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Journal of the International Society of Sports Nutrition logoLink to Journal of the International Society of Sports Nutrition
. 2026 May 3;23(1):2665547. doi: 10.1080/15502783.2026.2665547

Association between energy availability and sleep quality in elite female and male swimmers: a brief report

Emily A Lundstrom a,b, Mary Jane De Souza a, Megan E Conklin a, Nancy I Williams a,*
PMCID: PMC13142186  PMID: 42070107

Abstract

Background

There is a high prevalence of poor sleep quality and low energy availability (EA) in athletes during phases of intensive training, which poses significant risks for overreaching, improper recovery, and compromised training adaptations. To mitigate these risks, there is a need to explore the relationship between EA and sleep quality. To determine the interrelationships between EA and sleep quality, we assessed EA and sleep quality (sleep durationhrs, sleep debthrs, percent and hours of: slow-wave sleep (SWShrs/%), and rapid-eye movement (REMhrs/%)).

Methods

The participants included 26 elite male (n = 10; 83.8 ± 8.6 kg; BMI: 24.1 ± 1.9 kg/m2) and female (n = 16; 68.0 ± 5.6 kg; BMI: 22.5 ± 1.6 kg/m2) collegiate swimmers (aged 18–22 years). The descriptive data collected included age, weight, height, training data, and body composition measures. Using a wearable device and a dietary recording cell phone application, the collection of EA was matched to sleep data over a two-week period of heavy training. Pearson correlations were utilized to determine relationships between variables. When effects of sex were observed, linear regression analyses were utilized to control for sex-differences.

Results

Among all swimmers, 69% exhibited sub-optimal EA (<45 kcal/kg FFM/d). Male swimmers exhibited greater EA and SWS versus females (p < 0.05). EA was positively correlated with REMhrs (R = 0.64; p= 0.001) but not related to sleep debthrs. Regression analyses revealed that when controlling for sex, EA positively predicted SWShrs (R2 = 0.448; F = 9.35, p < 0.001), where higher EA predicted longer SWS durations. Controlling for sleep durationhrs, EA positively predicted REMhrs (R2 = 0.425; F = 8.509, p < 0.002) and negatively predicted sleep debthrs (R2 = 0.261; F = 4.055, p < 0.031), such that higher EA was predictive of longer durations of REM, and fewer hours of sleep debt. There was a trend toward a correlation between EA and sleep durationhrs in all swimmers (R = 0.33; p = 0.06).

Conclusion

Higher EA was significantly associated with greater REM and SWS durations, and with lower sleep debt in elite male and female college swimmers. Although cause and effect were not established, these findings provide preliminary evidence that adequate EA may support better sleep quality in elite collegiate swimmers. If this is confirmed, our results may suggest that athletes should get adequate sleep and consume adequate calories to support energy expenditure needs and optimize training and recovery. Future research should explore the underlying mechanisms and whether low EA causally impacts sleep quality.

1. Introduction

Given the frequent and intense training sessions and competitions, elite collegiate endurance athletes often struggle to adequately fuel for their high energy requirements [1–3], resulting in a high prevalence of low energy availability (EA) ranging from 25%–31% in males and females, respectively [4]. Chronically low EA can not only lead to detrimental health outcomes, such as the development of the Athlete Triad [5,6], it can also result in exacerbation of overtraining syndrome (OTS) [7], resulting in reduced sport performance [8]. A similarly high prevalence of inadequate sleep observed in elite collegiate athletes [9–11], is often attributed to the time demands of balancing academic responsibilities with rigorous training schedules [12–15]. Both adequate nutrition and sleep habits have been demonstrated to influence health [16–19] and adaptation to sport training [20–22]. Furthermore, preliminary evidence supports the potential relationships between energy status and sleep, whereby athletes with low EA present with worsened sleep quality and quantity compared to their adequately fueled counterparts [23–29]. Despite research suggesting an EA-sleep connection, there are conflicting results in the literature on whether this association exists, and prospective experimental evidence establishing cause and effect is lacking. That said, given the high prevalence of both EA and poor sleep in athlete populations, understanding these relationships is crucial for understanding and promoting optimal athlete health and performance outcomes.

