European Child & Adolescent Psychiatry

, Volume 26, Issue 9, pp 1129–1139 | Cite as

Sleep problems in children with attention-deficit hyperactivity disorder: associations with parenting style and sleep hygiene

  • Emma Sciberras
  • Jie Cheng Song
  • Melissa Mulraney
  • Tibor Schuster
  • Harriet Hiscock
Original Contribution

Abstract

We aimed to examine the association between sleep problems and parenting and sleep hygiene in children with attention-deficit/hyperactivity disorder (ADHD). Participants included 5–13-year-old children with DSM 5 defined ADHD and a parent-reported moderate-to-severe sleep problem (N = 361). Sleep was assessed using the parent-reported Children’s Sleep Habits Questionnaire. Parents also completed checklists assessing sleep hygiene, parenting consistency, and parenting warmth. Linear regression established prediction models controlling for confounding variables including child age and sex, ADHD symptom severity, comorbidities, medication use, and socio-demographic factors. More consistent parenting was associated with decreased bedtime resistance (β = −0.16) and decreased sleep anxiety (β = −0.14), while greater parental warmth was associated with increased parasomnias (β = +0.18) and sleep anxiety (β = +0.13). Poorer sleep hygiene was associated with increased bedtime resistance (β = +0.20), increased daytime sleepiness (β = +0.12), and increased sleep duration problems (β = +0.13). In conclusion, sleep hygiene and parenting are important modifiable factors independently associated with sleep problems in children with ADHD. These factors should be considered in the management of sleep problems in children with ADHD.

Keywords

ADHD Sleep Children Parenting Sleep hygiene Comorbidity 

Introduction

Children with attention-deficit/hyperactivity disorder (ADHD) suffer from more sleep problems compared to their healthy peers [1]. Certain parenting behaviors and poor sleep hygiene have been associated with sleep difficulties in typically developing children [2, 3]; however, relatively fewer studies have examined these risk factors in children with ADHD and sleep problems. Determining modifiable factors contributing to sleep problems in these children is important given that sleep problems have an independent impact on child functioning [4] and are far more common than in typically developing children. The identification of risk factors for sleep problems in this group may assist to optimize existing treatments [5, 6, 7].

Sleep problems affect up to 70% of children with ADHD and encompass a broad range of difficulties with both initiating and/or maintaining sleep [1]. Parent-reported data have indicated that children with ADHD have significantly higher rates of bedtime resistance, sleep onset difficulties, night awakenings, difficulty waking in the morning, and sleep disordered breathing compared to non-ADHD controls [8]. Studies using objective measures of sleep have found that children with ADHD have more fragmented sleep, a higher Apnea Hypopnea Index (indicative of obstructive sleep apnea), shorter sleep duration, and poorer sleep quality [8]. Furthermore, children with ADHD are more likely than their same-aged peers to experience sleep disorders including insomnia, sleep-related breathing disorders, sleep-related movement disorders, and circadian rhythm sleep-wake disorders [1]. The aetiology of sleep problems in children with ADHD is likely multifactorial including factors such as stimulant medication use [9] and coexisting psychiatric disorders [10]. There is evidence that sleep onset problems in children with ADHD are related to a delay in dim light melatonin onset, a marker of delayed circadian rhythms [11]. However, relatively few studies have examined other risk factors in children with ADHD and sleep problems including parenting and sleep hygiene.

Parenting may be a potentially modifiable risk factor for child sleep problems. However, only one small study (n = 67) of children with ADHD aged 5–12 years has examined this relationship in children with ADHD [10]. A lack of parenting consistency in daily household routines was associated with increased bedtime resistance; however, associations were not found for overall sleep, anxiety, and parasomnia difficulties [12]. Parenting stress was not independently associated with child sleep difficulties; however, other parenting dimensions such as parenting warmth were not considered [12]. Parental warmth may be an important possible risk factor for sleep problems given that the parents of children with ADHD have generally been found to display significantly less warmth and involvement compared to the parents of typically developing children [13]. Furthermore, in typically developing children, parental warmth has been associated with longer sleep duration [14].

Like parenting, sleep hygiene is a potentially modifiable risk factor for sleep problems in children with ADHD. Sleep hygiene describes parent and child practices to ensure readiness for sleep including a regular sleep/wake schedule, a suitable sleep environment, and set bedtime routine, and has been associated with better sleep in typically developing children [2, 15]. However, evidence as to whether sleep hygiene is associated with sleep problems in children with ADHD is conflicting [16, 17]. Weiss et al. [17] examined the effects of a sleep hygiene intervention in 27 children with ADHD aged 6–14 years, and found that by improving sleep hygiene, sleep onset latency also improved. In another study of children with ADHD aged 6–12 years (n = 74), sleep hygiene did not differ between those with and without sleep onset latency difficulties (greater than 30 min versus less than 30 min) [16]. However, this study did not assess other common sleep problems, such as bedtime resistance difficulties and night-time anxiety.

