Sleep Quality, Dietary Patterns, and Nutrition Knowledge in Ultramarathon Runners and American Football Players: A Comparative Cross-Sectional Study

ground: Nutrition and sleep are critical determinants of athletic performance and recovery. Direct comparative research between endurance and strength–power athletes remains limited. This study aimed to evaluate and compare nutritional knowledge, dietary habits, sleep quality, and Body Mass Index between ultramarathon runners and American football players, as well as to explore independent predictors of sleep quality. Methods: A cross-sectional study was conducted among 231 male athletes. To address group size disparity and mitigate statistical bias, a random undersampling technique was applied to create a balanced cohort of 86 athletes comprising 43 ultramarathon runners and 43 American football players. Nutritional parameters were assessed using the Kom- PAN questionnaire. Sleep quality was evaluated using the Pittsburgh Sleep Quality Index. Between-group comparisons were performed using the Mann–Whitney U test with False Discovery Rate correction. An integrated multiple regression model was constructed to identify predictors of global sleep quality. Results: Ultramarathon runners demonstrated significantly better overall sleep quality (p = 0.026) and higher nutritional knowledge (p < 0.001) compared to American football players. Differences in adherence to pro-healthy and non-healthy dietary patterns were not statistically significant after False Discovery Rate correction. The integrated multiple regression model revealed that the athletic discipline was the primary independent predictor of global sleep quality (p = 0.001), while dietary variables did not exhibit a significant independent effect. Furthermore, higher Body Mass Index was independently associated with better sleep scores within the multivariate model (p = 0.008). Conclusions: Significant sport-specific differences exist in BMI, nutritional knowledge,

The integrated multiple regression model revealed that the athletic discipline was the primary independent predictor of global sleep quality (p = 0.001), while dietary variables did not exhibit a significant independent effect. Furthermore, higher Body Mass Index was independently associated with better sleep scores within the multivariate model (p = 0.008). Conclusions: Significant sport-specific differences exist in BMI, nutritional knowledge, and sleep quality. Global sleep quality appears to be primarily associated with the specific physiological and environmental demands of the athletic discipline rather than individual dietary factors, which were not independently significant in the multivariable model. These findings suggest that recovery strategies in strength–power athletes may require a broader, multifactorial approach beyond nutritional education alone. Keywords: sleep quality; nutrition knowledge; sports nutrition; American football; ultramarathon Academic Editor: Christoforos D. Giannaki Received: 9 March 2026 Revised: 17 April 2026 Accepted: 20 April 2026 Published: 22 April 2026 Copyright: © 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

Nutrients 2026, 18, 1322 2 of 17 https://doi.org/10.3390/nu18091322 1. Introduction Nutrition and sleep quality represent two closely linked determinants of athletic performance, recovery capacity, and long-term physiological health. Adequate nutritional intake provides the substrate for energy production, promotes muscle protein synthesis, restores glycogen reserves, and modulates metabolic and endocrine processes that support sustained physical output [1]. Sleep, in parallel, is a fundamental biological function essential for cognitive performance, neuromuscular coordination, immune regulation, and recovery from training-induced stress [2,3]. In athletic populations, the interaction between these factors is particularly critical. Inappropriate dietary patterns may disrupt sleep through altered hormonal signaling, glycemic instability, or gastrointestinal distress [4–6], whereas insufficient or fragmented sleep can impair appetite control, metabolic efficiency, and nutrient assimilation [7,8]. This reciprocal relationship underscores the need for integrated strategies that address both nutrition and sleep as components of comprehensive athlete care. This investigation centers on two markedly different athletic groups: ultramarathon runners (UMs) and American football players (AFs), selected because of their contrasting physiological profiles and training demands. UMs are endurance athletes who perform prolonged bouts of predominantly aerobic exercise, often exceeding 50–100 km in competition [9]. Their performance relies heavily on oxidative metabolism and is supported by adaptations such as low body fat levels [10], elevated mitochondrial content, extensive capillary development, and superior cardiovascular efficiency [11]. From a nutritional perspective, these athletes require approaches that maximize glycogen availability, preserve electrolyte balance, and facilitate recovery from sustained metabolic and muscular load. Such strategies commonly emphasize carbohydrate availability, moderate protein intake, and diets rich in micronutrients [12]. Conversely, AFs engage in intermittent, high-intensity activity characterized by short-duration explosive efforts, rapid directional changes, and repeated maximal or near- maximal strength outputs [13,14]. Their physical characteristics differ substantially, with greater muscle mass, larger cross-sectional muscle area, and body compositions optimized for power production and physical contact [15]. The capacity to rapidly produce high levels of force and power via the phosphagen and glycolytic energy systems is essential for actions such as sprinting, tackling, blocking, and other explosive, position- specific movements. Consequently, well-developed anaerobic capacity and metabolic efficiency represent key physiological determinants of performance

