Sleep is essential for maintaining health and physical performance (Medic et al., 2017). Without sufficient sleep, a myriad of negative health consequences, such as disrupted metabolic and skeletal muscle function, and alterations to the hormonal milieu may occur (Morrison, Halson, et al., 2022). The National Sleep Foundation recommends that adults obtain 7–9 hr of sleep per night for maintaining health (Ohayon et al., 2017). However, sleep recommendations for athletic populations are less clear, with longer durations likely required, and influenced by the athlete’s training load and age (Walsh et al., 2020). Athletes appear to benefit from achieving ∼8 hr of sleep per night in order to feel well-rested (Sargent et al., 2021). Nevertheless, most athletes do not achieve sufficient sleep duration or sleep quality (Fox et al., 2020; Halson et al., 2022; Sargent et al., 2021), which is likely underpinned by a combination of various sport-related factors (e.g., timing of training and competition [Lastella et al., 2020] and long-haul travel [Janse van Rensburg et al., 2021]) and nonsport-related factors, such as age (Swinbourne et al., 2016) and sex (Schaal et al., 2011). This is an important issue because athletic performance can be impaired by poor sleep (Fullagar et al., 2015).
Pharmacological interventions exist to improve sleep but are often targeted at individuals with clinical sleep disorders such as insomnia. As such, the primary aim of the pharmacological agent is to improve overnight sleep, typically without focus on residual next-day effects. Consequently, pharmacological interventions can be associated with impairment of next-day cognitive performance (Frase et al., 2018). For example, commonly prescribed sleep medications may hinder an individual’s ability to perform complex psychomotor tasks, such as driving a car (Gunja, 2013). Interventions that may impede perceptual performance are likely to be detrimental to athletic performance when considering the need to complete highly demanding cognitive tasks, often under intense time pressure. In addition, pharmacological interventions often carry the risk of addiction and withdrawal upon cessation of use (Frase et al., 2018). Therefore, it is evident that some pharmacological interventions may not be suitable for use in an athletic population. One potential alternative to enhance sleep without the negative side effects of pharmacological interventions are nutritional interventions.
Several nutritional substances can improve sleep in healthy adults, including carbohydrates, tryptophan, theanine, and nucleotides (e.g., 5′AMP) (Doherty et al., 2019; Halson, 2014; Halson et al., 2020; Sutanto et al., 2021; Vlahoyiannis et al., 2021). These ingredients have been investigated both in isolation, and in combination, in healthy, untrained populations (Halson et al., 2020). For instance, a nutritional intervention with a combination of ingredients (e.g., high Glycemic Index carbohydrates, valerian, and theanine) reduced sleep onset latency (SOL) in healthy adult males without any impairment on next-day alertness, cognitive performance, or postural sway (Halson et al., 2020). Additionally, a very low carbohydrate diet can increase slow wave sleep and all stages of nonrapid eye movement (NREM) sleep in healthy adult males (Afaghi et al., 2008). Alertness, cognitive performance, and balance are important contributors to sports performance, but the impact of nutritional interventions targeted toward improving sleep on other aspects of sports performance (e.g., maximal effort) have not been examined. As athletes are often required to train in the morning, determining whether next-morning physical performance is influenced by presleep nutritional interventions is essential. A nutritional intervention that can improve sleep without hindering next-morning physical performance would be beneficial to many athletes.
The aim of the present study was to determine the effect of two different doses of a formulated nutritional intervention compared with placebo on objective, and subjective sleep, and next-morning physical performance, cognitive function, and balance in trained adult males. The two nutritional interventions (i.e., high dose, low dose) contained high Glycemic Index carbohydrate (but a low glycemic load), whey, tryptophan, theanine, and 5′AMP and were consumed 90 min before bedtime following a standard bout of daytime exercise.
Materials and Methods
Design and Procedures
A double-blind, placebo-controlled, crossover experimental design was used to examine the effectiveness of two different doses of a nutritional intervention on sleep and next-morning performance. Sleep was measured each night using polysomnography (PSG) during a 10.5-hr sleep opportunity (22:30 hr–08:00 hr). On Day 1, participants were familiarized with the exercise protocols and were trained on the cognitive and postural sway tasks. Night 1 was used to familiarize participants with the equipment for monitoring sleep. On Nights 2–4, participants received one of the two interventions, (i.e., high dose or low dose), or placebo in a randomized, counterbalanced order to mitigate potential order effects. The nutritional interventions were blinded by a researcher not involved in the study and the code was not revealed until all statistical analysis had occurred. A Latin Square randomization approach was used to ensure approximately the same number of potential trial orders were undertaken. A member of the research team allocated enrolled participants to the trial orders in accordance with the Latin Square randomization results. There were six potential sequences participants could complete the trial in, ABC (n = 3), ACB (n = 4), BAC (n = 1), BCA (n = 2), CAB (n = 4), and CBA (n = 3), with condition A, B, and C, representing high dose, low dose, and placebo, respectively (see Supplementary Figure S1 in Supplementary Materials [available online]). The nutritional interventions and placebo were provided to participants in liquid form (250 ml in volume). Ingredients for each nutritional intervention and placebo are provided in Table 1.
Ingredient Profiles of the Two Nutritional Interventions and Placebo Consumed by Trained Adult Males
| Nutritional intervention 1 | Nutritional intervention 2 | Placebo |
|---|---|---|
| 2.7-g high glycemic index carbohydrate | 2.7-g high glycemic index carbohydrate | Nonnutritive sweetener |
| 30-g whey | 40-g whey | Flavor |
| 0.641-g tryptophan | 0.855-g tryptophan | Color |
| 660-mg theanine | 660-mg theanine | |
| 53-μg 5′AMP | 53-μg 5′AMP | |
| Nonnutritive sweetener | Nonnutritive sweetener | |
| Flavor | Flavor |
Note. Color added to match the appearance of the whey protein present in nutritional Interventions 1 and 2.
On Nights 2–4, participants consumed either a nutritional intervention or placebo at 21:00 hr. Participants were given 5 min to consume the entire supplement or placebo and were supervised by a member of the research team. After consuming the intervention or placebo, participants were not permitted to consume any water until the morning. Participants rated their subjective sleepiness every 30 min from 20:00 hr to 22:00 hr. In the 30 min after waking in the morning, participants rated their perceptions of sleep (i.e., latency, quality, quantity) and gastrointestinal symptoms. At 09:00 hr, participants completed a 30-min test battery to assess subjective alertness, self-perceived capacity, cognitive performance, and postural sway. The tasks in the test battery were completed in the same order each day. At 10:00 hr, participants completed a 17-min warm-up followed by a 10-min maximal effort time trial on a stationary cycling ergometer. Each afternoon, participants completed a high-intensity cycling training session at approximately 16:00 hr to replicate training demands commonly encountered by athletes. See Supplementary Figure S2 in Supplementary Materials (available online) for a schematic overview of the study design.
