Sleep quality monitoring in individual sports athletes: parameters and definitions by systematic review

In the present review, we identify which instruments and parameters are used for sleep quality monitoring in individual sport athletes and which definitions were used for sleep quality parameters in this literature field. Systematic searches for articles reporting the qualitative markers related to sleep in team sport athletes were conducted in PubMed, Scopus and Web of Science online databases. The systematic review followed the Preferred Reporting Items for Systematic Reviews. The initial search returned 3316 articles. After the removal of duplicate articles, eligibility assessment, 75 studies were included in this systematic review. Our main findings were that the most widely used measurement instruments were Actigraphy (25%), Rating Likert Scales (16%) and Sleep Diary (13%). On sleep quality parameters (Sleep duration = 14%; Wake after sleep onset = 14%; Sleep Quality = 12%; Sleep Effciency = 11% and Sleep Latency = 9%), the main point is that there are different definitions for the same parameters in many cases reported in the literature. We conclude that the most widely used instruments for monitoring sleep quality were Actigraphy, Likert scales and Sleep diary. Moreover, the definitions of sleep parameters are inconsistent in the literature, hindering the understanding of the sleep-sport performance relationship.

Sleep Parameters - Athletic Performance - Sports Medicine - Athletes - Sport Performance

INTRODUCTION

Good sleep quality is a well-recognized predictor of physical and mental health, wellness and overall vitality[1]; conversely, a poor sleep quality may lead to accumulation of fatigue, drowsiness and changes in mood[2]. Due to this importance, sleep has been a topic much researched and debated recently in the sporting context[3],[4]. In this context, when it comes to establishing goals for athletes’ sleep, most recommendations focus on the number of hours spent in bed and on sleep hygiene strategies[2]. Although the number of hours in bed is a good reference to start improving sleep, athletes need to focus on sleep quality as well. Sleep quality refers to how well you sleep[1]. Uninterrupted sleep allows you to achieve the ideal amount of restorative sleep, which is essential for athletes[2],[5]. However, the quality of sleep can be more difficult to measure than the amount of sleep[1], especially in athletes.

Researchers verified the effects of training and competition on the sleep of elite athletes[4]. They found that their sleep quality, measured by sleep efficiency, was lower (3%-4%) the night before the competition compared with previous nights. The literature has shown that the sleep of the athletes was impaired on at least 1 night before an important competition[4],[5]. Furthermore, in sports practice differences have been observed in the sleep characteristics between individual and team sport athletes[5],[6]. Some of these characteristics are related with the sleep quality of the athletes[5]. For example, individual sport athletes had poorer sleep efficiency than team sports athletes[6]. In individual sports, the results of athletes are entirely their own. However, they do not have teammates to rely on or share the burden of a loss. Thus, pre-competition stress can contribute to reduced sleep and poor sleep quality[2],[5].

The term “sleep quality” has long been poorly defined yet ubiquitously used by researchers, clinicians and patients[7]. In addition, measuring sleep quality is more difficult than the amount of sleep because sleep quality is a subjective experience[1].This situation still remains, as reported by a recent systematic review and meta-analysis, which pointed to the best parameters for sleep quality monitoring in team sport athletes[3]. Therefore, the aims of the present study were to identify: 1) which instruments and parameters are used for sleep quality monitoring in individual sport athletes; and 2) which definitions were used for sleep quality parameters in in this literature field.

MATERIAL AND METHODS

Procedure

As a review methodology, we adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines[8]. The selection process and data extraction methods were completed by six authors (JGC, HSS, MS, CACF, RG and RRB). The quality appraisal was completed by two authors (JGC and RRB).

Search Strategy

Three electronic databases (PubMed, Scopus and Web of Science) were systematically searched from inception up to March 21st, 2020. The command line (“sleep” OR “sleep quality” OR “sleep quantity” OR “sleep behavior” OR “sleep disturbance” OR “sleep deprivation” OR “circadian rhythm”) AND (“individual sport” OR “individual sports” OR “athlete” OR “athletes”) was used during the electronic search.

