Periodization schedules training periods according to predicted timings of cumulative adaptations and has been at the foundation of exercise prescription. Recently, a selected body of work has highlighted that original research may be providing support for variation, but not for periodized variation. Furthermore, it has been suggested that the timings of expected adaptations have not been tested. However, it is not clear if these problems are present in meta-analyses on the subject, since they might have selected a distinct body or work. Therefore, our goal was to systematically review meta-analyses on exercise periodization, to verify whether the included periodized programs have been contrasted to two types of non-periodized programs (i.e., constant or varied) and also if the predictions concerning cumulative adaptations were tested. Data sources: Cochrane, EBSCO (Academic Search Complete, CINAHL Plus, MEDLINE, PsycINFO, SportDISCUS), ISI Web of Knowledge, PEDro, PubMed, Scielo, Scopus. Study eligibility criteria: Meta-analyses comparing periodized exercise programs with non-periodized programs. Participants and interventions: Humans following any form of training periodization. Study appraisal and synthesis methods: A checklist was used to verify whether studies included in the meta-analyses compared periodized to constant or varied, non-periodized programs, as well as whether predictions concerning the timing of adaptations were tested. None of the 21 studies included in the two meta-analyses compared periodized programs with varied, non-periodized programs. The accuracy of the predictions concerning the proposed timings of adaptations was not scrutinized by any of the 21 studies. The studies in question have focused only on strength training, meaning they are limited in scope. The limitations found in these meta-analyses suggest that consultation of original research on the subject is advisable. Systematic review registration number (PROSPERO): CRD42018111338.

The aim of this study was to investigate the effect of leg blood flow restriction (BFR) applied during a 3-a-side futsal game on strength-related parameters. Twelve male futsal players were randomly assigned into two groups (n = 6 for each group) during 10 training sessions either with or without leg BFR. Prior to and post-training sessions, participants completed a series of tests to assess anabolic hormones and leg strength. Pneumatic cuffs were initially inflated to 110% of leg systolic blood pressure and further increased by 10% after every two completed sessions. In comparison with baseline, the resting post-training levels of myostatin (p = 0.002) and IGF-1/MSTN ratio (p = 0.006) in the BFR group changed, whereas no change in the acute level of IGF-1 and myostatin after exercise was observed. Peak torque of knee extension and flexion increased in both groups (p < 0.05). A trend of increased neural activation of all heads of the quadriceps was observed in both groups, however, it was statistically significant only for rectus femoris in BFR (p = 0.02). These findings indicated that the addition of BFR to normal futsal training might induce greater neuromuscular benefits by increasing muscle activation and augmenting the hormonal response.

Background: To date, no study has examined the hydration status of runners competing in a 24-h winter race under extremely cold environmental conditions. Therefore, the aim was to examine the effect of a 24-h race under an average temperature of -14.3°C on hydration status. Methods: Blood and urine parameters and body mass (BM) were assessed in 20 finishers (women, n = 6; men, n = 14) pre- and post-race. Results: Five (25%) ultra-runners had lower pre-race plasma sodium [Na+] and 11 (52%) had higher pre-race plasma potassium [K+] values than the reference ranges. Post-race plasma [Na+], plasma osmolality, urine osmolality and urine specific gravity remained stable (p > 0.05). The estimated fluid intake did not differ (p > 0.05) between women (0.30 ± 0.06 L/h) and men (0.46 ± 0.21 L/h). Runners with a higher number of completed ultra-marathons (r = -0.50, p = 0.024) and higher number of training kilometers (r = -0.68, p = 0.001) drank less than those with lower running experience. Pre-race and post-race plasma [Na+] were related to plasma osmolality (r = 0.65, p = 0.002, r = 0.69, p < 0.001, respectively) post-race, but not to fluid intake (p > 0.05). BM significantly decreased post-race (p = 0.002) and was not related to plasma [Na+] or fluid intake (p > 0.05). Post-race hematocrit and plasma [K+] decreased (p < 0.001) and transtubular potassium gradient increased (p = 0.008). Higher pre-race plasma [K+] was related to higher plasma [K+] loss post-race (p = -0.85, p < 0.001). Conclusion: Hydration status remained stable despite the extremely cold winter weather conditions. Overall fluid intake was probably sufficient to replenish the hydration needs of 24-h runners. Current recommendations may be too high for athletes competing in extremely cold conditions.