The unique demands of swimming, characterized by high-volume training regimes and early morning practice sessions [1,13,15,30], pose challenges to athletes in achieving adequate energy balance [2], and sufficient sleep duration and quality [13,15]. Moreover, factors such as academic responsibilities and demanding training schedules [3,13,15,31], competition schedules and travel demands [13,15,18,31], and psychosocial stressors [3,14,18] may further complicate the relationship between EA and sleep quality among elite collegiate swimmers. Previous research has extensively examined the individual effects of EA [8,32,33] and sleep [18,22,34,35] on athletic performance and health outcomes; however, limited attention has been given to their interplay, particularly in elite collegiate swimmers. Understanding the associations between EA and sleep quality in this population is crucial for optimizing health and training adaptations, minimizing injury risk, and promoting overall well-being. Therefore, this study aims to investigate the associations between EA and sleep quality in elite swimmers, with consideration of a critical time-period during the competitive season associated with the highest prevalence of low EA, poor sleep, and heavy training. To our knowledge, this is the first study to directly examine the link between EA and objective measures of sleep quality in elite collegiate swimmers during heavy training.

2. Materials and methods

2.1. Experimental design

This is a subanalysis of data from a cross-sectional study that measured EA, sleep, training load, and swimming performance in 26 elite collegiate swimmers (10 males and 16 females) [36]. Prior to data collection, the study was approved by the University Institutional Review Board, and informed consent was obtained. Data collection occurred during the intensified training period of the athletes' season and lasted a total of 6 weeks. Data collection encompassed the weeks prior to tapering, or resting, for championship competition. Collection of EA for individual athletes occurred over a three-day period, encompassing two weekdays and one weekend day using a mobile dietary recording application. The sleep data were matched to the EA recording dates.

2.2. Participants

The participants for this study consisted of 27 elite collegiate swimmers (11 males and 16 females; 18–22 years) from a NCAA Division 1 Swim team. While 27 total swimmers were enrolled in the overall study, one was excluded, leaving 26 (10 males and 16 females) swimmers for the present analysis. All the participants were free of injury, were able to train without modifications, and in good health. The swimmers were NCAA, national and international competitors, with many having competed in the Olympic Games or Olympic Trials competitions. The menstrual status of the female participants was not tracked, which we acknowledge as a limitation given the relevance of menstrual function to EA.

2.3. Anthropometrics and body composition

Total body mass was measured using a traditional physician's scale (Seca Model 770; Seca, Hamburg, Germany) reporting mass in kilograms to the nearest 0.01 kg, while height measurements were recorded in centimeters to the nearest 0.1 cm. Next, using these measurements, weight to height ratio in kg/m2 was calculated to obtain the body mass index of each participant. A dual-energy X-ray absorptiometry (DXA) machine (Hologic Horizon-W, Model 201331) was used to assess body composition measures of fat-free mass (FFM), lean body mass (LBM), fat mass (FM), and total percent body fat (BF%) in all participants. DXA scans were performed by an International Society of Clinical Densitometry-certified technician on the study team.

2.4. Training

As detailed elsewhere [2,3,36], participants were required to attend all scheduled trainings as part of their participation in the study. The coaches scheduled and provided workout programs for these trainings. These training sessions included both in-water and weightlifting sessions, which provided the training plan to each athlete and the study staff at the beginning of each week. Measures of training load (defined as; session rating of perceived exertion, yardage swam per session and per day, and training intensity derived from specific swim workouts using wearable technology data captured during each session), were utilized as checks to ensure that high-intensity training and heavy training load was consistently maintained throughout the entire data collection period. Training measurements for individual athletes were collected to match 3-day dietary recording and sleep measurements.

2.5. Energy intake (EI)

The application MyFitnessPal premium was utilized as a tool for participants to document their dietary EI by keeping a 72-h diet log, two week-days and one weekend-day (Under Armour, Baltimore, MD). During the initial laboratory visit, study personnel provided detailed instructions on how the diet logs should be completed. Specifically, participants were instructed to record all the calorie-containing substances that they consumed as well as the time of consumption, location of consumption, whether it was a meal or snack, meal type (breakfast, lunch, or dinner), preparation details, and brand of calorie-containing substances. Upon receiving the 3-day diet logs from each participant, study personnel examined each log to ensure accuracy. After accuracy was established, dietary EI was calculated as the total value of all calorie-containing substances reported throughout the 72-h food log recording period.

