Gender and socioecological level differences in stakeholders' perceptions of athlete attrition factors in an Australian high-performance pathway system.

OBJECTIVES: This study aimed to build on previous work by the authors. It examines how socioecological level and gender influence high-performance sport system (HPSS) stakeholders' perspectives of the relative importance and feasibility to address athlete attrition factors within an Australian high-performance pathway system (HPPS). DESIGN: Mixed methods. METHODS: Sub-analysis was conducted of rating data from 30 participants who had contributed to identifying 83 statements in 13 clusters in a previous Concept Mapping study. The 13 clusters were statistically analysed in 'R' using cumulative link mixed models (CLMMs) to determine differences in perceived importance and feasibility between 1) socioecological levels, and 2) genders. RESULTS: Mean ratings for 11 and three of the 13 clusters were statistically significantly different between at least two of the five socioecological levels, for importance and feasibility, respectively. Athletes had the largest variation in mean ratings from the most (athlete health 4.59), to least (performance potential 2.83) important cluster, when compared to the other four socioecological levels. There were statistically significant differences between the ratings between genders (Men/Women) for two clusters for each rating scale: Importance: 'athlete health' (M3.33:W3.84 [p 0.012]); 'performance potential' (M3.35:W2.57; [p 0.001]), Feasibility: 'abuse and mismanagement of health' (M2.97:W3.68; [p 0.000]) and 'athlete health' (M2.54:W3.33; [p 0.000]). CONCLUSIONS: This study highlights the need to implement more robust athlete attrition monitoring protocols. It also highlights the importance of listening to youth athletes' voices, and enabling equal gender representation to ensure holistically tailored environments are created to retain talented athletes in high-performance pathway programmes.

Stakeholder insights into athlete attrition in the high-performance pathway.

OBJECTIVES: Primary: To gain a system-wide perspective on factors leading to athlete attrition from a high-performance sport system (HPSS). Secondary: To identify what a sample of system-wide stakeholders and past athletes value as the most important and feasible attrition factors to address to retain talented athletes. DESIGN: Mixed-methods. METHODS: Concept mapping was used for qualitative data collection and quantitative data analysis. Sixty-one participants including: (i) past athletes from an Australian state sporting institute; (ii) their families; and (iii) internal and external stakeholders to a HPSS who supported past athletes. RESULTS: Participants brainstormed 83 unique statements (i.e. attrition factors) that were mapped into 13 clusters of attrition factors following multidimensional scaling and hierarchical cluster analysis performed on the participants sorting data: 'abuse and mismanagement of health'; 'athlete health'; 'limited support/resourcing'; 'coaching'; 'inconsistent processes'; 'financial and career support'; 'pathway structure'; 'organisational dynamics'; 'competitive stress'; 'performance potential'; 'challenges with selection and transition'; 'psychological state'; and 'competing non-sport priorities'. 'Abuse and mismanagement of health' had the highest mean importance (3.76 out of 5) and feasibility (3.31) rating. The 13 clusters were further grouped into four overarching domains: 'sport system policy, structure and processes'; 'pathway structure, transition and support'; 'individual athlete health and capability'; and 'whole-of-life demands and priorities'. The domain 'sport system policy, structure and processes' contained the most important and feasible clusters. CONCLUSIONS: Macro (system-level) and micro (intrapersonal and interpersonal) level athlete attrition factors should be considered together. Athlete health was considered the most important athlete retention issue to address.

Sleep Quality in Elite Athletes: Normative Values, Reliability and Understanding Contributors to Poor Sleep.

BACKGROUND: The aims of this retrospective study were to (i) provide a description of sleep quality in elite athletes as measured by the Pittsburgh Sleep Quality Index (PSQI), (ii) provide normative PSQI data, (iii) identify differences across sex and sport, (iv) identify components that contribute to high PSQI scores and (v) assess PSQI test-retest reliability. METHODS: The PSQI was completed by 479 athletes (371 female and 108 male) across 20 Olympic team and individual sports. For ordinal and categorical variables, the Wilcoxon rank sum test and Chi Squared tests were used, respectively. A random forest regression was built to determine the importance of each PSQI component. Test-retest reliability was assessed using two-way mixed effects intraclass correlation coefficients. RESULTS: Fifty-two percent of athletes had a global PSQI score ≥ 5. Team sport athletes reported significantly longer sleep onset latency times but longer sleep durations compared with individual sport athletes. Sleep onset latency and sleep quality made the greatest contribution to the global PSQI scores. The PSQI demonstrated variability over periods of 2 months or more, with a minimal detectable change of 3 arbitrary units (AU). CONCLUSION: Long sleep onset latency and poor perceived sleep quality made the greatest contribution to the high PSQI scores observed in approximately half of elite athletes investigated. The PSQI should be administered at regular intervals due to variability within individuals over periods of 2 months or more. Individual questionnaire items or component scores of the PSQI may be useful for practitioners in guiding decision-making regarding sleep interventions in athletes.

