How Sleep Affects Recovery and Performance in Basketball: A Systematic Review

Citation: Ochoa-Lácar, J.; Singh, M.; Bird, S.P.; Charest, J.; Huyghe, T.; Calleja-González, J. How Sleep Affects Recovery and Performance in Basketball: A Systematic Review. Brain Sci. 2022,12, 1570. https:// doi.org/10.3390/brainsci12111570 Academic Editor: Maria Comas Soberats Received: 24 October 2022 Accepted: 11 November 2022 Published: 18 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 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 (https:// creativecommons.org/licenses/by/ 4.0/). brain sciences Review How Sleep Affects Recovery and Performance in Basketball: A Systematic Review Javier Ochoa-Lácar 1,*, Meeta Singh 2, Stephen P. Bird 3, Jonathan Charest 4,5, Thomas Huyghe 6 and Julio Calleja-González 7 1Independent Researcher, 31011 Pamplona, Spain 2Henry Ford Sleep Disorders Center, Detroit, MI 48202, USA 3School of Health and Medical Sciences, University of Southern Queensland, Ipswich, QLD 4350, Australia 4Centre for Sleep & Human Performance, Calgary, AB T2X 3V4, Canada 5Department of Kinesiology, University of Calgary, Calgary, AB T2N 1N4, Canada 6Department of Sports Science, Universidad Católica de Murcia, 30107 Murcia, Spain 7Department of Physical Education and Sport, University of the Basque Country, 48940 Vitoria, Spain *Correspondence: javier[email protected] Abstract: Background: Sleep is considered an essential component related to physiological and psychological recovery in athletes and particularly in basketball, given the impact of condensed travel and game schedules on player health and performance. Objective: The aim of this systematic review is to examine studies published to date on sleep and basketball performance. Methodology: All scientific articles that reported a relationship between sleep and its possible impact on performance in basketball are included. The research processes followed the PRISMA criteria, and the relevant articles were extracted (PubMed, WOS, Scopus) as of December 31, 2021. Results: Twenty-eight articles were selected for inclusion and data extraction, with 27 demonstrating that sleep is a vital component in the recovery of basketball players and their corresponding on-court performance. Three central themes that we identified: (1) the quality and extension of sleep (the better quality and more extension of sleep, better performance and lower probability of injury); (2) influence of the players circadian rhythm (travel and game scheduling do not currently facilitate or take this into account); and (3) higher training loads and/or increased stress may jeopardize a subsequent good night’s sleep, which should be taken into account when scheduling practices and workouts. Conclusion: The current systematic review regarding sleep and basketball performance and highlights that there is a strong relationship between both variables. Collectively, the evidence supports the critical influence of sleep on player recovery and basketball performance and risk for injury. Keywords: sports performance; basketball; sleep; recovery; travel; circadian rhythms 1. Introduction Basketball is an intermittent sport made up of high intensity repeated accelerations, decelerations, change of direction and jump landings, which places a high demand on players, both physically and cognitively [ 1 ]. In this team sport, the main physical actions that determine performance are accelerations and decelerations, changes of direction, and vertical jumps [ 2 ]. In addition to the importance of developing explosive power [ 3 – 5 ], aerobic capacity plays a key role, acting as a basis for the recovery of the player [ 6 ] and optimal recovery with methods with scientific evidence (foam roll, cold water Immersion and others) after practices and matches play a key role on team sport performance [7]. The scientific literature concludes that sports performance is not limited to the training performed by the athlete, but other factors as well, including both extrinsic and intrinsic variables [ 8 , 9 ]. In this sense, individualized recovery strategies are considered instrumental in achieving and sustaining the highest possible level of performance [ 10 , 11 ], and specifically in elite basketball ecosystems [ 12 ], where the players spend a much greater proportion Brain Sci. 2022,12, 1570. https://doi.org/10.3390/brainsci12111570 https://www.mdpi.com/journal/brainsci Brain Sci. 2022,12, 1570 2 of 20 of their time recovering than they do in training [ 6 ]. For instance, recovery strategies (Foam roll, ergo nutritional aids, cold water Immersion and others) help basketball players decrease their feeling of fatigue, an attribute that is directly related to athletic performance [ 13 ], considering the balance between happiness vs. Wellness, during the recovery process in high performance Sport [14]. One recovery strategy in particular, sleep, is commonly described as a determining factor in the recovery of players after a competition or after intense training session [ 15 , 16 ]. As such, it is theorized that a lack of sleep will foster negative consequences for performance. This appears to be critical in sports where motor coordination, decision-making and aerobic