If parenting practices and sleep hygiene are associated with sleep problems in children with ADHD, then interventions could be optimised to include more of a focus on these modifiable variables. Although behavioral sleep interventions have been shown to be effective in improving sleep in children with ADHD, in one study, 30–34% of those in the intervention group continued to experience moderate/severe sleep problems 3 and 6 months later, suggesting that further refinement of interventions is needed [5].

As such, this study aimed to examine associations between sleep problem severity and parenting (warmth and consistency) and sleep hygiene in children with ADHD. We hypothesized that greater parenting consistency and increased parental warmth would be associated with reduced sleep problem severity in children with ADHD. Although the findings from previous studies have been inconclusive, we expected sleep hygiene to be associated with less severe sleep problems in children with ADHD.

Methods

Study design

This study used baseline, cross-sectional data from a randomized controlled trial examining the effectiveness of a behavioral sleep intervention for 5–13-year-old children with ADHD in Victoria and Queensland, Australia. The study protocol has been reported in detail elsewhere [18], but is briefly described below. Ethics approval was obtained from The Royal Children’s Hospital Human Research Ethics Committee (34072).

Participants and recruitment

Pediatricians (n = 46) identified all children with ADHD aged between 5 and 12 years seen at their clinic within the past 12 months. A study invitation letter was sent to all identified families, and families who did not choose to “opt-out” within 2 weeks were telephoned by the research team to screen for eligibility.

To be eligible children needed to have been diagnosed with ADHD by their pediatrician and needed to meet DSM 5 symptom criteria for ADHD by parent report using the 18-item ADHD Rating Scale IV [19], participants were eligible if they also satisfied additional study-designed questions assessing ADHD symptom duration (“did your child have these symptoms for six months or longer before he/she was diagnosed with ADHD?”) and impairment (“are these symptoms present at home, school or when out socially e.g. in the park, visiting friends?”), reflecting DSM 5 diagnostic criteria.

Furthermore, parents needed to rate the child’s sleep as being a moderate–severe sleep problem to be eligible. To assess this parents were asked ‘Has your child’s sleep been a problem for you over the past 4 weeks?’ and if ‘yes’, they are asked to rate severity (mild, moderate, or severe) [4]. This measure has been used in multiple studies and has good correspondence with the Children’s Sleep Habits Questionnaire [4, 20]. In addition, the child’s sleep problems needed to meet the International Classification of Sleep Disorders (ICSD) criteria for chronic insomnia disorder or delayed sleep-wake phase disorder [21] as assessed via study-designed questions. In accordance with ICSD criteria, children must be experiencing difficulty initiating or maintaining sleep and have associated impairments. To meet criteria for delayed sleep-wake phase disorder, there also needed to be evidence of difficulty awakening and improved sleep when the child could choose their own sleep-wake schedule. Children were also eligible if they were experiencing sleep-related anxiety assessed via study-designed questions.

Exclusion criteria included parent report of any of the following conditions: serious medical condition (e.g., cerebral palsy), intellectual disability (IQ <70), or suspected obstructive sleep apnea (OSA). Caregivers who responded ‘sometimes’ or ‘usually’ to the three OSA items from the Children’s Sleep Habits Questionnaire (CSHQ) were subsequently contacted by a pediatrician on the research team (HH) to ask further about the OSA symptoms and child’s history. If OSA was suspected, these children were excluded and referred to appropriate clinical services as per usual care. Children taking medications for sleep (e.g., Melatonin) were eligible if they continued to have sleep problems according to the criteria described above.

Eligible and interested families were mailed or emailed a participant information statement, consent form, and baseline questionnaire. Study data were collected and managed using REDCap electronic data capture tools hosted onsite [22].

Measures

Comorbidities

The presence of autism spectrum disorder was assessed via parent report of diagnosis (yes/no) while internalizing (separation anxiety disorder, panic disorder, generalized anxiety disorder, social phobia, specific phobia, obsessive–compulsive disorder, major depressive disorder, and dysthymia) and/or externalizing comorbidities (conduct disorder and oppositional defiant disorder) were assessed using the telephone-administered Anxiety Disorders Interview Schedule for Children IV (ADISC-IV) completed with parents [23]. In children with ADHD, screening positive for two or more anxiety disorders produces high specificity and sensitivity for a clinically meaningful anxiety disorder [24]. Therefore, our study defined internalizing comorbidities as the presence of two or more anxiety disorders, or one mood disorder.

