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Graham J. Mytton, David T. Archer, Alan St Clair Gibson and Kevin G. Thompson

Purpose:

To assess the reliability and stability of 400-m swimming and 1500-m running competitions to establish the number of samples needed to obtain a stable pacing profile. Coaches, athletes, and researchers can use these methods to ensure that sufficient data are collected before training and race strategies are constructed or research conclusions are drawn.

Method:

Lap times were collected from 5 world and European championship finals between 2005 and 2011, resulting in the capture of data from 40 swimmers and 55 runners. A cumulative mean for each lap was calculated, starting with the most recent data, and the number of races needed for this to stabilize to within 1% was reported. Typical error for each lap was calculated for athletes who had competed in more than 1 final.

Results:

International swimmers demonstrated more reproducible performances than runners in 3 of the 4 laps of the race (P < .01). Variance in runners’ lap times significantly decreased by 1.7–2.7% after lap 1, whereas variance in swimmers’ lap times tended to increase by 0.1–0.5% after lap 1. To establish a stable profile, at least ten 400-m swimmers and forty-four 1500-m runners must be included.

Conclusions:

A stable race profile was observed from the analysis of 5 events for 1500-m running and 3 events for 400-m swimming. Researchers and athletes can be more certain about the pacing information collected from 400-m swimming than 1500-m running races, as the swimming data are less variable, despite both events being of similar duration.

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Andrew J. Carnes and Sara E. Mahoney

/power activities, session rating of perceived exertion scores 24 were collected immediately upon the conclusion of each WOD, with a cumulative mean of 8.4 (0.9). Four sample CFE sessions, each including a strength activity and a “WOD” as recommended by the online CFE program, 20 are described in Table  1 . Table

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Melissa G. Hunt, James Rushton, Elyse Shenberger and Sarah Murayama

 remaining outliers in a consistent way across all HRV data. Filtering within gHRV performs an automatic function that calculates the cumulative mean threshold based on the points and eliminates points that exceed this threshold or fall outside of accepted physiological values ( Rodríguez-Liñares, Lado, Vila, Méndez