2.6. Energy expenditure

Exercise energy expenditure (EEE) was measured by the WHOOP (WHOOP Inc., Boston, MA), which uses heart rate and tri-axial accelerometry to analyze heart rate and prompting participants to input their type of exercise and confirm its completion. This is described in further detail elsewhere [36]. Importantly, the WHOOP has demonstrated acceptable validity and reliability against gold-standard measurements and validated surrogate heart rate measurements during exercise [37,38]. Together, these data points allow the WHOOP to calculate EEE. The participants were required to wear the WHOOP at all times during the entire study duration.

2.7. Energy availability (EA)

EA was calculated using the formula EA = (EI − EEE)/(FFM) (kcal/kg FFM). As aforementioned, EI was determined from the information entered by participants into the MyFitnessPal application for 3-days to determine EI in kilocalories. WHOOP data was used to determine EEE, which was also used in kilocalories. Sleep data were retrieved for days corresponding to 3-day dietary recording for analysis. For descriptive purposes in this study, EA was determined to be optimal if EA ≥ 45 kcal/kg FFM/d, based on widely accepted values published in the literature [5,6,35].

2.8. Sleep quality

Sleep data were collected by the WHOOP via a triaxial accelerometer, 3-axis gyroscope, and optical sensors. WHOOP utilizes a proprietary sleep detection and sleep staging algorithm to produce various sleep measures (WHOOP Inc., Boston, MA) that includes integrations of measurements of physiological signals such as heart rate, heart rate variability, respiratory rate and movement to determine sleep–wake states and sleep stages. WHOOP has been demonstrated validity and reliability against gold-standard measurements and other wearables for sleep measurement [39–41]. While the WHOOP's advanced technology and sleep algorithms allow for the auto-detection of sleep, the application dashboard also allows users to confirm sleep times or manually adjust sleep times to further improve the accuracy of sleep data. The specific sleep variables measured by the WHOOP are total sleep time (duration), sleep disturbances, sleep efficiency, and amount of time (hours) and percentage of time in each of the four main stages of sleep (wake, light sleep, slow-wave sleep, and rapid eye movement sleep), and cycle duration. For analysis of sleep data against measures of EA, sleep data were collected on matched days with 3-day dietary recordings.

2.9. Statistical analyses

The data were analyzed using SPSS Statistical Software (version 26, Chicago, IL). All variables were tested for normality and outliers prior to conducting the statistical analyses. First, normality was tested using the Shapiro-Wilke statistic. Then, outliers were located, removed, and Levene's test was utilized to determine the homogeneity of variance. Frequency analysis was used to describe the number of participants who exhibited sub-optimal EA (EA < 45 kcal/kg FFM/d), as defined in the literature [5,6]. For the primary analyses, participants were analyzed as a whole group and by sex. Independent t-tests were utilized to determine group differences in variables of interest between sexes and for the secondary descriptive analysis of EA in our cohort between athletes with higher EA (HEA – individuals above the median split (50%) exhibiting higher EA) and lower EA (LEA – individuals below the median split (50%) exhibiting lower EA) groups, performed separately by sex (due to the sex difference in EA). Correlations were calculated using Pearson's correlation analysis to identify any relationships of interest. Linear regression analyses were utilized to determine statistically significant predictors of EA on sleep quality when sex differences were evident. Data were reported as mean ± SD, and a p-value of <0.05 was considered statistically significant.

3. Results

3.1. Participant characteristics

The participants enrolled at the beginning of the study consisted of 27 NCAA Division I collegiate swimmers (11 males and 16 females). There were no dropouts. One male participant was excluded from the analyses because it prematurely entered the taper phase of reduced-volume training before the data collection period ended. Therefore, a total of 26 participants were utilized for this study. Among these 26 participants, 24 participants identified themselves as Caucasian, 1 participant identified themselves as African American/Caribbean, and 1 participant identified themselves as Latin American. Further descriptive information of the participants is included in Table 1.

Table 1.

Participant characteristics.

