Association of Training Characteristics with Critical Power in Competitive Recreational Cyclists and Triathletes

Endurance athletes often employ a training zone approach to classify their training intensity, where Zones 1, 2, and 3 (Z1-Z3) correspond to low, moderate, and high intensities. Research has shown that many elite athletes across a multitude of endurance sports employ polarized training distributions (TIDs), i.e., they spend a large percentage of their training in Z1 with much of the remainder in Z3 and little training in Z2. This appears to be beneficial for performance in these populations. The typical TIDs among recreationally competitive endurance athletes and their impact on performance are less well understood. PURPOSE: The purpose of this study was to examine the TIDs of recreationally competitive cyclists and triathletes and the impact of training characteristics on cycling performance. METHODS: Participants (n = 19, age = 31.7 ± 10.7 years; height = 176.6 ± 8.8 cm; weight = 70.8 ± 8.6 kg, relative CP = 4.3 ± 0.7 W/kg) submitted raw activity files, which they had previously uploaded to Strava, a popular workout tracking site. We used a workout analysis program (Golden Cheetah, V3.5) and R statistical language to analyze training and racing data. We determined each athlete’s highest critical power (CP) relative to body weight by first finding the highest maximum mean power (MMP) over 2, 5, and 10 minutes achieved over the course of a single week and then employing the linear power inverse of time CP model. We considered relative CP (W/kg) as our proxy for endurance performance, as it highly correlates with race performances. We then extracted values for estimated maximal aerobic power (MAP), training volume (training hours), training intensity (mean training power as a percent of MAP), training frequency (number of sessions), and training polarization (polarization index (PI) calculated from percent time in power Z1-3) for the 12 weeks leading up to the performance measure. We determined the association of the training characteristics on relative CP while controlling for participant age and fitness (MAP) by employing a linear regression. RESULTS: Only 4 of 23 participants employed a polarized training approach as defined by a PI \u3e 2.0. Athletes spent on average 71.0 ± 9.5% of their training time in Z1, 16.1% ± 6.1 in Z2, and 12.9% ± 7.3 in Z3. They completed 74.9 ± 22.9 sessions and amassed 110.3 ± 46.9 hours of training time over the 12 weeks leading up the performance measure. In this preliminary analysis of 19 participants, we were unable to detect a statistically significant effect of polarization on relative CP, when controlling for age, fitness, and all other training variables. Yet, polarization was the explanatory variable with the largest impact on relative CP, b (SE) = 0.25 (0.55), t = 0.457, p = 0.656. CONCLUSION: Most of the recreationally competitive cyclists and triathletes in our study did not employ a polarized TID, despite data from elite cohorts and laboratory studies showing its benefits. More research into the effect of TID on performance and health in recreationally competitive athletes is needed to confirm its benefits in this population

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

While many of the architectural details of future exascale-class high performance computer systems are still a matter of intense research, there appears to be a general consensus that they will be strongly heterogeneous, featuring "standard" as well as "accelerated" resources. Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi. Any software infrastructure that claims usefulness for such environments must be able to meet their inherent challenges: massive multi-level parallelism, topology, asynchronicity, and abstraction. The "General, Hybrid, and Optimized Sparse Toolkit" (GHOST) is a collection of building blocks that targets algorithms dealing with sparse matrix representations on current and future large-scale systems. It implements the "MPI+X" paradigm, has a pure C interface, and provides hybrid-parallel numerical kernels, intelligent resource management, and truly heterogeneous parallelism for multicore CPUs, Nvidia GPUs, and the Intel Xeon Phi. We describe the details of its design with respect to the challenges posed by modern heterogeneous supercomputers and recent algorithmic developments. Implementation details which are indispensable for achieving high efficiency are pointed out and their necessity is justified by performance measurements or predictions based on performance models. The library code and several applications are available as open source. We also provide instructions on how to make use of GHOST in existing software packages, together with a case study which demonstrates the applicability and performance of GHOST as a component within a larger software stack.Comment: 32 pages, 11 figure

