Sleep Medicine 12 (2011) 209–214

Contents lists available at ScienceDirect

Sleep Medicine

journal homepage: www.elsevier .com/locate /s leep

Review Article

Normal sleep in African-Americans and Caucasian-Americans: A meta-analysis

Megan E. Ruiter a,⇑, Jamie DeCoster b, Lindsey Jacobs a, Kenneth L. Lichstein a

a Department of Psychology, The University of Alabama, Tuscaloosa, AL 35487, USAb Institute for Social Science Research, The University of Alabama, Tuscaloosa, AL, USA

a r t i c l e i n f o

Article history:Received 8 September 2010Received in revised form 10 November 2010Accepted 29 December 2010

Keywords:EthnicityMeta-analysisNormal sleepAfrican-AmericansCaucasian-AmericansPolysomnography

1389-9457/$ - see front matter Published by Elsevierdoi:10.1016/j.sleep.2010.12.010

⇑ Corresponding author. Tel.: +1 205 348 4962; faxE-mail address: meruiter@crimson.ua.edu (M.E. Ru

a b s t r a c t

Objective: This meta-analysis was designed to estimate the average magnitude of ethnic differencesbetween African-Americans and Caucasian-Americans in normal sleep and to identify moderators ofthese differences.Methods: Included studies had to have (1) sufficient information to estimate the difference between Afri-can-Americans and Caucasian-Americans on measures of subjective or objective sleep, (2) adult samples,and (3) samples of normal sleepers. Fourteen studies representing 1010 African-Americans and 3156Caucasian-Americans aged 18 years and older met these criteria.Results: Significant ethnic differences were found, with mean effect sizes ranging from �.23 to .57. Afri-can-Americans had poorer sleep continuity and duration, less slow wave sleep, and a greater proportionof stage 2 sleep. Differences in sleep continuity and duration variables were moderated by several biopsy-chosocial factors, whereas sleep architecture differences were not influenced by any examined moderat-ing factor.Conclusions: African-Americans slept worse objectively and subjectively than Caucasian-Americans.Sleep continuity and duration were moderated by biopsychosocial factors whereas sleep architecturewas not. Implications and future research are discussed.

Published by Elsevier B.V.

1. Introduction

Ethnicity is a common contributing factor to differences inhealth; however, its influence on normal sleep patterns is not fullyunderstood. Research on the influence of ethnicity on sleep in nor-mal individuals has increased in the last 10 to 15 years, primarilyfocusing on comparisons between African-Americans (AA) andCaucasian-Americans (CA), although the findings have not alwaysbeen consistent. One recent review [1] suggested that AA sleepworse both objectively and subjectively than CA. AA have beenshown to have longer sleep onset latencies [2,3], poorer sleep qual-ity [2–4], lighter sleep [5–8], and less deep sleep [5–7,9,10]. How-ever, other studies found contradictory results. One studyindicated that there were no differences in reported trouble initiat-ing sleep and maintaining asleep between the two ethnic groups[4], whereas another indicated that CA reported greater trouble ini-tiating and maintaining sleep [11]. There is also debate over ethnicdifferences in REM measures such as REM latency, density, andamount [4–8]. Overall, there do appear to be some differences be-tween AA and CA in normal sleep, although what aspects of normalsleep differ and the extent of those differences have not beenresolved.

B.V.

: +1 205 348 5784.iter).

Ethnic differences in the experience of normal sleep are likely tobe affected by moderating factors such as psychosocial stressors[10], health care utilization [12], health behaviors, psychiatricand medical disorders [12], anatomy [13], gender, and age. Ethnicdifferences and their moderating factors may have potential impli-cations for the definition of normal sleep, how ethnicity affects thisdefinition, the prevention of disordered sleep, and the develop-ment of clinical goals for the treatment of disordered sleep. In or-der to establish the existence and magnitude of ethnic differencesin normal sleep patterns, a meta-analysis was conducted to consol-idate results from the published literature [3,5–10,13–18]. Bothobjective and subjective sleep variables were examined. The stud-ies were executed across different settings and samples. The goalsof this study are to better define the normal sleep experienceacross ethnic groups and to determine whether the ethnic differ-ences are moderated by relevant biopsychosocial factors. Thisstudy was approved by The University of Alabama Institutional Re-view Board (#EX-07-CM-029).

