Can an Incremental Step Test Be Used for Maximal Lactate Steady State Determination in Swimming? Clues for Practice.

We aimed to compare the velocity, physiological responses, and stroke mechanics between the lactate parameters determined in an incremental step test (IST) and maximal lactate steady state (MLSS). Fourteen well-trained male swimmers (16.8 ± 2.8 years) were timed for 400 m and 200 m (T200). Afterwards, a 7 × 200-m front-crawl IST was performed. Swimming velocity, heart rate (HR), blood lactate concentration (BLC), stroke mechanics, and rate of perceived exertion (RPE) were measured throughout the IST and in the 30-min continuous test (CT) bouts for MLSS determination. Swimming velocities at lactate threshold determined with log-log methodology (1.34 ± 0.06 m∙s-1) and Dmax methodology (1.40 ± 0.06 m∙s-1); and also, the velocity at BLC of 4 mmol∙L-1 (1.36 ± 0.07) were not significantly different from MLSSv, however, Bland-Altman analysis showed wide limits of agreement and the concordance correlation coefficient showed poor strength of agreement between the aforementioned parameters which precludes their interchangeable use. Stroke mechanics, HR, RPE, and BLC in MLSSv were not significantly different from the fourth repetition of IST (85% of T200), which by itself can provide useful support to daily practice of well-trained swimmers. Nevertheless, the determination of MLSSv, based on a CT, remains more accurate for exercise evaluation and prescription.

Bioimpedance Vector Patterns Changes in Response to Swimming Training: An Ecological Approach.

BACKGROUND AND AIM: Monitoring bioelectric phase angle (PhA) provides important information on the health and the condition of the athlete. Together with the vector length, PhA constitutes the bioimpedance vector analysis (BIVA) patterns, and their joint interpretation exceeds the limits of the evaluation of the PhA alone. The present investigation aimed to monitor changes in the BIVA patterns during a training macrocycle in swimmers, trying to ascertain if these parameters are sensitive to training load changes across a 13-week training period. METHODS: Twelve national and international level swimmers (four females; eight males; 20.9 ± 1.9 years; with a competitive swimming background of 11.3 ± 1.8 years; undertaking 16-20 h of pool training and 4-5 h of dry-land training per week and 822.0 ± 59.0 International Swimming Federation (FINA) points) were evaluated for resistance (R) and reactance (Xc) using a single frequency phase sensitive bioimpedance device at the beginning of the macrocycle (M1), just before the beginning of the taper period (M2), and just before the main competition of the macrocycle (M3). At the three-time assessment points, swimmers also performed a 50 m all-out first stroke sprint with track start (T50 m) while time was recorded. RESULTS: The results of the Hotelling T2 test showed a significant vector displacement due to simultaneous R and Xc changes (p < 0.001), where shifting from top to bottom along the major axis of the R-Xc graph from M1 to M2 was observed. From M2 to M3, a vector displacement up and left along the minor axis of the tolerance ellipses resulted in an increase in PhA (p < 0.01). The results suggest a gain in fluid with a decrease in cellular density from M1 to M2 due to decrements in R and Xc. Nevertheless, the reduced training load characterizing taper seemed to allow for an increase in PhA and, most importantly, an increase of Xc, thus demonstrating improved cellular health and physical condition, which was concomitant with a significant increase in the T50 m performance (p < 0.01). CONCLUSIONS: PhA, obtained by bioelectrical R and Xc, can be useful in monitoring the condition of swimmers preparing for competition. Monitoring BIVA patterns allows for an ecological approach to the swimmers' health and condition assessment without resorting to equations to predict the related body composition variables.

The Cellular Composition of the Innate and Adaptive Immune System Is Changed in Blood in Response to Long-Term Swimming Training.

Competitive swimming requires high training load cycles including consecutive sessions with little recovery in between which may contribute to the onset of fatigue and eventually illness. We aimed to investigate immune changes over a 7-month swimming season. Fifty-four national and international level swimmers (25 females, 29 males), ranging from 13 to 20 years of age, were evaluated at rest at: M1 (beginning of the season), M2 (after the 1st macrocycle's main competition), M3 (highest training load phase of the 2nd macrocycle) and M4 (after the 2nd macrocycle's main competition) and grouped according to sex, competitive age-groups, or pubertal Tanner stages. Hemogram and the lymphocytes subsets were assessed by automatic cell counting and by flow cytometry, respectively. Self-reported Upper Respiratory Symptoms (URS) and training load were quantified. Although the values remained within the normal range reference, at M2, CD8+ decreased (M1 = 703 ± 245 vs. M2 = 665 ± 278 cell µL-1; p = 0.032) and total lymphocytes (TL, M1 = 2831 ± 734 vs. M2 = 2417 ± 714 cell µL-1; p = 0.007), CD3+ (M1 = 1974 ± 581 vs. M2 = 1672 ± 603 cell µL-1; p = 0.003), and CD4+ (M1 = 1102 ± 353 vs. M2 = 929 ± 329 cell µL-1; p = 0.002) decreased in youth. At M3, CD8+ remained below baseline (M3 = 622 ± 245 cell µL-1; p = 0.008), eosinophils (M1 = 0.30 ± 0.04 vs. M3 = 0.25 ± 0.03 109 L-1; p = 0.003) and CD16+56+ (M1 = 403 ± 184 vs. M3 = 339 ± 135 cell µL-1; p = 0.019) decreased, and TL, CD3+, and CD4+ recovered in youth. At M4, CD19+ were elevated (M1 = 403 ± 170 vs. M4 = 473 ± 151 cell µL-1; p = 0.022), CD16+56+ continued to decrease (M4 = 284 ± 131 cell µL-1; p < 0.001), eosinophils remained below baseline (M4 = 0.29 ± 0.05 109 L-1; p = 0.002) and CD8+ recovered; monocytes were also decreased in male seniors (M1 = 0.77 ± 0.22 vs. M4 = 0.57 ± 0.16 109 L-1; p = 0.031). The heaviest training load and higher frequency of URS episodes happened at M3. The swimming season induced a cumulative effect toward a decrease of the number of innate immune cells, while acquired immunity appeared to be more affected at the most intense period, recovering after tapering. Younger athletes were more susceptible at the beginning of the training season than older ones.