PEDIATRICS Vol. 118 No. 3 September 2006, pp. 1124-1132 (doi:10.1542/10.1542/peds.2005-3118)
ARTICLE |
Associations Between Sleep Problems, Anxiety, and Depression in Twins at 8 Years of Age
a Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, King's College London, London, United Kingdom
b Psychology Department, Goldsmiths College, University of London, London, United Kingdom
c Deparment of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
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ABSTRACT |
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OBJECTIVES. Associations between sleep and internalizing problems are complex and poorly understood. To better understand these covarying difficulties, genetic and environmental influences were estimated by using a twin design.
METHODS. Three hundred 8-year-old twin pairs reported on their anxiety and depression by completing the Screen for Childhood Anxiety Related Emotional Disorders and the Children's Depression Inventory. Parents reported on their children's sleep problems by completing the Child Sleep Habits Questionnaire.
RESULTS. Children reported by their parents to have different types of sleep problems self-reported more depression symptoms than those without. The correlation between total sleep-problem score and depression was moderate. That between sleep problems and anxiety was smaller and was not examined further. The association between sleep problems and depression was mainly explained by genes, and there was substantial overlap between the genes influencing sleep problems and those influencing depression. There was smaller influence from environmental factors making family members alike, and environmental factors making family members different decreased the association between sleep problems and depression.
CONCLUSIONS. A range of sleep difficulties are associated with depression in school-aged children, and the overall association between the 2 difficulties may be largely influenced by genes.
Key Words: sleep problems • anxiety • depression • twins
Abbreviations: ECHO—Emotions, Cognitions, Heredity and Outcome • TEDS—Twins Early Development Study • SES—socioeconomic status • CSHQ—Child Sleep Habits Questionnaire • CDI—Children's Depression Inventory • A—genetic influence • C—shared environmental influence • E—nonshared environmental influence • AIC—Akaike information criterion • CI—confidence interval
Sleep problems commonly co-occur with anxiety and depression (for reviews see refs 1 and 2) and can also be thought of as symptoms of these difficulties. Although these associations are widely acknowledged by clinicians and researchers alike, they are poorly understood, probably partly because of their complexity. One indication of this complexity comes from noting bidirectional associations between sleep and internalizing problems.3, 4 Furthermore, anxiety and depression may be associated with sleep problems in different ways. For example, the results of an epidemiologic study demonstrate a longitudinal association between sleep problems and anxiety but not depression.5 Similarly, it is possible that certain types of sleep problems, as compared with others, are more strongly associated with internalizing symptoms. Indeed, one study reported that internalizing problems are associated with bedtime resistance but not night waking.6 The use of different methods of assessment makes the picture more complicated still, because depression in children is regularly associated with sleep problems that are assessed subjectively (eg, using questionnaires)7 but not objectively (eg, using polysomnography).8
Age seems to be a key factor in associations between sleep and internalizing problems, and research suggests that these associations become stronger throughout childhood.3, 9 Midchildhood is an age period that has been underresearched, and this is particularly noteworthy given the important developmental changes occurring during this time. Focusing on children of this age may also have methodologic advantages, because this is arguably the youngest age at which children can accurately report on their own internalizing symptoms.10
Given the complexity of the associations between sleep problems, anxiety, and depression, the origins of these associations need to be examined from different perspectives. The use of behavioral genetic techniques provides one way of examining the origins of co-occurring problems.11 Only a few behavioral genetic studies have addressed the associations between sleep and internalizing difficulties, and these studies focused on preschool-aged children.12–14 The studies emphasized the importance of environmental influences making family members alike (shared environment) on these associations. Behavioral genetic research suggests that results are only applicable to the age group being investigated, because the magnitude of genetic and environmental influences on a trait may vary with age. For example, research suggests that the heritability of anxiety increases with age,15 and this may also be true for certain sleep characteristics.16
Given the complexity of the associations between sleep problems, anxiety, and depression and a dearth of research examining these associations in school-aged children, our purpose with this study was to examine these difficulties in a twin sample aged 8 years. Three specific aims were proposed. The first and second aims were to report the phenotypic associations between sleep problems and anxiety and depression, respectively. The final aim was to estimate genetic and environmental influences on the association(s) between sleep and internalizing difficulties.
