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The purpose of the present study was to investigate the psychological antecedents of potentially harmful dimensions of exercise behaviour, and their effects, in turn, on common physical health problems in South African distance runners. More specifically, the main research objectives were to explore (1) the effects of perfectionism, Type A behaviour pattern, and achievement goal orientations on running addiction and training load and (2) the impact of running addiction and training load on self-reported running injuries and upper respiratory tract infections (URTIs). Further research aims were to examine the impact of running addiction on training load and to investigate certain interrelationships among perfectionism, Type A behaviour, and achievement goal orientations. In this chapter, the research hypotheses, along with the design and methodological aspects of the study, are presented and discussed.

The Research Hypotheses

In line with the study objectives and in accordance with relevant theory and research, a number of research hypotheses were formulated and tested. These suppositions, together with a brief rationale for each statement, are presented below.

Direct Effects

Hypothesis 1
Perfectionism has direct, positive effects on running addiction risk.
Hypothesis 2
Perfectionism has direct, positive effects on training load.
Rationale for Hypotheses 1 and 2
It has been posited that perfectionists’ self-worth is contingent upon their level of achievement in relation to others (Ellis, 2002). It is conceivable that irrational cognitions such as this may promote maladaptive striving among committed runners, manifesting in potentially harmful training patterns. Perfectionistic self-presentation concerns may also motivate and encourage an unhealthy preoccupation with exercise due to impression-management needs (Flett & Hewitt, 2005). Further, some perfectionists may become dependent on running as a means of coping with perceived stress and negative affect. Finally, the empirical literature supports the hypothesis that perfectionism predicts problem exercise behaviour in distance runners (Hall et al., 2007a; 2007b; 2009).
Hypothesis 3
Type A behaviour has direct, positive effects on running addiction risk.
Hypothesis 4
Type A behaviour has direct, positive effects on training load.

Rationale for Hypotheses 3 and 4

The desire to promote and maintain a positive sense of self-worth via the attainment of unrealistically high performance standards (Martin et al., 1989) may foster excessive achievement striving among Type A runners. Consequently, these individuals may be inclined to adopt unhealthy or self-defeating training habits in order to achieve their goals. Also, the various dysfunctional cognitions and behaviours associated with the Type A construct may increase stress and negative effect (Martin et al., 1989; Smith & Anderson, 1986). Therefore, some Type A runners may use exercise as a maladaptive coping strategy, increasing the risk for dependence. In empirical research, Type A behaviour has been positively related to running injury risk (Diekhoff, 1984; Ekenman et al., 2001; Fields et al., 1990). It is plausible that dysfunctional training behaviours may account for this relationship (Ekenman et al., 2001; Fields et al., 1990).
Hypothesis 5
Task goal orientation has a direct, positive effect on training load.
Hypothesis 6
Ego goal orientation has a direct, positive effect on running addiction risk.
Hypothesis 7
Ego goal orientation has a direct, positive effect on training load.
Rationale for Hypotheses 5, 6, and 7
Achievement goal theory and a growing body of research suggest that a task goal orientation is primarily adaptive in achievement contexts, whereas an ego goal orientation is more likely to be maladaptive (Biddle et al., 2003; Conroy et al., 2003; Elliott & Dweck, 1988; Isoard-Gautheur et al., 2012; Lemyre et al., 2003; Roberts et al., 2007; Tenenbaum et al., 2005). It has been stated that task-involved runners should tend to exert effort and persist in the face of obstacles or setbacks as achievement is self-referenced and perceived as controllable (Hall et al., 2007a). Tasks goals should also promote flexible achievement striving in sport and exercise contexts as self-worth is not contingent upon achievement (Hall et al., 2007a). Conversely, when ego-involved athletes fail to demonstrate ability, self-worth is threatened, and repeated attempts at self-validation could promote dysfunctional patterns of achievement striving (Hall et al., 2007a). Therefore, both task and ego orientation may predict heavy training loads but ego goals may also foster a more intense and rigid approach to training. In support of these assertions, burnout in athletes has been positively related to ego orientation and inversely associated with task orientation (Lemyre et al., 2003). Potential causes of burnout include heavy physical training demands, insufficient recovery between workouts, and/or psychosocial stress (Raedeke, 2014).
Hypothesis 8
Running addiction has a direct, positive effect on self-reported upper respiratory tract infections.
Hypothesis 9
Running addiction has a direct, positive effect on self-reported running injuries.
Hypothesis 10
Training load has a direct, positive effect on self-reported upper respiratory tract infections.
Hypothesis 11
Training load has a direct, positive effect on self-reported running injuries.
Rationale for Hypotheses 8, 9, 10, and 11
Physiological conceptions of stress and models of exercise and infection imply that physical and/or psychosocial stressors can increase the risk of URTIs and overuse injuries in distance runners (Appaneal & Perna, 2014; Nieman, 2001; Selye, 1975). Hypothesized mediating mechanisms in this regard include stress-induced neuroendocrine changes leading to immunosuppression and impaired muscle repair ability (Appaneal & Perna, 2014). Since intense training and competition are both physically and psychologically demanding (Adams & Kirkby, 2001; Hancock & Hancock, 2014), it follows that heavier training loads may increase runners’ susceptibility to infectious illness and injuries. Similarly, the compulsive and inflexible behaviour of addicted exercisers may enhance their exposure to psychosocial stress (Berczik et al., 2012) while simultaneously predicting greater physical training stress. Consequently, addicted exercisers may also be susceptible to overtraining and its adverse physical and psychological effects (Adams & Kirkby, 2001).
In support of these assertions, higher training volumes and marathon-type competitions have been linked to increased URTI incidence in several studies involving runners (Heath et al., 1991; Linde, 1987; Nieman et al., 1990b; Peters & Bateman, 1983; Peters et al., 1993; Robson-Ansley et al., 2012). There is also some evidence that overuse injury incidence is positively related to exercise/running addiction risk (Diekhoff, 1984; Ekenman et al., 2001; Layman & Morris, 1991; Lichtenstein et al., 2014; Rudy & Estok, 1989) and to specific dimensions of training load (Nielsen et al., 2012; Johnston et al., 2003; Ryan et al., 2006; van Gent et al., 2007).
Hypothesis 12
Running addiction has a direct, positive effect on training load.
Rationale for Hypothesis 12
Models of exercise addiction suggest that qualitatively harmful exercise behaviour is linked to heavier exercise loads. Individuals addicted to exercise are likely to train excessively and without limitations (Adams & Kirkby, 2001). More specifically, exercise addiction is expected to predict higher training frequency and intensity, and increased training volume over time (de Coverley Veale, 1987; Downs & Hausenblas, 2014; Hausenblas & Downs, 2002a; Rudy & Estok, 1989). Consistent with these ideas, positive associations between exercise addiction and training frequency, intensity, and/or duration have been observed in a number of investigations (Downs et al., 2004; Gapin et al., 2009; Hagan & Hausenblas, 2003; Hausenblas & Downs, 2002b; Iannos & Tiggemann, 1997; Layman & Morris, 1991; Lichtenstein et al., 2014; Terry et al., 2004; Youngman & Burnett, 2008).

