Gatorade Sports Science Institute

Góndola, enseñanza y aprendizaje de las ciencias (puede ser referenciada como Góndola, Ens Apr Cien.) nació en el año 2006 como un medio para la divulgación del conocimiento en el campo educativo y de investigación en la enseñanza y el aprendizaje de las ciencias naturales (física, química, biología, astronomía) y las matemáticas. Desde entonces, se ha consolidado en un escenario de fortalecimiento para la comunidad académica de profesores de ciencias naturales tanto en formación como en ejercicio profesional en los diferentes niveles educativos. De acuerdo con la clasificación de áreas científicas de la organización para la Cooperación y el Desarrollo Económico (OCDE), la revista pertenece al área de conocimiento de ciencias de la educación y al área específica (disciplina) de educación. Desde el 2013, la revista es editada y publicada por el Centro de Investigaciones y Desarrollo Científico (CIDC) de la Universidad Distrital Francisco José de Caldas con sede en Bogotá, Colombia. Góndola, enseñanza y aprendizaje de las ciencias es una publicación semestral de alcance nacional e internacional, y cuenta con la colaboración de académicos e investigadores reconocidos en el área. Publica artículos en español, inglés y portugués, que son sometidos a un sistema de arbitraje doble ciego. Actualmente, está indexada en Emerging Source Citation Index (ESCI), Actualidad Iberoamericana, Google académico, OAJI, ERIHPLUS, MIAR, Fuente académica Plus (EBSCO), CLASE, REDIB, IRESIE, Journal TOCs, Dialnet, Latindex, Capes Qualis, DOAJ y Sherpa Romero. Los artículos sometidos a la revista deben ser originales, inéditos (no haber sido presentados a otras revistas), de calidad y que contribuyan al campo de la enseñanza y aprendizaje de las ciencias. enseñanza, aprendizaje, ciencias naturales, matemáticas, educación.

The purpose of this study was to compare a wearable microfluidic device and standard absorbent patch in measuring local sweating rate (LSR) and sweat chloride concentration ([Cl-]) in elite basketball players. Participants were 53 male basketball players (25 ± 3years, 92.2 ± 10.4kg) in the National Basketball Association's development league. Players were tested during a moderate-intensity, coach-led practice (98 ± 30min, 21.0 ± 1.2 °C). From the right ventral forearm, sweat was collected using an absorbent patch (3M Tegaderm™ + Pad). Subsequently, LSR and local sweat [Cl-] were determined via gravimetry and ion chromatography. From the left ventral forearm, LSR and local sweat [Cl-] were measured using a wearable microfluidic device and associated smartphone application-based algorithms. Whole-body sweating rate (WBSR) was determined from pre- to postexercise change in body mass corrected for fluid/food intake (ad libitum), urine loss, and estimated respiratory water and metabolic mass loss. The WBSR values predicted by the algorithms in the smartphone application were also recorded. There were no differences between the absorbent patch and microfluidic patch for LSR (1.25 ± 0.91mg·cm-2·min-1 vs. 1.14 ±0.78mg·cm-2·min-1, p = .34) or local sweat [Cl-] (30.6 ± 17.3mmol/L vs. 29.6 ± 19.4mmol/L, p = .55). There was no difference between measured and predicted WBSR (0.97 ± 0.41L/hr vs. 0.89 ± 0.35L/hr, p = .22; 95% limits of agreement = 0.61L/hr). The wearable microfluidic device provides similar LSR, local sweat [Cl-], and WBSR results compared with standard field-based methods in elite male basketball players during moderate-intensity practices.

