Authors: Billymo Rist1, Anthea C Clarke1, Tony Glynn2, Alan J. Pearce1
1 School of Allied Health, La Trobe University, Bundoora, Melbourne, AUSTRALIA
2 Fit Mind Consulting, Spencer Street, West Melbourne, Melbourne, Australia
School of Allied Health, La Trobe University, Bundoora, Melbourne, AUSTRALIA
Ph: +61 400392964
Billymo Rist is a PhD Candidate at La Trobe University in Melbourne Australia. His Research interests include biomarkers, stress, psychology of performance and neuroscience of sport and injury
Anthea Clarke, PhD, is a lecturer in sport and exercise science in the Department of Dietetics, Human Nutrition, and Sport at La Trobe University, Australia. Her research interests include applied sport science and application to team sports, and female athlete physiology.
Tony Glynn MPsych is a Performance and Clinical Psychologist with 20 years’ experience. Tony is currently performance psychologist for the Melbourne Vixens Netball Team, Victorian Sailing Team, and Tennis Australia and clinical psychologist at the Royal Children’s Hospital Melbourne.
Alan J Pearce PhD is an adjunct associate professor at La Trobe University, Melbourne Australia and Director of NeuroSports Labs, Melbourne. Alan has an interest in the neuroscience of exercise and sport and injury, with over 200 publications across neurophysiology, exercise physiology and psychology of exercise.
Stressors Associated with Professional Australian Rules Football Athletes Across a Competitive Season
Objective: This study explored psychophysiological stress in professional Australian Rules football athletes across the course of one competitive season.
Methods: A sample of eight players listed with one professional Australian football club participated in this study. Each week during the competitive season (22 weeks), players self-reported their general fatigue and sleep using a paper-based scale, as well as providing a salivary cortisol measure. Testing occurred 48-hours after competition. Participants’ weekly performance rating scores based on a points system metric of players’ data obtained during competitive matches were also recorded by the club each week.
Results: A significant inverse relationship was observed between cortisol and performance ratings, sleep and fatigue, and sleep and performance ratings. There was a significant predictive relationship observed, with cortisol levels and performance rankings (R2 = .35, F (6,74) = 7.06, p<.001). There was no significant relationship between performance and fatigue or performance and sleep.
Conclusions: This study shows a significant relationship between performance outcomes and psychophysiological stress in professional Australian football players. Professional clubs should look towards objective assessment protocols to measure athlete psychological stress to enhance current practice of self-report stress measures.
Keywords: Professional Sport,Stress, Cortisol, Performance, Sports Psychology
Achievement within professional sport is predicated on consistent performance outcomes in high pressure environments. Stress is an inherent factor within the landscape of professional sport directly affecting the individuals who participate at the highest levels. Therefore, the ability for professional athletes to consistently perform at a high level over the course of a competitive season across both physical and mental domains is critical. For athletes, to maintain consistent high-performance week in and week out while dealing with stress is a skill that sets them apart from the majority of the general population. However, if athletes fail to manage their physical and mental wellbeing, or external pressures become overwhelming, it can exacerbate the stress response (10; 11; 26), and consequently impact physical performance outcomes.
Professional athletes are often required to cope with a number of specific competitive stressors that include the interplay of personal goal development and performance pressures, team culture, selection, and coaching. In relation to environmental stressors, these can be organized to form four categories: 1) leadership and personnel issues (e.g., the coach’s behaviors and interactions, expectations, media), 2) cultural and team issues (e.g., the team atmosphere, roles, goals), 3) logistical and environmental issues (e.g., facilities, selection, travel), and 4) performance and personal issues (e.g., injuries, finances, career transitions) . If stressors remain unaddressed, research suggests athletes may experience poor well-being, burnout, and impaired preparation and performance during competition (17). Specifically, fatigue and sleep are two factors that have been implicated as playing a role in athletes’ ability to consistently manage their overall wellbeing and performance outcomes (15; 24).
