K`o0o@ @@@ @@@@  I o oP EN DB o`P   1 / > etF 8 FAtkinson2005 Batterham2005  Batterham2005 Downey2005 Hopkins2005 Hopkins2005 Hopkins2005 Hopkins2005 Hopkins2005 Hopkins2005  Hopkins2005  Hopkins2005 Hopkins2005Marshall2005 Mellow2005 Paton2005  Paton2005 Peltola2005  Taylor-Mason2005 c[Ji  B y  ^ R Z  D  +  AuthorsJournalsKeywords                                o @ &  l   Hopkins, W G 2005"Editorial: copyright control Sportscience9c 21-22}81http://sportsci.org/jour/05/inbrief.htm#editoriall'd]Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz Hopkins, W G 2005EPO abuse: a test case Sportscience9r 22-232+http://sportsci.org/jour/05/inbrief.htm#EPO\'d]Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nzx Hopkins, W G 2005$Sport scientists' top 10 sites Sportscience9 234-http://sportsci.org/jour/05/inbrief.htm#Top10 'd]Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz Hopkins, W G 20054-A spreadsheet for fully controlled crossoverso Sportscience9e244.http://sportsci.org/jour/05/inbrief.htm#spread'd]Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz Marshall, S W 2005B;Commentary on making meaningful inferences about magnitudese Sportscience9 43-44rHBclinical significance, confidence limits, statistical significance'Departments of Epidemiology and Orthopedics, University of North Carolina at Chapel Hill, Chapel Hill NC 27599-7435, USA. Email: Smarshall=AT=unc.edu Mellow, P 2005,%Copyright-free images and informationi Sportscience9e244.http://sportsci.org/jour/05/inbrief.htm#images'leSport and Recreation, AUT University, Auckland 1020, New Zealand. Email: peter.mellow=AT=clear.net.nzPaton, C D Hopkins, W G 2005tnCompetitive performance of elite olympic-distance triathletes: reliability and smallest worthwhile enhancement Sportscience9t 1-5rF?competition, error, race, reproducibility, testing, variabilitya |PURPOSE. The reliability of competitive performance of athletes in a given sport provides an estimate of the smallest worthwhile change in performance, which is crucial when testing athletes and when assessing factors that affect performance in that sport. We have therefore analyzed the reliability of ath-letes competing in international Olympic-distance triathlons. METHODS. We obtained official results from websites for triathlons performed before drafting in the cycling stage was permitted. We analyzed times for 103 athletes who en-tered two or more of nine such races over 19 months. Our measure of reliabil-ity was the typical race-to-race variation of an athlete's time, derived as a coef-ficient of variation by analysis of log-transformed times. RESULTS. (a) Typical race-to-race variations were: swim 1.6%, cycle 2.3%, and run 3.6%. When combined independently or dependently with the durations of each phase (20, 60 and 35 min), these variations yielded predicted variations in total time of 1.6% or 2.6% respectively, whereas the observed variation was 1.8%. (b) Transition times, which were available for three races, averaged 89 s for the swim-cycle and cycle-run transitions combined. Between-athlete variation in these times in each race was 5.2, 5.6 and 7.8 s, or ~0.1% of the mean total time of 115 min. (c) Analysis of reliability between all possible pairs of races showed no substantial effect of time between pairs (14-567 days). (d) Reliabil-ity between pairs of races held in normal environmental temperatures was better than when at least one of the pair was held in hot conditions (typical variations of 1.6% and 2.0% respectively). (e) The top 10% of triathletes, who averaged 3.4% faster than the average triathlete, had substantially smaller variations than the other triathletes for total time (1.1%) and for each of the three stages (swim, 1.2%; cycle, 1.3%; run, 2.5%). In triathlons where drafting in the cycle stage is permitted, variation in total time of the top triathletes is probably determined by the run alone and is therefore ~0.8%. CONCLUSIONS. (a) Factors that affect performance of individual elite triathletes act largely in-dependently in the three phases. (b) No worthwhile gains in performance are possible in the transitions. (c) Elite triathletes' performance is remarkably sta-ble over a 19-month period. (d) The outcome of a triathlon staged in a hot environment is somewhat less predictable than normal. (e) The smallest impor-tant change in race time for a top triathlete (half the variation in total time) is ~0.5%, which in current triathlons has to be achieved via changes of at least 1.2% in running speed.,&http://sportsci.org/jour/05/wghtri.htm'XRCentre for Sport and Exercise Science, The Waikato Institute of Technology, Hamilton. Email: Carl.Paton=AT=wintec.ac.nz. Sport and Recreation, Auckland University of Technology, Auckland 1020, New Zealand. Sport and Recreation, Faculty of Health, Auckland University of Technology, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz Paton, C D 2005jdCommentary on high-resistance training improves 40-km time-trial performance in competitive cyclists Sportscience9 32'~wCentre for Sport and Exercise Science, Waikato Institute of Technology, Hamilton, NZ. Email: Carl.Paton=AT=wintec.ac.nz Peltola, E 2005NGCommentary on competitive performance of