There is interest in the potential of sweat cytokines to serve as non‐invasive biomarkers to monitor immune system function, but many practical questions remain. For example, sweat constituents collected from tattooed (TAT) skin regions may differ from skin regions without permanent tattooing. The purpose of this investigation was to determine if tattooed skin (TAT) differed from contralateral non‐tattooed skin (NT) for sweat cytokines including: EGF, IL‐10, IL‐1α, IL‐1β, IL‐6, IL‐8, TNF‐α, and IL‐31 concentrations and excretion rates during a group fitness exercise session. Moderately trained individuals (males n=8, females n=8, 34±8 y) with at least one permanent tattoo on the upper body (wrist, forearm, tricep, bicep, scapula, shoulder, or torso) participated in a ~60 minute outdoor (~27±3°C, 63±4% RH) group fitness exercise session. Absorbent patches (3M Tegaderm™ + Pad) were used to collect sweat on both TAT and NT skin. Patches were monitored during the exercise session and removed upon moderate saturation. Sweat cytokine concentrations were measured using Multiplex (EMD Millipore, MagPix) customized kits. Local sweat rate (LSR) was calculated from sweat mass over patch surface area (11.9 cm2) and duration of exercise (56±2 min). Excretion rates were calculated as the product of LSR and cytokine concentration. To evaluate differences between TAT and NT skin, paired t‐tests or Wilcoxon signed rank tests were used. Significance level was set at p<0.05. Data are presented as mean±SD for normally and non‐normally distributed data. There were no differences in LSR for TAT vs. NT skin regions (1.20±0.62 vs 1.24±0.53 mg/cm2/min; p=0.70). There were no differences between TAT and NT skin for concentrations of EGF (45±29 vs 53±31 pg/mL; p=0.10), IL‐10 (0.03±0.04 vs 0.07±0.13 pg/mL; p=0.21), IL‐1β (1.66±1.12 vs 1.86±1.07 pg/mL; p=0.55), IL‐8 (0.60±0.50 vs 0.56±0.64 pg/mL; p=0.27), IL‐1α (618±550 vs 807±913 pg/mL; p=0.3) or TNF‐α (0.40±0.05 vs 0.40±0.04 pg/mL; p=0.85). There was below minimum or no level of detection for TAT and NT skin for IL‐6 and IL‐31. Excretion rates were significantly different between TAT and NT skin for EGF (0.05±0.04 vs 0.06±0.03 pg/cm2/min; p=0.04). In summary, EGF excretion rate was lower in TAT skin than NT skin during exercise, but TAT did not affect the concentrations or excretion rates of any other cytokines measured.

Skeletal muscle mass losses with age are associated with negative health consequences, including an increased risk of developing metabolic disease and the loss of independence. Athletes adopt numerous nutritional strategies to maximize the benefits of exercise training and enhance recovery in pursuit of improving skeletal muscle quality, mass, or function. Importantly, many of the principles applied to enhance skeletal muscle health in athletes may be applicable to support active aging and prevent sarcopenia in the healthy (non-clinical) aging population. Here, we discuss the anabolic properties of protein supplementation in addition to ingredients that may enhance the anabolic effects of protein (e.g. omega 3 s, creatine, inorganic nitrate) in older persons. We conclude that nutritional strategies used in pursuit of performance enhancement in athletes are often applicable to improve skeletal muscle health in the healthy older population when implemented as part of a healthy active lifestyle. Further research is required to elucidate the mechanisms by which these nutrients may induce favourable changes in skeletal muscle and to determine the appropriate dosing and timing of nutrient intakes to support active aging.

Gut microbiome influences athletes' physiology, but because of the complexity of sport performance and the great intervariability of microbiome features, it is not reasonable to define a single healthy microbiota profile for athletes. We suggest the use of specific meta-omics analysis coupled with innovative computational systems to uncover the hidden association between microbes and athlete's physiology and predict personalized recommendation.

