Abstract
Optimal post-exercise nutrition is critical for maximizing recov- ery and subsequent performance. However, athletes often lack knowledge of guidelines, leading to suboptimal practices, particularly inadequate carbohydrate intake for glycogen resynthesis. This study aimed to assess the adherence of Hungarian endurance athletes to nutritional recommendations, identifying deficits and guiding the development of effective educational strategies.Methods: A cross-sectional study surveyed 113 amateur Hungar- ian endurance athletes (mean age 40.04±9.89 years) training≥3 times/week using a self-developedonline questionnaire. A ten-item composite measure, the Post-Exercise Nutrition Recommendation Adherence Score (PENRAS, max 10 points), was calculated to assess adherence. Statistical analyses, including ANOVA and regression, were used to explore factors influencing PENRAS and nutritional practices.Results: The overall mean PENRAS was 5.32±1.52, indicating room for improvement. The most pronounced deficit was observed in quantitative knowledge, with only 1.8% of participants correctly identifying the optimal carbohydrate content required for rapid glycogen resynthesis. Con- currently, high protein content (58.4%) was mentioned by a higher percentage than high carbohydrate content (52.2%) as an aspect of post-exercise meal
nutritional practices.Results: The overall mean PENRAS was 5.32±1.52, indicating room for improvement. The most pronounced deficit was observed in quantitative knowledge, with only 1.8% of participants correctly identifying the optimal carbohydrate content required for rapid glycogen resynthesis. Con- currently, high protein content (58.4%) was mentioned by a higher percentage than high carbohydrate content (52.2%) as an aspect of post-exercise meal planning. Triathletes had significantly higher PENRAS than runners (6.28 vs. 4.97,p= 0.001). Higher PENRAS was also significantly associated with consultation with a dietitian (p= 0.018). Reliance on professionals positively predicted knowledge, while online sources were a significant negative predictor. Higher PENRAS was associated with better meal planning and earlier post-exercise meal timing.Conclusions: Endurance athletes’ post-exercise nutritional practices are suboptimal. The findings emphasize the need for targeted interventions prior- itizing education on carbohydrate intake and redirecting athletes towardsevidence-based information to improve adherence and performance outcomes. Keywords:endurance athletes; post-exercise nutrition; nutrition knowledge; recovery; PENRAS; triathlon 1. Introduction The close relationship between athletic performance and nutrition has become in- creasingly evident over the past few decades. Consequently, to optimize performance Nutrients2025,17, 3629 https://doi.org/10.3390/nu17223629
Nutrients2025,17, 3629 2 of 21 and maximize training adaptations, it is crucial for athletes to ensure adequate nutritional intake and establish dietary strategies that are precisely customized to their training load. By appropriately scheduling their macronutrient intake, athletes can support their training goals, whether they aim to optimize body composition, rapidly replenish glycogen stores, or promote protein synthesis [1]. According to study results, athletes’ nutrition is often not aligned with their training load and fails to provide the necessary nutrient supply for high-level performance [2,3]. Achieving appropriate performance requires a sport-specific approach. In the case of endurance sports, adequate carbohydrate intake is particularly important. However, in today’s diet culture, which often stigmatizes carbohydrates and is permeated by miscon- ceptions, both amateur and professional athletes frequently fail to reach the necessary carbohydrate intakes tailored to their training load [4–6]. The root of inadequate nutrient intake is often a lack of knowledge regarding scientific guidelines [7–9]. Athletes typically base their habits regarding sports nutrition and the use of dietary supplements on their own experiences, the accounts of other athletes, or informa- tion from online sources, with few being familiar with professional recommendations [7,8]. However, the effective translation and application of post-exercise nutritional rec- ommendations often remain suboptimal [7]. When considering the composition of post-exercisemeals, athletes often encounter recommendations tailored forresistance-type training. Applying these recommendations in the context of endurance sports may not optimally support the achievement of maximum athletic performance. For example, many athletes consume protein-containing dietary supplements in the hope of faster recovery and a more favorable body composition (i.e., greater protein synthesis and increased fatty acid oxidation), but they often overlook replenishing their depleted glycogen stores. Ac- cording to dietary records, a higher proportion of amateur runners meet the recommended minimum protein intake after exercise than they do for carbohydrate intake [8]. Key determinants of carbohydrate utilization during exercise include the volume (total duration) and intensity of the activity, as well as the athlete’s total energy expenditure, and exercise economy, the latter being important since the energy cost required for a given performance output is highly individual. Higher-intensity exercise leads