elite track-and-field athletesv Sportscience9g 25-26eF?competition, error, race, reliability, reproducibility, testingw("http://sportsci.org/jour/05/ep.htmTaylor-Mason, A Mt 2005f_High-resistance interval training improves 40-km time-trial performance in competitive cyclistsf Sportscience9  27-31g"endurance, strength, VO2maxwInterval training at race-specific high cadences improves endurance cycling performance, but there is evidence that adding resistance to reduce the ca-dence might be more effective. AIM. To determine the effect of high-resistance interval training on endurance performance of male cyclists during the competi-tion phase of a season. METHODS. In a randomized controlled trial, 10 cy-clists in a control group maintained usual training and competing while 12 cy-clists in an experimental group replaced part of their usual training with high resistance interval training twice weekly for 8 wk. Mean power in a 40-km simu-lated time trial, maximal oxygen consumption (VO2max), incremental peak power, body composition, and leg strength were measured before and after training. RESULTS. Relative to control training, there were clear beneficial effects of resistance training on 40-km mean power (7.6%, 90% confidence limits 5.0%). There were also clear beneficial effects on incremental peak power (3.5%, 4.2%), VO2max in ml.min 1.kg 1 (6.6%, 7.0%), and sum of 8 skinfolds ( 12%, 11%). Effects on body mass ( 1.6%, 1.9%) and thigh mus-cle area (0.6%, 2.7%) were possibly trivial. Effects on VO2max in L.min 1 and three measures of isokinetic leg strength were unclear, owing to large errors of measurement. CONCLUSIONS. High-resistance interval training produces a major enhancement in endurance power of athletes in the competitive season. The benefits of this form of training should transfer to competitive performance.'leAmy Taylor-Mason. Kinetic Edge Cycling, Box 25941, Auckland, New Zealand. Email: amy=AT=kecycling.com @     h Atkinson, G 2005:3Commentary on a decision tree for controlled trials  Sportscience9\ 40-41,%analysis, bias, crossover, randomized0,&http://sportsci.org/jour/05/wghamb.htm'Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool L3 2ET, UK. Email: G.Atkinson=AT=livjm.ac.uk"Batterham, A M Hopkins, W Gh 20054-Making meaningful inferences about magnitudes\ Sportscience9{ 6-13HBclinical significance, confidence limits, statistical significance>8A study of a sample provides only an estimate of the true (population) value of an outcome statistic. A report of the study therefore usually includes an infer-ence about the true value. Traditionally, a researcher makes an inference by declaring the value of the statistic statistically significant or non-significant on the basis of a p value derived from a null hypothesis test. This approach is confusing and can be misleading, depending on the magnitude of the statistic, error of measurement, and sample size. We use a more intuitive and practical approach based directly on uncertainty in the true value of the statistic. First we express the uncertainty as confidence limits, which define the likely range of the true value. We then deal with the real-world relevance of this uncertainty by taking into account values of the statistic that are substantial in some posi-tive and negative sense, such as beneficial and harmful. If the likely range overlaps substantially positive and negative values, we infer that the outcome is unclear; otherwise, we infer that the true value has the magnitude of the observed value: substantially positive, trivial, or substantially negative. We refine this crude inference by stating qualitatively the likelihood that the true value will have the observed magnitude (e.g., very likely beneficial). Quantita-tive or qualitative probabilities that the true value has the other two magnitudes or more finely graded magnitudes (such as trivial, small, moderate, and large) can also be estimated to guide a decision about the utility of the outcome.,&http://sportsci.org/jour/05/ambwgh.htm'School of Health and Social Care, University of Teesside, Middlesbrough, UK; Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz &L^ "Batterham, A M Hopkins, W Gh 2005,%A decision tree for controlled trialsv Sportscience9d 33-392,%analysis, bias, crossover, randomizedbLFA controlled trial is used to estimate the effect of an intervention. We present here a decision tree for choosing the most appropriate of five kinds of con-trolled trial for numeric outcome measures. A time series or quasi-experimental design is used when there is no opportunity for a separate control group or control treatment. In this design, the weakest of the five, a series of measurements taken before the intervention serves as a baseline to estimate change resulting from the intervention. In trials with a separate control group, the usual design is a fully controlled parallel-groups trial, in which subjects are measured before and after their allocated control or experimental treatment. A posts-only design, in which subjects are measured only after their treatment, can be more efficient when poor reliability of the outcome measure over the time frame of the intervention makes large sample