American football is physically and psychologically demanding, thus highlighting the potential need for adequate sleep for recovery. PURPOSE: To investigate self-reported sleep habits over the course of a seven-week football season and determine how game load may impact sleep. METHODS: Professional football players (n = 24; 25.9 ± 2.7 y) were recruited from the Alliance of American Football league. A customized sleep survey was used to ask about sleep duration (SLD), “How many hours of sleep did you get last night?” and sleep quality (SQ), “What was your quality of sleep”, while a total sleep score (TSS) was calculated using SLD and SQ, with higher scores meaning better sleep. Players completed the sleep survey sent via automated text message at 0800h the day before each game (GD-1), game day (GD) and each day following game day (GD+1, GD+2, GD+3, GD+4, and GD+5) during the seven-week season. Game load was recorded and defined as total snaps played for each game. A mixed-effects model was used to measure changes in all variables, while a Pearson’s correlation coefficient was used to assess relationships between game load and sleep variables. Two, three, and four-day averages for all sleep variables were calculated and correlated with the respective game load. RESULTS: A significant interaction for GD x time (p < 0.05) for SLD was observed, where players slept less on GD (6.1 ± 1.3 h) and GD+2 (6.7 ± 0.7 h) compared to GD-1 (8.5 ± 0.9 h) for week three, and GD+5 (6.3 ± 0.8 h) compared to GD-1 (8.4 ± 0.6 h) for week seven. Overall, SLD was higher on GD-1 (8.1 ± 0.9 h) than all other time points (p < 0.05). No significant main effect or interaction was found for SQ (p > 0.05). Overall, the TSS was significantly higher on GD-1 (7.0 ± 1.8 AU; p < 0.01) compared to GD, GD+2, GD+3, and GD+4 (5.4 ± 2.2; 5.6 ± 1.6; 5.8 ± 1.4; 6.1 ± 0.9 AU; respectively). Significant positive correlations for game load were found with SQ for two (r = .34, R2 = 0.119, p < 0.05), three (r = .39, R2 = 0.155, p < 0.01) and four (r = .50, R2 = 0.247, p < 0.01) day averages and the TSS for two (r = .29, R2 = 0.083, p < 0.05), three (r = .29, R2 = 0.084, p < 0.05), and four (r = .37, R2 = 0.139, p < 0.01) day averages, but not for SLD. CONCLUSIONS: Professional football players reported impaired SLD and TSS up to 5 days following a football game. Higher game load was associated with better SQ in the days following the game.

Serum cytokine values are used as indicators to examine a variety of health and performance‐related interventions in physiology. However, commonly utilized enzyme‐linked immunosorbent assay (ELISA) test kits are often not comparable against one another, making interpretation of absolute values between studies and throughout the literature difficult. To address this concern, the purpose of this study was to determine if serum cytokine (IL‐1β, IL‐6) ELISA measurements are comparable across two laboratory sites (Site 1: Frisco, TX and Site 2: Barrington, IL) using the same samples and methods. At Site 1, thirty‐three blood samples were collected from the antecubital vein of fasted professional American football players, and 300 μL were aliquoted into 6 cryovials after clotting and centrifugation and frozen immediately at −80°C. Half of the serum aliquots for the 33 samples were shipped from Site 1 and received frozen to Site 2. ELISA vs ELISA between sites testing procedure was standardized for spectrophotometer (Biotek), ELISA manufacturer (Sigma‐Aldrich), sample handling, lot number, and time of analysis. All assay procedures were performed per the manufacturer instructions with no technical issues. Both sites used four parameter non‐linear logistical standard curves with a r2=1 for all assays. Cytokine values are reported as mean ± SD. Site 1 versus Site 2 serum cytokine concentrations were compared using paired samples t‐tests with 95% CI. Coefficient of variation (CV%) was used to make within and between site comparisons. Results indicated that IL‐1β: [n= 25; (Site 2) 38.2 ± 41.7 pg/mL, (Site 1) 33.9 ± 35.2 pg/mL] had an inter‐site CV% of 15.4 and intra‐assay Site 1 and Site 2 CV% of 7.5 and 10.9. IL‐6: [n=33 (Site 2) 341.9 ± 453.3 pg/mL, (Site 1) 417.4 ± 698.1 pg/mL] had an inter‐site CV% of 36.9 and intra‐assay Site 1 and Site 2 CV% of 8.4 and 12.3. Nine IL‐1β samples were lost due to overflow in optical density during analysis. Paired samples t‐tests showed a significant mean difference for IL‐1β [4.2 (95% CI, −8.2 to −0.3) pg/mL, p = 0.034] between sites and no significant mean difference for IL‐6 [75.5 (95% CI, −14.3 to 165.3)] pg/mL, p = 0.096 between sites. Inter‐site CV’s were ~2× the intra‐assay CVs, indicating that caution should be taken when interpreting the absolute values of cytokines measured across laboratories using ELISA assays and from multiple publications. The source of the large inter‐assay variation for IL‐6 was predominately located towards the lower limits of detection, identifying a need for manufacturers to develop higher sensitivity assays across these markers. Future research is warranted to understand possible causes of variability in absolute cytokine values across two laboratories using the same samples and the same ELISA kits.Support or Funding InformationThis study was funded by and the authors are employed by the Gatorade Sports Science Institute, a division of PepsiCo, Inc. The views expressed in this abstract are those of the authors and do not necessarily reflect the position or policy of PepsiCo, Inc.

