Evaluation of the association of carbohydrate quality on enjoyment of physical activity and burnout in athletes

Abstract

Background

Both the quantity and quality of carbohydrates consumed significantly influence athletes’ performance by improving muscle glycogen stores. This study aimed to evaluate the association of carbohydrate quality on enjoyment of physical activity and burnout in athletes.

Methods

The study included 139 active athletes aged 19–35 years. A Demographic Structure Questionnaire, Athlete Burnout Questionnaire, and Physical Activity Enjoyment Scale were administered to the participants. To assess carbohydrate quality, food frequency data were collected, and carbohydrate quality was scored based on fibre intake, the whole/total grain ratio, glycaemic index, and solid/total carbohydrate ratio. The score obtained was divided into 5 quartiles, with Q5 representing the highest and Q1 the lowest carbohydrate quality. Data were analyzed using SPSS 24 software.

Results

Of the athletes, 76 were volleyball players, 36 were football players, and 27 participated in other sports. The age of the participants in the Q2 group was lower than in the Q4 group. The highest carbohydrate and protein intake was observed in the Q5 group, and the lowest in the Q1 group. The Physical Activity Enjoyment Scale score of the athletes in the Q5 group was significantly higher than that of the Q1 group (p < 0.05). There was a negative correlation between the Carbohydrate Quality Index and Athlete Burnout Questionnaire scores, glycaemic index, carbohydrate intake from liquids and refined grains, and a positive correlation between the Physical Activity Enjoyment Scale, carbohydrate intake from solids, and whole grains (p < 0.05).

Conclusion

Higher carbohydrate quality is positively associated with increased physical activity enjoyment and reduced burnout in athletes; however, further studies are needed on this topic.

Introduction

Both the quality and quantity of carbohydrates are crucial for performance in athletes [1]. Carbohydrates, in particular, are emphasized more during periods of intense exercise (such as training or competitions) [2]. One of the main reasons for this is that carbohydrates provide a higher energy yield in terms of adenosine triphosphate (ATP) per liter of oxygen compared to fats [3]. While fats generate 122 ATP per unit of fatty acid, carbohydrates produce 2 mol ATP through anaerobic respiration and 36 mol ATP through aerobic respiration. However, when considering the rate of energy production over time, the ATP yield from carbohydrates is significantly higher. Therefore, a high intake of carbohydrates is known to substantially improve physical performance during prolonged, intense activity [4].

Having full muscle glycogen stores before exercise plays a crucial role in enhancing performance during activity and promoting recovery afterward. The most effective way to achieve muscle glycogen synthesis is by consuming sufficient carbohydrate sources [2, 5]. Glucose ingested with food is converted into glucose 6-phosphate by the hexokinase enzyme in the cells, then glucose 1-phosphate is formed, and finally, the resulting UDP-glucose participates in glycogen formation through the action of the glycogen synthase enzyme [6]. Carbohydrate quality can vary based on factors such as whether it comes from whole grains or legumes, its dietary fiber content, and its glycaemic index or glycaemic load. The type of carbohydrates consumed by athletes also varies depending on the type, duration, and timing of exercise, including pre-, during-, and post-exercise needs [7, 8].

Before a competition, athletes are advised to consume more fibrous, low glycaemic index carbohydrate foods [9]. Consuming a low glycemic index diet before exercise leads to lower carbohydrate oxidation rates during exercise, thereby increasing fat utilization [10]. However, during exercise, they need to consume high glycaemic index, low-fiber foods (i.e., lower-quality carbohydrates) to meet the energy demands of exercise [9]. Consuming a high glycemic index diet after a meal stimulates the glycogen synthase enzyme in the muscles and accelerates glycogen synthesis [11]. Additionally, carbohydrate-rich foods are essential for rapid recovery after exercise [2, 12,13,14]. This shows that the timing of exercise is closely linked to carbohydrate quality in athletes.

Some studies investigating the effect of carbohydrate quality on sports performance highlight the importance of carbohydrates in exercise performance and endurance [15, 16], as carbohydrate quality significantly impacts athletes. A reduction in muscle and glycogen stores due to low carbohydrate intake before or during exercise leads to lower blood sugar levels during activity. Furthermore, when carbohydrates are depleted, muscles begin to fatigue as they cannot access sufficient energy, and energy starts to be produced from non-carbohydrate sources [17].

Carbohydrates are essential for replenishing glycogen stores and preventing low blood sugar, both of which help enhance performance [4]. Improved performance contributes to increased enjoyment of physical activity and reduces feelings of burnout in athletes. The aim of this study was to assess the impact of carbohydrate quality on burnout and physical activity enjoyment in athletes.

