Sleep and Attention in Children With ADHD and Typically Developing Peers

Abstract

Objective: The objective of this study was to examine the relationships between sleep and attention in both typically developing (TD) children and children with ADHD. Method: The current study examined sleep and attention in 50 children, from 6 to 12 years of age (25 ADHD, 25 TD). Attention was measured using the Conners’ Parent Rating Scale–Revised: Long Version and the Attention Network Test–Interaction (ANT-I), which provided an objective measure of alerting, orienting, and executive attention. Sleep was objectively measured using actigraphy. Results: Children with ADHD had poorer alerting and executive attention on the ANT-I, as well as poorer parent-reported attention. In addition, poor sleep predicted performance on alerting attention for children with ADHD and TD children, whereas the interaction between poor sleep and ADHD diagnosis predicted executive attention scores. Conclusion: The findings of the current study highlight the importance of ensuring children are getting good quality sleep to optimize attention, particularly for children with ADHD.

Keywords

ADHD sleep attention actigraphy Attention Network Test–Interaction (ANT-I)

Individuals of all ages can experience sleep problems; however, children are particularly vulnerable to the effects of poor sleep, as childhood is a time of major developmental change. Research has demonstrated that in middle-to-late childhood, total sleep time often falls short of what is recommended to maintain healthy daytime functioning (Crabtree & Williams, 2009). In fact, a National Sleep Foundation Poll conducted in 2008 found that, on average, elementary school–age children sleep approximately 8 to 9 hr despite recommendations by various health organizations for 10 to 11 hr of sleep (O’Brien, 2009). The reasons for poor sleep are quite extensive, including factors such as hectic family schedules and living arrangements, extracurricular demands, television, computer, cell phone use in the bedroom after bedtime, early school start times, and primary sleep disorders (O’Brien, 2009; Sadeh, 2007).

The sparse pediatric research examining the impact of poor sleep suggests that children who sleep less well are at greater risk for a variety of impairments. Common consequences of poor sleep include deficits in neurobehavioral functioning such as poor attention, poor academic achievement, and difficulty with behavioral and emotional regulation (Konofal, Lecendreux, & Cortese, 2010; Sadeh, 2007). According to Sadeh, Gruber, and Raviv (2003), the prefrontal cortex seems to be most sensitive to poor sleep, which in turn leads to numerous deficits in behavioral regulation and cognitive functioning, specifically in executive functions. The prefrontal cortex is the area of the brain thought to be associated with executive attention, and deficits in this area are highly prevalent in children with ADHD (Jones & Harrison, 2001).

Importantly, correlational studies conducted with typically developing (TD) children have repeatedly found that poor sleep is associated with increased inattention, hyperactivity, poor concentration, and poor school performance (Epstein, Chillag, & Lavie, 1998; Fallone, Acebo, Seifer, & Carskadon, 2005; Sadeh et al., 2003). In general, literature suggests that attention is particularly vulnerable to the effects of poor sleep. This seems logical given that the prefrontal area of the brain is linked to attention and has been suggested to be the brain area most sensitive to poor sleep quality (Jones & Harrison, 2001). Vriend et al. (2012) also found that poor sleep quality was associated with impairments in attention. Furthermore, a study by Gruber et al. (2007) demonstrated that sleep efficiency was positively correlated with attention measured on a continuous performance task. Experimental sleep manipulation studies lend additional support to the idea that poor sleep, including limited sleep quantity or poor sleep quality, is associated with impairments in attention. For instance, Fallone et al. (2005) found that a single night of sleep restriction in TD children led to a significant increase in teacher ratings of inattention.

It is not surprising that children with ADHD have been identified as a group of children with very high rates of sleep problems (Gruber, 2009). Although the prevalence of sleep problems is high in TD children, with approximately 30% of children displaying sleep difficulties, the rates in children with ADHD are even higher, affecting up to 80% (Cortese, Faraone, Konofal, & Lecendreux, 2009). Although past research has established a relationship between ADHD and sleep, the exact nature of this relationship is unclear. For example, it is not known if children with ADHD are more sensitive to the effects of poor sleep compared with their TD peers.

