Relationship Between Hand Kinematics, Hand Hydrodynamic Pressure Distribution and Hand Propulsive Force in Sprint Front Crawl Swimming

Comparison of foot pressure distribution and foot kinematics in undulatory underwater swimming between performance levels

This study aimed to elucidate the foot kinematics and foot pressure difference characteristics of faster swimmers in undulatory underwater swimming (UUS). In total, eight faster and eight slower swimmers performed UUS in a water flume at a flow velocity set at 80% of the maximal effort swimming velocity. The toe velocity and foot angle of attack were measured using a motion capture system. A total of eight small pressure sensors were attached to the surface of the left foot to calculate the pressure difference between the plantar and dorsal sides of the foot. Differences in the mean values of each variable between the groups were analysed. Compared to the slower swimmers, the faster swimmers exhibited a significantly higher swimming velocity (1.53 ± 0.06 m/s vs. 1.31 ± 0.08 m/s) and a larger mean pressure difference in the phase from the start of the up-kick until the toe moved forward relative to the body (3.88 ± 0.65 kPa vs. 2.66 ± 1.19 kPa). The faster group showed higher toe vertical velocity and toe direction of movement, switching from lateral to medial at the time of generating the larger foot pressure difference in the up-kick, providing insight into the reasons behind the foot kinematics of high UUS performance swimmers.

Smartpaddle® as a New Monitoring Feature: A Comparison Between Inertial Measurement Unit- and Strain Gauge-Based Devices on Tethered Swimming Forces

Smartpaddle® is a novel wearable device based on inertial measurement units (IMU) for in-field arm-stroke kinetics and kinematics analysis in swimming. However, the lack of data regarding its agreement and reliability, coupled with restricted access to raw data, emphasizes the need to evaluate it against a well-established strain gauge (SG) reference method for assessing swimming forces. Thus, this study aimed to investigate the agreement and reliability between the Smartpaddle® and strain gauge in a 30-s all-out arms-only tethered swimming test. Twelve trained young adult swimmers performed a test-retest 30-s all-out arms-only tethered swimming trial. Peak and mean forces were obtained from IMU (PFIMU and MFIMU) and SG (PFSG and MFSG) simultaneously. Statistical differences and correlations were found in both peak (PFSG = 158.46 ± 48.85 N, PFIMU = 75.47 ± 12.05 N, p < 0.001, r = 0.88) and mean (MFSG = 69.62 ± 16.36 N, MFIMU = 30.06 ± 5.42 N, p < 0.001, r = 0.84) forces between devices, presenting elevated systematic errors for both variables. No differences were found in IMU data between test-retest conditions in both peak (PFIMU = 75.47 ± 12.05 N, PFIMU = 75.45 ± 11.54 N, p = 0.99, ICC = 0.96) and mean (MFIMU = 30.06 ± 5.42 N, MFIMU = 30.21 ± 5.83 N, p = 0.80, ICC = 0.95) forces, with negligible systematic errors. In conclusion, although the Smartpaddle® device is not directly comparable to the strain gauge reference method, it has demonstrated high reliability levels in test-retest trials.

Effects of anthropometrics, thrust, and drag on stroke kinematics and 100 m performance of young swimmers using path-analysis modeling

The aim of this study was to understand the interactions between anthropometric, kinetic, and kinematic variables and how they determine the 100 m freestyle performance in young swimmers. Twenty-five adolescent swimmers (15 male and 10 female, aged 15.75 ± 1.01 years) who regularly participated in regional and national competitions were recruited. The 100 m freestyle performance was chosen as the variable to be predicted. A series of anthropometric (hand surface area-HSA), kinetic (thrust and active drag coefficient (CDA )), and kinematic (stroke length (SL); stroke frequency (SF), and swimming speed) variables were measured. Structural equation modeling (via path analysis) was used to develop and test the model. The initial model predicted performance with 90.1% accuracy. All paths were significant (p < 0.05) except the thrust-SL. After deleting this non-significant path (thrust-SL) and recalculating, the model goodness-of-fit improved and all paths were significant (p < 0.05). The predicted performance was 90.2%. Anthropometrics had significant effects on kinetics, which had significant effects on kinematics, and consequently on the 100 m freestyle performance. The cascade of interactions based on this path-flow model allowed for a meaningful prediction of the 100 m freestyle performance. Based on these results, coaches and swimmers should be aware that the swimming predictors can first meaningfully interact with each other to ultimately predict the 100 m freestyle performance.

