Mixed model repeated measures ANOVAs did not show any significant interactions between time and sex for motivation-related or mental health outcomes. For competence, the main effect for sex was significant (F(1, 96) = 5.696, p ≤ 0.02), indicating that male athletes had significantly higher scores than female athletes over T1 (5.64±1.04 versus 5.19±1.18), T2 (5.70±1.01 versus 5.13±1.13), and T3 (5.57±1.04 versus 5.14±1.22). There was a significant main effect for time for both task (F(2, 192) = 8.772, p ≤ 0.0001) and ego (F(2, 192) = 6.971, p ≤ 0.001) climates, and main effect for sex for ego climate (F(1, 96) = 4.450, p ≤ 0.04). There were no significant main effects for sex for any mental health outcome. There were significant
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main effects for time for TMD (F(4.17, 650) = 9.591, p ≤ 0.0001), depressive (F(4.28, 650) = 31.080, p ≤ 0.0001) and anxiety (F(3.89, 650) = 5.971, p ≤ 0.0001) symptoms, and sleep quality (F(4.14, 650) = 4.075, p ≤ 0.003), indicating that scores for each mental outcome significantly improved over time.
6.4.4 Correlations
Supplementary Tables 6.2 and 6.3 illustrate significant correlations between motivation-related and mental health variables at and between weeks one and 13, and weeks one and 25, respectively. When adjusted for potential Type I error of multiple testing, significant small, positive correlations were found at T1 between amotivation and TMD (r = 0.260, p ≤ 0.003), depressive symptoms (r = 0.269, p ≤ 0.002), and anxiety symptoms (r = 0.292, p ≤ 0.001). There were significant small-to-moderate, negative correlations between depressive symptoms and autonomy (r = -0.252, p ≤ 0.004), and anxiety symptoms and several motivation-related variables: competence (r = -0.389, p ≤ 0.001); autonomy (r = -0.322, p ≤ 0.001); relatedness (r = -0.249, p ≤ 0.004); task climate (r = -0.262, p ≤ 0.002). At T2 and T3, anxiety symptoms were positively correlated with amotivation (T2, r = 0.381, p ≤ 0.001; T3, r = 0.401, p ≤ 0.001), and negatively correlated with competence (T2, r = -0.327, p ≤ 0.001; T3, r = -0.397, p ≤ 0.001), autonomy (T2, r = -0.292, p ≤ 0.001; T3, r = -0.378, p ≤ 0.001), and relatedness (T2, r = -0.281, p ≤ 0.001; T3, r = -0.338, p ≤ 0.001). At T2, anxiety symptoms had a small, positive correlation with ego climate (r = 0.254, p ≤ 0.003), but this was non-significant at T3. TMD was moderately correlated with amotivation at both time points (T2, r = 0.401, p ≤ 0.001; T3, r = 0.313, p ≤ 0.002), and was moderately, negatively correlated with competence (r = -0.327, p ≤ 0.001) and autonomy (r = -0.354, p ≤ 0.001) only at T3. There was a small, positive correlation between T2 depressive symptoms and amotivation (r = 0.264, p ≤ 0.002) that was not evident at T3.
In terms of T1 motivation-related variables being associated with later mental health outcomes, the only correlation common to both later time points was between depressive symptoms and amotivation (T2, r = 0.254, p ≤ 0.003; T3, r = 0.291, p ≤ 0.004). Amotivation had a small correlation with T2 TMD (r = 0.275, p ≤ 0.001), and competence had a small-to- moderate negative correlation with anxiety symptoms at both T2 (r = -0.298, p ≤ 0.001) and T3 (r = -0.351, p ≤ 0.001). There was a large discrepancy regarding associations between T1 mental health outcomes and later motivation-related variables, with 10 significant associations at T2 and only one at T3. T1 TMD was positively correlated with T2 amotivation (r = 0.262, p ≤ 0.002), and T1 depressive symptoms were negatively correlated with T2 competence (r = -
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0.291, p ≤ 0.001) and autonomy (r = -0.304, p ≤ 0.001). There were small-to-moderate correlations between T1 anxiety symptoms and several motivation-related variables at T2: amotivation (r = 0.260, p ≤ 0.003); competence (r = -0.323, p ≤ 0.001); autonomy (r = -0.280, p ≤ 0.001); relatedness (r = -0.283, p ≤ 0.001); task climate (r = -0.259, p ≤ 0.003); ego climate (r = 0.271, p ≤ 0.002).