minimum protein intake after exercise than they do for carbohydrate intake [8]. Key determinants of carbohydrate utilization during exercise include the volume (total duration) and intensity of the activity, as well as the athlete’s total energy expenditure, and exercise economy, the latter being important since the energy cost required for a given performance output is highly individual. Higher-intensity exercise leads to a greater rate of carbohydrate oxidation, making muscle glycogen and blood glucose the predominant energy sources. In contrast, at lower intensities, a larger proportion of energy is derived from fat oxidation, thereby preserving glycogen stores. The rate and proportion of carbohydrate utilization vary substantially across different sports disciplines, depending on the nature of the exercise and its metabolic demands. In endurance-based events such as marathon running, road cycling, or cross-country skiing, athletes perform sustained, moderate-to-high-intensity exercise that places a high demand on carbohydrate availability. The primary nutritional goal in these cases is to maintain a high rate of exogenous carbohydrate oxidation, which underlies the widely recommended intake of 60–90 g·h −1 to spare muscle glycogen [10]. Conversely,ultra-enduranceevents, such as Ironman triathlons or ultramarathons, involve a lower relative intensity but ex- tremely high total volume and cumulative energy expenditure. Although the reliance on fat oxidation is higher, the sheer duration of these events can necessitate carbohydrate intakes of up to 120 g·h −1 (when using multiple transportable carbohydrates) to mitigate severe energy deficits and sustain central nervous system function [10]. During long-duration, high-intensity training sessions, muscle and liver glycogen stores become depleted, a process that contributes to fatigue and diminishes perfor- mance [10]. According to current sports nutrition guidelines, following an endurance-type training load, an intake of 1–1.2 g·BW −1 ·h −1 of carbohydrate is recommended during the four-hour period after exercise to ensure the fastest possible replenishment of glycogen
Nutrients2025,17, 3629 3 of 21 stores in the muscles and liver [11–16]. The importance of rapid replenishment is par- ticularly high when the recovery window is narrow (i.e., less than 8 h between training sessions), a highly relevant factor for triathletes and other endurance athletes due to their high training volume. The rate of glycogen resynthesis is highest during the first 1–2 h post-exercise. Consuming adequate carbohydrates in this critical period is essential for ath- letes engaging in frequent and intensive endurance training, as it enables proper recovery before the next training session [13,14,17]. Questionnaires designed to assess nutritional knowledge are often general and, in some cases, time-consuming to complete [18–26]. Even if they include a few questions regarding recovery, this is often insufficient to fully assess the complexity of the subject. The importance of this topic is underscored by a survey utilizing the Comprehensive Evaluation of Athlete’s Knowledge Questionnaire (CEAC-Q), specifically designed to assess carbohydrate knowledge among endurance athletes. Within this survey, the lowest rate of correct answers was recorded in the questionnaire’s recovery section [27]. In the case of the PEAKS-NQ, selecting appropriate recovery meals/nutrients is similarly noted as one of the more challenging sports nutrition concepts [23]. Ultra-endurance athletes, on the other hand, reached significantly higher scores in the recovery questions and performed significantly worse in the fluid and supplement questions [28]. To address this identified gap, we developed the Post-Exercise Nutrition Recommendation Adherence Score (PENRAS) as a specific, multi-faceted composite measure. Our research aims to comprehensively assess Hungarian endurance athletes’ knowledge of and adherence to post-exercise nutritional recommendations—a key area not extensively covered in the existing literature. Furthermore, we investigated the influence of sociodemographic factors, sport discipline, and sources of nutrition information on both knowledge and practical habits. Obtaining this insight is crucial for developing more effective educational strategies for this population, as the pivotal role of sports nutrition professionals in significantly improving athletes’ nutritional knowledge and practices is well established [29–31]. 2. Materials and Methods 2.1. Study Design The present study investigated the knowledge and habits related to post-workout nu- trition among a cohort of