Caffeine Supplementation Strategies Among Endurance Athletes

Caffeine is widely accepted as an endurance-performance enhancing supplement. Most scientific research studies use doses of 3–6 mg/kg of caffeine 60 min prior to exercise based on pharmacokinetics. It is not well understood whether endurance athletes employ similar supplementation strategies in practice. The purpose of this study was to investigate caffeine supplementation protocols among endurance athletes. A survey conducted on Qualtrics returned responses regarding caffeine supplementation from 254 endurance athletes (f = 134, m =120; age = 39.4 ± 13.9 y; pro = 11, current collegiate athlete = 37, recreational = 206; running = 98, triathlon = 83, cycling = 54, other = 19; training days per week = 5.4 ± 1.3). Most participants reported habitual caffeine consumption (85.0%; 41.2% multiple times daily). However, only 24.0% used caffeine supplements. A greater proportion of men (31.7%) used caffeine supplements compared with women (17.2%; p = 0.007). Caffeine use was also more prevalent among professional (45.5%) and recreational athletes (25.1%) than in collegiate athletes (9.4%). Type of sport (p = 0.641), household income (p = 0.263), education (p = 0.570) or working with a coach (p = 0.612) did not have an impact on caffeine supplementation prevalence. Of those reporting specific timing of caffeine supplementation, 49.1% and 34.9% reported consuming caffeine within 30 min of training and races respectively; 38.6 and 36.5% used caffeine 30–60 min before training and races. Recreational athletes reported consuming smaller amounts of caffeine before training (1.6 ± 1.0 mg/kg) and races (2.0 ± 1.2 mg/kg) compared with collegiate (TRG: 2.1 ± 1.2 mg/kg; RACE: 3.6 ± 0.2 mg/kg) and professional (TRG: 2.4 ± 1.1 mg/kg; RACE: 3.5 ± 0.6 mg/kg) athletes. Overall, participants reported minor to moderate perceived effectiveness of caffeine supplementation (2.31 ± 0.9 on a four-point Likert-type scale) with greatest effectiveness during longer sessions (2.8 ± 1.1). It appears that recreational athletes use lower caffeine amounts than what has been established as ergogenic in laboratory protocols; further, they consume caffeine closer to exercise compared with typical research protocols. Thus, better education of recreational athletes and additional research into alternative supplementation strategies are warranted

Caffeine Supplementation Strategies Among Endurance Athletes

Caffeine is widely accepted as an ergogenic aid for endurance performance. Many laboratory studies use doses of 3-6 mg/kg of caffeine 60 min prior to exercise. It is unclear if endurance athletes employ similar supplementation schemes in practice. Further, there is a paucity of data regarding caffeine consumption in this population. PURPOSE: The purpose of this study was to investigate caffeine supplementation strategies and consumption among endurance athletes. METHODS: A survey conducted on Qualtrics returned responses regarding caffeine supplementation from 247 endurance athletes (f = 129, m =118; age = 40.4 ± 18.4 y; pro = 11, current/former collegiate athlete = 67, recreational = 169; running = 95, triathlon = 80, cycling = 54, other = 18; training days per week = 5.4 ± 1.3). Descriptive statistics were calculated using SPSS V26. Pearson chi-square tests of independence were performed to investigate potential associations between a variety of grouping variables and caffeine use. Further, supplementation schemes were analyzed. Finally, athletes’ perception of the effectiveness of caffeine were examined. RESULTS: The majority of participants reported habitual caffeine consumption (84.2%; 34.8% multiple times daily). Yet, only 23.5% reported using caffeine supplements. A greater percentage of men (30.5%) used caffeine supplements compared with women (17.1%; p = .013). Athlete status was significantly associated with caffeine consumption (p = .004). Caffeine use was more prevalent among professional (36.4%) and recreational athletes (28.4%) compared with current/former collegiate athletes (9.0%). There were no significant differences in caffeine supplementation when comparing across type of sport (p = .505), household income (p = .191), education (p = .453) or working with a coach (p = .560). While not statistically significant (p = .064), 53.4% of those using caffeine supplements reported placing among the top 3 in their age group in the past year, compared with only 39.7% of those not using caffeine supplements. Sixty-eight athletes (27.5%) reported that they specifically timed caffeine supplementation around training (60.3% only before, 14.7% only during, 25.0% before and during sessions). Seventy-seven (31.2%) athletes reported timing caffeine intake around races (55.8% before, 13.0% during, 31.2% both). Of those reporting specific timing of caffeine use, 47.3% and 33.9% reported consuming caffeine within 30 min of training sessions and races respectively; 40.0% and 35.5% used caffeine 30-60 min before training and races; 12.7% and 36.6% reported taking caffeine \u3e60 min before training and races. The most frequently reported interval of supplementation during training (64.0%) and races (45.2%) was every 60-90 minutes. Those reporting specific amounts of caffeine consumed before training (n = 27) and races (n = 14), used 1.8 ± 1.0 mg/kg and 2.4 ± 1.3 mg/kg respectively. On average, 53.6% and 39.1% of athletes reported that caffeine exerted no effects to only minor effects during various types of training and racing respectively. A greater percentage of athletes reported moderate and major effects during more intense training as well as longer training sessions and races (52.7 - 72.7%). CONCLUSION: Most athletes in the present study did not follow typical laboratory protocols that have elicited ergogenic effects of caffeine. Better education among athletes and coaches or research into more diverse supplementation schemes are needed

