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.

The male athletes' carbohydrate and protein intake more closely approximated recommendations overall than that of the female athletes. The most common shortfall was carbohydrate intake during exercise, as only 18% of male and 29% of female athletes consumed 3060 g carbohydrate/hr during practice/competition.

BackgroundWe developed a digital dietary analysis tool for athletes (DATA) using a modified 24-h recall method and an integrated, customized nutrient database. The purpose of this study was to assess DATA’s validity and relative validity by measuring its agreement with registered dietitians’ (RDs) direct observations (OBSERVATION) and 24-h dietary recall interviews using the USDA 5-step multiple-pass method (INTERVIEW), respectively.MethodsFifty-six athletes (14–20 y) completed DATA and INTERVIEW in randomized counter-balanced order. OBSERVATION (n = 26) consisted of RDs recording participants’ food/drink intake in a 24-h period and were completed the day prior to DATA and INTERVIEW. Agreement among methods was estimated using a repeated measures t-test and Bland-Altman analysis.ResultsThe paired differences (with 95% confidence intervals) between DATA and OBSERVATION were not significant for carbohydrate (10.1%, -1.2–22.7%) and protein (14.1%, -3.2–34.5%) but was significant for energy (14.4%, 1.2–29.3%). There were no differences between DATA and INTERVIEW for energy (-1.1%, -9.1–7.7%), carbohydrate (0.2%, -7.1–8.0%) or protein (-2.7%, -11.3–6.7%). Bland-Altman analysis indicated significant positive correlations between absolute values of the differences and the means for OBSERVATION vs. DATA (r = 0.40 and r = 0.47 for energy and carbohydrate, respectively) and INTERVIEW vs. DATA (r = 0.52, r = 0.29, and r = 0.61 for energy, carbohydrate, and protein, respectively). There were also wide 95% limits of agreement (LOA) for most method comparisons. The mean bias ratio (with 95% LOA) for OBSERVATION vs. DATA was 0.874 (0.551-1.385) for energy, 0.906 (0.522-1.575) for carbohydrate, and 0.895(0.395-2.031) for protein. The mean bias ratio (with 95% LOA) for INTERVIEW vs. DATA was 1.016 (0.538-1.919) for energy, 0.995 (0.563-1.757) for carbohydrate, and 1.031 (0.514-2.068) for protein.ConclusionDATA has good relative validity for group-level comparisons in athletes. However, there are large variations in the relative validity of individuals’ dietary intake estimates from DATA, particularly in athletes with higher energy and nutrient intakes. DATA can be a useful athlete-specific, digital alternative to conventional 24-h dietary recall methods at the group level. Further development and testing is needed to improve DATA’s validity for estimations of individual dietary intakes.

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When a stimulus is applied to one part of the body, pain sometimes occurs in a distant site. This distant pain is called referred pain. The aims of this project were: To describe the prevalence of referred pain in subjects with temporomandibular disorders (TMD) at baseline and 8-year follow-up and the prevalence of persistence of referred pain at follow-up. Another aim was to identify risk factors for having referred pain at baseline and for predicting its persistence at follow-up. Finally, we wanted to determine whether referred pain affects the prognosis of patients with a TMD diagnosis. For each objective, we explored demographics such as gender, age, income, education level, and race. Other factors investigated included facial pain duration, somatization, somatization without pain, depression, anxiety, characteristic pain intensity (CPI), graded chronic pain scale (GCPS), number of other pains (headache, chest, back or stomach), and TMD diagnosis (myofascial pain, disk displacement, arthralgia or degenerative joint disease DJD). Methods: This secondary analysis included the data sets from the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) Validation (baseline) and IMPACT (follow-up) studies. It focused on a subclassification pain diagnosis termed "myofascial pain with referral". Subjects included in our analysis were TMD cases at baseline (n = 614) and TMD cases at follow-up (n = 286). Results. 26.4% of TMD cases had pain with referral at baseline and 36.4% at follow-up. The sites most likely to refer pain were extraoral sites (temporalis, masseter and mandible) at both baseline and follow-up. Female gender was associated with a higher

It is imperative to recognize the long-term implications of musculoskeletal disorders caused by or worsened by childhood obesity. It is also important to recognize that the ability to exercise comfortably is a key factor to developing a healthy lifestyle and maintaining a healthy body weight. Efforts to develop reasonable and acceptable programs to increase physical activity by all facets of society should be supported. Further research into the long-term implications of childhood musculoskeletal disorders related to childhood obesity is necessary.

We measured the agreement of our novel, digital dietary analysis tool for athletes (DATA) with the 24‐h dietary recall interview using the USDA 5‐step multiple‐pass method (INTERVIEW). Fifty‐six athletes (14–20 yr; 42 male, 16 female) completed DATA and INTERVIEW in a randomized counter‐balanced order (back‐to‐back on the same day). Intraclass correlations (ICC) and paired comparisons (expressed as % difference between methods) with 95% confidence intervals were used to determine statistical agreement between DATA and INTERVIEW. The ICC's between DATA and INTERVIEW were significant for energy (0.86, 0.76–0.92), carbohydrate (0.89, 0.81–0.94), total fat (0.75, 0.58–0.86), protein (0.89, 0.81–0.93), water (0.85, 0.75–0.91), sodium (0.85, 0.75–0.91), iron (0.83, 0.72–0.90), and calcium (0.91, 0.85–0.95). In addition, the differences between methods were not significant for energy (−1.1%, −9.1 to 7.7%), carbohydrate (0.2%, −7.1 to 8.0%), total fat (−6.1%, −17.8 to 7.4%), protein (−2.7%, −11.3 to 6.7%), water (−1.2%, −9.1% to 7.3%), sodium (−3.2%, −13.3 to 8.1%), iron (−8.4%, −18.3 to 2.7), and calcium (−0.4%, −10.0 to 10.3%). The DATA digital program is a valid 24‐h dietary analysis tool and can be used by sports health professionals looking for a quick, user‐friendly, athlete‐specific alternative to conventional 24‐h dietary recall methods. This study was funded by the Gatorade Sports Science Institute.