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	Cover Letter 
Article Type: Review Article 
Significance of research:
Sleep is perhaps the most important component to physiological and cognitive recovery and performance. Sleep is paramount for human function in normal day to day life but is especially important for athletic populations, as their requirement to recover and perform at a high level is crucial. Much research has been completed establishing the science behind the importance of sleep for both and mental and physical recovery. Polysomnography represents the gold standard for monitoring of sleep but represents an impractical option for many athletic populations. Many sporting teams use subjective questionnaires to combat this impracticality. Therefore the significance of this research is to review the accuracy of subject and objective sleep monitoring, review objective methods of sleep monitoring and lastly review what previous research has used to monitor sleep in an athletic population.
Agreement of authors for publication:
Alexander Neale Eagles1: Agreed, corresponding author 
Phone: +614 405569218
Email address: a.n.eagles23@gmail.com 
Fax: +617 5459 4880
Qualifications: Bachelor of Science, Doctor of Physiotherapy. 
Dale Ingham Lovell1: Agreed 
Phone: +61 7 5459 4464 
Email address:  HYPERLINK "mailto:dlovell@usc.edu.au" dlovell@usc.edu.au 
Fax: +617 5459 4880
Qualifications: Bachelor of Science (Hons), Doctor of Philosophy.
1Sciences Faculty of Science, Health and Education, University of the Sunshine Coast, Maroochydore, Queensland, Australia
Number of figures and tables: 
Six tables 
Three appendices  

No financial support or grant aid was received in conjunction with this study and no conflicts of interest are declared.

Sleep monitoring techniques: a review of the literature and implications for athletic based monitoring

Alexander N Eagles and Dale l Lovell

Abstract
Aim: The aim of this current article is to review the literature to: 1) establish the validity and reliability of the Bodymedia Sensewear Unit as an objective sleep monitoring option for athletes 2) determine the correlation between objective and subjective sleep measures and 3) review previous research that has monitored sleep in an athletic population.
Methods: An electronic literature search was conducted using Pubmed, Cochrane Library, PEDro, CINAHL, AMED, EMBASE and Web of Science. Articles then underwent specific inclusion and exclusion criteria to be included in the critical appraisal process. Articles were then critiqued and analysed with a study design specific tool. 
Results: Two articles were sourced addressing the validation of the Bodymedia Sensewear Unit. Three articles were identified addressing the association between subjective and objective sleep data. Two articles were identified addressing sleep in athletic populations. There were no studies representing level I evidence, two studies represented level II evidence, while the remaining 5 studies represented level III evidence.
Conclusions: The Bodymedia Sensewear unit offers a practical and reliable option for monitoring sleep in athletic populations. Subjective sleep data extracted from athletic populations should be interpreted with caution given the established poor correlation between subjective and objective sleep data. While within healthy ranges athletes have poorer sleep patterns than the general population and interstate air travel does not appear to influence sleep.

Key Words: sleep monitoring, subjective sleep monitoring, objective sleep monitoring, athlete sleep monitoring, bodymedia sensewear.