Eligibility criteria and selection process

Three authors (JGC, HdSS and MS) reviewed and identified the titles and abstracts based on the following inclusion criteria:

1. The study was written in English;

2. The study was published as an original research in a peer-reviewed journal as a full text article

3. Data were reported specifically for individual sport athletes;

4. Study performed during the athlete’s sporting career;

5. The participants were competitive athletes (defined as Olympic, Paralympic, international, professional, semi-professional, national, regional, youth elite or division I collegiate);

6. Sleep quality parameters were included;

7. The participants had not used chronic medication/drugs.

Quality Assessment

Two authors (JGC and RRB) evaluated the quality of all studies using evaluation criteria ([Table 1]) based on a study by Saw et al.[9]and used by Claudino et al.[3]. Scores were allocated based on how well each criterion was met, assuming a maximum possible score of 8 (low risk of bias) if some doubt was found, the third author (JCS) made the decision. Studies with a risk of bias score 4 or less were considered poor and were excluded.

Table 1

Risk of bias assessment criteria.

Criteria		Definition	Scoring
			0	1	2
A	Peer-reviewed	Study published in peer- reviewed journal	No	Yes	-
B	Number of participants	Number of participants included in study findings	<5	jun/30	>31
C	Population defined	Age, gender, sport, time experience (or level) was described	No	Partly	Yes
D	Experimental design	Experimental design of the study period was described and replicable	No	Partly	Yes
E	Sleep parameters	The sleep parameters were described	No	Yes	-

RESULTS

The initial search returned 3316 articles ([Figure 1]). After the removal of duplicate articles (n=1568), a total of 1748 studies were retained for full-text screening. Following the eligibility assessment, 1657 studies were excluded, as they did not meet the set inclusion criteria. Thus, 76 studies published between 1997 and 2020 were included for assessing the risk of bias. After that phase, we included 75 studies in this systematic review.

Figure 1 Study selection preferred reporting items for systematic reviews and meta- analyses flow diagram.

Characteristics of the studies and Risk of bias

The pooled sample size and age were 2841 participants and 22.8±6.2 years, respectively. About 19% of the sample were swimmers, 15% cyclists, 8% track & fields athletes and rowers, 7% gymnasts and triathletes, 6% judo athletes, 3% shooters, 2% canoeing, martial mixed arts athletes, runners, sailing and taekwondo athletes and 1% each were badminton players, biathlon athletes, bowlers, dancers, diving athletes, jiu-jitsu athletes, karate athletes, tennis players, short track speed skaters, paracyclists, weightlifters, racewalkers, paratriathletes and mountain bikers. Regarding the competitive level, the studies included presented the following distribution: National (n=28; 27%), International (n=24; 23%), Elite (n=18; 17%), Regional (n=10; 10%), Collegiate (n=7; 7%), Youth Elite (n=7; 7%), Paralympic (n=5; 5%) and Olympic athletes (n=4; 4%). The pooled duration of the interventions was, on average, 8 weeks (range, 1-78 weeks). Only one study was excluded from the review because it showed a risk of bias with a score <4 ([Table 2]). The average bias score for the studies was 7 (range, 5-8 weeks).

Table 2

Assessment of risk of bias in the studies included.

