A Systematic Review of Sleep and Circadian Rhythms in Children with Attention Deficit Hyperactivity Disorder

Bondopadhyay, Upasana; Díaz-Orueta, Unai; Coogan, Andrew N.

doi:10.1177/1087054720978556

Cited by 37 publications

(44 citation statements)

References 209 publications

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“…Only the extent of sleep deficit was slightly higher in children with ADHD on school days than on free days as compared with controls. These findings are in line with the results of a recent meta-analysis [4], which revealed longer SOL and lower sleep efficiency, and with a systematic review on schoolaged children with ADHD [27], which confirmed the occurrence of sleep disturbances using subjective and objective measurement methods in ADHD. With regard to IIV, children with ADHD showed higher IIV than typically developing children only in the SOL, which confirms the results of some earlier studies [39,70], but also contradicts others [40,41] that did not find greater IIV in children with ADHD.…”

Section: Discussionsupporting

confidence: 90%

Actigraphy-Derived Sleep Profiles of Children with and without Attention-Deficit/Hyperactivity Disorder (ADHD) over Two Weeks—Comparison, Precursor Symptoms, and the Chronotype

et al. 2021

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Although sleep problems are common in children with ADHD, their extent, preceding risk factors, and the association between neurocognitive performance and neurobiological processes in sleep and ADHD, are still largely unknown. We examined sleep variables in school-aged children with ADHD, addressing their intra-individual variability (IIV) and considering potential precursor symptoms as well as the chronotype. Additionally, in a subgroup of our sample, we investigated associations with neurobehavioral functioning (n = 44). A total of 57 children (6–12 years) with (n = 24) and without ADHD (n = 33) were recruited in one center of the large ESCAlife study to wear actigraphs for two weeks. Actigraphy-derived dependent variables, including IIV, were analyzed using linear mixed models in order to find differences between the groups. A stepwise regression model was used to investigate neuropsychological function. Overall, children with ADHD showed longer sleep onset latency (SOL), higher IIV in SOL, more movements during sleep, lower sleep efficiency, and a slightly larger sleep deficit on school days compared with free days. No group differences were observed for chronotype or sleep onset time. Sleep problems in infancy predicted later SOL and the total number of movements during sleep in children with and without ADHD. No additional effect of sleep problems, beyond ADHD symptom severity, on neuropsychological functioning was found. This study highlights the importance of screening children with ADHD for current and early childhood sleep disturbances in order to prevent long-term sleep problems and offer individualized treatments. Future studies with larger sample sizes should examine possible biological markers to improve our understanding of the underlying mechanisms.

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Section: Discussionsupporting

confidence: 90%

Actigraphy-Derived Sleep Profiles of Children with and without Attention-Deficit/Hyperactivity Disorder (ADHD) over Two Weeks—Comparison, Precursor Symptoms, and the Chronotype

et al. 2021

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“…The effect on sleep onset latency is of significant importance, given that problems with prolonged sleep onset latency are more common in children with ADHD than among healthy peers [ 32 , 33 ]. About one-third of children with ADHD have difficulties falling asleep [ 3 , 44 ], with up to one hour of sleep onset latency between children with ADHD and healthy peers [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

“…Children with ADHD have an increased risk of poor health outcomes, compared to healthy peers. The negative behaviors associated with ADHD cause difficulties with interpersonal interactions and educational outcomes, and are likely to be exacerbated by sleep disturbances [ 3 ]. The prevalence of sleep disturbances among children with ADHD is high (up to 70%) [ 4 , 5 ], compared to healthy peers (20–30%) [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…Sleep disturbance includes both initiation and maintenance of sleep. The most common sleep disturbances among children with ADHD are: bedtime refusal (e.g., resisting going to bed), difficulties in initiating sleep (e.g., insomnia), difficulties staying asleep (e.g., nighttime awakening), disproportionate daytime sleepiness, and fragmented sleep [ 3 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…No positive effects on sleep were seen in the study, although both parents and children favored weighted blankets over their usual blanket [ 34 ]. In summary, there is no convincing evidence of the effect [ 28 ] or the economic impact [ 27 , 35 ], nor any qualitative studies from children’s and parents’ perspectives on using weighted blankets to improve sleep in children with ADHD [ 3 ]. In filling this gap [ 35 ], an RCT by our research group is underway to explore the effects of an intervention with weighted blankets on both sleep and everyday life for children with ADHD and sleep problems [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

