### Abstract

Wheelchair basketball met a rapid growth in recent decades and became one of the most popular and spectacular sports for people with disabilities. Researchers’ efforts to perform tests evaluating the physiological and technical characteristics of the disable athletes have been based on the adoption of tests, used for healthy athletes (7, 15). In addition, different types of disabilities obligated the International Wheelchair Basketball Federation to establish classification degree for the athletes, ranging from 1 to 4.5, according to their disability. The purpose of this study was to evaluate the Greek elite basketball players’ technical skills and to compare their performance, (a) with their classification degree and (b) with recent literature. Fourteen (N=14) Greek wheelchair basketball players, all members of the national team, volunteered to perform six skill tests: (a) 20m sprint, (b) free throws, (c) lay-ups, (d) obstacle dribble, (e) pass for accuracy, and (f) pass for distance. The high classification degree athletes, demonstrated significantly higher performance than those with low classification, only in obstacle dribble test (p <.01), but the trend indicated that athletes with high classification degree demonstrated better performance on tests requiring physical abilities (sprint, lay-ups, obstacle dribble, pass for distance), while those with low classification degree performed better on tests requiring skills and concentration (free throws, pass for accuracy). These results are in accordance with recent literature, although Greek basketball players, demonstrated lower performance compared with those of other countries, where wheelchair basketball is widespread (13). The difference between high and low classification players in obstacle dribble test, caused to the lack of abdominal muscles, while overall performance is affected by the frequency of training and years of involvement with the sport, before the time point of injury (9).

**Key words:** wheelchair, basketball, technical skills

### Introduction

Sporting activities for people with physical disabilities became widespread in recent years. Wheelchair basketball, which is regarded as one of the most popular and spectacular sports for people with disabilities, devised at the end of the Second World War. Specifically, in 1944 the British government commissioned Dr. Guttmann to establish a foundation for care and hospitalization of world-war II spinal cord injured soldiers, in the area of Stoke Mandeville Hospital. Specifically, the team called “The Flying Wheels of Birmingham” is the one that has the legal right to invoke that have devised the wheelchair basketball (1946). The evaluation of the wheelchair basketball players’ technical skills has interested researchers and trainers in the past (4, 14-15). The evaluation methods for the technical characteristics of wheelchair basketball players, mainly based on similar tests used for healthy players (1-2, 11).

The ability to perform the technical skills required for the sport, characterized by the different type and degree, of the players’ disabilities. Each athlete is classified according to degree of disability, and the ability to perform certain tests such as wheelchair sprint, stopping, obstacle dribbling, holding the ball, etc. The classification system for wheelchair basketball, which has been established by the International Wheelchair Basketball Federation (IWBF), with five classification points (1-4.5), differs than U.S.A. applied system, which classifies the players in a three points scale (1-3). The main purpose of the studies so far, is to evaluate the athletes with different classification, and to investigate methods to improve their technical skills.

Brasile (4) demonstrated the performance of wheelchair basketball players which were classified according to the U.S.A. applied system (Classification I, II and III). Participants were evaluated in the following skill tests: (a) obstacle dribble, (b) free-throws, (c) dribbling and shooting the ball, and (d) pass for accuracy. The results showed that: (a) the classification II and III athletes demonstrated higher performance than the others, and (b) classification II athletes demonstrated highest performance.

Moreover, Vanlerberghe and Slock (14) evaluated 30 wheelchair athletes which were classified in the 3 points scale (I, II, III) and they applied: (a) two tests for shooting accuracy (shot under the basket and rebound; obstacle dribble, shot and rebound), (b) two tests for ball-handling (obstacle dribble and dribble around wheelchairs), and (c) two tests for passing ability (speed pass and long pass).

Results revealed significant differences between athletes with different physical disabilities. Athletes of III classification revealed the highest performance, while athletes of I classification revealed the lowest. However, researchers have argued that these specific skill tests can hardly be a reliable method for the evaluation of the wheelchair basketball players.

Also, Brasile (5) divided a sample of 79 wheelchair basketball athletes into three groups, according to their classification in order to evaluated their technical ability in six skill tests: (a) obstacle dribble, (b) 1 minute free throws using the strong hand, (c) 1 minute free throws using the weak hand, (d) pass for accuracy using the strong hand, (e) pass for accuracy using the weak hand and (f) 20m speed run. The skill tests’ results revealed that, athletes of II and III classification referred similar performance between them, but both of them higher than the athletes of I classification. These findings led the researcher to the conclusion that skill tests’ results are influenced by both of the training time and the previous experience in basketball.

Similar results were referred in a recent study by Ergun, Duzgun and Aslan (9), which evaluated 32 wheelchair basketball players. Subjects with low disability lagged behind in lay ups test, in 20m speed run, in shooting around the basket, as well as in obstacle dribble. Additionally, there were detected significant differences between athletes of different coaching experience to the tests of 20m speed run, obstacle dribble and passing for accuracy. Moreover, “age” may be an important factor that affects the performance of the athletes in wheelchair basketball.

Brasile (6) applied six field tests to evaluate twelve male and twelve female wheelchair basketball athletes in the following tests: (a) obstacle dribble, (b) free throws, (c) rebound and shot with the strong hand, (d) rebound and shot with the weak hand, (e) pass for accuracy with the strong hand and (f) pass for accuracy with the weak hand. Within the female group were revealed significant differences in tests requiring capability and discipline (rebounding and shooting the ball, obstacle dribble). In contrast, male athletes revealed improved performance in tests requiring higher power level and especially to those that were related with distance (passing for accuracy and free throws).

Finally, Molik et al. (13) evaluated 109 Poles and Lithuanian wheelchair basketball players in six skill tests. The results of the study revealed that athletes with low classification demonstrated lower performance, compared to athletes with a high classification degree. Particularly, no significant differences were detected between athletes of 1 and 2 classification degree. Reversely there were detected significant differences between athletes of 3 and 4.5 classification degree.

As is evident from reviewing the literature, the topic of wheelchair athletes’ skills is incomplete, and more incomplete regarding the high level athletes. The purpose of the present study is (a) to document the performance of elite basketball players’ in the technical skills, (b) to compare their performance in relation to their classification degree, and (c) to compared and discuss their performance with previous studies.

### Methods

#### Participants

Fourteen (N=14) wheelchair basketball athletes aged 30.1±6.6, all of them members of the national team, volunteered to participate in the present study (See Table 1). The types of their disability were the following: (a) one athlete with incomplete quadriplegia (injury on 6th and 7th cervical), (b) seven athletes with paraplegia (injury on 7th cervical to 12th thoracic), (c) one athlete with poliomyelitis and (d) six amputated athletes. They were divided in two groups of 7 athletes, according to their classification. The first group (n1=7) consisted from athletes of 1-2.5 and the second (n2=7) of 3-4.5 classification degree.

#### Skill tests

The six skill tests which assign the technical characteristics of the wheelchair basketball players and were applied in the present study are the following:

*20m speed run:* Subject takes a position behind the baseline and on the signal starts covering a 20m distance as fast as possible. In a two-minute period the subject had two attempts and the best is recorded (See Figure 1).

*Free throws:* Subject shoots 40 free throws in a series of 20 at a time. A 2-minutes rest inserted between the trials. One point was given for each basket made (See Figure 2).

*Obstacle dribble:* Subject starts on the signal at the tight side of the first obstacle and maneuvers through the course as fast as possible, pushing the wheelchair and dribbling the ball, accordingly the U.S.A. NWBA rules. The test is repeated without rest for one more time. Each dribbling violation adds 5 seconds to the trial time and each time the subject, ball, or wheelchair touch an obstacle, one second added to the trial time. One test trial was given to the subjects, for the familiarization with the test (See Figure 3).

*Lay-up:* Two cones are positioned on the 3-point line, perpendicular to the intersection, of the side lines of the free throw lane and the baseline. The subject takes position out of the 3-point line and starts with the signal to make as many lay-ups as possible within two minutes. After each attempt, he takes his own rebound, dribbles the ball around the opposite cone, preparing for the next lay up. The score was the total amount of the attempts, plus the total number of the successful lay ups (See Figure 4).

*Pass for distance:* The subject places the wheelchair so that the front wheels are behind the base line. Using the chest pass, he tries to pass the ball as far as possible. Subject was performed six attempts and the total of the measured distance was recorded (See Figure 5).

*Pass for accuracy:* The target in the specific test are three concentric rectangles of different sizes (50.8cm X 25.4cm, 101.6cm X 63.5cm and 152.4cm X 101.6cm), drown to smooth wall. The base of the larger rectangle is 60.96cm from the ground and the passing line is 10m (for 2-4.5 classification) or 7.5m (for 1 and 1.5 classification) from the wall. Subjects at the signal take position behind the line and perform 10 passes towards the wall any way the wish (i.e., chest pass, overhead, baseball), but discount any passes where the ball bounces first. If the ball hits the line or inside the smallest rectangle, subjects received 3 points which was the highest score. Two points received for the middle and one for the outer rectangle. Subjects should receive three warm up tosses from their distance and finally, only one trial of ten passes was allowed (See Figure 6).

#### Statistical analysis

Six separate (one for each skill test) independent samples t-tests were conducted to detect possible differences between the groups, and for all the carried skill tests. Significance level was set at p<0.05.