Introduction
Sleep and adequate rest are thought to be one of the most important components of recovery, not only in regards to sport but simple day to day living [ HYPERLINK \l "_ENREF_1" \o "Laidler, 2012 #1"  ADDIN EN.CITE <EndNote><Cite><Author>Laidler</Author><Year>2012</Year><RecNum>1</RecNum><DisplayText><style face="superscript">1</style></DisplayText><record><rec-number>1</rec-number><foreign-keys><key app="EN" db-id="x0fz5zsse9av5vew92s5t0wcrrxs5az0tsav">1</key></foreign-keys><ref-type name="Magazine Article">19</ref-type><contributors><authors><author>Laidler, Denise Campbell</author></authors></contributors><titles><title>The Importance of Sleep</title><secondary-title>Black Enterprise</secondary-title></titles><pages>76-79</pages><volume>42</volume><number>12</number><keywords><keyword>African American/Caribbean/African</keyword><keyword>United States--US</keyword><keyword>9190:United States</keyword></keywords><dates><year>2012</year></dates><pub-location>New York, United States, New York</pub-location><publisher>Earl G. Graves Publishing Company, Inc.</publisher><accession-num>1026587287</accession-num><urls><related-urls><url>http://search.proquest.com/docview/1026587287?accountid=26503</url></related-urls></urls><remote-database-name>OxResearch; ProQuest Central</remote-database-name><language>English</language></record></Cite></EndNote>1]. The sleep-wake cycle is governed by the circadian rhythm, a biological, physiological and biochemical process that controls energy levels and wakefulness over 24hrs [ HYPERLINK \l "_ENREF_2" \o "Beersma, 2007 #2"  ADDIN EN.CITE <EndNote><Cite><Author>Beersma</Author><Year>2007</Year><RecNum>2</RecNum><DisplayText><style face="superscript">2</style></DisplayText><record><rec-number>2</rec-number><foreign-keys><key app="EN" db-id="x0fz5zsse9av5vew92s5t0wcrrxs5az0tsav">2</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Beersma, Domien G. M.</author><author>Gordijn, Marijke C. M.</author></authors></contributors><titles><title>Circadian control of the sleep�wake cycle</title><secondary-title>Physiology &amp; Behavior</secondary-title></titles><periodical><full-title>Physiology &amp; behavior</full-title></periodical><pages>190-195</pages><volume>90</volume><number>2�3</number><keywords><keyword>Circadian rhythm</keyword><keyword>Sleep regulation</keyword><keyword>Mathematical models</keyword><keyword>Two-process model</keyword><keyword>Opponent processes model</keyword></keywords><dates><year>2007</year></dates><isbn>0031-9384</isbn><urls><related-urls><url>http://www.sciencedirect.com/science/article/pii/S0031938406003970</url></related-urls></urls><electronic-resource-num>http://dx.doi.org/10.1016/j.physbeh.2006.09.010</electronic-resource-num></record></Cite></EndNote>2]. The effect of lack of sleep has been shown to be detrimental to both physical and mental performance [ ADDIN EN.CITE  ADDIN EN.CITE.DATA  HYPERLINK \l "_ENREF_3" \o "Walker, 2004 #4" 3,  HYPERLINK \l "_ENREF_4" \o "Sinnerton, 1992 #5" 4]. Athletic performance requires sports specific strength and conditioning accompanied by correct motor skill acquisition [ HYPERLINK \l "_ENREF_5" \o "Rogers, 2006 #64"  ADDIN EN.CITE <EndNote><Cite><Author>Rogers</Author><Year>2006</Year><RecNum>64</RecNum><DisplayText><style face="superscript">5</style></DisplayText><record><rec-number>64</rec-number><foreign-keys><key app="EN" db-id="0599fdrvzpa2efeep0dpv5ahw90wxw0f02r2">64</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Rogers, R. G.</author></authors></contributors><auth-address>Department of Obstetrics and Gynecology, University of New Mexico Health Sciences Center, MSC10 5580, ACC Fourth Floor, Albuquerque, NM 87131-0001, USA. rrogers@salud.unm.edu</auth-address><titles><title>Mental practice and acquisition of motor skills: examples from sports training and surgical education</title><secondary-title>Obstetrics and gynecology clinics of North America</secondary-title></titles><periodical><full-title>Obstetrics and gynecology clinics of North America</full-title></periodical><pages>297-304, ix</pages><volume>33</volume><number>2</number><keywords><keyword>Clinical Competence</keyword><keyword>Curriculum</keyword><keyword>Humans</keyword><keyword>Imagination</keyword><keyword>Motor Skills</keyword><keyword>Practice (Psychology)</keyword><keyword>Specialties, Surgical</keyword><keyword>Sports</keyword></keywords><dates><year>2006</year></dates><urls><related-urls><url>http://europepmc.org/abstract/MED/16647605</url></related-urls></urls></record></Cite></EndNote>5]. Walker & Stickgold (2004) demonstrated lack of sleep to hinder motor skill consolidation due to the concept of sleep dependent brain plasticity changes. While reduced sleep has not been shown to impede maximal skeletal muscle function [ HYPERLINK \l "_ENREF_4" \o "Sinnerton, 1992 #5"  ADDIN EN.CITE <EndNote><Cite><Author>Sinnerton</Author><Year>1992</Year><RecNum>5</RecNum><DisplayText><style face="superscript">4</style></DisplayText><record><rec-number>5</rec-number><foreign-keys><key app="EN" db-id="x0fz5zsse9av5vew92s5t0wcrrxs5az0tsav">5</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Sinnerton, SA</author><author>Reilly, T.</author></authors></contributors><titles><title>Effects of sleep loss and time of day in swimmers</title><secondary-title>Biomechanics and Medicine in Swimming</secondary-title></titles><pages>347</pages><volume>6</volume><dates><year>1992</year></dates><isbn>0419156003</isbn><urls></urls></record></Cite></EndNote>4] it  minimises the sub-maximal ability of skeletal muscle and reduces immune function [6]. Sleep deprivation has also been shown to have profound negative influence on cognition [7]. The ability to adjust and respond rapidly in a changing environment, relies on the functional integrity of the prefrontal cortex, which is negatively impacted by sleep loss  HYPERLINK \l "_ENREF_7" \o "Couyoumdjian, 2010 #56" [8]. Other negative psychological influences as a result of sleep loss include poor mood states and decision making skills [9]. This is of significance given the multitude of stimuli the brain needs to process during a professional sporting game such as tennis, rugby or soccer. Given that sleep and adequate rest are such an integral parts of basic recovery [10], the importance of accurately monitoring sleep in an athletic population is key due to the performance requirements of the athletes. Ones sleep-wake cycle can be influenced by a number of components including nutrition, activity level and travel. Despite the established importance of sleep [ HYPERLINK \l "_ENREF_1" \o "Laidler, 2012 #1" 1] there remains confusion and a lack of consistency in terms of how to best obtain reliable sleep data within athletic populations. 

