Search Results

Purpose: To quantify the impact of successive competitions on swimming performance in world-class swimmers. Methods: An entire data set of all events swum during a new competition named the International Swimming League was collected. A Bayesian linear mixed model has been proposed to evaluate whether a progression could be observed during the International Swimming League’s successive competitions and to quantify this effect according to event, age, and gender. Results: An overall progression of 0.0005 (0.0001 to 0.0010) m/s/d was observed. The daily mean progression (ie, faster performance) was twice as high for men as for women (0.0008 [0.00 to 0.0014] vs 0.0003 [−0.0003 to 0.0009] m·s⁻¹). A tendency toward higher progression for middle distances (200 and 400 m) and for swimmers of a higher caliber (above 850 FINA [Fédération Internationale de Natation] points) was also observed. Swimmers between 23 and 26 years of age seemed to improve their swimming speed more in comparison with the other swimmers. Conclusions: This new league format, which involves several competitions in a row, seems to allow for an enhancement in swimming performance. Coaches and their support staff can now adapt their periodization plan in order to promote competition participation.

Purpose: To measure the proportion of French swimmers that progressed, stagnated, or regressed during the 2020 national championship compared with previous ones. Method: Individual best performances were collected at the French national championships from 2000 to 2020. Yearly proportions of swimmers who improved, stagnated, or regressed in performances were compared with their previous performances. Results: In 2020, the proportion of swimmers with performance regression has significantly increased (33% vs 17% in 2019). Women showed a higher proportion of performance regression (41%) than men (26%, P < .0001) in 2020. Only 39% of women and 53% of men experienced progression in 2020 (vs 60.8% [3.7%] and 66.7% [5.2%], respectively, in the previous years). Only the 2008 and 2009 championships showed a regression proportion that did not increase with age. The 2010 championship (the year of swimsuits ban) showed a higher proportion of regressing athletes than these previous years. Long-distance events showed higher proportion of performance regression (36.2% [0.5%]) for 400-, 800-, and 1500-m races than for short-distance ones (32.1% [3.2%]; 50-, 100-, and 200-m events). Breaststroke events showed higher regression (42.4%) than other styles (30.5% [2.1%]). Younger swimmers more often improved their performance than older ones (61.9% [8.5%] for swimmers less than 18 y of age vs 20.0% [10.8%] for those 25 y and older). Conclusion: A high proportion of swimmers experienced performance regression during the 2020 French national championships. A higher impact was observed among female, long-distance, and breaststroke swimmers. Eight weeks of lockdown without training may have led to poorer swimming performances.

Purpose: Short sleep duration and poor sleep quality are common in swimmers. Sleep-hygiene strategies demonstrated beneficial effects on several sleep parameters. The present study assessed the impact of a multisession sleep-hygiene training course on sleep in elite swimmers. Methods: Twenty-eight elite swimmers (17 [2] y) participated. The sleep-hygiene strategy consisted of 3 interventions. Sleep was measured by actigraphy for 7 days before the beginning of the intervention (baseline), after the first collective intervention (postintervention), after the second collective intervention (postintervention 2), and, finally, after the individual intervention (postintervention 3). The Epworth Sleepiness Scale (ESS) was completed concurrently. Swimmers were classified into 2 groups: nonsomnolent (baseline ESS score ≤ 10, n = 13) and somnolent (baseline ESS score ≥ 11, n = 15). Results: All swimmers had a total sleep time of <8 hours per night. Sixty percent of swimmers were moderately morning type. Later bedtime, less time in bed, and total sleep time were observed in the somnolent group compared with the nonsomnolent group at baseline. An interaction between training course and group factors was observed for bedtime, with a significant advance in bedtime between baseline, postintervention 2, and postintervention 3 for the somnolent group. Conclusions: The present study confirms the importance of implementing sleep-hygiene strategies, particularly in athletes with an ESS score ≥11. A conjunction of individual and collective measures (eg, earlier bedtime, napping, and delaying morning training session) could favor the total sleep time achieved.