Anaerobic Performance in Female Collegiate Wrestlers During Ovulation Versus the Mid-luteal Phase of the Menstrual Cycle: A Pilot Study

Anaerobic performance may vary during different phases of the menstrual cycle. The greatest differences occur between the late-follicular phase (i.e., ovulation) and the mid-luteal phase. Optimal anaerobic performance may be observed during the mid-luteal phase. PURPOSE: To explore differences in upper and lower body anaerobic performance during ovulation versus the mid-luteal phase of the menstrual cycle in collegiate female wrestlers. METHODS: Six female collegiate wrestlers (age = 18.6 ± 0.2 yrs; height = 165.0 ± 0.5 cm; body mass = 79.7 ± 9.6 kg; lean body mass = 45.6 ± 2.8 kg; % body fat = 31.4 ± 6.6%) performed both upper and lower body Wingate tests, each lasting 30 seconds, during the ovulation and the mid-luteal phases of the menstrual cycle. Upper and lower body tests were performed 24 hours apart. Menstrual cycle phases were determined by calendar tracking, reverse estimation of ovulation, and administration of a urinary luteinizing hormone test assessed daily until positive results indicated ovulation. Lower body power was measured using a Velotron cycle ergometer, with a resistance of 0.075 kg/kg applied after a 5-second sprint at a resistance of 1 kg (50 W). Peak power (W) and relative power (W/kg) were measured. Upper body power was measured using a Monark hand ergometer with a 0.045 kg/kg resistance applied after a 5-second sprint at a resistance of 0.5 kg (25 W). Peak power (W) and relative power (W/kg) was calculated using rotation count, weight applied, and distance per rotation. Paired t-tests were used to analyze differences in means during the ovulation vs mid-luteal phases with a significance level of 0.05. RESULTS: There were no significant differences between trials for any variables measured. Lower body peak power (W) was 848.3 ± 126.1W vs 855.0 ± 143.9W. Lower body relative power (W/kg) was 11.8 ± 0.7W/kg vs 11.9 ± 0.8W/kg. Upper body peak power (W) was 162.1 ± 29.6 vs 160.2 ± 13.2W. Upper body relative power (W/kg) was 2.3 ± 0.4W/kg vs 2.2 ± 0.2W/kg. CONCLUSION: There may not be an optimal timing of significantly increased anaerobic performance in regard to menstrual phase in these wrestlers