Sleep problem severity

Child sleep problem severity was assessed using the parent-reported CSHQ, a validated 33-item questionnaire (α = 0.78) [25]. The CSHQ measures sleep difficulties across eight domains: bedtime resistance (α = 0.76), sleep onset delay (single item), daytime sleepiness (α = 0.76), sleep duration (α = 0.62), sleep anxiety (α = 0.67), parasomnias (α = 0.60), sleep disordered breathing, and night wakings (α = 0.67). The total CSHQ score ranges from 33 to 99, with higher scores indicating more sleep difficulties. Our study focused on the CSHQ subscales instead of the total score, to examine predictors of specific sleep difficulties. The sleep disordered breathing subscale was excluded, because it was part of study exclusion criteria.

Sleep hygiene

There were no valid measures of sleep hygiene available for this age group. As such, parent-reported child sleep hygiene was measured on a five-point Likert scale using six study-designed questions (α = 0.79) assessing: (1) consistency of bedtime routine on school nights; (2) consistency of bedtime routine on non-school nights; (3) consistency of bedtime on school nights; (4) consistency of bedtime on non-school nights; (5) whether the child falls asleep using electronics; and (6) presence of electronic devices in the bedroom. To aid the interpretation of results, responses to each item were dichotomized based on sleep hygiene recommendations. Bedtime routine reported as “almost never”, “occasionally”, and “half the time” was considered as poor bedtime routine (coded as 1), while “often” or “nearly always” was considered as good bedtime routine (coded as 0). Similarly, bedtime consistency reported as “almost never”, “occasionally”, or “half the time” was considered as poor (coded as 1), while responses of “often” or “nearly always” were coded as good (0). Responses to falling asleep while using electronics and presence of electronics in bedroom were also dichotomized with a response of “yes” to falling asleep using electronics coded as 1, and “no” coded as 0. The presence of electronic devices in the bedroom was coded as 0 for “none” and 1 for one or more device. Each dichotomized item was summed to give a composite score, with higher scores indicating poorer sleep hygiene (range 0–6).

Parenting consistency and warmth

Parenting consistency and warmth were assessed via parent report using validated scales of good-to-excellent reliability developed for use in the Longitudinal Study of Australian Children [26]. Parenting consistency (α = 0.76) was measured using five items (e.g., “when you give this child an instruction or make a request to do something, how often do you make sure that he/she does it?”). Parental warmth (α = 0.87) was measured using six items (e.g., how often do you tell this child how happy he/she makes you?”). Both scales were rated on a five-point Likert scale, where higher scores representing more positive parenting behaviors.

Parental mental health

The Kessler (K6) was used to measure parental mental health (α = 0.85). K6 is a standardized and validated measure of psychological distress [27]. A score of 13 or more suggests high risk for a serious mental illness.

Other variables

Child demographic variables included child age and sex and current stimulant medication use (yes/no). Family demographic variables included whether the child was living in a single parent family (yes/no), primary caregiver education level, and neighborhood socio-economic advantage measured using the Socio-Economic Indexes for Areas (SEIFA) [28]. SEIFA ranks areas in Australia according to relative socio-economic disadvantage and advantage, with higher scores representing less disadvantage.

Statistical analysis

We report means, standard deviations, and ranges for quantitative variables and absolute and relative frequencies (percentages) for categorical variables. Possible confounding variables were identified a priori using directed acyclic graphs representing suspected presence and absence of causal pathways between measured child or parenting characteristics and the outcomes of interest. These included: child age and sex, ADHD symptom severity, internalizing and externalizing disorders (yes/no according to ADISC-IV), autism spectrum disorder (yes/no), ADHD stimulant medication use (yes/no), parent mental health (K6 score >13), parent high school completion (yes/no), parent tertiary degree completion (yes/no), single parent status (yes/no), and SEIFA score.

Spearman correlation coefficients were calculated to explore unadjusted bivariate associations between outcome variables, suspected predictor variables, and potential confounding variables. Multiple linear regression analyses were used to establish prediction models for each sleep problem subscale (bedtime resistance, delayed sleep onset, daytime sleepiness, sleep anxiety, parasomnias, night waking, and sleep duration) with the pre-specified predictors (parenting consistency, parental warmth, and sleep hygiene) and confounding variables. To assess the explanatory capability of a predictor variable regarding the outcome measures of interest, we evaluated the regression coefficients (β) and the associated 95% confidence intervals (CIs) after adjusting for possible confounding variables. To quantify overall explanatory performance of the final regression models, unadjusted and adjusted coefficients of determination (R2 fraction of variance explained) were reported. A sensitivity analysis found that there were no associations between melatonin use and any of the sleep domains, as such melatonin use was not retained in the final models.