2. Methods

2.1. Literature search

The purpose of the literature search was to extract the popula-tion of studies that have investigated the relationship between eth-

210 M.E. Ruiter et al. / Sleep Medicine 12 (2011) 209–214

nicity and sleep in normal sleepers. Several computerized dat-abases with published and non-published studies were examinedincluding PsycINFO (1887–May 2009), Dissertation Abstracts Inter-national (1861–May 2009), PubMed (1948–May 2009), Medline(1950–May 2009), Web of Science (1985–May 2009), SumSearch,Tripdatabase, Google Scholar, and Cochrane. The search procedurepaired terms related to CA or AA ethnic identity such as white, Cau-casian, black, African American, ethnicity, and race with sleep-relatedterms such as sleep and normal sleep. The reference sections of theprevious review [1] and articles matching inclusion criteria for thepresent study were examined to identify more relevant studies.Hand searches of the journals Sleep and Behavioral Sleep Medicinewere conducted. The search included conference presentationsand abstracts, as well as dissertations not subjected to peer review;although only one such article was found that met study criteria.Authors with a significant volume of research devoted to sleepand ethnic research were contacted via email for unpublishedstudies and/or previously collected data. Fig. 1 represents thescreening procedures for inclusion into the meta-analysis and themain reasons studies were excluded.

2.2. Inclusion criteria

Studies included in the meta-analysis met the following crite-ria: (1) the study contained sufficient information to estimate thedifference between African-Americans and Caucasian-Americanson measures of subjective or objective sleep. (2) The study usedan adult sample. (3) The study investigated normal sleepers as de-fined by the absence of suspected or diagnosed sleep disorders.Subjective measures of sleep were extracted from sleep diaries orretrospective questionnaires regarding recent sleep patternsexcluding for bedtimes and wake times. Objective sleep measureswere defined as any sleep variable extracted from polysomnogra-phy (PSG) or actigraphy.

(n = 2)

(n = 7)

Potentially relevant studies identified and screened for retrieval (n = 1,785)

Studies retrieved for more detailed evaluation (n = 31)

Studies excluded, with reasonsa

(n = 1,754)

Studies excluded, with reasonsb

(n = 9)

Potentially appropriate studies to be included in the meta-analysis (n = 23)

Studies excluded from meta-analysis, with reasonsc

Studies included in meta-analysis (n =16)

Studies withdrawn, by outcome, with reasonsd

Studies with usable information by outcome (n = 14)

Fig. 1. Flow chart of screening studies for inclusion into the meta-analysis.

2.3. Coding effect sizes and moderators

Two independent reviewers (M.E.R. and L.J.) coded all effectsizes and moderator variables. Disagreements were resolved byreferring to the original data source. If this did not resolve the is-sue, a third independent reviewer was consulted (K.L.L.). If a studydid not have sufficient information to make an informed decisionabout the study’s quality or to calculate its effect size, the primaryauthor was contacted for additional information. If the primaryauthor could not be contacted, then the criterion in question wascoded as unavailable for that particular study.