Authors	A	B	C	D	E	−
Taylor et al.[10]	1	1	2	1	1	6
Netzer et al.[11]	1	1	2	2	1	7
Reilly et al.[12]	1	1	1	1	1	5
Straub et al.[13]	1	1	1	2	1	6
Jurimae et al.[14]	1	1	2	1	1	6
Kinsman et al.[15]	1	1	2	1	1	6
Wall et al.[17]	1	1	1	1	1	5
Jurimae et al.[18]	1	1	2	1	1	6
Kinsman et al.[19]	1	2	2	1	1	7
Manfredini et al.[20]	1	1	1	1	1	5
Blumert et al.[21]	1	1	2	1	1	6
Leeder et al.[22]	1	2	1	2	1	7
Silva et al.[23]	1	1	2	2	1	7
Filaire et al.[24]	1	1	2	2	1	7
Hoshikawa et al.[25]	1	1	2	1	1	6
Lahart et al.[26]	1	0	1	2	1	5
Lastella et al.[27]	1	2	2	2	1	8
Lastella et al.[6]	1	1	2	2	1	7
Killer et al.[28]	1	1	2	2	1	7
Lastella et al.[29]	1	1	2	2	1	7
Suppiah et al.[30]	1	1	2	2	1	7
Chamari et al.[31]	1	0	2	2	1	6
Chennaoui et al.[32]	1	1	2	2	1	7
Kölling et al.[33]	1	2	2	2	1	8
Kölling et al.[34]	1	2	2	2	1	8
Louis et al.[35]	1	1	2	2	1	7
Main et al.[36]	1	1	1	1	1	5
McCloughan et al.[37]	1	1	2	1	1	6
Main et al.[36]	1	1	1	1	1	5
McCloughan et al.[37]	1	1	2	1	1	6
Sargent et al.[38]	1	1	1	2	1	6
Suppiah et al.[39]	1	1	1	1	1	5
Suppiah et al.[40]	1	1	1	1	1	5
Sperlich et al.[41]	1	1	2	1	1	6
Brandt et al.[42]	1	2	2	2	1	8
Cullen et al.[43]	1	1	1	2	1	6
Crowcroft et al.[44]	1	1	2	2	1	7
Dunican et al.[45]	1	1	2	2	1	7
Foss et al.[46]	1	1	2	2	1	7
Ortigosa-Márquez et al.[47]	1	1	2	2	1	7
Rodrigues et al.[48]	1	1	2	2	1	7
Sartor et al.[49]	1	1	2	2	1	7
Shields et al.[50]	1	1	2	2	1	7
Woods et al.[51]	1	1	2	2	1	7
Cheikh et al.[52]	1	1	2	2	1	7
Chtourou et al.[53]	1	1	2	2	1	7
Dumortier et al.[54]	1	1	2	2	1	7
El-Shobaki et al.[55]	1	1	1	1	1	5
Flatt et al.[56]	1	1	2	2	1	7
Kennedy et al.[57]	1	1	2	2	1	7
Martin et al.[58]	1	2	2	2	1	8
Peacock et al.[59]	1	1	2	2	1	7
Rosa et al.[60]	1	1	2	2	1	7
Rundfeldt et al.[61]	1	1	2	2	1	7
Silva et al.[62]	1	2	2	2	1	8
Stevens et al.[63]	1	1	2	2	1	7
Suppiah et al.[64]	1	2	2	1	1	7
Tabben et al.[65]	1	1	2	2	1	7
Daaloul et al.[66]	1	1	2	2	1	7
Lastella et al.[67]	1	1	2	2	1	7
Ramos-Campo et al.[68]	1	1	2	2	1	7
Romdhani et al.[69]	1	1	2	2	1	7
Saw et al.[9]	1	2	2	2	1	8
Aloulou et al.[93]	1	1	2	2	1	7
Andrade et al.[94]	1	1	1	1	1	5
Gudmundsdottir et al.[95]	1	2	1	2	1	7
Mah et al.[96]	1	1	2	1	1	6
Ramos-Campo et al.[97]	1	1	1	1	1	5
Roberts et al.[98]	1	1	2	2	1	7
Silva et al.[99]	1	2	2	2	1	8
Silva and Paiva[100]	1	2	1	1	1	6
Stavrou et al.[101]	1	2	2	1	1	7
Surda et al.[102]	1	2	2	1	1	7
Walsh et al.[103]	1	1	1	2	1	6
Carazo-Vargas and Moncada-Jiménez[104]	0	1	1	0	1	3*
Carter et al.[105]	1	1	1	1	1	5
Rosa et al.[106]	1	1	2	1	1	6
Mello et al.[107]	1	1	1	1	1	5

*Study excluded: risk of bias less than 4.

Findings

Initially, to permit an adequate reading flow, the summaries of the 75 studies included in the systematic review are described online supplementary in a table. Twenty-one measurement instruments were used for monitoring sleep quality in individual sport athletes ([Table 3]). The following instruments were the most prevalent: Actigraphy (n=36; 25%); Rating Likert Scales (n=23; 16%) and Sleep Diary (n=18; 13%).

Table 3

Instruments used for sleep quality monitoring.

Instruments	%	n
Actigraphy	25%	36
Rating Likert Scales (sleep quality)	16%	23
Sleep diary	13%	18
Epworth Sleepiness Scale	8%	11
Polysomnography	6%	8
RESTQ-Sport	5%	7
Pittsburgh Sleep Quality Index	6%	9
Sleep log	3%	4
Sleep-EEG	2%	3
Karolinska Sleepiness Scale	3%	4
Visual Analogue Scale (VAS)	2%	3
Karolinska Diary	1%	2
Multi-component Training Distress Scale	1%	2
Rating Likert Scales (jetlag)	1%	2
Pediatric Day time Sleepiness Scale	1%	2
Berlin Questionnaire	1%	1
Insomnia Severity Index	1%	1
Groningen Sleep Quality Questionnaire	1%	1
Sleepiness Questionnaire	1%	1
Sleep Questionnaire	2%	3
Spiegel Sleep Inventory	1%	1
Total	100	142

The definition and procedures used for the sleep quality parameters are presented in [Table 4]. Regarding sleep quality parameters (Sleep duration = 14%; Wake after sleep onset = 14%; Sleep Quality = 12%; Sleep Efficiency = 11% and Sleep Latency = 9%), there are different definitions for the same parameters in many cases reported in the studies.