Subjective sleep questionnaires are a common method to monitor sleep in athletic populations due the ease of application and compliance levels [11]. Subjective sleep questionnaires are commonly used in the literature as an additional measure to objective tests [11]. However research suggests it has limited value and poor correlations to objective sleep measures, thereby bringing into question the worth of such data [12-15]. 

Polysomnography (PSG) is considered the gold standard of objective sleep monitoring [11,13] HYPERLINK \l "_ENREF_12" \o "Kushida, 2001 #9" . Polysomnography gives a comprehensive recording of the physiological changes that occur during sleep. PSG monitors brain (Electroencephalogram), eye movements (Electroocculogram), muscle activity (Electromyogram), heart rhythm (Electrocardiogram) and air flow (thermistor or thermocouple sensor). Although PSG is considered to be the gold standard of sleep monitoring [3] HYPERLINK \l "_ENREF_12" \o "Kushida, 2001 #9" , it requires significant expense and subjects are required to stay at a specialised sleep laboratory to be monitored. Therefore the method represents an impractical option for monitoring sleep patterns in an athletic environment. Wristwatch actigraphy (WA) and the Bodymedia Sensewear unit offer a more practical option for measuring sleep in an athletic population. WA is worn on the non-dominate wrist of the participant and provides a measure of accelerations to create an activity pattern. WA has been the monitoring device of choice for research addressing different sleep environments and has been shown to have a strong correlation to PSG [13,16]. The Bodymedia Sensewear unit (BSU) uses a 2-axis accelerometer, heat flux sensor, galvanic skin response sensor (GSR), skin temperature sensor, and a near-body ambient temperature sensor and optional polar heart rate monitor to provide a comprehensive record of sleep [17,18]. The BSU has been show to higher correlations with polysomnography than other portable monitoring devices [17]. 
Therefore the aim of this current article is to review the literature to: 1) establish the validity and reliability of the Bodymedia Sensewear Unit as an objective sleep monitoring option for athletes 2) determine the correlation between objective and subjective sleep measures and 3) review previous research that has monitored sleep in an athletic population. Article inclusion will be based on specific criteria. Articles deemed appropriate for the review will undergo a critical appraisal and deconstruction of methodology in order to determine their value and impact towards addressing the aims. 