Nonlinear Analysis of the Hand and Foot Force-Time Profiles in the Four Competitive Swimming Strokes

Human locomotion on water depends on the force produced by the swimmer to propel the body forward. Performance of highly complex motor tasks like swimming can yield minor variations that only nonlinear analysis can be sensitive enough to detect. The purpose of the present study was to examine the nonlinear properties of the hand/feet forces and describe their variations across the four competitive swimming strokes performing segmental and full-body swimming. Swimmers performed all-out bouts of 25 m in the four swimming strokes, swimming the full-body stroke, with the arm-pull only and with the leg kicking only. Hand/foot force and swimming velocity were measured. The Higuchi's fractal dimension (HFD) and sample entropy (SampEn) were used for the nonlinear analysis of force and velocity. Both the arm-pull and leg kicking alone were found to produce similar peak and mean hand/foot forces as swimming the full-body stroke. Hand force was more complex in breaststroke and butterfly stroke; conversely, kicking conditions were more complex in front crawl and backstroke. Moreover, the arm-pull and kicking alone tended to be more complex (higher HFD) but more predictable (lower SampEn) than while swimming the full-body stroke. There was no loss of force production from segmental swimming to the full-body counterpart. In conclusion, the number of segments in action influences the nonlinear behavior of the force produced and, when combining the four limbs, the complexity of the hand/foot force tends to decrease.

Performance Tiers within a Competitive Age Group of Young Swimmers Are Characterized by Different Kinetic and Kinematic Behaviors

The present study aimed to analyze swimmers' in-water kinetic and kinematic behaviors according to different swimming performance tiers within the same age group. An amount of 53 highly trained swimmers (girls and boys: 12.40 ± 0.74 years) were split up into 3 tiers based on their personal best performance (i.e., speed) in the 50 m freestyle event (short-course): lower-tier (1.25 ± 0.08 m·s^-1); mid-tier (1.45 ± 0.04 m·s^-1); and top-tier (1.60 ± 0.04 m·s^-1). The in-water mean peak force was measured during a maximum bout of 25 m front crawl using a differential pressure sensors system (Aquanex system, Swimming Technology Research, Richmond, VA, USA) and defined as a kinetic variable, while speed, stroke rate, stroke length, and stroke index were retrieved and considered as kinematic measures. The top-tier swimmers were taller with a longer arm span and hand surface areas than the low-tier, but similar to the mid-tier. While the mean peak force, speed and efficiency differed among tiers, the stroke rate and stroke length showed mixed findings. Coaches should be aware that young swimmers belonging to the same age group may deliver different performance outcomes due to different kinetic and kinematic behaviors.

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

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.

Reliability of using a pressure sensor system to measure in-water force in young competitive swimmers

The aim of this study was to analyze the reliability of using a differential pressure system to measure in-water force in young competitive swimmers. Ten boys and five girls (12.38 ± 0.48 years, 49.13 ± 6.82 kg, 159.71 ± 7.99 cm) were randomly assigned to perform two maximum bouts of 25 m front crawl on different days (trial one, T1; trial two, T2), one week apart. A differential pressure system composed of two hand sensors (Aquanex System, v.4.1, Model DU2, Type A, Swimming Technology Research, Richmond, VA, United States) was used to measure the peak (RF_PEAK) and the mean (RF_MEAN) resultant force of the dominant and non-dominant hands (in Newton, N). Reliability was analyzed by computing the intraclass correlation coefficient (ICC), typical error (TE), smallest worthwhile change (SWC), coefficient of variation (CV%), standard error of measurement (SEM), and the minimal detectable change (MDC). Bland–Altman plots with 95% limits of agreement were also analyzed. The results showed no differences between T1 and T2 in all variables (p > 0.05). The ICC showed “excellent” reliability (ICC > 0.90) for the RF_PEAK and RF_MEAN in both hands. The CV% was rated as “good” (<5%) and TE was smaller than SWC in all variables. The Bland-Altman plots showed high reliability with a small bias (RF_PEAK dominant, -0.29 N; RF_PEAK non-dominant, -0.83 N; RF_MEAN dominant, 0.03 N; RF_MEAN non-dominant, 0.50 N). The pressure sensor system (Aquanex System) seems to be a reliable device for measuring the hand resultant force during front crawl in young swimmers and can be used to monitor the changes over time.