capacity are all fundamentally important [16–20]. Indeed, basketball is a prime example of the abovementioned characteristics [ 21 ]. To date, literature examining sleep and recovery on basketball considers sleep as one of the main recovery strategies available to players, alongside nutrition and hydration [ 2 , 6 ]. These factors are also considered as the most efficient recovery practices by the players themselves [22]. Furthermore, lack of sufficient regeneration may lead to a loss of performance due to the accumulation of holistic stress in the athlete’s body [ 23 ]. There are also publications that relate lack of sleep with negative effects on performance, both physically and cognitively, and injury [ 24 ]. In general, science presents the perverse effects on sports performance due the lack of quality sleep and, consequently, there is a growing interest to improve the sleep habits of athletes [15,25]. As such, it is essential to understand why basketball ecosystems, specially at the highest level, are so disruptive for sleep and therefore, for recovery. Due to diverse factors, such as scheduling, traveling, late practices and games, and electronic devices use, the probabilities of a solid and constant sleep, night after night, are scarce for elite basketball players. Finally, novel scientific studies examining the impact of air traveling direction, flight time, flight duration, average flight altitude, frequency and magnitude of height changes during flight, air cabin conditions, oxygen saturation levels, and athlete chronotype are warranted to help paint a clearer picture on how different stressors impact wellness and performance due to traveling [ 26 ]. These factors may represent significant problems in terms of athlete health and performance. Considering the large body of knowledge on sleep and sport performance, surprisingly, for the best of the authors’ knowledge, there remains limited research that systematically and critically examines the health and performance effects of daytime sleepiness in basketball. 2. Objectives The main purpose of this systematic review was to explore the underlying physiological and psychological effects of sleep on health and performance in basketball. Secondly, we aim to extend previous literature by providing up to date, best-practice recommendations for sleep during a competitive basketball season. 3. Methodology and Procedures 3.1. Search Strategy This systematic review focused on the relationship between sleep, health and basketball player performance and followed the guidelines established by Preferred Reporting Items for Systematic Review and Meta-Analyses [ 27 – 29 ]. The main parameter used for the inclusion-exclusion of articles has been the PICOS Model [ 30 ], as well as the OLE scale [ 31 ]. We have used the following inclusion criteria: P (Population): professional and amateur basketball players; I (Intervention): effect of sleep on performance; C (Control): does not apply; O (Outcome): consequence of sleep-in basketball performance; S (Study Design): all kinds. Therefore, a structured computer search was undertaken in different specialized online database media (PubMed, Web of Science [database that includes other databases such as Brain Sci. 2022,12, 1570 3 of 20 BCI, BIOSIS, CCC, DIIDW, INSPEC, KJD, RSCI and SCIELO, all of high quality], Scopus). This search ended on 31 December 2021. The search terms included a mix of medical subject headings (MeSH) and free-text words for key concepts related to recovery, young, basketball, and players. The following search equation was used to find the relevant articles: ["sleep basketball" (MeSH Terms) OR"sleep" (all fields)] AND ["basketball" (all fields)]. We have also done it in Spanish, using the formula ["sleep basketball " (MeSH TERMS) OR "sleep" (all the fields)] AND ["Basketball (all fields)] OR ["sleep" (all fields)] AND ["Basketball" (all fields)]. There were no filters applied to the athlete’s physical fitness level, race, or age to increase the power of the analysis. The search has been carried out independently by two authors, JOL and J.C-G, disagreements were discussed with the third author (S.B) The search terms selected were limited to sleep and basketball. No other terms were used additionally to increase the power of analysis. Through this equation, all relevant articles from this given field were obtained. The reference sections of all identified articles were also examined using the "Snowball Methods" strategy, based on the examination of reference sections of the selected articles [32]. This search method allowed us to apply the PICOS Model [ 33 ] on all the results obtained, which was the main inclusion-exclusion criterion of the articles for analysis. These database reviews have been carried out with the data obtained up to December 2021. Regarding the data collection process, the methodology used was the reading of each of the selected articles by the authors of this review. After carefully reviewing all articles, the authors finally excluded 3 more articles (2 opinion articles and one narrative review [ 18 , 26 , 34 ]). Hence, 25 articles were included in this study. The authors presented a summary table with the most relevant information