Participants
Seventeen healthy, trained adult males (McKay et al., 2021) completed this study (mean ± SD; age: 25.4 ± 6.5 years, height: 179.3 ± 7.2 cm, mass: 74.2 ± 10.0 kg). Out of 31 volunteers screened, 21 were enrolled between March 2022 and August 2022, with four withdrawing before completion of the trial (see Supplementary Figure S1 in Supplementary Materials [available online]). Participants completed a general health questionnaire, a preexercise screening tool by Exercise Sports Science Australia (Norton & Norton, 2011) (502.6 ± 8.4 min of weighted physical activity/exercise per week) and The Pittsburgh Sleep Quality Index (Buysse et al., 1989) (score: 3.0 ± 1.1) to assess eligibility. Participants had experience participating in sports, such as Australian Rules Football, Australian Rules Football refereeing, football (soccer), basketball, cricket, tennis, CrossFit, and Muay Thai. Participants were excluded from the study if they had a clinically diagnosed sleep disorder, had a change in medication over the study period known to affect sleep, had any musculoskeletal injuries, were smokers, or were shift workers. Participants were informed of the experimental procedures, provided with an opportunity to ask questions, and gave signed written consent prior to participation. Participants were instructed to maintain their regular sleep/wake patterns in the week prior to the study and to avoid alcohol in the 24 hr prior to the study. Sample size calculation (n = 18) was based on the number of participants who completed a similar sleep and nutritional intervention protocol which demonstrated an improvement in SOL (Halson et al., 2020) and a power calculation using the R package pwr (Champely et al., 2018). The power for the sample size was calculated using a medium effect size of 0.50 (which was the difference between groups in a similar study; Halson et al., 2020). The returned power was 0.90, which equates to a 10% chance of type II error and a 5% chance of type I error. As such, only 14 participants were required for the study to be powered at the conventional 0.80. The experimental protocol was approved by Central Queensland University’s Human Research Ethics Committee (0000021915), and reciprocal approval was obtained from the Australian Catholic University Human Research Ethics Committee (2022-2526R). This study was registered as a clinical trial with Australia New Zealand Clinical Trial Register on September 2, 2021, trial ID NCT05032729. The full-trial protocol and study details are available online at https://clinicaltrials.gov/study/NCT05032729
Living Conditions
Participants lived and slept in a purpose-built accommodation suite at Central Queensland University’s Appleton Institute in Adelaide, Australia. Six participants can be accommodated within the suite concurrently, which is configured similarly to a serviced apartment with each participant having their own private bedroom, lounge room, and bathroom. During the day when participants were not undertaking testing, they were permitted to engage in routine sedentary activities, such as reading, using laptops or tablets, and watching television. Participants were not permitted to undertake any additional exercise outside the two allocated sessions each day and were not permitted to sleep outside of the scheduled time in bed. Researchers monitored participants for compliance using close-circuit television and in-person monitoring.
Meals
Nutritional intake was standardized for each participant for the duration of the study. All meals provided to participants were calorie-controlled, and the same approximate number of calories was provided at each respective meal opportunity (e.g., breakfast, lunch, and dinner). Participants were provided with breakfast, lunch, and dinner at 11:30 hr, 13:30 hr, and 19:00 hr, respectively. Additionally, participants were provided with an opportunity to eat a standardized snack (i.e., identical item consumed each day) at 08:45 hr, and a second snack after the completion of their afternoon exercise training session (∼16:45 hr). Participants were provided with a 250-ml electrolyte sports drink (Gatorade, PepsiCo.) after the maximal effort time trial and simulated training session. On average, participants consumed 8,992 ± 3,307 kJ per day. Additionally, water was available ad libitum throughout the day from 08:00 hr until 21:00 hr. Participants were not permitted to consume any food or beverages apart from water, outside of the designated meal and snack times. Furthermore, participants were not permitted to consume caffeine or alcohol at any time during the protocol.
Sleep
Sleep was recorded using PSG equipment (Grael V1, Compumedics) with a standard montage of electrodes. Electrodes were applied in the 60 min prior to lights out and included three electroencephalograms (C4-M1, F4-M1, O2-M1), two electrooculograms (left/right outer canthus), and a submental electromyogram. All sleep records were blinded and manually scored in 30-s epochs by the same technician according to established criteria (Iber, 2007). The following dependent variables were calculated from each sleep recording: total sleep time (min), which reflects the time spent in any stage of sleep (i.e., N1, N2, N3, rapid eye movement [REM]) during time in bed; time spent in Stages N1, N2, N3, and REM sleep (min); SOL (min), which represents the time between lights out to the first epoch of any stage of sleep (i.e., N1, N2, N3, REM); wake after sleep onset (min), which reports the time spent in bed awake minus SOL; sleep efficiency (%), which represents total sleep time divided by time in bed × 100; arousals, (count); arousals in NREM sleep (count); arousals in REM sleep (count); awakenings (count); stage shifts (count); stage REM onset latency (min); and stage N3 onset latency. For one participant, objective PSG data were not obtained for the placebo condition due to a technical error with the recording but the remaining conditions were included for analysis.
Subjective Sleepiness
Subjective sleepiness was assessed using the Karolinska Sleepiness Scale (KSS) (Åkerstedt & Gillberg, 1990). The KSS is a 9-point scale where 1 = “extremely alert,” and 9 = “very sleepy, great effort to keep awake, fighting sleep.” Participants were instructed to circle the number on the scale that corresponded to their current level of sleepiness.
Subjective Sleep Quality, Subjective Sleep Duration, Subjective Sleep Latency
Subjective sleep quality was assessed using a 7-point scale, where 1 = “extremely poor,” 2 = “very poor,” 3 = “poor,” 4 = “average,” 5 = “good,” 6 = “very good,” and 7 = “extremely good” (Miller et al., 2020). Subjective sleep quantity and subjective SOL were assessed verbally by asking participants “How much sleep do you think you got?” and “How long did it take you to fall asleep?” (Miller et al., 2020).
Gastrointestinal Symptom Scale
The presence of gastrointestinal symptoms was assessed using a 16-item questionnaire (Halson et al., 2020). Participants used a 10-point Likert scale to rate if they had experienced a gastrointestinal symptom since bedtime the previous night. Possible responses ranged from 1 (no problem at all) to 10 (the worst it has ever been).
