The sporting environment places unique physical and psychological stressors on elite athletes, making them ideal candidates for athlete monitoring. For a start, they must cope with the physical strain and accompanying injury risk of heavy training loads and increasing competition demands (Soligard et al., 2016). Next, they must withstand busy travel schedules, with many athletes spending substantial time away from home (Gupta et al., 2016). These elements have a significant psychological burden as well, such as the anxiety that may accompany an injury lay-off (Reese, Pittsinger, & Yang, 2012), or the disturbed sleep inflicted by arduous journeys (Gupta et al., 2016). In addition to this brief list of sport-related challenges facing elite athletes, they encounter many of the same daily demands as everyone else, such as family conflict and illness (Howells & Fletcher, 2015). Thus, though elite athletes are at the upper echelons of the physical activity continuum, they may be more susceptible to adverse mental health than people realise (Doherty et al., 2016). In addition to monitoring mental health in elite sport, it is important to assess other factors, particularly those with an evidence-based link to performance, training, and well-being, and those that can be improved through intervention.
6.2.4 The Current Study
As stated in Chapters Four and Five, findings regarding athlete motivation and mental health are not definitive, particularly given the blend of protective (e.g., challenge of self- improvement; high physical activity levels) and risk (e.g., societal emphasis on rewards;
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demanding lifestyles) factors they may face. Earlier chapters also provided substantial evidence of the associations between motivation and mental health, making concurrent investigation of these variables very informative. Widespread calls for longitudinal studies (e.g., Clancy et al., 2016), coupled with the utility of athlete monitoring (e.g., Saw et al., 2015), make the examination of these constructs ideal from both a content and design perspective. The primary objective of this study, aligning with the aims of this thesis, was to characterise the motivation and mental health profiles of elite club athletes across 21-37 weeks and to examine associations between these variables over time. As in the previous chapter, it was hypothesised that (i) scores for intrinsic motivation and task climate would exceed extrinsic motivation/amotivation and ego climate, respectively, (ii) motivation-related variables would remain relatively stable over time; and, (iii) student-athletes would report high satisfaction (e.g., scores above the midpoint) of their basic needs. Given the mixed findings in the literature regarding athlete mental health, investigations of mood states, depressive and anxiety symptoms, and sleep quality, along with their potential associations with motivation-related variables, were exploratory.
6.3 Methods
6.3.1 Participants and Procedure
The participants from Chapter Four were included in this study. Briefly, 215 athletes (65% female, 35% male) were recruited from 11 teams across six sports (basketball, Gaelic football, hockey, hurling, rugby, and soccer). They ranged in age from 18 to 37 (M = 22.8, SD = 4.1) years, and had 12.5 (SD = 5.0) years sport experience on average. Three teams were categorised as “competitive-elite,” and eight teams were categorised as “semi-elite” (Swann et al., 2014). The procedures from Chapter Five were used in this study, though over a longer time frame. Specifically, seven psychometric inventories were administered using Survey Monkey between April 2015 and May 2016, with data collection, termed “monitoring” among the athletes, lasting the full athletic season (21-37 weeks depending on the team). Each team began data collection in its pre-/early-season phase, and ended in its late-season phase (Supplementary Table 6.1). Given 21 time points and a correlation of 0.5 between repeated measures, an a priori power analysis indicated that 33 participants would be needed to have 95% power for detecting a medium-sized effect (g = 0.3) when using the traditional .05 criterion of statistical significance (Faul et al., 2007). Athletes were briefed on the importance of psychological monitoring for their performance and well-being, and strongly encouraged to comply with data collection, with the promise of individualised reports at the end of the study.
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Based on their established recall periods, TMD and depressive symptoms were measured weekly, and sleep quality and anxiety symptoms were measured monthly. Consistent with previous research that measured motivation at 10-24 week intervals and beyond (Stenling et al., 2016; Vink et al., 2015), motivation, basic needs satisfaction, and motivational climate were measured at weeks one (T1), 13 (T2), and 25 (T3), with one team reaching the 37-week mark (T4).
6.3.2 Measures
Motivation was measured using the 18-item SMS-II (Pelletier et al., 2013). The SMS-II asks athletes “why do you practice your sport?” and provides a seven-point Likert scale for each response. It provides scores for intrinsic, integrated, identified, introjected, external, and non- regulations. Alternatively, a composite score, the SDI, can be computed (SDI = [3*intrinsic]+[2*integrated]+[1*identified]+[-1*introjected]+[-2*external]+[-3*non];
Vallerand, 1997). The SMS-II has demonstrated adequate reliability in previous studies (Cronbach's alpha of 0.73-0.86; Pelletier et al., 2013).
Athletes’ perceptions of competence, autonomy, and relatedness were measured using the BNSSS (Ng et al., 2011). This 20-item scale asks athletes how they feel when participating in their main sport, provides a seven-point Likert scale for responses, and has reliability coefficients of 0.61-0.82 for the five subscales (Ng et al., 2011). The autonomy subscale can be further broken down into internal perceived locus of causality, volition, and choice.
Athletes’ perception of the motivational climate typically experienced on their teams was assessed using the 33-item PMCSQ-II (Newton et al., 2000). The PMCSQ-II uses the stem “On this team…” and provides scores for perceived task and ego climates, with responses indicated on a five-point Likert scale. These subscales can be further subdivided into effort/improvement, important role, and cooperative learning (task climate components) and punishment for mistakes, unequal recognition, and intra-team rivalry (ego climate components). Evidence to support the reliability of the inventory (Cronbach’s alpha coefficients of 0.88 and 0.87 for task and ego climates, respectively) was provided by Newton et al. (2000).
TMD was measured using the POMS-B (McNair et al., 1971). The POMS-B consists of 30 adjectives describing how the respondent may be feeling “right at this moment” for five negative mood states (tension, depression, anger, fatigue, and confusion) and one positive mood state (vigour). TMD is calculated by subtracting the total for the vigour items from the total for the negative mood states items, with higher scores indicating greater TMD (range of -
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20-100). The subscales of the POMS-B have demonstrated adequate reliability (Cronbach's alpha of 0.71-0.88; Yeun & Shin‐Park, 2006).
Depressive symptom severity was assessed using the 16-item QIDS-SR (Rush et al., 2003), which measures nine symptom domains (sad mood, concentration, self-criticism, suicidal ideation, interest, energy/fatigue, sleep disturbance, appetite/weight, and psychomotor agitation/retardation) according to the Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013). Respondents are asked to rate symptoms from the prior seven days, with the following depressive symptoms classifications based on total score: none (0-5), mild (6-10), moderate (11-15), severe (16-20), and very severe (21-27). Rush et al. (2003) have provided evidence to support the reliability of the inventory (Cronbach’s alpha of 0.86).
Sleep quality was measured using the PSQI (Buysse et al., 1989). The PSQI uses 19 items to generate seven component scores that quantify overall sleep quality for the preceding month (good quality = 0-5; poor quality = >5): subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The component scores of the PSQI have a reliability coefficient of 0.83 (Buysse et al., 1989).
Anxiety symptoms were measured using the STAI-Y2 (Spielberger et al., 1983), which asks athletes to rate how they generally feel on a four-point Likert scale in response to 20 items. Individuals with a score greater than or equal to one standard deviation above age-related norms (~50) were classified as having high anxiety symptoms (range of 20-80; Spielberger et al., 1983). The STAI-Y2 has demonstrated adequate reliability (Cronbach's alpha of 0.90; Spielberger et al., 1983).