Although there is a triad of symptoms for ADHD (inattention, impulsivity, and hyperactivity; Diagnostic and Statistical Manual of Mental Disorders [5th ed.; DSM-5]; American Psychiatric Association [APA], 2013), difficulty with attention tends to persist into adolescence and adulthood (Beiderman, Mick, & Faraone, 2000). The attention deficits in this population have been explored extensively using a variety of cognitive tasks. Recently, the Attention Network Test–Interaction (ANT-I; Callejas, Lupianez, Funes, & Tudela, 2005) has become one of the primary tasks used to assess the nature of attention deficits in children with ADHD and to parse out individual differences in attention. For example, Mullane, Corkum, Klein, McLaughlin, and Lawrence (2011) found that when comparing attention in TD children and children with ADHD using the ANT-I, children with ADHD were at a considerable disadvantage.

As noted, poor sleep appears to affect attention in both children with ADHD and TD children, but little is known about whether or not poor sleep affects attention in a similar manner in these populations. Therefore, the current study sought to examine the relationship between poor sleep and attention in children with ADHD compared with TD controls. Based on previous literature (Beiderman & Spencer, 1999; Fallone, Acebo, Arnedt, Seifer, & Carskadon, 2001; Fallone et al., 2005; Sadeh, 2007), we expected that (a) children with ADHD would demonstrate significantly poorer sleep and poorer attention, (b) poorer sleep would predict weaker attention, and (c) attention of children with ADHD would be affected by poor sleep to a greater extent than attention of their TD peers.

Method

Participants

The sample for this study consisted of 50 participants (41 males, 9 females) recruited from two larger studies. Participants completed a 1-week baseline assessment during which time their typical sleep and daytime functioning were assessed. Twenty-five participants with ADHD were matched in terms of age and sex with 25 TD children. All children were between the ages of 6 and 12 years. Children in the ADHD group were all medication naive and had been rigorously diagnosed by registered psychologists specializing in ADHD.¹ None of the children had neurological disorders or were currently taking medication that may have interfered with their sleep. This study was approved by the Dalhousie University Research Ethics Board and the IWK Health Centre Research Ethics Board.

Procedure

Parental consent and child assent were collected for all participants. To collect baseline sleep measurements, participants were given an actigraph, which is a small, computerized, wristwatch-like device that collects data generated by the participant’s movement (Sadeh & Acebo, 2002). Participants were required to wear the actigraph each night for a 1-week period. Furthermore, the parents of participants were asked to complete a sleep diary for the week, recording sleep and wake times for their child participating in the study. All participants were asked to follow their typical sleep schedules. Data collected during the baseline week were analyzed in conjunction with information provided in the sleep diaries to ensure the baseline week was representative of a typical sleep schedule for the child. Following the baseline data collection week, participants returned to the laboratory for a testing session.

Both studies used similar testing procedures that involved various cognitive, emotional, and behavioral measures. For a detailed description of the testing procedure, please refer to Vriend et al. (2013). The measure of interest for the current study, the ANT-I (Callejas et al., 2005), was included in a larger battery of tests. The ANT-I fell approximately 45 min into the test battery, was preceded by an emotion task, and followed by a sleepiness scale and self-report questionnaire. Participants were asked to complete one practice block and one experimental block of the ANT-I on a laptop computer. The practice block took approximately 2 min while the experimental block was 8 min in duration.

While participants completed the test battery, parents completed a variety of questionnaires in a separate room. To measure attention, parents of the participants completed the Conners’ Parent Rating Scale–Revised: Long Version (CPRS-R:L; Conners, Sitarenios, Parker, & Epstein, 1998).