The increased awareness within professional sport of the overlaps between the athlete, their stress and wellbeing, the team environment, and how they intersect with performance has led to an increasingly scientific approach to monitoring the external and internal stressors experienced by athletes (3; 20). The premise of the adoption in these monitoring practices is to provide coaches and support staff (e.g. strength and conditioning) with a greater degree of certainty when prescribing and adjusting physical training loads, with the intention of optimizing adaptation and performance while reducing the risk of overtraining, injury, and illness (14). The predominant approach undertaken by professional sporting organizations in monitoring athletes is the implementation of self-report measures such as questionnaires and diaries which are relatively simple and form an inexpensive approach to monitoring athlete responses (14). Supporting literature for the implementation of self-report measures advocates the use of questionnaires with evidence of good validity and reliability (29). In reality, however, the more common approach is fort high-performance staff to develop their own short-from with athlete self-report measures in an attempt to improve compliance and increase relevance (25). Self-developed assessment tools are likely at the expense of validity and reliability, and inconsistencies in these methods highlight a number of concerns. Using non-or poorly-validated measures, for example, makes it difficult to interpret data appropriately while the daily collection method may also lead to questionnaire fatigue resulting in athletes responding in a random manner (26). Additionally, athletes have raised concerns around data security within these environments and the lack of informed consent practices, which may lead to manipulation of responses by athletes (25). In other words, athletes may not provide truthful responses for fear that their data may be used against them in team selection or determination of contract status. To address this manipulation of responses, there have been suggestions to incorporate objective measures to improve precision in quantifying athlete stress.
Utilizing psychophysiological biomarkers such as cortisol can provide an objective marker of the body’s ability to cope and respond to the stress associated with performance. The addition of this approach may overcome inherent issues with self-reported measures, improving preparation and performance across the course of a season (21).Two key systems in the neuroendocrine response to stress are the hypothalamic-pituitary-adrenal (HPA) axis and the autonomic nervous system (ANS), which drive responses to assist the body in coping with the applied stressor. The HPA axis releases cortisol, a stress hormone that facilitates action in response to an acute stress or threat (13). With improvements in reliability and sensitivity (9), salivary markers of HPA axis and ANS activity have steadily increased in popularity, offering a viable non-invasive alternative to blood collection methods. Historically, when measured in blood, stress markers such as cortisol have been analyzed in a laboratory by highly skilled technicians using processes that are expensive and can take several hours or days. However, small portable point-of-care (POC) devices are now available to make this process easier to conduct in the applied setting.
To date, research examining the link between cortisol and performance has produced divergent findings which is likely due to the inconsistency within the methodological protocols implemented in these studies (18). For example, cortisol can fluctuate dependent upon when it is collected, therefore, caution is required when interpreting salivary cortisol research that has only utilized single or minimal salivary cortisol sampling points and variation in collection times. While it has been recognized that excessive cortisol release may indicate physical stress, more recently attention has turned to the nexus between cortisol release and psychological stress, with mood disorders often linked with excess cortisol secretion (33). Within professional sport, workplace stressors that may impact on athletes include rigidity in training schedules, high risk of injury, and social expectations from their sporting community (for example public appearances and social media). However, understanding the strength of the relationship between cortisol, psychological stress, and performance in athletes is still to be determined.
The aim of this research was to examine the impact of stress on performance in professional Australian Rules football athletes across the course of one full competitive season. Using cortisol as a physiological marker of stress (22), in conjunction with validated self-report measures of fatigue and sleep as proxy measures of stress (16; 28) were observed in Australian football athletes from one team. The relationship between sleep, fatigue and stress in elite/professional athletes has been well researched (2; 31). Hours of sleep has been demonstrated to effect athletes stress levels both positively and negatively (2). In addition physical and mental fatigue has been shown to play a role in athlete stress outcomes (31). Specifically, the objective of this study was to explore the relationship between cortisol, self-report measures of fatigue and sleep, and performance rankings following every match of the competitive Australian Football League (AFL) season in 2019. A secondary objective was to investigate the predictive ability of these variables on athlete performance rankings during competition.
This research examined professional athletes participating in Australian Rules football. Athletes who participate in the elite competition (Australian Football League, AFL) are full-time professionals, and have to conform to a regimented schedule of training and competition, continued stress of weekly selection or de-selection, high risk of injuries, and regular social media abuse (23). Consequently, it is well described that professional players in the AFL are linked with a distinctive collection of psychophysiological stressors inherent in participating at this level. It has been reported that AFL players experience chronic stress that has manifested into mental illness (23).
Eight players from one professional AFL club volunteered to participate in the study. Utilizing athletes from one club allows for the study to employ a homogenous group of athletes who have similar training regimes and performance expectations (32). Inclusion criteria included players aged 18 years or older and being an elite level athlete, as defined by their level of competition (employed at the AFL club as a full-time contracted player). Participants were excluded if they were currently injured or had a previously diagnosed mental health disorder. Participants indicated they were not taking any medication and being part of a professional Australian Football team assisted with controlling of exercise workloads, dietary habits. The University Human Research Ethics Committee (HEC18041) approved all methods, the AFL team gave organizational consent, and participants gave signed informed consent prior to data collection. All data was collected weekly during the course of the regular competitive season (April-September 2019).