#### Classification

It is very possible, wheelchair basketball athletes because of their differences in disability degree, mobility, physical condition and training experience, to perform the technical skills by a completely different way. The skill’s performance was evaluated during games, from specialized observers called “classificators.” A basketball team comprehends athletes with high disability degree such as spinal cord injuries (e.g., quadriplegia), as well as athletes with low disability degree (e.g., amputation, other disabilities). The athletes are classified from 1 to 4.5, accordingly their basketball skills performance. The high classification degree corresponds to athletes with high functional capacity (therefore lower level of disability). The aim of this classification method is the compulsory participation of all the disable athletes in the games. These regulations have been applied since the early 1940’s, years of the game’s establishment. The first classification methods were based on the athletes’ anatomical characteristics, rather than their functional, so the athletes were classified with base their disability and not on their performance in games. Since 1984 a new classification system is in operation which primarily classified the athletes in four degrees (1, 2, 3, 4). Later, some changes were demonstrated, but the most important was the addition of the half degrees (1.5 – 2.5 – 3.5 – 4.5). The U.S.A National Wheelchair Basketball Association (N.W.B.A.) has established a different classification system, which is consistent by three degrees (1 – 2 – 3). So, the athletes are classified and the total of the in-bounce players’ degree must not exceed a specific number. The International Wheelchair Basketball Federation decided for the international games and tournaments, the limit total degree for the in-bounce players to be the 14. For the national and local championships, the Federations allow the participant teams to come in the games with more limit degrees (e.g., 14.5 or 15).

### Results

Table 2 presents the athletes’ classification and their performance in all the technical skills.

The results of the t-test process are presented in table 3. Significant differences detected only for the obstacle dribble test.

### Discussion

This study examined the performance of a sample of high level wheelchair athletes in basketball skills. It was well-documented that athletes with low classification degree, demonstrated lower performance than those with high classification, but not statistically significant. However, significant differences were presented only to the obstacle dribble test. These results are in accordance with previous studies of considerable researchers (7-8, 14). It is discussed below the results regarding the skill tests separately.

#### 20m speed run

For wheelchair basketball the speed ability holds an important role. Specifically, after adjusting the 24¨ regulation, the individual and team speed, became imperative. Brasile (4-5) referred differences in speed run, between the athletes of 2-3 and 1 classification degree, while Ergun et al (9) referred that training experience affects the speed run ability. Contrary to these researches, no significant differences were detected between the two groups in the present study but, on the one hand Brasile (3-8) used different classification method and on the other hand Ergun (9) detected differences only between the athletes of various experience.

Free throws

Although significant differences were not detected between the groups in this test, it is obvious that small differences, appears to be between the groups (20.7 vs 18.4). The results are in accordance with resent literature however, a point of attention regarding free throw shooting performance is the different technique between the players (10, 12), the different type of the wheelchair, their age and the training level before the injury (5), as well as after it (9).

#### Obstacle dribble

Regarding the obstacle dribble, significant differences were observed between the groups in the present study (55.5sec vs 47.1sec, p<0.001). These results are in accordance with literature, while in both of the studies (8-9, 14) which investigated obstacle dribble, were detected significant differences between the athletes with different classification level. Obviously, in this test, many repeated changes of direction in conjunction with controlling the ball, requiring full activation of the abdominal muscles. In these muscle groups, the difference between athletes of varying classification level, is obvious and has an important role in performance, especially in tests involving abrupt changes of direction. An important finding regarding the obstacle dribble test is the difference between Greek and U.S.A. wheelchair athletes. Vanlerberghe and Slock (14), referred values of 47.1 and 43 sec accordingly for low and high classification athletes.

These differences in performance among the Greek and U.S.A. wheelchair athletes, can be justified by the low level of Greek wheelchair basketball and the fact that their involvement in the sport is more leisure, as well as they do not train more than three times a week during the season. On the other hand, basketball in the U.S.A. is highly developed and the national team is among the top teams in the world while the Greek wheelchair basketball national team, is classified in division III of Europe.

#### Lay ups

Contrary to Ergun et al. (9) results, in this study were not detected significant differences between the groups. Specifically, the low classification athletes referred 9.1±2.3 purposeful efforts, while the high classification athletes 11.1±2.2. Although there is a lack of significance, the difference between the groups (9.1 vs 11.1) highlights a strong trend of the high classification athletes, to perform better scores in the specific test.

#### Pass for accuracy

Significant differences between these groups were not observed. However, it has to be noticed that in this test, the low classification athletes were performed their efforts closer to the target, compared to their co-participants with high classification level, which may have influenced the results. It seems that there is need for further investigation, to explore a better method, for assessing the passing test for accuracy.

#### Pass for distance

No significant differences were detected between the groups (12.1 vs 10.5). These results are in accordance with Vanlerberghe and Slock (14), they are reasonable and explained by the fact that the upper body of the athletes is not damaged, so they don’t lack of power and they can throw the basketball away.

### Conclusions

This study investigated the technical characteristics of elite basketball players with disabilities. Overall, although significant differences were not revealed between high and low classification athletes, the trend indicates that athletes with high classification degree are better on tests requiring physical abilities, while those with low classification degree performed better on tests requiring skills and concentration. It is also important to take into consideration the fact that the Greek athletes with disabilities do not train regularly and intensively and had no training experience before the injury. Future research should focus on planning and application of training programs, in order to ascertain the influence of organized and intensive training to the improvement of their physical and technical skills.

### Application In Sports
The organized and intensive training in athletes with disabilities is efficient and it is very important for their performance, from time to time to be evaluated through valid and reliable tests. The frequent applications of test functions as motive for the athletes, so they are more concentrated, energetic, and effective during practice.

### Acknowledgments

The authors thank all the wheelchair basketball players, participating in this study, for their maximum efforts to achieve the best performance. Their contribution made this research possible.

### Tables

#### Table 1
Anthropometric characteristics of Greek elite wheelchair basketball players

#### Table 2
Technical characteristics of Greek elite wheelchair basketball players

#### Table 3
t-test results for the six skill tests within the group

### Figures

#### Figure 1
20m speed run

#### Figure 2
Free throws. 2 series of 20 shot

#### Figure 3
Obstacle dribble

#### Figure 4
Lay-ups (2 min)
”)

#### Figure 5
Long pass (6 trials)
”)

#### Figure 6
Pass for accuracy (10 trials)
”)

### References

1. American Alliance for Health, Physical Education, Recreation and Dance (AAHPERD) (1984). Basketball for boys and girls: skill test manual. VA Reston.
2. Apostolidis, N., Nassis, G., Bolatoglou, T., & Geladas, N. (2003). Physiological and technical characteristics of elite young basketball players. The Journal of Sport Medicine and Physical Fitness, 43, 157-163.
3. Brasile, F. (1984). A wheelchair basketball skill test. Sports and Spokes, 9(7), 36-40.
4. Brasile, F. (1986). Do you measure up? Sports and Spokes, 12(4), 43-47.
5. Brasile, F. (1990). Performance evaluation of wheelchair athletes: More than a disability classification level issue. Adapted Physical Activity Quarterly, 7, 289-297.
6. Brasile, F. (1993). Evaluation the elite. Sports and Spokes,19(3), 52-55.
7. Brasile, F. (1996a). Wheelchair basketball skills proficiencies versus disability Classification. Adapted Physical Activity Quarterly, 3, 6-13.
8. Brasile, F., & Hendrick, B. (1996b). The relationship of skills of elite wheelchair basketball competitors to the international functional classification system. The Recreate Journal, 30, 114-127.
9. Ergun, N., Duzgun, I., & Aslan, E. (2008). Effect of the number of years of experience on physical fitness, sports skills and quality of life in wheelchair basketball players. Fizyoterapi Rehabilitasyon, 19(2), 55-63.
10. Goosey-Tolfrey, V., Butterworth, D., & Morriss, C. (2002). Free throw shooting technique of male wheelchair basketball players. Physical Activity Quarterly, 19, 238-250.
11. Hopkins, D. R. (1979). Using skill tests to identify successful and unsuccessful basketball performers. Research Quarterly for Exercise and Sport, 50, 381-387.
12. Malone, L.A., Gervais, P.L., Steadward, R.D., & Sanders, R.H. (1999, July). Parameters of ball release in wheelchair basketball free throw shooting. Oral presentation at the XVII International Symposium on Biomechanics in Sports, Edith Cowan University, Perth, Western Australia.
13. Molik, B., Kosmol, A., Laskin, J.J., Morgulec-Adamowicz, N., Skucas, K., Dabrowska, A., Gajewski, J., & Ergun, N. (2010). Wheelchair basketball skill tests: differences between athletes’ functional classification level and disability type. Fizyoterapi Rehabilitasyon, 21(1), 11-9.
14. Vanlerberghe, J.O.C., & Slock, K. (1987). A study of wheelchair basketball skills. International Perspective of Adopted Physical Activity. Champaign Illinois: Human Kinetics.
15. Vanlandewijck, Y.C., Daly, D.J., & Theisen, D.M. (1999) Field test evaluation of aerobic, anaerobic, and wheelchair basketball skill performances. International Journal of Sports Medicine, 20, 548-54.

### Corresponding Author

N. Apostolidis, Phd
National & Kapodistrian University of Athens, Faculty of Physical Education & Sport Science
Daphne – Athens, 17237 Greece
<napost@phed.uoa.gr>
+302107276085

Dr. E. Zacharakis is Lecturer to the Faculty of Physical Education and Sport Science of the Athens University. He is teaching Basketball techniques and tactics (Undergraduate). He was head coach of the Greek wheelchair basketball team, participated to the Olympic Games in Athens 2004. His research interest is focused on wheelchair basketball, concerning the technical and physiological characteristics.