Measures

Actigraphy

An actigraph is a small, computerized, wrist watch-like device, worn on the nondominant wrist of the participant. The actigraph is based on an acceleration sensor that collects data generated by physical motion, which can then be translated into a numeric representation (Sadeh, Hauri, Kripke, & Lavie, 1995). Actigraphy has been shown to yield a high agreement rate with polysomnography (PSG), the “gold standard” of sleep measurement, suggesting actigraphy provides a valid and reliable measure of sleep (de Souza et al., 2003; Paquet, Kawinska, & Carrier, 2007). Participants all wore a Micro-Mini Octagonal Basic Motionlogger Actigraph. Actigraphs record a variety of sleep variables including sleep duration, sleep minutes, and sleep efficiency.

While actigraphy is capable of recording a number of sleep parameters, pediatric research examining the link between sleep and daytime functioning has focused on two key variables; sleep minutes as a measure of sleep quantity and sleep efficiency as a measure of sleep quality. As such, the current study uses these two variables in the main analyses, but includes other actigraphy-derived variables for descriptive purposes (e.g., sleep duration and sleep latency). Sleep duration calculates the amount of time spent sleeping, whereas sleep minutes is a measure of time spent sleeping that accounts for night waking. Sleep latency provides a measure of the time from lights out to sleep onset and sleep efficiency calculates the ratio of sleep duration to sleep minutes. Parents completed a sleep diary (Corkum, Tannock, & Moldofsky, 1998), which was used as a supplementary aid in scoring and verifying actigraph data.

ANT-I

The ANT-I is a computerized task that measures the three networks that make up the attention system (Callejas et al., 2005; Posner & Petersen, 1990); the executive control network, the orienting network, and the alerting network. The executive control network is assessed using Flanker trials and Simon trials. Approximately half of the trials are Flanker trials, while the remainders are Simon trials. Flanker trials involve presenting the target (the central arrow) with two flanker arrows on each side and presenting the arrows either above or below the fixation point (Callejas et al., 2005). On congruent trials, all five arrows point in the same direction. However, during incongruent trials, the flanker arrows and the central target arrow point in opposite directions. On Simon trials, the target arrow is presented alone, pointing in either direction. Congruent trials involve presenting the target arrow in a spatial location congruent with its direction (Callejas et al., 2005). The executive network is measured by examining the difference in reaction time (RT) between congruent trials and incongruent trials.

The alerting network is measured by comparing the RT of trials with a tone preceding the target with trials that did not present a tone prior to presenting the target (Callejas et al., 2005). The orienting network is measured by comparing trials with a valid asterisk cue, an invalid asterisk cue, or no cue at all. On two thirds of the trials, a cue (asterisk) is presented for 50 ms. A valid cue trial involved presenting the asterisk cue to the same location as the preceding target arrow, whereas an invalid cue trial involved presenting the asterisk cue in the opposite location of the upcoming target (Callejas et al., 2005). The orienting network is measured by examining the difference in RT on trials with a valid cue compared with an invalid cue.

The trials begin as follows: Using designated keys on a computer keyboard, the participants are asked to indicate the direction of an arrow as soon as it is presented on the computer screen. In each trial, five events occur. First, the participant is presented with a fixation period for a random duration, during which a cross is presented as a central fixation point. On 50% of the trials, the tone is presented for 50 ms prior to the warning cue. The warning cue (an asterisk) is then presented on two thirds of trials for 100 ms. There is a short fixation period following the warning cue, which is then followed by the target (central arrow). The target remains on the screen until the participant responds or until 3,000 ms has passed. The task involves one block of 24 practice trials followed by one block of 96 experimental trials. The task takes approximately 16 min to complete. The ANT-I is run in E-Prime (Version 1.1; Psychology Software Tools, Inc., 2002, Sharpsburg, PA).

CPRS-R:L

The CPRS-R:L (Conners et al., 1998) is a widely used tool for collecting information from parents concerning their children’s ADHD and related symptoms. The CPRS-R:L is an 80-item questionnaire composed of 14 different scales. The Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) criteria for Inattentive Type ADHD was used for this study, which contains 9 items designed to target inattentive behaviors. Items on the CPRS-R:L are rated on a 4-point rating scale ranging from 0 (not true at all) to 3 (very much true). These items are the same as the inattention diagnostic criteria for ADHD in the DSM-IV. The CPRS-R:L has good reliability and concurrent validity (Conners et al., 1998), making this scale an appropriate tool for research.

