CONTEXT:Among American sports, football has the highest incidence of exertional heat stroke (EHS), despite decades of prevention strategies. Based on recent reports, 100% of high school and college EHS football fatalities occur during conditioning sessions. Linemen are the at-risk population, constituting 97% of football EHS deaths. Linemen heat up faster and cool down slower than other players. EVIDENCE ACQUISITION:Case series were identified from organized, supervised football at the youth, high school, and collegiate levels and compiled in the National Registry of Catastrophic Sports Injuries. Sources for event occurrence were media reports and newspaper clippings, autopsy reports, certificates of death, school-sponsored investigations, and published medical literature. Articles were identified through PubMed with search terms "football," "exertional heat stroke," and "prevention." STUDY DESIGN:Clinical review. LEVEL OF EVIDENCE:Level 5. RESULTS:Football EHS is tied to (1) high-intensity drills and conditioning that is not specific to individual player positions, (2) physical exertion as punishment; (3) failure to modify physical activity for high heat and humidity, (4) failure to recognize early signs and symptoms of EHS, and (5) death when cooling is delayed. CONCLUSION:To prevent football EHS, (1) all training and conditioning should be position specific; (2) physical activity should be modified per the heat load; (3) understand that some players have a "do-or-die" mentality that supersedes their personal safety; (4) never use physical exertion as punishment; (5) eliminate conditioning tests, serial sprints, and any reckless drills that are inappropriate for linemen; and (6) consider air-conditioned venues for linemen during hot practices. To prevent EHS, train linemen based on game demands.Strength-of-Recommendation Taxonomy:n/a.

athletes is weak at present, at least if the athlete follows standard sports nutrition guidelines to support muscle building, e.g., a positive energy balance and sufficient dietary protein intake. • O3FA ingestion may facilitate the remodeling of skeletal muscle protein when the athlete is unable to consume or tolerate ingestion of an optimal per serving dose of protein (~0.3 g/kg body mass) during post-exercise recovery. • Early evidence supports a prehabilitative/rehabilitative role of O3FA in maintaining muscle mass during short-term injury-induced muscle disuse that results in a prolonged period of limb immobilization, e.g., leg brace or leg cast. Case studies in a real-life injury setting are required to advance knowledge regarding the protective role of O3FA on muscle mass and quality during injury recovery. • Initial findings do not support the idea that increasing O3FA ingestion will promote high-quality weight loss during short-term periods of energy restriction. This notion applies to athletes with the goal to reduce fat mass and preserve muscle mass such as weight-category sports, esthetic sports or sports that demand a particularly high power-to-mass ratio. • Recent studies provide promising evidence regarding a role for O3FA in accelerating recovery from intense “muscle damaging” exercise, although further work is required across a range of sport-specific contexts. • Further research is warranted to better understand the optimum dose of O3FA and the ratio of eicosapentaenoic acid (EPA) to docosahexaenoic acid (DHA) to best support muscle recovery in athletes, and across a range of performance contexts, e.g., energy restriction, injury-induced muscle disuse, muscle repair and recovery. OMEGA-3 FATTY ACIDS FOR TRAINING ADAPTATION AND EXERCISE RECOVERY: A MUSCLE-CENTRIC PERSPECTIVE IN ATHLETES

Abstract Davis, JK, Wolfe, AS, Basham, SA, Freese, EC, and De Chavez, PJD. Neuromuscular, endocrine, and perceptual recovery after a youth American football game. J Strength Cond Res 35(5): 1317–1325, 2021—American football is a high-intensity intermittent sport consisting of various movements and repeated collisions which highlights the importance of adequate recovery from a game to prepare for the next competition. Therefore, the purpose of this study was to determine the time course of recovery markers after a youth American football game. Thirteen male American football youth players were monitored for 7 days after a single football game. Baseline measures were taken 28 hours pregame for lower-body neuromuscular function by countermovement jumps (CMJs) to determine peak power (PP), jump height (JH), flight time (FT), and takeoff velocity (TOV). Saliva was analyzed for cortisol, testosterone, and C-reactive protein (CRP). Perceptual recovery was assessed by the modified profile of mood states (POMS), perceived recovery status (PRS), and a daily wellness questionnaire. These measures were repeated immediately postgame (30 minutes) and at 20, 44, 68, 92, 116, and 140 hours postgame. Compared with baseline values, there was a significant decrease (p < 0.05) in CMJ PP, JH, and TOV up to 68 hours postgame and FT 44 hours postgame. No significant difference existed among time points for salivary testosterone and CRP. Cortisol levels significantly increased postgame compared with baseline (p < 0.05). Total mood disturbance, assessed by POMS, and daily wellness markers for energy were significantly decreased (p < 0.05), whereas daily wellness markers for soreness were significantly increased (p < 0.05) immediately after the game. Players exhibited a significant decrease in PRS up to 44 hours postgame (p < 0.05), similar to the decrease in neuromuscular function. Neuromuscular function and PRS are impaired for up to 44–68 h postgame.