of each study, in an easy-to-digest format, which in turn, helped the authors analyze, categorize and classify all the data necessary to carry out this review (Table 3). Titles and abstracts were selected to review the full text. Two authors (J.O-L. and J.C-G.) searched for independently published studies, and disagreements on all outcomes were resolved by a third author (S.B) 3.2. Inclusion and Exclusion Criteria Studies included in this systematic review had to meet the following inclusion criteria: (I) the study population comprised basketball players (College level or above); (II) sleep measurements were recorded; (III) the effects of sleep were compared with one or more indicators of basketball performance; and (IV) study designs included all possible designs: quantitative, qualitative, and/or mixed-method model with experimental, quasi-experimental, and/or non-experimental research design, utilizing primary and/or secondary data sources. Again, in our case, the PICOS model would be as follows: Professional, semi-professional, college, amateur and disabled basketball players (P); Effects of sleep on performance (I); Not applicable (C); Relationship of sleep with specific performance in basketball (O); and all, including quantitative, qualitative, and/or mixed-method model witexperimental, quasi-experimental, and/or non-experimental research design, utilizing primary and/or secondary data sources (S). All search titles and abstracts were collated to identify duplicates and possible missing studies. Titles and abstracts were screened for further full-text review. The search for published studies was conducted independently by two authors (JOL and h JC-G) and disagreements were resolved through discussions with another author (M.S). We have also applied the OLE scale [30], as follows (Table 1): Brain Sci. 2022,12, 1570 4 of 20 Table 1. OLE Scale. Level Evidence Level 1 Meta-analysis of high-quality randomized controlled trials (RCTs) or RCTs Level 2 Lower quality RCTs or prospective comparative studies Level 3 Case studies or retrospective studies Level 4 Cases without comparison of control groups Level 5 Case reports or expert opinions We have applied the OLE scale to the 25 studies analyzed, being such that (Table 2): Table 2. Applied OLE Scale. OLE Nº Studies L1 3 L2 0 L3 4 L4 14 L5 4 3.3. Selection of Studies Once the inclusion and exclusion criterion has been applied, the information obtained was evaluated by the two authors (JOL and JC-G) to define their selection. The year of publication, the authors, the sample size, the type of study and the characteristics of each of them were taken into account. Afterwards, all the relevant information was put into an Excel sheet (Microsoft Inc, Seattle, WA, USA) independently by two authors (JOL and JC-G) and the disagreements were resolved through discussions with the third author (J.CH). 3.4. Extraction of Information Once the works to be analyzed were selected, the following information was designated from each of them: year, author and publication; sample size; applied methodology; variables analyzed; results. In reference to the methodology applied for each of the studies analyzed, 1 of these 25 articles were a systematic review of the literature [ 35 ], and there were also 2 articles using questionnaires for the participants [ 22 , 36 ]. Apart from these, the rest of the articles studied were based on mixed linear models of statistical analysis, where we highlight the use of Bonferroni (2 articles: [ 37 , 38 ]), MANOVA (4 articles: [ 39 – 42 ]), Student’s T (4 articles: [38,43–45] ), regression analysis (7 articles: [46–52] and the use of Spearman correlation [45]. 3.5. Assessment of the Quality of the Studies Considering the potential limitations of the studies included in this systematic review, and in order to draw reliable conclusions, the Cochrane Collaboration Guidelines were followed [ 27 ]. Thus, the two authors independently assessed methodological quality and risk of bias (JOL and JC-G), while disagreements were resolved by a third-party assessment (T.H). In the Cochrane Risk of Bias tool [ 28 ], the following elements were included and divided into different domains: (1) selection bias (elements, random sequencing, allocation and concealment), (2) performance bias (blinding of participants and staff), (3) detection bias (blinding of outcome assessment), (4) assertion bias (incomplete outcome data), (5) reporting bias (selective reporting) and (6) other bias (other sources of bias). The assessment of risk of bias was characterized as (a) low-risk (plausible bias that is not likely to seriously alter the results), (b) unclear risk (plausible bias that raises some doubt about the results), or (c) high risk (plausible bias that seriously weakens confidence in the results). Brain Sci. 2022,12, 1570 5 of 20 Finally, observational studies such as those reviewed in this paper may suffer from this possibility; In addition, there is the possible limitation of the existence of different ways of reacting to sleep in each individual (since it is impossible to know whether the athletes with the best sleep outcomes are performing better on the court or they just happen to be the best players.) However, the