Results

Demographic Variables

Demographic variables were compared between groups using one-way between-participant ANOVAs. The analyses indicated that for age, the ADHD group (M = 108.20 months, SD = 21.12 months) did not significantly differ from the TD group (M = 107.94 months, SD = 16.66 months), F(1, 48) = 0.02, p = .96. Socioeconomic status (SES; Boyd, 2008) did not differ between the ADHD group (M = 42.19, SD = 12.78) and the TD group, M = 44.89, SD = 10.08, F(1, 48) = 0.51, p = .76. Chi-square analysis demonstrated that groups did not significantly differ on sex, χ²(1) = .12, p = .74 (ADHD: 20 males, 5 females; TD: 19 males, 6 females). As expected, the two groups significantly differed on the CPRS-R:L ADHD Index F(1, 48) = 81.15, p = .00, with participants with ADHD scoring significantly higher (M = 72.08, SD = 9.04) than TD participants (M = 48.40, SD = 9.04).

Attention Variables

Refer to Table 1 for means and standard deviations for the attention variables.² The two groups significantly differed on parent report of inattention, CPRS-R:L; F(1, 48) = 71.44, p = .00, with participants with ADHD scoring significantly higher (M = 71.20, SD = 9.38) than TD participants (M = 47.64, SD = 10.31). Based on the objective measure of attention, there was a significant difference between the ADHD and TD group in RT on two of the three ANT-I networks; the alerting network, F(1, 48) = 9.05, p =.001, with participants in the ADHD group (M = 689.96 ms, SD = 618.61 ms) taking longer to respond than TD participants (M = 264.61 ms, SD = 341.97 ms) and the executive network RT, F(1, 48) = 8.12, p =.01, with ADHD participants performing significantly slower (M = 1,121.44 ms, SD = 760.68 ms) than TD participants (M = 483.49 ms, SD = 820.88 ms). However, the two groups did not statistically differ in RT on the orienting network, F(1, 48) = 0.49, p = .48, although the average score of participants in the ADHD group (M = 223.61 ms, SD = 471.44 ms) was slower than the TD group (M = 141.55 ms, SD = 344.27 ms), the standard deviations were very large, and as such the groups did not differ significantly.

Table 1.

Means, Standard Deviations, and One-Way Between-Subjects ANOVA for Attention Variables.

	ADHD	TD
Variable	M (SD)	M (SD)	F value	Significance
Conners’ ADHD index	83.83 (9.75)	48.40 (9.54)	F(1, 48) = 81.15	.00***
Conners’ Parent Rating Scale–Inattentive Scale	71.20 (9.38)	47.64 (10.31)	F(1, 48) = 71.44	.00***
ANT-I alerting network (RT)	689.96 (618.61)	264.60 (341.97)	F(1, 48) = 9.05	.001***
ANT-I orienting network (RT)	223.61 (471.44)	141.55 (344.27)	F(1, 48) = 0.49	.48
ANT-I executive network (RT)	1,121.44 (760.68)	483.49 (820.88)	F(1, 48) = 8.12	.01**

Note. Conners’ Parent Rating Scale–Revised: Long Inattentive Scale values represent T-scores (M = 50; SD = 10). TD = typically developing; ANT-I = Attention Network Test–Interaction; RT = reaction time (ms).

p < .05. **p < .01. ***p < .001.