Effect sizes were calculated for each study representing ethnicdifferences on objective and subjective total sleep time (TST), sleepefficiency (SE), sleep-onset latency (SOL), wake after sleep onset(WASO), REM percentage, stage 1 percentage, stage 2 percentage,slow wave sleep percentage (SWS), and sleep-disordered breathing(RDI/AHI/ODI4). The mean age of each study sample, the percent-age of women in each study sample, and mean body mass indexwere coded as continuous moderators. The location of sleeprecordings (i.e., home vs. laboratory); the presence or absence ofadequate psychometric data for subjective sleep measures; pres-ence or absence of weight restrictions as part of a study’s inclusioncriteria; presence or absence of screening and exclusion for mentaldisorders; presence or absence of full-time employment as part ofa study’s inclusion criteria; and the presence or absence of anymedication use were coded as dichotomous categorical modera-tors. The inter-rater reliability estimates, as determined by intra-class correlations for continuous moderators and Cohen’s kappafor categorical moderators ranged from .769 to 1.00 with a medianof .908.

2.4. Calculation of effect sizes

In this meta-analysis, the effect sizes were computed as theunbiased standardized mean difference between the two ethnicgroups, Hedges g⁄ [19]. Effect sizes were calculated with the assis-tance of Comprehensive Meta-Analysis [20], a software packagedeveloped by Biostat. Positive effect sizes indicated that AA hadhigher values than CA on the corresponding sleep variable. Formost studies, effect size calculations were based on reportedmeans and standard deviations. Effect size calculations for studiesthat did not provide these statistics were computed through otherstatistical tests such as mean differences and p values. When stud-ies reported more than one value for a sleep variable (such as whenstudies reported separate values for weekdays and weekends), thevalues were averaged to provide a single effect size.

2.5. Analyses of effect sizes

The mean weighted effect size was calculated for each of thesleep variables, where the weight for each study was calculatedas the inverse of the variance of its effect size. In addition, hetero-geneity analyses were conducted to determine significant variationin the effect sizes beyond what would be expected from samplingerror. The heterogeneity statistic Q was used to estimate this var-iability. Q follows a chi-square distribution with k � 1 degrees offreedom, where k is the number of effect sizes. A significant valueof Q indicates that the effect sizes significantly vary between stud-ies, suggesting the need for moderator analyses to explain the var-iability. Cohen’s [21] guidelines were used to interpret thestrengths of the effect sizes.

Once the mean effect sizes were computed, fail-safe N calcula-tions [22] were conducted for those that were significant. The pur-pose of fail-safe N calculations is to determine the potential impactof publication bias by determining the number of nonsignificant,unpublished studies and future studies that would need to be

M.E. Ruiter et al. / Sleep Medicine 12 (2011) 209–214 211

added to the meta-analysis to reduce the significant effect sizes tonon-significance. This set of procedures provides a denotation ofthe stability of the results.

3. Results

3.1. Descriptive analyses

Fourteen studies with non-overlapping samples were included.This analysis assessed 1010 AA and 3156 CA with ages rangingfrom 18 to 98 years. The studies were all from peer-reviewed jour-nals, with the exception of one abstract submitted for viewing at anational conference. The median publication date of the studieswas 2003. The approximate mean age of the participants was48.5 years, and 54.1% of the participants were women of the stud-ies that reported these data. All of the studies were conducted inthe United States. Ten studies provided information on objectivesleep measures (see Table 1 in online Supplementary material).Nine studies used PSG and one used actigraphy. Six studies pro-vided information about self-reported sleep experiences (see Ta-ble 1 in online Supplementary material). From these studies,numerical data on sleep duration were extracted. Histograms ofthe effect sizes for each sleep variable were examined for possibleoutliers, but none were observed.

The definition of ‘‘normal sleepers’’ varied between studies andwere mostly determined through exclusionary criteria that focusedon mental and physical health status, as well as diagnosed and sus-pected sleep disorders. Some studies excluded all major medicalconditions and diagnosed psychiatric disorders, whereas othersonly excluded certain medical conditions, screened for psychiatricconditions but did not exclude them, excluded only sleep disor-ders, or did not report screening for any comorbidities. For the pur-poses of this review, normal sleepers were defined as participantsselected for participation because they were without any previ-ously suspected or diagnosed sleep disorders. Certain studies[5,8,9,13] over-sampled or specifically selected patients at highrisk for sleep disorders. In those studies, only the data from controlgroups were used, and/or data only from participants who hadundergone PSG and were free from sleep disorders after being in-cluded in the study.