Subjective Alertness and Self-Perceived Capacity
Alertness was assessed using a visual analog scale (VAS). Participants placed a mark on a 100-mm horizontal line anchored by the statements “struggling to remain awake” and “extremely alert and wide awake.” A VAS was also used to measure self-assessed ability to perform the cognitive performance tasks (VAS Performance) (Kosmadopoulos et al., 2016). Participants responded to the question “How well do you think you will perform” by placing a mark between the statements “extremely poorly” and “extremely well” on a 100-mm line.
Cognitive Performance
Sustained attention was assessed using the psychomotor vigilance task (PVT-192; Ambulatory Monitoring Inc.) (Dinges & Powell, 1985). The psychomotor vigilance task is a handheld device with an upper surface that contains a four-digit LED display and two push-button response keys. Participants attended to the LED display for the duration of the test (10 min) and pressed the appropriate response key with the thumb of their dominant hand as quickly as possible after the appearance of a visual stimulus (presented at a variable interval of 2–10 s). If the correct response key was pressed, the LED display exhibited the participant’s response time (in milliseconds) for 500 ms. If the wrong response key was pressed, an error message was displayed (ERR). If a response was made prior to the stimulus being presented, a false start message was displayed (FS). For all analyses, anticipated responses (i.e., those with response time less than 100 ms) were excluded. Dependent measures were number of lapses, which were defined as a response time greater than 500 ms (count), mean response time (ms), and false starts (count) (Basner & Dinges, 2011).
Postural Sway
Postural sway was assessed using an Accusway computerized force platform (AMTI) in conjunction with Swaywin software (AMTI) (Sargent et al., 2012). The force platform measures both three‐dimensional forces (Fx, Fy, Fz) and three‐dimensional moments (Mx, My, Mz) involved in balance. These provide center of pressure coordinates, which allow postural sway to be calculated. Participants performed two trials each for 30 s—one trial with their eyes open and the other trial with their eyes closed. The outcome variable recorded during the postural sway assessment was the area of the 95% confidence ellipse enclosing the center of pressure (Area 95, cm2).
Heart Rate
Heart rate was monitored continuously during both the maximal effort time trial and simulated training session using a Polar M400 heart rate monitor (M400, Polar Electro).
Cycling Warm-Up Protocol
Prior to the maximal effort time trial and simulated training session, participants completed a 17-min incremental warm-up based on rating of perceived exertion (RPE) using the Borg Scale (Borg, 1982) on a stationary cycling ergometer (Wattbike Trainer, Wattbike Ltd.). The warm-up consisted of 6 min of cycling at a self-determined RPE six, progressing to 6 min at RPE 13, followed by 3 min at RPE 16, and then 2 min of rest.
Maximal Effort Time Trial
Exercise performance was measured using a 10-min maximal effort time trial performed on a stationary cycling ergometer. Participants were instructed to produce the highest average power they could during the time trial. While cycling, participants were blinded to heart rate and power output, but were provided with a verbal update of elapsed time every minute. The dependent variables obtained during the time trial were mean RPE (6–20), mean heart rate (beats per minute [bpm]), and mean power output (watts; W).
Simulated Training Session
A standardized exercise session was conducted to replicate a typical training session of an endurance-trained athlete. Participants completed 3 × 5-min intervals on a stationary cycling ergometer, with the instructions provided to produce the highest average power they could during each interval. A 5-min rest was provided between each interval. While cycling, participants were blind to heart rate and power output, but were provided with a verbal update of time elapsed after every minute.
Statistical Analysis
All data were analyzed with linear mixed effects models using separate models built for each outcome variable of interest, with condition included as a fixed effect and participant ID included as a random effect using the R package lme4 (Bates et al., 2014; Team RC, 2013). A random intercept for participant was included to account for intraindividual dependencies and interindividual heterogeneity arising from the repeated measures on each participant. All models were estimated using Restricted Estimated Maximum Likelihoods from the lme4 package. All p values were obtained using type III analysis of variance with Satterthwaite’s tests with Kenward–Roger degrees of freedom as implemented in the R package CAR (Fox & Weisberg, 2011). Results were reported as mean estimates with alpha set at p < .05. Pairwise comparisons were performed to understand the magnitude of effects between conditions and estimate appropriately weighted effect sizes from the linear mixed models. The magnitude of differences were assessed using Cohen’s d effect size statistic, and 95% confidence intervals (CIs) using the t_to_d function in the effectsize package, where the t value from the linear mixed model is divided by the square root of the degrees of freedom error from the same model and interpreted as trivial, <.20; small, .20–.49; moderate, .50–.79, and large ≥ .80 (Lakens, 2013). Sleep stage distributions during each condition were plotted using histograms. Sleep hypnogram data (recorded in 30-s epochs) were binned into 5-min intervals, and the percentage of each stage (i.e., wake, Stage N1 sleep, Stage N2 sleep, Stage N3 sleep, and REM sleep) was calculated. The percentages were plotted on the y-axis, with each stage stacked on top of each other to represent the overall distribution, while the 5-min bins were plotted along the x-axis.
Results
Sleep
For the sleep variables, there were no statistically significant main effects between the low-dose intervention, high-dose intervention, and placebo (Tables 2 and 3). There were some minor differences between conditions in the duration of Stage N1 sleep, Stage N3 sleep, and the number of arousals in REM sleep. Specifically, the duration of Stage N1 (i.e., “light” sleep) sleep (Figure 1) was likely higher in the high-dose intervention compared to placebo (7.4 min; Cohen’s d: 0.4; 95% CI [0.03, 0.76]; see Supplementary Table S1 in Supplementary Materials [available online]); and the duration of Stage N3 (i.e., “deep” sleep) sleep (Figure 2) was lower in the low-dose intervention compared with placebo (−13.6 min; Cohen’s d: −0.37; 95% CI [−0.73, −0.004]; see Supplementary Table S1 in Supplementary Materials [available online]). Additionally, the number of arousals observed during REM sleep (Figure 3) was likely lower in the low-dose intervention compared with placebo (−7.6 arousals; Cohen’s d: −0.46; 95% CI [−0.82, −0.083]; see Supplementary Table S1 in Supplementary Materials [available online]). Individual responses for total sleep time, SOL, wake after sleep onset, and sleep efficiency are presented in Figure 4.