Players completed two paper-based single item self-report measures about their general fatigue and sleep, as well as providing a salivary cortisol measure. Participants completed each measure once per week at the same time and day (48-hours post-match). All measures took less than 10 minutes to complete. Player performance was quantified using performance rating data provided by the club (see sub-section on performance).
To assess fatigue the fatigue numeric rating scale was used. Instructions direct the participants to rate their overall fatigue over a 7-day recall period on a single-item. The single-item numeric rating scale was a graphic visualization of fatigue severity. The scoring line was 10 mm in length yielding an 11-point scale (0 = none, 10 = unbearably severe). The fatigue numeric rating scale has good psychometric properties with interclass correlation coefficient (ICC) of 0.82 (16) (12).
The sleep quality scale  was used to assess athlete’s perception of sleep quality. Participants rated their overall quality of sleep over a 7-day recall period on a visual analogue scale, whereby the participants marked a number score from 0 to 10, according to the following five categories: 0 = terrible, 1–3 = poor, 4–6 = fair, 7–9 = good, and 10 = excellent. The sleep quality scale has good psychometric properties with interclass correlation coefficient of 0.74 (28).
Athletes were instructed to complete salivary cortisol measures 48 hours post competition, with each sample collected within 15 minutes after waking up (22). For the collection of saliva, participants were required to place the oral fluid collector swab in their mouth on top of the tongue and close their mouth. Participants had to continue swabbing until the indicator on the swab had turned blue in color and 0.5 ml of saliva has been obtained. Saliva collection took approximately 30-seconds. Once collected, each sample was analyzed within 15 minutes of collection using a Lateral Flow Device (LFD) reader (iPro Cube) provided to the participant (8). Reliability of baseline resting cortisol in an AFL player cohort has reported intraclass correlation coefficient of 0.79 (22).
Player rating points for each athlete were provided by the club analytics team for interpretation within this study. Live data was captured and analyzed by club statisticians and data scientists allowing detailed player performance metrics to be assessed. Performance scores are determined by a rating system e.g., effective kick = 4 points, goal = 8 points, ineffective disposal = – 4 points etc. This point system allows players performance scores to be tallied week to week for each individual. Performance scores were collected immediately post-match for the entirety of the 22-week season.
Data analyses were completed using Jamovi software (www.jamovi.org, Version 1.0.8). A multiple linear regression model was developed to determine the strength directionality of the association between athlete’s performance scores, cortisol levels, fatigue, and sleep. Scores from the participant’s performance were correlated with their cortisol levels, fatigue, and sleep. Pearson’s correlations and linear regression models were used to determine the relationships between athlete’s performance, cortisol, and self-reported fatigue and sleep. Alpha was set at p<0.01. Given the small sample size of this study, the researchers chose to apply a more rigorous alpha criterion than previous studies with a similar sample size (5). This modification was to overcome the likelihood of type I error, and to allow for better contrast to studies with larger samples that utilized the same alpha level (30) (1). The assumptions of homogeneity of variance, linearity and residuals were met and approximately normally distributed.
All participants completed all data (cortisol, fatigue, sleep) collection points, while performance scores were captured for every game that participants were selected to play (see Tables 1 and 2). Pearson’s correlation coefficients showed that there was a significant negative relationship between cortisol and performance ratings (r= -.40, p= .001), sleep and fatigue (r= -.68, p= .001) and a positive correlation between sleep and performance ratings (r= -.35, p= .001), and sleep and wellness (r=.69, p=.001). There were no other significant correlations (see Table 3).
Table 1. AFL Athletes (n=8) individual mean scores on cortisol, performance, fatigue, and sleep.
|Participant||Cortisol (nmol/l, 0- 40)||Performance (Score 0 -115)||Fatigue (Score 0 – 10)||Sleep (Score 0 -10)|
Table 2. AFL Athletes (n=8) group data across the course of an entire season
|Cortisol (nmol/l, 0- 40)||Performance (Score 0 -115)||Fatigue (Score 0 – 10)||Sleep (Score 0 -10)|
Linear regression was carried out to investigate the relationship between participants performance ratings, cortisol levels and fatigue and sleep ratings. Cortisol levels accounted for performance ranking (R2= .35, F (6,74) = 7.06, p< 001). There was no significant relationship between performance and fatigue (p= .090) or performance and sleep (p= .022). For performance, there was a 1.63 nmol/l decrease in cortisol levels for every standard deviation increase in performance scores (Table 3). Therefore, 35% of the variation in performance can be explained by the model containing cortisol levels.