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Parents’ Experiences of Weighted Blankets’ Impact on Children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Sleep Problems—A Qualitative Study

Larsson

Aili

Nygren

et al. 2021

IJERPH

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Sleep disturbances are common among children with attention-deficit/hyperactivity disorder (ADHD). While pharmacological treatment has increased dramatically, parents often prefer non-pharmacological interventions. Research on experiences of weighted blankets and their effect in sleep improvement is scarce. The aim of this study was to explore parents’ experiences of weighted blankets for children with ADHD and sleep problems, and the impact on their children’s sleep. The explorative design was based on qualitative content analysis. Interviews were conducted with a purposeful sample of 24 parents of children with ADHD and sleep problems, after completing a sleep intervention with weighted blankets for 16 weeks. Parents reported that children sleeping with weighted blankets: (1) achieved satisfactory sleep, including improved sleep onset latency, sleep continuity, and sleep routines; (2) achieved overall well-being, including improved relaxation and reduced anxiety; and (3) mastered everyday life, including improved balance in life, family function, and participation in school and leisure activities. This study brings forward novel aspects of the effects of improved sleep among children with ADHD. The findings contribute to the understanding of potential positive effects of an intervention with weighted blankets critical for clinical practice to improve sleep, well-being, and everyday life of children with ADHD and their families.

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Machine Learning–Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children

Kim¹,

Kim

Pack

et al. 2023

JAMA Netw Open

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ImportanceEarly detection of attention-deficit/hyperactivity disorder (ADHD) and sleep problems is paramount for children’s mental health. Interview-based diagnostic approaches have drawbacks, necessitating the development of an evaluation method that uses digital phenotypes in daily life.ObjectiveTo evaluate the predictive performance of machine learning (ML) models by setting the data obtained from personal digital devices comprising training features (ie, wearable data) and diagnostic results of ADHD and sleep problems by the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version for Diagnostic and Statistical Manual of Mental Disorders, 5th edition (K-SADS) as a prediction class from the Adolescent Brain Cognitive Development (ABCD) study.Design, Setting, and ParticipantsIn this diagnostic study, wearable data and K-SADS data were collected at 21 sites in the US in the ABCD study (release 3.0, November 2, 2020, analyzed October 11, 2021). Screening data from 6571 patients and 21 days of wearable data from 5725 patients collected at the 2-year follow-up were used, and circadian rhythm–based features were generated for each participant. A total of 12 348 wearable data for ADHD and 39 160 for sleep problems were merged for developing ML models.Main Outcomes and MeasuresThe average performance of the ML models was measured using an area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the Shapley Additive Explanations value was used to calculate the importance of features.ResultsThe final population consisted of 79 children with ADHD problems (mean [SD] age, 144.5 [8.1] months; 55 [69.6%] males) vs 1011 controls and 68 with sleep problems (mean [SD] age, 143.5 [7.5] months; 38 [55.9%] males) vs 3346 controls. The ML models showed reasonable predictive performance for ADHD (AUC, 0.798; sensitivity, 0.756; specificity, 0.716; PPV, 0.159; and NPV, 0.976) and sleep problems (AUC, 0.737; sensitivity, 0.743; specificity, 0.632; PPV, 0.036; and NPV, 0.992).Conclusions and RelevanceIn this diagnostic study, an ML method for early detection or screening using digital phenotypes in children’s daily lives was developed. The results support facilitating early detection in children; however, additional follow-up studies can improve its performance.

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A Systematic Review of Sleep and Circadian Rhythms in Children with Attention Deficit Hyperactivity Disorder

Cited by 37 publications

References 209 publications

Actigraphy-Derived Sleep Profiles of Children with and without Attention-Deficit/Hyperactivity Disorder (ADHD) over Two Weeks—Comparison, Precursor Symptoms, and the Chronotype

Actigraphy-Derived Sleep Profiles of Children with and without Attention-Deficit/Hyperactivity Disorder (ADHD) over Two Weeks—Comparison, Precursor Symptoms, and the Chronotype

Parents’ Experiences of Weighted Blankets’ Impact on Children with Attention-Deficit/Hyperactivity Disorder (ADHD) and Sleep Problems—A Qualitative Study

Machine Learning–Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children

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