Methods

This descriptive, cross-sectional study was carried out in Türkiye, between January 1 and May 1, 2023, and included 139; male and female volunteer professional athletes aged 19 to 35 years who are actively engaged in sports. Only athletes actively participating in sports (least three days a week with each session lasting at least 60 min) were included; individuals engaging in irregular sports activities were excluded. The minimum sample size was calculated as 103 participants, providing a 5% margin of error and 80% statistical power, using GPower 3.1. Initially, 159 individuals were contacted; however, participants with incomplete anthropometric, demographic, or dietary data were excluded, resulting in a final sample size of 139. The study was conducted following the principles of the Declaration of Helsinki and received ethical approval from the Ankara Yıldırım Beyazıt University Health Sciences Ethics Committee (approval date: December 08, 2022; decision number: 19-1235). After receiving ethics committee approval, the necessary permissions were also obtained from the Turkish Ministry of Youth and Sports. Informed consent was obtained from all participants prior to participation.

Data collection tools

Data were collected using the Demographic Structure Questionnaire, Athlete Burnout Scale (ABQ), Physical Activity Enjoyment Scale (PACES), and the Food Consumption Frequency Scale.

Demographic structure questionnaire

The Demographic Structure Questionnaire which has been provided as supplementary material (Supplementary File 1) included questions about participants’ personal characteristics, including anthropometric measurements such as gender, age, body weight, and height. It also gathered information on their sports discipline, water consumption, physical activity level, reasons for engaging in sports, and their nutritional habits before, during, and after training.

Body weight (kg) and height (cm) were measured by the researchers. Participants were measured while standing upright, looking straight ahead, with the top of the ears and the outer corner of the eyes aligned in a parallel plane (Frankfort Plane) [18]. BMI values were classified according to WHO criteria and calculated using the formula [19].

Athlete burnout questionnaire

The Athlete Burnout Questionnaire was used to determine the level of burnout in athletes. The Turkish version of the scale, originally developed by Raedeke and Smith [20], was validated and tested for reliability by Kelecek et al. [21]. The Cronbach’s alpha coefficients for the scale were 0.75 for the “Decreased Sense of Achievement” sub-dimension, 0.87 for the “Emotional/Physical Exhaustion” sub-dimension, and 0.83 for the “Depersonalization” sub-dimension. The scale consists of 13 questions scored on a five-point Likert scale (1-Never, 2-Sometimes, 3-Often, 4-Frequently, 5-Always) and is divided into three sub-dimensions: “Decreased Sense of Achievement,” “Emotional/Physical Exhaustion,” and “Depersonalisation.” The total score can range from 15 to 65, with higher scores indicating greater higher levels of burnout [20, 21].

Physical activity enjoyment scale

The Physical Activity Enjoyment Scale was used to assess the level of enjoyment athletes experience during physical activity. Developed by Mullen et al. [22], the scale has a Cronbach’s alpha value of 0.955, and its Turkish validity and reliability were confirmed by Özkurt et al. [23]. The scale consists of 8 questions, scored on a scale from 1 to 7, with a maximum score of 56 and a minimum of 8 higher scores indicate a greater enjoyment of physical activity.

Carbohydrate quality index calculation

The data obtained from the Food Consumption Frequency Questionnaire, which included 132 food items, were analyzed using the Beslenme Bilgi Sistemi (BeBiS) software [24], an internationally recognized database, to calculate the participants’ average daily energy and nutrient intake, as well as the Carbohydrate Quality Index (CQI). The food consumption frequency was assessed on a daily, weekly, monthly, and annual basis, and participants were asked to report portion sizes for each food item consumed at these frequencies. Additionally, we sourced the GI for certain foods from the University of Sydney GI database [25]. To calculate the CQI score, four components were used: fibre intake, whole/total grain ratio, glycaemic index, and solid/total carbohydrate ratio. Each component was scored on a 1–5 scale (with reverse scoring for the glycaemic index), and the total CQI score was calculated as the sum of these four components, ranging from a minimum of 4 to a maximum of 20 points. The resulting scores were divided into five quartiles, with Q1 representing the lowest and Q5 representing the highest carbohydrate quality [7] (Table 1).

Table 1 Criteria used to calculate carbohydrate quality

Data evaluation

Statistical analyses were performed using IBM SPSS Statistics 24 software. The Independent Samples t-test (t-table value) was used to compare the measurement values of two independent groups with normally distributed data, while the Mann-Whitney U test (Z-table value) was used for groups without normal distribution. The ANOVA test (F-table value) was used to compare the measurement values of three or more groups with normally distributed data, and the Kruskal-Wallis H test (χ2-table value) was used for data that were not normally distributed. Normality of the data was assessed using the Shapiro-Wilk test for small sample sizes and the Kolmogorov-Smirnov test for larger samples. The Pearson correlation coefficient was applied to examine the relationship between two quantitative variables with normal distribution, while the Spearman correlation coefficient was used for non-normally distributed data. Multiple linear regression was performed to determine the association of variables on the CQI score.