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METHODS |
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Participants
All participants are members of the Emotions, Cognitions, Heredity and Outcome (ECHO) study, which consists of 300 twin pairs who were aged 8 (mean, 8 years 6 months; range, 8 years 2 months to 8 years 11 months) at wave 1 of data collection. ECHO is a spin-off study from the longitudinal Twins Early Development Study (TEDS),17 which focuses on twins born in England and Wales during 1994–1996. Data collection took place at the Institute of Psychiatry (London, United Kingdom) with the exception of a small number of families who were visited in their homes. The ECHO study received ethical approval from the Maudsley Hospital Ethics Committee (London). Informed consent from parents was obtained by mail before data collection.
To maximize power, a selected-extremes design was used, with the majority of twin pairs being selected for high levels of parent-reported anxiety at 7 years old (this was the most thoroughly assessed relevant phenotype at the 7-year assessment). Of the 5745 families in the TEDS, parental data were available for 5343 twin pairs, and 1378 of these families contained at least one child (or proband) scoring in the top 15% of this scale. Of these families, 967 were excluded because they had withdrawn from the TEDS (n = 5 pairs), they were participating in other concurrent spin-off studies (n = 211 pairs), they did not live within a 2-hour travel radius of the Institute of Psychiatry (n = 676 pairs), or at least one of the twins had a major medical condition such as spina bifida, cerebral palsy, or autism (n = 75 pairs). This left 411 potential proband pairs. Another 30 families had moved to a different house, leaving 381 proband families to be invited to participate, of whom 247 pairs took part (65%). For the controls (n = 3965 pairs), the same criteria were applied, which excluded 2794 pairs whom had withdrawn from the TEDS (n = 25 pairs), were participating in other concurrent spin-off studies (n = 737 pairs), did not live within a 2-hour travel radius of the Institute of Psychiatry (n = 1930 pairs), or had a major medical condition such as spina bifida, cerebral palsy, or autism (n = 102 pairs). This left 1171 potential control families, from which a random 92 pairs were invited to participate; 53 (58%) of these pairs participated.
Therefore, the total number of families seen was 300. After testing, data from 11 (4%) twin pairs were considered unusable because during the testing session it became apparent that at least one of the twins had a neurologic impairment, autistic spectrum disorder, severe receptive language impairment, or persistent difficulties with attention. The decision to exclude these data was based on consultation with a senior clinical and research psychologist. Another 10 (3%) families did not have scores for all the measures reported here because of missing data. Data from these 10 families are included wherever possible.
Fifty-seven percent of the sample were girls, and 43% were boys. The majority of the families participating in the ECHO study was white (n = 256 [87%]). Most mothers were employed (n = 215 [74%]) and remained in education until 18 years or later (n = 157 [54%]). Similarly, most fathers were employed (n = 269 [93%]) and remained in education until 18 years or later (n = 175 [61%]). We used the TEDS composite measure of socioeconomic status (SES) (qualifications and current employment for both parents and mother's age at the birth of her first child) to compare families that did and did not take part and found that participants were of higher SES than nonparticipants (t = 4.93; df = 809; P < .001). There were also slight differences on the basis of the gender-by-zygosity group, with monozygotic pairs (both male and female) more likely to take part (14.1% vs 12.3% opted in and out of the study, respectively, for monozygotic males and 19.0% vs 13.0% for monozygotic females) and dizygotic male pairs less likely to take part (10.0% vs 15.4%, respectively; 
2 = 14.20; df = 5; P < .05). However, there were no differences on the basis of anxiety scores in the children (mean, 13.55 and 13.36 opted in and opted out, respectively; P was not significant) or ethnicity (13.4% vs 9.6% opted in and out respectively; P was not significant).
Procedures and Instruments
Zygosity
A parent-rated instrument was used to assign twin zygosity. This resulted in unambiguously identifying 95% of the twin pairs as monozygotic or dizygotic. For the remaining 5%, DNA was collected from cheek swabs, and zygosity was assigned by using highly polymorphic markers that yield an accuracy of 99.9%.18 The final sample used in these analyses consisted of 96 monozygotic twin pairs, 192 dizygotic twin pairs, and 1 pair of unknown zygosity who refused participation in a DNA test and was excluded from twin analyses.
Anxiety-Selection Variable
Anxiety was examined in the TEDS sample when the participants were aged 7 years using a parent-reported anxiety scale made up of 21 items assessing the anxiety-related behaviors most commonly assessed in young children (namely, general distress [negative mood], separation anxiety, shyness/inhibition, and fears). Each item was rated on a 3-point scale (0 = never; 1 = sometimes; 2 = often). Items were taken from existing measures that are considered reliable and valid,19–23 and this scale has been used in previous research.24 The internal consistency (
) of this measure was .81. Twins with at least one member scoring in the top 15% of this measure were selected as our high-anxiety pairs in the ECHO study, whereas control pairs included children who both scored below this cutoff. The entire sample was analyzed as one group, taking this selection process into account (see below).
