P3.258: Energy Availability, Macronutrient Intake, and Sleep Outcomes among Adolescent Athletes

Background: Adolescent athletes are vulnerable to inadequate nutritional intake and sleep, yet whether nutrition and sleep are associated among adolescents is unclear. The relative energy deficiency in sport conceptual model proposes that sleep disturbances are associated with low energy availability (EA; inadequate energy available to support physiologic functioning after accounting for exercise energy expenditure). There is mixed evidence regarding the relationships between macronutrient intake (e.g., carbohydrate, protein, and fat) and sleep health. Our purpose was to investigate the relationships between EA, macronutrient intake, and sleep outcomes among adolescent athletes.

Methods: We conducted a cross-sectional study of healthy 13-18 year old athletes. Participants recorded daily dietary intake (via smartphone application) and exercise energy expenditure via wrist-worn actigraphy/heart rate monitor for 7 days, and completed a dual-energy x-ray absorptiometry scan to obtain fat free mass (FFM). Daily EA was calculated: [energy intake (kcal) – exercise energy expenditure (kcal)]/FFM. Sleep was also measured via the wrist-worn monitor capturing total sleep duration, sleep efficiency, and wake after sleep onset (WASO). The intraindividual variability (IIV) of sleep duration, efficiency, and WASO was calculated as the ratio of the 7-day standard deviation to the 7-day average of the respective variables. Linear regression models were used to assess associations between: (1) 7-day average EA and sleep outcomes, and (2) macronutrient intake and sleep outcomes. We also conducted an ANCOVA to compare sleep outcomes while controlling for sex across three EA categories: low EA ( < 30 kcal/kg/FFM), suboptimal EA (30-45 kcal/kg/FF), and optimal EA (>45 kcal/kg/FFM).

Results: A total of 57 adolescent athletes (15.4±1.4 years old, 63% female) were included (Table 1). No significant associations were observed between EA and any sleep outcome. Post-hoc comparisons revealed no statistically significant differences in sleep outcomes between low EA and suboptimal EA, low EA and optimal EA, or suboptimal EA and optimal EA. However, macronutrient intake was associated with WASO IIV, with higher protein intake associated with less variability in WASO (estimate = -10.5, p = 0.03) and higher carbohydrate intake associated with increased variability in WASO (estimate = 3.6, p = 0.04) (Figure 1). No significant relationships were found between macronutrient intake and sleep duration or efficiency.

Conclusion: Among adolescent athletes, EA was not significantly associated with sleep outcomes. While macronutrient intake was not associated with sleep duration or efficiency, lower protein intake and higher carbohydrate intake was associated with higher variability in WASO. These findings suggest that less protein and more carbohydrate consumption may play a role in increased sleep fragmentation, which can be associated with poorer sleep quality. Future research should include more long-term prospective assessment of nutritional intake and sleep variables to better understand the interplay of EA, macronutrient consumption, and sleep among adolescent athletes.