  All (n = 26) Male (n = 10) Female (n = 16) p-value
Mean ± SD Mean ± SD Mean ± SD
Demographics        
Height (cm) 178.7 ± 7.8 186.4 ± 4.9 174.0 ± 4.8 0.001
Weight (kg) 74.1 ± 10.3 83.8 ± 8.6 68.0 ± 5.6 0.001
Age (years) 19.6 ± 1.1 20.1 ± 1.0 19.3 ± 1.0 0.094
Fat free mass (kg) 56.5 ± 11.0 68.6 ± 6.7 49.0 ± 4.3 0.001
Fat mass (kg) 16.0 ± 2.8 13.6 ± 2.1 17.5 ± 2.1 0.001
Body mass index (kg/m2) 23.1 ± 1.9 24.1 ± 1.9 22.5 ± 1.6 0.024
Average training volume (yds/s) 6100 ± 950 5900 ± 1000 6250 ± 850 0.687
Energy        
Average EI (kcal/day) 2935 ± 991 3959 ± 716 2294 ± 437 0.001
Average EEE (kcal/day) 717 ± 260 901 ± 280 601 ± 170 0.002
Average EA (kcal/kg FFM/d) 38.6 ± 11.0 44.4 ± 9.7 35.1 ± 10.5 0.033
Sleep        
Matched to dietary recording dates        
Sleep duration (h) 6.8 ± 0.9 6.6 ± 1.1 7.0 ± 0.7 0.303
REM duration (h) 1.7 ± 0.7 1.9 ± 0.9 1.5 ± 0.5 0.157
SWS duration (h) 1.2 ± 0.4 1.5 ± 0.3 1.0 ± 0.3 0.008
Sleep debt (h) 1.2 ± 0.5 1.2 ± 0.6 1.2 ± 0.5 0.834
REM % 22.1 ± 8.0 23.1 ± 9.8 21.4 ± 6.8 0.599
SWS % 14.0 ± 4.1 16.4 ± 3.8 12.4 ± 3.6 0.013
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Yds/s, yards per training session; EI, energy intake; EEE, exercise energy expenditure; EA, energy availability; FFM, fat-free mass; REM, rapid eye movement; SWS, slow-wave sleep; SD: standard deviation; Bold formatting denotes statistical significance (p < 0.05) between variables.

3.2. Descriptive energy, sleep, training, and performance characteristics in all swimmers

Descriptive information of energy, body composition, and sleep variables for all swimmers is presented in Table 1. Male swimmers had higher EA compared to female swimmers (p < 0.05, 95% CI [0.81, 17.75]). Among all swimmers, 69% (18/26) exhibited sub-optimal EA (EA < 45 kcal/kg FFM/d, range: 13.3–42.7 kcal/kg FFM/d), and 31% (8/26) of swimmers exhibited an EA at or above the optimal value (EA ≥ 45 kcal/kg FFM/d, range: 45.1–60.8 kcal/kg FFM/d). When examining the data by sex, 50% (5/10) male swimmers (EA: 26.5–42.7 kcal/kg FFM/d) and 81% (13/16) of the female swimmers (13.3–44.4 kcal/kg FFM/d) exhibited sub-optimal EA (EA < 45 kcal/kg FFM/d)

There were no differences in sleep durationhrs, however, the male swimmers had greater SWShrs (p = 0.008, 95% CI [0.12, 0.66]) and higher SWS% compared to female swimmers (p = 0.013, 95% CI [0.93, 7.07]) when sleep was matched to days of dietary recording.

3.3. Sleep and EA in male and female swimmers

Table 2 describes the differences in sleep quality between LEA and HEA when using the median split value of EA as a cut-point and analyzing by sex. For male swimmers, the EA ranges for both groups were 26.5–42.7 kcal/kg FFM/d for LEA, and 45.1–60.8 kcal/kg FFM/d for HEA, respectively. For female swimmers, EA ranged from 13.3–33.8 kcal/kg FFM/d for LEA and 34.6–55.5 kcal/kg FFM/d for HEA, respectively. In the male swimmers, there were no significant differences between the HEA and LEA groups for any of the sleep variables. In the female swimmers, sleep debthrs was greater in the LEA group versus the HEA group (1.46 ± 0.41 vs 0.85 ± 0.37; p = 0.008, 95% CI [0.19, 1.03]). As well, REMhrs and SWShrs were lower in the LEA group than in the HEA group. Visual depictions of significant differences in sleep variables between the EA groups are presented in Figure 1.