Screen for Childhood Anxiety Related Emotional Disorders
At 8 years of age, anxiety was examined in the ECHO sample using the Screen for Childhood Anxiety Related Emotional Disorders (SCARED),25, 26 which is a 41-item self-report questionnaire including items such as "I am nervous" and "I'm scared to go to school," each rated on a 3-point Likert scale (0 = not true; 2 = often true). The items were summed to produce an overall anxiety score, with higher scores indicating greater anxiety. This measure has been shown to have excellent concurrent face validity27 and psychometric properties.26 The internal consistency () of this measure was .88.
Child Sleep Habits Questionnaire
Parents reported on their 8-year-old children's sleep problems using an abbreviated version of the Child Sleep Habits Questionnaire (CSHQ).28 This version of the CSHQ consists of 33 items including, for example, "child struggles at bedtime (cries, refuses to stay in bed, etc)," each rated on a 3-point scale (1 = rarely; 3 = usually). The CSHQ taps into 8 aspects of commonly reported childhood sleep problems: bedtime resistance, sleep-onset delay, sleep duration, sleep anxiety, night wakings, parasomnia, sleep-disordered breathing, and daytime sleepiness. Parents reported on the most recent typical week. When necessary, items (eg, "child goes to bed at the same time at night") were recoded, so a higher score on this scale indicates greater sleep problems. The reliability and validity of this measure are demonstrated elsewhere.28, 29 Four items in the CSHQ ("I get scared if I sleep away from home," "I worry about sleeping alone," "I have nightmares about something bad happening to my parents," and "I have nightmares about something bad happening to me") are similar to those included in the Screen for Childhood Anxiety Related Emotional Disorders. These items were removed from the CSHQ total score to avoid potential interpretation difficulties caused by overlapping items. The internal consistency () of the remaining items was .75.
Children's Depression Inventory
Depressive symptoms were examined by using the Children's Depression Inventory (CDI),30 a 27-item self-report questionnaire that examines affective, cognitive, and behavioral signs of depression. For each item the child must select 1 of 3 statements that best reflects how they have been feeling for the past few weeks. For example, the child can choose between the statements "I have fun in many things," "I have fun in some things," and "nothing is fun at all." Each statement is coded 0 (least depressive) to 2 (most depressive), and a sum score of all the responses is made. The CDI demonstrates good reliability and validity.31, 32 One item concerning thoughts about suicide was removed from the questionnaire for ethical reasons (it was deemed inappropriate for 8-year-old children). Furthermore, to avoid the potential problem of overlapping items, 2 items pertaining to trouble sleeping and tiredness were removed from the CDI. The internal consistency () of the remaining items was .82. Of note, preliminary analyses demonstrated similar results regardless of whether overlapping items were removed from the measures.
Statistical Analyses
Data Preparation
Because the sleep-problem subscales were heavily skewed and some contained just a few items, they were dichotomized for analyses. Specifically, for each subscale participants scored 0 if they were not reported to have the sleep difficulty assessed by the subscale and 1 if they were. Because of the small number of items in certain subscales, none of the items tapping into anxiety were removed from these subscales.
Correcting for the Selection Variable
All analyses were conducted in Mx,33 which is one of the most popular statistical programs designed to deal with genetically sensitive data. Mx was used to control for the selected nature of the sample. As described previously, the twins participating in the current investigation were selected from a larger sample of twins. Pairs in which either twin scored in the top 15% for anxiety at age 7 were selected for participation in our investigation. A smaller random sample of control twin pairs from which neither twin scored in the top 15% was also selected. This method of recruiting participants typically increases the means, decreases the variances, and decreases the covariance of correlated variables.34
To correct for ascertainment, analyses on all variables including descriptive statistics, correlations among the measures, and genetic analyses were conducted jointly with the 7-year screening variable from the TEDS sample. This effectively links the study data back to the distribution of scores from these individuals on the original selection variable, available on the entire sample, and uses the association between the test variables and the selection variable to estimate the distribution that the test variables would have had if the entire sample had been assessed. This is somewhat similar to using a weight but is more accurate because it uses maximum likelihood to estimate the corrected distributions, variances, and covariances. Thus, statistically, the technique treats the TEDS participants not included in the ECHO sample as "missing" in the testing phase.35 The reasons for this approach are twofold. First, practical considerations meant that we could only see a small proportion of the total TEDS sample, and selecting from the extremes increases not only the power but also the likelihood of including children with clinically significant difficulties. Second, by including controls and grounding our analyses within the larger unselected TEDS sample, we were able to generalize our conclusions to the whole TEDS sample rather than to the specific selected group that we examined.
