New Recruit to SMG Technologies’ Internship Program
Patrick Campbell is currently a doctoral candidate at the Queensland University of Technology (QUT), and the latest successful recruit to join the SMG Technologies PhD Internship Program.
He completed his undergraduate degree in Exercise and Movement Science in 2014, and attained first class Honours in 2015, with a thesis examining “The specificity of pre-professional rugby union training in preparation for competitive match-play”. Pat combines his academic credentials with experience as a strength and conditioning coach previously working in GPS Rugby Club and Goalball Queensland, and is currently working with Wheelchair Basketball Queensland.
The PhD research will examine the viability and utility of implementing customised subjective ‘wellness’ questionnaires in monitoring the athlete training response. Subjective load monitoring is becoming increasingly popular, with self-report questionnaires (84%) being the most common measure of fatigue monitoring in a survey of high performance sport practitioners (Taylor et al., 2012). Of these self-report questionnaires, Taylor et al., (2012) reports 80% were a compilation of a shortened customised questionnaires which have been simplified to include just 5–12 items (e.g., perceived ratings of fatigue, muscle soreness, stress, sleep and mood).
Previous research of more extensive questionnaires such as the Profile of Mood States (POMS) or the Recovery-Stress Questionnaire (RESTQ-S) has indicated support for the effectiveness of varying psychological measures in clinical and applied literature in monitoring athlete fatigue, overtraining, and adaptation/maladaptation (Meeusen et al., 2013). However, established questionnaires (e.g., POMS, RESTQ-S) are considered too lengthy, non-specific for athletes and are impractical for frequent use in applied settings (Gallo et al., 2015).
Presently however, despite increasing research into customized questionnaires (Gallo et al., 2015; Gastin et al., 2013; Thorpe et al., 2015), there is a lack of scientific rigour and evidence surrounding the use of monitoring the training response utilising customised questionnaires (Saw et al., 2015b; Taylor et al., 2012). Additionally, despite their widespread use, there is no consensus of how the questionnaires can be used to plan, adjust and optimise athlete loading (Gallo et al., 2015; Saw et al., 2015b; Taylor et al., 2012). Further to this, when implementing athlete monitoring it should be clear as to how the data can be interpreted and used (Halson, 2014). Therefore, this body of research aims to provide insight and contribute solutions into these problems.
Pat’s responsibility with the internship program at SMG, will also include data analysis case studies based on wellness, internal load responses, external load responses and mixed-method approaches. The premise behind studying this concept is to develop an overarching case study showing the utility of a centralized database in identifying potential problems or training errors that may arise. The two case studies Pat is focusing on are Randwick Rugby Union Club and Pymble Ladies’ College.
It was through SMG’s extensive support and interest in partnering with research programs and institutions across a range of fields that led to the collaboration between Pat and SMG. A world-leading organisation is a natural fit for Pat to work with and a team willing to further their products and reputation globally through applying scientific accuracy to their solutions. Pat’s research will add value across all products, including SportsMed Elite, Baseline, FitBox, FitYou and Enterprise, where the measure of wellness is fundamental.
Benefits to SMG’s clientele are numerous and will include the verification and possible refinement of current subjective measures, and the items included within the measures, ensuring both the reliability and validity of our monitoring practices. This may assist in optimising the composition of our questionnaires, which may enhance practices and time-efficiency. Further, the research may also assist in providing efficient data analysis and interpretation, ensuring clientele have confidence and certainty when implementing changes in response to the monitoring practices.
References
Gallo, T. F., Cormack, S. J., Gabbett, T. J., & Lorenzen, C. H. (2015). Pre-training perceived wellness impacts training output in Australian football players. Journal of sports sciences, 1–7.
Gastin, P. B., Meyer, D., & Robinson, D. (2013). Perceptions of wellness to monitor adaptive responses to training and competition in elite Australian football. The Journal of Strength & Conditioning Research, 27(9), 2518–2526.
Halson, S. L. (2014). Monitoring training load to understand fatigue in athletes. Sports Medicine, 44(2), 139–147.
Meeusen, R., Duclos, M., Foster, C., Fry, A., Gleeson, M., Nieman, D., . . . Urhausen, A. (2013). Prevention, diagnosis, and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. Medicine and science in sports and exercise, 45(1), 186–205.
Saw, A. E., Main, L. C., & Gastin, P. B. (2015a). Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. British Journal of Sports Medicine, bjsports-2015–094758.
Saw, A. E., Main, L. C., & Gastin, P. B. (2015b). Role of a self-report measure in athlete preparation. The Journal of Strength & Conditioning Research, 29(3), 685–691.
Taylor, K., Chapman, D., Cronin, J., Newton, M., & Gill, N. (2012). Fatigue monitoring in high performance sport: a survey of current trends. Journal of Australian Strength and Conditioning, 20(1), 12–23.
Thorpe, R. T., Strudwick, A. J., Buchheit, M., Atkinson, G., Drust, B., & Gregson, W. (2015). Monitoring Fatigue During the In-Season Competitive Phase in Elite Soccer Players. International journal of sports physiology and performance.