Basketball players face multiple challenges to in-season recovery. The purpose of this article is to review the literature on recovery modalities and nutritional strategies for basketball players and practical applications that can be incorporated throughout the season at various levels of competition. Sleep, protein, carbohydrate, and fluids should be the foundational components emphasized throughout the season for home and away games to promote recovery. Travel, whether by air or bus, poses nutritional and sleep challenges, therefore teams should be strategic about packing snacks and fluid options while on the road. Practitioners should also plan for meals at hotels and during air travel for their players. Basketball players should aim for a minimum of 8 h of sleep per night and be encouraged to get extra sleep during congested schedules since back-to back games, high workloads, and travel may negatively influence night-time sleep. Regular sleep monitoring, education, and feedback may aid in optimizing sleep in basketball players. In addition, incorporating consistent training times may be beneficial to reduce bed and wake time variability. Hydrotherapy, compression garments, and massage may also provide an effective recovery modality to incorporate post-competition. Future research, however, is warranted to understand the influence these modalities have on enhancing recovery in basketball players. Overall, a strategic well-rounded approach, encompassing both nutrition and recovery modality strategies, should be carefully considered and implemented with teams to support basketball players' recovery for training and competition throughout the season.

Although a large body of research has investigated various hormonal and immunological responses to exercise, few studies have assessed the biological significance of those responses utilizing critical difference values (CDV) and biological variation (BV) in the context of monitoring biomarkers in professional athletes. PURPOSE: To assess salivary hormone changes over a professional American football season and determine if individual monitoring of these biomarkers is valuable. METHODS: Professional American football players (n=24) were recruited to provide weekly saliva samples over the course of a seven week season. Saliva samples were collected between 0600 and 0800 hours following an overnight fast and a mouth rinse with distilled water. Eight samples (two baseline and six weekly samples) were collected per player and analyzed for salivary testosterone (T), cortisol (C), uric acid (sUA), and immunoglobulin A concentration (SIgA). Player data were included for analysis if they provided samples at ≥70% of all collection time points (n=17). Data were analyzed using parametric statistics after confirmation of normality by Shapiro-Wilk and Reed‘s Criterion tests. The within-subject biological variation, CDV, and index of individuality (II) were calculated in accordance with the methods of Frasier and Harris. Lastly, relative percent change from baseline for each weekly collection was assessed using repeated measures one-way ANOVA. RESULTS: The CDV for salivary T, C, sUA, and SIgA were 27.5%, 61.3%, 48.0%, and 59.2%; while BV was 10.8%, 26.1%, 20.5%, and 25% respectively. II was calculated as 0.93, 0.52, 0.59, and 0.65 (arbitrary units) for T, C, sUA, and SIgA, respectively. All hormones exhibited significant differences between players (p<0.001), however were not significantly different between weeks (P>0.05). CONCLUSION: These data suggest that individual players experience week-to-week variation in salivary hormone response over a professional American football season, however team-wide fluctuations are minimal. Furthermore, the relatively low II values may imply that these salivary biomarkers are useful in terms of monitoring meaningful individual changes across a season.

PURPOSE: The purpose of this study was to investigate the perceptual wellness responses and time course of recovery following an American football game and if those responses vary across a professional American football season. METHODS: Twenty-four male, American football players (25.9 ± 2.7 y) were recruited to complete a standardized daily wellness survey 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. The surveys were obtained each morning via automated text messages to assess perceptions of energy, motivation, stress, and soreness utilizing 10-point Likert scales. A composite daily wellness score (DWS) was created where a higher score indicated better overall wellness. Eight players met the minimum survey response rate set at 70% and were therefore included in the study. All variables were used to determine the time course of perceptual recovery following a game, as well as cumulative recovery across the season. A mixed-effects model was used to measure changes in all markers including the DWS. RESULTS: There were no significant interactions for day x week (p > 0.05) across the season for the DWS or individual wellness markers. DWS was significantly higher on GD-1 (28.4 ± 6.1; p < 0.01) than GD+1, +2, +4, +5 (23.0 ± 6.0; 25.5 ± 6.3; 26.3 ± 6.2; 27.0 ± 6.1, respectively), but lower than GD (31.5 ± 4.1) and similar to GD+3 (26.8 ± 5.5; p > 0.05). Perceived energy was significantly lower on GD+1, +3, +4 (5.6 ± 1.9; 5.9 ± 1.9; 5.9 ± 2.1, respectively) compared to GD-1 (6.9 ± 2.1; p < 0.05). Perceived motivation was significantly higher on GD (8.5 ± 1.6) compared to GD-1 (7.2 ± 1.9; p < 0.05), but then declined on GD+1 (5.3 ± 2.4) and GD+2 (5.7 ± 2.3). Perceived muscle soreness was the lowest on GD (1.9 ± SD) and significantly higher the days following (GD+1: 5.4 ± 2.1; GD +2: 4.1 ± 2.2; GD+4 3.8 ± 1.8; and GD +5: 3.8 ± 1.9) compared to GD-1 (3.0 ± 1.6; p < 0.05). There was no daily effect on perceived stress (p > 0.05). CONCLUSIONS: Perceptual wellness markers are negatively impacted immediately after and days following a professional football game, and those affects remained consistent across the season. The DWS and individual markers of perceptual wellness may take up to 5 days to return to pregame levels and should be considered when planning player training.

