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Sonographically detectable intratendinous blood flow (IBF) is found in 50%-88% of Achilles tendinopathy patients as well as in up to 35% of asymptomatic Achilles tendons (AT). Although IBF is frequently associated with tendon pathology, it may also represent a physiological regulation, for example, due to increased blood flow in response to exercise. Therefore, this study aimed to investigate the acute effects of a standardized running exercise protocol on IBF assessed with Doppler ultrasound (DU) Advanced dynamic flow in healthy ATs. 10 recreationally active adults (5 f, 5m; 29 +/- 3years, 1.72 +/- 0.12m, 68 +/- 16kg, physical activity 206 +/- 145minute/wk) with no history of AT pain and inconspicious tendon structure performed 3 treadmill running tasks on separate days (M1-3) with DU examinations directly before and 5, 30, 60, and 120minutes after exercise. At M1, an incremental exercise test was used to determine the individual anaerobic threshold (IAT). At M2 and M3, participants performed 30-minute submaximal constant load tests (CL1/CL2) with an intensity 5% below IAT. IBF in each tendon was quantified by counting the number of vessels. IBF increased in five ATs from no vessels at baseline to one to four vessels solely detectable 5minutes after CL1 or CL2. One AT had persisting IBF (three vessels) throughout all examinations. Fourteen ATs revealed no IBF at all. Prolonged running led to a physiological, temporary appearance of IBF in 25% of asymptomatic ATs. To avoid exercise-induced IBF in clinical practice, DU examinations should be performed after 30minutes of rest.
Increased Achilles (AT) and Patellar tendon (PT) thickness in adolescent athletes compared to non-athletes could be shown. However, it is unclear, if changes are of pathological or physiological origin due to training. The aim of this study was to determine physiological AT and PT thickness adaptation in adolescent elite athletes compared to non-athletes, considering sex and sport. In a longitudinal study design with two measurement days (M1/M2) within an interval of 3.2 +/- 0.8 years, 131 healthy adolescent elite athletes (m/f: 90/41) out of 13 different sports and 24 recreationally active controls (m/f: 6/18) were included. Both ATs and PTs were measured at standardized reference points. Athletes were divided into 4 sport categories [ball (B), combat (C), endurance (E) and explosive strength sports (S)]. Descriptive analysis (mean SD) and statistical testing for group differences was performed (cy = 0.05). AT thickness did not differ significantly between measurement days, neither in athletes (5.6 +/- 0.7 mm/5.6 +/- 0.7 mm) nor in controls (4.8 +/- 0.4 mm/4.9 +/- 0.5 mm, p > 0.05). For PTs, athletes presented increased thickness at M2 (Ml: 3.5 +/- 0.5 mm, M2: 3.8 +/- 0.5 mm, p < 0.001). In general, males had thicker ATs and PTs than females (p < 0.05). Considering sex and sports, only male athletes from B, C, and S showed significant higher PT-thickness at M2 compared to controls (p <= 0.01). Sport-specific adaptation regarding tendon thickness in adolescent elite athletes can be detected in PTs among male athletes participating in certain sports with high repetitive jumping and strength components. Sonographic microstructural analysis might provide an enhanced insight into tendon material properties enabling the differentiation of sex and influence of different sports.
Background: Data on electrocardiographic and echocardiographic pre-participation screening findings in paediatric athletes are limited.
