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Towards a Grand Unified Theory of sports performance

Glazier PS

Hum Mov Sci, 2017 Dec;56(Pt A):139-156.
PMID: 26725372 DOI: 10.1016/j.humov.2015.08.001

Sports performance is generally considered to be governed by a range of interacting physiological, biomechanical, and psychological variables, amongst others. Despite sports performance being multi-factorial, however, the majority of performance-oriented sports science research has predominantly been monodisciplinary in nature, presumably due, at least in part, to the lack of a unifying theoretical framework required to integrate the various subdisciplines of sports science. In this target article, I propose a Grand Unified Theory (GUT) of sports performance-and, by elaboration, sports science-based around the constraints framework introduced originally by Newell (1986). A central tenet of this GUT is that, at both the intra- and inter-individual levels of analysis, patterns of coordination and control, which directly determine the performance outcome, emerge from the confluence of interacting organismic, environmental, and task constraints via the formation and self-organisation of coordinative structures. It is suggested that this GUT could be used to: foster interdisciplinary research collaborations; break down the silos that have developed in sports science and restore greater disciplinary balance to the field; promote a more holistic understanding of sports performance across all levels of analysis; increase explanatory power of applied research work; provide stronger rationale for data collection and variable selection; and direct the development of integrated performance monitoring technologies. This GUT could also provide a scientifically rigorous basis for integrating the subdisciplines of sports science in applied sports science support programmes adopted by high-performance agencies and national governing bodies for various individual and team sports.

Matched MeSH terms: Athletic Performance/physiology*
Enhanced performance with elastic resistance during the eccentric phase of a countermovement jump

Aboodarda SJ, Yusof A, Abu Osman NA, Thompson MW, Mokhtar AH

Int J Sports Physiol Perform, 2013 Mar;8(2):181-7.
PMID: 23428490

To identify the effect of additional elastic force on the kinetic and kinematic characteristics, as well as the magnitude of leg stiffness, during the performance of accentuated countermovement jumps (CMJs).

Matched MeSH terms: Athletic Performance/physiology*
Fulltext Effects of music on arousal during imagery in elite shooters: A pilot study

Kuan G, Morris T, Terry P

PLoS One, 2017;12(4):e0175022.
PMID: 28414741 DOI: 10.1371/journal.pone.0175022

Beneficial effects of music on several performance-related aspects of sport have been reported, but the processes involved are not well understood. The purpose of the present study was to investigate effects of relaxing and arousing classical music on physiological indicators and subjective perceptions of arousal during imagery of a sport task. First, appropriate music excerpts were selected. Then, 12 skilled shooters performed shooting imagery while listening to the three preselected music excerpts in randomized order. Participants' galvanic skin response, peripheral temperature, and electromyography were monitored during music played concurrently with imagery. Subjective music ratings and physiological measures showed, as hypothesized, that unfamiliar relaxing music was the most relaxing and unfamiliar arousing music was the most arousing. Researchers should examine the impact of unfamiliar relaxing and arousing music played during imagery on subsequent performance in diverse sports. Practitioners can apply unfamiliar relaxing and arousing music with imagery to manipulate arousal level.

Matched MeSH terms: Athletic Performance/physiology*
Measuring Vertical Jump Height With Artificial Intelligence Through a Cell Phone: A Validity and Reliability Report

Tan ECH, Weng Onn S, Montalvo S

J Strength Cond Res, 2024 Sep 01;38(9):e529-e533.
PMID: 38953840 DOI: 10.1519/JSC.0000000000004854