Table 2.

Sleep and energy availability.

Sleep Male swimmers
 Female swimmers
Lower EA (LEA) Higher EA (HEA) p-value Lower EA (LEA) Higher EA (HEA) p-value
26.5–42.7
kcal/kg FFM/d		 45.1–60.8
kcal/kg FFM/d		 13.3–33.8
kcal/kg FFM/d		 34.6–55.5
kcal/kg FFM/d
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
Sleep Debthrs 1.5 ± 0.7 0.9 ± 0.4 0.146 1.5 ± 0.4 0.9 ± 0.4 0.008
REM Durationhrs 1.5 ± 0.5 2.4 ± 1.1 0.150 1.2 ± 0.3 1.9 ± 0.5 0.006
SWS Durationhrs 1.4 ± 0.4 1.5 ± 0.2 0.485 0.9 ± 0.3 1.2 ± 0.3 0.002
REM % 26.1 ± 8.7 26.8 ± 9.8 0.150 22.4 ± 5.8 22.2 ± 4.1 0.917
SWS% 16.3 ± 4.9 16.5 ± 2.9 0.929 11.7 ± 2.2 13.2 ± 4.7 0.444
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EA, energy availability; LEA, lower EA group; HEA, higher EA group; FFM, fat-free mass; REM, rapid-eye movement; SWS, slow wave sleep; SD, standard deviation. t-tests are described with p values in the table. Bold formatting denotes statistical significance (p < 0.05) between variables.

Figure 1.

A three panel bar graph shows sleep debt, REM sleep, and slow wave sleep in male and female swimmers by energy availability. The three panel bar graph shows sleep debt, REM sleep, and slow wave sleep in male and female swimmers, categorized by energy availability. Panel 1, Sleep Debt, has a vertical axis from 0.0 to 2.5 hours. For male swimmers, EA in lower 50 percentage is 1.4 hours, EA in upper 50 percentage is 0.9 hours. For female swimmers, EA in lower 50 percentage is 1.4 hours, EA in upper 50 percentage is 0.9 hours, with a significant difference indicated by an asterisk. Panel 2, REM Sleep, has a vertical axis from 0 to 4 hours. For male swimmers, EA in lower 50 percentage is 1.6 hours, EA in upper 50 percentage is 2.3 hours. For female swimmers, EA in lower 50 percentage is 1.2 hours, EA in upper 50 percentage is 1.9 hours, with a significant difference indicated by an asterisk. Panel 3, Slow wave Sleep, has a vertical axis from 0.0 to 2.0 hours. For male swimmers, EA in lower 50 percentage is 1.4 hours, EA in upper 50 percentage is 1.5 hours. For female swimmers, EA in lower 50 percentage is 0.8 hours, EA in upper 50 percentage is 1.2 hours, with a significant difference indicated by an asterisk.

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Comparison of sleep debthrs, REMhrs, and SWShrs between lower EA and higher EA groups in both male and female swimmers. Statistically significant differences between EA groups are noted with *p < 0.05. EA; energy availability, REM; rapid-eye movement, SWS; slow-wave sleep.

3.4. Sleep and EA in all swimmers

Regarding relationships between EA and sleep variables, there was no correlation between EA and sleep durationhrs in all swimmers (r = 0.33; p = 0.09). However, when examining sleep quality, average EA was positively correlated with REMhrs in all swimmers (r = 0.639, p < 0.001), male swimmers alone (r = 0.744, p = 0.014), and female swimmers alone (r = 0.515, p = 0.041) (Figure 2). The average EA was positively correlated with SWShrs in all swimmers (r = 0.607, p < 0.001) and female swimmers (r = 0.570, p = 0.021) but not in male swimmers (r = 0.389, p = 0.267). EA was positively correlated with SWS% in all swimmers (r = 0.488, p = 0.012). When examining by sex, there were no evident relationships between EA and SWS% in either sex.

Figure 2.