Methods
The methodology undertaken to find relevant literature was a three-fold process. Stage one of the review focused on previous literature using the BSU to monitor sleep. Stage two focused on previous research that analysed the relationship between subjective and objective sleep data. The third stage of the review soured previous research that measured sleep parameters on an athletic population. An electronic literature search was performed using the following search engines for all stages of the selection process.
Pubmed
Cochrane Library
PEDro
CINAHL
AMED
EMBASE
Web of Science
Stage one key search terms included, Validation of Sensewear armband, Sensewear AND sleep, Sleep monitoring quality AND quantity. There was limited research within these areas with the first above two search terms yielding 12 and 21 results respectively. The third search term, Sleep monitoring quality AND quantity search term yielded 75 results and required limits which included 2002-2013, English, Humans, RCT, Clinical trails & Reviews. Limits used narrowed the results to 8 articles. Due to the lack of research regarding Sensewear and monitoring of sleep only one article from all searches was deemed relevant to this review due to the inclusion criteria outlined below. A second article located on Bodymedia�s website ( HYPERLINK "http://www.bodymedia.com/" www.bodymedia.com/) focused at addressing the validation of Sensewear for sleep monitoring. The article is an internal study and not published under any scientific journal. Table 1 shows the yield from each strategy using the above search string and limits set for stage one. 
Inclusion criteria:
Bodymedia�s Sensewear used to measure sleep 
Sensewear results compared to an establish objective measure

Stage two search terms included Subjective sleep assessment AND objective sleep assessment. Terms yielded 385 results. Limits used include 2002-2013, Humans, English and RCT. After limits used results yielded 31 articles, none of which were deemed applicable due to the inclusion criteria outlined below. Sleep assessment questionnaire AND polysomnography (actigraphy) terms yielded 39 results. Limits used included Human and English. After limits the search was narrowed to 33 articles. The search included 14 duplicate hits from the previous search. Only one of which was included in the review due to the selection inclusion criteria outline below. The Sleep quality AND subjective measures AND objective measures terms resulted in 328 articles. Limits used included Humans, English, 2002-2013 & RCT. This narrowed the results to 54 articles 44 of which were duplicate hits from previous searches with none satisfying the below inclusion criteria. Table 3 shows the yield from each strategy using the above search string and limits set for stage one. 


Inclusion criteria:
Direct statistical analysis between objective and subjective sleep data
Sleep subjective and objective data compared from the same nights 

Stage three search terms included Sleep quality AND athletes terms which yielded 38 results. Limits used included 2002-2013, Humans & English. After limits search was narrowed to 24 articles. From these 24, 2 were deemed relevant for selection when considering inclusion criteria outlined below. 
Sleep measurement AND athletes search terms yielded 15 results. Limits as described above narrowed the search to 9, all of which doubled with the previous search terms. Athletes AND (sleep quality OR sleep quantity) yielded 40 results, after apply limits as described above the search was narrowed to 24 with 2 duplicate hits from the previous searches. The 2 duplicates were the articles that had met the inclusion criteria from the first search terms used. Table 5 shows the yield from each strategy using the above search string and limits set for stage one. 
Inclusion criteria:
Must be an athletic population 
Sleep monitoring included at least one objective measure 

The outcome of selection process resulted in the following:
Sensewear validation studies; 2 articles
Correlation studies between subjective and objective sleep data; 3 articles
Sleep monitoring on an athletic population; 2 articles
Each article was then critically appraised (Tables 2,4,6) using the McMasters critical appraisal tool for quantitative research (Appendix 1) or cohort design specific CASP critical appraisal checklist to evaluate the quality of the study (Appendix 2). 