### Abstract

The purpose of this study was to investigate physical health-compromising behavior differences of sports fans (highly identified) to those of non-sports fans (less identified). The justification for the study was that if highly identified sports fans were found to engage in elevated health risk behaviors, targeting this group would represent a significant opportunity for health policy makers to achieve a significant impact on the health and wellness of a large segment of Americans while at the same time reducing the costs associated with obesity and unhealthy living practices. Electronic surveys were forwarded to individuals at an American University located in the mid-south region of the country. A sample of 515 participants responded. Highly identified sports fans had significantly higher health risk behaviors than non-sports fans on a range of health behavior measures, including: higher fat consumption, more fast food consumption, less vegetable consumption, greater consumption of refined as opposed to whole grains, and an increased amount of alcohol consumed on days they chose to drink. Additionally, using height and weight data to calculate Body Mass Index (BMI), highly identified sports fans were found to have a higher BMI. Recommendations for future research and applications of the findings to sport are presented.

**Key words:** health, behavior, identification, sport, marketing

### Introduction

According to the Centers for Disease Control and Prevention (9), chronic diseases such as heart disease, cancer, and diabetes are the leading causes of death and disability in the United States accounting for seven out of ten U.S. deaths annually. Approximately 133 million Americans have at least one chronic disease and this has increased dramatically over the last three decades with young Americans’ diagnosis of chronic diseases quadrupling over the past four decades (9). Also, worldwide, chronic diseases are reaching epidemic proportions, affecting individuals of all ages and nationalities with some 388 million people expected to die from one or more chronic diseases in the next ten years (10). Not only are chronic diseases devastating based on mortality rates but also bring with them high levels of morbidity which limit daily living and reduce quality of life.

Another consequence of an increase in the prevalence of chronic disease is the economic toll exerted on the economy of the United States. As a nation, the United States spends two trillion dollars per year on health care and it has been projected that more than 200 million Americans alive today will develop a chronic illness which in turn will equate to a cost of $1 in every $4 spent in the US going toward health care (1). This trend is also occurring internationally. Within the next ten years China, India, and the United Kingdom are projecting losses in national income of US$828 billion due to reduced economic productivity associated with chronic disease (10).

Risk factors associated with the development of chronic diseases such as high blood pressure, high blood cholesterol, smoking, being over weight or obese (BMI greater than 25.0), inactivity, and poor diet provides a depressing snap shot of the future development of chronic disease. Risk factor data elucidates the future chronic disease burden and provides information necessary for the development of preventive interventions (33). Lifestyle, behavioral risk factors, and social and environmental conditions have now become the key determinants of the public’s health (31). Controlling disease risk factors must be addressed as a major component in the fight against chronic disease development.

One of the primary ways health prevention workers seek to control disease risk factors and alter personal behaviors is by educating the public through social marketing initiatives. Andreasen (2) defined social marketing as “the application of commercial marketing technologies to the analysis, planning, execution and evaluation of programs designed to influence the voluntary behaviour of target audiences in order to improve their personal welfare and that of society” (p. 7). Distinguished by its emphasis on non tangible products such as ideas, attitudes, and lifestyle changes, social marketing has been described as a process serving to “increase the acceptability and ideas or practices in a target group, solve problems, introduce and disseminate ideas and issues, and as a strategy for translating scientific knowledge into effective education programs” (19, p. 2).

A key component of the social marketing process is market segmentation. The emphasis placed on market segmentation, or knowing one’s audience brings precision to audience analysis, allowing health prevention efforts to collect vital information for the formulation of better targeted and more effective messages leading to more appropriate message design, more effective message delivery, and better reception by the public (22). Reaching large, targeted segments of the U.S. population with appropriate marketing of risk reduction education and interventions can begin reducing the disastrous course of chronic disease development.

One particularly large, readily identifiable, and commercially lucrative segment of the U.S. population is the sports fan. Distinguished from the casual sports observer, a sports fan is defined as someone who is “interested in and follow(s) a sport, team, and/or athlete” (47, p. 2). Sports fans have long been the target of Corporate America, as marketers have understood the positive ‘return on investment’ (ROI) benefits associated with marketing their goods and services to sports fans through sponsorships and traditional advertising. Corporate executives choose to link their messages to the objects of sports fans’ attention to gain message credibility and increase message receptivity, as sporting events are well accepted and have a strong fan following. This study was designed to compare the health risk behaviors of sports fans and non-sports fans on the premise that those who have a heightened interest in following sports may be a perfect segment to which health prevention education efforts could be directed.

***Sports consumption and sports fandom in America.*** Sports for entertainment purposes have become an increasingly prominent leisure activity as well as an important part of the American economy in contemporary society. The sports business is one of the largest and fastest growing industries in the United States. A recent research report (26) estimated the size of the entire U.S. sports industry to be $414 billion as of 2010. The same publication reported that in 2010 annual company spending for sports advertising has reached $27.3 billion. The pervasiveness of sports fandom in contemporary society is even further highlighted by the continued increase in attendance figures, the amount and extensiveness of sports coverage through various forms of media such as radio, television, and print publications, as well the emergence of and use of new media technologies such as the internet and social networking (47).

Recognizing that both sports fans and non-sports fans are likely to consume, in some form or another, sports entertainment products, the study of the former as a unique market segment requires a distinction be made between sports fans and casual observers. An increasingly common psychological construct used to measure the degree to which one is a sports fan is team identification. Team identification refers to the extent to which a person feels psychologically connected to a team (47) and as the personal commitment and emotional involvement customers have with a sport organization (34). Concerning issues related to self-esteem and the self-concept, contemporary thinking on identification is rooted in the literature on social identity theory (35-37). Tajfel (1981) defined social identity as “the aspect of individuals’ self-concept which derives from their knowledge of their membership in a social group (or groups) together with the value and emotional significance attached to that membership” (35, p. 251).

Team identification is a useful construct for distinguishing between sports fans and non-sports fans because the degree to which one is attached or identified to a particular team reflects the extent to which the organization is linked to the self given its essentiality in facilitating utilitarian, experiential, or symbolic needs (26). For the highly identified individual, the role of team follower is a central component of their identity. These individuals readily present themselves as a fan of their team to others, view association with their team as a reflection and extension of themselves, and see the team’s successes and failures as their own (47). In contrast, for the casual observer, or lower identified person, the role of team follower is a peripheral component to self-concept. As a result, researchers examining the phenomena of sports fandom have reported that sports fans are more likely to spend a great deal more of time, energy, and resources following their teams than non-sports fans (11,44) and are more loyal to teams during periods of poor performance (24,41).

Investigating the link between sports fandom and health, researchers have consistently found team identification to have a positive relationship to measures of psychological health such as social self-esteem and social well-being, vigor, extroversion, and frequency of positive emotions, as well a negative relationship to loneliness and alienation (4,43,45,48). It was reasoned that identification with a sports team may perform an important psychological role for individuals in contemporary society (4). A strong identification with a specific sports team has been thought to provide a buffer from feelings of depression and alienation and fosters feelings of belongingness and self-worth as traditional social and community ties have declined in the wake of the erosion of the nuclear family and neighborhoods, faith in political institutions and religion, and increased geographic mobility and industrialization (4,12,20).

Despite the increased attention being given by scholars to the study of the psychological outcomes associated with sports fandom, there have only been a scant number of studies focused on the physical health of those who follow sports (3,8,18) and these studies have primarily focused on acute incidence of negative health events associated with watching a sports event. For example, Barone-Adesi, Vizzini, Merletti, and Richiardi (3) examined hospital admissions for acute myocardial infarction (AMI) among the Italian population during three international football competitions: the World Cup 2002, the European Championship 2004, and the World Cup 2006. They did not find an increase in the rates of admission for AMI on the days of football matches involving Italy in either the single competitions or the three competitions combined, and thus concluded the cardiovascular effects of watching football matches were small. Conversely, Carroll, Ebrahim, Tilling, Macleod, and Smith (8) examined hospital admissions for a range of diagnoses on days surrounding England’s 1998 World Cup football matches. The results indicated the risk of admission for AMI increased by 25% on the day of a home team loss in a big game and on the two following days. Kloner, McDonald, Leeka, and Poole (18) investigated changes in death rates when a local football team participated in and won the Super Bowl and when a local team participated in and lost the Super Bowl. Two events were examined, namely: 1) the January 20, 1980 game between the Los Angeles Rams and Pittsburgh Steelers (which Los Angeles lost); and 2) the January 22, 1984 game between the Los Angeles Raiders and Washington Redskins (which Los Angeles won). The researchers concluded the emotional stress of loss and/or the intensity of a game played by a sports team in a highly publicized rivalry such as the Super Bowl could trigger total and cardiovascular deaths.

These studies suggest that individuals who care about the outcome of a sporting event are more likely to experience negative acute health consequences as a result of the stress associated with the experience of watching their team. However, what is lacking in the literature is a discourse on the health related lifestyle behaviors of sports fans that may ultimately lead to the acute incidents described above.

The purpose of the present study was to investigate physical health-compromising behavior differences of sports fans (highly identified) to those of non-sports fans (less identified). If highly identified sports fans are found to engage in elevated health risk behaviors, targeting this group may represent a significant opportunity for health policy makers to achieve a significant impact on the health and wellness of a large segment of Americans while at the same time reducing the costs associated with obesity and unhealthy living practices.