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Bivariate Correlations

In addition to the posited directional relationships described above, it was expected that several of the personality and motivational variables would be related to one another. These predicted associations were as follows:
Hypothesis 13
Perfectionism is positively related to Type A behaviour
Rationale for Hypothesis 13
Beliefs concerning the importance of high personal standards of achievement for self-validation and self-worth purposes purportedly underlie both the perfectionism and Type A constructs (Flett et al., 1994; Flett et al., 2011). Also, it has been maintained that perfectionism stems from high parental expectations and conditional acceptance (Frost et al., 1990). Similar parental influences on the development of Type A behaviour have been recognized. For example, it has been suggested that the parents of Type A individuals may be overly-demanding and castigatory (Flett et al., 1994). Therefore, it is plausible that these personality constructs are related. This assertion is supported by empirical research (Flett et al., 1994; Flett et al., 2011).
Hypothesis 14

Perfectionism is negatively related to task goal orientation.

Rationale for Hypothesis 14
The literature suggests that perfectionism typically predicts maladaptive achievement behaviour and outcomes (Bovornusvakool et al., 2012; D’Souza et al., 2011; Ellis, 2002; Flett & Hewitt, 2005; Hall et al., 2007a). Conversely, task goal orientation has been associated mainly with adaptive motivation-related behaviour (Biddle et al., 2003; Conroy & Hyde, 2014; Elliott & Dweck, 1988; Lemyre et al., 2003; Roberts et al., 1998; Roberts et al., 2007; Tenenbaum et al., 2005). Therefore, it is plausible that these variables may be inversely related in a distance running context.
Hypothesis 15
There is a positive correlation between task goal orientation and ego goal orientation.
Rationale for Hypothesis 15
According to the hierarchical model of achievement motivation, achievement goals can be differentiated on an approach–avoidance dimension, in addition to how competence is defined (Elliot & Thrash, 2001). In brief, therefore, goals may represent the motive to either attain competence (approach goals) or avoid incompetence (avoidance goals). This approach has yielded four sets of goals, specifically mastery-approach, mastery-avoidance, performance-approach, and performance-avoidance (Elliot & Thrash, 2001). Mastery-approach and performance-approach goals are equivalent to conceptions of task and ego goals in the dichotomous model (Roberts et al., 2007). This suggests that task and ego goals share an approach dimension, which could result in a positive relationship between the two goal orientations. Recent research is consistent with this idea (Hall et al., 2007a; Ozkan et al., 2012).
The set of research hypotheses described above was expressed in a conceptual model which was subsequently tested using the powerful, multivariate statistical technique of structural equation modelling (SEM). The proposed model is pictured in Figure 4.1. The single-headed arrows attached to straight lines in the diagram represent hypothesized direct effects, while the doubled-headed arrows attached to curved lines indicate predicted bivariate correlations. The technique of SEM is discussed in more depth later in this chapter.

