Regular Articles

46
Introduction
47 48 49 Football is an intermittent team sport that requires a high level of tactical, technical, and physical skill to succeed. During the match, players engage in a random combination of explosive and powerful activities, along with technical and tactical maneuvers, intermittently over a 90-minute game duration¹. In modern football, the competitive phase represents a crucial period where teams engage in official 50 51 52 53 matches, competitions, or tournaments. During this phase, the workload, whether from training sessions or matches, is intensive². Teams strive to reach their peak performance, requiring refined management and meticulous assessment of players' potential to achieve collective performance 3. 54 55 It is acknowledged that sleep is an important strategy among others for psychological and physiological recovery 4 . Sleep promotes muscle regeneration by allowing the release of growth hormones, which stimulate muscle protein synthesis and repair damaged tissues during exercise. Additionally, it helps 56 57 58 59 60 combat oxidative stress. Some studies have shown that chronic sleep deprivation is associated with an increase in injuries among football players 5. They indicate that players with poor sleep quality are more prone to a higher incidence of musculoskeletal injuries 6 . For elite athletes, 8 hours of sleep per night is considered a crucial need to feel rested 7. However, due to intense training, football players often face a 61 lack of sleep. They frequently sleep less than 7 hours due to various factors related to football or not 8 . 62 63 64 65 On the other hand, the importance of nutrition is indisputable for professional footballers, as it constitutes a crucial element alongside other key factors in players' performance and recovery 9. According to the latest UEFA consensus (Union of European Football Associations), adequate nutrition contributes to meeting the increased demands of matches and training sessions 10 . It recommends that 66 67 68 69 players should have a balanced diet, including macronutrients for necessary energy and micronutrients such as vitamins and minerals to support the immune system, recovery, and injury prevention. A metanalysis studies indicate that professional football players may not adequately meet their energy needs and that they have suboptimal energy intake compared to estimates of their energy expenditures 11 . 70 71 The interrelation between sleep and nutrition was discussed approximately 30 years ago 12 , but in the field of sports, it remains relatively unexplored, particularly regarding the effect of sleep on eating behavior. Research has shown that the usual duration of sleep is generally associated with a higher caloric intake as well as an absolute or r relative intake of nutrients or foods 13 . Prolonged periods of 72 73 74 wakefulness could promote increased meal frequency, including unhealthy snacks, and alter the timing 75 76 of food intake, such as consuming late at night or during the night 14 . On the other hand, physiological sleep deprivation has been shown to affect appetite, including leptin and ghrelin, as well as hormonal 77 78 79 homeostasis related to metabolism, such as cortisol, insulin sensitivity, and growth hormones. Recent research has also focused on the role of sleep in the quality of diet, highlighting a general association between shorter sleep duration and lower diet quality as well as irregular eating behaviors. Regarding 80 81 dietary habits, it has been reported that players who sleep fewer hours tend to follow a lower-quality diet . Taking this into consideration and based on the available literature, the field of nutrition associated with sleep in professional football in Morocco remains relatively unexplored. This has inspired us to launch 82 83 84 85 this project, firstly to fill this information gap and enrich the scientific community, not only for Morocco but also for Africa, especially North Africa, and the Mediterranean region as a whole. This study aimed 86 87 to explore the potential relationships between sleep quality, nutritional status, and dietary habits among Moroccan Professional male football players during the competitive. To achieve this, we attempted to 88 89 90 address the following questions: (a) To what extent do Moroccan man football players adhere to scientific sleep guidelines and UEFA (Union of European Football Associations)? (b) What variations exist in the sleep status among players? (c) Are there correlations between sleep quality and nutritional 91 balance in terms of total food intake? Finally, (d) can quality sleep nights promote healthier food decisions and, consequently, influence food choices and nutritional behaviors on the following day? 92 93
94
Methodology
95
Study design and competitive level:
96 97 98 We conducted an observational study using a cross-sectional design to explore the relationships between sleep quality and nutritional status among a total of 49 professional male football players who voluntarily participated in this study in the Moroccan professional league "Botola-Pro" over two consecutive weeks during the competitive phase. Each week included a training microcycle with a rest 99 100 101 day, five training days, and a match at the end of the week, distributed as follows (match days (MDs) occurred at 6:00 p.m. on Sundays. Training sessions or Training Days (TDs) took place on Tuesday (MD+2), Wednesday (MD+3), Thursday (MD-3), Friday (MD-2), and Saturday (MD-1). No training sessions or Rest Days (RDs) occurred on Mondays (MD+1) Fig1. The sleep quality was evaluated using the Pittsburgh Sleep Quality Index (PSQI), and the dietary intake was assessed using the 24-hour dietary recall technique. All these measures were conducted daily from Monday to Sunday at 10 a.m. A correlation analysis was performed to explore the relationships between sleep quality and dietary intake. This study was conducted following the ethical principles outlined in the Declaration of Helsinki and received approval from the regional ethics committee of the Ibn Rôchd University Hospital Center in Casablanca, under the jurisdiction of the Ministry of Health in Morocco (Approval No: 22/2022). All participants voluntarily participated in the study and had the option to withdraw at any time. Place Figure 1 113 102 103 104 105 106 107 108 109 110 1 1 1 1 112
1114
Participants.