cross-sectional muscle area, and body compositions optimized for power production and physical contact [15]. The capacity to rapidly produce high levels of force and power via the phosphagen and glycolytic energy systems is essential for actions such as sprinting, tackling, blocking, and other explosive, position- specific movements. Consequently, well-developed anaerobic capacity and metabolic efficiency represent key physiological determinants of performance in American football [16]. Nutritional priorities in this population include sufficient protein intake to support muscle hypertrophy, carefully timed carbohydrates to fuel intense activity, and high overall energy consumption to meet caloric requirements [17,18]. Sleep plays a crucial role in this group, as the combination of intense training and mechanical stress from collisions necessitates substantial recovery to reduce injury risk, enhance strength adaptations, and preserve cognitive function for tactical performance [19,20]. The pronounced differences between these two athlete populations, endurance- based versus power-based, aerobic versus anaerobic metabolism, and lean versus muscular body composition, create a valuable framework for examining sport-specific demands and performing an exploratory comparison of baseline lifestyle behaviors such as dietary practices, nutrition-related knowledge, and sleep quality. Although considerable research has explored nutrition and sleep independently within specific sports, direct comparisons between athlete groups with such divergent metabolic and physical characteristics remain limited. The influence of specific sport disciplines on athlete physiology is multidimensional. These differences extend beyond metabolic adaptations and body compositions. They also encompass other biological parameters determined by the training environment. Research

Nutrients 2026, 18, 1322 3 of 17 https://doi.org/10.3390/nu18091322 indicates that the type of sport practiced significantly modifies oral health status and salivary parameters in athletes [21]. The lack of comparative research on sleep quality and nutritional habits between predominantly aerobic and anaerobic disciplines underscores the necessity of formulating discipline-specific recommendations that are precisely aligned with the underlying physiological demands, including, for example, the identification of optimal sleep patterns to support recovery and performance, as well as the development of tailored dietary strategies that best meet the metabolic and nutritional requirements characteristic of a given discipline. This gap is significant, as extrapolating findings from one sport to another may result in recommendations that are not optimally aligned with sport-specific needs. Therefore, the present study, by describing and comparing these parameters between these two disciplines, may serve as an introduction to the development of such recommendations. Accordingly, the main objective of this study was to compare nutritional knowledge, dietary behaviors, and sleep quality, and Body Mass Index (BMI) between UM and AF. By analyzing two groups situated at opposite ends of the spectrum in terms of energy requirements, training intensity, and physiological phenotype, this research aims to document these differences and provide the empirical data necessary for the development of discipline-specific recommendations. They may contribute to the development of targeted interventions that enhance performance and support long-term health, reinforcing the importance of individualized approaches in sports nutrition and sleep management. 2. Materials and Methods 2.1. Study Design and Participants This cross-sectional study was conducted between January and March 2021. Its primary aim was to compare dietary patterns, nutrition knowledge, and sleep quality between UM and AF. The study group consisted of active professional players of the Panthers Wroclaw American football team (n = 43), while the comparison group included ultra-endurance runners (n = 188). The study protocol was approved by the Bioethics Committee of Wroclaw Medical University (approval no. KB-22/2021 and KB-601/2021). All participants provided written informed consent prior to participation. The study was conducted in accordance with the Declaration of Helsinki. Participation was voluntary and anonymous. Participants were recruited from active