Results 
Tables 2,4 & 6 highlight the key aspects of each paper within its focus area. In total only 7 articles met the criteria to be included in the review. Two articles were sourced addressing the validation of the BSU. Three articles were identified addressing the association between subjective and objective sleep data. Two articles were identified addressing sleep in athletic populations.
Overall there were no studies representing level I evidence, two studies represented level II evidence a cohort study and a cross sectional design, while the remaining 5 studies were a correlation design and represented level III evidence. Study design and level were determined for each article base on the Scottish Intercollegiate Network (SIGN) evidence based clinical guidelines for the nation health service  HYPERLINK \l "_ENREF_18" \o "Harbour, 2001 #69"  ADDIN EN.CITE <EndNote><Cite><Author>Harbour</Author><Year>2001</Year><RecNum>69</RecNum><DisplayText><style face="superscript">18</style></DisplayText><record><rec-number>69</rec-number><foreign-keys><key app="EN" db-id="x0fz5zsse9av5vew92s5t0wcrrxs5az0tsav">69</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Harbour, Robin</author><author>Miller, Juliet</author></authors></contributors><titles><title>A new system for grading recommendations in evidence based guidelines</title><secondary-title>BMJ: British Medical Journal</secondary-title></titles><periodical><full-title>BMJ: British Medical Journal</full-title></periodical><pages>334</pages><volume>323</volume><number>7308</number><dates><year>2001</year></dates><urls></urls></record></Cite></EndNote>18 (Appendix 3). Between studies there was a total of 5697 participants (note 5407 came from a single study), mean ages of the participants was not able to be determined as only 3 out of the 7 studies included specific age information. Between studies average nights monitored included 283 (note 266 of those night came from a single study).

Table 1: Search strategy for aim 1) reliability and validity of the Bodymedia Sensewear Unit
Table 2: Yield for Bodymedia Sensewear Unit validity and reliability studies
Table 3: Search strategy for aim 2) the correlation between subjective and objective sleep data
Table 4: Yield for Studies that examine correlations between subjective and objective sleep data
Table 5: Search strategy for aim 3) review the literature that examines sleep in athletic populations
Table 6: Yield for studies that examine sleep in an athletic population
Discussion 
Sensewear validity and reliability research: Level III evidence, Table 2
Two articles, one internal unpublished paper and one external published paper were sourced addressing the validation of Bodymedia�s Sensewear product. 
Sunseri et al., (2009) reported on 35 subjects between three patient groups. The two main patient populations in the study are considered here. Population one (n=15) included normal sleeper, no further age parameters and or subject information was recorded. Sensewear data was plotted against subjective questionnaire data over one night. While the authors stated a 93.2% accuracy of Sensewear in comparison to the subjective questionnaires no other details were given. In population two (n= 10) Sensewear data was plotted against PSG & video monitoring. Data collection was via the researches and not a neutral source and therefore contains bias. Sensewear outcomes comparative to the gold standard PSG were as follows: Sedentary activities 91.9% accuracy, True negative identification of wakefulness 92.8%, True positive identification: sleep/sedentary activities 87%, PSG minute-by minute agreement with Sensewear armband 85.3%, Overall prediction of sleep 99.1%, Overall minute-by-minute prediction of wake 50.5%. PSG Data intervals were in 30-second epochs, while Sensewear data recorded in 1-minute epochs and required to be split to match for subsequent analysis. The authors then went onto concluded Sensewear is a valid and reliable means of testing sleep parameters. However the McMasters University critical appraisal tool for quantitative research highlighted major floors in the research. The authors did not describe the sample size in detail nor did the authors justify the sample size in each of the sub groups. Ages, sex, recruitment strategies, inclusion and exclusion criteria were not stated. Outcome measures within population one were not valid given that previous research demonstrates a poor correlation between subjective and objective sleep data [12-15]. Outcome measures were valid for population two. The research is unreliable as it is of a poor design, has clear bias and remains unpublished externally.
O�Driscoll et al., (2012) published externally addressing the validation of Sensewear as a sleep monitoring device.  The authors reported on 50 patients, 18 years and older referred for sleep disorder investigations. Patients were monitored overnight with PSG and Sensewear armband. PSG Data intervals were recorded in 30-second epochs, while Sensewear data recorded in 1-minute epochs and required to be split to match for subsequent for analysis. Analysis of data found epoch-by-epoch agreement rate (79.9	 �	 1.6%), Sensewear sleep (sensitivity, 88.7	 �	 1.5%) Sensewear wake (specificity 49.9	 �	 3.6%). The authors went on to conclude the Sensewear armband provides a reasonable estimation of sleep but a poor estimation of wake. This study was superior than the internal validation study competed by the Bodymedia company, but found similar results as Sunseri et al., (2009). The study scored well on the McMasters University critical appraisal tool for quantitative research (Table 1). There is a clear lack of research evaluating the effectiveness of the BSU to monitor sleep. 
This review yielded only 2 relevant articles, only the later of which has clinical relevance. From the limited research the BSU is an accurate substitute from PSG for monitoring sleep quality but has a poor ability to detect wake. 