115 116 1117 118 119 120 121 122 This comprised a total of forty-nine male professional football players aged 24.9 ± 3.1 years from three Moroccan clubs who were included in the sample for this study. Inclusion criteria required them to be professional players, while exclusion criteria applied to those who did not participate in training sessions and matches, were injured, or had incomplete PSQI or dietary surveys. Ultimately, our final sample consisted of 5 goalkeepers, 19 defenders, 14 midfielders, and 11 forwards. All solicited players actively participated in this project and underwent comparable training loads. Research data were collected using survey forms and face-to-face interviews; for 24-hour dietary recall (24HR) 15-17 , the Pittsburgh Sleep Quality Index (PSQI) 18 , and anthropometric measurements. 123 124
Anthropometric and body composition measurements
125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 session was held during the week before the study commencement to familiarize all players with the Pittsburgh Sleep Quality Index (PSQI) Arabic version 21 . The purpose of this session was to clarify the survey completion process by providing practical examples. Throughout the two-week observation period, participants were kindly asked to fill out the questionnaire within the first two hours after waking 147 148 149 up. They were then instructed to return it t to the principal investigator the following day at 10 a.m., just before the training session commenced. Each time, the principal investigator provided a fresh copy for participants to complete the next day after their night of sleep, and this process continued seamlessly. The measurement of body composition values is carried out using the InBody 120 bioelectrical impedance analyzer, developed by InBody Co., Ltd. This device utilizes advanced technology by applying an electric current of 150μA (± 50μA) at frequencies of 20 and 100 kHz throughout the body. The results provided by this analyzer encompass various critical parameters for a comprehensive evaluation of body composition. the measurements include total weight, Body Mass Index (BMI), body muscle mass (BMM), body fat mass (BFM), and basal metabolism (BM) estimate of energy expenditure at rest. The precision error for Fat-Free Mass (FFM), Fat Mass (FM), and % Body Fat (%BF) is less than 2% in 30 subjects. Athletes were directed to remove their shoes, and height measurements were taken using a stadiometer with a precision of ±0.1 mm for utmost accuracy (Portable Stadiometer-Seca 225, Hamburg, Germany) 19,20 .
Assessment of sleep quality
The assessment of sleep quality is ensured through the use of the Pittsburgh Sleep Quality Index (PSQI). 18 . This instrument is considered a valid means to evaluate individual sleep quality over the preceding month and comprises a total of 19 questions. These 19 self-evaluation questions combine to form 7 "components," including subjective sleep quality, sleep latency, sleep duration, habitual efficiency, disturbances, use of medication, and daytime dysfunction. The overall score, ranging from 0 to 21, is calculated by summing the points from all subsections. A score exceeding 5 indicates poor sleep quality, while a score equal to or less than 5 indicates good sleep quality 18 .  A pre-data collection training 150
Assessment of diet intake:
151 152 153 154 155 156 157 158 159 The 24-hour recall dietary technique was employed to assess food consumption over a period of fourteen consecutive days 15-17.  The scientific community has endorsed a seven-day data collection period as optimal, as it aligns with obtaining accurate nutritional estimates for the comprehensive analysis of routine nutrient and energy intake while minimizing variability in coding errors 22,23 . This approach was implemented to ensure the accuracy of the gathered data. The Data on food consumption, including snacks, dietary supplements, and other consumables, were collected. To enhance accuracy, researchers assisted football players in estimating quantities consumed more precisely by using everyday household items such as spoons, glasses, and dinnerware. This complementary approach facilitated a more thorough examination of the food diaries 16,24 . Furthermore, we employed the Remote Food Photographic Method (RFPM)²² . 160 161 162 163 164 165 166 167 The Nutrilog 3.30 professional software, utilizing the Ciqual 2020 food composition database, was employed to analyze dietary intake data. This extensive database provided detailed information on the total energy intake (TEI) expressed in kilocalories (kcal), the nutritional composition of various ingredients, including carbohydrates (CHO), sugars, proteins, fatty acids, salt, vitamins, minerals, and the energy content of meals 26 Similarly, macronutrient intakes were assessed and reported in grams (g) and grams per kilogram of body mass (g/kg BM) for comparison with "Union of European Football Associations" UEFA guidelines¹ . 