= 43), while the comparison group included ultra-endurance runners (n = 188). The study protocol was approved by the Bioethics Committee of Wroclaw Medical University (approval no. KB-22/2021 and KB-601/2021). All participants provided written informed consent prior to participation. The study was conducted in accordance with the Declaration of Helsinki. Participation was voluntary and anonymous. Participants were recruited from active club athletes competing in either endurance (ultramarathon) or strength–power (American football) disciplines. To be eligible for inclusion, athletes had to be active club members, aged 18 to 30 years, male, and free from chronic illnesses and provide written consent to participate in the study. Athletes were excluded if they did not meet these criteria, including being under 18 years of age, having any chronic illness, lacking club player status, or declining participation. Additional exclusions were applied after data verification: incomplete questionnaire responses or failure to comply with embedded verification questions resulted in removal from the final dataset. Only male athletes were ultimately included in the analyzed cohort. Data were collected using an electronic questionnaire that was available to participants for several weeks, with no time limit for completion. In the case of questions or concerns, participants were able to contact the project coordinator. The survey included a demographic and author-designed questionnaire, the KomPAN questionnaire for dietary habits and nutrition knowledge assessment, and the Pittsburgh Sleep Quality Index (PSQI). A detailed description of the recruitment process, along with inclusion and exclusion criteria, is presented in Figure 1.

Nutrients 2026, 18, 1322 4 of 17 https://doi.org/10.3390/nu18091322 Figure 1. Study selection process. 2.2. KomPAN Dietary habits and nutrition knowledge were assessed using the validated KomPAN questionnaire developed by the Polish Academy of Sciences [22]. The instrument evaluates the frequency of consumption of selected food groups and allows calculation of two indices: the Pro-Healthy Diet Index (PHD) and the Non-Healthy Diet Index (NHD), expressed as percentage values (0–100%). Overall diet quality was determined using the Diet Quality Index (DQI), calculated as the difference between PHD and NHD scores, with possible values ranging from −100 to 100. Higher positive scores indicate a more health-promoting dietary pattern, whereas negative values reflect a predominance of unhealthy dietary components. Based on the responses, participants receive a score that reflects their dietary tendency as low (−100– −26), medium (−25–25), or high (25–100). Nutrition knowledge was evaluated using a 25-item single-choice test included in the KomPAN questionnaire, with response options being “True,” “False,” or “I’m not sure”. Each correct answer was awarded one point, while incorrect or uncertain responses receive zero points, resulting in a total score ranging from 0 to 25. The total score reflects the participant’s level of Dietary Knowledge (DK). Based on their responses, participants receive a score that reflects their dietary tendency as insufficient (0–8), sufficient (9–16), or good (17–25). To ensure data reliability, control questions were embedded within the survey. Responses failing validation checks were excluded from the final dataset. The KomPAN questionnaire has demonstrated satisfactory reliability and internal consistency in previous studies [23,24]. 2.3. PSQI

Nutrients 2026, 18, 1322 5 of 17 https://doi.org/10.3390/nu18091322 Sleep quality was assessed using the PSQI [25]. This questionnaire consists of 19 self- rated items that generate seven component scores: • Subjective sleep quality (C1): Assigning a score based on the subjective evaluation of sleep quality by the respondent. • Sleep latency (C2): Allocating zero points for a latency period of less than 15 min and three points for a latency exceeding 60 min. • Sleep duration (C3): Awarding zero points for achieving 7 or less hours of sleep. • Habitual sleep efficiency (C4): Scoring contingent on the ratio of actual hours of sleep to the time spent in bed. • Sleep disturbances (C5): A scoring system dependent on the presence of disturbances affecting the continuity of night sleep, such as feeling too hot or too cold, experiencing unsettling dreams, or discomfort in breathing. • Use of sleep medication (C6): Scoring in relation to the frequency of sleep medication usage by the respondent. • Daytime dysfunctions (C7): Scoring based on the frequency with which a lack of nocturnal rest impacts daytime behavioral disruptions, such as eating habits or participation in meetings. The sum of these components yields a global score ranging from 0 to 21 points, with higher scores indicating poorer sleep quality. In the present study, a global PSQI score >5 was considered indicative of poor sleep quality. The PSQI is a validated and widely used instrument with established psychometric properties [26]. 2.4. Statistical Analysis Statistical analyses were performed using the R statistical environment. To address the initial disparity in group sizes and mitigate the risk of statistical bias, a random undersampling technique was employed. A subgroup of 43 participants was randomly selected from the UM cohort to match the sample size of the AF, creating a perfectly balanced dataset (n = 86). This approach reduced the size of the original UM sample and may have affected the representativeness of the analyzed data. A fixed random seed was utilized to ensure the full reproducibility of this procedure. The normality of continuous variables was evaluated using the Shapiro–Wilk test. Due to