Genetic Analyses
Twin studies compare within-pair similarity for groups of monozygotic twins, who are genetically identical, and dizygotic twins, who shared half their segregating genes.11 This information can be used to disentangle genetic (A), shared environmental (C), and nonshared environmental (E) influences on single traits and correlations between traits. Nonshared environmental influences act to make family members different, and estimates also incorporate error. The selected nature of the sample was also controlled when running genetic analyses in Mx. Maximum-likelihood methods of raw data analyses were used to test genetic models. A correlated-factors model was tested, which allows genetic, shared, and nonshared environmental influences on one phenotype (eg, sleep problems) to correlate with the same factors on the other phenotype (eg, depression). Consequently, the correlations between sleep problems and depression are mediated via genetic, shared, and nonshared environmental routes (see Fig 1).
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A saturated model, which estimates the maximum number of parameters to describe variances, covariances, and means of the variables, was fitted to data from each measure. The fit of the genetic model (which contained the same number of variables as the saturated model) was calculated by subtracting the fit statistic (provided by Mx for raw data modeling as minus twice the log likelihood [–2LL]) for the saturated model from that of the genetic model. The –2LL statistic in itself does not provide useful information about the fit of the model. However, the difference in –2LL between the genetic and saturated model provides a relative measure of fit because differences in –2LL between models are distributed as
2 and the saturated model is considered to provide the perfect fit to the data. The Akaike information criterion (AIC) is calculated as 2 – 2df, and lower (eg, negative) values indicate a better fit. Information about the precision of parameter estimates (and their explained variance) was obtained by likelihood-based confidence intervals (CIs). |
RESULTS |
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Descriptive Statistics
All parent-reported sleep problems occurred frequently in this selected sample. Daytime sleepiness was reported most frequently (n = 508 [91%] parents responded their children showed
1 signs of daytime sleepiness at least sometimes), with sleep-disordered breathing reported least often (n = 160 [29%]). Approximately half of the twins in the sample were reported by their parents to have other sleep difficulties (bedtime resistance, n = 332 [59%]; sleep-onset delay, n = 280 [50%]; sleep-duration problems, n = 209 [37%]; sleep anxiety, n = 322 [57%]; night waking, n = 229 [41%]; parasomnia, n = 399 [71%]). Self-reported anxiety was higher in individuals with parent-rated bedtime resistance (mean, 29.64; SD, 12.04) than in those without (mean, 26.50; SD, 13.27; P = .045). Anxiety scores were not significantly different for those with and without reports of other sleep problems. In contrast, self-reported depression scores were significantly higher in individuals with parent-reported bedtime resistance, sleep-onset delay, sleep anxiety, and parasomnia than in those without (see Fig 2). There were also nonsignificant trends for individuals with reports of all the other sleep difficulties to show higher levels of depression. Given that a range of different sleep difficulties were associated with depression, a total sleep-problem scale was used for additional analyses.
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The means and SDs for the total scores controlled for the selected nature of the data (sleep problems: mean, 36.95; SD, 5.72; anxiety: mean, 28.52; SD, 12.52; and depression: mean, 8.36; SD, 6.41). There were significant gender differences for the anxiety-selection variable (female: mean, 9.28; SD, 5.88; male: mean, 8.39; SD, 5.84; P < .001) and for the ECHO anxiety variable (female: mean, 29.91; SD, 12.17; male: mean, 26.73; SD, 12.62; P < .01). Means were estimated in genetic models and were allowed to differ between the genders. There were no significant mean differences between monozygotic and dizygotic twins for sleep problems, anxiety, or depression.
The phenotypic correlations between parent-rated sleep problems and self-rated anxiety and depression after controlling for the selected nature of the data were small but significant (r = .12, P < .01; and r = .20, P < .001, respectively). There was also a moderate association between anxiety and depression (r = .37, P < .001). Given that the links between sleep problems and anxiety were particularly small, this association was not examined in behavioral genetic models. Because many types of sleep problems were associated with depression and the overall association was more substantial than that with anxiety, this association was decomposed in a behavioral genetic model.