results appear robust in terms of their conclusions. We attach a summary table with all the relevant information of the selected articles (Table 3): Brain Sci. 2022,12, 1570 6 of 20 Table 3. Summary table of the 25 studies included in the systematic review. Year Author Title Main Topic N Level Methodological Approach Measured Variable Outcome 1997 Steenland K, Deddens JA [53] Effect of travel and rest on performance of professional basketball players Sleep and commuting 8495 NBA season games (1987–1995) Professional Each game, one observation (data from the NBA)—regression analysis Objective: performance statistics More time between games improves performance. Circadian rhythms positively affect from west to east 2011 Mah et al. [54] The Effects of Sleep Extension on the Athletic Performance of Collegiate Basketball Players Sleep and performance 11 male Collegiate Fixed-effects linear regression models Objective: performance on sprints, free throws, 3-pointers, reaction time Subjective: levels of sleepiness and mood Optimal sleep helps reaching peak performance 2012 Zhao et al. [41] Red Light and the Sleep Quality and Endurance Performance of Chinese Female Basketball Players Sleep and performance 20 female Professional Cohort study, mixed ANOVA Subjective: Pittsburgh Sleep Quality Index, Objective: aerobic test Red light improved quality of sleep 2017 Staunton et al. [55] Sleep patterns and match performance in elite Australian basketball athletes. Sleep and performance 17 female Elite Prospective cohort study comparing total and quality of sleep vs. performance (bball efficiency statistic) Objective: triaxial accelerometers and EFF (basketball efficiency) Game schedule can affect sleep patterns 2017 Tsunoda et al. [44] Correlation between sleep and psychological mood states in female wheelchair basketball players on a Japanese national team Sleep and performance 17 female Elite Spearman’s correlation + Student’s t Subjective: Pittsburgh Sleep Quality Index (PSQI), Profile of Mood States (POMS-SF) Vigor is related to sleep and performance 2017 Heishman et al. [42] Comparing performance during morning vs. Afternoon training sessions in intercollegiate basketball players Sleep and workloads 10 male Collegiate Retrospective study t-test Objective: CMJ and player readiness (Omegawave), Subjective: self-reported sleep quantity Less sleep, poorer performance Brain Sci. 2022,12, 1570 7 of 20 Table 3. Cont. Year Author Title Main Topic N Level Methodological Approach Measured Variable Outcome 2018 Bonnar et al. [34] Sleep interventions designed to improve athletic performance and recovery: a systematic review of curret approaches Sleep and performance 218 athletes, 18–24 yrs, various sports from 10 studies Collegiate Systematic review (PRISMA 2016) of PubMed, PsycInfo, and WebofScience Subjective: sleep interventions survey Sleep is key for recovery and performance 2018 Thornton et al. [50] Impact of shortcompared to long-haul international travel on the sleep and wellbeing of national wheelchair basketball athletes Sleep and commuting 11 male Elite Linear mixed models determined effects of travel length on sleep and jet lag, fatigue and vigor feelings Subjective: personal ratings on sleep, jet lag, fatigue and vigor during travel phases, competition phases, and base phases Long travel is more fatigue-inducing than short travel, affects vigor 2018 Murray et al. [21] Recovery practices in Division 1 collegiate athletes in North America Sleep and performance 152 division 1 athletes (bball, football, soccer) Collegiate 16-item questionnaire Subjective: attitude towards recovery: sleep, nutrition, cold water immersion, compression garments Only a few players use sleep as recovery 2018 Mutsuzaki et al. [37] Comparison of sleep status among three Japanese national wheelchair basketball teams Sleep and performance 44 Japanese national team wheelchair players: 14 top male, 18 top female and 12U-23 male Elite Bonferroni test + Student’s t Subjective: Pittsburgh Sleep Quality Index (PSQI) Older players sleep worse than young ones. Women have more insomnia than men 2018 Jonathan Roy and Genevieve Forest [44] Greater circadian disadvantage during evening games for the National Basketball Association (NBA), National Hockey League (NHL) and National Football League (NFL) team travelling westward Sleep and circadian rhythms 5 years of regular season games (5909 NBA games, 5640 NHL, and 1280 NFL games) Professional T-tests and analysis of variance and single linear regression models Objective: teams’ winning percentages Circadian rhythms affect performance. Disadvantages for teams traveling westward Brain Sci. 2022,12, 1570 8 of 20 Table 3. Cont. Year Author Title Main Topic N Level Methodological Approach Measured Variable Outcome 2019 Jones et al. [56] Association between late-night tweeting and next-day game performance among professional basketball players Sleep and performance 112 NBA players Professional Merge of 2 public databases of social media and performance Objective: late night social media activity vs. next day game performance (individual NBA statistics) Late night social media activity negatively affects performance 2019 Daniel et al. [57] Effect of the