practical habits. Obtaining this insight is crucial for developing more effective educational strategies for this population, as the pivotal role of sports nutrition professionals in significantly improving athletes’ nutritional knowledge and practices is well established [29–31]. 2. Materials and Methods 2.1. Study Design The present study investigated the knowledge and habits related to post-workout nu- trition among a cohort of Hungarian endurance athletes, using a self-developed online ques- tionnaire between March 2022 and June 2023. The questionnaire (Supplementary Materials) was administered via Google Forms software (Google LLC, Mountain View, CA, USA). A link to the questionnaire was delivered to the participants via email and various social me- dia platforms. The questionnaire was actively available and collected responses throughout this period, from March 2022 until it was closed in June 2023. Respondents were recruited in part through expert sampling, given that the authors had connections with endurance athlete teams. Additionally, snowball method was applied, as the study was promoted online through a variety of social media platforms. Individuals were eligible to take part in this study if they were healthy endurance athletes aged over 18 years and engaged in endurance training at least three times per week. We specified in the recruitment advertisement that we were seeking responses exclusively from amateur endurance athletes. This was intended to exclude professional or elite athletes whose training loads and access to nutritional resources may differ significantly from the general amateur athletic population. The research population consisted of non-elite, dedicated amateur triathletes and runners who participate in races recreationally. All experimental procedures were approved by the local Ethics Committee (Sci- entific and Research Committee of the Medical Research Council, Hungary; Decision IV/573-1/2022/EKU) and were conducted in accordance with the Declaration of Helsinki.
Nutrients2025,17, 3629 4 of 21 All participants were provided with information about the aim of the research and were informed of the confidential use of their data for statistical purposes, and that the results would be published in scientific presentations and academic papers. Participants provided their electronic informed consent before completing the questionnaire. The overall course of the research, including the timeline for data collection and participant flow, is summarized in Figure. Figure 1.Course of the Study and Participant Flow. 2.2. Participants A total of 126 completed questionnaires were initially received. Thirteen (13) partici- pants were excluded from the final analysis because their self-reported training frequency did not meet the minimum three-times-per-week inclusion criterion. The final analytical sample consisted of 113 participants with a mean age of 40.04 years (SD= 9.89), ranging from 19 to 70 years. The age distribution of respondents was close to symmetrical, with a median age of 41 years. The gender distribution in our sample was relatively balanced, with 57.5% (n= 65) female and 42.5% (n= 48) male respondents. The majority of respondents were highly educated, with 78.8% (n= 89) possessing a university degree. The remaining 21.2% had completed secondary education, including vocational school (n= 11) and high school (n= 13). In accordance with the inclusion criteria, the sample consisted of endurance athletes performing endurance training a minimum of three times per week. 23.9% (n= 27) of the participants identified themselves as triathletes. 43.4% of the sample exclusively participated in running, while one participant reported cycling as the sole endurance activity. 17.7% practiced both running and cycling on a regular basis, 6.2% combined running and swimming, and 8% regularly performed running, cycling, and swimming without participating in triathlon competitions. For later statistical analysis, participants were classified into three groups based on their sports: runners (those who exclusively ran), triathletes, and mixed endurance athletes (all other participants engaging in multiple endurance sports). This categorization was based solely on the discipline(s) practiced by the athletes and not on self-reported weekly training volume. Group comparisons of demographic variables are presented in Section. 2.3. Questionnaire The questionnaire was created in