### Methods

#### Participants

Using an electronic survey distribution software platform, electronic surveys were forwarded to community members at a University located in the U.S. mid-south who were in possession of a valid email account. Participants accessed the survey by clicking on a link contained in the body of an introductory email message. Email reminders were sent at two and four week intervals following the initial invitation. A sample of 515 students took part in the investigation.

#### Procedures

Upon clicking on the hyperlink contained in the body of the email communication participants were taken to the survey homepage where further instructions were provided and consent was sought. Continuation to the first section of the survey questionnaire was taken as consent to participate. In total, the survey comprised of four sections. The first section contained one question asking participants to identify their absolute favorite sports team. The purpose of this question was to have participants self-report a subject as a frame of reference to use when answering the questions contained in section two of the survey.

The second section comprised the team cognitive-affective identification subscale from the Team Identification Scale (TIS) developed and tested by Dimmock, Grove, and Eklund (11). Cognitive-affective identification was operationalized as one’s knowledge of membership to a group and the emotional significance of membership to that group. The scale contained 8 Likert-scale items with response options ranging from 1 (strongly disagree) to 6 (strongly agree). Thus, higher numbers represent greater levels of identification. A sample item from cognitive-affective identification scale read, “When I talk about my favorite team, I say ‘we’ rather than ‘they’”. Acceptable test-retest reliability coefficients for the cognitive-affective subscale (r = .72, p = .01) were reported (11). As noted above, subjects targeted the team they personally identified in section one when completing the team identification scale. The eight items comprising the cognitive-affective team identification scale were summed and then averaged to form a single index of identification (Cronbach’s alpha = .91). A median split was performed on the participants’ scale scores to establish two groups: participants with a low level of identification with the team (n = 255, scale range = 1 to 3.49) and participants with a high level of team identification (n = 260, scale range = 3.5 to 6).

The third section of the survey contained eleven questions assessing participants’ self-reported health risk behaviors, including: two questions related to physical activity, six questions related to eating practices, and one question each for alcohol use, tobacco use, and Sexually Transmitted Diseases (STD) and Acquired Immunodeficiency Syndrome (AIDS) risk. The questions in this section were adopted from the Comprehensive Assessment Plus Personal Wellness Profile developed by Wellsource, Inc. The Personal Wellness Profile has been found to be a reliable and valid questionnaire to assess an individual’s level of wellness in clinical and non-clinical setting (7).

The fourth and final section of the survey assessed the participants’ demographic information including position at the university (i.e., student, staff, faculty, or administrator) age, sex, as well as two physical descriptive characteristics, namely: height and weight. Height and weight data enabled the researchers to calculate each participant’s BMI. BMI, which is a ratio of weight in proportion to height, was calculated from self-reported weight and height data using the imperial BMI formula (weight in pounds multiplied by 703 over height in inches squared). BMI was defined using the following standardized categories: underweight (BMI = < 18.5); normal weight (BMI = 18.5 – 24.9); overweight (BMI = 25 – 29.9); obese (BMI of 30 or greater).

#### Data Analysis

The analysis involved testing for physical health risk behavior differences in the measure of identification for those participating in the research. PASW Statistics program version 18 was used to compute a series of several independent ANOVAs for this purpose.

### Results

#### Descriptives

Descriptive analysis (frequencies and percent) of variables under study is displayed in Table 1. The majority of respondents were female (64.3%) and married (39.8%). The majority of those responding were aged 18 to 24 (34.6%) followed by 30 to 39 (17.3%) and 25 to 29 (15.9%). In terms of ethnicity, 76.3% of the respondents were Caucasian and 13.8% were African American. Finally, student respondents represented the largest group in the sample (69.7%), followed by faculty (12.6%) and staff members (11.1%). Means and standard deviations for team identification as a function of variables under study are displayed in Table 2.

#### Group Differences

Analysis of variance (ANOVA) results for team identification (high identification and low identification) as a function of variables under study are reported in Table 3. The results of the one-way ANOVA revealed significant differences between the self-reported health behaviors of low identified individuals and high identified individuals for each of the following dimensions: breakfast frequency, F(1,513) = 5.35, p < .05; fat intake, F(1,513) = 4.13, p < .05; fast food consumption frequency, F(1,513) = 4.17, p < .05; vegetable consumption frequency, F(1,513) = 3.34, p < .10; breads and grains consumption, F(1,513) = 3.54, p < 1.0; and alcohol consumption, F(1,513) = 16.63, p < .05. Additionally, the ANOVA results revealed a significant difference in the BMI of low identified individuals and high identified individuals, F(1,513) = 5.36, p < .05. For each of the results reported above, analysis of the dimension means for each group indicated that high identified sports fans have poorer health related behaviors than low identified subjects. No significant group differences were found for the following self-reported health behaviors: aerobic exercise frequency, strength training exercise frequency, unhealthy snack consumption, smoking frequency, and risk factor for AIDS and STDs.

### Discussion

The objective of this study was to develop an understanding of the health related lifestyle behavior disparities among sports fans and non-sports fans using team identification as a proxy for sports fandom. The results indicated that sports fans have a significantly higher BMI than do non-sports fans and engage in riskier health related behaviors than do their non-sports fan counterparts on a range of measures. All of the measures on which the two groups differed related to diet and food consumption choices. Sports fans were found to eat breakfast less often than non-sports fans, consume foods higher in fat more often, consume fast food on a more regular basis, consume vegetables less often, consume refined grains as opposed to whole grains more often, and consume more alcoholic beverages on the days they chose to drink than do non-sports fans. The two groups did not significantly differ on the following measures: aerobic exercise frequency, strength training exercise frequency, unhealthy snack consumption, smoking frequency, and risk factor for STD and AIDS. It is important to note that no differences were found between the two groups on the measures of physical activity because both groups were equally inactive.
Given that this study represents, to our knowledge, the first attempt to scientifically investigate health related lifestyle behaviors of sports fans, there are not any direct explanations in the literature to explain why the observed differences exist. However, an examination of the literature related to the lifestyle of sports fans in general may provide some clues, or insight, into possible causes. The level of identification one has to an organization has been found to relate to the nature of a consumer’s interaction with the organization (40). Strongly identified sports fans often make heavy financial and/or time commitments toward following their favorite team and devote significant portions of their day to that pursuit (11,44). This time commitment includes time reading about one’s favorite team on the internet and in magazines, listening to the radio, watching the team play and also engaing in discussion about the team with others.
Additionally, there is evidence in the literature demonstrating an inverse relationship between mass media consumption (viewing hours), and intake of healthy food choices such as fruits and vegetables (6). It was suggested this relationship may be the result of the replacement of healthy foods by foods highly advertised on television (6). It is conceivable this rationale may apply for highly identified sports fans, who have been found to exhibit a bias towards the brands and products that sponsor their favortite teams and events than do lower identified indivuals (14,30).

### Conclusion

The findings from this study should be interpreted in light of several limitations that could be addressed in follow-up research on health risk behaviors of sports fans. Among them, due to the cross-sectional nature of this investigation, errors in recall by the study participants may be present. Additionally, as self-report behaviors were used to measure the variables under study, the reliability in the accuracy of participant responses may be questioned. The underreporting of energy, or food intake, using self-report instruments has been documented in the literature (17,21,23). Finally, the present findings might only be generalizable to a primarily student population in U.S. mid-south.

To address issues related to generalizability and to verify the results found here, future researchers may wish to replicate this study using a national sample of sports fans. Additionally, future research should examine the reasons why health behavior disparities exist between those persons who self-report having a higher level of identification to a sports team than those reporting a lower identification. Finally, to improve education efforts, future research may also be conducted for the purposes of gaining an understanding of sports fans attitudes about health related behaviors and health in general.

### Applications In Sport

The results of the current study suggest health educators and policy makers seeking to make a significant positive contribution to the fight against preventable chronic illnesses resulting from unhealthy lifestyles would do well to follow the lead of corporate America in targeting the large and identifiable segment of the population who are identified sports fans. Writing on the societal and environmental factors affecting food choice and physical activity, Booth, Mayer and Sallis (5) noted changes in these behaviors require intervention and commitment to action at multiple levels and that education based obesity-prevention strategies are most effective when there exists environmental modifications supported by partnerships with relevant sectors outside traditional health domains, including researchers, educators, government, and industry. Thus, educating sports fans about healthy living practices must involve collaboration with the objects of fans’ attention, namely the college athletic departments, leagues, teams, and athletes they follow. These sports organizations and entities already very well recognize the importance of community outreach as a part of their business models. Notwithstanding the desire to positively contribute to the betterment of the communities in which they are situated, sport organizations engage in socially responsible initiatives for strategic reasons as well. Organizations that do ‘good’ have been found to gain a competitive advantage in the marketplace and are more likely to succeed than those who do not (27,28). As a result, many sport organizations have implemented focused strategies towards achieving a competitive marketplace advantage by becoming ‘good’ corporate citizens. Thus, partnering in programs designed to educate their most devoted followers about strategies towards achieving a healthy lifestyle would serve the dual role of contributing to the overall success of the organization while at the same time positively impacting the health of those in the communities they serve.

