Interaction of Kinematic, Kinetic, and Energetic Predictors of Young Swimmers’ Speed

Morais, Jorge E.; Barbosa, Tiago M.; Bragada, José A.; Ramírez‐Campillo, Rodrigo; Marinho, Daniel A.

doi:10.1123/ijspp.2022-0430

Cited by 3 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The selection of the variables included in predictive models normally depends on the determinant factors of the underlying event. In swimming, the selection should follow the concepts of kinematic, kinetic, and energetic performance determinants (e.g., [33][34][35]) and studies detailing predictive models (e.g., [10,35,36]). However, even variables that present contradictory results in the literature, given the inevitable heterogeneity in methodologies and subjects, should also be explored using artificial models [26].…”

Section: Resultsmentioning

confidence: 99%

Swimming Performance Interpreted through Explainable Artificial Intelligence (XAI)—Practical Tests and Training Variables Modelling

Carvalho,

Goethel,

Silva

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Explainable artificial intelligence (XAI) models with Shapley additive explanation (SHAP) values allows multidimensional representation of movement performance interpreted on both global and local levels in terms understandable to human intuition. We aimed to evaluate the swimming performance (World Aquatics points) predictability of a combination of demographic, training, anthropometric, and biomechanical variables (inputs) through XAI. Forty-seven swimmers (16 males), after completing a training questionnaire (background and duration) and anthropometric assessment, performed, in a randomised order, a 25 m front crawl and three countermovement jumps, at maximal intensity. The predicted World Aquatics points (516 ± 159; mean ± SD) were highly correlated (r2 = 0.93) with the 529 ± 158 actual values. The duration of swimming training was the most important variable (95_SHAP), followed by the countermovement jump impulse (37_SHAP), both with a positive effect on performance. In contrast, a higher percentage of fat mass (21_SHAP) corresponded to lower World Aquatics points. Impulse, when interpreted together with dryland training duration and stroke rate, shows the positive effects of upper and lower limb power on swimming performance. Height should be interpreted together with arm span when exploring positive effects of anthropometric traits on swimming performance. The XAI modelling highlights the usefulness of specific training, technical and physical testing, and anthropometric factors for monitoring swimmers.

show abstract

Section: Resultsmentioning

confidence: 99%

Swimming Performance Interpreted through Explainable Artificial Intelligence (XAI)—Practical Tests and Training Variables Modelling

Carvalho,

Goethel,

Silva

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

The effects of six sprint interval training sessions on muscle oxygenation and swimming performance in untrained swimmers

Dalamitros,

Tzivanis,

Martín-Rodríguez

et al. 2024

Front. Sports Act. Living

View full text Add to dashboard Cite

The current study examined the changes in muscle oxygenation values and swimming performance after six sessions of sprint interval training during a three-week period in untrained swimmers. Twelve swimmers of both genders (age: 23.5 ± 5.6yrs) executed the twice-weekly experimental training protocol (EXP, n = 12), consisting of a 4 × 50 m front-crawl swimming (repeated sprint training—RST) with maximal intensity, and 2 min of passive recovery in between, after a short in-water warm-up. The control group (CON, n = 9) performed a continuous swimming set (200 m) at 120 b pm−1, with the same weekly frequency. Performance times in two maximum swim trials (400 m: T400 and 50 m: T50), muscle oxygenation of the deltoid muscle (SmO2) immediately after T400 and T50, 1-min heart rate recovery (HRR1) after T400, T50, and swim strokes during both swim trials (S/T400, S/T50) were assessed. For the EXP group, T400 improved by 2.4 (p = 0.011). In contrast, T50 presented no significant improvement (1%, p > 0.05). SmO2 decreased at T400 (5.5%, p = 0.017) and increased at T50 (3.7%, p = 0.030). HRR1 improved after T400 (7.9%, p = 0.002), T50 (4.6%, p = 0.005) and RST (9.6%, p = 0.002). S/T400 and S/T50 remained relatively unchanged (p > 0.05). The CON group presented no significant changes in any of the variables examined. In conclusion, six sprint interval training sessions can improve aerobic capacity over a 3-week training period, as indicated by the enhanced T400 performance and the reduced HRR1 values, in previously trained swimmers. Finally, the sensitivity of the near-infrared spectroscopy method to detect short-term training-induced changes is highlighted.

show abstract

The relationship between maximal lactate accumulation rate and sprint performance parameters in male competitive swimmers

Sengoku,

Shinno,

Kim

et al. 2024

Front. Sports Act. Living

View full text Add to dashboard Cite

This study aimed to examine the relationship between the maximal lactate accumulation rate (ċLamax) and sprint performance parameters in male competitive swimmers. Seventeen male competitive swimmers volunteered to perform a 20 m maximal front crawl sprint without pushing off the wall from a floating position. ċLamax was determined by the 20-m sprint time and blood lactate measured before and after the 20 m sprint. For the sprint performance parameter, a 50 m time trial with the front crawl swimming stroke was conducted, and the times taken from 0 to 15 m, 15–25 m, 25–35 m, and 35–45 m were analyzed. A semi-tethered swimming test was conducted to investigate the load-velocity profile of each swimmer. From the load-velocity profile, theoretical maximal velocity (V0), maximal load (L0) and relative maximal load (rL0) were examined. The slope of the load-velocity profile was also determined. According to the results, ċLamax correlated with 50 m front crawl performance (r = −.546, p < .05). Moreover, a higher ċLamax was related to faster 0–35 m section time. Furthermore, ċLamax correlated with L0 (r = .837, p < .01), rL0 (r = .820, p < .01), and load-velocity slope (r = .804, p < .01). ċLamax is a good indicator of 50 m front crawl performance in male swimmers, and higher glycolytic power contributes to the faster time at the beginning of the sprint race. ċLamax could also evaluate the ability of a swimmer to apply force to the water during high-intensity swimming.

show abstract

Interaction of Kinematic, Kinetic, and Energetic Predictors of Young Swimmers’ Speed

Cited by 3 publications

References 29 publications

Swimming Performance Interpreted through Explainable Artificial Intelligence (XAI)—Practical Tests and Training Variables Modelling

Swimming Performance Interpreted through Explainable Artificial Intelligence (XAI)—Practical Tests and Training Variables Modelling

The effects of six sprint interval training sessions on muscle oxygenation and swimming performance in untrained swimmers

The relationship between maximal lactate accumulation rate and sprint performance parameters in male competitive swimmers

Contact Info

Product

Resources

About