### 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.
| Variables | n | % | |
|---|---|---|---|
| Sex | Male | 178 | 35.7 |
| Female | 320 | 64.3 | |
| System Missing | 17 | 3.3 | |
| Total | 515 | 100.00 | |
| Age | 18-24 | 178 | 34.6 |
| 25-29 | 82 | 15.9 | |
| 30-39 | 89 | 17.3 | |
| 40-49 | 72 | 14.0 | |
| 50-59 | 48 | 9.3 | |
| 60+ | 27 | 5.2 | |
| System Missing | 19 | 3.7 | |
| Total | 515 | 100.00 | |
| Relationship Status | Single | 134 | 26.0 |
| In a Relationship | 127 | 24.7 | |
| Married | 205 | 39.8 | |
| Seperated | 3 | 0.6 | |
| Divorced | 28 | 5.4 | |
| Widowed | 6 | 1.2 | |
| System Missing | 12 | 2.3 | |
| Total | 515 | 100.00 | |
| Ethnicity | Black | 71 | 13.8 |
| White | 393 | 76.3 | |
| Hispanic | 13 | 2.5 | |
| Asian | 12 | 2.3 | |
| Native American | 4 | 0.8 | |
| Other | 7 | 1.4 | |
| Missing | 15 | 2.9 | |
| Total | 515 | 100.00 | |
| Institution Status | Student | 359 | 69.7 |
| Staff Member | 57 | 11.1 | |
| Faculty | 65 | 12.6 | |
| Administrator | 8 | 1.6 | |
| Dual Role | 16 | 3.1 | |
| System Missing | 10 | 1.9 | |
| Total | 515 | 100.00 | |
#### Table 2
Means and standard deviations for team identification as a function of variables under study.
| Dependant Variable | n | Mean | S.D. | Std. Error |
|---|---|---|---|---|
| Body Mass Index (BMI) | ||||
| Low identifiers | 255 | 25.09 | 5.52 | .796 |
| High identifiers | 260 | 2.81 | 2.37 | .147 |
| Aerobic Exercise | ||||
| Low identifiers | 255 | 2.94 | 2.44 | .153 |
| High identifiers | 260 | 2.81 | 2.37 | .147 |
| Strength Training | ||||
| Low identifiers | 255 | 2.70 | 1.61 | .101 |
| High identifiers | 260 | 2.56 | 1.61 | .100 |
| Eat Breakfast | ||||
| Low identifiers | 255 | 1.42 | 1.67 | .104 |
| High identifiers | 260 | 1.76 | 1.69 | .105 |
| Healthy Snack Consumption | ||||
| Low identifiers | 255 | 1.36 | 1.27 | .079 |
| High identifiers | 260 | 1.40 | 1.31 | .081 |
| Fat Intake | ||||
| Low identifiers | 255 | 1.80 | 1.90 | .119 |
| High identifiers | 260 | 2.15 | 2.05 | .127 |
| Fast Food Consumption | ||||
| Low identifiers | 255 | 1.53 | 1.30 | .081 |
| High identifiers | 260 | 1.77 | 1.32 | .082 |
| Vegetable Consumption | ||||
| Low identifiers | 255 | 2.53 | 1.64 | .103 |
| High identifiers | 260 | 2.80 | 1.81 | .112 |
| Refined Grains Consumption | ||||
| Low identifiers | 255 | 2.73 | 1.47 | .083 |
| High identifiers | 260 | 3.29 | 1.87 | .110 |
| Alchohol Consumption | ||||
| Low identifiers | 255 | 2.73 | 1.47 | .083 |
| High identifiers | 260 | 3.29 | 1.87 | .110 |
| Smoking Behavior | ||||
| Low identifiers | 255 | 1.16 | 1.92 | .120 |
| High identifiers | 260 | 1.29 | 2.12 | .131 |
| STD Risk Behavior | ||||
| Low identifiers | 255 | 0.91 | 1.38 | .086 |
| High identifiers | 260 | 1.06 | 1.44 | .089 |
#### Table 3
Analysis of Variance (ANOVA) Results
| Independent Variable
Team Identification |
|||
|---|---|---|---|
| Dependent Variables | df | F | p |
| Body Mass Index (BMI) | 1 | 5.36 | .021 |
| Aerobic Exercise Frequency | 1 | .352 | .553 |
| Strength Training Exercise Frequency | 1 | .928 | .336 |
| Eat Breakfast Frequency | 1 | 5.35 | .021 |
| Unhealthy Snack Consumption | 1 | .143 | .705 |
| Fat Intake (High vs. Low) | 1 | 4.13 | .043 |
| Fast Food Consumption Frequency | 1 | 4.17 | .042 |
| Vegetable Consumption Frequency | 1 | 3.34 | .068 |
| Breads and Grains Consumption (Refined vs. Whole) | 1 | 3.54 | .061 |
| Alchohol Consumption | 1 | 16.63 | .000 |
| Smoking Frequency | 1 | .545 | .461 |
| Risk Factor for AIDS and STDs | 1 | 1.571 | .211 |
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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.