Stressed and Not Sleeping: Poor Sleep and Psychological Stress in Elite Athletes Prior to the Rio 2016 Olympic Games.

PURPOSE: Psychological stress is reported to be an important contributor to reduced sleep quality and quantity observed in elite athletes. The purpose of this study was to explore the association between psychological stress and sleep and to identify if specific aspects of sleep are disturbed. METHODS: One hundred thirty-one elite athletes (mean [SD], male: n = 46, age 25.8 [4.1] y; female: n = 85, age 24.3 [3.9] y) from a range of sports completed a series of questionnaires in a 1-month period approximately 4 months before the 2016 Rio Olympic Games. Questionnaires included the Pittsburgh Sleep Quality Index; Recovery-Stress Questionnaire; Depression, Anxiety, and Stress Scale (DASS 21); and Perceived Stress Scale (PSS). RESULTS: Regression analysis identified the PSS and DASS stress as the main variables associated with sleep. A PSS score of 6.5 or higher was associated with poor sleep. In addition, a PSS score lower than 6.5 combined with a DASS stress score higher than 4.5 was also associated with poor sleep. Univariate analyses on subcomponents of the Pittsburgh Sleep Quality Index confirmed that PSS is associated with lower sleep quality (t99 = 2.40, P = .018), increased sleep disturbances (t99 = 3.37, P = .001), and increased daytime dysfunction (t99 = 2.93, P = .004). DASS stress was associated with increased sleep latency (t94 = 2.73, P = .008), increased sleep disturbances (t94 = 2.25, P = .027), and increased daytime dysfunction (t94 = 3.58, P = .001). CONCLUSIONS: A higher stress state and higher perceived stress were associated with poorer sleep, in particular increased sleep disturbances and increased daytime dysfunction. Data suggest that relatively low levels of psychological stress are associated with poor sleep in elite athletes.

A confirmatory factor analysis of the Life Orientation Test-Revised with competitive athletes.

Current reviews outside of sport indicate that the Life Orientation Test-Revised (LOT-R) items load on two separate factors (optimism and pessimism) and, therefore, should be treated as independent constructs. However, researchers in the sport sciences continue to use the single composite score reflecting a unidimensional definition of optimism. Hence, I sought to explicitly examine the factor structure of the LOT-R with athletes. Results of this study support the two-factor structure, which is consistent with accumulating evidence outside of sport and exercise science. This contribution is an important first step toward continued examination of this instrument and future work on the influence of optimism (versus pessimism) on sport and exercise behavior.

Development of a measure of rehabilitation adherence for athletic training.

CONTEXT: Rehabilitation adherence is accepted as a critical component for attaining optimal outcomes. Poor adherence is recognized as a problem in the athletic training setting. Measurement has been inconsistent, and no measure has been developed for athletic training settings. OBJECTIVE: To identify indicators of sport-injury rehabilitation adherence relevant to athletic training and develop a Rehabilitation Adherence Measure for Athletic Training (RAdMAT) based on these indicators. DESIGN: Mixed methods, 3 steps. SETTING: College athletic training facility. PARTICIPANTS: Practicing certified athletic trainers (ATCs; n=7) generated items, experts (n=12) reviewed them, and practicing ATCs (n=164) completed the RAdMAT for their most, average, and least adherent athlete. MAIN OUTCOME MEASURE: RAdMAT. RESULTS: The RAdMAT is 16 items with 3 subscales. Subscales and total have good internal consistency and clearly discriminate among adherence levels. CONCLUSIONS: The RAdMAT is based on scholarly literature and clinical practice, making it particularly appropriate for use in athletic training clinical practice or for research purposes.