Purpose: To compare the dynamics of maximal oxygen uptake ( $\dot{\mathrm{V}}{\mathrm{O}}_2$ ), blood lactate ([La]_b), total energy expenditure (E _tot), and contributions of the aerobic (E _aer), alactic anaerobic (E _an,al), and lactic anaerobic (E _an,lac) metabolic energy pathways over 4 consecutive 25-m laps (L_0–25, L_25–50, etc) of a 100-m maximal freestyle swim. Methods: Elite swimmers comprising 26 juniors (age = 16 [1] y) and 23 seniors (age = 24 [5] y) performed 100 m at maximal speed and then 3 trials (25, 50, and 75 m) at the same pace as that of the 100 m. [La]_b was collected, and $\dot{\mathrm{V}}{\mathrm{O}}_2$ was measured 20 s postexercise. Results: The estimated energetic contributions for the 100-m trial are presented as mean (SD): E _aer, 51% (8%); E _an,al, 18% (2%); E _an,lac, 31% (9%). $\dot{\mathrm{V}}{\mathrm{O}}_2$ increased from L_0–25 to L_25–50 (mean = 3.5 L·min⁻¹; 90% confidence interval [CI], 3.4–3.7 L·min⁻¹ to mean = 4.2 L·min⁻¹; 90% CI, 4.0–4.3 L·min⁻¹) and then stabilized in the 2nd 50 m (mean = 4.1 L·min⁻¹; 90% CI, 3.9–4.3 L·min⁻¹ to mean = 4.2 L·min⁻¹; 90% CI, 4.0–4.4 L·min⁻¹). E _tot (juniors, 138 [18] kJ; seniors, 168 [26] kJ), E _an,al (juniors, 27 [3] kJ; seniors, 30 [3] kJ), and E _an,lac (juniors, 38 [12] kJ; seniors, 62 [24] kJ) were 11–58% higher in seniors. Faster swimmers (n = 26) had higher $\dot{\mathrm{V}}{\mathrm{O}}_2(4.6\mathrm{L}·{\min }^{-1}$ , 90% CI 4.4–4.8 L·min⁻¹ vs 3.9 L·min⁻¹, 90% CI 3.6–4.2 L·min⁻¹), and E _aer power was associated with fast performances (P < .001). Conclusion: Faster swimmers were characterized by higher $\dot{\mathrm{V}}{\mathrm{O}}_2$ and less time to reach the highest $\dot{\mathrm{V}}{\mathrm{O}}_2$ at ∼50 m of the 100-m swim. Anaerobic qualities become more important with age.

Purpose: Pacing, or the distribution of energy expenditure, is particularly important in swimming; however, there is limited research examining pacing profiles in long-distance freestyle events. This study aimed to characterize the pacing profiles of elite male 1500-m freestyle swimmers using a novel method to provide a detailed analysis of different race segments. Methods: The race data for 327 male 1500-m freestyle long-course races between 2010 and 2019 were analyzed retrospectively. The raw 50-m split times for each lap were converted to a percentage of overall race time. The races were classified as a fast-, average-, or slow-start strategy (laps 1–2); as an even, negative, or positive pacing strategy (laps 3–28); and as a fast-, average-, or slow-finish strategy (laps 29–30) to give an overall pacing profile. Results: Slow- and average-start strategies were associated with faster overall 1500-m times than a fast-start strategy (mean = −21.2 s; 90% confidence interval, −11.4 to −32.3 s, P = .00). An even pacing strategy in laps 3 to 28 yielded faster overall 1500-m times than a positive pacing strategy (−8.4 s, −3.9 to −13.0 s, P = .00). The overall 1500-m times did not differ substantially across the finish strategies (P = .99). The start strategy differed across age groups and nationalities, where younger swimmers and swimmers from Australia and Great Britain typically spent a lower percentage of race time in laps 1 to 2 (faster start strategy; −0.10%, −0.01% to −0.23%, P ≤ .02). Conclusion: Adopting a relatively slower start strategy helps conserve energy for the latter stages of a 1500-m freestyle race.

Purpose: To measure core temperature (T _core) in open-water (OW) swimmers during a 25-km competition and identify the predictors of T _core drop and hypothermia-related dropouts. Methods: Twenty-four national- and international-level OW swimmers participated in the study. Participants completed a personal questionnaire and a body fat/muscle mass assessment before the race. The average speed was calculated on each lap over a 2500-m course. T _core was continuously recorded via an ingestible temperature sensor (e-Celsius, BodyCap). Hypothermia-related dropouts (H group) were compared with finishers (nH group). Results: Average prerace T _core was 37.5°C (0.3°C) (N = 21). 7 participants dropped out due to hypothermia (H, n = 7) with a mean T _core at dropout of 35.3°C (1.5°C). Multiple logistic regression analysis found that body fat percentage and initial T _core were associated with hypothermia (G ² = 17.26, P < .001). Early T _core drop ≤37.1°C at 2500 m was associated with a greater rate of hypothermia-related dropouts (71.4% vs 14.3%, P = .017). Multiple linear regression found that body fat percentage and previous participation were associated with T _core drop (F = 4.95, P = .019). There was a positive correlation between the decrease in speed and T _core drop (r = .462, P < .001). Conclusions: During an OW 25-km competition at 20°C to 21°C, lower initial T _core and lower body fat, as well as premature T _core drop, were associated with an increased risk of hypothermia-related dropout. Lower body fat and no previous participation, as well as decrease in swimming speed, were associated with T _core drop.