intake of high or low glycemic index high carbohydratemeals on athletes’ sleep quality in pre-game nights Sleep and performance 9 male Elite Cross-sectional study with a crossover design (3-day championship) Subjective: sleep latency (LAT), sleep efficiency (EFIC), wake after sleep onset (WASO), sleep time through actigraphy and sleep diary, satiety, sleepiness Objective: dietary intake, glycemic response, salivary cortisol and melatonin Food intake during the day affects night sleep more tan the glycemic index 2019 Clemente et al. [38] Perceived Training Load, Muscle Soreness, Stress, Fatigue, and Sleep Quality in Professional Basketball: A Full Season Study Sleep and workloads 15 male Professional Descriptive longitudinal study, mixed ANOVA Subjective: internal load (RPE) and wellness (muscle soreness, stress, fatigue, and sleep quality) Lower sleep quality and higher fatigue when playing 2 games in a week 2020 Fox et al. [36] Losing Sleep Over It: Sleep in Basketball Players Affected by Game But Not Training Workloads Sleep and performance, sleep and workloads 7 male Semi-pro Observational study—linear mixed model + Bonferroni tests Objective: player loads and heart rate Subjective: RPE sleep duration and quality Higher loads mean shorter sleep duration and lower quality 2020 Fox et al. [48] The Effect of Game-Related Contextual Factors on Sleep in Basketball Players Sleep and performance 9 male Semi-pro Linear mixed models and effect sizes to compare duration and quality of sleep with game outcomes Objective: game outcome and score margin vs. subsequent night sleep duration Subjective: sleep quality Players should sleep more hours after games Brain Sci. 2022,12, 1570 9 of 20 Table 3. Cont. Year Author Title Main Topic N Level Methodological Approach Measured Variable Outcome 2020 Lastella et al. [49] The Impact of Training Load on Sleep During a 14-Day Training Camp in Elite, Adolescent, Female Basketball Players Sleep and workloads 11 female Elite Separate linear mixed models and effect size analysis assessed differences in sleep behaviors depending on the training load Objective: wrist activity monitors Subjective: RPE No effects 2020 Doeven et al. [58] Managing Load to Optimize Well-Being and Recovery During Short-Term Match Congestion in Elite Basketball. Sleep and workloads 16 male Elite Monitor loads and recovery during a full season Subjective: RPE and wellbeing (sleep, fatigue, stress, soreness, and mood) during a full season Workloads negatively affect sleep and wellbeing 2020 Watson et al. [51] Decreased Sleep Is an Independent Predictor of In-Season Injury in Male Collegiate Basketball Players Sleep and performance 19 male Collegiate Separated mixed-effect logistic regression model Subjective: mood, fatigue, stress, soreness, Objective: sleep duration (hours), and previous day’s training load and injuries More sleep, less risk of injury 2020 Cammarano et al. [45] Sleep and perceived effort during a collegiate women’s basketball season Sleep and workloads 14 female Collegiate Multiple regression analysis Objective: sleep quantity Subjective: sleep quality (self-reported measures) and RPEs (preand in-season) No effects 2021 Fox et al. [47] The Association Between Sleep and In-Game Performance in Basketball Players Sleep and performance 8 male Semi-pro Linear regression with cluster-robust standard error to quantify performance–sleep association Objective: performance (player statistics and composite performance statistics, such as offensive and defensive rating and player efficiency) Good quality of sleep before a game, better performance Brain Sci. 2022,12, 1570 16 of 20 5.2. Sleep and Workloads The results of the studies analyzed show that the loads of the workouts affect sleep in the following way: higher loads prevent athletes from having quality sleep that same night [ 36 ]. This affects performance the next day [ 39 , 47 , 57 ], so coaches should take this into account when planning and deciding their workout intensity and schedules. 5.3. Sleep and Circadian Rhythm Circadian rhythms affect all players and coaches and they determine the best and worst times of the day to train and compete [ 43 ]. This is not being taken into account when scheduling training or competitions [ 62 ]. If this variable were taken into account consistently, the performance of players could be increased in a similar way. Future lines of research should focus on this to further develop this idea. 5.4. Sleep and Traveling In professional competitions, it is increasingly common to travel hundreds or thousands of kilometers per week in order to meet the assigned matches [ 26 , 61 ]. This has a significant toll players health and performance: long journeys negatively affect recovery of athletes, through factors such as the physical toil of travel and disruptions to the circadian rhythm [ 18 , 51 , 60 ]. Therefore, the performance of the players tends to be worse and the probability of injuries, higher. Again, this is not being taken into account when choosing competition calendars. The respective organizations should take this into account to improve competitions and better protect players, who are the most important assets that clubs have [18]. 