The purpose of this study was twofold: first, to compare the central adiposity (CA), cardiovascular fitness (CF), and physical activity (PA) in children with different weight status, and second, to determine the associations between moderate to vigorous physical activity (MVPA) and measures of adiposity [CA and body mass index (BMI)] and CF. A sample of 244 children (boys = 120 and girls = 124), 9.7-10.8 years of age (10.3 ± 0.3 years), was measured for stature, body mass, waist circumferences, and 20-m multi-stage fitness test. PA was recorded with ankle mounted accelerometer. BMI groups were used to classify children as underweight (UW), normal weight (NW), and overweight (OW). The prevalence of being OW was 21.7 and 25% in boys and girls, respectively. Only 5.3% of the participants were found to accumulate recommended amount (≥60 min/day) of MVPA. Boys were significantly outperformed girls in terms of CF. Moreover, they were significantly more engaged in moderate and vigorous physical activities than girls. Regardless of gender, results indicated that OW children had significantly higher values in all anthropometric parameters and lower level of CF than their UW and NW counterparts. In girls, OW children were found to accrue less time engaging in MVPA than the children in UW and NW groups. In boys, OW children were found to accrue less time engaging in vigorous activities than UW and NW children. Results also showed that there were no significant differences between UW and NW girls and boys in respect to CF. Besides, UW girls were found to accrue more time engaging in MVPA than NW girls. MVPA was found to be significantly and negatively correlated with BMI and waist circumference and significantly and positively correlated with CF in both boys and girls. These discrepancies and associations highlight the considerable influences of MVPA on weight status and CF in children.

Physical fitness components that relate with performance in marathon running, e.g., aerobic capacity and body composition, have been studied extensively. On the other hand, data on components of the health-related physical fitness, such as flexibility and muscle strength, were missing in this sport. Therefore, the aim of the present study was to profile force-velocity (F-v) characteristics, muscle strength and flexibility in female recreational marathon runners and to examine their relationship with age, race time and anthropometric characteristics (body fat percentage, fat-free mass - FFM, and total thigh muscle cross-sectional area - CSA). Thirty three female marathon runners (age 40.0 ± 8.9 years, body fat percentage 19.5 ± 4.6% and personal record 4:34 ± 0:39 h:min), separated into three age groups (<35, 35-45 and >45 years) and three performance groups (race time <4:15 h:min, 4:15-4:45 h:min and >4:45 h:min), performed sit-and-reach test (SAR), isometric muscle strength tests, squat jump, countermovement jump and F-v test on a cycle ergometer. The main findings of the present study were that (i) participants had moderate scores of body composition and physical fitness considering norms of the general population, (ii) the <35 age group had better jumping ability than 35-45 and >45 age group, and the older age group had lower F0, Pmax and rPmax than their younger counterparts, (iii) the slowest performance group scored the highest in SAR, and (iv) isometric strength, F0 and Pmax correlated largely with body mass and FFM. Considering the lack of existing data on anaerobic power and neuromuscular fitness of female marathon runners, the findings reported in this study would be useful for strength and conditioning trainers to monitor the training of their athletes. Even if these parameters were not related to race time, they should be monitored regularly as they were either component of health-related physical fitness (muscle strength and flexibility) or could help runners (anaerobic power) under specific circumstances such as ascends during a race.

Swimming induced pulmonary edema (SIPE) is a complication that can occur during exercise with the possibility of misdiagnosis and can quickly become life threatening; however, medical literature infrequently describes SIPE. Therefore, the aim of this review was to analyse all individual cases diagnosed with SIPE as reported in scientific sources, with an emphasis on the diagnostic pathways and the key facts resulting in its diagnosis. Due to a multifactorial and complicated pathophysiology, the diagnosis could be difficult. Based on the actual literature, we try to point out important findings regarding history, conditions, clinical findings, and diagnostic testing helping to confirm the diagnosis of SIPE. Thirty-eight cases from seventeen articles reporting the diagnosis of SIPE were selected. We found remarkable differences in the individual described diagnostic pathways. A total of 100% of the cases suffered from an acute onset of breathing problems, occasionally accompanied by hemoptysis. A total of 73% showed initial hypoxemia. In most of the cases (89%), an initial chest X-Ray or chest CT was available, of which one-third (71%) showed radiological signs of pulmonary edema. The majority of the cases (82%) experienced a rapid resolution of symptoms within 48 h, the diagnostic hallmark of SIPE. Due to a foreseeable increase in participation in swimming competitions and endurance competitions with a swimming component, diagnosis of SIPE will be important, especially for medical teams caring for these athletes.

