Abstract
BackgroundIce swimming for 1 mile and 1 km is a new discipline in open-water swimming since 2009. This study examined female and male performances in swimming 1 mile (‘Ice Mile’) and 1 km (‘1 km Ice event’) in water of 5 °C or colder between 2009 and 2015 with the hypothesis that women would be faster than men.MethodsBetween 2009 and 2015, 113 men and 38 women completed one ‘Ice Mile’ and 26 men and 13 completed one ‘1 km Ice event’ in water colder than +5 °C following the rules of International Ice Swimming Association (IISA). Differences in performance between women and men were determined. Sex difference (%) was calculated using the equation ([time for women] – [time for men]/[time for men] × 100). For ‘Ice Mile’, a mixed-effects regression model with interaction analyses was used to investigate the influence of sex and environmental conditions on swimming speed. The association between water temperature and swimming speed was assessed using Pearson correlation analyses.ResultsFor ‘Ice Mile’ and ‘1 km Ice event’, the best men were faster than the best women. In ‘Ice Mile’, calendar year, number of attempts, water temperature and wind chill showed no association with swimming speed for both women and men. For both women and men, water temperature was not correlated to swimming speed in both ‘Ice Mile’ and ‘1 km Ice event’.ConclusionsIn water colder than 5 °C, men were faster than women in ‘Ice Mile’ and ‘1 km Ice event’. Water temperature showed no correlation to swimming speed.
Highlights
Ice swimming for 1 mile and 1 km is a new discipline in open-water swimming since 2009
Based upon the fact that Lynne Cox was the first person to swim in icy water [20] before the first man [21] and the recent findings that women were able to beat men in open-water swimming in low water temperatures (
We considered repeated finishes in our analysis since it was hypothesized that extensive previous experience was important for successful ice swimming [16, 17]
Summary
On the IISA website, the results of successful swims are presented with date, name of the athletes, sex, water temperature, and wind chill. Statistical analysis Each set of data was tested for normal distribution (D’Agostino and Pearson omnibus normality test) and for homogeneity of variances (Levene’s test) before statistical analyses. A mixed-effects regression model with swimmer as random variable to consider swimmers who completed several events was used to analyse changes in swimming speed across years. Experience (i.e., number of previous successful swims), water temperature, wind chill and calendar year as fixed variables. We considered interaction effects between sex, experience, water temperature and wind chill. Between water temperature and swimming speed was tested using Pearson correlation coefficients.
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