6. Conclusions This work of review of the most current scientific literature shows that sleep and circadian rhythms are very fashionable topics that are acquiring a lot of importance in the competitive environment of basketball in recent seasons. There are finally solid scientific conclusions that crown sleep as a basic practice of recovery of the athlete and it is clear that it affects their performance and health. Dismissing the use of good sleep practices and not having into account the diverse circadian rhythms of our athletes can have deleterious consequences on basketball performance. 7. Practical Applications Education in sleep hygiene and its important consequences should come from the hand of the staff of each team, to enhance the only natural (and free) tool that exists to recover the body from the stress to which we subject it in each training and game [ 67 , 68 ]. Players must understand how to create a positive pre-sleep environment (avoiding the use of stimulants, social media and bright light before going to bed, especially blue light, using amber tinted glasses and night-shift-mode for the electric devices as a countermeasure, setting a cooler and quiet environment without distractions) and how this can affect their performance, both in numbers and statistics and in injuries. On the other hand, coaches should take more into account natural circadian rhythms when scheduling loads for both morning and evening workouts, allowing more hours of sleep for their players after a particularly hard training day or match. The same applies after a long and heavy trip, when facing the next training. Sleep should be considered as one more recovery practice and therefore should have the same importance as the usual practices in these cases (cold immersion, ergogenic aids, massages, compression garments . . . ). As for professional basketball clubs and leagues, it seems necessary that more attention be paid to the negative impact of travel on the performance and health of players. Now that it is clearer how they suffer in their performance and how the chances of injury increase, both factors that condition the spectacle of this sport and thus, are key when it comes to attracting fans (and therefore, the money necessary to keep everything moving), sleep Brain Sci. 2022,12, 1570 17 of 20 cannot be considered trivial. Moreover, in the main professional leagues of the world (NBA, Euro league), where long trips to different time zones are common, emphasis should be placed on providing enough hours of rest between trips, on avoiding the classic schedule of two consecutive games (back-to-back games) and even the time of the game itself. We must remember that in the NBA it has been shown [ 44 ] how when a team from the West travels to the East the time of the game benefits him by coinciding with its peak of the circadian rhythm. We believe that all these recommendations can increase the value of the professional sport of basketball at several levels, from the purely sports’ side to the organizational and even throughout the economic part. By improving both the show and the organization of it, it must necessarily result in a greater attraction on the part of the spectator and a more efficient use of budgets by the clubs. All the information collected in this work should be taken into account by both the players and the coaches of the teams (staff members are also affected by these conditions, as Calleja-González et al pointed out in 2021 [ 66 ]), the managers of the clubs and by the organizations of the professional leagues themselves, in order to facilitate a safer and more coherent environment with the circadian rhythms and recovery times of the human being. 8. Future Lines of Research Basketball is a sport that requires more accurate future research on sleep and with a larger population and duration over time. We know that at the elite of the sport, the NBA [ 26 ], attention is beginning to be drawn to such an important issue [ 18 ] and we believe that progress has to be made in that direction. The approach to sleep, therefore, should be personalized, always taking into account the individuality of each player and being able to provide a tailor-made solution. 9. Limitations The number of articles related to this topic that have been based on professional players is very small, due to the difficulty of examining this type of profile. That is why in this review it has been decided to also include the information provided by the studies with semi-professional and amateur players. Regarding the risk of bias and the quality of the methodology used, disagreements have been discussed between the two authors, JOL and JC-G, until reaching an agreement on the subject. Author Contributions: J.O.-L. and J.C.-G.: conceived and designed the research, analyzed and interpreted the data, drafted the article and approved the final version submitted for publication. J.O.- L., T.H., M.S., J.C. and J.C.-G.: analyzed and interpreted the data, critically reviewed the article, and approved the final version submitted for publication. J.O.-L., S.P.B. and J.C.-G.: critically reviewed the article and ap-proved the final version submitted for publication. 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