### Tables

#### Table 1
Descriptive analysis (frequencies and percent) of classification variables.

#### Table 2
Means and standard deviations for team identification as a function of variables under study.

#### Table 3
Analysis of Variance (ANOVA) Results

### References

1. 2009 Almanac of Chronic Disease. The Impact of Chronic Disease on U.S. Health and Prosperity: A Collection of Statistics and Commentary. Available at: http://www.fightchronicdisease.org/sites/default/files/docs/2009_PFCDAlmanac.pdf. Accessed March 29, 2011.
2. Andreasen, A. (1995). Marketing social change: Changing behavior to promote health, social development, and the environment. San Francisco, CA: Jossey-Bass.
3. Barone-Adesi, F., Vizzini, L., Merletti, F., & Richiardi, L. (2010). It is just a game: Lack of association between watching football matches and the risk of acute cardiovascular events. International Journal of Epidemiology, 39(4), 1006-1013.
4. Branscombe, N.R., & Wann, D.L. (1991). The positive social and self-concept consequences of sport team identification. Journal of Sport and Social Issues, 15, 115-127.
5. Booth, S.I., Mayer, J., & Sallis, J.F. (2001). Environmental and societal factors affect food choice and physical activity: Rationale, influences, and leverage points. Nutrition Reviews, 59(3), S21-S36.
6. Boynton-Jarrett, R., Thomas, T.N., Peterson, K.E., Wiecha, J., Sobol, A.M., & Gortmaker, S.L. (2003). Impact of television viewing patterns on fruit and vegetable consumption among adolescents. Pediatrics, 112(6), 1321-1326.
7. Brown, S.C., Geiselman, P.J., Copeland, A.L., Gordon, C, & Richard-Eaglin, A. (2008). Reliability and validity of the Personal Wellness Profile (PWP) questionnaire in African American college women. Journal of Cultural Diversity, 15(4), 163-167.
8. Carroll, D., Ebrahim, S., Tilling, K., Macleod, J., & Smith, G.D. (2002). Admissions for myocardial infarction and World Cup football: Database survey. British Journal of Medicine, 325, 1439–1442.
9. Centers for Disease Control and Prevention. Chronic Disease Prevention and Health Promotion – Statistics and Tracking. Available at: http://www.cdc.gov/chronicdisease/stats/index.htm. Accessed March 29, 2011.
10. Daar, A.S., Singer, P.A., Persad, D.L., Pramming, S., Matthews, D., Beaglehole, R., Bernstein, A., Borysiewicz, L., Colagiuri, S., Ganguly, N, Glass, R., Finewood, D., Koplan, J., Nabel, E., Sarna, G., Sarrafzadegan, N, Smith, R., Yach, D., & Bell, J. (2007). Grand challenges in chronic non-communicable diseases. Nature, 450, 494-496.
11. Dimmock, J.A., Grove, J.R., & Eklund, R.C. (2005). Re-conceptualizing team identification: New dimensions and their relationship to intergroup bias. Group Dynamics, 9, 75-86
12. Farber, B.A. (1983). Introduction: A critical perspective on burnout. In Farber BA, ed. Stress and Burnout in the Human Service Professions. New York, NY: Pergamon.
13. Grier, S., & Bryant, C.A. (2005). Social marketing in public health. Annual Review of Public Health, 26, 319-339.
14. Gwinner, K.P., & Eaton, J. (1999). Building brand image through event sponsorship: The role of image transfer. Journal of Advertising, 28(4), 47-57.
15. Hill, R. (2001). The marketing concept and health promotion: A survey and analysis of “recent health promotion” literature. Social Marketing Quarterly, 2, 29-53.
16. Jensen. M. (2009). You’re Killing me. Available at: http://obit-mag.com/articles/youre-killing-me. Accessed March 28, 2011.
17. Johansson, L., Solvoll, K., Bjorneboe, G.E., & Drevon, C.A. (1998). Under- and over-reporting of energy intake related to weight status and lifestyle in a nationwide sample. American Journal of Clinical Nutrition, 68, 266-274.
18. Kloner, R.A., McDonald, S., Leeka, J., & Poole, W.K. (2009). Comparison of total and cardiovascular death rates in the same city during a losing versus winning Super Bowl Championship. American Journal of Cardiology, 103(12), 1647-1650.
19. Lefebvre, R.C., & Flora, J.A. (1988). Social marketing and public health intervention. Health Education Quarterly, 15(3), 299-315.
20. Levinson, H. (1965). Reciprocation: The relationship between man and organization. Administrative Science Quarterly, 9, 370-390.
21. Lichtman, S.W., Pisarska, K., Berman, E.R., Perstone, M., Dowling, H., Offenbacher, E., Weisel, H., Heshka, S., Matthews, D.E., & Heymsfield, S.B. (1992). Discrepancy between self-reported and actual caloric intake and exercise in obese subjects. New England Journal of Medicine, 327, 1893-1898.
22. Ling, J.C., Franklin, A.K., Linsteadt, J.F., & Gearon, S.A. (1992). Social marketing: Its place in public health. Annual Review of Public Health, 13, 341-362.
23. Macdiarmid, J., & Blundell, J. (1998). Assessing dietary intake: Who, what, and why of under-reporting. Nutrition Research Reviews, 11, 231-253.
24. Madrigal, R. (1995). Cognitive and affective determinants of fan satisfaction with sporting event attendance. Journal of Leisure Research, 27, 205-227.
25. Park, C.W., MacInnis, D.J., & Priester, J. (2006). Beyond attitudes: Attachment and consumer behavior. Seoul National Journal, 12(2), 3-36.
26. Plunkett Research, Ltd. Sports Industry Overview. Available at: http://www.plunkettresearch.com/sports%20recreation%20leisure%20market%20research/industry%20statistics. Accessed March 14, 2011.
27. Porter, M.E., & Kramer, M.R. (2002). The competitive advantage of corporate philanthropy. Harvard Business Review, 80(12), 56-68.
28. Porter, M.E., & Kramer, M.R. (2006). Strategy & society: The link between competitive advantage and corporate social responsibility. Harvard Business Review, 84(12), 78-92.
29. Randolph, W., & Viswanath, K. (2004). Lessons learned from public health mass media campaigns: Marketing health in a crowded media world. Annual Review of Public Health, 25, 419-437.
30. Roy, D.P., & Cornwell, T.B. (2004). The effects of consumer knowledge on responses to event sponsorships. Psychology & Marketing, 21(3), 185-207.
31. Seigel, M., & Lotenberg, L.D. (2007). Marketing Public Health: Strategies to Promote Social Change, 2nd ed. Sudbury, MA: Jones and Bartlett Publishers.
32. SmallStep Adult & Teen. Campaign PSAs. Available at: http://www.smallstep.gov/. Accessed March 31, 2011.
33. Suhrcke, M., Nugent, R.A., Stuckler, D., & Rocco, L. (2006). Chronic Disease: An Economic Perspective. London: Oxford Health Alliance.
34. Sutton, W.A., McDonald, M.A., & Milne, G.R. (1997). Creating and fostering fan identification in professional sports. Sports Marketing Quarterly, 6(1), 15-22.
35. Tajfel, H. (1981). Human Groups and Social Categories. Cambridge, UK: Cambridge University Press.
36. Tajfel, H. (1982). Social Identity and Intergroup Relations. Cambridge, UK: Cambridge University Press.
37. Tajfel, H., & Turner, J.C. (1979). An integrative theory of intergroup conflict. In Austin W, Worchel S, eds. The Social Psychology of Intergroup Relations (pp. 33-48). Pacific Grove, CA: Brooks/Cole.
38. Tajfel, H., & Turner, J.C. (1986). The social identity theory of intergroup behavior. In Worchel S, Austin W, eds. Psychology of Intergroup Relations, 3rd ed (pp. 7-24). Chicago: Nelson-Hall.
39. Theodorakis, N.D., Dimmock, J., Wann, D., & Barlas, A. (2010). Psychometric evaluation of the team identification scale among Greek sport fans: A cross-validation approach. European Sport Management Quarterly, 10(3), 289-305.
40. Thomson, M., McInnis, D., & Park, C.W. (2005). The ties that bind: Measuring the strength of consumers‘ emotional attachments to brands. Journal of Consumer Psychology, 15(1), 77-91.
41. Wakefield, K.L. (1995). The pervasive effects of social influence on sporting event attendance. Journal of Sport and Social Issues, 19, 335-351.
42. Walsh, D.C., Rudd, R.E., Moeykens, B.A., & Moloney, T.W. (1993). Social marketing for public health. Health Affairs, Summer, 104-119.
43. Wann, D.L. (1994). The “noble” sports fan: The relationships between team identification, self-esteem, and aggression. Perceptual and Motor Skills, 78, 864-866.
44. Wann, D.L., & Branscombe, N.R. (1993). Sports fans: Measuring degree of identification with the team. International Journal of Sport Psychology, 24, 1-17.
45. Wann, D.L., Dimmock, J.A., Grove, J.R. (2003). Generalizing the team identification – psychological health model to a different sport and culture: The case of Australian Rules Football. Group Dynamics, 7, 289-296.
46. Wann, D.L., Inman, S., Ensor, C.L., Gates, R.D., & Caldwell, D.S. (1999). Assessing the psychological well-being of sport fans using the Profile of Mood States: The importance of team identification. International Sports Journal, 5, 81-90.
47. Wann, D.L., Melnick, M.J., Russell, G.W., Pease, D.G. (2001). Sports Fans: The Psychology and Social Impact of Spectators. New York: Routledge Press.
48. Wann, D.L., & Pierce, S. (2005). Measuring sport team identification and commitment: An empirical comparison of the Sport Spectator Identification Scale and the Psychological Commitment to Team Scale. North American Journal of Psychology, 5, 365-372.
49. Weiner, I.B., Nezu, A.M., Nezu, C.M., & Geller, P.A. (2003). Handbook of Psychology Volume 9: Health Psychology. Hoboken, NJ: John Wiley and Sons.