Purpose: Little information is available on the association of hydration beliefs and behaviors in endurance athletes and exercise-associated hyponatremia (EAH). The aim of the present study was to determine hydration beliefs and behaviors in endurance athletes. Method: A 100 and 38 recreational athletes [107 mountain bikers (MTBers) and 31 runners] competing in seven different endurance and ultra-endurance races completed pre- and post-race questionnaires, and a subgroup of 113 (82%) participants (82 MTBers and 31 runners) also provided their blood samples. Result: More than half of the participants had some pre-race (59%), mid-race (58%), and post-race (55%) drinking plan. However, the participants simultaneously reported that temperature (66%), thirst (52%), and plan (37%) affected their drinking behavior during the race. More experienced (years of active sport: p = 0.002; number of completed races: p < 0.026) and trained (p = 0.024) athletes with better race performance (p = 0.026) showed a more profound knowledge of EAH, nevertheless, this did not influence their planned hydration, reported fluid intake, or post-race plasma sodium. Thirteen (12%) hyponatremic participants did not differ in their hydration beliefs, race behaviors, or reported fluid intake from those without post-race EAH. Compared to MTBers, runners more often reported knowledge of the volumes of drinks offered at fluid stations (p < 0.001) and information on how much to drink pre-race (p < 0.001), yet this was not associated with having a drinking plan (p > 0.05). MTBers with hydration information planned more than other MTBers (p = 0.004). In comparison with runners, more MTBers reported riding with their own fluids (p < 0.001) and planning to drink at fluid stations (p = 0.003). On the whole, hydration information was positively associated with hydration planning (n = 138) (p = 0.003); nevertheless, the actual reported fluid intake did not differ between the group with and without hydration information, or with and without a pre-race drinking plan (p > 0.05). Conclusion: In summary, hydration beliefs and behaviors in the endurance athletes do not appear to affect the development of asymptomatic EAH.

Purpose: Hypohydration and hyperhydration are significant disorders of fluid metabolism in endurance performance; however, little relevant data exist regarding multi-stage endurance activities. The aim of the present study was to examine the effect of running seven marathons in 7 consecutive days on selected anthropometric, hematological and biochemical characteristics with an emphasis on hydration status. Methods: Participants included 6 women and 20 men (age 42.6 ± 6.2 years). Data was collected before day 1 (B1) and after day 1 (A1), 4 (A4), and 7 (A7). Results: The average marathon race time was 4:44 h:min (ranging from 3:09 - 6:19 h:min). Plasma sodium, plasma potassium and urine sodium were maintained during the race. Body mass (p < 0.001, η2 = 0.501), body fat (p < 0.001, η2 = 0.572) and hematocrit (p < 0.001, η2 = 0.358) decreased. Plasma osmolality (Posm) (p < 0.001, η2 = 0.416), urine osmolality (Uosm) (p < 0.001, η2 = 0.465), urine potassium (p < 0.001, η2 = 0.507), urine specific gravity (Usg) (p < 0.001, η2 = 0.540), plasma urea (PUN) (p < 0.001, η2 = 0.586), urine urea (UUN) (p < 0.001, η2 = 0.532) and transtubular potassium gradient (p < 0.001, η2 = 0.560) increased at A1, A4, and A7 vs. B1. Posm correlated with PUN at A1 (r = 0.59, p = 0.001) and A4 (r = 0.58, p = 0.002). The reported post-race fluid intake was 0.5 ± 0.2 L/h and it correlated negatively with plasma [Na+] (r = -0.42, p = 0.007) at A4 and (r = -0.50, p = 0.009) at A7. Uosm was associated with UUN at A1 (r = 0.80, p < 0.001), at A4 (r = 0.81, p < 0.001) and at A7 (r = 0.86, p < 0.001) and with Usg (r = 0.71, p < 0.001) at A1, (r = 0.52, p = 0.006) at A4 and (r = 0.46, p = 0.02) at A7. Conclusions: Despite the decrease in body mass, fluid and electrolyte balance was maintained with no decrease in plasma volume after running seven marathons in seven consecutive days. Current findings support the hypothesis that body mass changes do not reflect changes in the hydration status during prolonged exercise.

Purpose: Excessive or inappropriate non-steroidal anti-inflammatory drug (NSAID) use during ultra-endurance events could cause potential risk to athletes' health. Reports on NSAID consumption in mountain bikers or ultra-mountain bikers are scarce. Therefore, the aim of this study was to investigate the prevalence of NSAID consumption immediately before, during and immediately after a mountain bike (MTB) race and to compare NSAID consumption in two different MTB competitions. Methods: This observational study took place at a three-stage MTB race (SMTB) (n = 63) and at a 24-h MTB race (24MTB) (n = 68), both held in the Czechia in 2017. NSAID consumption was evaluated via self-reported electronic questionnaires. Results: Of all finishers (n = 131), fourteen (10%) consumed NSAID at least once during the competition day (immediately before, during or immediately after the race). The number of NSAID consumers was the same in both competitions. Nevertheless, only three athletes (2%), all of them from the 24MTB, consumed NSAID during the race and 5% of all mountain bikers reported consumption after the race. In contrast to the SMTB, the intake reported by the 24MTB participants was quite homogeneous in terms of the timing of NSAID consumption. The NSAID users were older (p = 0.043) than the non-users. Ibuprofen was most commonly used by 79% of all consumers. Conclusion: The prevalence of NSAID use was higher in the older participants and seems to be lower in comparison with results from studies about runners, ultra-runners and triathletes suggesting that it is determined by the discipline (i.e., cycling). On the other hand, the timing of NSAID consumption was probably affected by the competition character (e.g., MTBS or 24MTB). Future studies should focus on a larger sample size of cyclists from various disciplines.