Result

Of the athletes who participated in the study, 115 were male and 24 were female. No statistically significant relationship was found between the ABQ and PACES scores based on gender, sports branch, or the reason for engaging in sports (p > 0.05). The PACES score for athletes in the Q5 carbohydrate quality group was found to be statistically higher than that of athletes in the Q1 group (p < 0.05) (Table 2).

Table 2 Athletes’ scale scores by demographic characteristics and CQI quartiles

The average age of athletes in the Q4 group (25.5 ± 5.62 years) was statistically higher than those in the Q2 group (21.6 ± 4.40 years). No statistically significant differences were observed between body weight, BMI, duration of licensure, or the number of weekly training sessions and the CQI score (p > 0.05) (Table 3).

Table 3 Anthropometric measurements, training status, and demographic distribution of CQI quartiles among athletes

Among male athletes, 33.9% were in the Q5 group, 23.5% in Q3, 20.9% in Q2, 12.2% in Q1, and 9.6% in Q4. Among female athletes, 9.2% were in Q1, 29.2% in Q3, 16.7% in Q2, 12.5% in Q4, and 12.5% in Q5. A majority of football players (58.3%) were in the Q5 group, along with 32.9% of volleyball players and 37% of athletes from other disciplines (Table 3).

The Q5 group had the highest intake of carbohydrates (627.9 ± 247.4 g), protein (216.5 ± 63.2 g), fibre (109.3 ± 42.1 g), vitamin A (2383.5 ± 1249.7 µg), vitamin E (33.0 ± 9.8 mg), vitamin K (279.5 ± 121.1 µg), vitamin B₂ (4.2 ± 1.4 mg), vitamin B₃ (77.5 ± 22.9 mg), vitamin B₉ (762.1 ± 278.0 µg), vitamin C (289.7 ± 134.8 mg), potassium (8610.0 ± 3308.3 mg), calcium (1771.3 ± 650.6 mg), magnesium (1073.2 ± 377.1 mg), phosphorus (3611.6 ± 1183.8 mg), and iron (40.8 ± 15.2 mg) (p < 0.05) (Table 4).

Table 4 Distribution of Macro- and micronutrient intake by CQI

There was a negative correlation between the CQI score and the ABQ score (r: -0.205, p: 0.016), and a statistically significant weak positive correlation with the PACES score (r: 0.290, p: 0.001). A significant negative correlation was found between the glycaemic index, carbohydrates from refined grains and liquids, and CQI, while a positive correlation was observed with carbohydrates from solid and whole grains (p < 0.05) (Table 5).

Table 5 Association between CQI and selected variables

Multiple linear regression analysis revealed a significant effect of independent variables on the CQI score (F = 22.148, p < 0.01). According to single regression analysis, the ABQ score (β: -0.207, p: 0.014) had a negative association on the CQI index. The PACES score (β: 0.277, p: 0.001), carbohydrate intake (β: 0.695, p < 0.001), protein intake (β: 0.529, p < 0.001), fibre (β: 0.740, p < 0.001), vitamin B6 (β: 0.540, p < 0.001), vitamin B₉ (β: 0.597, p < 0.001), calcium (β: 0.329, p < 0.001), phosphorus (β: 0.593, p < 0.001), potassium (β: 0.682, p < 0.001), and magnesium (β: 0.623, p < 0.001) all had a positive association on CQI levels (Table 6).

Table 6 Regression analysis of scale scores and selected food groups

Discussion

The study examined 139 active athletes aged 19–35 to assess the relationship between carbohydrate quality, physical activity enjoyment, and burnout. Athletes in the Q5 carbohydrate quality group had significantly higher PACES scores than those in Q1. A weak but significant positive correlation was found between CQI and PACES scores, while CQI showed a negative correlation with ABQ scores.

In this study, a negative correlation was found between the ABQ score and total carbohydrate quality, although this relationship was weakly significant (Table 5). Furthermore, the ABQ scale score was found to have a negative impact on the CQI score (Table 6, p < 0.05), indicating that higher burnout levels may be associated with lower carbohydrate quality. Previous research supports this relationship; for instance, Cox et al. [26] reported that regular intensive exercise could lead to decreased performance, which aligns with Havemann’s [27] findings that many athletes fail to consume sufficient carbohydrates to replenish muscle glycogen stores. Additionally, Edin [28] demonstrated that dietary fiber supplementation with high-soluble carbohydrate quality may help manage fatigue in basketball players. Given that the majority of athletes in this study were football and volleyball players, who typically follow intensive training programs, it is plausible that individuals in the Q1 group—those with lower carbohydrate quality—experience higher levels of burnout.

