Individualizing Training in Swimming: Evidence for Utilizing the Critical Speed and Critical Stroke Rate Concepts

Which of the Physiological vs. Critical Speed Is a Determinant of Modern Pentathlon 200 m Front Crawl Swimming Performance: The Influence of Protocol and Ergometer vs. Swimming Pool Conditions

BACKGROUND

Modern pentathlon includes horse riding, fencing, swimming, shooting and cross-country running. Events can last many hours during which the athletes face almost maximal energy and physiological demands, and fatigue. Early recognition and prevention of injuries and overuse syndromes can be achieved by refining the individual training loads. The purpose of the study was to determine which parameter could be the most accurate predictor of swimming working capacity determinants in pentathletes.

METHODS

Fourteen male pentathletes performed a continuous maximal incremental test in the swimming flume ergometer to measure peak oxygen uptake (VO₂peak), and five swimming tests in a 50 m swimming pool to detect critical velocity (CV); velocity at 2 and 4 mM·L^-1 of blood lactate (v2, v4) and energy cost (EC).

RESULTS

The 200 m swimming time was 2:18-2:32 m:s (340 FINA points). CV was 1.21 ± 0.04 m·s^-1, v2 was 1.14 ± 0.09 and v4 1.23 ± 0.08 m·s^-1. VO₂peak was 3540.1 ± 306.2 mL·min^-1 or 48.8 ± 4.6 mL·kg^-1·min^-1. EC at 1.24 m·s^-1 was 45.7 ± 2.4 mL·kg^-1·min^-1. Our main finding was the large correlation of CV with 200 m swimming performance; Conclusions: Among all the protocols analysed, CV is the most predictive and discriminative of individual swimming performance in this group of pentathletes. It appears as the most suitable test to constantly refine their swimming training loads for both performance enhancement and health promotion.

Physiological Responses and Stroke Variables during Arm Stroke Swimming Using Critical Stroke Rate in Competitive Swimmers

The current study examined the physiological responses and stroke variables at critical stroke rate (CSR), 105% CSR, and 110% CSR in order to utilize CSR for prescription arm stroke swimming. Nine male national-level collegiate swimmers performed an all-out 200 m and 400 m for determining the CSR. Participants performed three sets of 6 × 100 m (with 10 s of rest between each bout), the stroke rate for each set was enforced at CSR, 105% CSR, and 110% CSR. Mean swimming velocity, heart rate, and rate of perceived exertion were found to increase with each set (p < 0.05). Blood lactate concentration did not differ between the CSR and the 105% CSR (3.3 ± 1.4 vs. 3.5 ± 1.5 mmol/L) but was higher in 110% CSR (5.1 ± 1.6 mmol/L) than in the other two sets (p < 0.05). There was no difference in the stroke rate between all bouts in each set, and the stroke length did not change from the second to sixth bout in each set. This study suggested that training intensity for CSR and 105% CSR correspond to threshold level, and 110% CSR corresponds to high-intensity training level. It was also suggested that training in the CSR−110% CSR range could be performed without regard to SL reduction.

Monitoring the Heart Rate Variability Responses to Training Loads in Competitive Swimmers Using a Smartphone Application and the Banister Impulse-Response Model

PURPOSE

First, to examine whether heart rate variability (HRV) responses can be modeled effectively via the Banister impulse-response model when the session rating of perceived exertion (sRPE) alone, and in combination with subjective well-being measures, are utilized. Second, to describe seasonal HRV responses and their associations with changes in critical speed (CS) in competitive swimmers.

METHODS

A total of 10 highly trained swimmers collected daily 1-minute HRV recordings, sRPE training load, and subjective well-being scores via a novel smartphone application for 15 weeks. The impulse-response model was used to describe chronic root mean square of the successive differences (rMSSD) responses to training, with sRPE and subjective well-being measures used as systems inputs. Changes in CS were obtained from a 3-minute all-out test completed in weeks 1 and 14.

RESULTS

The level of agreement between predicted and actual HRV data was R2 = .66 (.25) when sRPE alone was used. Model fits improved in the range of 4% to 21% when different subjective well-being measures were combined with sRPE, representing trivial-to-moderate improvements. There were no significant differences in weekly group averages of log-transformed (Ln) rMSSD (P = .34) or HRV coefficient of variation of Ln rMSSD (P = .12); however, small-to-large changes (d = 0.21-1.46) were observed in these parameters throughout the season. Large correlations were observed between seasonal changes in HRV measures and CS (changes in averages of Ln rMSSD: r = .51, P = .13; changes in coefficient of variation of Ln rMSSD: r = -.68, P = .03).

CONCLUSION

The impulse-response model and data collected via a novel smartphone application can be used to model HRV responses to swimming training and nontraining-related stressors. Large relationships between seasonal changes in measured HRV parameters and CS provide further evidence for incorporating a HRV-guided training approach.