Nutrient Adequacy in Endurance Athletes

Adequate nutrition is critical to optimal performance in endurance athletes. However, it remains unclear if endurance athletes are consuming enough energy, macronutrients, and micronutrients. PURPOSE: The purpose of this study was to determine if endurance athletes are meeting their nutritional requirements and whether it varies by gender. METHODS: Endurance athletes (n=44), 39.0±14.2 y, participated in the study. Dietary intake was assessed using the five-step multiple-pass 24-hour recall method, a validated measure, that involved asking the participants to recall in detail the type and amount of foods and beverages they consumed the previous day. Energy, macronutrient, and micronutrient intakes were computed from the recalls using the ESHA Food Processor Diet Analysis Software. Nutritional adequacy was calculated by comparing the nutrient intakes of the participants with nutrient standards set by the Food and Nutrition Board, Institute of Medicine, the American College of Sports Medicine (ACSM), the Dietary Guidelines for Americans, and the American Heart Association (AHA). Fisher’s Exact test was used to compare the proportion of male and female endurance athletes that did not meet the requirements for energy, macronutrient, and micronutrient intakes. RESULTS: Over 50% of male athletes did not consume enough water, protein, carbohydrates, dietary fiber, linoleic acid, α-linolenic acid, eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), vitamins D, E, and K, pantothenic acid, biotin, manganese, chromium, zinc, molybdenum, choline, potassium, and magnesium. More than 50% of female athletes did not consume enough protein, carbohydrates, linoleic acid, α-linolenic acid, EPA, DHA, vitamins D, E, and B12, pantothenic acid, thiamine, biotin, manganese, chromium, zinc, molybdenum, choline, and potassium. About 50% of male and female athletes consumed more than the recommended amount of total fat, saturated fat, cholesterol, and sodium. Many athletes (male: 20%; female: 8%) did not meet the energy requirements. A significantly higher portion of male athletes compared to female athletes did not meet the nutrient requirements for dietary fiber (70.0% and 24.0%, respectively; p ≤ 0.001), α-linolenic acid (90.0% and 60.0%, respectively; p = 0.04), and total water (75.0% and 40.0%, respectively; p = 0.03). CONCLUSION: Many endurance athletes are not meeting the nutrient requirements for energy, water, and several macronutrients and micronutrients, with some differences by gender. These results need to be confirmed by a larger study. Endurance athletes would benefit from dietary counseling by a registered dietitian

The Relationship between Dietary Intake and Sleep Quality in Endurance Athletes

Athletes have a high prevalence of poor sleep quality. It is unknown if dietary intake affects sleep quality in athletes. PURPOSE: To examine if sleep quality in endurance athletes is related to dietary intake. METHODS: Endurance athletes (n=187), 42.0±13.7 y, participated in the study. Participants completed questionnaires on demographics, dietary intake, and sleep quality. Sleep quality was assessed using the Athlete Sleep Screening Questionnaire (ASSQ), a validated tool, with scores ranging from 0-40 (higher scores indicate poorer sleep quality). The ASSQ subscales included sleep difficulty (SD), chronotype (C), and sleep disordered breathing (SDB). ASSQ-SD was categorized as having none (0-4), mild (5-7), moderate (8-10), and severe (11-17) SD. ASSQ-C was categorized as morning (\u3e4) or evening (higher risk for sleep issues) (≤4) type. ASSQ-SDB was categorized as difficulty breathing (\u3e1) or not ( RESULTS: ASSQ score was 22.3±3.96, indicating average sleep quality among athletes. ASSQ-SD score showed that 33.7% of athletes had no SD, and 38.5%, 21.9%, and 5.9% had mild, moderate, and severe SD, respectively. ASSQ-C score was 9.4±2.82, and 93% of athletes were morning type and 7% were evening type. ASSQ-SDB score indicated that 79.1% of athletes had normal and 20.9% had disordered breathing. Preliminary analyses revealed that ASSQ scores were significantly related to vegetable (p=.038) and caffeinated beverage (p=0.034) intake, but not to the other dietary variables. Significantly higher ASSQ score, (i.e., poorer sleep quality) was found in athletes who consumed ≥5 servings/d (24.0±4.0) of vegetables compared with \u3c1 \u3e(20.9±3.18, p=.011) or 1-2 (21.6±4.11, p=.030) servings/d. Athletes who drank \u3e2.5 cups/d of caffeinated beverages had higher ASSQ score or poorer sleep quality versus those who consumed 3 cups/d of milk had a higher disordered breathing score (.69±.947) versus those who drank 1-2 (.18±.521, p=.009) and \u3c1 \u3e(.30±.641, p=.016) cups/d. Athletes who consumed /d of whole grains had a higher ASSQ-DBS score (.48±.79) versus those who consumed 3-4 servings/d (.09±.401, p=.029). ASSQ-SD was not related to any of the dietary variables. CONCLUSIONS: Increased vegetable and caffeinated beverage consumption were associated with decreased sleep quality. Less whole grains and fruits were associated with evening chronotype. Athletes who consumed more milk and less whole grains had increased disordered breathing