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Purpose: Few data are available on sleep characteristics of elite track-and-field athletes. Our study aimed to assess (1) differences in sleep between sexes and among different track-and-field disciplines, (2) the effect of individualized sleep-hygiene strategies on athletes’ sleep parameters, and (3) daytime nap characteristics in track-and-field athletes. Methods: Sleep characteristics of 16 elite Olympic-level track-and-field athletes (male: n = 8; female: n = 8) were assessed during the preseason period, at baseline (T0), and during the in-season period, after the adoption of individualized sleep-hygiene strategies (T1). Sleep parameters were objectively monitored by actigraphy for a minimum of 10 days, for each athlete, at both T0 and T1. A total of 702 nights were analyzed (T0 = 425; T1 = 277). Results: Female athletes displayed better sleep efficiency (88.69 [87.69–89.68] vs 91.72 [90.99–92.45]; P = .003, effect size [ES]: 0.44), lower sleep latency (18.99 [15.97–22.00] vs 6.99 [5.65–8.32]; P < .001, ES: 0.65), higher total sleep time (07:03 [06:56–07:11] vs 07:18 [07:10–07:26]; P = .030, ES: 0.26), earlier bedtime (00:24 [00:16–00:32] vs 00:13 [00:04–00:22]; P = .027, ES: 0.18), and lower nap frequency (P < .001) than male athletes. Long-distance runners had earlier bedtime (00:10 [00:03–00:38] vs 00:36 [00:26–00:46]; P < .001, ES: 0.41) and wake-up time (07:41 [07:36–07:46] vs 08:18 [08:07–08:30]; P < .001, ES: 0.61), higher nap frequency, but lower sleep efficiency (88.79 [87.80–89.77] vs 91.67 [90.95–92.38]; P = .013, ES: 0.44), and longer sleep latency (18.89 [15.94–21.84] vs 6.69 [5.33–8.06]; P < .001, ES: 0.67) than athletes of short-term disciplines. Furthermore, sleep-hygiene strategies had a positive impact on athletes’ total sleep time (429.2 [423.5–434.8] vs 451.4 [444.2–458.6]; P < .001, ES: 0.37) and sleep latency (14.33 [12.34–16.32] vs 10.67 [8.66–12.68]; P = .017, ES: 0.19). Conclusions: Sleep quality and quantity were suboptimal at baseline in Olympic-level track-and-field athletes. Large differences were observed in sleep characteristics between sexes and among different track-and-field disciplines. Given the positive effect of individualized sleep-hygiene strategies on athlete’s sleep, coaches should implement sleep education sessions in the daily routine of top-level athletes.

Purpose: To investigate the influence of menstrual-cycle (MC) phase on measures of recovery status, that is, resting heart rate, perceived sleep quality, and physical and mental readiness to train, among female endurance athletes. Methods: Daily data were recorded during 1 to 4 MCs (ie, duration ≥21 and ≤35 d, ovulatory, luteal phase ≥10 d) of 41 trained-to-elite-level female endurance athletes (mean [SD]: age 27 [8] y, weekly training: 9 [3] h). Resting heart rate was assessed daily using a standardized protocol, while perceived sleep quality and physical and mental readiness to train were assessed using a visual analog scale (1–10). Four MC phases (early follicular phase [EFP], late follicular phase, ovulatory phase, and midluteal phase [MLP]) were determined using the calendar-based counting method and urinary ovulation-prediction test. Data were analyzed using linear mixed-effects models. Results: Resting heart rate was significantly higher in MLP (1.7 beats·min⁻¹, P = .006) compared with EFP without significant differences between the other MC phases. Perceived sleep quality was impaired in MLP compared with late follicular phase (−0.3, P = .035). Physical readiness to train was lower both in ovulatory phase (−0.6, P = .015) and MLP (−0.5, P = .026) compared with EFP. Mental readiness to train did not show any significant differences between MC phases (P > .05). Conclusions: Although significant, the findings had negligible to small effect sizes, indicating that MC phase is likely not the main determinant of changes in measures of recovery status but, rather, one of the many possible stressors.