A three panel scatter plot shows REM sleep versus average EA for all swimmers, male swimmers, and female swimmers. The three panel scatter plot shows REM sleep in hours on the vertical axis versus average EA in kilocalories per kilogram FFM per day on the horizontal axis. Panel A, All Swimmers, n equals 26, shows a positive correlation with R equals 0.639, p less than 0.001. Panel B, Male Swimmers, n equals 10, shows a positive correlation with R equals 0.744, p equals 0.014. Panel C, Female Swimmers, n equals 16, shows a positive correlation with R equals 0.515, p equals 0.041. All three panels show data points generally increasing from left to right.

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The relationship between average EA and REM sleep in (A) All Swimmers, Panel (B) Male Swimmers, and Panel (C) Female Swimmers.

When EA was analyzed for its potential influence on measures of sleep quality, linear regression analyses revealed that when controlling for sex, EA was a significant predictor of SWShrs (R2 = 0.448; F = 9.35, p < 0.001). Linear regression analyses also revealed that when controlling for sleep durationhrs, EA was a significant predictor of REMhrs (R2 = 0.425; F = 8.509, p < 0.002). Finally, when controlling for sleep durationhrs, EA was a significant predictor of sleep debthrs (R2 = 0.261; F = 4.055; p = 0.031).

4. Discussion

This study is one of few to provide preliminary evidence of the interrelationships between EA on measures of sleep quality in elite swimmers. Most notably, we found that over half (69%) of the swimmers exhibited sub-optimal EA (EA < 45 kcal/kg FFM/d), and we found that female swimmers with higher EA had significantly better sleep quality for several sleep measures, including SWShrs and REMhrs. EA was also positively associated with REMhrs and SWShrs in all swimmers. Further, EA was found to be a significant predictor of SWShrs, and sleep debthrs when controlling for the confounding variables of sex and sleep durationhrs. Considered together, these findings suggest that EA may be related to sleep quality, whereby greater EA is associated with better sleep quality in elite collegiate swimmers.

4.1. Differences in energy availability between male and female swimmers

The present study identified important sex differences in EA. Among male swimmers, 50% (5/10) exhibited a sub-optimal EA, whereas 81% (13/16) of female swimmers exhibited sub-optimal EA of <45 kcal/kg FFM/d. This sex difference in EA has been documented as a common trend in the literature [1,42,43]: however, limited head-to-head research is available to support differences in EA between males and females. Potential explanations for the higher prevalence of sub-optimal EA may result from sociocultural pressures placed on female athletes to maintain a thin, lean physique, versus the pressures on males to exhibit a strong, muscular physique, which is often exacerbated in the context of sport participation [44]. In addition, our study demonstrated that 69% (18/26) of all swimmers exhibited sub-optimal EA while 31% (8/26) of swimmers exhibited an EA at or above optimal. It is important to note no single threshold of EA constitutes the point at which physiological consequences ensue [5,6,45], but at this time, an EA of ≥45 kcal/kg FFM/d is used as the accepted cut-point for “optimal” EA that ensures that all physiological functions are energetically equipped for proper functioning [5,6,45]. In the context of sleep, higher EA provides the energy necessary for physiological recovery processes that take place during sleep [46,47], and may allow for more efficient sleep cycles that encompass longer periods of SWS and REM sleep. Thus, the high prevalence of sub-optimal EA exhibited in this cohort of swimmers may contribute to compromised sleep architecture and/or reduced sleep quality, which may impact recovery from training.

