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Scott Douglas

Since its humble beginnings at the end of World War II, wheelchair basketball has incorporated a classification system for its players. The classification system ensures equal representation among team players and fosters positions and roles that are unique to the various levels of disability represented on a team (Goodwin et al., 2009). The increasingly competitive nature of this global game has necessitated an increasingly high level of coaching expertise. The purpose of this commentary is to take a practical look at the International Wheelchair Basketball Federation Player Classification System and the challenges it presents to a wheelchair basketball coach during the chaos of a game.

Open access

Roberta Gaspar, Natalia Padula, Tatiana B. Freitas, João P.J. de Oliveira and Camila Torriani-Pasin

necessary to analyze interventions based on available physical exercises in order to provide evidence-based recommendations. 14 – 16 The ability to describe, classify, and code information and measures on a wide range of health issues requires common structures and language. The International Classification

Open access

Salomé Aubert, Joel D. Barnes, Chalchisa Abdeta, Patrick Abi Nader, Ade F. Adeniyi, Nicolas Aguilar-Farias, Dolores S. Andrade Tenesaca, Jasmin Bhawra, Javier Brazo-Sayavera, Greet Cardon, Chen-Kang Chang, Christine Delisle Nyström, Yolanda Demetriou, Catherine E. Draper, Lowri Edwards, Arunas Emeljanovas, Aleš Gába, Karla I. Galaviz, Silvia A. González, Marianella Herrera-Cuenca, Wendy Y. Huang, Izzeldin A.E. Ibrahim, Jaak Jürimäe, Katariina Kämppi, Tarun R. Katapally, Piyawat Katewongsa, Peter T. Katzmarzyk, Asaduzzaman Khan, Agata Korcz, Yeon Soo Kim, Estelle Lambert, Eun-Young Lee, Marie Löf, Tom Loney, Juan López-Taylor, Yang Liu, Daga Makaza, Taru Manyanga, Bilyana Mileva, Shawnda A. Morrison, Jorge Mota, Vida K. Nyawornota, Reginald Ocansey, John J. Reilly, Blanca Roman-Viñas, Diego Augusto Santos Silva, Pairoj Saonuam, John Scriven, Jan Seghers, Natasha Schranz, Thomas Skovgaard, Melody Smith, Martyn Standage, Gregor Starc, Gareth Stratton, Narayan Subedi, Tim Takken, Tuija Tammelin, Chiaki Tanaka, David Thivel, Dawn Tladi, Richard Tyler, Riaz Uddin, Alun Williams, Stephen H.S. Wong, Ching-Lin Wu, Paweł Zembura and Mark S. Tremblay

on their HDI classification to cover costs associated with the Global Matrix 3.0 initiative. Three different tiers of registration fees ($500 USD for the low HDI countries, $750 USD for the medium HDI countries, $1000 USD for the high HDI countries, and $1500 USD for the very high HDI countries) were

Open access

Kathryn Mills, Aula Idris, Thu-An Pham, John Porte, Mark Wiggins and Manolya Kavakli

Objectives: To determine the validity and reliability of the peak frontal plane knee angle evaluated by a virtual reality (VR) netball game when landing from a drop vertical jump. Study Design: Laboratory. Methods: Forty participants performed 3 drop vertical jumps evaluated by 3-dimensional motion analysis and 3 drop vertical jumps evaluated by the VR game. Limits of agreement for the peak projected frontal plane knee angle and peak knee abduction were determined. Participants were given a consensus category of “above threshold” or “below threshold” based on a prespecified threshold angle of 9° during landing. Classification agreement was determined using kappa coefficient, and accuracy was determined using specificity and sensitivity. Ten participants returned 1 week later to determine intrarater reliability, standard error of the measure, and typical error. Results: The mean difference in detected frontal plane knee angle was 3.39° (95% confidence interval [CI], 1.03° to 5.74°). Limits of agreement were −10.27° (95% CI, −14.36° to −6.19°) to 17.05° (95% CI, 12.97° to 21.14°). Substantial agreement, specificity, and sensitivity were observed for the threshold classification (κ = .66; 95% CI, .42 to .88; specificity = 0.96; 95% CI, 0.78 to 1.0; and sensitivity = 0.75; 95% CI, 0.43 to 0.95). The game exhibited acceptable reliability over time (intraclass correlation coefficient, ICC3,1 = .844), and error was approximately 2°. Conclusion: The VR game reliably evaluated a projected frontal plane knee angle. Although the knee angle detected by the VR game is strongly related to peak knee abduction, the accuracy of detecting the exact angle was limited. A threshold approach may be a more accurate approach for gaming technology to evaluate frontal plane knee angles when landing from a jump.