Table 3. Correlations across all variables and regressions between performance and cortisol, wellness, and sleep for AFL Athletes (n=8)
Figure 1: Mean scores to illustrate variability in measures of cortisol, and performance vs evenness of sleep and fatigue scores across the course of the AFL season as represented by each week (e.g., W1 is week 1). Cortisol levels and sleep score are represented on the left Y axis, and Performance and fatigue scores are represented on the right Y axis.
With reports that professional Australian Football players experience chronic stress manifesting into mental illness (23), the purpose of this study was to investigate the impact of psychological stress on performance in professional athletes across the course of one AFL season. In response to suggestions that self-reporting can be manipulated by athletes who may not report honestly (25), the inclusion of biomarkers such as cortisol, in addition to self-reported measures of fatigue and sleep, allowed for objectivity and refinement in the assessment of psychophysiological stress. The results of this research provide supporting evidence that there are significant differences in the sensitivity of psychophysiological markers of stress (cortisol) and self-report measures (fatigue and sleep) in interpreting a professional athlete’s stress response during the course of a competitive season. The findings of this study highlight the significant relationship of cortisol levels with individuals’ performance rankings, with lower levels of individual cortisol informing the likelihood of an athlete having a higher performance ranking during competition. In light of the current practice of using self-report measures as proxy measures of athlete stress, the findings of this study demonstrate the accuracy of assessing cortisol levels in predicting athletes’ performance, which was not identified through the self-report measures utilized (Figure 1). Despite an observed moderate negative correlation between sleep and fatigue which is supported by previous research (6), sleep and fatigue were not indicative of performance outcomes. While sleep is important for recovery, poor sleep is not always indicative of stress (4). In contrast, salivary cortisol is a specific marker of the autonomic nervous system (ANS) and, therefore, is directly linked to the physiological stress response which can manifest as deleterious recovery, overtraining, and prolonged psychological stress. The pattern of these results where self-report measures have failed to accurately capture athlete stress is consistent with previous research (25).
The flat line (illustrating minimal variability from week to week) nature of responses to the self-reported fatigue and sleep scores in this study are indicative of athletes’ behavior and responses to these questionnaires in the Australian football environment. Consistent with previous research by Saw et al, (25), athletes often report very little deviation from responses in self-report measures week to week (Figure 1). While the self-report measures of fatigue and sleep utilized in this study were published questionnaires with acceptable validity and reliability, professional Australian football clubs often use internally created, non-validated self-report measures due to their ease of use and low/no cost by not having to pay for licensed surveys. Likely, while the observed self-report measures in this study may not be sufficient to accurately monitor athletes for stress throughout a season, alternative internally created self-report measures may be even more removed from being able to monitor as intended. Therefore, this lack of rigor in data collection processes can lead to athletes who are experiencing abnormal levels of stress, failing to be appropriately acknowledged by support staff.
In aggregate, the findings indicate that there is an important role for the implementation of objective markers such as cortisol within the elite sporting landscape as it relates to assessing individual athletes’ holistic stress, and the relationship of this variable to performance outcomes. These results strongly imply that stress management is critical for athletes to have sustained performance in competition. Specifically, the finding demonstrates that cortisol is an appropriate means of monitoring stress for both individual athletes and high-performance staff. These insights allow for the implementation of appropriate interventions to overcome the issues of manipulation associated with self-report measures, and therefore, providing a higher likelihood of athletes avoiding the consequences of increased stress levels on performance outcomes.
