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

Background: 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, 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.

Authors

Institutions

Publication Details

Journal
Nutrients
Published
2026-04-22
DOI
https://doi.org/10.3390/nu18091322
Primary Topic
Sleep and related disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Aureliusz Kosendiak, Elżbieta Biernat, Zofia Kuźnik, Bartosz Bogusz Adamczak et al.
Nutrients
Sleep and related disorders
article

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

Aureliusz Kosendiak, Elżbieta Biernat, Zofia Kuźnik, Bartosz Bogusz Adamczak, Szymon Makles, Weronika Hariasz, Hanna Bazan
article en

Abstract

Background: 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, 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.

NutrientsVol. 18(9)
University of Opole (PL), SGH Warsaw School of Economics (PL), Wroclaw Medical University (PL), DSW University of Lower Silesia (PL)
Zero hunger
Openalex Percentile: Top 7%
Sleep and related disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.