Erik, HT, Onn, SW, and Montalvo, S. Vertical jump height with artificial intelligence through a cell phone: a validity and reliability report. J Strength Cond Res 38(9): e529-e533, 2024-This study estimated the reliability and validity of an artificial intelligence (AI)-driven model in the My Jump 2 (My Jump Lab ) for estimating vertical jump height compared with the Force Platform (FP). The cross-sectional study involved 88 athletes (33 female and 55 male athletes), performing a total of 264 countermovement jumps with hands on hips. "Jump heights were simultaneously measured using the FP and the My Jump 2 app." The FP estimated jump heights using the impulse-momentum method, whereas My Jump 2 used the flight-time method, with the latter using an AI feature for automated detection of jump take-off and landing. Results indicated high reliability for the AI model (intraclass correlation coefficient [ICC 1,3 ] = 0.980, coefficient of variation [CV] = 4.12) and FP (ICC 1,3 = 0.990, CV = 2.92). Validity assessment showed strong agreement between the AI model and FP (ICC 2,k = 0.973). This was also supported by the Bland-Altman analysis, and the ordinary least products regression revealed no significant systematic or proportional bias. The AI-driven model in My Jump 2 is highly reliable and valid for estimating jump height. Strength and conditioning professionals may use the AI-based mobile app for accurate jump height measurements, offering a practical and efficient alternative to traditional methods.

Matched MeSH terms: Athletic Performance/physiology
Fulltext A machine learning approach of predicting high potential archers by means of physical fitness indicators

Muazu Musa R, P P Abdul Majeed A, Taha Z, Chang SW, Ab Nasir AF, Abdullah MR

PLoS One, 2019;14(1):e0209638.
PMID: 30605456 DOI: 10.1371/journal.pone.0209638

k-nearest neighbour (k-NN) has been shown to be an effective learning algorithm for classification and prediction. However, the application of k-NN for prediction and classification in specific sport is still in its infancy. The present study classified and predicted high and low potential archers from a set of physical fitness variables trained on a variation of k-NN algorithms and logistic regression. 50 youth archers with the mean age and standard deviation of (17.0 ± 0.56) years drawn from various archery programmes completed a one end archery shooting score test. Standard fitness measurements of the handgrip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were conducted. Multiple linear regression was utilised to ascertain the significant variables that affect the shooting score. It was demonstrated from the analysis that core muscle strength and vertical jump were statistically significant. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the significant variables identified. k-NN model variations, i.e., fine, medium, coarse, cosine, cubic and weighted functions as well as logistic regression, were trained based on the significant performance variables. The HACA clustered the archers into high potential archers (HPA) and low potential archers (LPA). The weighted k-NN outperformed all the tested models at itdemonstrated reasonably good classification on the evaluated indicators with an accuracy of 82.5 ± 4.75% for the prediction of the HPA and the LPA. Moreover, the performance of the classifiers was further investigated against fresh data, which also indicates the efficacy of the weighted k-NN model. These findings could be valuable to coaches and sports managers to recognise high potential archers from a combination of the selected few physical fitness performance indicators identified which would subsequently save cost, time and energy for a talent identification programme.

Matched MeSH terms: Athletic Performance/physiology*
UEFA expert group statement on nutrition in elite football. Current evidence to inform practical recommendations and guide future research

Collins J, Maughan RJ, Gleeson M, Bilsborough J, Jeukendrup A, Morton JP, et al.

Br J Sports Med, 2021 Apr;55(8):416.
PMID: 33097528 DOI: 10.1136/bjsports-2019-101961

Football is a global game which is constantly evolving, showing substantial increases in physical and technical demands. Nutrition plays a valuable integrated role in optimising performance of elite players during training and match-play, and maintaining their overall health throughout the season. An evidence-based approach to nutrition emphasising, a 'food first' philosophy (ie, food over supplements), is fundamental to ensure effective player support. This requires relevant scientific evidence to be applied according to the constraints of what is practical and feasible in the football setting. The science underpinning sports nutrition is evolving fast, and practitioners must be alert to new developments. In response to these developments, the Union of European Football Associations (UEFA) has gathered experts in applied sports nutrition research as well as practitioners working with elite football clubs and national associations/federations to issue an expert statement on a range of topics relevant to elite football nutrition: (1) match day nutrition, (2) training day nutrition, (3) body composition, (4) stressful environments and travel, (5) cultural diversity and dietary considerations, (6) dietary supplements, (7) rehabilitation, (8) referees and (9) junior high-level players. The expert group provide a narrative synthesis of the scientific background relating to these topics based on their knowledge and experience of the scientific research literature, as well as practical experience of applying knowledge within an elite sports setting. Our intention is to provide readers with content to help drive their own practical recommendations. In addition, to provide guidance to applied researchers where to focus future efforts.