Supplementary Tables 6.4-6.6 illustrate significant correlations between T1 scores and T1T2 change scores, T1 scores and T1T3 change scores, and T1T2 and T1T3 change scores. When adjusted for potential Type I error of multiple testing, significant moderate correlations were found between T1 autonomy (r = -0.337, p ≤ 0.0001) and relatedness (r = - 0.313, p ≤ 0.0001) and T1T2 change in depressive symptoms, and between T1 ego climate and T1T2 change in sleep quality (r = 0.306, p ≤ 0.002). There were no significant correlations between T1 mental health scores and change scores for motivation-related variables. There was a moderate, positive correlation between T1T2 change in anxiety symptoms and T1T2 change in amotivation (r = 0.419, p ≤ 0.0001). T1T3 change in anxiety symptoms had a moderate correlation with T1T3 amotivation (r = 0.347, p ≤ 0.0001), competence (r = -0.304, p ≤ 0.002), and autonomy (r = -0.341, p ≤ 0.001). There were no significant correlations between T1T2 mental health scores and T1T3 motivation-related scores, or vice versa.
6.5 Discussion
Consistent with hypotheses, the athletes reported adaptive motivational patterns, though the mental health results were somewhat maladaptive. As discussed in Chapter Four, the athletes’ scores were consistent with the self-determination continuum, being highest for intrinsic regulation and then decreasing sequentially. This pattern was evident when the sample was divided into compliant and non-compliant sub-samples, with the compliant group reporting significantly better (higher autonomous, lower controlled) scores than the non-compliant group. The finding that athletes with higher quality (more self-determined) motivation complied with the monitoring protocol is not surprising. Numerous studies have shown that motivation is critical for compliance in related areas, such as injury rehabilitation (e.g., Everhart, Best, & Flanigan, 2015) and exercise (e.g., Herring, Sailors, & Bray, 2014). Therefore, an athlete who is more autonomously motivated to engage in sport may also be more autonomously motivated to engage in psychological monitoring. With this in mind, it may be effective to differentially motivate athletes to engage in monitoring in future studies or real- world scenarios. For example, autonomously motivated athletes may need few reminders to
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complete the inventories if monitoring is presented to them in particular ways, such as by evidencing the link between psychology and their own performance (identified regulation), or by highlighting optimal compliance levels and, therefore, tapping into their love of challenge (intrinsic regulation). In contrast, compliance-contingent rewards/punishments (external regulation) may be needed to motivate athletes who are characterised by controlled motivation. Such tailored strategies could be implemented to specific sub-groups of athletes throughout the monitoring period, thereby increasing compliance.
From a mental health perspective, the athletes’ average week one scores are just below and above the caseness cut-off for mild depression and poor sleep quality, respectively. Therefore, the sample could be characterised overall as poor sleepers, which echoes much previous research on elite athletes (Gupta et al., 2016). Similar to the motivation-related findings, the compliant athletes reported lower (better) scores than the non-compliant athletes across the four mental health outcomes, while also having a smaller proportion of athletes exhibiting moderate depressive symptoms (though more with mild depressive symptoms), poor sleep quality, and elevated anxiety symptoms. Given that poor mental health is maladaptive, affecting a host of factors within and beyond sport (Rice et al., 2016), it is somewhat unsurprising that compliance in a voluntary study would be lower among those with poorer mental health scores. However, such athletes are likely in greater need of psychological monitoring, which reinforces the tendency of elite athletes to conceal help-seeking behaviours (Gulliver, Griffiths, & Christensen, 2012).