Sleep, Subjective Sleepiness, and Sleep Questionnaire Outcomes of Trained Adult Males
| Outcome | Conditions (mean ± SD) | ||
|---|---|---|---|
| Placebo | Low dose | High dose | |
| Sleep | |||
| TST (min) | 508.3 ± 46.7 | 494.9 ± 42.8 | 509 ± 29.7 |
| WASO (min) | 49.5 ± 46.1 | 60.3 ± 47.7 | 45.1 ± 31.4 |
| SE (%) | 89.2 ± 8.2 | 86.8 ± 7.5 | 89.3 ± 5.2 |
| SOL (min) | 12.2 ± 12.7 | 13.1 ± 20 | 15.1 ± 15.4 |
| REM latency (min) | 78.9 ± 22.8 | 90.5 ± 26 | 93.8 ± 40.2 |
| Stage 3 latency (min) | 15.4 ± 9.7 | 16.8 ± 14.1 | 16.6 ± 7.9 |
| Stage 1 (min) | 30.9 ± 10.6 | 34.9 ± 19.0 | 37.4 ± 18.4 |
| Stage 2 (min) | 240.4 ± 29.9 | 236.9 ± 34.9 | 242.1 ± 26.7 |
| Stage 3 (min) | 139.6 ± 31.2 | 125.3 ± 21.9 | 132.3 ± 22.2 |
| REM (min) | 97.4 ± 31.1 | 97.7 ± 29.8 | 97.3 ± 29.7 |
| Arousals—total (count) | 120.9 ± 41.2 | 117.6 ± 43.4 | 126.5 ± 48.3 |
| Arousals—REM (count) | 35.2 ± 15.9 | 31.5 ± 16.9 | 39.1 ± 19.5 |
| Arousals—NREM (count) | 85.8 ± 31.1 | 86.1 ± 32.1 | 87.4 ± 34 |
| Awakenings (count) | 46.7 ± 46.3 | 57.2 ± 45.3 | 39.6 ± 26.5 |
| Stage shifts (count) | 167 ± 30 | 173.6 ± 34.7 | 175.6 ± 38.5 |
| Subjective sleepiness | |||
| KSS 20:00 hr (units) | 4.4 ± 1.3 | 4.7 ± 1.6 | 4.7 ± 1.6 |
| KSS 20:30 hr (units) | 5 ± 1.7 | 5 ± 1.4 | 5.3 ± 1.6 |
| KSS 21:00 hr (units) | 5.3 ± 1.4 | 5.2 ± 1.3 | 5.4 ± 1.6 |
| KSS 21:30 hr (units) | 5.5 ± 1.5 | 5.7 ± 1.4 | 5.8 ± 1.8 |
| KSS 22:00 hr (units) | 5.9 ± 1.8 | 6.0 ± 1.4 | 6.1 ± 1.9 |
| Subjective questionnaires | |||
| Presleep arousal (units) | 4.9 ± 6.5 | 5.2 ± 6.2 | 4.6 ± 6.7 |
| Perceived sleep quality (units) | 4.9 ± 1.1 | 4.8 ± 1.0 | 4.8 ± 1.0 |
| Perceived sleep quantity (hr) | 7.6 ± 1.2 | 7.6 ± 1.4 | 7.7 ± 0.9 |
| Perceived SOL (min) | 24.5 ± 18 | 22.4 ± 19.9 | 23 ± 17.7 |
Note. TST = total sleep time; WASO = wake after sleep onset; SE = sleep efficiency; SOL = sleep onset latency; REM = rapid eye movement; NREM = nonrapid eye movement; KSS = Karolinska Sleepiness Scale.
Statistical Outcomes for Sleep, Subjective Sleepiness, and Sleep Questionnaires of Trained Adult Males
| Outcome | F-statistic | df | p |
|---|---|---|---|
| Sleep | |||
| TST (min) | 1.585 | 2,31.27 | .221 |
| WASO (min) | 1.969 | 2,31.13 | .157 |
| SE (%) | 1.604 | 2,31.26 | .217 |
| SOL (min) | 0.281 | 2,30.24 | .757 |
| REM latency (min) | 2.194 | 2,31.37 | .128 |
| Stage 3 latency (min) | 0.112 | 2,31.43 | .894 |
| Stage 1 (min) | 2.518 | 2,31.05 | .097 |
| Stage 2 (min) | 0.214 | 2,31.47 | .809 |
| Stage 3 (min) | 2.140 | 2,31.24 | .135 |
| REM (min) | 0.038 | 2,31.11 | .963 |
| Arousals—total (count) | 1.154 | 2,31.10 | .329 |
| Arousals—REM (count) | 3.254 | 2,31.14 | .052 |
| Arousals—NREM (count) | 0.042 | 2,31.13 | .959 |
| Awakenings (count) | 1.363 | 2,31.30 | .271 |
| Stage shifts (count) | 1.082 | 2,31.17 | .351 |
| Subjective sleepiness | |||
| KSS 20:00 hr (units) | 0.348 | 2,32 | .708 |
| KSS 20:30 hr (units) | 0.723 | 2,32 | .493 |
| KSS 21:00 hr (units) | 0.320 | 2,32 | .728 |
| KSS 21:30 hr (units) | 0.254 | 2,32 | .778 |
| KSS 22:00 hr (units) | 0.208 | 2,32 | .813 |
| Subjective questionnaires | |||
| Presleep arousal (units) | 0.314 | 2,32 | .733 |
| Perceived sleep quality (units) | 0.047 | 2,32 | .954 |
| Perceived sleep quantity (hr) | 0.130 | 2,32 | .878 |
| Perceived SOL (min) | 0.274 | 2,32 | .762 |
Notes: TST = total sleep time; WASO = wake after sleep onset; SE = sleep efficiency; SOL = sleep onset latency; REM = rapid eye movement; NREM = nonrapid eye movement; KSS = Karolinska Sleepiness Scale; df = degrees of freedom.
—Violin plots indicating the effects of two different doses of a nutritional intervention and placebo on Stage N1 sleep duration. Colored lines (traveling from left to right, across each condition) represent each participant, black dots indicate the mean data point, vertical black lines reflect the SD of the data, and the shape of each plot shows the distribution density of data.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 35, 3; 10.1123/ijsnem.2024-0148
—Violin plots indicating the effects of two different doses of a nutritional intervention and placebo on stage N3 sleep duration. Colored lines (traveling from left to right, across each condition) represent each participant, black dots indicate the mean data point, vertical black lines reflect the SD of the data, and the shape of each plot shows the distribution density of data.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 35, 3; 10.1123/ijsnem.2024-0148
—Violin plots indicating the effects of two different doses of a nutritional intervention and placebo on arousals during rapid eye movement (REM) sleep. Colored lines (traveling from left to right, across each condition) represent each participant, black dots indicate the mean data point, vertical black lines reflect the SD of the data, and the shape of each plot shows the distribution density of data.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 35, 3; 10.1123/ijsnem.2024-0148
—Violin plots indicating the effects of two different doses of a nutritional intervention and placebo on (A) TST, (B) SOL, (C) WASO, and (D) SE. Colored lines (traveling from left to right, across each condition) represent each participant, black dots indicate the mean data point, vertical black lines reflect the SD of the data, and the shape of each plot shows the distribution density of data. TST = total sleep time; WASO = wake after sleep onset; SE = sleep efficiency; SOL = sleep onset latency.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 35, 3; 10.1123/ijsnem.2024-0148
The probability distribution of sleep stages for each condition is presented in Figure 5. During the first 3 hr of sleep, a longer N3 sleep duration was apparent during the nutritional intervention conditions compared to the placebo condition. Additionally, sleep throughout the final 2 hr of the high-dose condition appears to contain less periods of wake compared with the low dose and placebo.