4.2. Relationships between energy availability and sleep

The present study found relationships between EA and sleep quality. In our cohort of swimmers, female swimmers with higher EA (>34 kcal/kg FFM/d) had significantly better sleep quality, as evidenced by sleep debthrs, REMhrs, and SWShrs. Low EA has a demonstrated impact on circulating hormone levels, with decreases in leptin, and increases in ghrelin being observed during periods of low EA [5,6,48–51]. Additionally, leptin and ghrelin are involved in regulating the sleep‒wake cycle [52–54]. Maintaining adequate EA helps maintain adequate leptin and ghrelin concentrations [48,50,51], hormones that, when properly regulated, support healthy sleep‒wake cycles [51]. In contrast, low EA, as seen in the present study, can disrupt leptin and ghrelin regulation [47–49,55], potentially leading to worsened sleep quality. Increases in ghrelin [53] may increase wakefulness, leading to shorter sleep durations and thus shorter durations of SWS and REM, and greater accumulation of sleep debt. EA was positively correlated with REMhrs sleep in all, male, and female swimmers, and positively correlated with SWShrs in all swimmers and female swimmers, and EA was found to be a significant predictor of REMhrs sleep and SWShrs when controlling for the confounding variables of sex and sleep durationhrs. The literature supports that leptin inhibits lateral hypothalamic neurons from secreting orexin [53], a hormone that increases arousal and wakefulness, decreases REM sleep, and decreases SWS [54]. Therefore, when leptin concentrations are reduced under conditions of low EA [48,55], the lateral hypothalamic neurons may secrete more orexin, resulting in increased arousal and wakefulness, which may result in decreased REM sleep, and decreased SWS [56]. Additionally, ghrelin, a hormone that increases during low EA [49,57], has been found to increase orexin levels [54], further increasing arousal and wakefulness and also impairing REM and SWS [58]. Therefore, given the demonstrated role of leptin ghrelin, and orexin in moderating sleep and arousal [54,59], it is not surprising that lower EA was related to lower REMhrs and SWShrs. While the relationship between EA and the sleep quality variables of REM and SWS in athlete populations have yet to be explored, other research demonstrates relationships between sleep quality parameters and dietary intake in other active populations. Specifically, one study investigating the relationship between EA and sleep patterns in a population of athletic trainers found those with low EA were at significantly higher risk for experiencing sleep disturbances than those with optimal EA [60].

The present study found that EA was a significant predictor of sleep debthrs when controlling for the confounding variables of sex and sleepduration. A possible mechanism is the role of orexin, a neuropeptide that promotes wakefulness and increases when EA is low [54]. Because sleep debthrs reflects the deficit that accumulates when an athlete does not get sufficient sleep (WHOOP Inc., Boston, MA), elevated orexin in athletes with low EA may cause a delay in sleep onset or increased nighttime awakenings, reducing an athlete's total sleep duration and resulting in increased sleep debthrs [23,58]. Furthermore, our finding that elite collegiate swimmers with lower EA had significantly worse sleep quality for several sleep measures is not only informative for health maintenance, but it also has valuable, practical applications for athletes. Specifically, the finding that athletes with low EA had significantly worse sleep quality provides coaches with a strategic direction in implementing science-driven nutritional practices to support the health of their athletes. Coaches can work with sport dieticians to provide nutrition education and to encourage consistent energy intake distributed throughout the day, including well-timed snacks, to prevent extended periods of fasting [61] and to improve the likelihood of athletes achieving their caloric needs for their activity level [62]. Other strategies that could be implemented include: consuming a balanced meal 3–4 h before bedtime, incorporating a small pre-sleep snack, and encouraging athletes with early-morning training sessions to consume a light pre-workout snack and recovery meal immediately after completion of training to minimize energy deficits that may be contributing to disrupted sleep that night.

4.3. Limitations

To the best of our knowledge, this is the first study to examine the association between EA and sleep, as measured via a wearable device, in elite collegiate swimmers during heavy training. Nonetheless, it is imperative to acknowledge certain limitations. First, the DXA assessments were conducted following morning swim practice without standardized food or fluid restrictions, which may have introduced variability in body composition measures but better reflected athletes' habitual routines. As well, there is the potential inaccuracy of energy expenditure and sleep measurement when wearable technology is used, particularly in a sports context such as swimming, which warrants consideration. The WHOOP integrates multiple physiological signals, including heart rate, heart rate variability, respiratory rate, and movement, through proprietary algorithms to estimate sleep characteristics and daily energy expenditure. While these approaches do not replace gold-standard assessments such as polysomnography, indirect calorimetry, or doubly labeled water, they allow for continuous, low-burden monitoring in free-living conditions. As with all wrist-worn wearables, concerns remain regarding the precision of sleep staging and energy expenditure estimates, particularly in athletic populations. However, wrist-based monitoring offers substantial advantages in feasibility, compliance, and ecological validity for elite athletes training in free-living conditions. Future studies should aim to further validate wearable-derived sleep and energy expenditure metrics and, where possible, integrate them with laboratory-based gold standard methods (including employing the doubly labeled water (DLW) method and in-laboratory polysomnography, the gold standard for measuring sleep [63]) to confirm and extend the present findings to support an association between sleep and energy availability. Despite these limitations, the present findings support an association between sleep characteristics and energy availability in elite swimmers and provide a foundation for future mechanistic and intervention-based research.

