Page 125 - Petelin Ana. Ur. 2022. Zdravje otrok in mladostnikov / Health of children and adolescents. Proceedings. Koper: Založba Univerze na Primorskem/University of Primorska Press
P. 125
g time for 100m and 200m after six-week IMT, the similar results Nahtigal the effect of inspiratory muscle training on the maintenance of swimming abilities 125
(2015) found in 14- to 16-year olds after eight weeks of training.
The purpose of our study was to examine weather IMT also affects the
maintenance of swimming abilities during a swimming break. The purpose of
the research is particularly meaningful at the time, when restrictions and rec-
ommendations due to the declared epidemic of the covid-19 disease occasion-
ally limited the implementation of swimming activities in swimming pool fa-
cilities.
Methods
19 students (5 male and 14 female) of the first year of the undergraduate study
program Aplied Kinesiology participated in the research. They were divided
into an intervention (n = 11; 6 women, 5 men) and a control group (n = 8; 8
women, 0 men).
As a part of the study course, the participants were included in the elev-
en-day swimming course program, after which we took the first measurements.
The second measurements were taken ten days after the course. In the mean-
time, no swimming activities were performed. The intervention group per-
formed an IMT with the Powerbreathe Plus device (A division of Gaiam Ltd.,
Northfield Road, Southam, Warwickshire, CV47 ORD, UK). Exercise consist-
ed of 30 rapid maximum forced inhalations and relaxed exhalations, twice a
day. The intensity (resistance on the device) was set to 60% of the maximal in-
spiratory power (S-index) (McConnell, 2011; Finta, Boda, Nagy and Bender,
2020), which was measured in the first measurements. A specific intensity of
60% MIP has been justified as effective IMT based on optimal response in con-
text of blood flow and generated pressure (Sheel et al., 2001, McConnell and
Griffiths, 2010). The subjects were taught the independent and correct use of
the training device before the intervention.
Statistical package IBM SPSS Statistics 25.0 (Armonk, NY:IBM Corp.) was
used for statistical data processing. Shapiro-Wilk test was used to check the
normal distribution of variables. Analysis of the differences between the indi-
vidual variables between the groups in the initial state was performed using the
independent samples t-test or its non-parametric version, i.e. the Man-Whitney
U-test. The analysis of progress for normally distributed variables was checked
with the analysis of variance for repeated measurements (Two-way/Factorial
RM ANOVA) with the time and group factors (Field, 2009). We also calculat-
ed the interaction of time and group to determine the effect of the intervention.
For non-normally distributed variables, we used non-parametric version, the
Wilcoxon signed rank test. In accordance with selected test, we calculated the
effect size (eta squared (η2)), defining the effect size as small for values > 0.01,
medium for values > 0.06 and large for values > 0.14 (Cohen, 1988; Fritz, Morris
and Richler, 2012). After the analysis of variance, we performed a paired-sam-
ples t-test, to check statistically significant differences for individual variables
(2015) found in 14- to 16-year olds after eight weeks of training.
The purpose of our study was to examine weather IMT also affects the
maintenance of swimming abilities during a swimming break. The purpose of
the research is particularly meaningful at the time, when restrictions and rec-
ommendations due to the declared epidemic of the covid-19 disease occasion-
ally limited the implementation of swimming activities in swimming pool fa-
cilities.
Methods
19 students (5 male and 14 female) of the first year of the undergraduate study
program Aplied Kinesiology participated in the research. They were divided
into an intervention (n = 11; 6 women, 5 men) and a control group (n = 8; 8
women, 0 men).
As a part of the study course, the participants were included in the elev-
en-day swimming course program, after which we took the first measurements.
The second measurements were taken ten days after the course. In the mean-
time, no swimming activities were performed. The intervention group per-
formed an IMT with the Powerbreathe Plus device (A division of Gaiam Ltd.,
Northfield Road, Southam, Warwickshire, CV47 ORD, UK). Exercise consist-
ed of 30 rapid maximum forced inhalations and relaxed exhalations, twice a
day. The intensity (resistance on the device) was set to 60% of the maximal in-
spiratory power (S-index) (McConnell, 2011; Finta, Boda, Nagy and Bender,
2020), which was measured in the first measurements. A specific intensity of
60% MIP has been justified as effective IMT based on optimal response in con-
text of blood flow and generated pressure (Sheel et al., 2001, McConnell and
Griffiths, 2010). The subjects were taught the independent and correct use of
the training device before the intervention.
Statistical package IBM SPSS Statistics 25.0 (Armonk, NY:IBM Corp.) was
used for statistical data processing. Shapiro-Wilk test was used to check the
normal distribution of variables. Analysis of the differences between the indi-
vidual variables between the groups in the initial state was performed using the
independent samples t-test or its non-parametric version, i.e. the Man-Whitney
U-test. The analysis of progress for normally distributed variables was checked
with the analysis of variance for repeated measurements (Two-way/Factorial
RM ANOVA) with the time and group factors (Field, 2009). We also calculat-
ed the interaction of time and group to determine the effect of the intervention.
For non-normally distributed variables, we used non-parametric version, the
Wilcoxon signed rank test. In accordance with selected test, we calculated the
effect size (eta squared (η2)), defining the effect size as small for values > 0.01,
medium for values > 0.06 and large for values > 0.14 (Cohen, 1988; Fritz, Morris
and Richler, 2012). After the analysis of variance, we performed a paired-sam-
ples t-test, to check statistically significant differences for individual variables