into three groups based on their sports: runners (those who exclusively ran), triathletes, and mixed endurance athletes (all other participants engaging in multiple endurance sports). This categorization was based solely on the discipline(s) practiced by the athletes and not on self-reported weekly training volume. Group comparisons of demographic variables are presented in Section. 2.3. Questionnaire The questionnaire was created in Hungarian and consisted of multiple sections. The first section included demographic questions (gender, age, and highest educational level) and basic anthropometric data (height and weight). The second section focused on general
Nutrients2025,17, 3629 5 of 21 dietary habits, including the presence of food allergies and intolerances, dietary patterns followed, nutrition-related goals, the number of meals consumed on recovery and training days, meal planning routines, nutrition periodization strategies, sources of nutrition infor- mation, and prior consultation with a dietitian or sports nutritionist. Since no validated questionnaire specifically addressing post-exercise nutrition knowledge and habits of en- durance athletes was available at the time of the survey, we developed our own questions to assess this crucial topic. Given the study’s focus on this area, multiple questions were dedicated to key aspects, including the timing and composition of post-exercise meals, knowledge about optimal recovery windows, as well as awareness of the optimal car- bohydrate content of post-workout meals. Participants were asked about their typically consumed foods and beverages after exercise, as well as factors considered when choos- ing their post-exercise meal. Further sections included several questions related to the supplements used by the athletes and their fluid intake, as well as their training habits. All questions in the online questionnaire were set as mandatory response fields. This method effectively eliminated item-level missing data within the acceptedn= 113 sample. Consequently, the statistical analysis did not require imputation or handling of internal missing values for the core variables. 2.4. Data Analysis For the purpose of this study, the collected data were systematically processed and analyzed in four distinct stages: analysis of demographic data, the development of the Post-ExerciseNutrition Recommendation Adherence Score (PENRAS), mapping paths leading to higher PENRAS, and investigating the associations between PENRAS and dietary habits. The initial stage of analysis focused on the compilation and processing of demographic and foundational sports-related data for the respondents. Descriptive statistics were used to interpret demographic statistics and response frequencies. For the presentation of continuous variables, means and standard deviations (SD) were used, while categorical variables were presented as frequencies and percentages. For later analysis, participants were grouped into the following categories: runners, triathletes, and mixed endurance athletes. Respondents who participated in at least two endurance sports were classified as mixed endurance athletes. One respondent identified exclusively as a
frequencies. For the presentation of continuous variables, means and standard deviations (SD) were used, while categorical variables were presented as frequencies and percentages. For later analysis, participants were grouped into the following categories: runners, triathletes, and mixed endurance athletes. Respondents who participated in at least two endurance sports were classified as mixed endurance athletes. One respondent identified exclusively as a cyclist and could not be assigned to the above categories, so his data were excluded from analyses broken down by sports groups. Demographic differences between sport groups were analyzed via Pearson’s chi-square tests and Kruskal–Wallis tests. In addition to the demographic description, frequency analysis was used to examine the dietary patterns and nutrition-related goals of respondents. To evaluate the potential dif- ferences between these nutrition-related goals, a series of McNemar’s testswas performed. In the second block, a composite score was created that we named PENRAS (Post-ExerciseNutrition Recommendation Adherence Score). We assessed the athletes’ nutritional knowledge and level of adherence to guidelines related to recovery nutrition in the context of endurance training. This was done using a set of self-developed questions. To create a composite score, 10 individual response options were selected from these questions. This part of the questionnaire included two types of questions: those measuring theoreti- cal knowledge and those assessing actual practices. For the knowledge-based questions, response options included the correct quantities and timeframes, along with smaller and larger values to evaluate accuracy. Other items specifically investigated how athletes apply these habits in practice. The combination of knowledge-based and practice-based items was conceptually driven by the aim to measure overall compliance to post-exercise nutrition recommendations. Effective adherence requires both the necessary theoretical foundation