### Corresponding Author

Daniel R. Sweeney, PhD.
Department of Health Sciences
University of Arkansas at Little Rock
2801 S University Ave
Little Rock, AR 72204
<drsweeney@ualr.edu>
501-683-7575

Daniel Sweeney is an assistant professor of sport management and Donna Quimby an associate professor of exercise science and chair of the department.

### Abstract

The purpose of this study was to investigate the participation motives and goal orientations of participants in the Hong Kong Master Games. The participants were 108 men and 52 women (N=160). The age range of participants was 35 to 77 years old (M= 46.2, SD = 9.2). They were divided into three age groups (30-39 years old, n=32; 40-49 years old, n=96; above 50 years old, n=32). The _Participation Motivation Inventory_ (Gill, Gross & Huddleston, 1983), and the _Task and Ego Orientations Questionnaire_ (Duda & Whitehead, 1998) were utilized. The top five participation motives were fun, affiliation/friendship, fitness, skill development and achievement/status. The participation motives and the goal orientations for men and women were similar. The oldest adults had significantly higher scores on the eight participation motives: fun, skill development, fitness, team atmosphere, achievement/ status, affiliation /friendship, energy release and miscellaneous than the younger and middle age adults. The mean score on task orientation was higher than the ego orientation for all participants. The empirical results of this Hong Kong study support earlier studies (Vogel, Brechat, Leprete, Kaltenbach, Berthal & Lonsdorfer, 2009) that strongly encourage physical activity leaders to design sport and physical activity programs for adults in order to enhance their physical, social, psychological and mental well being.

**Key Words:** motivation, physical activity, task and ego orientation, master games

### Introduction

Historically, sociologically, politically, culturally and now medically, sport and physical activity has a long history of contributing to the overall evolution and positive growth of the human species (Bloom, Grant, & Watt, 2005). More recently a very strong body of evidence has been developed to support the theory that regular physical activity contributes to the overall health of the human species throughout the lifecycle- from childhood to old age, supporting the old adage that it is “never too early nor too late” to participate in sport and physical activity (Shepherd, 1995, Levy, 1998; Galloway & Jokl, 2000; Colcombe & Krame, 2003, U.S. Department of Health and Human Services, 2008). Furthermore, physical inactivity has serious health, economic and political implications in a world where health is at the core of a vibrant and prosperous society (Commonwealth of Australia, 2000; Conference Board of Canada, 2005). As the population of older adults in developed nations is increasing, “aging well” and successful active aging programs have become a critical area of scientific study related to geriatric health care (Graves, 2002) .

In Hong Kong, the proportion of population aged 35 to 64 and above 65 has increased from 28.3% and 3.2% in 1961 to 46.6 % and 12.8% in 2009 respectively (Hong Kong Census and Statistics Department, 2010). Sport and physical activities play such an important role in keeping the ever aging population healthy, governments at all levels, pay more attention and efforts to promote the concept on “Sport for all” to the general public (Canadian Fitness and Lifestyle Research Institute, 2005; Cheung, 2009).

For the past twelve years, the Leisure and Cultural Services Department has been organizing the Master Games to promote a physically active lifestyle for Hong Kong citizens. The emphasis of these games has been on participation and enjoyment rather than winning prizes (Leisure and Cultural Services Department, 2004). However, it also happened that there was very limited research conducted on investigating the motivations underlying participation among individuals aged 35 and above taking part in the Master games. However, if we hope to understand why Hong Kong people participate in sport and physical activity while other become couch potatoes and strain the health care system, motivational research of this kind is badly needed. Therefore, this study was designed to investigate these critical motivational determinants behind the participants in the Master Games in Hong Kong.

#### Motivations in Sports

Motivation comes from the Latin word “movere” which means “to move” and it is the energy or intensity underlying behavior (Carron, 1980). Motivation refers to those personality factors, social variables, or cognitions that come into play, enter into competition to attain some standard of excellence.

Gill, Gross and Huddleston (1983) had identified the motivations into eight main factors, which were achievement/ status, team atmosphere, fitness, energy release, skill development, affiliation/ friendship, fun and miscellaneous (e.g. like to use the equipment). Researchers stated that enjoyment, interest and competence motives were the internal factors which played an important role in motivating individuals to participate in sports (Scanlan, Stein & Ravizza, 1989a, 1989b; and Frederick & Ryan, 1993). Scanlan, Stein and Ravizza, (1989b) found that social and life opportunities (affiliation/friendship motive) and social recognition (factor of achievement/ status) were the other important factors to motivate people to take part in physical activity.

#### Goal Orientations in Sports

The Achievement goal theory was originally developed to explain educational achievement. This theory was widely applied in the context of sport and exercise researches (Lavallee, Kremer, Moram, & Williams, 2004). The two main achievement goals (task goal orientation and ego goal orientation) are the factors which determine a person’s motivation (Weinberg & Gould, 2003). Nicholls (1989) believed that goal orientations reflected an individual’s view of the world and were conceptually related to beliefs held on the cause of success. In addition, Ferrer and Weiss (2000) also stated that the strongest predictors of intrinsic motivation, effort and persistence were task goal orientation, perceived competence, and learning climate.

Individual with task-oriented goals focuses on self-referenced perceptions of personal competence and personal development, emphasis on mastery of skills, working hard, developing lifetime skills and improving from one point of time to the next. On the other hand, an ego-oriented goal individual focuses on surpassing or exceeding the performance of others and preferably with low effort (Duda & Nicholls, 1992).

There is a positive relationship between task-oriented and intrinsic motivation. Because the sport experience is an end in itself, by its defining features, the task-oriented individual focuses on the process rather than the competition outcomes when participating in sport. While the ego-involved goal perspective is more likely to decrease intrinsic motivation as the individual’s perceived ability and self-confidence are tied to how he/she compares with others (Duda, Chi, Newton, Walling & Catley, 1995, Cox 2007). For instance, task-oriented individuals who are assumed to experience intrinsic motivations and would like to choose a challenging task, show off their effort and have a strong work ethics as their motives, are more likely to focus on the skill development, fitness and team membership. The ego-oriented participants are assumed to show minimal effort, have low perceived competence, and more likely to protect self-worth with motives focusing on competition and recognition/ status (White & Duda, 1994; Robert & Treasure, 1995; and Roberts Treasure & Balague, 1998).

#### Gender Differences in Sports Participation

In motivation research, men valued self-competitive, reward, and skill improvement as their participation motives in physical activities. Whereas women valued self-expression, stress reduction, weight loss and relaxation, especially in weight control and appearance motives (Mathes, McGiven & Schneider, 1992). Furthermore, Frederick and Ryan (1993) reported that the main distinction of gender differences in sport participation was that men rated health and fitness, competition and challenge as the top participation motives; while women rated tension release, body-related and social factors as their top participation motives.

In goal achievement, most of the previous researches revealed that women had significantly higher scores in task orientation than men, and men had significantly higher score in ego orientation (Duda, 1989; Newton & Duda, 1993; Walling & Duda, 1995).

#### Age Differences and Sports Participation

Individuals have different reasons for participating in sports and physical activities. Rudman (1989) had investigated members enrolled in fitness program of a private sport club and reported that the younger participants (aged under 34 years) took part in sports because of the psychological benefits such as dealing with stress related to work and enhancing their physical attractiveness. For the participants of middle age (35-49 years), their participation motives were more the philosophical with ideological reasons such as family obligations and enjoyment/ fun. For the oldest participants (above 50 years), their participation motives were psychological and social reasons such as feeling younger and social networking with family members and friends.

Moreover, Kleiber and Kelly (1980) identified that both the younger adults with ages approximately 20 to mid-30s and the older adults who were above 60 years old chose the social goals as a reason for participating in sports, while the middle-age adults (35-50 years old) identified their participation goals as seeking close personal relationship. In addition, Brodkin andWeiss (1990) found that all the younger adults (23-39 years), the middle-aged adults (40-59 years) and the older adults (above 60 years) rated skill improvement, fun and being active as their main participation motives while engaging in swimming competition. They also found that being with friends was the most important motive for both the middle-age and the older adults.

Goal orientations may be changed by socialization experiences and aging over time. Brodkin and Weiss (1990) pointed out that young athletes who participated in sport looked more for social recognition than the middle-aged and older adults. Similarly, Duda and Tappe (1988) reported there was a decrease in competition objectives from younger to older men, if the exercise program became too challenging and they needed to perform with great physical competence. Older adults chose not to participate if the competence level rose too much. Thus, Kleiber and Kelly (1980) summarized that there was a movement away from an ego orientation in the middle-aged and the older adults, thus the middle-aged and the older adults would not be interested in physically demanding recreation activities.