sizes unavoidable. Cross-over studies, in which all the subjects receive all the treatments, are an option when the effects of the treatments wash out in an acceptable time. In fully con-trolled crossovers, subjects are measured before and after each treatment, whereas measurements are taken only after each treatment in a simple cross-over. Fully controlled crossovers, arguably the best of the five designs, are more efficient if the outcome measure becomes too unreliable over the wash-out period, and they provide an assessment of the effect of the treatment on each subject. In simple crossovers, individual assessment is possible only by including a repeat of the control treatment.,&http://sportsci.org/jour/05/wghamb.htm'School of Health and Social Care, University of Teesside, Middlesbrough, UK; Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz Downey, B M 2005RKCommentary on competitive performance of elite Olympic-distance triathletesn Sportscience9f42F?competition, error, race, reproducibility, testing, variability*#http://sportsci.org/jour/05/bmd.htm'\VEndurance Coach Ltd, Cudgen, NSW 2487, Australia. Email: brendon=AT=endurancecoach.com Hopkins, W G 2005D>Impact factors of journals in sport and exercise science, 2004 Sportscience9\ 14-16a0)academic, citation, publication, research A journal's impact can be measured as a factor representing the recent annual rate of citation of its average article. Exercise and sport-science journals with impact factors rising by more than 0.3 since last year include Acta Physiologica Scandinavica (now 2.1), Journal of Rehabilitation Medicine (1.6), Leisure Sci-ences (1.3), Pediatric Exercise Science (1.4), Physical Therapy (2.0), Scandi-navian Journal of Medicine and Science in Sports (1.7), and Sports Medicine (2.8). A noteworthy newcomer is Exercise and Sport Sciences Reviews (2.3). Journals maintaining their impact include American Journal of Sports Medicine (2.4), Archives of Physical Medicine and Rehabilitation (1.7), British Journal of Sports Medicine (1.3), European Journal of Applied Physiology (1.3), High Altitude Medicine and Biology (1.5), Human Movement Science (1.1), Interna-tional Journal of Sport Nutrition and Exercise Metabolism (0.9), International Journal of Sport Psychology (0.4), International Journal of Sports Medicine (1.4), Journal of Applied Biomechanics (0.4), Journal of Applied Physiology (2.8), Journal of Applied Sport Psychology (0.9), Journal of Athletic Training (1.3), Journal of Biomechanics (1.9), Journal of Sport and Exercise Psychology (1.4), Journal of Strength and Conditioning Research (0.9), Medicine and Sci-ence in Sports and Exercise (2.6), and Research Quarterly for Exercise and Sport (0.9). Two core journals fell by more than 0.4: Clinical Journal of Sport Medicine (1.4) and Journal of Sports Sciences (0.9).,%http://sportsci.org/jour/05/wghif.htm'd]Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz Hopkins, W G 2005rkCompetitive performance of elite track-and-field athletes: variability and smallest worthwhile enhancements} Sportscience9e 17-20F?competition, error, race, reliability, reproducibility, testinghPURPOSE. To describe the reproducibility of competitive performance of elite track-and-field athletes and to derive the smallest worthwhile enhancements of performance in these events. METHODS. The data were official results of events in 17 competitions of an annual series of the International Amateur Athletic Federation extending over 101 d. Typical within-athlete variability from competition to competition was derived as a coefficient of variation by re-peated-measures analysis of log-transformed times (for running and hurdling events) or distances (for jumping and throwing events). The smallest worth-while performance enhancement was taken as half the within-athlete variability. RESULTS and DISCUSSION. Within-athlete variabilities were as follows: run-ning and hurdling events up to 1500 m, 1.0%; longer runs and steeplechase, 1.4%; triple and high jump, 1.7%; pole vault and long jump, 2.4%; discus, jave-lin, and shot put, 2.8% (90% confidence limits all ~/1.13). The differences between events presumably reflect differing contributions of energy systems, pacing strategies, wind resistance and skill. Females may have had a little more variability in performance (~1.1) than males in some events, possibly because of less depth of competition. There was some evidence that variability increased with increasing time between competitions for the short running events (from ~0.7% for ~1 wk to ~1.1% for ~100 d). The top-half athletes in each event were less variable than the bottom-half in running and hurdling up to 1500 m (0.8 vs 1.1%) and in longer runs and steeplechase (1.1 vs 1.6%), but differences were unclear in the other events. A likely explanation is less consis-tent motivation in endurance athletes who were not in the medal stakes. CONCLUSIONS. Coaches and sport scientists should focus on enhancements of as little as 0.3-0.5% for elite track athletes through 0.9-1.5% for elite field athletes..(http://sportsci.org/jour/05/wghtrack.htm'd]Sport and Recreation, AUT University, Auckland 1020, New Zealand. Email: will=AT=clear.net.nz