168 169 170 The results of the 24-hour Recall were used to extract data on the players' diet. The frequency of consumption for each food item was calculated to assess the usual daily or weekly intake foods. The food groups, and beverages commonly consumed in Morocco were considered, including vegetables, 171 172 173 174 175 176 177 178 179 legumes, fruits, dairy products (milk, yogurt, and cheese), cereals (bread, cereals, rice, pasta, and couscous), meat (red meat: veal, lamb, camel, and goat; white meat: poultry and turkeys; processed meat), fish, Fats (this includes vegetable oils, butter, nuts, and seeds, providing essential fatty acids), olive oil, eggs, beverages (coffee, tea, and herbal infusions), sweets (sugar, jelly, candies, pastries, sweetened fruit juices), soda, and added fats (olive oil, other vegetable oils, and butter). An expert nutrition researcher carried out the investigation to guarantee the highest level of accuracy in the findings. It is also important to emphasize that the medical staff monitored the consumption of products and dietary supplements to ensure uniformity in intake.
Statistical analysis
180 181 182 183 184 185 186 The statistical analysis was conducted using the Statistical Package for the Social Sciences (SPSS) software (IBM, SPSS Statistics, Version 27, Chicago, IL). The collected data were presented using descriptive statistics, including the minimum (min) and maximum (max) values, the mean value (x), and the standard deviation (SD). The normal distribution of differences between data pairs was checked using Kolmogorov-Smirnov tests (p > 0.02 for all variables). Additionally, the difference between ""Good Sleep Quality" and "Poor Sleep Quality" was tested using the Student's t-test when the variable follows a normal distribution. This specifically applies to the following variables: TEI (Total energy intake), 187 188 189 190 191 192 193 194 195 196 197
Results
198 Anthropometric Measurements and Sleep quality (PSQI) of male professional football players 199 200 201 202 203 204 205 206 207 208
Place Table 1
209 210 211 212 213 214
Place Table 2
215 216 217 218 Carbohydrates “CHO” g/Kg BM (Body Mass), CHO %, Protein g/Kg BM (Body Mass), Protein %, Fat g/Kg BM (Body Mass), Fat %, and fibers. For variables that do not follow a normal distribution, including the seven "components" of the PSQI such as s subjective sleep quality, sleep latency, sleep duration, habitual efficiency, disturbances, use of medication, and daytime dysfunction, the Mann- Whitney test was employed. This test was also adopted for food groups, including the 15 elements mentioned earlier in the dietary assessment section. Furthermore, Pearson's correlation coefficient (r) was used to examine correlations between sleep quality and nutritional status variables, while Spearman's coefficient was used for other variables that do not follow a normal distribution. Statistically significant differences between the studied groups or associations between variables were confirmed if the p-value was less than 0.05. The confidence interval (CI) was set at 95%. This involves a total of 49 male professional football players. The descriptive characteristics of these participants are presented in the (table 1), where the data is expressed as mean ± standard deviation (SD), along with the corresponding maximum and minimum values. The average age of these players is 24.9 ± 3.1 years, spanning from 20.0 to 34.0 years. Concerning body weight, the mean is 73.4 ± 6.2 kg, with a range of 62.5 to 88.0 kg. The average height of the players is 181.2 ± 6.6 cm, varying between 166.0 and 192.0 cm. The body mass index (BMI) has an average of 22.3 ± 1.2, with values oscillating between 20.1 and 25.3. The percentage of body fat (BFM) shows an average of 10.3 ± 2.2%, ranging from 6.5% to 15.0%. Finally, the basal metabolic rate (BMR) has an average of 1813.2 ± 111.2 Kcal, with respective maximum and minimum values of 2016.9 and 1600.7 Kcal. The results reveal that, among the 49 participants, 36 are classified as having "Good Sleep" "73%" while 13 are classified as having "Poor Sleep" "27%" based on their total score on the Pittsburgh Sleep Quality Index (PSQI). The analyses, expressed as mean ± standard deviation (SD), unveil significant differences between these two groups, assessed through the Mann-Whitney test, for various components of the PSQI (table 2). For players experiencing "Good Sleep" the mean scores are significantly lower in key areas, including subjective sleep quality (1.0 ± 0.1 vs. 1.8 ± 0.4, p < 0.001), sleep latency (1.2 ± 0.4 vs. 1.6 ± 0.5, p < 0.05), sleep duration (0.6 ± 0.5 vs. 1.3 ± 0.5, p < 0.001), sleep disturbances (0.1 ± 0.2 vs. 0.5 ± 0.7, p < 0.001), and the total PSQI score (4.0 ± 0.2 vs. 6.9 ± 0.9, p < 0.001). 