = 63) believed that vitamin E supplementation was necessary to protect red blood cells from oxidative damage and to promote oxygen transport to muscles. Other researchers have reported athletes supplementing with vitamins C and E to enhance their immune system and prevent illness (Froiland, Koszewski, Hingst, & Kopecky, 2004; Neiper, 2005). Overall, the nutrition knowledge deficits identified in the present study confirm that many college athletes lack understanding of the roles of protein, vitamins, and minerals in the body, and thus lack the ability to assess whether their dietary intake of nutrients warrants use of a supplement. Education strategies for sports professionals and athletes should focus on the roles of selected nutrients in exercise, how to obtain adequate dietary intake of the nutrients, and how to evaluate need for nutrient supplementation.
The mean nutrition attitude score was high for both sexes. Seventy-one percent (n = 80) strongly agreed that “Eating healthy foods will improve my athletic performance.” Our findings about positive nutrition-related attitudes are consistent with those of Zawila and colleagues (2003), who reported that runners exhibited positive attitudes regarding nutrition education. College athletes may be receptive to learning how to improve their dietary intake to correct nutrient inadequacies that can impact their sport performance.
The mean diet quality for both males and females was greater than 80%, indicating an overall healthy diet among those surveyed. In regard to mean component scores, males and females alike had high scores (greater than 8) for vitamin A, vitamin C, and calcium. In contrast, mean scores for intake of vitamin E, potassium, fiber, and sodium were low, indicating a need for nutrition education moving dietary intake of these nutrients into line with dietary recommendations.
We found that neither nutrition knowledge nor attitude correlated with dietary intake; knowledge was less than 1% predictive of dietary intake. Conflicting results have been reported for athletes regarding relationships between nutrition knowledge and dietary intake. Wilta and colleagues (1995) found that dietary intake was 27% predictive of nutrition knowledge among runners and thus concluded that runners with greater nutrition knowledge make better food choices. On the other hand, Turner and colleagues (2001) reported that osteoporosis knowledge was only 3% predictive of dairy intake among athletes and thus concluded that, among college athletes, there was no significant correlation between knowledge of osteoporosis and intake of dairy products. In the present study, nutrition-related attitude was only 5% predictive of dietary intake, indicating that attitude about eating to support performance was not the primary influence on dietary intake. In addition, no significant correlations were found between knowledge of specific nutrients and actual dietary intake of the nutrients. While examining these relationships, we identified adequate intake with adequate knowledge (carbohydrate), poor intake with lack of knowledge (vitamin E), and adequate intake with lack of knowledge (protein and vitamin C). As a result of this study’s findings, we suggest that future research should explore factors other than nutrition knowledge and attitude that influence dietary intake among college athletes, since knowledge and attitude were not found here to be primary factors impacting dietary intake.
Address correspondence to: B. Malinauskas, Ph.D., R.D., Assistant Professor, Department of Nutrition and Dietetics, East Carolina University,
Greenville, NC 27858-4353, malinauskasb@ecu.edu
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Leisure constraints experienced by university students in Greece
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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