To maximize the clinical relevance of our analyses, we created nomograms based on the multiple regression models outcomes. Nomograms provide a graphical translation of regression models to allow a prediction of the risk for a particular clinical event. We established nomograms for the sleep problem outcomes that explained at least 10% of the variance in the adjusted model (bedtime resistance, parasomnias, sleep duration, and daytime sleepiness), but have only reported the nomogram for bedtime resistance to maintain the conciseness of the paper (the remaining nomograms can be found in the online supplementary materials). The model-based score points are displayed in the nomogram for each predictor variable value, which are then summarized for an individual’s covariate data. For the resulting total number of points, the corresponding predicted outcome can be deduced from the nomogram. The statistical software Stata 13.0 and R with the package “rms” were used for data analysis.

Results

Sample characteristics

Of the 701 families who were eligible, 361 (52%) returned completed questionnaires and consent forms. Although responders and non-responders were similar in terms of child age and gender distribution, and ADHD symptom severity, responders were more likely to be from more socially advantaged areas.

Table 1 shows the sample characteristics. The majority of the children were male (75%), taking stimulant medication (74%), and had internalizing (60%) or externalizing (50%) comorbidities. Most (93%) questionnaires were completed by mothers and the majority of primary caregivers had completed high school (70%). One quarter of the families were single parent households.
Table 1

Characteristics of children with ADHD and their families

Characteristics

N (%)a

Child

 

 Age, mean (SD), range

9.5 (1.7), 5.8–13.5

 Male

271 (75.1)

 ADHD symptom severity, mean (SD), range

39.3 (8.2), 19.0–54.0

 Stimulant medication use

268 (74.2)

 Sleep medication use

 

  Melatonin

124 (34.8)

  Clonidine

31 (8.8)

 Comorbidities

 

  Internalizingb

217 (60.3)

  Externalizing

183 (50.7)

  Both internalizing and externalizing

129 (35.8)

  Autism spectrum disorderc

140 (38.8)

Sleepd

 

 Total CSHQ score, mean (SD), range

58.4 (8.3), 39.0–81.5

  Bedtime resistance

1.7 (0.5), 1.0–3.0

  Sleep duration

2.1 (0.5), 1.0–3.0

  Sleep anxiety

1.9 (0.6), 1.0–3.0

  Night waking

1.8 (0.6), 1.0–3.0

  Parasomnias

1.6 (0.3), 1.0–3.0

  Daytime sleepiness

1.9 (0.5), 1.0–3.0

Sleep hygienee

 

 Bedtime routine school night

 

  Poor routine

108 (30.3)

  Good routine

249 (69.7)

 Bedtime routine non-school nights

 

  Poor routine

205 (42.2)

  Good routine

150 (57.8)

 Bedtime school nights

 

  Similar bedtime

259 (73.8)

  Different bedtime

92 (26.2)

 Bedtime non-school nights

 

  Similar bedtime

133 (38.1)

  Different bedtime

216 (61.9)

 Fall asleep using electronics

129 (35.7)

 Electronics in bedroom

 

  One or more

224 (62.1)

Family

 

 Primary caregiver, mother

331 (92.5)

 Single parent family

88 (24.6)

 Highest education level of primary caregiver

 

  Completed high school

252 (70.0)

  Completed tertiary/postgraduate degree

143 (39.9)

 SEIFA disadvantage, mean (SD), range

1011.79 (62.2), 783.3–1120.4

 Parent mental health K6 score, mean (SD), range

6.2 (5.0), 0.0–23.0

 Parenting consistency score, mean (SD), range

3.5 (0.8), 1.3–5.0

 Parental warmth score, mean (SD), range

4.0 (0.7), 2.1–5.0

CHSQ Children’s Sleep Habits Questionnaire, SEIFA Socio-Economic Index for Areas

aSample size ranges from 349 to 361

bInternalizing comorbidities defined as the presence of two or more anxiety disorders or one mood disorder

cParents were asked if their child had been diagnosed by a health professional with Autism Spectrum Disorder or Asperger

dSubscales are reported as mean item scores to aid interpretation

eSleep hygiene dichotomized. Falling asleep using electronics or presence of one or more electronic device in bedroom were grouped as yes/no

Sleep problem outcome variables

Table 2 presents the multiple linear regression models for each sleep domain. Prior to adjustment the outcomes daytime sleepiness, bedtime resistance, parasomnias, and sleep duration revealed the best prediction models, each explaining between 16 and 20% of the variance (Table 2). After adjustment, the models explain between 8 and 16% of the variance (with the exception of night waking). The prediction model for daytime sleepiness explained 16%, bedtime resistance explained 14%, sleep duration and parasomnias explained 12%, and sleep anxiety and delayed sleep onset explained about 8% of the variance, while the model for night waking did not explain any of the observed data dispersion.
Table 2

Association between sleep problems and sleep hygiene, parenting consistency, and parenting warmth

CSHQ subscales

Unadjusted R2

Adjusted R2

Spearman coefficients

Variables

β

95% CI

P

Bedtime resistance

0.18

0.14

−0.09

Child age

−0.06

−0.17 to 0.05

0.27

   

