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Purpose: Ice hockey is a team invasion sport characterized by repeated high-intensity skating efforts, technical and tactical skill, physical contact, and collisions requiring considerable levels of muscular strength. The purpose of this study was to evaluate the relationships between lower-body vertical force–time metrics and skating qualities in subelite female ice hockey players. Methods: A cross-sectional cohort design was employed utilizing 14 athletes (body mass = 66.7 [1.8] kg; height = 171.6 [6.2] cm; age = 21.1 [1.7] y). The relationships between metrics of lower-body strength collected from a drop jump, squat jump, countermovement jump, loaded countermovement jump, and an isometric squat and 4 skating qualities collected from a linear sprint, repeated sprint test, and a multistage aerobic test were evaluated. Results: The regression models revealed a positive relationship between relative peak force in the isometric squat and skating multistage aerobic test performance (r ² = .388; P = .017) and a positive relationship between repeated-sprint ability and eccentric mean force during the loaded countermovement jump (r ² = .595; P = .001). No significant relationships were observed between strength metrics and skating acceleration or maximal velocity. Conclusions: These data suggest that skating ability is most affected by relative isometric strength in female ice hockey players. It is recommended that practitioners focus training on tasks that improve relative force output. It is also recommended that isometric relative peak force be used as a monitoring metric for this cohort.

Purpose: To verify whether training the iron cross (IC) with assistive devices (herdos; H_IC) and added external load (LH_IC) to equate the moments of force developed on the rings could be considered an intermediate step between the nonoverloaded herdos situation (H_IC) and the IC performed on the rings. Methods: Relative levels of surface electromyography (sEMG) activity were normalized with respect to a standing IC before comparing gymnasts who can perform the IC on the rings (achievers) and gymnast who cannot (nonachievers) in the 2 herdos conditions (H_IC and LH_IC). Seven muscles were chosen for sEMG analysis, namely, pectoralis major (PM), latissimus dorsi, teres major, lower trapezius, serratus anterior, biceps brachii (BB), and triceps brachii. Additionally, 3 indices were calculated to measure levels of coactivation: Elbow_idx, Scapula_idx, and Shoulder_idx. Results: The bigger magnitude of differences in sEMG activity among situations was found for the PM and BB (F ≥ 30.7; P < .001). When comparing the global and the PM, teres major, BB, and triceps brachii activity across groups, nonachievers activated their musculature to a greater extent than the achievers independently of the herdos situation (P ≤ .046). Achievers’ Elbow_idx was the only index that was significantly higher (P ≤ .005) in the IC in comparison to LH_IC and H_IC. Conclusion: sEMG activity of PM and BB was particularly sensitive between situations, independently of the level of achievement. We recommend training the IC by adding external load in the herdos situation to increase muscle activity to levels closer to the rings situation but avoiding the potential factor of injuries.

Purpose: Advanced footwear technology is prevalent in distance running, with research focusing on these “super shoes” in competitive athletes, with less understanding of their value for slower runners. The aim of this study was to compare physiological and biomechanical variables between a model of super shoes (Saucony Endorphin Speed 2) and regular running shoes (Saucony Cohesion 13) in recreational athletes. Methods: We measured peak oxygen uptake (VO₂peak) in 10 runners before testing each subject 4 times in a randomly ordered crossover design (ie, Endorphin shoe or Cohesion shoe, running at 65% or 80% of velocity at VO₂peak [vVO₂peak]). We recorded video data using a high-speed camera (300 Hz) to calculate vertical and leg stiffnesses. Results: 65% vVO₂peak was equivalent to a speed of 9.4 km·h⁻¹ (0.4), whereas 80% vVO₂peak was equivalent to 11.5 km·h⁻¹ (0.5). Two-way mixed-design analysis of variance showed that oxygen consumption in the Endorphin shoe was 3.9% lower than in the Cohesion shoe at 65% vVO₂peak, with an interaction between shoes and speed (P = .020) meaning an increased difference of 5.0% at 80% vVO₂peak. There were small increases in vertical and leg stiffnesses in the Endorphin shoes (P < .001); the Endorphin shoe condition also showed trivial to moderate differences in step length, step rate, contact time, and flight time (P < .001). Conclusions: There was a physiological benefit to running in the super shoes even at the slower speed. There were also spatiotemporal and global stiffness improvements indicating that recreational runners benefit from wearing super shoes.

Purpose: The study examined the longitudinal interplay of anthropometric, metabolic, and neuromuscular development related to performance in adolescent national-level swimmers over 12 months. Methods: Seven male and 12 female swimmers (14.8 [1.3] y, FINA [International Swimming Federation] points 716 [51]) were tested before (T0) and after the preparation period (T1), at the season’s peak (T2), and before the next season (T3). Anthropometric (eg, fat percentage) and neuromuscular parameters (squat and bench-press load-velocity profile) were assessed on dry land. Metabolic (cost of swimming [C], maximal oxygen uptake [ $\dot{\mathrm{V}}{\mathrm{O}}_2\text{peak}$ ], and peak blood lactate [bLa_peak]) and performance (sprinting speed [v _sprint] and lactate thresholds [LT1 and 2]) factors were determined using a 500-m submaximal, 200-m all-out, 20-second sprint, and incremental test (+0.03 m·s⁻¹, 3 min), respectively, in front-crawl swimming. Results: v _sprint (+0.6%) and LT1 and 2 (+1.9–2.4%) increased trivially and slightly, respectively, from T0 to T2 following small to moderate strength increases (≥+10.2%) from T0 to T1 and $\dot{\mathrm{V}}{\mathrm{O}}_2\text{peak}$ (+6.0%) from T1 to T2. Bench-press maximal strength and peak power correlated with v _sprint from T0 to T2 (r ≥ .54, P < .05) and LT2 at T1 (r ≥ .47, P < .05). Changes in fat percentage and $\dot{\mathrm{V}}{\mathrm{O}}_2\text{peak}$ (T2–T1 and T3–T2, r ≤ −.67, P < .01) and C and LT2 (T2–T0, r = −.52, P = .047) were also correlated. Conclusions: Increases in strength and $\dot{\mathrm{V}}{\mathrm{O}}_2\text{peak}$ from preparation to the competition period resulted in improved sprint and endurance performance. Across the season, upper-body strength was associated with v _sprint and LT2, although their changes were unrelated.

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.