Table 4

Definitions of the main sleep quality parameters.

Sleep Parameters	Definitions (author)	Frequency
		n (%)
Sleep Duration	• Calculated from TRT minus SOL and including any wakefulness intervening after sleep onset[70]	31 (14.2%)
	• The sleep duration expressed as a percentage of time asleep from sleep start* to sleep end[22]
	• Sleep start to sleep end minus wake time[26]
	• Duration of sleep during a sleep period[27]
	• Sleep Period Time (SPT: time between sleep and awakening) - SOL) - WASO[71]
	• Time in bed from which SOL and WASO are subtracted to obtain the time spent asleep
	• Assumed Sleep time as determined by the algorithm, taking into account immobile time[72]
	• Estimated by 4 questions daily[73]
	• The amount of time spent in bed asleep[63]
	• The total amount of sleep obtained during a sleep period[74]
WASO	• Time spent awake between the start and end of sleep[25]	31 (14.2%)
	• The amount of time spent awake after sleep has been initiated as a percentage of sleep[75]
	• Number of continuous sections categorized as awake in the epoch-by-epoch wake/sleep categorization[28]
	• The amount of time spent awake after sleep has been initiated as a (%) percentage of sleep[29]
Sleep Quality	• Determined by WA by measures of sleep efficiency and fragmentation index[22]	27 (12.4%)
Sleep Efficiency	• Total Sleep time x 100/total min in bed with the lights out[76]	24 (11.0%)
	• The sleep duration expressed as a percentage of time asleep*from bedtime* to sleep end[22]
	• Was the sleep period a percentage of the time in bed[25]
	• Percentage of time spent asleep from sleep onset calculated by ((sleep duration - wake time)/sleep duration) x 100)[26]
	• Percentage of Time in bed that was spent asleep[71]
	• Percentage of Time in bed that was spent asleep[77]
	• Percentage of Time in bed actually spent asleep[30]
	• Calculated by means of actigraphy measurements and sleep diaries and the ratio between Total Sleep time (TST) and Time in bed[32]
	• The relation of Total Sleep time to time in bed, in percentage, is considered as sleep efficiency
Sleep Efficiency	• Actual Sleep time expressed as a percentage of time in bed[72]	24 (11.0%)
	• Sleep duration as a percentage of time in bed[37]
	• Estimated by 4 questions daily[73]
	• Indicates how much Time in bed is spent sleeping[59]
	• Sleep duration expressed as a percentage of time in bed[63]
	• The percentage of Time in bed that was spent asleep[74]
Sleep Latency	• Was determined from the time out until the start of sleep identified by the sensor[25]	19 (8.7%)
	• Time from Bed Time to sleep onset[29]
	• Time between bedtime and sleep onset time[71]
	• The amount of time between bedtime and sleep start[72]
	• The time it takes an individual to fall asleep[59]
	• The period of time between bedtime and sleep onset time[74]
	• The difference between sleep onset time* and bedtime as defined by the participant[22]
Time In Bed	• The difference between bedtime and get-up time as defined by the participant[22]	13 (6.0%)
	• Started from when the athletes laid in bed and the lights were switched off[25]
	• Time spent in bed attempting to sleep between bedtime and get-up time[71]
	• The total amount of time spent in bed between bedtime and get-up time[72]
Bed Time	• Self-reported clock time at which a participant went to bed to attempt to sleep[71]	12 (5.5%)
	• Were obtained together with subjective sleep duration. Variability was estimated by the difference in sleep duration during weekend days and weekdays[73]
Sleep Stage	• The total sleep stage values, expressed as a percentage of total Sleep time[70]	11 (5.0%)
Day Time Sleepiness	• Asking the individual to rate how likely they would be to doze off or fall asleep in eight common daily activities[78]	8 (3.7%)
Get-Up Time	• Self-reported clock time at which a participant got out of bed and stopped attempting to sleep[71]	6 (2.8%)
	• The self-reported clock time at which a participant got out of bed[72]
Moving Time	• The actual time spent moving* during time in bed[22]	6 (2.8%)
	• Time spent moving as a percentage of the assumed Sleep time, which is derived from the number of epochs whereby scores greater than zero were recorded (sum of duration of moving time epochs > 0)/sleep duration) x 100) and is an indicator of restlessness[26]
Moving Time	• Was the amount of time spent moving as a percentage of the time in bed[25]	6 (2.8%)
Sleep Fragmentation	• Sum of the mobile time (%) and the immobile bouts ≤1 min (%). The Fragmentation Index is an indication of the degree of fragmentation of the sleep period, and can be used as an indication of Sleep Quality[28]	5 (2.3%)
	• A measure of restlessness during sleep, using the percentage of time in bed[22]
Sleep Disturbance	• No definition	5 (2.3%)
Sleep Onset (Time)	• Clock time that a participant fell asleep at the start of a sleep period[71]	5 (2.3%)
Day Time Naps	• The total amount of sleep obtained during a daytime nap[75]	3 (1.4%)
Sleep Offset	• Clock time at which a participant woke at the end of a sleep period[71]	3 (1.4%)
Ease of Falling Asleep or Ease of Waking up	• No definition	2 (0.9%)
Amount of Dreaming	• No definition	1 (0.5%)
Calm Sleep	• No definition	1 (0.5%)
DeepSleep	• No definition	1 (0.5%)
Feeling Refreshed After Awakening	• No definition	1 (0.5%)
Feeling Sleep	• No definition	1 (0.5%)
Fell Asleep Time	• No definition	1 (0.5%)
Immobile Time	• The actual time spent immobile in bed[22]	1 (0.5%)