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.

Abstract Davis, JK, Laurent, CM, Allen, KE, Zhang, Y, Stolworthy, NI, Welch, TR, and Nevett, ME. Influence of clothing on thermoregulation and comfort during exercise in the heat. J Strength Cond Res 31(12): 3435–3443, 2017—Sport textiles of synthetic fiber have been proposed to have superior properties for keeping wearers cooler, drier, and more comfortable compared with natural fibers. The impact of various fiber content and fabric construction on thermoregulation and perceptual responses are not well understood. Eight male collegiate athletes performed 3 counterbalanced trials of 45-minute treadmill run at 60% of maximal oxygen uptake in an environmental chamber (32° C). Three different fibers, consisting of 100% cotton, a blend of natural fibers (50/50% cotton/soybean), and a synthetic fiber (100% polyester) with mesh loops to facilitate ventilation through the clothing, were tested. Heat strain indices, microenvironment temperature, ratings of perceived exertion (RPE), and clothing comfort were measured. Session RPE (S-RPE) and session thermal sensation (S-TS) were recorded 20 minutes after each trial. There was no effect of clothing on rectal, skin, and body temperatures, heart rate, RPE, or comfort measures (p ≥ 0.05). A significant effect was observed for synthetic fiber compared with cotton on S-RPE (p = 0.03), S-TS (p = 0.04), and the microenvironment temperature at the chest (p = 0.02). No significant difference was shown for any other fibers on S-RPE, S-TS, or other microenvironment areas (p ≥ 0.05). These results show that clothing fiber content and fabric construction had no effect on thermoregulation, RPE, or clothing comfort during moderate-intensity exercise in the heat; whereas synthetic fabric construction indeed effectively reduced regional microenvironment temperature and attenuated global exertion and TS, which may have important implications for exercise tolerance in the heat.

Team sport athletes face a variety of nutritional challenges related to recovery during the competitive season. The purpose of this article is to review nutrition strategies related to muscle regeneration, glycogen restoration, fatigue, physical and immune health, and preparation for subsequent training bouts and competitions. Given the limited opportunities to recover between training bouts and games throughout the competitive season, athletes must be deliberate in their recovery strategy. Foundational components of recovery related to protein, carbohydrates, and fluid have been extensively reviewed and accepted. Micronutrients and supplements that may be efficacious for promoting recovery include vitamin D, omega-3 polyunsaturated fatty acids, creatine, collagen/vitamin C, and antioxidants. Curcumin and bromelain may also provide a recovery benefit during the competitive season but future research is warranted prior to incorporating supplemental dosages into the athlete’s diet. Air travel poses nutritional challenges related to nutrient timing and quality. Incorporating strategies to consume efficacious micronutrients and ingredients is necessary to support athlete recovery in season.