### Method

#### Participants

A total of 160 participants (108 men and 52 women) at the Hong Kong Master Games, were invited to participate in this study. The participants were from 35 to 77 years old (M= 46.15 years old, SD= 9.2). They were divided into three age groups (30-39 years old, n=32; 40-49 years old, n=96; above 50 years old, n=32). Convenient sampling method was used and the selected eight events were: tennis, orienteering, distance run, swimming, badminton, squash, lawn bowls and gate ball.

#### Instruments

The measuring instruments used for the study were the _Participation Motivation Inventory_ (Gill, Gross & Huddleston, 1983) and _Task and Ego Orientation in Sport Questionnaire_ (Duda & Whitehead, 1998). The questionnaire was divided into three sections. The first part was the participation motives. The second part was the task and ego orientation; while the third part was the personal information, such as the frequency and duration of practicing.

The participants were requested to choose the most appropriate response that could best describe their personal feelings based on a 5-point Likert Scales. There were 30 items in the _Participation Motivation Inventory_. Participants responded to the statement: “I participated in the Master Games because …” by indicating their preferences from 1 (Very unimportant) to 5 (Very important). The scale revealed eight motivational factors: fun, achievement/status, team atmosphere, fitness, energy release, skill development, affiliation/ friendship and such miscellaneous motives as participation motives for sport and physical activity. The _Task and Ego Orientation in the Sport Questionnaire_ (TEOSQ) consisted of 13 items. The responses ranked the statement “I feel most successful in sports when …” from 1(strongly disagree) to 5(strongly agree). There were 7 items on task orientation and 6 items on ego orientation on the TEOSQ.

### Results

#### Participation Motives

The _Participation Motivation Inventory_ could be categorized into eight participation motives. The rank order of participation motive scores from the highest to the lowest were the following: Fun (M = 4.35, SD = 0.53 ); Affiliation /Friendship (M= 4.11, SD = 0.65); Fitness (M= 4.00, SD = 0.69); Skill development (M= 3.96, SD = 0.72); Achievement/ Status (M = 3.70, SD = 0.70); Team atmosphere (M = 3.58, SD = 0.99); Energy release (M = 3.36, SD = 0.74) and Miscellaneous (M = 2.99, SD = 0.82).

##### Gender

As the number of participant per cell was too small to conduct the 2 x 3 factorial design, two individual Multiple Analysis of Variance (MANOVA) were utilized to compare the mean vectors of the eight participation motives. The Wilks’ Lambda value for gender was not significant (p > .05) which revealed that the participation motives for men and women were similar. The means and standard deviations of the eight participation motives for men and women are listed in Table 1.

##### Age Group

The Wilks’ Lambda value for the age group was significant (p < .05). The discriminant functions obtained for the eight participation motives were significant.

Moreover, the oldest age group had significantly higher scores on skill development F(2, 157) = 3.4, p =.036; achievement/ status F(2, 157) =11.12, p =.000; team atmosphere F(2, 157) =9.18, p = .000; fitness F(2, 157) = 8.81, p = .000; energy release F(2, 157) =10.97, p = .000; skill development F(2, 157) =6.54, p = .002; affiliation/friendship F(2, 157) = 13.31, p = .000 and miscellaneous F(2, 157) = 9.68, p = .000. Post Hoc Tukey Tests were utilized and the results reported that the participants aged over 50 years had significantly higher scores than the participants aged 30 to 39 years for the seven participation motives except skill development. They also had significantly higher scores than the participants aged 40 to 49 years for seven participation motives except fun. The participation motives for the 30 to 39 age group and the 40 to 49 age group were similar. The means and standard deviations of the eight participation motives for three age groups are listed in Table 2.

##### Experience

There were 33 participants took part in this event for the first time and 127 of them had participated in this event before. The Wilks’ Lambda value for experience was not significant (p > .05) revealed that the participation motives for participants with different levels of experience in the Master Game were similar

#### Task and Ego Orientations

There were 13 items in the _Task and Ego Orientations in Sport Questionnaire_ and the top three goal orientation statements were “do my very best”; “something I learn makes me want to go and practice more”; and “work really hard”. Details are listed in tabled 3. The goal orientations of all participants in the Master Games was task orientation (M=4.02, SD = 0.51) rather than ego orientation (M=3.43, SD = 0.75).

##### Gender

The Wilks’ Lambda value for gender was not significant (p > .05) which revealed that the goal orientation scores for men and women were similar and the means and standard deviations of the eight participation motives for men and women are listed in Table 4.

##### Age Group

The Wilks’ Lambda value for the age group was significant (p < .05). The discriminant functions obtained for both ego and task orientations were significant.

Ego orientation, F(2, 157) = 4.09, p = .019; Task orientation, F(2, 157) =3.34, p = .038. The Tukey tests indicated that the oldest participants (aged over 50 years) had significantly higher mean ego orientation score than the youngest participants (aged 30-39 years). They also had significantly higher mean task orientation score than the 40-49 years old group.

##### Experience

The Wilks’ Lambda value for the experience group was significant (p < .05). The discriminant functions obtained for ego orientation was significant, F(1, 158) =14.08, p = .000. The means and standard deviations of the ego orientation score for the no experience group was M = 3.00, SD = .71; and the previous experience group was M = 3.54, SD =.73. The task orientation score for participants without and with previous experience was similar.

### Discussion

This study is concerned with participation motives and goal orientations of individuals participating in the Master Games. After comparing the eight dimensions of participation motives, fun and affiliation/friendship are the most influential motivators that encouraged individuals to take part in the Master Games. For the goal orientations of sport participation, most participants take part in physical activities to meet their task orientation needs.

This study supported previous research that participation motives for men and women were similar and having fun was an important motive (Shapiro, 2003).

The oldest adults (ages over 50 years) had the highest scores on most of the participation motives and they ranked “Affliation/Friendship” , “fun” and “fitness” as the top three motives. This supports previous research which indicated that older adult participated in physical activity for psychological and social purposes (Rudman,1989).

There were significant mean differences on goal orientation scores among the three age groups. Participants with ages above 50 years old had higher scores on ego orientation than participants between the 30 to 39 years old. This situation may be due to the fact that the older participants have more years of experience in the Master Games, thus they had more confidence in their ability as compared with the others.

Furthermore, the result on the task orientation score reflected that all participants would like to master their skill and they believed that success in competition would depend on practicing the skill and their effort. This finding does not support the finding of Steinberg, Grieve and Glass (2002) which stated that the ego orientation score for the over 50 years old male group was lower than the younger groups. This difference could be due to a cultural intervening variable, to be more precise, since Chinese culture assigns greater respect to the “elders” than Western culture, this finding was not unexpected.

#### Experience Difference on Participation Motives and Goal Orientations

Previous participation experience was one of the important factors which determined whether an individual would master a new skill and their attitudes towards the Master Games. The participation motives for people with different previous experience in the Master Games were similar.

For task orientation, no significant difference was found in the participation experience. On the other hand, participants with previous experience have significantly higher scores on the ego orientation than individuals without previous experience. The results supported that participation experience could enhance participants’ confidence in competition and they would like to out perform others. In other words, participation satisfaction socializes the participants into seeking more participation in order to gain more satisfaction and the positive cycle keeps repeating itself and it eventually becomes a self-fulfilling prophecy.

### Conclusion

In this study, the participation motives and goal orientations of men and women are similar. Older adults have higher mean score on the following seven motivational factors (“Fun”, “Achievement/ Status”, “Team atmosphere’, “Fitness”, “Energy release”, “Affiliation/Friendship” and “Miscellaneous”) than the youngest adults. Fun is an important motive for all participants.

For goal orientations, older participants have higher mean scores on ego orientation. The participation motives and task orientation score for participants with different experience are similar. Participants with previous experience have a higher ego orientation score than those without previous experience.

The application of this study to the world of Masters Sport and “leisure Sports” as well as “Serious Leisure” is very salient. As the Post-Industrial world “ages”, there will be a greater need for “leisure sports” whose main goal is “Health Promotion”.If leisure sports contribute to both a positive ego-enhancing psychological and physical outcome, then this will greatly reduce the pressure on the health care system in post-industrial medically oriented societies. The provision of professionally planned leisure sports for seniors is far more financially economic than the need for more long-term care and pharmaceutical solutions to caring for our aging populations. Greater emphasis needs to be placed in the development of curricula that addresses the growing need to educate future leaders in the delivery of leisure sports ranging from low-intensity activities such as walking, swimming, biking and skiing to more highly organized leisure sports that may be viewed as more “serious” forms of leisure sports (Stebbins, 2007) that require long-term training and professional coaching.

#### Recommendations for Future Studies

The sample size of the research should be larger, cross-cultural with more qualitative grounded research methods so that the study could be more representative and generalizable. In addition, cross-cultural case studies should be developed to gather more information on participation motives and goal orientations as impacted by different cultural and socialization patterns.

Most Western societies see a significant ageing of their population that will be further accentuated in the coming decades. Future research should carry out cost-benefit analysis of the value of Master Sports and Leisure Sports on reducing the medical costs of an ageing population that can maintain their “independence” as a result of these activities. The ability of older adults to function independently depends largely on maintenance of aerobic capacity and muscle strength. Furthermore some longitudinal studies suggest that physical activity is linked to a reduced risk of developing Dementia and Alzheimer’s disease.

Furthermore, links between theoretical model building and policy and management strategies need to be nurtured as there presently exists a disconnection between the two. It is recommended that all Masters Games should include a research and evaluation component for the betterment of the games and our ageing society.