DISCUSSION

Many athletes and coaches know that having a good night’s sleep is important. However, despite this, they are constantly having far less than they actually need. Like this, in this study we found which instruments, parameters and their definitions were used for sleep quality monitoring in individual sport athletes. Our main findings were that the measurement instruments most used were actigraphy, scales as Likert rating scales and sleep diary. Additionally, there are different definitions for the same parameters in many cases reported in the literature. The definition of sleep quality appeared in only one study, being determined by measures of sleep efficiency and fragmentation index.

Despite the influence that sleep has on sports performance, the present study is the first to show how the measurement instruments for monitoring the sleep in the individual sports were used. Previous research with team sport athletes[3]reported similar results to those of the present study. In general, the scientific literature suggests the use of sleep diaries, actigraphy, or polysomnography for clinical suspicion of sleep disorders evaluation[16],[79],[89]. The use of screening questionnaires contributes to identify poor sleep habits and potential sleep disorders[16]. The data obtained from the diaries and questionnaires can be informative for practitioners because the process is simple. The association of the sleep diary with actimetry has been recommended, because it is useful for tracking the sleep-wake pattern and for ensuring adequate time in bed[89],[90],[91]. This method is more adequate during periods of travel or high-intensity training, when there is high risk for insufficient sleep[90].

Also, the specificity of training and competition schedules is possibly the most influential factor that leads to inconsistent sleep among individual sports athletes[108]. For this reason, instruments with practical applications are more suitable for monitoring the sleep quality of athletes[16]. Thus, the use of activity monitors (actigraphy), smartphone applications and sleep questionnaires have become a reality in athletes’ daily practice[16],[89] ^,- [109]. In this sense, different instruments and information collected can complement each other and aggregate sleep data makes the assessment of sleep quality more robust and tolerant to noise and lack of data[109]. Our results signaling for the use of actigraphy, rating Likert scales and sleep diary for sleep monitoring. We, therefore, suggest that this holistic approach (individualized) to sleep assessment be used in individual sports.

On the other hand, the use of adequate instruments is of no use if the analyzed parameters are not properly defined. In our study, we identified different parameters for assessing sleep quality. In this regard, the National Sleep Foundation (NSF) recommended that the main variables that express sleep quality are latency, a number of an awakenings (>5 minutes), wake after sleep onset (WASO) and sleep efficiency[1]. However, the NSF did not find consensus regarding sleep architecture or nap-related variables as elements of good sleep quality[1]. This fact explains why we found only one study[22]that defined sleep quality. Despite its common usage, the literature highlights which sleep quality is a term without a clear definition[1],[7]. However, Kline[110]defines sleep quality “as one’s satisfaction with the sleep experience, integrating aspects of sleep initiation, sleep maintenance, sleep quantity, and refreshment upon awakening”. Sleep quality refers to subjective perceptions of one’s sleep, that should be borne in mind in coaching athletes before, during and after the competitions[16],[60],[71],[106],[111]. Like this, we highlight the role of sleep quality in individual sports is still an unexplored field of research.