219 220 221 It is noteworthy that no significant difference was observed between the two groups regarding sleep efficiency. Additionally, none of the players reported using sleep medication, explaining the absence of a significant difference in this component between the groups. 222 Sleep quality Energy intake and Nutritional status of the of male professional football players 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 The (table 3) provides a comprehensive analysis of the dietary habits of professional football players based on sleep quality. In terms of energy intake, the "Good Sleep" group exhibits a significantly higher total energy intake (3158.9±163.5 Kcal) compared to the "Poor Sleep Quality" group (2757.4±164.7 Kcal), with a notable overall difference (p < 0.001). Concerning nutritional intake, the Student t-test revealed significant variations, the group of football players with "Good Sleep " exhibits significantly higher ratios of carbohydrates (CHO %), carbohydrate intake per kilogram of body mass (CHO g/kg BM), and protein intake per kilogram of body mass (PRO g/kg BM) (p < 0.001 and p < 0.05, respectively), while the fat ratio (FAT %) is lower in this group (p < 0.05). Additionally, the dietary fiber intake is significantly higher with a significance level of (p < 0.05). The correlation study between the sleep quality score (PSQI) and nutritional status variables has revealed significant associations. The Pearson test showed that the PSQI score exhibits a significant negative correlation with total energy intake (TEI) (r = -0.687**, p < 0.01), carbohydrate intake per kilogram of body mass (CHO g/kg BM) (r =-0.499**, p < 0.01) also for carbohydrate (CHO%) and protein (CHO%) ratio with a (r =-0,294* p < 0.05), (r =-0,292* p < 0.05) consecutively,  in the other side and concurrently, the PSQI demonstrates a positive correlation with the percentage of fats (FAT%) and fat intake per kilogram of body mass (FAT g/kg BM). 239
Place Table 3
240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 However, the dietary intake frequency, assessed using the Mann-Whitney test, reveals significant disparities between the two groups. The results highlight statistically significant differences, the players with "Good Sleep" display significantly higher values than those with "Poor Sleep Quality" (p < 0.05 to p < 0.001), particularly in the consumption of vegetables and fish. Conversely, players with "Poor Sleep" present significantly higher values notably for the intake of processed meats, fats and butter, as well as sodas (p < 0.05 or p < 0.001). The Spearman test reveals a significantly negative correlation between the PSQI Score and the consumption of vegetables (r = -0.432**, p < 0.01) as well as fish (r = -0.298*, p < 0.05). Conversely, positive and significant correlations were observed with the consumption of processed meats (r = 0.387**), fats and butter (r = 0.375**, p < 0.01), and soda (r = 0.352*, p < 0.05). Regarding the frequency of daily meals, players with "Good Sleep Quality" show significantly higher frequencies, especially about the total number of meals per day, breakfasts per day, and snacks per day, with respective significance levels at (p < 0.001 and p < 0.05).  In addition to this the Spearman test results expose significant negative correlations between PSQI and meals per day (r = -0.476**,p<0,01) Specifically, there are strong negative correlations with the frequency of, breakfasts per day (r = - 0.477**, p<0,01), and snacks (r = -0.330**, p<0,01)
255
Discussion
256 257 258 259 The present research aimed to explore the possible relationship between sleep quality, nutritional status, and dietary habits among professional football players. The inclusion of scores from the Pittsburgh Sleep Quality Index (PSQI), along with data on energy intake and dietary frequency, allowed for a thorough analysis of how sleep statuses might influence nutritional behaviors. 260 261 262 263 264 265 To ensure alignment between training load and nutritional status, the UEFA 10 (Union of European Football Associations) has established recommendations indicating that caloric intake should be between 3400 and 4300 calories to optimize performance in football while also preserving health and preventing injuries for professional football players. Our study has revealed that the mean energy intake of the male professional football players was 3052,4±241,6. Kcal. The energy requirements of the football players we investigated exhibit noteworthy similarities across various leagues and player categories.  