—Sleep histograms representing the probability distribution of sleep stages across the low-dose (top) and high-dose (middle) nutritional interventions and the placebo (bottom). Data represent the percentage of epochs scored as Stage N1, Stage N2, Stage N3, REM, and wake (W) in 5-min bins. The concept for this figure is based on Figure 2 in Sargent et al. (2022). REM = rapid eye movement.
Citation: International Journal of Sport Nutrition and Exercise Metabolism 35, 3; 10.1123/ijsnem.2024-0148
Subjective Sleepiness, Sleep Quality, Sleep Duration, and Sleep Latency
No differences were observed in subjective sleepiness between conditions (Tables 2 and 3; see Supplementary Table S2 in Supplementary Materials [available online]). Additionally, no differences were observed in subjective sleep quality, subjective sleep duration, or subjective sleep latency between conditions (Tables 2 and 3; see Supplementary Table S2 in Supplementary Materials [available online]).
Cognitive Performance, Subjective Alertness, Self-Perceived Capacity, and Postural Sway
There was no difference in mean reaction time, number of lapses, or false starts between conditions during the psychomotor vigilance task (Tables 4 and 5; see Supplementary Table S2 in Supplementary Materials [available online]). In addition, there were no differences in subjective alertness, self-perceived capacity, or postural sway between conditions (Tables 4 and 5; see Supplementary Table S2 in Supplementary Materials [available online]).
Cognitive Function, Postural Sway, Subjective Sleepiness, Alertness, and Perceived Performance Testing Outcomes of Trained Adult Males
| Outcome | Conditions (mean ± SD) | ||
|---|---|---|---|
| Placebo | Low dose | High dose | |
| PVT—mean reaction time (ms) | 244.7 ± 33.2 | 244.8 ± 28.6 | 245.7 ± 36.8 |
| PVT—lapses (count) | 1.0 ± 1.5 | 0.9 ± 1.7 | 0.9 ± 1.2 |
| PVT—false starts (count) | 1.1 ± 1.6 | 0.6 ± 1.0 | 1.3 ± 1.8 |
| KSS (units) | 3.8 ± 1.3 | 3.8 ± 1.1 | 4.2 ± 1.5 |
| VAS alertness (units) | 64.2 ± 18.1 | 66.6 ± 17.7 | 66.3 ± 19.1 |
| VAS performance (units) | 67.7 ± 18.6 | 68.5 ± 16.6 | 63.9 ± 17.4 |
| Postural sway–area 95 (cm2) | 0.3 ± 0.2 | 0.3 ± 0.2 | 0.4 ± 0.2 |
Note. PVT = psychomotor vigilance task, KSS = Karolinska Sleepiness Scale, VAS = visual analog scale.
Statistical Outcomes for Cognitive Function, Postural Sway, Subjective Sleepiness, Alertness, and Perceived Performance Testing of Trained Adult Males
| Outcome | F-statistic | df | p |
|---|---|---|---|
| PVT—mean reaction time (ms) | 0.050 | 2,32 | .951 |
| PVT—lapses (count) | 0.021 | 2,32 | .979 |
| PVT—false starts (count) | 1.774 | 2,32 | .186 |
| KSS (units) | 1.873 | 2,32 | .170 |
| VAS alertness (units) | 0.801 | 2,32 | .458 |
| VAS performance (units) | 1.492 | 2,32 | .240 |
| Postural sway–area 95 (cm2) | 1.297 | 2,32 | .287 |
Notes: PVT = psychomotor vigilance task; KSS = Karolinska Sleepiness Scale; VAS = visual analog scale; df = degrees of freedom.
Gastrointestinal Symptom Scale
There was no difference in the number of gastrointestinal symptoms between conditions (mean ± SD; low dose: 1.8 ± 2.2; high dose: 1.3 ± 1.6; placebo: 1.6 ± 2.2; p: .477; see Supplementary Table S2 in Supplementary Materials [available online]).
Maximal Effort Time Trial and Simulated Training Session
No differences were observed in the maximal effort time trial performance between conditions (Table 6). Participants exercised at the same heart rate and power output for each simulated training session (mean ± SD; low dose: 144.5 ± 38.5 bpm and 173.3 ± 70 W; high dose: 153.0 ± 11.4 bpm and 182.4 ± 52.9 W; placebo: 154.0 ± 11 bpm and 181.4 ± 51.3 W; all p > .05; see Supplementary Table S2 in Supplementary Materials [available online]).
Maximal Effort Time-Trial Testing and Statistical Outcomes of Trained Adult Males
| Outcome | Placebo | Conditions | Statistical outcomes | |||
|---|---|---|---|---|---|---|
| Low dose | High dose | F-statistic | df | p | ||
| Mean power output (watts) | 173.3 ± 51.7 | 175.1 ± 50.6 | 176.2 ± 52.5 | 0.294 | 2,32 | .748 |
| Mean heart rate (bpm) | 150.3 ± 13.5 | 152.8 ± 15.3 | 151 ± 13.3 | 1.020 | 2,32 | .372 |
| Mean RPE (units) | 16.4 ± 1.1 | 16.2 ± 1.1 | 16.4 ± 1.1 | 1.525 | 2,32 | .233 |
Note. Data are mean ± SD. df = degrees of freedom; RPE = rating of perceived exertion; bpm = beats per minute.
Discussion
The aim of this study was to examine the effect of two different doses of a formulated nutritional intervention compared with placebo on objective and subjective sleep, next-morning physical performance, cognitive function, and postural sway in trained adult males. Objective sleep parameters do not appear to be significantly affected by both doses of the nutritional intervention and no improvements in sleep were observed. However, from a practical standpoint, the respective changes in sleep architecture may lack clinical significance, and are unlikely to cause any deleterious effects on performance (Ohayon et al., 2017). No differences in subjective sleep-related outcomes were observed after consumption of either dose of the nutritional intervention (Table 2). Additionally, participants were able to maintain next-morning physical performance, cognitive function, and postural sway after the consumption of each intervention. Consequently, the consumption of high and low doses of a proprietary blended supplement that contains tryptophan, high-Glycemic Index carbohydrate, theanine, 5′AMP, and whey protein does not appear to have a meaningful influence on objective sleep parameters, no effect on subjective sleep outcomes, and does not improve next-day performance.