Association between subjective and objective sleep data: Level II and Level II evidence, Table 4

Three articles were identified addressing the association between subjective and objective sleep data. Unruh et al., (2008) reported on 5407 Community dwelling adults mean age 63, range 45�99 (52% women) comparing PSG and subjective sleep questionnaires over one night. Sleep symptoms were obtained using a 5-point Likert scale and Epworth Sleepiness Scale (ESS). PSG Data intervals were in 30-second epochs. Strength of association between subjective and objective data was determined by Spearman�s correlation coefficients and yielded a low to moderate correlation coefficient (0.06-0.32). The Authors went on to conclude a low to moderate correlation between self reported sleep and objective measures. The study is of good quality when assessed against CASP Cohort appraisal checklist (Table 2). 

Moser et al., (2010) reported on 28 subjects, 13 males 15 females, mean age 53.3 � 21.3 years, range 21�86 years, comparing subjective sleeping habits compared to PSG data over 2 days. PSG Data intervals were in 30-second epochs. Subjective sleep information was obtain using a number of subjective measures; standardized self-rating scale for sleep and awakening quality (morning post), four different visual analog scales (VAS) morning and evening, drive (active�passive), mood (cheerful�sad), affectivity (interested�uninterested) and drowsiness (wake�tired) were presented to the subjects. Finally a Self-rating scale of wellbeing was completed the morning post each night. Paired t-test was used to test significant differences between data sets. When comparing subjective measures to one another, Student�s t-test showed significant differences  (T = 2.564; p = 0.016). The authors found significant correlations between objective stage wake and subjective somatic complaints (1st night: r = .592, p = 0.001; 2nd night: r = .546, p = 0.003) but concluded all other correlations between objective and subjective variables were not consistent over both nights. The study is of good quality when assessed against CASP Cohort appraisal checklist (Table 2).

Kushida et al., (2001) reported on 100 sleep-disordered subjects, 69 men and 31 women mean age 49�14.7yrs. Authors compared data on number of awakenings, sleep efficiency (SE) & total sleep time (TST) from PSG, actigraphy and subjective questionnaires over one night. Actigraphy and PSG data were recorded in intervals were in 30-second epochs, with the subjective sleep questionnaire completed the following morning. Paired t-tests, non-parametric sign tests, Pearsons correlations & Bonferroni probabilities were used to determine any statistical significant differences between data sets. No significant difference TST & SE (P=<0.05) PSG vs. combined actigraphic/Subjective data was found however a significant difference was found (P=<0.05) for Subjective vs. PSG number of awakenings (31�20.2). The authors went onto conclude subjective sleep reports markedly underestimate the number of awakenings. The study is of good quality when assessed against CASP Cohort appraisal checklist (Table 2).
The research identified does not examine subject and objective data correlations as a primary aim nor do they use a common method to extract subjective data. Collectively the research shows poor correlations between subjective and objective sleep data.