−0.02

Child gender

0.02

−0.08 to 0.12

0.76

   

0.16

Single parent family

0.12

0.01 to 0.23

0.03

   

−0.07

HS degree

0.03

−0.09 to 0.15

0.62

   

−0.03

Uni degree

0.06

−0.05 to 0.18

0.30

   

0.15

Parent K6

0.06

−0.05 to 0.17

0.23

   

−0.15

SEIFA

−0.13

−0.25 to −0.02

0.02

   

0.10

ADHD symptom severity

0.04

−0.07 to 0.15

0.49

   

−0.10

Stimulant med

−0.12

−0.22 to −0.02

0.02

   

0.00

ASD

−0.05

−0.16 to 0.06

0.39

   

0.15

Int. and Ext. comorbidities

0.08

−0.03 to 0.19

0.17

   

0.27

Sleep hygiene

0.20

0.09 to 0.31

0.001

   

−0.23

Parenting consistency

−0.16

−0.27 to −0.05

0.005

   

0.03

Parenting warmth

0.09

−0.02 to 0.19

0.10

Daytime sleepiness

0.20

0.16

0.25

Child age

0.25

0.14 to 0.35

<0.001

   

−0.18

Child gender

−0.24

−0.34 to −0.14

<0.001

   

0.02

Single parent family

−0.00

−0.11 to 0.10

0.99

   

0.00

HS degree

−0.00

−0.12 to 0.12

0.92

   

−0.00

Uni degree

0.01

−0.10 to 0.13

0.82

   

0.11

Parent K6

0.07

−0.04 to 0.18

0.20

   

−0.03

SEIFA

−0.02

−0.13 to 0.09

0.75

   

0.03

ADHD symptom severity

0.06

−0.05 to 0.17

0.31

   

−0.16

Stimulant med

−0.17

−0.28 to −0.07

0.001

   

−0.01

ASD

−0.06

−0.16 to 0.05

0.29

   

0.11

Int. and Ext. comorbidities

0.11

0.00 to 0.22

0.046

   

0.18

Sleep hygiene

0.12

0.01 to 0.23

0.04

   

0.05

Parenting consistency

0.08

−0.03 to 0.19

0.63

   

−0.03

Parenting warmth

0.02

−0.08 to 0.13

0.16

Parasomnias

0.16

0.12

−0.12

Child age

−0.11

−0.22 to 0.00

0.05

   

0.04

Child gender

0.08

−0.03 to 0.18

0.15

   

0.06

Single parent family

0.00

−0.10 to 0.11

0.95

   

−0.06

HS degree

0.04

−0.08 to 0.16

0.54

   

−0.07

Uni degree

−0.04

−0.15 to 0.08

0.54

   

0.15

Parent K6

0.05

−0.05 to 0.16

0.33

   

−0.07

SEIFA

−0.03

−0.14 to 0.09

0.66

   

0.25

ADHD symptom severity

0.21

0.10 to 0.33

<0.001

   

−0.02

Stimulant med

−0.03

−0.13 to 0.08

0.63

   

0.05

ASD

0.02

−0.09 to 0.13

0.77

   

0.23

Int. and Ext. comorbidities

0.12

0.01 to 0.24

0.03

   

0.11

Sleep hygiene

0.09

−0.03 to 0.20

0.15

   

−0.09

Parenting consistency

−0.03

−0.15 to 0.08

0.56

   

0.16

Parenting warmth

0.18

0.08 to 0.29

0.001

Sleep anxiety

0.12

0.08

−0.10

Child age

−0.7

−0.18 to 0.04

0.21

   

0.05

Child gender

0.05

−0.05 to 0.16

0.32

   

0.10

Single parent family

0.09

−0.02 to 0.20

0.11

   

−0.03

HS degree

0.03

−0.09 to 0.15

0.65

   

0.05

Uni degree

0.10

−0.02 to 0.22

0.10

   

0.14

Parent K6

0.06

−0.05 to 0.17

0.29

   

−0.08

SEIFA

−0.09

−0.21 to 0.02

0.12

   

0.10

ADHD symptom severity

0.03

−0.08 to 0.15

0.60

   

−0.09

Stimulant med

−0.08

−0.18 to 0.03

0.17

   

0.09

ASD

0.05

−0.06 to 0.17

0.35

   

0.19

Int. and Ext. comorbidities

0.12

0.00 to 0.23

0.045

   

0.09

Sleep hygiene

0.06

−0.06 to 0.18

0.32

   

−0.16

Parenting consistency

−0.14

−0.26 to −0.03

0.02

   

0.09

Parenting warmth

0.13

0.02 to 0.23

0.02

Night waking

0.04

−0.00

−0.08

Child age

−0.02

−0.14 to 0.10

0.74

   

−0.08

Child gender

−0.07

−0.18 to 0.04

0.20

   