Sleep Variables

Refer to Table 2 for the means and standard deviations of sleep variables. One-way between-groups ANOVAs indicated that groups did not significantly differ on any of the measures of sleep. The ADHD group did not statistically differ from the TD group on sleep minutes, F(1, 48) = 0.02, p = .96; sleep duration, F(1, 48) = 1.90, p = .18; sleep efficiency, F(1, 48) = 0.76, p = .90; or sleep latency, F(1, 44) = 0.08, p = .79. ADHD participants slept an average of 481.09 min (SD = 70.68), with a sleep duration of 573.20 min (SD = 44.06 min), an average sleep efficiency of 83.84% (SD = 9.76), and a mean sleep latency of 32.8 min (SD = 24.47 min). The TD group slept an average of 478.99 min (SD = 44.49 min), with an average sleep duration of 557.85 min (SD = 34.17 min), an average sleep efficiency of 85.89% (SD = 6.56), and a mean sleep latency of 31 min (SD = 31.02 min).

Table 2.

Means, Standard Deviations, and One-Way Between-Subjects ANOVA for Sleep Variables.

	ADHD	TD
Variable	M (SD)	M (SD)	F value	Significance
1. Sleep duration	573.20 (44.06)	557.85 (34.17)	F(1, 48) = 1.89	.18
2. Sleep minutes	481.09 (70.68)	478.99 (44.49)	F(1, 48) = 0.02	.96
3. Sleep efficiency	83.84 (9.76)	85.88 (6.56)	F(1, 48) = 0.76	.90
4. Sleep latency	32.8 (24.47)	31.0 (31.02)	F(1, 44) = 0.08	.79

Note. Sleep minutes, sleep duration, and sleep latency values reported in minutes. Sleep efficiency values reported in percentages. TD = typically developing.

p < .05. **p < .01. ***p < .001.

Sleep and Attention

As alerting attention, executive attention, and parent-reported inattention significantly differed between groups, the relationships between these variables, sleep efficiency and sleep minutes were further explored. Sleep minutes and sleep efficiency were chosen as the main variables of interest for the current study, as they provide a measure of both sleep quantity and sleep quality. Three separate hierarchical regression analyses were conducted in which the dependent variables were (a) ANT-I alerting attention, (b) ANT-I executive attention, and (c) parent-reported inattention (see Tables 3 -5). The independent variables for each analysis were sleep minutes and sleep efficiency for the first step, group (e.g., ADHD vs. TD) for the second step, and the interaction term for group, sleep minutes, and sleep efficiency for the third step.

Table 3.

Hierarchical Regression Analyses Predicting ANT-I Alerting Attention Through ADHD Diagnosis, Sleep Efficiency, and the Interaction Between Sleep Efficiency and ADHD Diagnosis.

ANT-I alerting	R ²	F value	β	Significance
Step 1
S.M.	.13	F(1, 48) = 3.53	0.53	.04*
S.E.	.13	F(1, 48) = 3.53	−0.24	.04*
Step 2
ADHD	.27	F(1, 48) = 5.59	−0.38	.00**
Step 3
Interaction	.32	F(1, 48) = 4.09	−4.10	.00**

Note. Values for sleep efficiency reported in percentage. For ADHD diagnosis, 1 = ADHD, 2 = TD. ANT-I = Attention Network Test–Interaction; S.M. = sleep minutes; S.E. = sleep efficiency; ADHD = ADHD diagnosis. Interaction = interaction between ADHD diagnosis, sleep minutes, and sleep efficiency.

p < .05. **p < .01. ***p < .001.

Table 4.

Hierarchical Regression Analyses Predicting ANT-I Executive Attention Through ADHD Diagnosis, Sleep Efficiency, and the Interaction Between Sleep Efficiency and ADHD Diagnosis.

ANT-I executive	R ²	F value	β	Significance
Step 1
S.M.	.14	F(1, 48) = 3.73	0.62	.03*
S.E.	.14	F(1, 48) = 3.73	−0.45	.03*
Step 2
ADHD	.24	F(1, 48) = 4.86	−0.33	.02*
Step 3
Interaction	.33	F(1, 48) = 4.25	−5.14	.01**

Note. Values for sleep efficiency reported in percentage. For ADHD diagnosis, 1 = ADHD, 2 = TD. ANT-I = Attention Network Test–Interaction; S.M. = sleep minutes; S.E. = sleep efficiency; ADHD = ADHD diagnosis; Interaction = interaction between ADHD diagnosis, sleep minutes, and sleep efficiency.

p < .05. **p < .01. ***p < .001.