27–29 266 267 268 269 270 271 272 273 274 275 276 The "Good Sleep" group exhibits a significantly higher total energy intake (3158.9±163.5 Kcal) compared to the "Poor Sleep Quality" group (2757.4±164.7 Kcal), with a notable overall difference (p < 0.001). Furthermore, the energy ratio distribution for CHO, proteins, and fats was 52±3 %, 15±2 % and 33±3 %, respectively. For players benefiting from "Good Sleep Quality" mean scores are significantly lower in key areas, including subjective sleep quality, sleep latency, sleep duration, and sleep disturbances. Our findings align with studies suggesting that elite athlete populations, often subjected to significant training loads and participating in competitions such as football matches, frequently face disturbances 8 . Studies report that 50% to 78% of elite athletes experience sleep disturbances, and 22% to 26% suffer from severe sleep disorders, reflecting on overall sleep quality 30 - 32 277
Place Table 4
278
The nutrition status was associated with the sleep quality
279 280 281 282 The correlation analyses have revealed significant associations between the PSQI score, total energy intake (TEI), and macronutrient intake (carbohydrates and Protein) per kilogram of body mass (g/kg BM) Fig 2, highlighting an interesting link between sleep quality and the nutritional habits of football players. 283 284
Energy macronutrients intake and sleep quality:
285 286 287 288 289 290 The results of this analysis reveal significant associations between various aspects of sleep and energy intake among professional football players. A significant negative correlation is observed between subjective sleep quality and total energy intake (r = -0,681, p < 0.001), indicating that those who enjoy better sleep quality tend to have a balanced nutritional intake. also, the sleep latency shows a negative significant correlation with total energy intake (r = -0.253, p < 0.050), suggesting that the time taken to fall asleep are significantly influence overall energy intake. A significant negative correlation is also observed between sleep duration and total energy intake (r = -0.462, p < 0.001), 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 meaning that longer sleep duration is associated with increased food intake. Additionally, a negative relationship is found between sleep disturbances and total energy intake (r = -0.373, p = 0.008), suggesting that fewer sleep disturbances are associated with greater energy consumption. Finally, daytime dysfunction shows no significant correlation with total energy intake (r = -0.235, p = 0.108), indicating that it does not significantly influence players' eating habits. Regarding the macronutrients, a significant negative correlation is observed between subjective sleep quality and carbohydrate consumption per kg of body mass (r = -0.473, p < 0.001), as well as a with protein (r = -0,288, p < 0.05). And also a significant negative correlation with sleep latency (r = - 0.314, p = 0.028) and (-0,321,p < 0.05) for carbohydrates and proteins respectively, a significant negative correlation is observed between PSQI score and carbohydrate consumption per kg of body mass (r = -0,499, p < 0.001), as well as with protein consumption per kg of body mass (r = -0,296, p <0.05) and the percentage of fat in the diet (r = -0,292, p < 0.05), suggesting that players who benefit from good sleep quality tend to have a balanced intake of macronutrients. This was confirmed by a linear regression, highlighting a significant relationship between sleep quality and nutritional status, as represented by total energy intake (TEI), carbohydrates, proteins, and percentage of fat. The correlation coefficients of these variables with sleep quality are respectively (R ²=0.48, R ²=0.28, R ²=0.092, and R ²=0.1). Several previous studies have examined the relationship between sleep and energy intake among 308 309 310 311 312 313 314 315 316 317 athletes, providing a solid foundation for our argumentation. For instance, the study conducted by Halson et al. (2016) 33 found a significant association between sleep quality and energy intake among professional rugby players. This study used validated questionnaires to assess sleep quality and energy intake, revealing that players with better sleep quality tended to have a more balanced energy intake. Another study, conducted by Mah et al. (2011) 34 , investigated the effects of sleep on dietary intake among college basketball players. Researchers utilized objective sleep measures, such as polysomnography, along with dietary journals to evaluate participants' energy intake. Their findings showed that longer nights of sleep were associated with healthier food choices and improved appetite regulation. 318 319 320 321 322 Furthermore, a longitudinal study conducted by Beccuti and Pannain (2011) 35examined the effects of sleep deprivation on energy intake and body weight among healthy adults. Results revealed that participants with insufficient sleep tended to consume more calories, particularly from carbohydrates and fats, which could contribute to long-term weight gain. Place Figure 2 323 324