Nutrients2025,17, 3629 6 of 21 (knowledge) and the successful application of this knowledge in a real-world scenario (practice). Each of the ten items was analyzed individually using frequency data. To assess which areas require the most focus in educational activities, we established three reference ranges based on the percentage of correct answers for each item: “low” (0–39%), “moderate” (40–69%), and “high” (70–100%). This categorization was based on the criteria thresholds used in a study assessing knowledge of carbohydrate guidelines of endurance athletes [27]. The ten items used as indicators to assess the participants’ post-exercise nutrition knowledge and habits were as follows: the optimal timing of post-exercise meals; the key aspects considered when selecting post-exercise nutrition; the recommended carbohydrate content of a post-exercise meal; and whether participants intentionally increase their carbohydrate and protein intake on days involving more intense training sessions. The scoring procedure for each item was defined according to specific criteria. For multidimensional response items, participants could provide multiple correct elements. For example, in the assessment of key aspects of post-exercise nutrition selection, one point was assigned for mentioning each of the following: high carbohydrate, high sodium, high protein, high energy content, easy digestibility, and low fat content. A summative approach was used, awarding one point for each correctly identified component, allowing a nuanced assessment of partial knowledge. For quantitative items (e.g., recommended carbohydrate intake), the scoring procedure was designed to assign partial credit for responses that demonstrated a degree of correct knowledge, such as awarding 0.5 points for the two answers closest to the optimum, while only the fully correct response received the full point. According to current guidelines, the optimal timing for nutrient intake to facil- itate post-exercise glycogen synthesis and rapid recovery is considered to be within two hours. Consequently, any timing options presented within this two-hour window (within30 min/60 min/2 h) were deemed correct [13,14,17]. Regarding the optimal car- bohydrate content of the post-exercise meal, one point was given to the correct answer (1–1.2 g·BW −1 ·h −1 ), and 0.5 points were given to the two options closest to the correct answer (0.8–1 g/BW/h
within two hours. Consequently, any timing options presented within this two-hour window (within30 min/60 min/2 h) were deemed correct [13,14,17]. Regarding the optimal car- bohydrate content of the post-exercise meal, one point was given to the correct answer (1–1.2 g·BW −1 ·h −1 ), and 0.5 points were given to the two options closest to the correct answer (0.8–1 g/BW/h and 1.2–1.6 g·BW −1 · h −1 ). This methodology was based on the guidelines published by the International Society of Sports Nutrition and the Position of the Academy of Nutrition and Dietetics, Dietitians of Canada, and the American College of Sports Medicine: Nutrition and Athletic Performance [12]. The minimum score that could be obtained was 0, and the maximum reachable score was 10. Descriptive statistics, including the mean, standard deviation, distribution characteristics (median, minimum and maximum values), and frequencies with their corresponding percentages, were reported for this scale. The PENRAS demonstrated an acceptable level of normality (skewness =−0.30, kurtosis =−0.50). The third analytical block focused on evaluating the predictors of PENRAS, specif- ically examining the influence of demographic and sport-related factors and analyzing the role and predictive value of various information sources utilized by the participants. To evaluate the association of demographic and sport-related factors on the PENRAS, univariate ANOVA was conducted, with sport group, gender, age, education level, and previous consultation with a dietitian entered as independent variables. Weekly endurance training volume was included in the model as a separate covariate to statistically control for its potential effect on the PENRASs. Due to the limited sample size, only main effects were tested, and no interaction terms were included. Partial eta squared (η 2) values were reported as effect sizes for significant predictors. Additionally, we analyzed the influence of various information sources on the par- ticipants’ knowledge score. The extent to which respondents utilized specific sources of
Description
This study assesses post-exercise nutrition knowledge among Hungarian amateur endurance athletes.