Methods and results: 10-15 year-old athletes (n = 343) were screened using electro- and echocardiography. The electrocardiogram (ECG) was normal in 220 (64%), mildly abnormal in 108 (31%), and distinctly abnormal in 15 (4%) athletes. Echocardiographic upper reference limits (URL, 97.5 percentile) for the left ventricular (LV) wall thickness in 10-11-year-old boys and girls were 9-10 mm and 8-9 mm, respectively; in 12-13-year-old boys and girls 9-10 mm; and in 14-15-year-old boys and girls 10-11 mm and 9-10 mm, respectively. Three athletes were excluded from competitive sports: one for symptomatic Wolff-Parkinson-White syndrome with a normal echocardiogram; one for negative T-waves in V-1-V-4 and a dilated right ventricle by echocardiography suggestive of (arrhythmogenic) right ventricular disease; and one for normal ECG and biscupid aortic valve including an aneurysm of the ascending aorta detected by echocardiography. Related to echocardiographic findings, the sensitivity and specificity of the ECG to identify cardiovascular abnormalities was 38% and 64%, respectively. The ECG's positive-predictive and negative-predictive values were 13% and 88%, respectively. The numbers needed to screen and calculated costs were 172 for ECG ( 7049), 172 for echocardiography ( 11,530), and 114 combining ECG and echocardiography ( 9323).
Conclusions: Compared to adults, paediatric athletes presented with fewer distinctly abnormal ECGs, and there was no gender difference in paediatric athletes' ECG-pattern distribution. A combination of ECG and echocardiography for pre-participation screening of paediatric athletes is superior to ECG alone but 30% more costly.
Background: Athletes may differ in their resting metabolic rate (RMR) from the general population. However, to estimate the RMR in athletes, prediction equations that have not been validated in athletes are often used. The purpose of this study was therefore to verify the applicability of commonly used RMR predictions for use in athletes. Methods: The RMR was measured by indirect calorimetry in 17 highly trained rowers and canoeists of the German national teams (BMI 24 ± 2 kg/m2, fat-free mass 69 ± 15 kg). In addition, the RMR was predicted using Cunningham (CUN) and Harris-Benedict (HB) equations. A two-way repeated measures ANOVA was calculated to test for differences between predicted and measured RMR (α = 0.05). The root mean square percentage error (RMSPE) was calculated and the Bland-Altman procedure was used to quantify the bias for each prediction. Results: Prediction equations significantly underestimated the RMR in males (p < 0.001). The RMSPE was calculated to be 18.4% (CUN) and 20.9% (HB) in the entire group. The bias was 133 kcal/24 h for CUN and 202 kcal/24 h for HB. Conclusions: Predictions significantly underestimate the RMR in male heavyweight endurance athletes but not in females. In athletes with a high fat-free mass, prediction equations might therefore not be applicable to estimate energy requirements. Instead, measurement of the resting energy expenditure or specific prediction equations might be needed for the individual heavyweight athlete.
Background Low back pain (LBP) is a common pain syndrome in athletes, responsible for 28% of missed training days/year. Psychosocial factors contribute to chronic pain development. This study aims to investigate the transferability of psychosocial screening tools developed in the general population to athletes and to define athlete-specific thresholds.
Methods Data from a prospective multicentre study on LBP were collected at baseline and 1-year follow-up (n=52 athletes, n=289 recreational athletes and n=246 non-athletes). Pain was assessed using the Chronic Pain Grade questionnaire. The psychosocial Risk Stratification Index (RSI) was used to obtain prognostic information regarding the risk of chronic LBP (CLBP). Individual psychosocial risk profile was gained with the Risk Prevention Index – Social (RPI-S). Differences between groups were calculated using general linear models and planned contrasts. Discrimination thresholds for athletes were defined with receiver operating characteristics (ROC) curves.
Results Athletes and recreational athletes showed significantly lower psychosocial risk profiles and prognostic risk for CLBP than non-athletes. ROC curves suggested discrimination thresholds for athletes were different compared with non-athletes. Both screenings demonstrated very good sensitivity (RSI=100%; RPI-S: 75%–100%) and specificity (RSI: 76%–93%; RPI-S: 71%–93%). RSI revealed two risk classes for pain intensity (area under the curve (AUC) 0.92(95% CI 0.85 to 1.0)) and pain disability (AUC 0.88(95% CI 0.71 to 1.0)).
Conclusions Both screening tools can be used for athletes. Athlete-specific thresholds will improve physicians’ decision making and allow stratified treatment and prevention.