The stability of motivation and basic needs satisfaction over time is consistent with the findings from Chapter Five. Though recent research has shown sport motivation to be a relatively stable disposition (Stenling et al., 2016; Stenling et al., 2015), the literature is mixed regarding basic needs satisfaction. Stenling et al. (2015) reported stable basic needs satisfaction over five months due to the stability of the coaches’ interpersonal styles. Similarly, Adie, Duda, and Ntoumanis (2012) reported stable basic needs satisfaction and coaching behaviours over two competitive seasons. However, other evidence indicates that athletes experience changes in basic needs satisfaction, predominantly because they perceive the behaviour of their coaches to change over time (Reinboth & Duda, 2006). The current data support that coaching behaviour is dynamic, in that perceptions of the motivational climate changed. It is possible that critical events throughout the season (e.g., important games, injury issues) influence coaching behaviour (Stenling et al., 2016), though these potential additional research questions were beyond the scope of this thesis. Notably, athletes’ perceptions were assessed in this thesis,
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rather than actual behaviours, so it would be ideal to triangulate such findings in future using coach observation (Smith, Tessier, et al., 2015).
The improvement in mental health outcomes in the current study echoes most of the findings from the previous chapter. Though anxiety symptoms improved significantly among the club athletes, it did not among the student-athletes in Chapter Four, which is consistent with the traditional conceptualisation of trait anxiety as a relatively stable construct (Spielberger et al., 1983). However, evidence has shown that trait anxiety is sensitive to change in response to even short-term targeted interventions. As examples, interventions involving cognitive behavioural therapies (Mitte, 2005), relaxation training (Manzoni, Pagnini, Castelnuovo, & Molinari, 2008), and exercise training (Herring, Jacob, Suveg, & O’Connor, 2011) produce moderate-to-large reductions in trait anxiety scores. Notably, the current data indicated that, based on age-related norms, anxiety was predominantly in the “normal” range for most athletes. The improvement in mental health supports that physical activity benefits mental health (Physical Activity Guidelines Advisory Committee, 2018). Beyond this well-established benefit, sport participation is associated with increases in well-being, vitality, and enjoyment, and reductions in stress and distress (Eime et al., 2013). In particular, team sport provides a like-minded support network that strengthens social bonds and, therefore, decreases athlete susceptibility to poor psychological outcomes (Miller & Hoffman, 2009). Although the benefits of sport participation are well known, there are risk factors associated with higher levels of performance, such as the extreme physical and psychological stressors that elite athletes face (Hughes & Leavey, 2012). In addition to the emergence of academic literature on the negative effects of elite sport participation (Rice et al., 2016), there has been a plethora of mainstream media coverage regarding elite athletes’ struggles with poor mental health (e.g., Cooper, 2017, April 11; Newberry, 2018, May 22). Nonetheless, this thesis supports that sport involvement is associated with better mental health.
It is plausible that the current sample’s semi/competitive-elite status is not as high and, therefore, demanding as other samples in the literature. For example, Newman et al. (2016) and Doherty et al. (2016) reported instances of depression among athletes competing at higher levels than those studied in this thesis. Conversely, Stenling et al. (2015) reported high and stable well-being among young elite skiers, suggesting that high-level participation was not associated with impaired mental health. This mixed evidence indicates that mental health trajectories are not fixed among athletes, and that research in this area remains somewhat exploratory.
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Many of the associations between variables reported in this chapter are consistent with theory. For example, the associations between amotivation and three mental health outcomes reinforces longstanding evidence of the maladaptive effects of non-self-determined motivation (Ryan & Deci, 2017). Similarly, task climate and the three basic psychological needs were inversely associated with anxiety symptoms, echoing the adaptive influence of these constructs (e.g., O’Rourke, Smith, Smoll, & Cumming, 2011). Although the significance of associations between variables was not always consistent over time, the magnitude of the associations remained relatively constant. That is, small- and moderate-sized associations persisted at T2 and T3, except for some minor decreases from just over to just under the moderate threshold (r = 0.30). In only two instances (T1 TMD and later competence and autonomy) did the association increase from small- to moderate-sized. Very rarely, the direction of the associations changed as the season progressed. Both anxiety symptoms and ego climate increased from T1 to T2, but then diverged at T3, with ego climate increasing again and anxiety symptoms decreasing. Thus, this association was somewhat inconsistent and requires further exploration.