0.06

Single parent family

0.04

−0.07 to 0.16

0.49

   

−0.06

HS degree

−0.04

−0.16 to 0.09

0.56

   

−0.06

Uni degree

0.02

−0.10 to 0.15

0.71

   

0.13

Parent K6

0.09

−0.03 to 0.20

0.13

   

−0.09

SEIFA

−0.02

−0.15 to 0.10

0.69

   

0.11

ADHD symptom severity

0.08

−0.04 to 0.20

0.21

   

−0.03

Stimulant med

−0.06

−0.17 to 0.11

0.33

   

0.01

ASD

−0.02

−0.14 to 0.10

0.73

   

−0.09

Int. and Ext. comorbidities

0.03

−0.09 to 0.15

0.64

   

−0.00

Sleep hygiene

−0.06

−0.19 to 0.06

0.30

   

−0.07

Parenting consistency

−0.06

−0.18 to 0.06

0.35

   

0.05

Parenting warmth

0.07

−0.04 to 0.18

0.24

Sleep duration

0.16

0.12

0.22

Child age

0.21

0.10 to 0.32

<0.001

   

−0.04

Child gender

−0.06

−0.16 to 0.04

0.25

   

0.05

Single parent family

−0.01

−0.12 to 0.10

0.85

   

−0.02

HS degree

−0.05

−0.17 to 0.07

0.44

   

0.07

Uni degree

0.16

0.04 to 0.27

0.009

   

0.13

Parent K6

0.14

0.04 to 0.25

0.01

   

−0.05

SEIFA

0.00

−0.11 to 0.12

0.98

   

0.15

ADHD symptom severity

0.09

−0.02 to 0.20

0.12

   

−0.01

Stimulant med

−0.01

−0.12 to 0.10

0.85

   

0.10

ASD

0.07

−0.04 to 0.18

0.20

   

0.15

Int. and Ext. comorbidities

0.09

−0.02 to 0.21

0.10

   

0.16

Sleep hygiene

0.13

0.02 to 0.25

0.02

   

−0.03

Parenting consistency

0.01

−0.11 to 0.12

0.91

   

−0.09

Parenting warmth

−0.06

−0.16 to 0.05

0.28

Delayed sleep onset

0.12

0.08

0.07

Child age

−0.01

−0.12 to 0.10

0.81

   

−0.12

Child gender

−0.12

−0.23 to −0.01

0.03

   

−0.04

Single parent family

−0.07

−0.18 to 0.04

0.20

   

0.13

HS degree

0.15

0.03 to 0.27

0.02

   

0.05

Uni degree

0.06

−0.06 to 0.18

0.35

   

0.09

Parent K6

0.10

−0.01 to 0.21

0.07

   

−0.04

SEIFA

−0.14

−0.26 to −0.02

0.02

   

0.04

ADHD symptom severity

0.05

−0.07 to 0.16

0.41

   

0.14

Stimulant med

0.15

0.04 to 0.25

0.008

   

0.00

ASD

0.01

−0.10 to 0.13

0.81

   

−0.01

Int. and Ext. comorbidities

−0.03

−0.14 to 0.10

0.66

   

0.11

Sleep hygiene

0.17

0.06 to 0.29

0.004

   

0.09

Parenting consistency

0.14

0.02 to 0.25

0.02

   

−0.05

Parenting warmth

−0.11

−0.22 to −0.00

0.04

Parenting consistency

After adjusting for confounding variables, greater parenting consistency was associated with decreased bedtime resistance (coefficient, β = −0.16, 95% confidence interval, CI = −0.27 to −0.05) and decreased sleep anxiety (β = −0.14, CI = −0.26 to −0.03), and with increased delayed sleep onset (β = +0.14, CI = +0.02 to +0.25) (see Table 2). Parenting consistency was not independently associated with other sleep domains.

Parenting warmth

In adjusted analyses, greater parental warmth was associated with a reduction in delayed sleep onset (β = −0.11, CI = −0.22 to −0.00), but with increased parasomnias (β = +0.18, CI = +0.08 to +0.29) and sleep anxiety (β = +0.13, CI = +0.02 to +0.23). Parenting warmth was not independently associated with other sleep domains.

Sleep hygiene

Adjusted models indicated that poorer sleep hygiene was associated with increased bedtime resistance (β = +0.20; CI = +0.09 to +0.31), increased daytime sleepiness (β = +0.12; CI = +0.01 to +0.23), increased sleep duration problems (β = + 0.13, CI = +0.02 to +0.25), and increased delayed sleep onset (β = +0.17, CI = +0.06 to +0.29).