Table 5.

Hierarchical Regression Analyses Predicting Parent-Reported Attention Through ADHD Diagnosis, Sleep Efficiency, and the Interaction Between Sleep Efficiency and ADHD Diagnosis.

CPRS-R:L inattention	R ²	F value	β	Significance
Step 1
S.M.	.04	F(1, 48) = 0.91	0.33	.41
S.E.	.04	F(1, 48) = 0.91	−0.28	.41
Step 2
ADHD	.61	F(1, 48) = 23.54	−0.78	.001**
Step 3
Interaction	.61	F(1, 48) = 13.64	0.88	.00***

Note. Values for sleep efficiency reported in percentage. For ADHD diagnosis, 1 = ADHD 2 = TD. CPRS-R:L = Conners’ Parent Rating Scale–Revised:Long; S.M. = sleep minutes; S.E. = sleep efficiency; ADHD = ADHD diagnosis; Interaction = interaction between ADHD diagnosis, sleep minutes, and sleep efficiency.

p < .05. **p < .01. ***p < .001.

Hierarchical regression analysis indicated that sleep minutes significantly predicted alerting network RT, β = 0.53, t(48) = 2.28, p = .03, and explained a significant proportion of variance in alerting network RT, R² = .13, F(1, 48) = 3.53, p = .04, with greater sleep minutes predicting better performance on alerting network RT. Sleep efficiency did not significantly predict alerting network RT, β = −0.24, t(48) = −1.03, p =. 31. Furthermore, ADHD diagnosis significantly predicted alerting network RT, β = −0.38, t(48) = −2.93, p = .00, and explained a significant proportion of variance in alerting network RT, R² = .27, F(1, 48) = 5.9, p = .00. However, the interaction between ADHD diagnosis, sleep minutes, and sleep efficiency did not significantly predict alerting network RT, β = −0.41, t(48) = −1.69, p = .10, but did account for a significant proportion of variance in alerting network RT, R² = .32, F(1, 48) = 4.09, p = .00.

Sleep minutes, β = −0.62, t(48) = −2.71, p = .01, and sleep efficiency, β = −0.45, t(48) = −1.97, p = .05, were both significant predictors of executive network RT and accounted for significant proportion of the variance, R² = .14, F(1, 48) = 3.73, p = .03, with greater sleep minutes and sleep efficiency predicting lower executive network RT scores. ADHD diagnosis also significantly predicted executive network RT, β = −0.33, t(48) = −2.50, p = .02, and accounted for a significant amount of variance in executive network RT, R² = .24, F(1, 48) = 4.86, p = .01. Likewise, the interaction between ADHD diagnosis, sleep minutes, and sleep efficiency was a significant predictor of executive network RT, β = −0.51, t(48) = −2.13 p = .04, and accounted for a significant proportion of variance, R² = .33, F(1, 48) = 4.25, p < .001.

Finally, neither sleep minutes, β = 0.33, t(48) = 1.35, p = .18, nor sleep efficiency, β = −0.28, t(48) = −1.16, p = .25, significantly predicted parent-reported inattention on the CPRS-R:L Inattentive Scale or accounted for a significant amount of variance, R² = .04, F(1, 48) = 0.91, p = .41. ADHD diagnosis significantly predicted reports of inattention on the CPRS-R:L, β = −0.78, t(48) = −8.14, p < .001, and accounted for a significant proportion of variance in parent-reported inattention, R² = .61, F(1, 48) = 23.54, p < .001. However, the interaction between ADHD diagnosis, sleep minutes, and sleep efficiency did not significantly predict parent-reported inattention, β = 0.88, t(48) = 0.48, p = .64, but did account for a significant proportion in variance, R² = .61, F(1, 48) = 13.64, p = .00.

