During visit 3 subjects completed the same protocols as visit 2 except they

During visit 3 subjects completed the same protocols

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During visit 3, subjects completed the same protocols as visit 2, except they completed a single 4 × 5-min inter- val series, this time at 16 km·h −1 for men and 15 km·h −1 for women, wearing each shoe. After an ~ 5-min break, the subjects completed a maximal aerobic power (VO 2 peak) test at the same running velocity, in their own shoes. Treadmill gradient was increased by 1% each minute until volitional exhaustion. The VO 2 peak was deff ned as the highest 30-s mean value obtained during the test. 2.3 Statistics The effects of shoe condition on running economy and biomechanical measures was analyzed using a spreadsheet for post-only crossovers [ 24 ]. The value of the ADI shoe for each dependent variable was included as a covariate to improve precision of the estimate of the effects. Effects were estimated in percent units via log transformation, and uncertainty in the estimate was expressed as 90% conff dence limits (CLs). The effect size (ES), which represents the mag- nitude of the difference between the two conditions in terms of standard deviation (SD), was calculated from the log- transformed data by dividing the change in the mean by the average SD of the two conditions. The threshold values for the magnitudes of effects on all measures for small, moder- ate, large, very large, and extremely large effects were 0.2, 0.6, 1.2, 2.0, and 4.0, respectively, of the between-subject SD in the control condition [ 25 ]. To analyze potential relationships underlying the effect of shoes on aerobic energy expenditure, changes in aero- bic energy expenditure were plotted against changes in mechanism variables, and the scatterplots were inspected for any linear trend. Resulting correlation coeffficients were converted into 90% CLs using a spreadsheet [ 26 ]. The con- tribution of each of the changes in biomechanical measures to the change in aerobic energy expenditure was investi- gated by using the change scores of each log-transformed measure as a covariate in the spreadsheet for the analysis of the effect of the shoe condition. The change in aerobic
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335 Running Economy in Flats vs. Spikes energy expenditure associated with the measure was given by evaluating the effect on aerobic energy expenditure of a difference in the covariate equal to the mean change in the measure, while the change in aerobic energy expendi- ture independent of the measure was given by adjusting the effect of the treatment to zero change in the measure [ 25 ]. To investigate the extent to which one mechanism variable explained the contribution of each other measure, each bio- mechanical measure was included as an additional covariate in separate analyses. These multiple linear regressions could not be performed with the post-only crossover spreadsheet but were realized instead using the Linest function in Excel (Microsoft Corporation, Redmond, WA, USA).
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  • Fall '16
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