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: The aim of this study was to describe individual training characteristics, racing strategies, and periodization in preparation for the Tour de France in 2 world-class road cyclists finishing in the top 5 of the general classification. Methods: Week‐by‐week power meter training and racing data of 2 (A and B) road cyclists (age: 29 and 23 y; maximum oxygen consumption: 83 and 81 mL·min⁻¹·kg⁻¹; and relative 20‐min record power output: 6.9 and 6.5 W·kg⁻¹) in the preparation phase (December–July/August) leading up to the Tour de France were retrospectively analyzed. Weekly volume and intensity distribution in power zones were considered. Results: Cyclists A and B completed 46 and 19 races, 22.5 (6.3) and 18.2 (5.1) h·wk⁻¹, with a pyramidal intensity distribution of 81.0%–13.3%–5.7%, and 88.8%–7.9%–3.3% in zone 1–zone 2–zone 3. Cyclist B spent 14 days at altitude. Increased high-intensity volume and polarization index occurred during race weeks. During periods without racing, training intensity progressively increased. Strength training was performed during November and December but not during the following months. During tapering, total exercise volume and time at high intensity decreased. Conclusion: These data provide novel insights into the periodization of world-class road cyclists in advance of a top 5 placing in the Tour de France general classification.

Purpose: To analyze the associations between physiological and biomechanical variables with the FINA (International Swimming Federation) points (ie, swimming performance) obtained in 1500-m front-crawl swimming to determine whether these variables can be used to explain triathletes’ FINA points. Methods: Fourteen world-class, international and national triathletes (10 male: 23.24 [3.70] y and 4 female: 23.36 [3.76] y) performed a 1500-m front-crawl swimming test in a short-course pool. Heart rate (HR), oxygen uptake ( ${\dot{\mathrm{V}}\mathrm{O}}_2$ ), and blood lactate concentrations were obtained before and after the test. HR was also measured during the effort. Highest ${\dot{\mathrm{V}}\mathrm{O}}_2$ value ( ${\dot{\mathrm{V}}\mathrm{O}}_2\text{peak}$ ) was estimated by extrapolation. Clean swimming speed, turn performance, stroke rate, stroke length, and stroke index (SI) were obtained by video analysis. Results: Average 1500-m performance times were 1088 (45) seconds and 1144 (31) seconds for males and females, respectively. HR after the effort, ${\dot{\mathrm{V}}\mathrm{O}}_2\text{peak}$ , aerobic contributions, total energy expenditure, energy cost, and turn performance presented moderate negative associations with swimming performance (r ≈ .5). In contrast, respiratory exchange ratio, anaerobic alactic contribution, clean swimming speed, stroke length, and SI were positively related, with clean swimming speed and SI having a strong large association (r ≈ .7). A multiple stepwise regression model determined that 71% of the variance in FINA points was explained by SI and total energy expenditure, being predictors in 1500-m front-crawl swimming. Conclusions: Swimming performance in triathletes was determined by the athletes’ energy demands and biomechanical variables. Thus, coaches should develop specific technique skills to improve triathletes’ swimming efficiency.

Purpose: To determine whether there are bilateral and interdigit differences in the maximal force production of experienced climbers and whether these differences are mediated by ability level or preferred style of climbing. Methods: Thirty-six male climbers (age 30 [9.4] y) took part in a single-session trial to test their maximal force production on both hands. The tests included a one-arm maximal isometric finger flexor strength test (MIFS) and a one-arm individual MIFS. Bilateral differences were analyzed by strongest hand (defined as the hand that produced the highest MIFS value) and dominance (defined as the writing hand). Results: A pairwise t test found that MIFS was significantly greater for the strongest hand (mean difference = 4.1%, 95% CI, −0.052 to 0.029, P < .001), with handedness explaining 89% of the variation. A 2-way mixed-model analysis of variance determined that there were no interactions between preferred style (bouldering or sport climbing) and MIFS or between ability level (advanced or elite) and MIFS. Conclusions: Climbers have significant finger flexor strength bilateral asymmetries between their strongest and weakest hand. Moreover, when dominance is controlled, this difference in strength is present, with the dominant hand producing more force. Neither preferred style of climbing nor the ability level of the climbers could explain these asymmetries. As such, practitioners should consider regularly monitoring unilateral strength, aiming to minimize the likelihood of large bilateral asymmetry occurring.