For this reason, understanding the sports requirements is vital for adequate sleep, as well as, for adequate sleep evaluation. Each sport represents a unique variable combination to deal with sleep management. Disturbed sleep patterns and increased incidences of illness have been shown in ultra-endurance athletes[85]and sprint cyclists[86],[87]. It has been observed a greater reporting of poor sleep in individual sports compared with team sports[88]. These differences were explained by the lower pressure and anxiety experienced in team sports compared with individual ones due to the performance responsibility, e.g., in team-sports, being divided by the team members[3],[92]. Conversely, individual sports athletes could go to bed earlier, wake up earlier, and obtain less sleep than athletes from team sports[6]. This feature may favor a sleep debt condition and then, impairment of aspects related to physical restoration, compromising sports performance.

From a practical point of view, individual and team sports differ in most aspects, but mainly the dimension of the sport’s internal logic. Internal logic is defined as a system of specific motor characteristics necessary for the performance of particular sports gestures[80]. In addition, internal logic is associated with aspects of a modality that never changes, such as the existence of interaction with opponents. This means, if there are peculiar aspects of the modalities (individual or team-sports) which require that the players act in a specific way (from the point of view of the realized movement) during their practice. Thus, in team sports, there will usually be interaction with adversary whereas in individual sports, interaction with adversary may or may not exists[3]. In addition, the duration and intensity of the individual or team modalities are also very different. These differences may influence, to a great extent, the type of stress generated, the state of mood and, consequently, the sleep duration or sleep quality in different sports modalities[81],[82],[83],[84].

Properly addressing the sleep needs of athletes requires understanding the complexity of variables influencing circadian and homeostatic factors and cooperation of a multidisciplinary team of coaches and physicians. Sleep management should include goals to all athletes as well as individualized approaches[16]. In this context, is necessary strategies of education about healthy sleep habits and sleep hygiene[1],[16]. Besides, cooperation of coaches and staff to identify athletes at risk and, the identification of outside factors influencing sleep, including stress, injuries and medications are fundamental for sleep monitoring of the athletes[16].

The results of this review suggest that sleep quality should be studied in individual sport athletes using easy and inexpensive methods, such as questionnaires/diary, actigraphy or Likert rating scales. The current state of development in the area proposes a promising future about the use of artificial intelligence (AI) to integrate sleep quality in the 24-h monitoring of the athletes[112]. This is because the trend of using 24-hour monitoring (wearable devices or smartphones) and the use of prediction algorithms can contribute to discovering how sleep quality can be improved in athletes. Improving athletes’ sleep quality is important because it is vital for levels of mental and physical performance, general well-being and for the recovery process. Sleep-related technologies are useful for monitoring and also for aid intervention[109].

The main limitation of our study was not to analyze the level of instability (coefficient of variation) of the sleep quality parameters due to the impossibility of grouping given the different definitions for the same parameter. The literature[3]suggests a scale for the CV with CV >30%=large and CV <10%=small[3]. Variables with a large CV are less likely (OR) to detect statistically significant differences during repetitive measurement. In the case of monitoring the quality of sleep, performing this analysis contributes to better reliability of the measures repeated daily or in specific situations (jet lag, training, competition, etc.).

In conclusion, the present study found that the instruments most widely used for monitoring sleep quality were actigraphy, Likert rating scales and questionnaires. Moreover, the definitions of sleep parameters are inconsistent in the literature. This situation does not favour the understanding of the sleep-sport performance relationship. Thus, we suggest creating an international consensus for sleep evaluation in high-performance athletes.

Conflict of Interests

The authors have no conflict of interests to declare.

Funding source

This study was supported out on the financial support of the National Council for Scientific and Technological Development (CNPq) by process 432153/2018-7 (MCTIC/CNPq Nº 28/2018).

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Publikationsverlauf

Eingereicht: 21. Dezember 2019

Angenommen: 22. Juni 2020

Artikel online veröffentlicht:
09. November 2023