Discussion

The objectives of the current study were to (a) build upon previous findings that children with ADHD exhibit significant impairments in attention and sleep, (b) examine whether sleep predicted difficulties with attention, and (c) determine whether this relationship significantly differed in children with ADHD versus TD children. The results of the current study demonstrate that children with ADHD significantly differ from TD children on a variety of attention measures, including parent-reported attention (CPRS-R:L), as well as alerting and executive attention as measured by the ANT-I. However, children with ADHD did not significantly differ from TD children on measures of orienting attention. The findings of the current study also indicate that newly diagnosed, medication naive children with ADHD do not significantly differ from TD participants on sleep duration, sleep minutes, or sleep efficiency, as measured by actigraphy. Most importantly for this study, we found that sleep minutes (a measure of sleep quantity), not sleep efficiency (a measure of sleep quality), significantly predicted alerting attention RT in both children with ADHD and TD children but did not significantly predict parent-reported attention. ADHD diagnosis significantly predicted parent-reported attention scores, alerting attention RT, and executive attention RT. Finally, the interaction between ADHD diagnosis, sleep minutes, and sleep efficiency significantly predicted executive attention RT but did not significantly predict alerting or parent-reported attention. This suggests that sleep minutes and sleep efficiency predicts alerting and parent-reported attention in a similar manner in children with ADHD and TD children. However, sleep minutes and sleep efficiency predicted executive attention differently in children with ADHD and TD children.

On the basis of previous literature, the first hypothesis of the current study proposed that children with ADHD would demonstrate significantly poorer attention and sleep than TD children. In terms of attention, the results of the current study indicate that children with ADHD significantly differed on both alerting and executive attention RT of the ANT-I compared with TD children, with the ADHD group scoring significantly lower on both of these network tasks. The ADHD group did not significantly differ from the TD group on orienting attention RT, which is consistent with previous literature. For example, Johnson et al. (2008) found that the mean RT of children with ADHD was significantly slower than TD children on both the alerting and executive network of attention measured by the ANT-I. Mullane et al. (2011) also demonstrated similar findings using the ANT-I, suggesting that children with ADHD experience significantly weaker alerting and executive attention when compared with TD children, yet demonstrate relatively intact orienting attention. Likewise, the two groups significantly differed on parent report of attention, with children with ADHD being rated as having significantly more problems with inattention than TD children. These findings confirm that both the subjective and objective measures of attention used in this study had adequate sensitivity to detect differences in attention in children with versus without ADHD.

The current study also examined the differences between children with ADHD and TD children on sleep variables, including sleep duration, sleep minutes, and sleep efficiency; the two groups did not significantly differ on any of the sleep variables examined. At first glance, these findings may seem surprising given the overwhelming perception of poor sleep as a common occurrence in children with ADHD. However, it is important to note that a recent analysis of review papers by Corkum and Coulombe (2013), which included three existing meta-analyses and five systematic reviews, concluded that there was evidence for parent-reported sleep problems but these were not verified on objective measures of sleep, with the exception of increased motor movements during sleep.

Based on past research (Fallone et al., 2001; Fallone et al., 2005; Sadeh, 2007), it was expected that poor sleep would predict poor attention regardless of whether or not the child had ADHD. This prediction was largely based on the observation that TD children who experience poor sleep demonstrate a significant increase in observed inattentive behaviors (Blunden, Hoban, & Chervin, 2006; Fallone et al., 2001; Fallone et al., 2005; O’Brien, 2009). Although previous literature suggests that children with ADHD might exhibit similar patterns of behavior (Owens, 2009), findings have been largely based on subjective measures of both sleep and attention. The results of the current study demonstrated that sleep minutes predicted alerting attention, and both sleep minutes and sleep efficiency significantly predicted executive attention. These findings are consistent with past literature that suggests that children with poor sleep demonstrate significant impairments in performance on objective tasks of attention, such as the Continuous Performance Task (Gruber et al., 2007; Konofal et al., 2010; Moreau, Rouleau, & Morin, 2013) and the Children’s Color Trails Test (Vriend et al., 2012). However, sleep minutes and sleep efficiency did not significantly predict parent-reported attention. Together, the results partially support the second hypothesis that a negative correlation would exist between poor sleep and attention.

