Using Statistical Parametric Mapping to Compare the Propulsion of Age-Group Swimmers in Front Crawl Acquired with the Aquanex System

Abstract

Understanding the difference in each upper limb between age groups can provide deeper insights into swimmers’ propulsion. This study aimed to: (1) compare swimming velocity and a set of kinematical variables between junior and juvenile swimmers and (2) compare the propulsion outputs through discrete and continuous analyses (Statistical Parametric Mapping—SPM) between junior and juvenile swimmers for each upper limb (i.e., dominant and non-dominant). The sample was composed of 22 male swimmers (12 juniors with 16.35 ± 0.74 years; 10 juveniles with 15.40 ± 0.32 years). A set of kinematic and propulsion variables was measured at maximum swimming velocity. Statistical Parametric Mapping was used as a continuous analysis approach to identify differences in the propulsion of both upper limbs between junior and juvenile swimmers. Junior swimmers were significantly faster than juveniles (p = 0.04, d = 0.86). Although juniors showed higher propulsion values, the SPM did not reveal significant differences (p < 0.05) for dominant and non-dominant upper limbs between the two age groups. This indicates that other factors (such as drag) may be responsible for the difference in swimming velocity. Coaches and swimmers should be aware that an increase in propulsion alone may not immediately lead to an increase in swimming velocity.

Association between the Upper Quarter Dynamic Balance, Anthropometrics, Kinematics, and Swimming Speed

“…A nylon cable that unfolded over the trial from a speedometer device (SpeedRT, ApLab, Rome, Italy) and attached to a belt tied around the swimmer’s waist was used to measure swim speed. The speedometer transmitted the signal to software (Speed RT 2013, ApLab, Rome, Italy) that was streaming the swimmer´s speed in real-time [ 38 ]. After an auditory signal, the swimmer performed a push-off from the headwall without underwater kicking.…”

Using Statistical Parametric Mapping as a statistical method for more detailed insights in swimming: a systematic review

“…This includes the sample size, participants’ age, body mass, height, years of experience, pool length where data was collected, the type of trial or event, and the participants’ FINA points that characterize their competitive level. Most studies (N = 6, 66.7%) ( Martens et al, 2016 ; Gaudet et al, 2018a ; 2018b ; Blache et al, 2018 ; Gonjo and Olstad, 2021 ; Gourgoulis and Nikodelis, 2022 ) evaluated adult swimmers, and three studies (33.3%) investigated young swimmers ( Ruiz-Navarro et al, 2021 ; Morais et al, 2022c ; 2023 ). Five studies (55.6%) ( Martens et al, 2016 ; Blache et al, 2018 ; Gonjo and Olstad, 2021 ; Morais et al, 2022c ; Gourgoulis and Nikodelis, 2022 ) evaluated only male swimmers, and four studies (44.4%) included males and females concurrently ( Gaudet et al, 2018a ; 2018b ; Ruiz-Navarro et al, 2021 ; Morais et al, 2023 ).…”

Mass point versus whole‐body modeling of skiers for performance evaluation in alpine skiing

“…It is common in research to compare the average ski turn cycle data, where the main issue with such discrete data is that it reduces the dimensionality of potential information available by disregarding or not considering insightful details throughout the entire ski turn cycle. 12 To compare the results of the MP and LS models within a ski turn cycle at individual data points, a continuous statistical analysis of the time series data through statistical parametric modeling (SPM) may be performed. 13 This analysis provides information on the exact time within the ski turn cycle at which the differences occur, which may be of paramount importance.…”

“…13 This analysis provides information on the exact time within the ski turn cycle at which the differences occur, which may be of paramount importance. 12 The aims of the present study were: (a) to examine the deviations in diff e mech between the MP and LS skier models, and thus to investigate whether the MP model is sufficiently accurate for evaluating skiing performance, (b) to compare the energy fluctuations derived with the two skier models, and (c) to investigate the influence of the gate setup on the differences in diff e mech and on the differences in energy fluctuations between the two skier models in giant slalom. It was hypothesized that in the turny gate setup, where the ratio between the horizontal and total gate displacement is larger than in the open gate setup (Figure 1), the e RKE accounts for a larger portion of (2)…”

“…To compare the results of the MP and LS models within a ski turn cycle at individual data points, a continuous statistical analysis of the time series data through statistical parametric modeling (SPM) may be performed 13 . This analysis provides information on the exact time within the ski turn cycle at which the differences occur, which may be of paramount importance 12 …”