Low energy availability (LEA) has been associated with several physiological consequences, but its impact on sleep has not been sufficiently investigated, especially in the context of young athletes. This study examined the potential association between energy availability (EA) status and objective sleep quality in 42 male rugby players (mean age: 16.2 ± 0.8 years) during a 7-day follow-up with fixed sleep schedules in the midst of an intensive training phase. Participants’ energy intake was weighed and recorded. Exercise expenditure was estimated using accelerometry. Portable polysomnography devices captured sleep on the last night of the follow-up. Mean EA was 29.3 ± 9.14 kcal·kg FFM⁻¹·day⁻¹, with 47.6% of athletes presenting LEA, 35.7% Reduced Energy Availability (REA), and 16.7% Optimal Energy Availability (OEA). Lower sleep efficiency (SE) and N3 stage proportion, along with higher wake after sleep onset (WASO), were found in participants with LEA compared to those with OEA (p = 0.04, p = 0.03 and p = 0.005, respectively, with large effect sizes). Segmented regression models of the EA-sleep outcomes (SE, sleep onset latency [SOL]), WASO and N3) relationships displayed two separate linear regions and produced a best fit with a breakpoint between 21–33 kcal·kg FFM⁻¹·day⁻¹. Below these thresholds, sleep quality declines considerably. It is imperative for athletic administrators, nutritionists, and coaches to conscientiously consider the potential impact of LEA on young athletes’ sleep, especially during periods of heavy training.

The aim of this study was to examine the effect of delayed evening mealtime on sleep quality in young athletes. Twelve rugby players (age 15.8 ± 0.7 years) participated in a crossover within-participant design. Adolescents spent five consecutive days in each of two conditions, separated by a 2-week washout period: routine dinner (3.5 hr before bedtime) and late dinner (LD, 1.5 hr before bedtime). Other mealtimes as well as bedtime and wake-up time were usual and remained the same in both conditions. Their schedules, dietary intakes, and physical activity were controlled and kept constant throughout the study. Sleep was assessed using polysomnography on the first and the last nights in the individual rooms of the boarding school. An increase in total sleep time by 24 min (p = .001, d = 1.24) and sleep efficiency by 4.8% was obtained during LD (p = .001, d = 1.24). Improvement in sleep efficiency was mainly due to a lower wake after sleep onset (−25 min, p = .014, d = −3.20), a decrease of microarousals (−25%, p = .049, d = −0.64), and awakenings ≥90 s (−30%, p < .01, d = −0.97) in LD compared to routine dinner. There were no significant differences in sleep architecture except for a shorter slow-wave sleep (N3) latency (−6.9 min, p = .03, d = −0.778) obtained during LD. In this study, evening dinner 1.5 hr before bedtime leads to better quality and less fragmented sleep compared to evening dinner 3.5 hr before bedtime in young athletes.

This study aimed to determine the short-term effect of two isocaloric diets differing in the ratio of protein–carbohydrate on melatonin levels, sleep, and subsequent dietary intake and physical activity in healthy young men. Twenty-four healthy men took part in a crossover design including two sessions of three days on isocaloric diets whether high-protein, low-carbohydrate (HPLC) or low-protein, high-carbohydrate (LPHC) followed by 24-h free living assessments. Sleep was measured by ambulatory polysomnography pre-post-intervention. Melatonin levels were assessed on the third night of each session on eight-point salivary sampling. Physical activity was monitored by accelerometry. On day 4, participants reported their 24-h ad-libitum dietary intake. LPHC resulted in better sleep quality and increased secretion of melatonin compared to HPLC. A significant difference was noted in sleep efficiency (p < 0.05) between the two sessions. This was mainly explained by a difference in sleep onset latency (p < 0.01) which was decreased during LPHC (PRE: 15.8 ± 7.8 min, POST: 11.4 ± 4.5 min, p < 0.001). Differences were also noted in sleep staging including time spent on REM (p < 0.05) and N1 (p < 0.05). More importantly, REM latency (PRE: 97.2 ± 19.9 min, POST 112.0 ± 20.7 min, p < 0.001) and cortical arousals (PRE: 7.2 ± 3.9 event/h, POST 8.5 ± 3.3 event/h) increased in response to HPLC diet but not LPHC. On day 4, 24-h ad-libitum energy intake was higher following HPLC compared to LPHC (+64 kcal, p < 0.05) and explained by increased snacking behavior (p < 0.01) especially from carbohydrates (p < 0.05). Increased carbohydrates intake was associated with increased cortical arousals.

Background: A better understanding of the influence of energy balance on sleep in adolescents, particularly those with obesity, could help develop strategies to optimize sleep in these populations. The purpose of this study was to investigate sleep under ad libitum-vs-controlled diets adjusted to energy requirement (eucaloric) among adolescents with obesity and their normal weight controls. Methods: Twenty-eight male adolescents aged between 12 and 15 years, n = 14 adolescents with obesity (OB: BMI ≥ 90th centile) and n = 14 normal weight age matched controls (NW), completed an experimental protocol comprising ad libitum or eucaloric meals for three days, in random order. During the third night of each condition, they underwent in home polysomnography (PSG). Results: An interaction effect of energy intake (EI) was detected (p < 0.001). EI was higher during ad libitum compared to the eucaloric condition (p < 0.001) and in OB compared to NW (p < 0.001) in the absence of any substantial modification to macronutrient proportions. Analyses of energy intake distribution throughout the day showed a significant interaction with both a condition and group effect during lunch and dinner. Sleep improvements were noted in OB group during the eucaloric condition compared to ad libitum with reduced sleep onset latency and N1 stage. Sleep improvements were correlated to reduced EI, especially during the evening meal. Conclusion: Simply adjusting dietary intake to energy requirement and reducing the energy proportion of the evening meal could have therapeutic effects on sleep in adolescents with obesity. However, positive energy balance alone cannot justify worsened sleep among adolescents with obesity compared to normal weight counterparts.

Despite the importance of sleep monitoring in the context of sport, few studies to date examined night-to-night sleep variability among adolescent athletes. This study compared night-to-night sleep variation between junior rugby players and age-matched non-athlete adolescents across seven consecutive nights of the in-season competitive phase. This investigation is based on data from a single centre, observational prospective study including 30 adolescents (15 junior rugby players and 15 non-athlete age-matched controls). Sleep was continuously monitored using ambulatory electroencephalogram (EEG) recordings. While the non-athlete controls catch-up on their sleep debt during the weekend by delaying their wake-up time, junior rugby players opt for an earlier bedtime to cope with sport-related travel (Fri: −00:57 h:min; p < 0.001; Sat: −01:58 h:min; p < 0.001) or early school (Mon: −00:55 h:min; p < 0.001). Night-to-night sleep examination identified greater sleep disturbances in junior rugby players the nights before and after the competition SE (Fri: −11%; p < 0.001; Sat: −9 min; p < 0.01). Junior rugby players showed higher IIV in sleep duration (CV_TST: + 5.8%; P < 0.001), efficiency (CV_SE: + 3.8%; p < 0.001) and staging (CV_N2: + 5.4%; p < 0.001; CV_N3: + 4.5; p < 0.01 IIV; REM: + 16.4%; p < 0.01). Higher IIV in the young athletes’ sleep outcomes could make them even more vulnerable to health and wellness concerns (i.e. overtraining, injury). The study results show the urgent need for an appropriate consideration of sleep regularity in young athletes.