Although the present results support the link between cortisol levels and performance in athletes, it is appropriate to consider limitations of the research. Due to the requirement of a high number of data points to be collected by participants longitudinally across the course of the season, the researchers were only able to obtain compliance from a small sample who were likely to be selected for every match. Therefore, a decision was made to implement a stricter alpha level to overcome the limitations of other studies with similar sample sizes to avoid a potential type I error (30). Additionally, employing an alpha level of p < 0.01 allows for a more suitable comparison to similar studies with larger sample sizes that use the same alpha level criteria (1; 7) Additionally, while the self-report markers utilized in this study which are associated with stress have been demonstrated as reliable and valid, with good psychometric properties, little change over the course of the season was found. The rationale for using these surveys was to engage in compliance given that players have demanding daily training schedules. However, longer form measures of fatigue and sleep are available (19; 27) which have stronger reliability and validity and may provide more accurate findings regarding self-reported stress. Furthermore, athletes that participated in this study were unlikely to be de-selected allowing for maximum collection of all data points. Therefore, results may not be applicable to entry level athletes who may not be selected to play on a regular basis. Additionally, cortisol is a highly sensitive marker of stress and a number of different variables can effect an individual’s cortisol levels (e.g., exercise, food, medication) (8). While the cortisol collection protocol in this study was consistent and developed to overcome any confounding variables, these factors cannot be completely ruled out in influencing results. To overcome these limitations, future studies should incorporate additional examination of other endocrine factors (e.g., salivary immunoglobin A) that may shed more light on variables which may be important to assessing athletes psychophysiological stress and its association to performance
Application to Sport
The results of this study suggest several applied implications for practitioners working with athletes in the applied sporting environment. Professional sporting teams should employ a multimodal approach to the assessment of stress by incorporating psychophysiological measures such as cortisol to overcome accuracy issues inherent in self-report measures. Employing a multimodal approach to the assessment of athlete stress provides a more accurate profile of the athlete experience within the high-performance environment, and allows for a more timely and individualized approach to the utilization of interventions to manage stress and ultimately optimize performance outcomes during competition.
The present study has enhanced the understanding of the relationship between optimal assessment protocols of stressors within the professional Australian football environment and the link between performance outcomes and psychophysiological stress. Furthermore, it provides a framework for high-performance sports to continually adapt their assessment of stress to overcome concerns with current preferred practices of assessment. Therefore, mitigating the negative consequences of stress and allowing for enhanced performance over longer periods of time for athletes. It is with great hope that the current research will stimulate further investigation into this important area of athlete stress and performance.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. All authors have substantially contributed in writing and critically reviewing the manuscript. No funding was provided for the completion of this study. AJP currently receives partial research salary funding from Sports Health Check charity. AJP has previously received partial research funding from the Australian Football League, Impact Technologies Inc., and Samsung Corporation. Other authors declare that they have no competing interests.
- Biau, D. J., Kernéis, S., & Porcher, R. (2008). Statistics in brief: the importance of sample size in the planning and interpretation of medical research. Clinical orthopaedics and related research, 466(9),2282-2288.
- Biggins, M., Cahalan, R., Comyns, T., Purtill, H., & O’Sullivan, K. (2018). Poor sleep is related to lower general health, increased stress and increased confusion in elite Gaelic athletes. The Physician and sportsmedicine, 46(1),14-20.
- Coutts, A., & Cormack, S. (2014). Monitoring the training response. High-performance training for sports.71-84
- Cummiskey, J., Natsis, K., Papathanasiou, E., & Pigozzi, F. (2013). Sleep and athletic performance. European Journal of Sports Medicine, 1(1).
- Dettl-Rivera, M. G., Gill, D. L., Reifsteck, E., Education, P., SCAT, A., Hill, R., & Dettl, M. G. Self-efficacy in college athletics: An exploratory study.
- Dickinson, R. K., & Hanrahan, S. J. (2009). An investigation of subjective sleep and fatigue measures for use with elite athletes. Journal of Clinical Sport Psychology, 3(3),244-266.
- Dmitrašinović, G., Pešić, V., Stanić, D., Plećaš-Solarović, B., Dajak, M., & Ignjatović, S. (2016). ACTH, cortisol and IL-6 levels in athletes following magnesium supplementation. Journal of medical biochemistry, 35(4),375.
- Ducker, K. J., Lines, R. L., Chapman, M. T., Peeling, P., McKay, A. K., & Gucciardi, D. F. (2020). Validity and reliability evidence of a point of care assessment of salivary cortisol and α-amylase: a pre-registered study. PeerJ, 8,e8366.
- Fisher, R., McLellan, C., & Sinclair, W. (2017). The validity and reliability for a salivary cortisol point of care test. Journal of Athletic Enhancement, 2015.
- Forsdyke, D., Gledhill, A., & Ardern, C. (2017). Psychological readiness to return to sport: three key elements to help the practitioner decide whether the athlete is REALLY ready? In: BMJ Publishing Group Ltd and British Association of Sport and Exercise Medicine.