Foods with high carbohydrate quality enhance physical performance by providing energy needed by athletes and filling glycogen stores [15]. In this study, the PACES scale score of the athletes in the Q5 group was statistically higher than those in the Q1 group (Table 2). PACES scale score was found to have a positive relation on CQI (Table 6) (p < 0.05). This study aligns with a study conducted in Japan, which showed that adding fiber to the diet to improve carbohydrate quality enhances athletic performance and provides endurance against exercise-induced stress [29]. Similarly, in a study investigating the effect of low-carbohydrate and grain-based diets on endurance training, it was emphasized in another study that, compared to a low-carbohydrate diet, a grain-based diet causes fewer interruptions during training, which is beneficial for performance [30]. It is thought that the reason for this is that good carbohydrate quality affects physical performance and causes athletes to enjoy exercise.

Nutrition is crucial for regulating performance, which tends to decrease with age in athletes. This study found that the age of athletes in the Q4 group was higher than that of those in the Q2 group (p < 0.05). In the study conducted by Yüksel et al. [31] no correlation was found between carbohydrate quality and age. It is thought that the reason for this is that age is an important factor on performance, especially in athletes, and they are careful about carbohydrate consumption to regulate performance.

As the amount of whole grain and pulp in the diet increases, carbohydrate quality increases. In some studies on carbohydrate quality, it was observed that the highest whole grain and pulp intake was in the group with the highest carbohydrate quality [31,32,33]. In this study, the lowest fibre intake was found in the Q1 group and the highest in the Q5 group (Table 5). This study aligns with a previous study in which carbohydrate quality was divided into five quartiles. It was found that individuals in the Q5 group consumed the highest amounts of fiber and whole grains, while those in the Q1 group consumed the lowest amounts of fiber and grains [7]. These findings suggest that individuals with lower carbohydrate quality tend to have inadequate fiber and whole grain intake, which may contribute to reduced diet quality.

The glycemic index (GI), one of the key factors determining carbohydrate quality, plays a crucial role for athletes [34, 35]. During competition, athletes tend to prefer foods with a lower GI, while higher GI foods are favored during the recovery period [36]. In this study, a statistically significant negative correlation was found between GI and CQI (r = -0.665, p < 0.001) (Table 5). These findings are consistent with previous research examining the impact of dietary fiber on exercise performance, as the study observed the lowest GI in the Q5 group, whereas the highest GI was found in the Q1 group [28]. Additionally, several studies have reported that carbohydrate quality tends to decrease as the glycemic index increases [7, 32, 33].”

Dietary fibre is one of the factors that improve carbohydrate quality and fruits, vegetables and whole grains are rich in dietary fibre. In this study, the highest intake of vitamins A, E, K, B₁, B₂, B₃, B₉ and C; Na, P, K, Mg and Fe minerals were found in the Q5 group (Table 4). This is consistent with the findings reported by Zape et al. [7], who found that the highest levels of vitamin C, B3, E, A, and B9, as well as the minerals Fe, Ca, Mg, and Zn, were found in the Q5 group. Similarly, in another study, it was found that participants in the Q5 group had the highest intake of vitamin C, B₁ and B₉; Mg and K minerals [31]. This is thought to be due to the consumption of the cereal group, which is rich in micronutrients, by the individuals in the Q5 group.

This study has some limitations. First, since the study was based on a cross-sectional design, causal relationships could not be established. Future studies could use longitudinal designs to better assess causality. Participants were observed only once during the data collection process, and information provided by the participants themselves was used. This may have led to issues such as social desirability bias and response errors. Using dietary tracking apps or biomarkers in future research could help improve data accuracy. In addition, the analyses examining the relationship between carbohydrate quality, physical activity enjoyment, and burnout may have been influenced by confounding factors such as the participants’ demographic characteristics. Moreover, the quality and quantity of carbohydrates consumed by athletes are influenced by the frequency of food intake and vary across periods before, during, and after exercise, making it challenging to generalize the results. Since all these limitations may affect the generalizability of the study’s findings, further research is needed in the future.

As a result, this study found that carbohydrate quality may have a limited association with burnout and physical performance in sports. This limitation is likely due to the small effect sizes observed in the analysis. However, the literature review revealed a limited number of studies on this subject, highlighting the need for further research in this area. Future studies could benefit from using longitudinal designs or randomized controlled trials to establish causality more effectively.