• The mechanical loading and muscle activation of four common exercises used in osteoporosis prevention for early postmenopausal women

      Montgomery, Gallin; Abt, Grant; Dobson, Catherine; Smith, Tina; Evans, Will; Ditroilo, Massimiliano (Elsevier, 2018-12-11)
      High impact exercise can reduce postmenopausal bone loss, however stimulus frequency (loading cycles per second) can affect osteogenesis. We aimed to examine the effect of stimulus frequency on the mechanical loading of four common osteoporosis prevention exercises, measuring body acceleration and muscle activation with accelerometry and electromyography (EMG), respectively. Fourteen early postmenopausal women completed randomised countermovement jumps (CMJ), box-drops (BD), heel-drops (HD) and stamp (STP) exercises for continuous and intermittent stimulus frequencies. Sacrum accelerometry and surface electromyography (EMG) of four muscles were recorded. CMJ (mean ± SD: 10.7 ± 4.8 g & 10.0 ± 5.0 g), BD (9.6 ± 4.1 g & 9.5 ± 4.0 g) and HD (7.3 ± 3.8 g & 8.6 ± 4.4 g) conditions generated greater peak acceleration than STP (3.5 ± 1.4 g & 3.6 ± 1.7 g) across continuous and intermittent trials. CMJ and BD generated greater acceleration gradients than STP across continuous and intermittent trials. CMJ generated greater rectus femoris EMG than all other exercises, CMJ and BD generated greater semitendinosus and tibialis anterior EMG than HD across continuous and intermittent trials. CMJ and BD provide greater peak acceleration than STP and remain similar during different stimulus frequencies. CMJ, BD and HD may exceed STP in maintaining postmenopausal bone health.
    • Misuse of "Power" and Other Mechanical Terms in Sport and Exercise Science Research.

      Winter, Edward M; Abt, Grant; Brookes, F B Carl; Challis, John H; Fowler, Neil E; Knudson, Duane V; Knuttgen, Howard G; Kraemer, William J; Lane, Andrew M; van Mechelen, Willem; et al. (Lippincott Williams & Wilkins, 2016-10)
      Despite the Système International d'Unitès (SI) that was published in 1960, there continues to be widespread misuse of the terms and nomenclature of mechanics in descriptions of exercise performance. Misuse applies principally to failure to distinguish between mass and weight, velocity and speed, and especially the terms "work" and "power." These terms are incorrectly applied across the spectrum from high-intensity short-duration to long-duration endurance exercise. This review identifies these misapplications and proposes solutions. Solutions include adoption of the term "intensity" in descriptions and categorizations of challenge imposed on an individual as they perform exercise, followed by correct use of SI terms and units appropriate to the specific kind of exercise performed. Such adoption must occur by authors and reviewers of sport and exercise research reports to satisfy the principles and practices of science and for the field to advance.
    • Oxygen uptake during modern dance class, rehearsal, and performance.

      Wyon, Matthew A.; Abt, Grant; Redding, Emma; Head, Andrew; Craig, N.; Sharp, C. (Allen Press, 2004)
      The aim of the present study was to examine whether the workload, expressed in oxygen uptake and heart rate, during dance class and rehearsal prepared the dancer for performance. Previous research on the demands of class and performance has been affected by equipment limitations and could only provide limited insight into the physiological demands placed on the dancer. The present study noted that dance performance had significantly greater mean oxygen uptake and heart rate than noted in both class and rehearsal (p < 0.05). Further analysis noted that, during class and rehearsal, heart rates were rarely within the aerobic training zone (60-90%HRmax, where HRmax is the maximum heart rate). Dance performance placed a greater demand on the aerobic and anaerobic glycolytic energy systems than seen during class and rehearsal, which placed a greater emphasis on the adenosine triphosphate-creatine phosphate system. Practical implications suggest the need to supplement training within dance companies to overcome this deficit in training demand.
    • Power, precision, and sample size estimation in sport and exercise science research

      Abt, Grant; Boreham, Colin; Davison, Gareth; Jackson, Robin; Nevill, Alan; Wallace, Eric; Williams, Mark; Sports Performance. (Informa UK Limited, 2020-06-19)
      The majority of papers submitted to the Journal of Sports Sciences are experimental. The data are collected from a sample of the population and then used to test hypotheses and/or make inferences about that population. A common question in experimental research is therefore “how large should my sample be?”. Broadly, there are two approaches to estimating sample size – using power and using precision. If a study uses frequentist hypothesis testing, it is common to conduct a power calculation to determine how many participants would be required to reject the null hypothesis assuming an effect of a given size is present. That is, if there’s an effect of the treatment (of given size x), a power calculation will determine approximately how many participants would be required to detect that effect (of size x or larger) a given percentage of the time (often 80%). Power calculations as conducted in popular software programmes such as G*Power (Faul et al., 2009) typically require inputs for the estimated effect size, alpha, power (1 – ᵦ), and the statistical tests to be conducted. All of these inputs are subjective (or informed by previous studies) and up to the researcher to decide the most appropriate balance between type 1 error rate (false positive), type 2 error rate (false negative), cost, and time. In contrast, estimating sample size via precision involves estimating how many participants would be required for the frequentist confidence interval or Bayesian credible interval resulting from a statistical analysis to be of a certain width. The implication is that a narrower confidence interval or credible interval allows a more precise estimation of where the “true” population parameter (e.g., mean difference) might be.