Both univariate twin correlations were greater for monozygotic than dizygotic twins (sleep problems: monozygotic, .74; dizygotic, .38; depression: monozygotic, .31; dizygotic, .22), which suggests genetic influence. The bivariate twin correlation (depression for one twin and sleep problems for the co-twin; see Table 1) is greater in monozygotic compared with dizygotic twins, which indicates genetic influence on the associations between sleep problems and depression.
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Model Fitting
The bivariate genetic model provided a reasonable fit to the data. Specifically, it did not fit significantly less well than the saturated model (
2 = 121.10; df = 111; P = .24) and the AIC value was negative (AIC = –100.90). The parameter estimates and 95% CIs for the bivariate genetic model are shown in Table 2. These values demonstrate that genetic influences are most important (accounting for 61% of the variance) for sleep problems, with shared and nonshared environment also playing a significant role. There is also important nonshared environmental influence on depression, accounting for 70% of the variance. Genetic and shared environmental influences are smaller (and nonsignificant) and explain the rest of the variance. The bivariate genetic correlation (rA) and the bivariate shared environmental correlation (rC) are moderate. The bivariate nonshared environmental correlation (rE) is small and negative, suggesting that nonshared environmental factors that increase sleep problems may decrease depression and vice versa. Although nonsignificant paths (as indicated by CIs spanning 0) could be dropped individually, they could not be dropped simultaneously because the phenotypic correlation (r = .20, P < .001) is significant. The influences on the bivariate correlations could not be distinguished because the sample size is small and there is an associated lack of power. Nonetheless, the results show an interesting trend whereby genetic influences are almost entirely responsible for the sleep-depression relationship. The phenotypic correlation resulting from each of the 3 sources of variance was calculated by 
for sleep problems x rA x for depression, 
for sleep problems x rC x for depression, and 
for sleep problems x rE x for depression, which adds up to the phenotypic correlation (see Table 2). The genetic influence on the association between sleep problems and depression was .19 (
x .64 x 
). Overall, genetic factors seem to be most important in explaining the association between sleep problems and depression, whereas environmental influences are, in general, small.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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There are 3 particularly interesting results of this study. First, there was little evidence of a substantial association between childhood anxiety and sleep problems. In particular, different sleep problems (except bedtime resistance) were not associated with anxiety scores, and the overall association was small. Second, there seems to be a more robust association between sleep problems and depression, and participants with different types of sleep problems reported higher levels of depression than those without. Third, the association between sleep problems and depression seems to be largely mediated by genetic factors with smaller environmental influences. These results are particularly noteworthy given the use of different raters to examine different phenotypes. The findings from our study are discussed below and followed by a summary of the study limitations.
A great deal of literature examining associations between sleep and internalizing problems in childhood focuses on depression and not anxiety. This may reflect the finding reported here of a stronger concurrent association between sleep difficulties and depression, although links with anxiety have also been reported36 (for review, see ref 2). Previous research has also examined sleep difficulties in association with combined anxiety and depression in reflection of the finding that parents have difficulty distinguishing between these phenotypes.3 The possibility that the magnitude of the associations with anxiety and depression differs suggests that it may be useful to distinguish these phenotypes in this type of research, although there are also good reasons for "lumping" anxiety and depression together (eg, factor analyses demonstrate that anxiety and depression can be combined into an internalizing domain37). The overall picture is unclear, because longitudinal research has demonstrated stronger links between childhood sleep problems and anxiety as compared with depression.5
Although depression is typically examined in association with certain types of sleep disturbances such as difficulties getting to sleep, night waking, and early waking, the results of this study suggest that symptoms of depression may be associated with a wider range of sleep difficulties including sleep anxiety and parasomnia. This finding is perhaps unsurprising, given that different sleep problems may co-occur38 and that previous research in adolescents and adults has linked depression with a range of different sleep problems.39
The association between sleep problems and depression reported here was mainly a result of genetic influences. Although this was not found in a previous study that focused on preschool-aged children,14 this finding fits well with previous literature suggesting an increasingly important role of genes with age for a range of phenotypes.15, 16 The large overlap between genes influencing depression and those influencing sleep problems suggest that it may be worth exploring whether genes that are known to influence depression are associated with sleep problems and vice versa. Although this study was not designed to identify specific genes and there are likely to be a wide array of systems involved in the association between sleep problems and depression, serotonin genes are obvious candidates for additional exploration. These genes play a role in a range of physiologic functions including eating and sleeping patterns as well as depression.40–42 Dopamine, which is considered important in sleep regulation43 and has been associated with depression,44 may also be important.