Sleep monitoring in an athletic population: Level II and Level III evidence, Table 6 
Research addressing sleep in an athletic population is limited to two articles in this review. Richmond et al. (2004) reported on 10 professional AFL players duration of sleep (SLD), sleep efficiency (SE), number of wakings (NW) and total wake time after sleep onset (WT) with actigraphy and subjective sleep quality (SQ) assessed with rating scale over 5 non game nights for baseline measurements. A second sample of the same population was then taken over 3 nights, (N1, N2 & N3) with the game on the second night. Actigraphy was recorded in 1 minute epochs with subjective sleep quality assessed during the night and morning post. One-way repeated ANOVEAs compared to baseline showed, SLD on N1 was increased when home and away (by 51 and 105 mins respectively, p<0.05), while all other measures of sleep pattern were unchanged. On N2, SLD was decreased to a similar degree whether home or away (by 68 and 64 mins respectively, p<0.05), while all other measures of sleep pattern were unchanged. By N3 all measures of sleep pattern had returned to baseline values. Relative to baseline, Pearson�s correlation coefficients showed perception of SQ was worst on N2 of a home game. The authors went onto conclude that interstate travel by elite AFL players has no adverse effects on sleep pattern on the night before a game. Study limitations included lack of information around the subject population and justification of sample size. The study scored well on the McMasters University critical appraisal tool for quantitative research (Table 3). 
Leeder et al., (2012) reported on 67 subjects. Group 1 consisted of 47 athletic subjects monitored over 4 days in a home environment. Group 2 consisted of 20 control subjects data obtained from University of Surrey Clinical Research Centre. Actigraphy was scored in one-minute epochs. One-way ANOVEA and two-way analysis of variance (athlete vs. control and gender effect) indicated a significantly less time asleep seen in the athletes compared to the controls (p=0.27). SPSS measures for centrality and spread showed a significant effect of gender on �time awake� (mean difference: 12 minutes higher in males; 95% likely range: 3 to 21 minutes) and �sleep efficiency� (mean difference: 2.4 lower in males; 95% likely range: 0.1 to 4.8). Athletes showed poorer markers of sleep quality than an age and sex matched non-athletic control group (Sleep efficiency: 80.6+6.4% and 88.7+3.6%, respectively, Fragmentation Index: 36.0+12.4 and 29.8+9.0, respectively). The authors concluded that while athletes remained within the range for healthy sleep, poorer characteristics of sleep are seen in the athlete group. The study is of good quality when assessed against CASP Cohort appraisal checklist (Table 3).
The limited research into athletic populations and sleep parameters suggests a) interstate travel does not significantly influence normal sleep patterns b) players tend to sleep more the night before a game and less the night of a game and c) generally poorer characteristic of sleep quality are seen in the athletic population. These findings should be considered with caution given the variations in populations, research design and limited research in the area.


Conclusion 
The current literature review and critical appraisal identified 7 relevant articles (2 cohort design & 5 correlation design). In conclusion we found 1) The BSU to be a reliable means of monitoring sleep but provides a poor ability to detect wakefulness, the current research lacks information addressing recruitment of patients and patient details (Table 2). 2) Poor correlations between subjective and objective data, no common subjective measure were used and populations varied between studies (Table 4). 3) Athletes while within healthy ranges have poorer sleep patterns than the general population; compilation of data was not possible due to the different populations, aims and measurements of the studies (Table 6). 
This literature review and critical analysis has identified that the BSU offers a practical and reliable option for monitoring sleep in athletic populations. Subjective sleep data extracted from athletic populations should be interpreted with caution given the established poor correlation between subjective and objective sleep data. There is a lack of research addressing the monitoring of sleep parameters in athletic populations, which is of concern given the multiple establish effects of sleep deprivation. 

References:
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13.	Kushida CA, Chang A, Gadkary C, Guilleminault C, Carrillo O, Dement WC. (2001) Comparison of actigraphic, polysomnographic, and subjective assessment of sleep parameters in sleep-disordered patients. Sleep Medicine 2: 389-396.
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19.	Harbour R, Miller J. (2001) A new system for grading recommendations in evidence based guidelines. BMJ: British Medical Journal 323-334.










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����������������������]��^���gd�o�h�"]�h^�"gd-��h]�hgd-�����&`#$gd�,�(�|. ��A!�"�#��$��%�������.:pg���. ��A!�"�#��$��%�������|	D<EndNote><Cite><Author>Walker</Author><Year>2004</Year><RecNum>4</RecNum><DisplayText><style face="superscript">3, 4</style></DisplayText><record><rec-number>4</rec-number><foreign-keys><key app="EN" db-id="x0fz5zsse9av5vew92s5t0wcrrxs5az0tsav">4</key></foreign-keys><ref-type name="Journal Article">17</ref-type><contributors><authors><author>Walker, M. 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