### Tables

#### Table 1
Means and Standard Deviations of eight motives for men and women.

#### Table 2
Means and Standard Deviations of eight motives for participants in three age groups.

#### Table 3
Rank Order on goal orientations for participants in the Master Games (N=160).

#### Table 4
Means and Standard Deviations of goal orientations for men and women.

#### Table 5
Means and Standard Deviations of goal orientations for participants in three age groups.

### References

1. Bloom, M., Grant, M., & Watt, D. (2005). Strengthening Canada: The socio-economic benefits of sport participation. Ottawa, Canada: Conference Board of Canada.
2. Brodkin, P., & Weiss, M. R. (1990). Developmental differences in motivation for participating in competitive swimming. Journal of Sport and Exercise Psychology, 12(3), 248-263.
3. Canadian Fitness and Lifestyle Research Institute (2005). Fend off health problems with activity. Ottawa: Canada
4. Carron, A. (1980). Social psychology of sport. New York: Movement Publications.

5. Census and Statistics Department, Hong Kong Special Administration Region. (2010). Hong Kong Population Projections 2010-2039. Retrieved on Aug 5, 2010 from <http://www.censtatd.gov.hk/products_and_services/products/publications/statistical_report/population_and_vital_events/index_cd_B112001504_dt_latest.jsp>

6. Cheung, S.Y. (2009). Healthy Exercise for Longevity Project. Hong Kong: Health Exercise for Longevity Committee. 張小燕 (2009).「健體助延年」計劃 : 長者運動與健康狀況之關係及對香港協辦奧運馬術支持度調查. 「健體助延年」計劃籌備委員會.
7. Cheung, S. Y. (1998). Senior citizens general gymnastic award scheme. The Journal of the International Council for Health, Physical Education, Recreation, Sport, and Dance, 34(3), 51-54.
8. Colcombe, S., & Krame, A.F. (2003). Fitness effects on the cognitive function of older adults: A Meta-Analytic study. Psychological Science. 14(2), 125.
9. Commonwealth of Australia. (2000). Physical inactivity in Australia. The commonwealth department of health and aged care and the Australian sports commission.
10. Cox, R.H. (2007). Sport Psychology: Concepts and Applications (6th ed.). New York: The Mc Graw-Hill Companies, Inc.
11. Duda, J. L. (1989). Relationship between task and ego orientation and the perceived purpose of sport among high school athletes. Journal of Sport and Exercise Psychology, 11, 318-335.
12. Duda, J. L., & Nicholls, J. G. (1992). Dimensions of achievement motivation in schoolwork and sport. Journal of Education Psychology, 84(3), 290-299.
13. Duda, J. L., & Tappe, M. K. (1988). Predictors of personal investment in physical activity among middle-aged and older adults. Perceptual and Motor Skills, 66, 543-549.
14. Duda, J. L., & Whitehead, J. (1998). Measurement of goal perspectives in the physical domain. In J. L. Duda (Ed.), Advances in sport and exercise psychology measurement (pp. 21-48). Morgantown, WV: Fitness Information Technology.
15. Duda, J. L., Chi, L., Newton, M. L., Walling, M. D., & Catley, D. (1995). Task and ego orientation and intrinsic motivation in sport. International Journal of Sport Psychology, 26, 40-63.
16. Ferrer, C. E., & Weiss, M. R. (2000). Predictors of intrinsic motivation among adolescent students in physical education. Research Quarterly for Exercise and Sport, 71(3), 267-279.
17. Frederick, C. M., & Ryan, R. M. (1993). Differences in motivation for sport and exercise and their relations with participation and mental health. Journal of Sport Behavior, 16(3), 124-146.
18. Galloway, M.T., & Jokl, P. (2000) .Aging successfully: The importance of physical activity in maintaining health and function. Journal of the American Academy of Orthopaedic Surgeons. 18(1), 37-44.
19. Gill, D. L., Gross, J. B., & Huddleston, S. (1983). Participation motivation in youth sports. International Journal of Sport Psychology, 14, 1-14.
20. Givvin, K. B. (2001). Goal orientations of adolescents, coaches, and parents: Is there a convergence of beliefs? The Journal of Early Adolescence, 21(2), 227-247.
21. Graves, S. (2002). Successful active aging programs. American College of Sports Medicine Interest Group on Aging.
22. Kleiber, D. A., & Kelly, J. R. (1980). Leisure, socialization, and the life cycle. In S. E. Iso-Ahola (Ed.), Social psychological perspectives on leisure and recreation (pp.91-137). Springfield, Ill.: C. C. Thomas.
23. Lavallee, D., Kremer, J., Moram, A. P., & Williams, M., (2004). Sport Psychology-Contemporary Themes. New York, NY: Palgrave Macmillan.
24. Leisure and Cultural Services Department (2004). Master games 2004/05, introduction. Retrieved August 22, 2004, from <http://www.lcsd.gov.hk/me/mastergame/en/index.php>
25. Levy, J. (1978). Play behavior. New York: Wiley.
26. Levy, J. (1994). Leisure and retirement in the new age wave society. World Leisure and Recreation Journal, 34(1), 5-10.
27. Levy, J., Losito, V., Levy, R. (1994). Ontario Senior Games: A longitudinal study. The First International Conference On Prevention. West Virginia University School of Medicine, Charleston, West Virginia, October 27-30
28. Levy, J. (1998) Physical activity: Top anti-aging pill. Del Condominium Life, Spring/Summer, 28-29.
29. Li, F., Harmer, P., & Acock, A. (1996). The task and ego orientation in sport questionnaire: Construct equivalence and mean differences across gender. Research Quarterly for Exercise and Sport, 68(2), 228-238.
30. Mathes, S. A., McGivern, A. T., & Schneider, C. M. (1992). The influence of participation and gender on employees’ motives for involvement in a corporate exercise program. Journal of Sport Management, 6(1), 1-14.
31. McGuire, L.C., Strine, C.A., Okoro, I.B., Ahluwalia, I.B., & E.S. Ford. (2007). Healthy lifestyle behaviors among older U.S. adults with and without disabilities, behavioral risk factor surveillance system. Chronic Disease Prevention. 4(1).
32. Newton, M., & Duda, J. L. (1993). Elite adolescent athletes’ achievement goals and beliefs concerning success in tennis. Journal of Sport and Exercise Psychology, 15, 322-341.
33. Nicholls, J. G. (1989). The competitive ethos and democratic education. Cambridge, MA: Harvard University Press.
34. Roberts, G. C., & Treasure, D. C. (1995). Achievement goals, motivational climate and achievement strategies and behaviors in sport. International Journal of Sport Psychology, 26, 64-80.
35. Robert, G. C., Treasure, D. C., & Balague, G. (1998). Achievement goals in sport: the development and validation of the perception of success question. Journal of Sport Science, 16, 337-347.
36. Rudman, W. J. (1989). Age and involvement in sport and physical activity. Sociology of Sport Journal, 6, 228-246.
37. Scanlan, T. K., Stein, G. L., & Ravizza, K. (1989a). An in-depth study of former elite figure skaters: I. introduction to the project. Journal of Sport and Exercise Psychology, 11, 54-64.
38. Scanlan, T. K., Stein, G. L., & Ravizza, K. (1989b). An in-depth study of former elite figure skaters: II. sources of enjoyment. Journal of Sport and Exercise Psychology, 11, 65-83.
39. Shapiro, D. R. (2003). Participation motives of Special Olympics athletes. Adapted Physical Activity Quarterly, 20, 150-165.
40. Shephard, R. J. (1995). Physical activity, health, and well-being at different life stage. Research Quarterly for Exercise and Sport, 66(4), 298-302.
41. Stebbins, R.A. (2007). Serious leisure: A perspective for our time. New Brunswick, NJ: Transaction Publishers.
42. Steinberg, G., Grieve, F. G.., & Glass, B. (2002). Achievement goals across the lifespan. Journal of Sport Behavior, 23(3), 298-306.
43. U.S. Department of Health and Human Services. (2008). Physical Activity Guidelines for Americans: Physical Activity Guidelines Advisory Committee Report 2008. Retrieved on Aug 12, 2010 from <http://www.health.gov/PAGUIDELINES/committeereport.aspx>
44. Vogel, T., Brechat, P.M., Lepretre, G., Kaltenbach, M., Berthel, J., & Lonsdorfer, J. (2009). Health benefits of physical activity in older patients: A review. International Journal of Clinical Practice, 63 (2), 303-320.
45. Walling, M. D., & Duda, J. L. (1995). Goals and their associations with beliefs about success in and perceptions of the purposes of physical education. Journal of Teaching in Physical Education, 14, 140-156.
46. Weinberg, R. S., & Gould D. (2003). Foundations of sport and exercise psychology (3rd ed). Champaign, IL: Human Kinetics.
47. White, S. A., & Duda, J. L. (1994). The relationship of gender, level of sport involvement, and participation motivation to task and ego orientation. Journal of Sport Psychology, 25, 4-18.
48. Wilfrid, S. (1995) Why sports matter. Wilson Quarterly, 19, (1), 1-15.
49. Wong, A. K. Y., & Cheung, S. Y. (2005). Functional fitness level of older women in Hong Kong. International Journal of Eastern Sports and Physical Education, 3(1), 273-282.

### Corresponding Author

Prof. Siu Yin Cheung
Department of Physical Education, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China.
Telephone: (852) 3411-5637
Fax: (852) 3411-5757
E-Mail: <cheungsy@hkbu.edu.hk>