The third hypothesis proposed that attention would be affected by poor sleep to a greater extent in children with ADHD than in TD children. Interestingly, the interaction between sleep efficiency, sleep minutes, and ADHD diagnosis significantly predicted executive attention but did not significantly predict alerting or parent-reported attention, suggesting that the executive attention of children with ADHD may be especially vulnerable to poor sleep while alerting attention may be affected similarly in both groups. These results are not surprising given the overlap in the central nervous system centers that regulate sleep and those that regulate attention in the prefrontal cortex (Jones & Harrison, 2001). In addition, these findings are consistent with a recent study conducted by Moreau and colleagues (2013), which suggested that poor sleep, in particular shorter sleep duration, was associated with a variety of executive functioning problems as reported by parents. It may be that when ADHD and sleep problems coexist these may act to create a double deficit in terms of executive attention and functioning.

There are a number of strengths and limitations that need to be considered when interpreting the results of this study. An important strength of this study was the use of both objective and subjective measures of attention and sleep. As previously mentioned, a large portion of research correlates subjective measures of attention with subjective measures of sleep, which can be problematic given informant bias. However, the current study used not only subjective measures of attention but also objective measures of attention and sleep, with both of these measures considered to be reliable and valid (Callejas et al., 2005; Fan, McCandliss, Sommer, Raz, & Posner, 2002; Sadeh & Acebo, 2002; Sadeh et al., 1995). Another strength of the current study is that participants were matched by age and sex and did not significantly differ on demographic variables such as SES. Furthermore, the ADHD participants were rigorously diagnosed by registered clinical psychologists specializing in ADHD, and all children with ADHD were medication naive, suggesting that the findings were not influenced by medication status. Furthermore, the sample size of the current study was comparable with those examining similar hypotheses in the field. Nonetheless, a larger sample size would have allowed more in-depth analysis that may have permitted analyses examining differences in sex or ADHD presentations.

In conclusion, poor sleep is a risk factor for attention problems, particularly alerting attention, in both children with ADHD and TD children. Moreover, executive attention in children with ADHD may be particularly vulnerable to the negative effects of poor sleep. As noted previously, many TD children, as well as the majority of children with ADHD, may experience poor sleep (Blunden, Hoban, & Chervin, 2006; Cortese et al., 2009), which underscores the importance of our findings. The findings of the current study highlight the importance of ensuring children are getting good quality sleep to optimize attention, particularly in children with ADHD.

Footnotes

Acknowledgements

The authors would like to extend their sincere gratitude to the children and families who participated, and the research staff and volunteers who assisted with the collection of these data.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by Canadian Institutes of Health Research (FRN 94299) and Dalhousie Psychiatry Research Fund.

Notes

Author Biographies

Jessica Waldon is a school psychologist with the Halifax Regional School Board.

Jennifer Vriend is a clinical psychologist with the Queensview Professional Services. Her research interests include the impact of sleep deprivation on cognitive functioning and emotion processing in children with and without attention disorders.

Fiona Davidson is presently a graduate student in the Clinical Psychology PhD program at Dalhousie University, supervised by Dr. Corkum. Her research interests include the impact of sleep deprivation on behavior, academic, and cognitive functioning in children with ADHD.

Penny Corkum is a registered psychologist and professor in the Clinical Psychology Program in the Department of Psychology & Neuroscience at Dalhousie University. She is also cross-appointed in the Departments of Psychiatry and Pediatrics. Dr. Corkum is the Director of the Colchester East Hants ADHD Clinic and Scientific Staff at the IWK Health Centre. Dr. Corkum’s primary research focus is child mental health disorders and sleep disorders.

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