- Forsdyke, D., Gledhill, A., & Ardern, C. (2017). Psychological readiness to return to sport: three key elements to help the practitioner decide whether the athlete is REALLY ready? In (Vol. 51, pp. 555-556): BMJ Publishing Group Ltd and British Association of Sport and Exercise Medicine.
- Gladman, D., Nash, P., Goto, H., Birt, J. A., Lin, C.-Y., Orbai, A.-M., & Kvien, T. K. (2020). Fatigue numeric rating scale validity, discrimination and responder definition in patients with psoriatic arthritis. RMD open, 6(1),e000928.
- Guilliams, T. G., & Edwards, L. (2010). Chronic stress and the HPA axis. The standard, 9(2),1-12.
- Halson, S. L. (2014). Monitoring training load to understand fatigue in athletes. Sports medicine, 44(2),139-147.
- Halson, S. L. (2014). Sleep in elite athletes and nutritional interventions to enhance sleep. Sports Medicine, 44(1),13-23.
- Kim, H.-J., & Abraham, I. (2017). Measurement of fatigue: Comparison of the reliability and validity of single-item and short measures to a comprehensive measure. International journal of nursing studies, 65,35-43.
- Larner, R. J., Wagstaff, C., Thelwell, R., & Corbett, J. (2017). A multistudy examination of organizational stressors, emotional labor, burnout, and turnover in sport organizations. Scandinavian journal of medicine & science in sports, 27(12),2103-2115.
- Lautenbach, F., Laborde, S., Klämpfl, M., & Achtzehn, S. (2015). A link between cortisol and performance: An exploratory case study of a tennis match. International Journal of Psychophysiology, 98(2),167-173.
- Mota, D. D., & Pimenta, C. A. (2006). Self-report instruments for fatigue assessment: a systematic review. Research and Theory for Nursing Practice, 20(1),49-78.
- Rano, J., Fridén, C., & Eek, F. (2018). Effects of acute psychological stress on athletic performance in elite male swimmers. The Journal of sports medicine and physical fitness, 59(6),1068-1076.
- Rist, B., Glynn, T., Clarke, A., & Pearce, A. (2020). The Evolution of Psychological Response to Athlete Injury Models for Professional Sport. The Journal of Science and Medicine, 2(4),1-14.
- Rist, B., & Pearce, A. J. (2019). Tiered Levels of Resting Cortisol in an Athletic Population. A Potential Role for Interpretation in Biopsychosocial Assessment? Journal of Functional Morphology and Kinesiology, 4(1),8.
- Ruddock-Hudson, M., O’Halloran, P., & Murphy, G. (2014). The psychological impact of long-term injury on Australian football league players. Journal of Applied Sport Psychology, 26(4),377-394.
- Sargent, C., Lastella, M., Halson, S. L., & Roach, G. D. (2014). The impact of training schedules on the sleep and fatigue of elite athletes. Chronobiology International, 31(10),1160-1168.
- Saw, A. E., Main, L. C., & Gastin, P. B. (2015). Monitoring athletes through self-report: factors influencing implementation. Journal of sports science & medicine, 14(1),137.
- Shrier, I., Safai, P., & Charland, L. (2014). Return to play following injury: whose decision should it be? British journal of sports medicine, 48(5),394-401.
- Smith, S., & Trinder, J. (2001). Detecting insomnia: comparison of four self‐report measures of sleep in a young adult population. Journal of sleep research, 10(3),229-235.
- Snyder, E., Cai, B., DeMuro, C., Morrison, M. F., & Ball, W. (2018). A new single-item sleep quality scale: results of psychometric evaluation in patients with chronic primary insomnia and depression. Journal of Clinical Sleep Medicine, 14(11),1849-1857.
- Taylor, K., Chapman, D., Cronin, J., Newton, M. J., & Gill, N. (2012). Fatigue monitoring in high performance sport: a survey of current trends. J Aust Strength Cond, 20(1),12-23.
- Thiese, M. S., Ronna, B., & Ott, U. (2016). P value interpretations and considerations. Journal of thoracic disease, 8(9),E928.
- Thorpe, R. T., Atkinson, G., Drust, B., & Gregson, W. (2017). Monitoring fatigue status in elite team-sport athletes: implications for practice. International journal of sports physiology and performance, 12(s2),S2-27-S22-34.
- Yin, R. K. (2013). Validity and generalization in future case study evaluations. Evaluation, 19(3),321-332.
- Young, E. A., Abelson, J., & Lightman, S. L. (2004). Cortisol pulsatility and its role in stress regulation and health. Frontiers in neuroendocrinology, 25(2),69-76.