Research Design

The present study employed a cross-sectional research design and utilized a self-report method of data collection. This constituted a self-administered, online questionnaire that featured a closed-ended response format. Consistent with a cross-sectional design, data were collected in one survey on a single occasion. This type of design can be contrasted with a longitudinal study, which involves multiple observations over time of the variables of interest (Fife-Schaw, 1998).
The current research design could also be termed, retrospective, in that the independent and dependent variables were assessed simultaneously. This approach differs from a prospective design in which the predictor variables are assessed weeks or months before the outcome variable(s) (Petrie & Falkstein, 1998).
Several considerations guided the selection of the current research design and method of data collection. An important concern was the need to obtain a sufficiently large sample to produce reliable results. In this regard, a cross-sectional design may have several advantages over a longitudinal design. For example, the former approach avoids the problem of subject attrition, which occurs when subjects drop out of the study while it is in progress (Fife-Schaw, 1998). Also, a cross-sectional survey places fewer demands on participants, which may enhance subject compliance (Fife-Schaw, 1998).
Similarly, self-report assessments of health and exercise may be more appropriate for the current purpose than certain alternative methods of data collection. These include physician ratings (Petrie & Falkstein, 1998) and the use of psychophysiological measures, such as heart rate monitors, to assess exercise intensity. Although ostensibly more objective than self-reports, these tools are likely to have financial and practical limitations when used for large-scale assessments. In contrast, the questionnaire method represents a simple and economical method of collecting health and exercise data for a large number of people. Furthermore, self-report measures of physical activity rate highly in the areas of acceptability, practicality, convenience, and information specificity (Hausenblas & Giacobbi, 2004). It has also been claimed that subjective measures of illness or injury are generally valid indicators of physical health status (Nowak, 1991). Moreover, self-report health assessments have been shown to have good construct and predictive validity (Molnar et al., 2006).

Research Participants and Sampling

A convenience sample consisting of 220 South African distance runners took part in the current study. Of these participants, 123 were male and 94 were female. Three respondents neglected to indicate their gender. A convenience sample has been defined as “a non-random sample that is chosen for practical reasons” (McBurney, 1998, p. 160). Participation criteria were that runners were aged 18 and older and took part in competitive running events of at least 800 metres in distance. The minimum age of participants was set at 18 in order to avoid the problem of obtaining parental consent.
The recruitment of participants incorporated several steps. First, a well-known South African road running website (i.e., was consulted in order to obtain a directory of athletic clubs countrywide. The next step was to identify clubs that have websites and thus may be reasonably large and have adequate communication networks. From this list, a total of 60 running clubs from across the country were selected.
Subsequent to this, an email was sent to club officials (e.g., secretary or chairperson) advising them of the study and requesting their assistance with the research. The designated individuals were asked to forward details of the study to their members and to attach the link to an online survey ( It was thought that the use of a ‘gatekeeper’ approach to subject recruitment (Barrett, 1998) could avoid the ethical and logistical problems associated with acquiring personal email addresses. A potential disadvantage of this strategy, however, is that the selected gatekeepers may fail to comply with the specific research request. A possible further problem is that gatekeepers may personally decide who should receive the survey, thus potentially biasing the sample of respondents (Barrett, 1998).
Several measures were taken in an effort to maximize the response rate. These included extending the survey response deadline and re-contacting gatekeepers to remind them about the study. A further strategy was to offer a pair of running shoes to the value of R1 200.00 as a lucky draw prize for participants. An attempt was also made to ensure that the survey was straightforward and could be completed within about 20 minutes. At the conclusion of the study, runners from 29 of the selected clubs (48%) had issued responses. Thus, it appeared that 52% of gatekeepers did not notify their members of the research for reasons that are subject to speculation. Of the responding clubs, 11 were based in Gauteng, nine in the Western Cape, eight in KwaZulu-Natal, and one in the Free State.
It should be noted that the use of an online survey may have resulted in a research sample that was more sophisticated and/or belonged to a higher socioeconomic bracket than the average runner. Therefore, the current sample may not be truly representative of the South African distance running population at large.