In meta-analysis, researchers can choose whether to treat astudy as either a fixed or a random effect in their analyses.Fixed-effects analyses are more powerful but the interpretationsof the results can only be applied to the studies included in themeta-analysis [19]. In contrast, random-effect analyses allow theresults to be generalized to a broader population of studies fromwhich the meta-analysis was drawn [19]. Although random-effectsanalyses are often preferable, due to the small number of studiesthat constitute each effect size we have decided to use fixed-effectsanalyses. Our small sample does not allow us to accurately esti-mate the random effect of study, and the reduced power inherentin random-effect analyses would make it difficult to draw anyinferences from our results.

3.2. Mean effect sizes

Table 2 (in online Supplementary material) provides a summaryof the effect sizes for each sleep variable measured and reported byat least four studies. The table includes the weighted mean effectsize, a 95% confidence interval around the weighted mean effectsize, the number of studies and participants involved, the standarderror for each effect size, the Q values of the homogeneity tests, thefail-safe N for each effect size, and the overall mean difference be-tween the two groups in the original metric. Subjective TST wasthe only self-reported sleep variable that could be analyzed be-

cause there were too few studies that reported group differencesin the other measures of the subjective experience of normal sleep.There was a small weighted mean effect size of �.23 for self-re-ported TST, indicating that AA report shorter sleep durations thanCA. Based on the low fail-safe N, the relative stability of the meaneffect size should be interpreted with caution. The significanthomogeneity test suggests that there may be several underlyingfactors that are affecting this difference.

TST and SE mean weighted effect sizes were �.48 and �.54,respectively. These effect sizes can be descriptively classified assmall and medium effect sizes, respectively. A small-to-mediumeffect size for SOL of .39 was also found; however, there was no sig-nificant effect size difference for WASO. Overall, these results indi-cate AA objectively have worse sleep continuity and duration thanCA. However, the fail-safe N for SOL was low and therefore its sta-bility should be interpreted with caution. The homogeneity testswere found to be significant for all of the significant effect sizes,and therefore indicate there may be factors that moderate themagnitude of these differences. There were no significant differ-ences between AA and CA on sleep-disordered breathing events.

The weighted mean Cohen’s g⁄ for SWS percentage and for stage2 percentage were �.55 and .57, respectively. Both of these effectsizes can be descriptively classified as medium effect sizes indicat-ing that AA have a smaller proportion of SWS than CA and a greaterproportion of stage 2 sleep than CA. The data for stage 2 percentagewere not found to be significantly heterogeneous, whereas the datafor SWS percentage were found to be significantly heterogeneous.There was no significant difference between AA and CA for REMpercentage or stage 1 percentage.

3.3. Moderators of effect sizes

Significant heterogeneity was found in the effect sizes for objec-tive TST, subjective TST, sleep efficiency, SOL, and SWS percentage.Moderator analyses were performed on these measures for all pre-viously mentioned moderators (see Table 3 in online Supplemen-tary material). The results of the analyses for categorical andcontinuous moderators can be found in Tables 4 and 5 (in onlineSupplementary material), respectively. Categorical moderatorswere analyzed by partitioning the homogeneity statistic Q into var-iability in the effect size explained by the moderator, QB, and var-iability in the effect size not explained by the moderator, QW.

Continuous moderators were analyzed using meta-analytic regres-sion procedures to determine the significance of the relationshipbetween the effect size and the moderator.

3.4. Objective total sleep time

The relationship between ethnicity and objective TST was notmoderated by age, gender or BMI. In concert with the BMI finding,studies that included only participants who were within their idealbody weight produced smaller effect sizes; nonetheless, the differ-ence between groups remained significant. The following modera-tor factors reduced the effect size difference between AA and CA tonon-significance: when the sleep recordings was conducted in alaboratory rather than in the home, when participants were em-ployed full-time, when studies that allowed participants with pos-sible mental illness were excluded, and when all medication wasexcluded from the studies.