The longitudinal associations reported indicate that motivation may influence, or predict, later mental health. The data show significant associations between T1 motivation- related variables and T2/T3 mental health outcomes, as well as between T1 motivation-related variables and longitudinal changes in mental health outcomes. These findings are consistent with previous longitudinal research within (Sheehan et al., 2018b) and beyond (e.g., Stenling et al., 2015) this thesis, and the cross-sectional pathways between these factors presented in Chapter Four (Sheehan et al., 2018a). The numerous significant associations between earlier mental health outcomes and later motivation-related variables suggest that there may be reciprocal relationships between the variables. That is, not only does motivation influence mental health in line with the aforementioned research, but mental health may also reciprocally influence motivation. Notably, however, there was no evidence that T1 mental health outcomes were associated with longitudinal changes in motivation-related variables. Still, the data suggest that, although motivation was found to be relatively stable in the current study, it is plausible that over a longer period that depressive symptoms or mood disturbances could promote amotivation. Similarly, the inverse associations between depressive symptoms and competence and autonomy are not unexpected, given that individuals with depression may experience reduced functioning (Doherty et al., 2016), and basic needs are critical nutriments for functioning (Ryan & Deci, 2017). Such associations were not found later in the season, potentially due to the significant improvement in depressive symptoms. The inverse
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associations between anxiety symptoms and later basic needs and task climate are unsurprising, given that anxiety is a primarily negative construct characterised by apprehension, tension, and thoughts of worry that are frequently associated with maladaptive consequences (Ford, Ildefonso, Jones, & Arvinen-Barrow, 2017). Thus, it seems logical for improved anxiety scores to be associated with improved adaptive motivation-related variables. Similarly, the positive associations between anxiety symptoms and amotivation and ego climate are somewhat expected. Indeed, research has shown a relationship between a performance climate and subsequent anxiety, concluding that ego cues may encourage athletes to perceive their circumstances as threatening (Carr & Wyon, 2003). Further, it seems plausible that the general predisposition to perceive circumstances as threatening (trait anxiety) may increase the perception of ego cues, which tend to threaten athletes’ self-esteem and competence. As outlined above, the significance of such associations was not always consistent, but their magnitude remained relatively consistent over time. Overall, many of the longitudinal associations reported here are consistent with or expand previous research presented within this thesis and the wider available literature.
6.5.1 Implications
Similar to Chapter Five, this study provides practical lessons for optimising the motivation and mental health of elite club athletes, particularly through coordinated psychological monitoring. Firstly, educational workshops could be designed to increase coaches’ knowledge of motivation, particularly their role in shaping the motivational climate. Although the diminishing task and developing ego climate did not appear to influence athletes’ motivation or basic needs satisfaction in the current study, it may do so over a longer period. Likewise, such a shift in the climate could undermine mental health, given the associations reported in this and previous chapters. While other architects of the motivational climate (e.g., parents, peers, et cetera) were not included in this thesis, it would likely be useful for them to upskill in this area as well. Secondly, educational workshops could be delivered to coaches and athletes alike regarding mental health. Although the scores for mental health outcomes improved over time, there was a large proportion of athletes categorised as having mild depressive symptoms or being poor sleepers. Informing stakeholders of risk factors for and consequences of poor mental health would, therefore, be beneficial, and may also encourage athletes to seek help if they are experiencing difficulties. Thirdly, coaches could consider implementing psychological monitoring with their athletes. Application-based monitoring is easy for athletes to use and coaches to analyse, thereby facilitating a quick feedback and flagging system. Issues with
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compliance in the current study may have been largely attributable to the delay in receiving feedback, as the intention was to observe trends over time, rather than intervene. Regular monitoring, however, would not have this issue, in that coaches could share results with athletes throughout the season with the purpose of maximising strengths and addressing difficulties.