Open access

Christiana M.T. van Loo, Anthony D. Okely, Marijka Batterham, Tina Hinkley, Ulf Ekelund, Soren Brage, John J. Reilly, Gregory E. Peoples, Rachel Jones, Xanne Janssen and Dylan P. Cliff

Background:

To validate the activPAL3 algorithm for predicting metabolic equivalents (TAMETs) and classifying MVPA in 5- to 12-year-old children.

Methods:

Fifty-seven children (9.2 ± 2.3y, 49.1% boys) completed 14 activities including sedentary behaviors (SB), light (LPA) and moderate-to-vigorous physical activities (MVPA). Indirect calorimetry (IC) was used as the criterion measure. Analyses included equivalence testing, Bland-Altman procedures and area under the receiver operating curve (ROC-AUC).

Results:

At the group level, TAMETs were significantly equivalent to IC for handheld e-game, writing/coloring, and standing class activity (P < .05). Overall, TAMETs were overestimated for SB (7.9 ± 6.7%) and LPA (1.9 ± 20.2%) and underestimated for MVPA (27.7 ± 26.6%); however, classification accuracy of MVPA was good (ROC-AUC = 0.86). Limits of agreement were wide for all activities, indicating large individual error (SB: −27.6% to 44.7%; LPA: −47.1% to 51.0%; MVPA: −88.8% to 33.9%).

Conclusions:

TAMETs were accurate for some SB and standing, but were overestimated for overall SB and LPA, and underestimated for MVPA. Accuracy for classifying MVPA was, however, acceptable.

Open access

Trey Burdette, Barry Joyner and Dan Czech

The Multidimensional Model for Sport Leadership (MML) (Chelladurai, 1980) posits that an athlete’s performance and satisfaction are functions of the congruency between the preferred leadership of student-athletes, the required behavior of the coach as dictated by the situation, and the actual behavior of the coach. As such, research in sport should examine how appropriate the model is to today’s athletic culture. Gender, one member characteristic, has been researched considerably, with conflicting results, while race and the amount of playing time have been largely ignored with preferential leadership. The purpose of this study was to classify student-athletes’ race, gender, and playing time by their preferred coaching behaviors. NCAA Division-I student-athletes (n = 140) in baseball, men’s and women’s basketball, men’s and women’s soccer, softball, and men’s and women’s volleyball were surveyed using the Revised Leadership Scale for Sport (RLSS). Using discriminant analysis, the authors attempted to predict the student-athlete gender, race, and playing time by their preferred coaching behavior scores. None of the models were significant, indicating a lack of variance between the classification groups. Future research on the importance of preferred coaching predictors is discussed.

Open access

David M. Werner and Joaquin A. Barrios

participants successfully completed 120 seconds without breaking, the unilateral effort was classified as a “no break” and the other side was tested identically. Participants were placed in the “break” classification if they had a form break at any time during the cumulative 240 seconds of bilateral testing

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Ítalo R. Lemes, Rômulo A. Fernandes, Bruna C. Turi-Lynch, Jamile S. Codogno, Luana C. de Morais, Kelly A.K. Koyama and Henrique L. Monteiro

addition, categories of medications were created based on the International Classification of Diseases. 23 Invoices obtained from BHUs were used to compute the dosage and market price of medication used by patients. All expenditures were computed in the Brazilian currency (Real) and converted to US

Open access

Viviane Ribeiro de Ávila, Teresa Bento, Wellington Gomes, José Leitão and Nelson Fortuna de Sousa

) rating scale. 25 Data Extraction The data extracted from the studies were: mean age, population, sample size, gender, study design, follow-up in months, instruments used, fracture classification, cause of fracture, surgical technique, and results of the SF-36 questionnaire. Evidence Synthesis Study

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Xiaolin Yang, Irinja Lounassalo, Anna Kankaanpää, Mirja Hirvensalo, Suvi P. Rovio, Asko Tolvanen, Stuart J.H. Biddle, Harri Helajärvi, Sanna H. Palomäki, Kasper Salin, Nina Hutri-Kähönen, Olli T. Raitakari and Tuija H. Tammelin

considered to consist of subgroups of individuals, but the group membership is unknown. Mixture modeling is a tool to statistically identify these homogeneous subgroups in a data-driven way. First, the latent profile analysis was carried out separately for both outcomes. The classification was based on the