3.5. Subjective total sleep time

In contrast with objective TST, the magnitude of the ethnic dif-ference in subjective TST was moderated by age, gender, and BMIsuch that studies with more female participants and participantswith higher BMIs demonstrated a larger ethnic difference, whereas

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studies with larger proportions of elderly participants exhibited asmaller ethnic difference. The adequacy of the psychometric datafor measurement of subjective TST did not explain a significantamount of variability in the effect size for subjective TST.

3.6. Sleep efficiency

Moderator analyses for ethnic differences in SE revealed thatgender moderated the ethnic difference, such that as the propor-tion of women in the studies increased, the magnitude of the dif-ference between AA and CA also increased. Similar to what wasfound with objective TST, the effect size difference was reducedto non-significance when the location of the sleep evaluation wasin a laboratory and when studies that allowed participants withpossible mental illness were excluded. Use of any type of medica-tion also accounted for a significant amount of variability in the ef-fect size for SE, such that the studies that included participantstaking medications produced larger effect sizes; but the differencestill remained significant when these studies were excluded. Final-ly, employment status and study-imposed weight restrictions didnot explain a significant amount of variability in the effect sizefor SE.

3.7. Sleep-onset latency

In concert with moderator analyses on SE, the effect size for SOLrevealed that gender moderated the ethnic difference such that asthe greater proportion of women the studies increased, the magni-tude of the difference between AA and CA also increased. Studiesconducted in the laboratory, studies that excluded participantswith possible mental illness, and studies that excluded participantsusing medications did not show significant differences between AAand CA on SOL. Studies including participants who were employedfull-time had smaller but still significant effect sizes, as did studiesincluding participants who were within their ideal body weight.

3.8. Slow wave sleep percentage

The effect size difference between AA and CA on SWS percent-age was not moderated by any factors analyzed within this review.

4. Discussion

Consistent with a previous review [1], the present meta-analy-sis concluded that normal-sleeping AA do not sleep as well as nor-mal-sleeping CA, particularly when sleeping at home in theirnatural environment. AA have shorter TST both objectively andby self-report than CA. AA have poorer SE and longer SOL thanCA. AA also have less SWS percentage and greater stage 2 percent-age than CA. WASO, REM percentage, stage 1 percentage, and mea-sures of sleep-related disordered breathing did not significantlydiffer between AA and CA. The significant effect sizes from thepresent study ranged between small to medium effects.

The effect size differences in subjective TST, SOL, and SE werestronger with samples comprising higher proportions of women.This is consistent with the previous literature [23], which hasshown that AA women have self-reported obtaining significantlyless TST than CA women. Despite this, it is still unclear why gendermoderates sleep across these ethnic groups or if AA women mayrepresent a vulnerable population for the emergence of abnormalsleep patterns. Potential unidentified social and cultural factorsthat explain the moderation of these effect sizes are socio-eco-nomic status [24] and neighborhood context [25].

Study samples with more elderly subjects exhibit a smaller dif-ference between AA and CA for subjective TST. The self-reported

ethnic difference appears to diminish as the average age of the par-ticipants increases. This result is compatible with prior researchthat concluded older AA report having significantly more TST thanolder CA [3]. Two hypotheses have been proposed to explain thisresult. The first hypothesis suggests older AA tend to appraiseany health complications they experience in a more positive man-ner than CA, leading to an underreporting of sleep disturbances[26,27]. The second hypothesis suggests that because AA have low-er life expectancies than CA, AA who survive to an advanced ageare more likely to engage in adaptive health behaviors, reducingthe differences in sleep disturbances [1]. Given the present analy-sis, the first hypothesis may be particularly relevant since objectivedifferences in TST and SE are not moderated by age despite self-re-ports to the contrary.