Shared environmental factors explained a moderate proportion of the association between sleep problems and depression, and there was overlap between these influences on each phenotype. However, the magnitude of the overlap suggests that not all shared environmental influences on sleep problems are shared with depression and vice versa. The consistent finding that shared environmental factors play a role in the association between sleep and internalizing problems fits well with acknowledging that sleep is the antithesis of vigilance and that a safe environment is paramount in allowing vigilance to lapse and sleep to occur (for review, see ref 45). Feelings of safety with regard to one's environment could also be associated with symptoms of depression.
The nonshared environment accounted for a negative proportion of the association between sleep problems and depression. This suggests that nonshared environmental influences increasing sleep problems may actually decrease depression, and vice versa, and that overall these influences act to reduce the association between sleep problems and depression. This is difficult to explain, because nonshared influences that have been associated with sleep difficulties are also considered to be associated with depression (eg, peer difficulties46).
The main limitation of this study is the relatively small sample used. This smaller size was necessary given financial and time constraints resulting in part from the thorough assessments of the phenotypes under investigation. The small sample meant that the CIs for certain parameters, including the bivariate correlations, were large and spanned zero. This limitation also meant that there was not power to examine possible gender effects. Added to the finding that the twins in the sample were mainly white and of above-average SES, it is clear that replication and extension of the current results in a larger, more-diverse sample would be useful. Another point to consider with regard to the sample is that although we excluded participants for a number of reasons (including experiencing major medical conditions), it is possible that some of the participants that we included in our sample had other difficulties that may have been associated with their levels of sleep problems, anxiety, and depression. For example, we did not assess (and therefore take into consideration in analyses) medication use.
Although the measures used in this study are an improvement on those used in previous studies in terms of reliability, validity, and detail of assessment,13, 14 the exclusive use of parent report to examine sleep problems has been criticized, because parents are not accurate at estimating sleep-quality variables such as frequency of night wakings.47–49 This limitation is attenuated in that the sleep questionnaire used in this investigation also taps into sleep patterns, which are considered to be accurately reflected in subjective reports. Nonetheless, the consistent differences found between subjective and objective measures of sleep problems means that the results of this study should not be applied to objectively examined sleep difficulties, and it is possible that objectively assessed sleep problems may have yielded quite different results.
Another issue worth addressing is that measures used in this study examined sleep problems and anxiety in the full range rather than at the extremes. Hence, the results of this study are not necessarily applicable to clinically significant problems. This consideration is important, given that previous research examining other phenotypes has demonstrated differences between genetic and environmental influences at the high extreme and in the full range (eg, depression50).
In addition to limitations specific to this study, those commonly leveled toward twin studies also need to be considered. A potential problem with the use of twins concerns generalizability. In the context of the current research, it is possible that the sleep patterns and problems experienced by twins differ from those experienced by nontwins, although a study comparing sleep difficulties in young twins and nontwins reported small group differences.51 For further discussion of challenges made to the twin method, see ref 11.
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CONCLUSIONS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONCONCLUSIONSREFERENCES |
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Overall, this study suggests that symptoms of depression in midchildhood are associated with a range of sleep difficulties, indicating that it may be useful to assess sleep difficulties in children who present with symptoms of depression and vice versa. This is particularly noteworthy given that >50% of pediatricians feel that they lack confidence in their ability to identify and treat sleep problems.52 There also seems to be a strong role for genes in the associations between common sleep problems and depression, although environmental influences may also play a role. An obvious next step to further unraveling the complex associations between sleep and internalizing problems is to undertake interdisciplinary study. Future research will examine whether cognitive biases mediate genetic influences on sleep and internalizing difficulties.
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ACKNOWLEDGMENTS |
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The ECHO project was supported by a Career Development Award from the United Kingdom Medical Research Council (to Dr Eley). Dr Gregory was supported by an Economic and Social Research Council Postdoctoral Fellowship during this research.
We thank the ECHO study members, Philippa Carter, David Clark, Georgina Hosang, Jennifer Lau, Helen Matthews, Fiona Mcleod, Maria Napolitano, Judith Owens, Robert Plomin, Jasmine Singh, and Lucy Stirling.
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FOOTNOTES |
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Accepted Apr 13, 2006.
Address correspondence to Alice M. Gregory, PhD, Box P080, Institute of Psychiatry, De Crespigny Park, London SE5 8AF, United Kingdom. E-mail: a.gregory{at}iop.kcl.ac.uk
The authors have indicated they have no financial relationships relevant to this article to disclose.
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