Frequency of dietary intake and sleep quality:
325 326 327 The analysis of correlations between sleep quality and the frequency of dietary intake among professional football players reveals several significant associations. The results show significant negative correlations between subjective sleep quality and the consumption of fibers (r = -0.183, p < 328 329 330 331 332 333 334 335 0.05), vegetables (r = -0.340, p < 0.01), fish (r = -0.186, p < 0.05), red meat (r = -0.091, p < 0.05), breakfast (r = -0.554, p < 0.01), snacks (r = -0.383, p < 0.01), as well as the number of meals per day (r = -0.554, p < 0.01). This suggests that players with poor sleep quality tend to consume less fiber, vegetables, fish, red meat, and have fewer snacks and meals per day. Conversely, significant positive correlations are observed between subjective sleep quality and the consumption of processed meat (r = 0.429, p < 0.01), olive oil (r = 0.355, p < 0.05), as well as soda (r = 0.438, p < 0.01). This suggests that players with poor sleep quality tend to consume more processed meat, olive oil, and soda. 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 These results indicate an association between sleep quality and the dietary habits of professional football players, highlighting the importance of sleep on players' dietary behavior the following days. Our findings align with other studies suggesting that training sessions and matches could have a negative impact on certain athletes 36,37 , with indirect repercussions on other aspects of players' daily lives, such as nutrition. This stems from the identified negative correlations between perceived effort and sleep quality 38 . Additionally, Watson et al  39 also observed that a reduction in sleep duration could be linked to negative effects during higher training loads. These findings underscore the importance of adequate sleep management for healthy dietary behaviors in professional football players 40-42 . This was confirmed by the positive association found between the total PSQI score and the frequency of consuming processed meat, fats and butter, as well as soda, being (r=0.387), (r= (r=0.375), and (r=0.352) respectively. This suggests that players experiencing sleep disturbances tend to consume more of these mentioned foods. This could be attributed, as reported by several players, to delays in waking up, as players may not have enough time to prepare these meals. Therefore, some of them prefer to opt for fast food. Additionally, after the end of training sessions or matches, fatigued players also tend to choose prepared meals, which may explain the increase in the consumed fat content and the decrease in the level of dietary fiber. 352 353 354 355 356 357 358 359 360 361 362 363 On the other hand, the relationship between lack of sleep and nutritional intake, including appetite, is likely largely due to the involvement of psychological and endocrine factors. Scientifically, the link between lack of sleep and nutritional intake can be explained by various physiological and biochemical mechanisms. Firstly, sleep deprivation disrupts the balance of hormones regulating hunger and satiety. For example, leptin, a hormone that suppresses appetite, decreases, while ghrelin, which stimulates appetite, increases with sleep deprivation. This hormonal dysregulation can lead to increased appetite and less controlled eating behaviors 43 . Additionally, lack of sleep activates the sympathetic nervous system, triggering the release of cortisol, a stress hormone, which can also stimulate appetite and promote consumption of calorie-rich food. Furthermore, lack of sleep impairs cognitive functions such as decision-making and impulse control, which can result in less healthy food choices. Lastly, lack of sleep can affect emotional state, increasing stress, anxiety, or depression, leading to seeking comfort in food. In summary, lack of sleep disrupts hormonal regulation, increases stress, impairs cognitive 364 365 functions, and affects emotional state, thereby contributing to changes in eating habits and increased nutritional intake 44 . 366
Study Limitations and Strength
367 368 369 370 While our study sheds light on these associations, certain limitations need to be acknowledged. Additionally, factors such as training load, stress, and individual differences related to playing position can influence these relationships and require further investigation. The practical implications of these findings are extensive, paving the way for targeted interventions aimed at improving the sleep quality of professional football players and, consequently, optimizing their athletic performance and overall 371 372 373 374 375 376