Numerous studies have reported on the thermoregulation and hydration challenges athletes face in team and individual sports during exercise in the heat. Comparatively less research, however, has been conducted on the American Football player. Therefore, the purpose of this article is to review data collected in laboratory and field studies and discuss the thermoregulation, fluid balance, and sweat losses of American Football players. American Football presents a unique challenge to thermoregulation compared with other sports because of the encapsulating nature of the required protective equipment, large body size of players, and preseason practice occurring during the hottest time of year. Epidemiological studies report disproportionately higher rates of exertional heat illness and heat stroke in American Football compared with other sports. Specifically, larger players (e.g., linemen) are at increased risk for heat ailments compared with smaller players (e.g., backs) because of greater body mass index, increased body fat, lower surface area to body mass ratio, lower aerobic capacity, and the stationary nature of the position, which can reduce heat dissipation. A consistent finding across studies is that larger players exhibit higher sweating rates than smaller players. Mean sweating rates from 1.0 to 2.9 L/h have been reported for college and professional American Football players, with several studies reporting 3.0 L/h or more in some larger players. Sweat sodium concentration of American Football players does not seem to differ from that of athletes in other sports; however, given the high volume of sweat loss, the potential for sodium loss is higher in American Football than in other sports. Despite high sweating rates with American Football players, the observed disturbances in fluid balance have generally been mild (mean body mass loss ≤2 %). The majority of field-based studies have been conducted in the northeastern part of the United States, with limited studies in different geographical regions (i.e., southeast) of the United States. Further, there have been a limited number of studies examining body core temperature of American Football players during preseason practice, especially at the high school level. Future field-based research in American Football with various levels of competition in hotter geographical regions of the United States is warranted.

PURPOSE:Cold water immersion (CWI) provides rapid cooling in events of exertional heat stroke. Optimal procedures for CWI in the field are not well established. This meta-analysis aimed to provide structured analysis of the effectiveness of CWI on the cooling rate in healthy adults subjected to exercise-induced hyperthermia. METHODS:An electronic search (December 2014) was conducted using the PubMed and Web of Science. The mean difference of the cooling rate between CWI and passive recovery was calculated. Pooled analyses were based on a random-effects model. Sources of heterogeneity were identified through a mixed-effects model Q statistic. Inferential statistics aggregated the CWI cooling rate for extrapolation. RESULTS:Nineteen studies qualified for inclusion. Results demonstrate CWI elicited a significant effect: mean difference, 0.03°C·min(-1); 95% confidence interval, 0.03-0.04°C·min(-1). A conservative, observed estimate of the CWI cooling rate was 0.08°C·min(-1) across various conditions. CWI cooled individuals twice as fast as passive recovery. Subgroup analyses revealed that cooling was more effective (Q test P < 0.10) when preimmersion core temperature ≥38.6°C, immersion water temperature ≤10°C, ambient temperature ≥20°C, immersion duration ≤10 min, and using torso plus limbs immersion. There is insufficient evidence of effect using forearms/hands CWI for rapid cooling: mean difference, 0.01°C·min(-1); 95% confidence interval, -0.01°C·min(-1) to 0.04°C·min(-1). A combined data summary, pertaining to 607 subjects from 29 relevant studies, was presented for referencing the weighted cooling rate and recovery time, aiming for practitioners to better plan emergency procedures. CONCLUSIONS:An optimal procedure for yielding high cooling rates is proposed. Using prompt vigorous CWI should be encouraged for treating exercise-induced hyperthermia whenever possible, using cold water temperature (approximately 10°C) and maximizing body surface contact (whole-body immersion).

Session rating of perceived exertion (SRPE) permits global effort estimations after an exercise bout and has shown promise for evaluating training load. However, factors mediating SRPE are not well understood. The purpose of this study was to compare SRPE between cycling and treadmill exercise at low and moderate intensities. In a counterbalanced order, male subjects (n = 7) completed a VO2max trial on a cycle ergometer and a motor-driven treadmill. Then, participants completed trials at 50 and 75% mode-specific VO2max on a cycle ergometer (BK75, BK50) and a treadmill (TM75, TM50) to achieve ∼ 400-kcal energy expenditure per trial. Acute RPE (i.e., during exercise) at 5 minutes, midway, and test termination were recorded with SRPE (20-minutes postexercise) expressed as overall (SRPEO), legs (SRPEL), and breathing also recorded were heart rate (HR) and change in rectal temperature (ΔTrec). Significance was accepted at p ≤ 0.05. Repeated-measures analysis of variance revealed significantly greater SRPE for higher intensities within each mode. Crossmodal comparisons also show a higher SRPE at moderate (75% VO2max) intensities [SRPEO] = BK75: 7.6 ± 1.0, TM75: 6.9 ± 1.3) vs. lower (50% VO2max) intensities (BK50: 4.6 ± 1.4, TM50: 4.6 ± 1.1). Within modes, SRPE corresponded well with ΔTrec and HR. Acute RPE was linked with intensity and drifted upward across time. Results indicated that overall and differentiated SRPEs are magnified with exercise intensity with the corresponding disruption in internal environment potentially mediating subjective responses. From a practical application standpoint, SRPE provides a subjective assessment for immediate evaluation of daily training. Results indicate that, when using SRPE to monitor training, consideration should be given to responses across differing exercise modes.