This study compared the ability to predict performance in half-marathon races through physiological variables obtained in a laboratory test and performance variables obtained in the Cooper field test. Twenty-three participants (age: 41.6 ± 7.6 years, weight: 70.4 ± 8.1 kg, and height: 172.5 ± 6.3 cm) underwent body composition assessment and performed a maximum incremental graded exercise laboratory test to evaluate maximum aerobic power and associated cardiorespiratory and metabolic variables. Cooper's original protocol was performed on an athletic track and the variables recorded were covered distance, rating of perceived exertion, and maximum heart rate. The week following the Cooper test, all participants completed a half-marathon race at the maximum possible speed. The associations between the laboratory and field tests and the final time of the test were used to select the predictive variables included in a stepwise multiple regression analysis, which used the race time in the half marathon as the dependent variable and the laboratory variables or field tests as independent variables. Subsequently, a concordance analysis was carried out between the estimated and actual times through the Bland-Altman procedure. Significant correlations were found between the time in the half marathon and the distance in the Cooper test (r = -0.93; p < 0.001), body weight (r = 0.40; p < 0.04), velocity at ventilatory threshold 1, (r = -0.72; p < 0.0001), speed reached at maximum oxygen consumption (vVO2max), (r = -0.84; p < 0.0001), oxygen consumption at ventilatory threshold 2 (VO2VT2) (r = -0.79; p < 0.0001), and VO2max (r = -0.64; p < 0.05). The distance covered in the Cooper test was the best predictor of time in the half-marathon, and might predicted by the equation: Race time (min) = 201.26 - 0.03433 (Cooper test in m) (R 2 = 0.873, SEE: 3.78 min). In the laboratory model, vVO2max, and body weight presented an R 2 = 0.77, SEE 5.28 min. predicted by equation: Race time (min) = 156.7177 - 4.7194 (vVO2max) - 0.3435 (Weight). Concordance analysis showed no differences between the times predicted in the models the and actual times. The data indicated a high predictive power of half marathon race time both from the distance in the Cooper test and vVO2max in the laboratory. However, the variable associated with the Cooper test had better predictive ability than the treadmill test variables. Finally, it is important to note that these data may only be extrapolated to recreational male runners.

Maximal heart rate (HRmax) is associated mostly with age, but age alone explains the variance in HRmax to a limited degree and may not be adequate to predict HRmax in certain groups. The present study was carried out on 3374 healthy Caucasian, Polish men and women, clients of a sports clinic, mostly sportspeople, with a mean age of 36.57 years, body mass 74.54 kg, maximum oxygen uptake (VO2max, ml∗kg-1 ∗min-1) 50.07. Cardiopulmonary exercise tests (CPET) were carried out on treadmills or cycle ergometers to evaluate HRmax and VO2max. Linear, multiple linear, stepwise, Ridge and LASSO regression modeling were applied to establish the relationship between HRmax, age, fitness level, VO2max, body mass, age, testing modality and body mass index (BMI). Mean HRmax predictions calculated with 5 previously published formulae were evaluated in subgroups created according to all variables. HRmax was univariately explained by a 202.5-0.53∗age formula (R 2 = 19.18). The weak relationship may be explained by the similar age with small standard deviation (SD). Multiple linear regression, stepwise and LASSO yielded an R 2 of 0.224, while Ridge yielded R 2 0.20. Previously published formulae were less precise in the more outlying groups of the studied population, overestimating HRmax in older age groups and underestimating in younger. The 202.5-0.53∗age formula developed in the present study was the best in the studied population, yielding lowest mean errors in most groups, suggesting it could be used in more active individuals. Tanaka's formula offers the second best overall prediction, while the 220-age formula yields remarkably high mean errors of up to 9 bpm. In conclusion, adding the studied variables in multiple regression models improves the accuracy of prediction only slightly over age alone and is unlikely to be useful in clinical practice.