The two doses of a nutritional intervention examined in the present study did not enhance sleep quality or quantity but likely had an influence on three components of sleep. However, these effects do not represent a meaningful change in sleep quality or quantity. A reduction of 13.5 min of “slow wave sleep” was observed after consuming the lower dose intervention compared to placebo; and a likely increase in “light sleep” of approximately 7 min was observed after consuming the higher dose intervention compared with placebo. The findings in this study contrast with previous results where a nutritional intervention with a similar ingredient profile improved SOL (mean ± SD; intervention: 9.9 ± 12.3 min vs. placebo: 19.6 ± 32.0 min) in a group of healthy adult males; Halson et al., 2020). However, the differences in SOL may be attributed to the different ingredient profiles (e.g., valerian) (Fernández-San-Martín et al., 2010). Therefore, the ingredient profile in the current study may not be as effective at improving sleep in trained adult males compared to interventions with other ingredients such as valerian. Exploring the relationship between additional foods and nutrients with links to improving sleep would be a beneficial next step.
Exercise performance was not influenced by either nutritional intervention. This is consistent with evidence reporting the effects of acute presleep alpha-lactalbumin consumption on sleep quality and time-trial performance, in which no improvement in sleep parameters (measured via ActiGraphy), or changes in 4-km cycling time-trial performance were observed (MacInnis et al., 2020). In the current study, cycling performance was consistent across trials, highlighting the participants were able to tolerate the exercise demands of the protocol. This is likely due to the participants’ previous training experience and ability to repeat high-intensity efforts across successive days. Furthermore, as there were no decrements in performance observed, the presleep ingestion of the nutritional interventions is likely safe for use by athletes who are required to undertake physical performance tasks in the morning.
Cognitive performance and postural sway were not affected by either dose of the nutritional supplement. This is an important finding as interventions to enhance sleep have the potential to induce a “hangover effect” and impair cognitive and psychomotor function (Brandt & Leong, 2017). For example, reduced alertness, slower reaction times, and greater daytime sleepiness may manifest the day after ingestion of prescription sleep medication (Vermeeren, 2004). In an athletic context, a slower reaction time may have negative implications for sporting performance, as numerous components of sport (e.g., agility) rely on fast and accurate decision making and response times (Morrison, Martin, et al., 2022; Sheppard & Young, 2006). Additionally, no adverse effects that are commonly reported after pharmaceutical sleep medications were observed the morning after either of the nutritional interventions. Therefore, the combination of ingredients in these nutritional interventions appears to not have any detrimental effects on cognitive function or balance in adult male athletes.
Although this study is the first to investigate the effects of two doses of a novel nutritional intervention to enhance sleep in well-trained adult males, there are limitations that should be noted. Athletes are typically reported as poor sleepers due to various sport-related and nonsport-related factors, such as early morning training sessions (Sargent et al., 2014), precompetition anxiety (Romyn et al., 2016), and travel (Halson et al., 2021; Walsh et al., 2020). In the current study, participants were provided a consistent sleep opportunity between 22:30 hr and 08:00 hr (i.e., 10.5 hr in bed), in a private distraction-free environment, without the typical demands of early morning training or evening competition. This may have incurred a ceiling effect, as the sleep environment may be more advantageous for facilitating “good” sleep than what an athlete would experience in their typical training schedule and living environment. Subsequently, the environment may make it difficult to observe improvements in sleep with the nutritional interventions. Assessing the efficacy of the two doses of the nutritional intervention during an athlete’s typical training and competition schedule, under “free-living” conditions may provide different results. Furthermore, whether the supplement has different effects on poor sleepers, which athletes often are, would be pertinent to explore. Second, only a single modality of exercise was conducted each morning to assess the effects of the nutritional interventions on physical performance. Whether performance was affected in a longer duration task or in an alternative modality of exercise (e.g., resistance training) would be valuable to assess, as athletes often undertake various modalities of training in the morning. Third, participants were required to abstain from caffeine consumption throughout the protocol. Whether habitual caffeine consumption habits (e.g., dose and timing) would affect results would require further exploration. Fourth, the nutritional interventions were only consumed in an acute setting and determining whether the repeated or chronic consumption of the interventions has an influence on sleep would be a valuable next step.
In conclusion, the consumption of a high- or low-dose proprietary blended supplement did not meaningfully influence objective or subjective sleep in trained adult males in this study. The two doses did not appear to affect next-morning physical performance or have any negative effects on cognitive performance or postural sway, suggesting it is safe for use in this context. It is speculated that improvements in sleep-related outcomes were not observed due to the removal of typical stressors associated with an athlete’s regular training and competition schedule (e.g., early morning training and evening competition) that could impair sleep. Alternatively, the combination of ingredients may not be as effective at enhancing sleep–wake behaviors compared to similar nutritional interventions that have used other ingredients for improving sleep. Future research investigating the efficacy of the two nutritional interventions when consumed during periods of typical training and competition in an athlete’s “free-living” conditions or when consumed chronically may be beneficial. Furthermore, exploring the benefits of consuming other foods and nutrients (e.g., antioxidants, fiber, ergothioneine) linked to improvements in sleep would be important.
Acknowledgments
Disclosure statement: This study was provided financial support by the Gatorade Sports Science Institute, a division of PepsiCo, Inc. Pahnke is employed by PepsiCo, Inc. and Nyman was employed at PepsiCo, Inc. at the time this study was conducted. Views expressed in this article are of the authors, and do not necessarily reflect the position or policy of PepsiCo, Inc. Funding: This research was funded by the Gatorade Sports Science Institute, a division of PepsiCo, Inc. Data Availability Statement: Data sets generated and analyzed within this study are not currently publicly available. However, the data can be made available by contacting the corresponding author. Contributor Role Taxonomy: Formal analysis, investigation, writing—original draft, writing review and editing: Morrison. Formal analysis, resources, writing review and editing, supervision, project administration: Weakley. Conceptualization, methodology, resources, writing—review and editing: Roach. Conceptualization, methodology, resources, writing—review and editing: Sargent. Methodology, investigation, data curation, writing—review and editing: Miller. Conceptualization, methodology, resources, funding acquisition, writing—review and editing: Nyman. Investigation, data curation, writing—review and editing: Gardiner. Investigation, data curation, writing—review and editing: Munteanu. Conceptualization, methodology, resources, funding acquisition: Pahnke. Conceptualization, methodology, resources, funding acquisition, supervision, writing—review and editing: Halson.