Personalized hydration strategies play a key role in optimizing the performance and safety of athletes during sporting activities. Clinicians should be aware of the many physiological, behavioral, logistical and psychological issues that determine both the athlete’s fluid needs during sport and his/her opportunity to address them; these are often specific to the environment, the event and the individual athlete. In this paper we address the major considerations for assessing hydration status in athletes and practical solutions to overcome obstacles of a given sport. Based on these solutions, practitioners can better advise athletes to develop practices that optimize hydration for their sports.

Life expectancy for those in the United States who reach 65 years old is an additional 20 years. Healthy food choices, dietary patterns, and physical activity that promotes functional fitness can help make older adults more independent and improve quality of life in their later years. This article answers the top questions about food and fitness after 50 years old that are posed by older adults with answers to help health professionals encourage their patients, clients, friends, and family eat well, move well, and be well. Questions range from “best” foods, diets, and exercise to maintain fitness, as well as questions on hydration, calcium intake, and dietary supplements.

This study determined the relations between regional (REG) and whole body (WB) sweating rate (RSR and WBSR, respectively) as well as REG and WB sweat Na+ concentration ([Na+]) during exercise. Twenty-six recreational athletes (17 men, 9 women) cycled for 90 min while WB sweat [Na+] was measured using the washdown technique. RSR and REG sweat [Na+] were measured from nine regions using absorbent patches. RSR and REG sweat [Na+] from all regions were significantly ( P < 0.05) correlated with WBSR ( r = 0.58-0.83) and WB sweat [Na+] ( r = 0.74-0.88), respectively. However, the slope and y-intercept of the regression lines for most models were significantly different than 1 and 0, respectively. The coefficients of determination ( r2) were 0.44-0.69 for RSR predicting WBSR [best predictors: dorsal forearm ( r2 = 0.62) and triceps ( r2 = 0.69)] and 0.55-0.77 for REG predicting WB sweat [Na+] [best predictors: ventral forearm ( r2 = 0.73) and thigh ( r2 = 0.77)]. There was a significant ( P < 0.05) effect of day-to-day variability on the regression model predicting WBSR from RSR at most regions but no effect on predictions of WB sweat [Na+] from REG. Results suggest that REG cannot be used as a direct surrogate for WB sweating responses. Nonetheless, the use of regression equations to predict WB sweat [Na+] from REG can provide an estimation of WB sweat [Na+] with an acceptable level of accuracy, especially using the forearm or thigh. However, the best practice for measuring WBSR remains conventional WB mass balance calculations since prediction of WBSR from RSR using absorbent patches does not meet the accuracy or reliability required to inform fluid intake recommendations. NEW & NOTEWORTHY This study developed a body map of regional sweating rate and regional (REG) sweat electrolyte concentrations and determined the effect of within-subject (bilateral and day-to-day) and between-subject (sex) factors on the relations between REG and the whole body (WB). Regression equations can be used to predict WB sweat Na+ concentration from REG, especially using the forearm or thigh. However, prediction of WB sweating rate from REG sweating rate using absorbent patches does not reach the accuracy or reliability required to inform fluid intake recommendations.