Furthermore, the size of the total sample of collegiate swimmers represented a sample of convenience, and may limit the generalizability of the present study's results. The significant sex differences between several of the variables of interest (i.e. EA, and SWShrs), required certain statistical analyses to be performed separately by sex (specifically, t-tests and correlation analyses), lowering the effective sample size for several of the analyses and thus further lowering the statistic power of the result. However, the strengths of the current study include its field-based setting, robust evaluation of EI and sleep, and use of validated sleep measurement wearable for the measurement of sleep (WHOOP Inc., Boston, MA) [39–41]. Lastly, it is essential to acknowledge that certain underlying pathways necessitate further exploration using randomized controlled trials and employing gold-standard measurements.

The theoretical frameworks described in the OTS model and relative energy deficiency in sport (RED-S) model suggest a relationship between EA and sleep, emphasizing the importance of further investigation. The OTS model proposes that nutrition and sleep are both contributing factors to athlete burnout [7,64], while the RED-S model suggests that reduced sleep quality is a consequence of low EA [6], and even includes “sleep disturbances” as a segment of the RED-S Health Conceptual Model. Our findings conceptually align with the existing theoretical frameworks describing the interplay between chronic low EA, heavy training, and worsened sleep outcomes. The observed preliminary associations support that low EA and poor sleep quality may collectively contribute to impaired recovery capacity in athletes undergoing heavy training loads. We acknowledge that the relationships discovered in this population could be bi-directional: that is, sleep quantity and quality could be influencing dietary intake habits and overall EA. While we are not aware of any study to date that has experimentally explored the effect of poor sleep quality on EA in an elite collegiate athlete population, research has found sleep quantity and quality to influence dietary intake in the general population. For instance, one study investigating the relationship between sleep deprivation and daily EI in a population of healthy adults found that sleep-restricted subjects consumed significantly more calories than control subjects [65]. Another study examining the effect of sleep restriction on food intake and weight gain found that insufficient sleep led to increased EI, while transition from insufficient to adequate sleep decreased EI, especially of calorically dense foods [66]. These studies demonstrate the potential of sleep habits in influencing eating behaviors, further emphasizing the need for future research using longitudinal and experimental designs to clarify causal pathways between EA, sleep, and recovery within the RED-S and OTS frameworks.

5. Conclusion

This study is among the first to demonstrate a direct association between EA and objectively measured sleep quality in elite collegiate swimmers. Specifically, swimmers with lower EA exhibited worse sleep quality, particularly, less SWS, less REM sleep, and more sleep debt. These findings provide preliminary evidence that low EA may be linked to impaired sleep quality during periods of heavy training, reinforcing the potential interconnection between nutrition and recovery in athletes. Given the high prevalence of both low EA and poor sleep quality within athlete populations, as well as the importance of adequate EA and proper sleep for athlete health and performance, addressing these factors is imperative. To avoid the negative consequences of poor sleep quality and low EA, athletes should get adequate sleep and consume adequate calories to support energy needs and optimize training and performance.

5.1. Future directions

While this study contributes novel findings, particularly finding that higher EA relates to better sleep quality in our population of elite endurance athletes, more research is needed to clarify the effects of EA and sleep quality measures on athletic performance. Particularly, randomized control trials should be conducted to greater explore the causal relationship between these variables. In addition, future studies should investigate hormonal markers, i.e. leptin, ghrelin, orexin, etc., to elucidate the mechanisms underlying these relationships. Finally, future research on the effects of EA and sleep quality measures on sport performance may focus on examining these relationships across sports, throughout different points of the training season, and across competitive levels to extend the results of these studies beyond elite collegiate swimmers.

Acknowledgments

The authors would like to thank the athletes for their involvement and extraordinary cooperation during this study.

Disclosure statement

No potential conflict of interest was reported by the author(s) .

Funding

This study was not funded.

Data availability statement

Data will be made available upon reasonable request.

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