6.5.2 Limitations
There are several potential limitations. A larger sample size could have been recruited in order to compensate for attrition, thereby allowing the compliance cut-off to be set at a higher percentage and longitudinal analyses to be conducted on a larger sample. Secondly, greater efforts could have been made to increase compliance, such as attendance at training and more frequent communication with coaches/managers. Thirdly, game schedules and results could have been monitored, similar to Chapter Five, as well as injury status and other potentially important factors, but these were beyond the scope of the current research aims. Lastly, individual sport athletes and foreign athletes could have been recruited in order to facilitate analyses by sport type and nationality.
6.5.3 Conclusion
Notwithstanding the aforementioned potential limitations, this is the first study to assess motivation-related and mental health variables among elite club athletes over a full competitive season with a comprehensive schedule of weekly, monthly, and quarterly data collection. As such, it addresses a gap in the literature and provides numerous real-world implications. The athletes reported adaptive motivational patterns, though their perceptions of task and ego climates decreased and increased, respectively, over time. Similar to the results in Chapter Five, almost half of the athletes experienced symptoms indicative of mild depression or poor sleep quality, but there were significant improvements in all mental health outcomes as the season progressed. It appears that participation in elite club sport was, therefore, beneficial for the athletes, such that high levels of autonomous motivation and basic needs satisfaction were maintained, and scores for TMD, depressive and anxiety symptoms, and sleep quality improved. The numerous significant associations that emerged from the data indicate that motivation and mental health are interlinked, reinforcing findings from Chapters Four and Five. Poorer mental health was associated with less adaptive motivation-related variables (e.g., amotivation and ego climate), while better scores were associated with more adaptive motivation-related variables (e.g., autonomous motivation and basic needs satisfaction). Novel associations were found between earlier mental health variables and later motivation-related variables. Although much research, including the model presented in Chapter Four, indicates
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that motivation precedes mental health, it is plausible that the reverse pathway also exists. As a final note, the data revealed differences between compliant and non-compliant athletes that would be useful to consider when implementing psychological monitoring among teams in the future. Overall, this study builds on those presented in previous chapters, while further contributing to the literature and providing applications for a variety of stakeholders.
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7.1 Introduction
Chapters 2-6 provided a multifaceted account of athlete motivation, a longstanding topic in sport psychology. The synthesis of available literature provided in Chapter Two presented a comprehensive summary of the samples, variables, research designs, and analyses included in research on competitive sport motivation over a 21-year period. Based on these and other findings highlighting the potentially important, but understudied, relationship between athlete motivation and mental health, mental health was included as a key topic in this thesis. Next, the aims, theoretical framework, and design of this thesis were refined, such that they were grounded in the available literature but designed to extend current knowledge. Given the utility and popularity of self-report measures highlighted in Chapter Two, a detailed methodological review, including bibliometric analysis, was provided in Chapter Three that sought to inform the choice of inventory used to assess motivation, the central construct of this thesis. Following this, the research design was piloted and finalised, leading to the empirical studies presented in Chapters 4-6. Chapter Four integrated motivation and mental health in a cross-sectional SEM among elite club athletes, thereby extending previous work in SDT. More specifically, this chapter supported that mental health outcomes can comprise the last step in the motivational sequence posited by the HMIEM. In addition to confirming previously established pathways among motivation-related variables (e.g., motivational climate and basic needs), the SEM uncovered novel pathways between the six motivation regulations and four mental health outcomes. Chapter Five longitudinally assessed the same variables among a subset of the overall sample, who were analysed separately because of their status as student-athletes and the associated brevity of their season. In Chapter Six, these analyses were applied and extended to the club athletes originally introduced in Chapter Four. As such, this chapter incorporates all of the key findings from Chapters 2-6 with a view to contributing to the literature and providing real-world applications, which can be summarised as follows:
Elite student-athletes experience adaptive motivational profiles, characterised by stable autonomous motivation and basic needs satisfaction, and a stronger task than ego motivational climate.
Elite club athletes experience adaptive motivational profiles, characterised by stable autonomous motivation and basic needs satisfaction, and a stronger task than ego motivational climate that moderately changes over time.
Over 40% of elite student-athletes and club athletes report symptoms indicative of mild depression or poor sleep quality.
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Mental health outcomes improve as the season progresses for elite student-athletes and club athletes.
Evidence supports that mental health should be considered as an outcome of motivation