References
Afaghi, A., O’Connor, H., & Chow, C.M. (2008). Acute effects of the very low carbohydrate diet on sleep indices. Nutritional Neuroscience, 11(4), 146–154.
Åkerstedt, T., & Gillberg, M. (1990). Subjective and objective sleepiness in the active individual. International Journal of Neuroscience, 52(1–2), 29–37.
Basner, M., & Dinges, D.F. (2011). Maximizing sensitivity of the psychomotor vigilance test (PVT) to sleep loss. Sleep, 34(5), 581–591.
Bates, D., Mächler, M., Bolker, B., & Walker, S. (2014). Fitting linear mixed-effects models using lme4. arXiv preprint arXiv:1406.5823.
Borg, G.A. (1982). Psychophysical bases of perceived exertion. Medicine & Science in Sports & Exercise, 14(5), 377–381.
Brandt, J., & Leong, C. (2017). Benzodiazepines and Z-drugs: An updated review of major adverse outcomes reported on in epidemiologic research. Drugs in R&D, 17(4), 493–507.
Buysse, D.J., Reynolds, C.F., III, Monk, T.H., Berman, S.R., & Kupfer, D.J. (1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry research, 28(2), 193–213.
Champely, S., Ekstrom, C., Dalgaard, P., Gill, J., Weibelzahl, S., Anandkumar, A., Ford, C., Volcic, R., De Rosario, H., & De Rosario, M.H. (2018). Package “pwr.” R Package Version, 1(2), Article 230.
Dinges, D.F., & Powell, J.W. (1985). Microcomputer analyses of performance on a portable, simple visual RT task during sustained operations. Behavior Research Methods, Instruments, & Computers, 17(6), 652–655.
Doherty, R., Madigan, S., Warrington, G., & Ellis, J. (2019). Sleep and nutrition interactions: Implications for athletes. Nutrients, 11(4), Article 822.
Fernández-San-Martín, M.I., Masa-Font, R., Palacios-Soler, L., Sancho-Gómez, P., Calbó-Caldentey, C., & Flores-Mateo, G. (2010). Effectiveness of Valerian on insomnia: A meta-analysis of randomized placebo-controlled trials. Sleep Medicine, 11(6), 505–511.
Fox, J., & Weisberg, S. (2011). Multivariate linear models in R. In An R companion to applied regression. Sage.
Fox, J.L., Scanlan, A.T., Stanton, R., & Sargent, C. (2020). Insufficient sleep in young athletes? Causes, consequences, and potential treatments. Sports Medicine, 50(3), 461–470.
Frase, L., Nissen, C., Riemann, D., & Spiegelhalder, K. (2018). Making sleep easier: Pharmacological interventions for insomnia. Expert Opinion on Pharmacotherapy, 19(13), 1465–1473.
Fullagar, H.H., Skorski, S., Duffield, R., Hammes, D., Coutts, A.J., & Meyer, T. (2015). Sleep and athletic performance: The effects of sleep loss on exercise performance, and physiological and cognitive responses to exercise. Sports Medicine, 45(2), 161–186.
Gunja, N. (2013). In the Zzz zone: The effects of Z-drugs on human performance and driving. Journal of Medical Toxicology, 9(2), 163–171.
Halson, S.L. (2014). Sleep in elite athletes and nutritional interventions to enhance sleep. Sports Medicine, 44(Suppl. 1), 13–23.
Halson, S.L., Johnston, R.D., Appaneal, R.N., Rogers, M.A., Toohey, L.A., Drew, M.K., Sargent, C., & Roach, G.D. (2021). Sleep quality in elite athletes: Normative values, reliability and understanding contributors to poor sleep. Sports Medicine, 52, 417–426.
Halson, S.L., Johnston, R.D., Piromalli, L., Lalor, B.J., Cormack, S., Roach, G.D., & Sargent, C. (2022). Sleep regularity and predictors of sleep efficiency and sleep duration in elite team sport athletes. Sports Medicine-Open, 8(1), Article 470.
Halson, S.L., Shaw, G., Versey, N., Miller, D.J., Sargent, C., Roach, G.D., Nyman, L., Carter, J.M., & Baar, K. (2020). Optimisation and validation of a nutritional intervention to enhance sleep quality and quantity. Nutrients, 12(9), Article 579.
Iber, C. (2007). The AASM manual for the scoring of sleep and associated events: Rules. Terminology and Technical Specification.
Janse van Rensburg, D.C., Jansen van Rensburg, A., Fowler, P.M., Bender, A.M., Stevens, D., Sullivan, K.O., Fullagar, H.H., Alonso, J.-M., Biggins, M., & Claassen-Smithers, A. (2021). Managing travel fatigue and jet lag in athletes: A review and consensus statement. Sports Medicine, 51(10), 2029–2050.
Kosmadopoulos, A., Zhou, X., Roach, G.D., Darwent, D., & Sargent, C. (2016). No first night shift effect observed following a nocturnal main sleep and a prophylactic 1-H afternoon nap. Chronobiology International, 33(6), 716–720.
Lakens, D. (2013). Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Frontiers in Psychology, 4, Article 863.
Lastella, M., Roach, G.D., Vincent, G.E., Scanlan, A.T., Halson, S.L., & Sargent, C. (2020). The impact of training load on sleep during a 14-day training camp in elite, adolescent, female basketball players. International Journal of Sports Physiology and Performance, 15(5), 724–730.
MacInnis, M.J., Dziedzic, C.E., Wood, E., Oikawa, S.Y., & Phillips, S.M. (2020). Presleep α-lactalbumin consumption does not improve sleep quality or time-trial performance in cyclists. International Journal of Sport Nutrition and Exercise Metabolism, 30(3), 197–202.
McKay, A.K., Stellingwerff, T., Smith, E.S., Martin, D.T., Mujika, I., Goosey-Tolfrey, V.L., Sheppard, J., & Burke, L.M. (2021). Defining training and performance caliber: A participant classification framework. International Journal of Sports Physiology and Performance, 17(2), 317–331.
Medic, G., Wille, M., & Hemels, M.E. (2017). Short-and long-term health consequences of sleep disruption. Nature and Science of Sleep, 151–161.
Miller, D.J., Sargent, C., Roach, G.D., Scanlan, A.T., Vincent, G.E., & Lastella, M. (2020). Moderate-intensity exercise performed in the evening does not impair sleep in healthy males. European Journal of Sport Science, 20(1), 80–89.