Ethnic differences in objective TST, SE, and SOL disappearedwhen studies examined participants in the lab. One possible inter-pretation of this result is the sleep environments of AA may bemore disruptive than that of CA, so that the differences disappearwhen the sleepers are examined in a foreign but neutral andnon-disruptive sleep setting, like a laboratory. If this is the case,it may highlight the need for greater public health education ofsleep hygiene, particularly in AA, or a greater investigation of thesleep environment in the context of socio-economic status. How-ever, the present analysis also showed that the ethnic disparityin SWS is not significantly moderated by study location, suggestingsleep architecture and perhaps sleep quality are immune to theshielding effect of a common sleep environment. This is in contrastto one previous study that documented an ethnicity-by-location ofsleep study interaction for SWS amount [7]. This study found thatAA have more SWS at home than in the laboratory, whereas theopposite result was found for CA.

Studies that included participants with lower BMI scoresshowed significantly smaller ethnic disparities in subjective TSTbut the data also suggest that there are a number of other factorsthat explain a significant amount of the variability in subjectiveTST. Participants within their ideal body weight also showed smal-ler but nonetheless significant ethnic differences on SOL relative tostudies with participants with mixed weights. Obesity has beenfound to be associated with shorter sleep duration in both self-re-ported [28] and actigraph measurements in the general population[29,30]. Also, epidemiological data have revealed ethnic disparitiesin obesity, such that African-Americans have a higher prevalencerate than CA [31]. These two findings may explain the mild moder-ating effect of obesity on subjective TST found in the current meta-analysis. AA represented in the studies may have had a higherobesity prevalence than CA, which consequently resulted in short-er self-reported sleep durations.

Full-time employment did significantly moderate the ethnicdifference in the amount of objective TST. Ethnic differences inSOL were also smaller among participants with full-time employ-ment but still significant. When the hours of work per week foreach ethnic group are essentially equalized, sleep and wake pat-terns are more likely to be similar as well. Alternatively, employ-ment status has been viewed as a rough correlate of activitylevel [28]. Therefore, it may not be the employment status in itselfthat is affecting the resulting ethnic sleep difference, but perhapsthe higher activity level implicit in employment.

Excluding studies with participants unscreened for mental ill-ness resulted in nonsignificant ethnic differences for TST, SOLand SE. Although none of the studies likely included participantswith severe mental illnesses because the participants were alldrawn from non-clinical populations, mild to moderate depressionand anxiety may have been present and impacted ethnic differ-ences in sleep parameters. Depression and anxiety symptoms havebeen related to sleep disturbance and changes in sleep architecture[32,33]. Research on ethnic differences in sleep parameters among

M.E. Ruiter et al. / Sleep Medicine 12 (2011) 209–214 213

clinically depressed populations found AA had less TST, more stage2 sleep, less SWS, less REM sleep, longer REM sleep latency, andlonger sleep-onset latency compared to CA even when other psy-chosocial factors were controlled [34,35]. The present results implythat possible mild mental illness within the included studies cansignificantly moderate the ethnic differences in sleep continuityand duration, but may not be severe enough to moderate sleeparchitecture as has been found in the literature. The review alsosuggests the typical profile of normal sleep and sleep disturbedby depression, historically defined by mostly CA participants,may not fully apply to AA.

Finally, excluding participants using prescription medicationsof any kind resulted in nonsignificant ethnic differences betweenAA and CA for objective TST and SOL, and reduced the differencein SE. There is large intra-individual variability in the therapeuticeffects, pharmacokinetics, and pharmacodynamics of medicationsthat disrupt sleep as a side effect. Some of this variability has beenpartially explained by ethnic differences in rates of metabolismwhether due to genetic or lifestyle factors or both [36]. One inter-pretation of the current results is that AA may be more sensitive toside effects of these agents affecting sleep than the CA. However,careful control or measurement of medications taken by partici-pants would be needed to substantiate this interpretation.