Other variables

A number of the confounding variables were independently associated with each of the sleep domains. Children living in single parent households had increased bedtime resistance, while children taking stimulant medication and those from neighborhoods with higher socio-economic advantage had lower levels of bedtime resistance. Older children, females and those with internalizing and externalizing comorbidities had higher levels of daytime sleepiness, while again, stimulant medication use was associated with lower levels of daytime sleepiness. Greater ADHD symptom severity and the presence of internalizing and externalizing comorbidities were associated with higher parasomnia scores, while internalizing and externalizing comorbidities were independently associated with sleep anxiety. Older child age, having a parent with a university degree, parent mental health difficulties, and ADHD symptom severity were all associated with increased sleep duration problems. Being female, taking stimulant medication, living in a less disadvantaged neighborhood, and having a parent who had completed high school were associated with delayed sleep onset.

Figure 1 shows the nomogram for the regression model of bedtime resistance. Nomograms can be used as a prediction tool for these sleep problems, by summing the points of predictor variables that apply to a certain clinical case. For example, a 7-year-old (20 points) girl (8 points) with poor sleep hygiene (55 points) whose parent has poor mental health (12 points) and shows a lack of parenting consistency (90 points) produces a total score of 185 (Fig. 1). This corresponds to a bedtime resistance score of 11.
Fig. 1

Nomogram of bedtime resistance. Single parent (0 = no partner; 1 = have partner), University degree (completed tertiary degree; 0 = no, 1 = yes), SEIFA (socio-economic indexes for areas), parent mental health (0 = no serious mental health disorder, 1 = probable serious mental health disorder), comorbid internalizing and externalizing disorders (0 = no, 1 = yes), sleep hygiene (higher score indicates poorer sleep hygiene), parental warmth (higher scores indicate greater parental warmth), parenting consistency (higher scores indicate greater parenting consistency)

Discussion

We found that parenting and sleep hygiene were associated with sleep problems in children with ADHD. Specifically, greater parenting consistency and better sleep hygiene were associated with decreased bedtime resistance, while better sleep hygiene was associated with lower levels of daytime sleepiness, less delayed sleep onset, and fewer sleep duration difficulties. Greater parental warmth was associated with decreased delayed sleep onset, and with increased sleep anxiety and parasomnias, while parenting consistency was associated with lower levels of sleep anxiety and more delayed sleep onset. These associations were independent of child age and gender, stimulant medication use, and internalizing and externalizing comorbidities.

Our finding that greater parenting consistency was associated with reduced bedtime resistance is consistent with that of Noble et al. [29]. We extend previous findings by demonstrating that higher parenting consistency is also associated with decreased sleep anxiety. Our findings suggest that caregivers with more consistent parenting behaviors may be better able to establish and follow through on bedtime routines, which may be particularly important for the behavioral sleep problems, such as bedtime resistance and sleep anxiety. We found that the relationship between parenting consistency and bedtime resistance and sleep anxiety was independent of key variables, such as ADHD symptom severity and comorbidity. Unexpectedly, our results showed that greater parenting consistency was associated with increased delayed sleep onset. However, the results should be interpreted with caution as the delayed sleep onset subscale comprises a single item with low validity against objective measures [30].

Surprisingly, we found that greater parental warmth was associated with increased parasomnias and sleep anxiety. To the best of our knowledge, this study is the first to examine parental warmth and sleep problems in children with ADHD. It is possible that parents who exhibit more warmth towards their child may also be more permissive, which may lead to later bedtimes and sleep deprivation which may increase parasomnias. However, we found no association between parenting warmth and sleep duration and there was only a weak association between parenting consistency and warmth in this study. Parents exhibiting more warmth may encourage their children to disclose their worries, feelings, or emotions around bedtime, which may explain the observed relationship with sleep anxiety. Related to this parenting warmth may be associated with more parental worries which could result in an over-reporting of worries. Another possibility is that parents who exhibit more warmth may spend more time with their children, which may provide more opportunities for them to be aware of their child’s sleep difficulties and reported them in our study. Future research needs to better clarify the causal nature of this association before any clinical recommendations can be made.

Sleep hygiene was associated with bedtime resistance, daytime sleepiness, and sleep duration. We also found variation in bedtime routines and bed times between school nights and non-school nights. Most parents reported good bedtime routine and regular bedtimes on school nights, with the reverse on non-school nights. This may reflect laxity of bedtime practices on non-school nights, which may affect the child’s circadian rhythm and contribute to poor sleep quality on school days. Previous studies examining the effect of sleep hygiene on sleep problems in primary school-aged children with ADHD have reached inconsistent conclusions. Our study extends previous research by having a larger sample size, taking into account possible confounding factors, such as ADHD medication use and comorbidities, and examining a broader range of sleep difficulties.