Matched MeSH terms: Athletic Performance/physiology*
The identification of high potential archers based on fitness and motor ability variables: A Support Vector Machine approach

Taha Z, Musa RM, P P Abdul Majeed A, Alim MM, Abdullah MR

Hum Mov Sci, 2018 Feb;57:184-193.
PMID: 29248809 DOI: 10.1016/j.humov.2017.12.008

Support Vector Machine (SVM) has been shown to be an effective learning algorithm for classification and prediction. However, the application of SVM for prediction and classification in specific sport has rarely been used to quantify/discriminate low and high-performance athletes. The present study classified and predicted high and low-potential archers from a set of fitness and motor ability variables trained on different SVMs kernel algorithms. 50 youth archers with the mean age and standard deviation of 17.0 ± 0.6 years drawn from various archery programmes completed a six arrows shooting score test. Standard fitness and ability measurements namely hand grip, vertical jump, standing broad jump, static balance, upper muscle strength and the core muscle strength were also recorded. Hierarchical agglomerative cluster analysis (HACA) was used to cluster the archers based on the performance variables tested. SVM models with linear, quadratic, cubic, fine RBF, medium RBF, as well as the coarse RBF kernel functions, were trained based on the measured performance variables. The HACA clustered the archers into high-potential archers (HPA) and low-potential archers (LPA), respectively. The linear, quadratic, cubic, as well as the medium RBF kernel functions models, demonstrated reasonably excellent classification accuracy of 97.5% and 2.5% error rate for the prediction of the HPA and the LPA. The findings of this investigation can be valuable to coaches and sports managers to recognise high potential athletes from a combination of the selected few measured fitness and motor ability performance variables examined which would consequently save cost, time and effort during talent identification programme.

Matched MeSH terms: Athletic Performance/physiology*
Unexpected-Disturbance Program for Rehabilitation of High-Performance Athletes

Teichmann J, Suwarganda EK, Lendewig C, Wilson BD, Yeo WK, Aziz RA, et al.

J Sport Rehabil, 2016 May;25(2):126-32.
PMID: 25658597 DOI: 10-1123/jsr.2014-0280

CONTEXT: The Unexpected-Disturbance Program (UDP) promotes exercises in response to so-called involuntary short- to midlatency disturbances.
OBJECTIVE: This study investigated the effectiveness of the UDP in the last 6 wk of rehabilitation.
DESIGN: Pre-post study with 2-tailed paired t tests for limited a priori comparisons to examine differences.
SETTING: National Sports Institute of Malaysia.
PARTICIPANTS: 24 Malaysian national athletes.
INTERVENTIONS: 7 sessions/wk of 90 min with 3 sessions allocated for 5 or 6 UDP exercises.
MAIN OUTCOMES: Significant improvements for men and women were noted. Tests included 20-m sprint, 1-repetition-maximum single-leg press, standing long jump, single-leg sway, and a psychological questionnaire.
RESULTS: For men and women, respectively, average strength improvements of 22% (d = 0.96) and 29% (d = 1.05), sprint time of 3% (d = 1.06) and 4% (d = 0.58), and distance jumped of 4% (d = 0.59) and 6% (d = 0.47) were noted. In addition, athletes reported improved perceived confidence in their abilities. All athletes improved in each functional test except for long jump in 2 of the athletes. Mediolateral sway decreased in 18 of the 22 athletes for the injured limb.
CONCLUSION: The prevention training with UDP resulted in improved conditioning and seems to decrease mediolateral sway.

Matched MeSH terms: Athletic Performance/physiology