There were some limitations to the present analysis. First, therewere several studies that did not have enough data to calculate ef-fect sizes or did not report information on the ethnic compositionsof the populations sampled. Consequently, these studies were ex-cluded from the analysis. Second, many of the studies reportingobjective sleep measures recruited participants from the southernCalifornia region. Therefore, the effect sizes on these measures maybe disproportionately influenced by ethnic differences in that re-gion and may not generalize to other parts of the United States.Third, the self-reported sleep data were primarily measured usingone-point retrospective questionnaires with unknown psychomet-rics; effect sizes on subjective TST could be underestimated.Fourth, there are likely many other moderating factors, such as so-cio-economic status (SES), sleep environment variables and sleephygiene behaviors that were not reported by a sufficient amountof the studies and thus could not be analyzed. SES, in particular,is likely to be a significant mediator of ethnicity sleep disparitieson a variety of sleep variables based on the results of two recentstudies [37,38] that found interactions between SES determinantsand ethnicity on sleep quality and complaints. One notable interac-tion between race and class on sleep quality was that racial dispar-ities between AA and CA in sleep quality was attenuated in low SESAA when income, education, employment and health status wereall considered, but remained significant in AA with moderate tohigh SES [38]. This finding further highlights the need to investi-gate other moderating factors within ethnic-class groups.

The main implication of the meta-analysis is that ethnicity is animportant factor when examining normative sleep, the relation-ship between sleep and health, and the perception of sleep. Under-standing the role of sleep and health from a multiethnicperspective may inform clinical decision making in the preventionand treatment of sleep disturbance. An example of preventingsleep disturbance from a multiethnic approach might be to exam-ine why perceptions of TST differ between normal-sleeping AA andCA to see if these differing perceptions lead to the underreportingof developing sleeping problems and long-term healthcare dispar-ities. An example of treating sleep disturbance from a multiethnicapproach might be to determine if and why certain ethnic groupsengage in different non-sleep promoting behaviors to seehow these differences affect cognitive-behavioral treatmentresponsivity.

Our meta-analysis revealed several gaps in this literature. First,there is a lack of data on ethnic differences on self-reported sleep

continuity and quality variables. Second, data on ethnic differenceson PSG-derived wake time and arousals were also limited and war-rant further investigation. Third, although the present meta-analy-sis revealed a nonsignificant difference in REM sleep percentagebetween AA and CA, there may be minor differences between AAand CA within specific age groups since REM proportion decreaseswith age [39]. It is also important to conduct further research onother REM indices such as REM latency, density and distributionbecause they may explain and impact cross-ethnic differences inprevalence rates of psychiatric disorders that have REM indicesas risk factors. Fourth, data on ethnic differences in sleep disorderssuch as insomnia, sleep apnea, narcolepsy and various sleep arou-sal disorders across the lifespan are lacking. Finally, ethnic differ-ences in treatment response to sleep interventions have not beeninvestigated, but need to be ascertained in order to provide betterhealthcare.

The verification that there are ethnic differences in normativesleep between African-Americans and Caucasian-Americans, par-ticularly when AA are sleeping in their home environment, is valu-able because it improves our understanding of possible healthdisparities and refines our expectations and standards of what is‘‘normal’’ sleep across ethnic groups. Overall, AA sleep worse thanCA on both PSG-derived and self-report measures. Biopsychosocialfactors moderated ethnic differences in sleep continuity, whereasnone of these factors moderated ethnic differences in sleep archi-tecture. Investigations on the relationship between ethnicity andsleep are still limited. Future studies should attempt to examinethe relationship between sleep and ethnicity directly. It is also rec-ommended these studies aim to include a wider variety of ethnicgroups. Finally, it may also be important to investigate the roleof ethnic identity and discrimination since it has been found to af-fect objective sleep [10].

Financial support

None.

Conflicts of Interest

The ICMJE Uniform Disclosure Form for Potential Conflicts ofInterest associated with this article can be viewed by clicking onthe following link: doi:10.1016/j.sleep.2010.12.010.

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