sample size of the AF, creating a perfectly balanced dataset (n = 86). This approach reduced the size of the original UM sample and may have affected the representativeness of the analyzed data. A fixed random seed was utilized to ensure the full reproducibility of this procedure. The normality of continuous variables was evaluated using the Shapiro–Wilk test. Due to the non-normal distribution of the data, non-parametric methods were applied. Categorical variables were presented as frequencies and percentages, and between-group differences were analyzed using Fisher’s exact test. Continuous variables were summarized using medians and interquartile ranges. Between-group comparisons for continuous variables were conducted using the Mann–Whitney U test. To rigorously control the False Discovery Rate (FDR) arising from multiple testing, all resulting p-values were adjusted using the Benjamini–Hochberg procedure. The magnitude of the differences was evaluated by calculating the biserial rank correlation coefficient (r) as the effect size. To identify the independent predictors of global sleep quality and account for potential confounding factors, an integrated multiple linear regression model was constructed. The global PSQI score served as the dependent variable. The athletic group, BMI, DK, PHD, NHD scores were included as independent predictors. The assumption of no multicollinearity was verified and satis fied using the Variance Inflation Factor, yielding values below 1.6 for all variables. To minimize the penalty arising from the False Discovery Rate (FDR) correction while preserving statistical power, correlation analyses were specifically targeted at the PSQI components that demonstrated significant between-group differences in the initial analysis (global PSQI score, sleep latency, and daytime dysfunction). This hypothesis-

Nutrients 2026, 18, 1322 6 of 17 https://doi.org/10.3390/nu18091322 driven approach allowed for a reduction in the number of tested variables. Spearman’s rank correlations with FDR correction were performed independently for both athletic groups. The level of statistical significance was set at p < 0.05 for all analyses. 3. Results 3.1. Characteristics of the Baseline Group The study population consisted of 188 UMs and 43 AFs, all of whom were male. Significant differences were observed in body composition and residence; while 62.8% of runners maintained a normal weight (mean BMI = 24.1), most football players (83.7%) were classified as overweight (mean BMI = 29.1). Geographical distribution showed that over half of the runners (51.1%) and nearly two-thirds of football players (65.1%) resided in cities with more than 100,000 inhabitants. Furthermore, a marked disparity in nutritional knowledge was identified, as 24.5% of runners achieved a “good” mark, whereas 100% of football players were rated as having only “sufficient” knowledge. Subjective assessments revealed that 73.4% of runners perceived their sleep as “good,” compared to only 55.8% of football players. The characteristics of the baseline group are shown in Table 1. Table 1. Characteristics of the baseline group. Variables Runners, n = 188 [IQR] (%) Football Players, n = 43 [IQR] (%) Gender Male 188 (100.0) 43 (100.0) Place of residence Rural area 34 (18.1) 7 (16.3) City <50,000 * 41 (21.8) 8 (18.6) City 50,000–100,000 * 17 (9.0) 0 (0.0) City 100,000+ * 96 (51.1) 28 (65.1) Body Mass Index Mean 24.1 [22.6–25.4] 29.1 [25.9–31.7] Underweight 2 (1.1) 0 (0.0) Normal weight 118 (62.8) 7 (16.3) Overweight 68 (36.2) 36 (83.7) Nutritional knowledge mark Insufficient 17 (9.0) 0 (0.0) Sufficient 125 (66.5) 43 (100.0) Good 46 (24.5) 0 (0.0) Sleep quality Good 138 (73.4) 19 (55.8) Poor 50 (26.6) 24 (44.2) Note: n is the number of observations; * number of inhabitants. 3.2. Categorical Characteristics of the Balanced Groups To address the initial group size disparity and minimize the risk of statistical bias, an undersampling technique was employed, resulting in a perfectly balanced cohort of 86 athletes comprising 43 AF and 43 UM.