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Ethical Considerations

In conducting the current investigation, every effort was made to ensure that the study complied with current international standards of conduct governing psychological research. In this regard, key ethical issues, such as informed consent, confidentiality, anonymity, voluntary participation, and subject debriefing, were identified and addressed. For example, respondents were advised of the nature and purpose of the study prior to participation. At the same time, they were assured of the confidentiality of all data collected, the anonymity of subjects, and the voluntary nature of involvement. An option was also provided for participants to receive feedback on the study results. Given the nature of the research, it was not foreseen that participation would impact negatively on the welfare of respondents. The Ethics Committee of the Department of Psychology at the University of South Africa provided ethical clearance for the study.

Measuring Instruments

An adapted version of the Multidimensional Perfectionism Scale (MPS) (Frost et al., 1990) was used to measure the construct of perfectionism. The original instrument is a 35-item Likert-type scale that assesses factors hypothesized to be related to perfectionism. The measure consists of six subscales, namely Personal Standards, Concern over Mistakes, Doubts about Actions, Parental Criticism, Parental Expectations, and Organization

Defining Exercise and Related Terms
Health Risks of Distance Running
Running Injuries
Upper Respiratory Tract Infections
Overtraining, Running Injuries, and URTIs
Exercise Addiction
Rationale behind the Present Study
Psychological Antecedents of Exercise Behaviour
Effects of Exercise Behaviour on Running Injuries
Effects of Exercise Behaviour on URTIs
Objectives of the Present Study
Significance of the Present Study
Structure of the Thesis
Disciplinary Context of the Current Study
Sport and Exercise Psychology
Health Psychology
Exercise Addiction
Defining Addiction
Defining Exercise Addiction
Theories and Models of Exercise Addiction
Personality and Motivational Influences on Exercise Behaviour
Personality Traits and Exercise Behaviour
Achievement Goals and Exercise Behaviour
Exercise Behaviour, Injuries, and URTIs
Contemporary Models of Disease
Stress-Related Perspectives on Exercise and Health
Summary and Conclusions
Research Methodologies in Exercise Research
Exercise Addiction Studies
Overuse Injury and URTI Research
Personality and Exercise Behaviour: The Research
Perfectionism: Correlates and Consequences
Type A Behaviour Pattern: Correlates and Consequences
Relationship between Perfectionism and Type A Behaviour
Achievement Goals and Exercise Behaviour: The Research
Exercise Behaviour, Injuries, and URTIs: The Research
Psychosocial Stress, Athletic Injuries, and URTIs
Exercise and Overuse Injuries
Exercise and URTIs
Relationship between Exercise Addiction and Exercise Load
Summary and Conclusions
The Research Hypotheses
Direct Effects
Bivariate Correlations
Research Design
Research Participants and Sampling
Ethical Considerations
Measuring Instruments
Type A Behaviour Pattern
Achievement Goal Orientations
Running Addiction
Training Load
Running Injuries
Upper Respiratory Tract Infections
Demographic and Additional Information
Techniques of Statistical Analysis
Model Specification
Descriptive Statistical Analysis
Participant Characteristics
Descriptive Statistics: Personality and Motivation
Descriptive Statistics: Running Addiction and Training Load
Descriptive Statistics: URTIs and Running Injuries
Normality of Data Distribution
Correlational Statistical Analysis
Structural Equation Modelling Analysis
Parameter Estimation
Model Fit Assessment
Model Modification
SEM Path Analysis Model
Descriptive Statistics
Type A Behaviour Pattern
Achievement Goal Orientations
Running Addiction
Training Load
Running Injuries
Upper Respiratory Tract Infections
Structural Equation Modelling
Original Structural Equation Model
SEM Path Analysis Model
Modified SEM Path Analysis Model
Background to the Research Problem
Objectives of the Current Study
Research Hypotheses and Methodological Issues
Major Findings and Conclusions
Prevalence of Running Addiction
Prevalence of Running Injuries and URTIs
Psychological Predictors of Running Addiction
Psychological Predictors of Training Load
Effects of Running Addiction and Training Load on Injury Risk
Effects of Running Addiction and Training Load on URTI Risk
Limitations of the Study
Contributions and Applications of the Study
Recommendations for Future Research
Concluding Remarks

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