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Isaac Selva Raj, Stephen R. Bird, Ben A. Westfold, and Anthony J. Shield

Reliable measures of muscle strength and functional capacity in older adults are essential. The aim of this study was to determine whether coefficients of variation (CVs) of individuals obtained at the first session can infer repeatability of performance in a subsequent session. Forty-eight healthy older adults (mean age 68.6 ± 6.1 years; age range 60–80 years) completed two assessment sessions, and on each occasion undertook: dynamometry for isometric and isokinetic quadriceps strength, 6 meter fast walk (6MFWT), timed up and go (TUG), stair climb and descent, and vertical jump. Significant linear relationships were observed between CVs in session 1 and the percentage difference between sessions 1 and 2 for torque at 60, 120, 240 and 360°/s, 6MFWT, TUG, stair climb, and stair descent. The results of this study could be used to establish criteria for determining an acceptably reliable performance in strength and functional tests.

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Aisha Chen, Sandhya Selvaraj, Vennila Krishnan, and Shadnaz Asgari

correlation; and .9 to 1, very high correlation. To evaluate the statistical difference between intrasession reliabilities, t statistic was employed following the application of Fisher Z transformation. 5 To measure absolute reliability, we calculated the coefficient of variation (CV) for Δ COP of each

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Pui W. Kong and Norma G. Candelaria

This study aimed to 1) determine the suitability of using spanning set (SS) to measure knee angle variability in the entire gait cycle and 2) assess the sensitivity of SS magnitude to the order of polynomial fitted to the standard deviation (SD) curves of the mean ensemble curves. Eight runners performed 10 over-ground barefoot running trials, followed by 8 min of accommodation, and then another 10 trials. Knee angle variabilities before and after accommodation were assessed using the SS and two conventional methods: mean coefficient of variation and mean SD. The sensitivity of the SS magnitude was assessed by calculating SS using (n–2), (n–1), (n+1), and (n+2)th orders of polynomials, where nth is the best fit order. Variability decreased after accommodation using the conventional methods (p < .05) but not the SS. The SS magnitude was sensitive to the order of polynomial. It is concluded that the SS may not be appropriate for measuring knee kinematic variability in the entire gait cycle during over-ground barefoot running.

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Aki Salo and Paul N. Grimshaw

Eight trials each of 7 athletes (4 women and 3 men) were videotaped and digitized in order to investigate the variation sources and kinematic variability of video motion analysis in sprint hurdles. Mean coefficients of variation (CVs) of individuals ranged from 1.0 to 92.2% for women and from 1.2 to 209.7% for men. There were 15 and 14 variables, respectively, in which mean CVs revealed less than 5% variation. In redigitizing, CVs revealed <1.0% for 12 variables for the women's trials and 10 variables for the men's trials. These results, together with variance components (between-subjects, within-subject, and redigitizing), showed that one operator and the analysis system together produced repeatable values for most of the variables. The most repeatable variables by this combination were displacement variables. However, further data processing (e.g., differentiation) appeared to have some unwanted effects on repeatability. Regarding the athletes' skill, CVs showed that athletes can reproduce most parts of their performance within certain (reasonably low) limits.

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Paul G. Taylor, Raul Landeo, and Jennifer Coogan

The purpose of this study was to explore movement variability of throwing arm and ball release parameters during the water polo shot and to compare variability between successful (hit) and unsuccessful (miss) outcomes. Seven injury free, subelite, females completed 10 trials of the 5 m water polo penalty shot. Intraindividual coefficient of variation percentage (CV%) values were calculated for elbow and wrist angular displacement, wrist linear velocity and ball release parameters (height, angle and velocity). Coordination variability (elbow/wrist angular displacement) was calculated as the CV% of the mean cross-correlation coefficient. Elbow and wrist displacement variability decreased to 80% of throwing time then increased toward release. Wrist linear velocity variability reduced toward release. Individual CV% values ranged between 1.6% and 23.5% (all trials), 0.4% and 20.6% (hit), and 0.4% and 27.1% (miss). Ball release height and velocity variability were low (< 12%; all trials) whereas release angle variability was high (>27%; all trials). Cross-correlation results were inconclusive. Roles of the elbow and wrist in production of stable ball release height and velocity and control of the highly variable release angle in the water polo shot are discussed and suggested for further study. Optimal levels of variability warrant future investigation.

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Andrea J. Braakhuis, Kelly Meredith, Gregory R. Cox, William G. Hopkins, and Louise M. Burke

A routine activity for a sports dietitian is to estimate energy and nutrient intake from an athlete’s self-reported food intake. Decisions made by the dietitian when coding a food record are a source of variability in the data. The aim of the present study was to determine the variability in estimation of the daily energy and key nutrient intakes of elite athletes, when experienced coders analyzed the same food record using the same database and software package. Seven-day food records from a dietary survey of athletes in the 1996 Australian Olympic team were randomly selected to provide 13 sets of records, each set representing the self-reported food intake of an endurance, team, weight restricted, and sprint/power athlete. Each set was coded by 3–5 members of Sports Dietitians Australia, making a total of 52 athletes, 53 dietitians, and 1456 athlete-days of data. We estimated within- and between- athlete and dietitian variances for each dietary nutrient using mixed modeling, and we combined the variances to express variability as a coefficient of variation (typical variation as a percent of the mean). Variability in the mean of 7-day estimates of a nutrient was 2- to 3-fold less than that of a single day. The variability contributed by the coder was less than the true athlete variability for a 1-day record but was of similar magnitude for a 7-day record. The most variable nutrients (e.g., vitamin C, vitamin A, cholesterol) had ~3-fold more variability than least variable nutrients (e.g., energy, carbohydrate, magnesium). These athlete and coder variabilities need to be taken into account in dietary assessment of athletes for counseling and research.

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Sara R. Sherman, Clifton J. Holmes, Bjoern Hornikel, Hayley V. MacDonald, Michael V. Fedewa, and Michael R. Esco

included in the final analysis. Analyses were performed using SPSS (version 23.0; IBM Corp, Armonk, NY) and Microsoft Excel 2016 software. The RMSSD mean and coefficient of variation (CV) were calculated (CV = [SD/mean] × 100; %) for each of the 2 daily recordings on an Excel spreadsheet. The RMSSD

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Michael J. Duncan, Darren Richardson, Rhys Morris, Emma Eyre, and Neil D. Clarke

( Atkinson & Nevill, 1998 ). All analysis was performed using IBM SPSS Statistics (version 26; IBM, Armonk, NY). Results Intraclass correlation coefficients and coefficient of variation (CV) indicated good to excellent reliability and relatively small variability for the UGhent dribbling test running ( R

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Pedro L. Valenzuela, Guillermo Sánchez-Martínez, Elaia Torrontegi, Javier Vázquez-Carrión, Zigor Montalvo, and G. Gregory Haff

–v slope—from which the F–v IMB is calculated—seems to be poorer (coefficient of variation [CV] >10%). 11 In this context, the aim of the present study was to analyze the differences in F–v variables computed using the Samozino method from loaded jumps performed under unconstrained and constrained

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Gemma N. Parry, Lee C. Herrington, Ian G. Horsley, and Ian Gatt

-subject coefficient of variation (CV%) were calculated with 95% confidence intervals to determine relationships between test–retest. Boxing style correlational differences were tested by applying the Mann–Whitney U test for side-to-side differences (the second aim). All standard error of the mean and smallest