(73.4) 19 (55.8) Poor 50 (26.6) 24 (44.2) Note: n is the number of observations; * number of inhabitants. 3.2. Categorical Characteristics of the Balanced Groups To address the initial group size disparity and minimize the risk of statistical bias, an undersampling technique was employed, resulting in a perfectly balanced cohort of 86 athletes comprising 43 AF and 43 UM. The categorical characteristics of this balanced sample are presented in Table 2. A significant difference was observed in BMI categories, with the vast majority of AFs classified as overweight (83.7%), whereas most UMs maintained a normal weight (69.8%) (p < 0.001). DK also differed significantly between the disciplines; while all AF demonstrated sufficient knowledge, a considerable proportion of UM (27.9%) achieved a good knowledge rating (p < 0.001). Regarding global sleep quality

Nutrients 2026, 18, 1322 7 of 17 https://doi.org/10.3390/nu18091322 categories, the AF group exhibited a higher prevalence of poor sleep (55.8%) compared to the UM group (32.6%) (p = 0.050). No significant categorical differences were found in the prevalence of low or medium adherence to NHD and PHD patterns. Table 2. Categorical characteristics of the balanced groups ( n = 86). Variable Category AF UM p-Value (Fisher) BMI Normal 7 (16.3%) 30 (69.8%) <0.001 Overweight 36 (83.7%) 13 (30.2%) <0.001 PHD Low 34 (79.1%) 29 (67.4%) 0.330 Medium 9 (20.9%) 14 (32.6%) 0.330 NHD Low 38 (88.4%) 43 (100%) 0.055 Medium 5 (11.6%) 0 (0%) 0.055 DK Good 0 (0%) 12 (27.9%) <0.001 Sufficient 43 (100%) 28 (65.1%) <0.001 Insufficient 0 (0%) 3 (7%) <0.001 Sleep quality Good 19 (44.2%) 29 (67.4%) 0.050 Poor 24 (55.8%) 14 (32.6%) 0.050 Note: AF—American football players, UM—ultramarathon runners, BMI—Body Mass Index, PHD—Pro-Healthy Diet Index, NHD—Non-Healthy Diet Index, DK—Dietary Knowledge. 3.3. Comparison of All Analyzed Continuous Variables A detailed comparison of all analyzed continuous variables, utilizing the Mann– Whitney U test with False Discovery Rate (FDR) correction, is detailed in Table 3. The analysis confirmed robust sport-specific disparities. The largest effect size was observed for BMI, which was significantly higher in AFs than in UMs (p < 0.001, r = 0.632). UMs achieved significantly higher scores in continuous DK compared to AFs (p < 0.001, r = 0.499). Notably, after FDR correction, only the DQI score remained significantly different between groups (p = 0.040), while the NHD (p = 0.130) and PHD (p = 0.491) scores did not. However, global PSQI scores remained significantly higher in AFs, indicating worse overall sleep (p = 0.026, r = 0.280). Within the PSQI components, AFs experienced significantly worse daytime dysfunction (p = 0.003, r = 0.366), longer sleep latency (p = 0.026, r = 0.275), and poorer subjective sleep quality (p = 0.048, r = 0.244). Table 3. Comparison of all analyzed continuous variables (n = 86). Variable AF [IQR] UM [IQR] Corrected p-Value (FDR) Effect Size (r) BMI 28.72 [5.72] 24.57 [2.78] <0.001 0.632 PHD 23.8 [6.25]

The study compares sleep quality and nutrition knowledge between ultramarathon runners and American football players.