377
Conclusion
378 379 380 381 382 383 384 385 386
Data availability statement
387 388 The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author 389 390 391 392 393 The authors would like to acknowledge and would like to express heartfelt gratitude to the professional club, players and to those who helped us in the collect of the data and all that participated in this study. Also, to the Department of Biology at the Faculty of Sciences Ben M'sik, Hassan 2 University. 394 395 well-being. However, the exact correlation between sleep quality and dietary habits remains a research topic requiring in-depth exploration for a more profound understanding of this complex relationship; longitudinal studies and clinical trials could provide a robust foundation for developing specific and tailored recommendations in the field of footballers' nutrition and sleep management. Our results suggest an association between sleep quality and dietary intake in athletes, highlighting the potential influence of sleep quality on the dietary choices of professional football players. These findings underscore the importance of considering sleep quality in the nutritional planning of footballers, particularly with a view to optimizing their energy intake, especially in terms of macronutrients, to ensure optimal athletic performance. Players are advised to seek guidance from qualified professionals in both sports' nutrition for any nutritional periodization and sleep specialists to ensure optimal adaptations to training and recovery. 397 398 399 400 This study was conducted following the ethical principles outlined in the Declaration of Helsinki and received approval from the regional ethics committee of the Ibn Rôchd University Hospital Center in Casablanca, under the jurisdiction of the Ministry of Health in Morocco (Approval No: 22/2022). All participants voluntarily participated in the study and had the option to withdraw at any time. 401 402 403 "The authors have no conflicts of interest to declare". 404 405 Funding Sources 406 “This study was not supported by any sponsor or funder”. 407 408 409 Conceptualization: OUKHEDA M, TAKI H, SAILE R. Data curation: OUKHEDA M, TAKI H, SAILE R. Formal analysis: OUKHEDA M, TAKI H. Investigation: OUKHEDA M, TAKI 410 411 412 413 414 415 416 417 418 419 H, SAILE R. Methodology: OUKHEDA M, TAKI H, SAILE R. Project administration: TAKI H, SAILE R. Resources: all authors. Software: OUKHEDA M, TAKI H. Supervision: TAKI H, SAILE R. Validation: TAKI H, SAILE R. Writing—original draft: OUKHEDA M, TAKI H. Writing—review & editing: OUKHEDA M, TAKI H, SAILE R. 420
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Energy intake, nutritional status, and diet intake Frequency according to sleep quality "Good Sleep" and "Poor Sleep" of the professional football players 570 571 Table 4. Correlation between Sleep quality and Energy intake, nutritional status, and diet intake Frequency of the professional football players 572 573 574 575 Figure 1: Protocol of study design and training microcycle (MD: match day; MD+1: one days after match day); MD+2: two days after match day; MD+3: three days after match day; MD- 3: three days before match day; MD-2: two days before match day; MD-1: one day before match day). 576 577 Figure 2: Association between nutritional status and score of PSQI. The dote represent the soccer players participants (n= 49);and the area is for confidence interval (CI:95%) 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 S 8 7 9 1 14
 16
 17 11
SD: standard deviation; BMI: body mass index; BFM: body fat mass; BMR Basal metabolic rate.
PSQI: Pittsburgh Sleep Quality Index Correlation is significant at the 0.01 level (2-tailed). ** Correlation is significant at the 0.05 level (2-tailed). *
TEI: Total Energy Intake (Keal); CHO %: Ratio of Carbobydrates; CHO g/kg BM: Carbobydrate's g/kg BM; PRO %: Ratio of Proteins; PRO g/kg BM: Protein's g/kg BM, Ratio of FAT % BM, PSQI, Pittsburgh Sleep Quality Index. • Froquency of consumption per day: **Froquency of consumption per week. Dairy products (milk, yogurt, and chocse), cercals (bread, cercals, rice, pasta, and couscous), beve infissions), sweets (Sugar, jelly, candies, pastrics, and sweetened fruit juices), white meat (poultry and turkeys), and rod meat (veal, lamb, camel, and goat). Variables ar deviation). Statistically significant differences are defined as P<0.05.
of FAT %, FAT g/kg BM, PSQI, Pittsburgh Sleep Quality Index. Horizontal line: 1) Subjective sleep quality; 2) Sleep latency; 3) Sleep duration; 4) Sleep efficiency; 5) Sleep disturbance; 6) Daytime dysfunction; 7) PSQI Score. Correlation is significant at the 0.01 level (2-tailed). ** Correlation is significant at the 0.05 level (2-tailed). *