Morrison, M., Halson, S.L., Weakley, J., & Hawley, J.A. (2022). Sleep, circadian biology and skeletal muscle interactions: Implications for metabolic health. Sleep Medicine Reviews, 66, Article 101700.
Morrison, M., Martin, D.T., Talpey, S., Scanlan, A.T., Delaney, J., Halson, S.L., & Weakley, J. (2022). A systematic review on fitness testing in adult male basketball players: Tests adopted, characteristics reported and recommendations for practice. Sports Medicine, 52(7), 1491–1532.
Norton, K., & Norton, L. (2011). Pre-exercise screening. Guide to the Australian adult pre-exercise screening system exercise and sports science Australia.
Ohayon, M., Wickwire, E.M., Hirshkowitz, M., Albert, S.M., Avidan, A., Daly, F.J., Dauvilliers, Y., Ferri, R., Fung, C., & Gozal, D. (2017). National sleep foundation’s sleep quality recommendations: First report. Sleep Health, 3(1), 6–19.
Romyn, G., Robey, E., Dimmock, J.A., Halson, S.L., & Peeling, P. (2016). Sleep, anxiety and electronic device use by athletes in the training and competition environments. European Journal of Sport Science, 16(3), 301–308.
Sargent, C., Darwent, D., Ferguson, S.A., & Roach, G.D. (2012). Can a simple balance task be used to assess fitness for duty? Accident Analysis & Prevention, 45, 74–79.
Sargent, C., Halson, S., & Roach, G.D. (2014). Sleep or swim? Early-morning training severely restricts the amount of sleep obtained by elite swimmers. European Journal of Sport Science, 14(Suppl. 1), S310–315.
Sargent, C., Kosmadopoulos, A., Zhou, X., & Roach, G.D. (2022). Timing of sleep in the break between two consecutive night-shifts: The effect of different strategies on daytime sleep and night-time neurobehavioural function. Nature and Science of Sleep, 231–242.
Sargent, C., Lastella, M., Halson, S.L., & Roach, G.D. (2021). How much sleep does an elite athlete need? International Journal of Sports Physiology and Performance, 1, Article 650.
Schaal, K., Tafflet, M., Nassif, H., Thibault, V., Pichard, C., Alcotte, M., Guillet, T., El Helou, N., Berthelot, G., & Simon, S. (2011). Psychological balance in high level athletes: Gender-based differences and sport-specific patterns. PLoS One, 6(5), Article 19007.
Sheppard, J.M., & Young, W.B. (2006). Agility literature review: Classifications, training and testing. Journal of Sports Science, 24(9), 919–932.
Sutanto, C.N., Loh, W.W., & Kim, J.E. (2021). The impact of tryptophan supplementation on sleep quality: A systematic review, meta-analysis, and meta-regression. Nutrition Review, 10, Article 27.
Swinbourne, R., Gill, N., Vaile, J., & Smart, D. (2016). Prevalence of poor sleep quality, sleepiness and obstructive sleep apnoea risk factors in athletes. European Journal of Sport Science, 16(7), 850–858.
Team RC. (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www. R-project. org/
Vermeeren, A. (2004). Residual effects of hypnotics: Epidemiology and clinical implications. CNS Drugs, 18(5), 297–328.
Vlahoyiannis, A., Giannaki, C.D., Sakkas, G.K., Aphamis, G., & Andreou, E. (2021). A systematic review, meta-analysis and meta-regression on the effects of carbohydrates on sleep. Nutrients, 13(4), Article 1283.
Walsh, N.P., Halson, S.L., Sargent, C., Roach, G.D., Nedelec, M., Gupta, L., Leeder, J., Fullagar, H.H., Coutts, A.J., Edwards, B.J., Pullinger, S.A., Robertson, C.M., Burniston, J.G., Lastella, M., Le Meur, Y., Hausswirth, C., Bender, A.M., Grandner, M.A., & Samuels, C.H. (2020). Sleep and the athlete: Narrative review and 2021 expert consensus recommendations. British Journal of Sports Medicine, 10, Article 2025.
Nontechnical Summary
Sleep is essential for athletes to perform, yet athletes often experience poor sleep, which may hinder physical performance and cognitive function. To enhance sleep, athletes may turn to consuming pharmacological interventions (e.g., sleeping medication). However, the use of pharmacological interventions to improve sleep may be detrimental, with “hangover” type effects potentially manifesting the next morning, which may impair cognitive performance. Presleep nutritional interventions have shown promise with their ability to improve sleep without inducing next-morning cognitive impairments and may be a viable alternative to pharmacological interventions. The aim of this study was to investigate the effects of two different doses of a nutritional intervention versus a placebo on sleep and next-day cognitive function and physical performance, in healthy trained adult males.
The study used a double-blind, repeated-measures, placebo-controlled study design to assess the effects of a low- and high-dose nutritional intervention versus placebo on sleep. The nutritional interventions contained high Glycemic Index carbohydrates, whey, tryptophan, theanine, and 5′AMP, all ingredients linked to improving sleep. Seventeen healthy, adult, physically trained male participants completed the study. All participants were required to attend and stay in a purpose-built sleep laboratory for four nights, where their sleep quality and quantity were measured using gold-standard measurement techniques (i.e., polysomnography). While in the sleep laboratory, participants consumed a standardized diet and did not consume any alcohol or caffeine.
Each night participants consumed either a low- or high-dose nutritional intervention or placebo at 21:00 hr, prior to bedtime. Participants were provided a 10.5-hr sleep opportunity between 22:30 hr and 08:00 hr where their sleep was measured. Each morning participants were required to undertake cognitive and balance testing as well as a high-intensity cycling time trial to assess the influence of the nutritional intervention on cognitive function, balance, and physical performance. In the afternoon, participants completed a high-intensity cycling exercise session to replicate the typical demands of an athlete’s schedule. When participants were not exercising, they were asked to undertake sedentary activities and rest.
There were no significant effects on objective sleep, subjective sleep, cognitive function, or balance, or physical performance observed after consuming the low- or high-dose nutritional interventions. Although the nutritional interventions did not improve sleep, there were also no negative effects on next-morning cognitive function, balance, or physical performance.
The nutritional interventions did not improve sleep in our study. This may have been due to the composition of the nutritional interventions, with the combination and/or dose of ingredients included not being effective for improving the sleep of participants. Another potential reason for not seeing improvements in sleep after the consumption of the nutritional interventions is that the participants recruited were already good sleepers and had limited room to improve. Whether the interventions would be more effective in poor sleepers or when consumed as a part of an athlete’s day-to-day life would be important to consider in future research. A positive finding from our study was the nutritional interventions did not induce any negative next-morning effects on cognitive function or physical performance.
