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Young athletes may be at risk for low energy availability (LEA) or dietary habits that are indicative of eating disorders. Thus, the purpose of the current study was to investigate the prevalence of LEA among high school athletes and examine those at risk for eating disorders. A secondary aim was to examine relationships between sport nutrition knowledge, body composition, and LEA. Methods: 94 male (n = 42) and female (n = 52) mean ± SD age: 18.09 ± 2.44 y; height: 172.6 ± 9.8 cm; body mass: 68.7 ± 14.5 kg; BMI: 22.91 ± 3.3 kg·m−2) athletes completed a body composition assessment and electronic versions of the abridged sports nutrition knowledge questionnaire (ASNK-Q), brief eating disorder in athletes questionnaire (BEDA-Q), and the low energy availability for females questionnaire (LEAF-Q; females only). Results: 52.1% of female athletes were classified as being at risk for LEA. Moderate inverse relationships existed for computed LEAF-Q scores and BMI (r = −0.394; p < 0.01). A total of 42.9% of males (n = 18) and 68.6% of females (n = 35) were at risk for eating disorders, with females being at greater risk (p < 0.01). Body fat percentage was a predictor (β = −0.095; p = −0.01) for eating disorder risk status. For every 1 unit increase in body fat percentage, athletes were 0.909 (95% CI: 0.845–0.977) times less likely to be classified as at risk for an eating disorder. Male (46.5 ± 13.9) and female (46.9 ± 11.4) athletes scored poorly on the ASNK-Q, with no differences between sex (p = 0.895). Conclusions: Female athletes were at a greater risk for eating disorders. No relationships existed between sport nutrition knowledge and %BF. Female athletes with a higher %BF had a lower risk for an eating disorder and risk for LEA.

Early morning training sessions may affect sleep quantity in athletes. The purpose of this study is to examine differences in total sleep time of collegiate athletes on nights prior to early morning training sessions relative to non-training nights. Wristwatch monitors equipped with photoplethysmography and accelerometer technology were worn by 18 NCAA Division III collegiate athletes (Age: 20.1 ± 1.6 years, Height: 1.81 ± 0.02 m, Body Mass: 91.2 ± 6.5 kg, Body Fat %: 20.8 ± 1.6%) during a two-week period of training to monitor total sleep times. Athletes recorded time in and out of bed using a sleep diary, anxiety levels due to having to wake up in the morning, and perceived recovery status (PRS) upon waking the next day. The data were divided into: nights before non-training days (NT) and Training days (TD). Data were analyzed using univariate analysis. All athletes obtained significantly less total sleep on nights before TD relative to NT (NT: 8:15 ± 1:03 vs. TD: 6:08 ± 0:59 hh:mm; p < 0.05). There was a positive relationship between total sleep time and recovery status (p < 0.01). Anxiety scores were inversely related to total sleep time (p < 0.01). Next-day recovery status was inversely related to anxiety scores (p < 0.001). College athletes obtained significantly less total sleep time on nights before early morning training sessions (< 0700) during the off-season, regardless of sex and sport. Coaches should consider later training sessions or promote optimal sleep quantity in order to minimize the risks associated to early morning training sessions.