### 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

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### 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.

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### 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>