In contrast to some previous reports [9], stimulant medication use was associated with decreased bedtime resistance, which could be due to suppression of ADHD symptoms around bedtime. As expected, stimulant medication was associated with lower levels of daytime sleepiness which likely reflects the medication masking the effects of sleep deprivation during the day. Past studies have also indicated a close link between internalizing and externalizing behaviors and sleep problems [10, 31], and indeed, we found that co-occurring internalizing and externalizing comorbidities were independently associated with increased daytime sleepiness, parasomnias, and sleep anxiety but not bedtime resistance, night waking, and sleep duration, which could be due to the inclusion of other variables in our models, such as parenting factors and sleep hygiene.

We demonstrated a more clinically relevant interpretation of our results through the use of a nomogram presented for bedtime resistance, a novel approach in pediatric sleep medicine. Parenting consistency, sleep hygiene, and neighborhood disadvantage contributed most to the severity of bedtime resistance. For parasomnias, ADHD symptom severity, parental warmth, and child age appear to have the greatest contributions. Child age was the strongest predictor of daytime sleepiness and of sleep duration, with sleep hygiene also playing a role in sleep duration. In the future, nomograms may allow clinicians to tailor treatment according to a child’s risk for specific sleep problems by taking various risk factors into consideration. However, the nomograms generated in the current study are limited in their clinical utility given that none of our adjusted models explained more than 16% of the variance. Further research should identify other predictors to build into these nomograms to create a clinical tool for better assessing sleep difficulties in children with ADHD.

Our study had some limitations. First, we used subjective measures to assess child sleep problems. However, subjective measures tend to capture the behavioral sleep problems in children with ADHD better than more objective measures [32]. We also used a study-designed measure of sleep hygiene as there were no valid measures of sleep hygiene for this age group. We also dichotomized the sleep hygiene items to aid interpretation of findings; this has limited our ability to comment on variation in sleep hygiene practices and their influence on sleep problems. It is possible that there was social desirability bias as parents reported on their own parenting behaviors. Further research using objective measures to measure parenting behaviors, such as direct observation of parent–child interactions, could be considered; however, these approaches are costly and usually not feasible for large-scale studies. We did conduct multiple analyses, which may have affected our Type I error rate and we did not have a non-ADHD control group. All participants in this study identified as having a sleep problem; thus, we focused on the relationship between our key exposure variable of interest and sleep problem severity as opposed to merely the presence of a sleep problem. Future studies should replicate our findings using a longitudinal study design. Responders differed to non-responders, and as such, results of our study are more likely to generalize to families of children from more socially advantaged areas. Finally, there is a need to further investigate the associations between parenting, sleep hygiene, and delayed sleep onset. Our measure of delayed sleep onset comprised a single item of unclear validity. As such, the results regarding delayed sleep onset must be interpreted with caution.

Conclusion

Parenting domains and sleep hygiene are associated with sleep difficulties, each related to different aspects of the sleep profile of children with ADHD. Surprisingly, greater parental warmth was associated with increased bedtime resistance and sleep anxiety in this population. For children with ADHD experiencing sleep difficulties, parenting and sleep hygiene may be important modifiable factors and should be considered in the future management of their sleep problems. Nomograms may be a useful tool for tailoring the assessment and management of sleep difficulties in these children.

Notes

Acknowledgements

This study was funded by the Australian National Health and Medical Research Council (NHMRC Grant 1058827). ES is supported by an NHMRC Early Career Fellowship (1037159) and NHMRC Career Development Award (1110688). TS is supported by funding from the Royal Children’s Hospital Foundation to the Melbourne Children’s Trial Centre. HH is supported by an NHMRC Career Development Award (607351). MCRI is supported by the Victorian Government’s Operational Infrastructure Support Program. Data were collected and managed using the REDCap electronic data capture tools hosted at MCRI. REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

Ethics approval was granted by the Human Research Ethics Committees of The Royal Children’s Hospital (#34072). The research has been conducted in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. All persons gave their informed consent prior to their inclusion in the study.

Supplementary material

787_2017_1000_MOESM1_ESM.doc (82 kb)
Supplementary material 1 (DOC 81 kb)
787_2017_1000_MOESM2_ESM.doc (89 kb)
Supplementary material 2 (DOC 89 kb)
787_2017_1000_MOESM3_ESM.doc (72 kb)
Supplementary material 3 (DOC 72 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Emma Sciberras
    • 1
    • 2
    • 3
  • Jie Cheng Song
    • 2
  • Melissa Mulraney
    • 2
    • 3
  • Tibor Schuster
    • 2
    • 5
  • Harriet Hiscock
    • 2
    • 3
    • 4
  1. 1.Deakin UniversityGeelongAustralia
  2. 2.Murdoch Childrens Research InstituteParkvilleAustralia
  3. 3.The University of MelbourneParkvilleAustralia
  4. 4.Centre for Community Child HealthThe Royal Children’s HospitalParkvilleAustralia
  5. 5.Department of Family MedicineMcGill UniversityMontrealCanada

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