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Record W3158522858 · doi:10.1093/sleep/zsab072.764

767 Sleep architecture and sleep problems in adolescents and young adults with and without ADHD: A systematic review and meta-analysis

2021· review· en· W3158522858 on OpenAlexaboutno aff
Finja Marten, Lena Keuppens, Dieter Baeyens, Bianca Boyer, Marina Danckaerts, Saskia Van der Oord

Bibliographic record

VenueSLEEP · 2021
Typereview
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsnot available
Fundersnot available
KeywordsSleep onset latencySleep (system call)Sleep hygieneMeta-analysisSleep onsetPsychologySleep architectureAudiologyClinical psychologyMedicinePolysomnographyInsomniaPsychiatrySleep qualityInternal medicineElectroencephalography

Abstract

fetched live from OpenAlex

Abstract Introduction During the past years, an increasing number of articles has focused on comparing sleep in youths with and without ADHD. However, so far no meta-analysis has been conducted summarizing the findings. Therefore, the current study assesses sleep architecture (i.e. the basic sleep structure), sleep problems, and sleep hygiene. Sleep was assessed both subjectively and objectively and the two groups were compared on multiple variables. Methods Two researchers independently performed a literature search (1980–2020). Studies using a case-control design comparing sleep in youths (12–25 years) with and without ADHD were included. Study quality was evaluated using the Newcastle-Ottawa Scale. Standardized mean differences were calculated for each outcome domain being reported by at least two studies. Results 10379 publications were screened, resulting in 11 studies and 52 effect sizes (nADHD=2377, ncontrol=21687). These effect sizes were summarized into 7 objective and 11 subjective variables measuring sleep. Two objective sleep variables were significantly worse in the ADHD group; total sleep time (z=2.16, p=.03) and sleep onset latency (z=2.39, p=.02). The two groups did not differ on sleep efficiency, sleep onset/offset time, and time in bed. Comparing the groups on subjective variables resulted in the same pattern, with total sleep time (z=21.27, p<.001) being significantly shorter in the ADHD group, and sleep onset latency (z=15.39, p<.001) and wake after sleep onset (z=13.50, p<.001) being significantly longer. Additionally, the ADHD group reported a significantly lower sleep efficiency (z=20.15, p<.001) and subjective sleep satisfaction (z=3.50, p<.001). Wake time and number of awakenings during the night were not significant. Youths with ADHD also reported significantly more sleep problems, including insomnia (z=6.38, p<.001), daytime sleepiness (z=26.68, p<.001) and sleep disturbances (z=8.00, p<.001). Due to only two studies measuring it, with a focus on different variables, sleep hygiene could not be included. Conclusion In general, youths with ADHD have a disrupted sleep architecture and experience more sleep problems compared to their typically developing peers. Consequently, sleep assessment should become a routine part during the diagnostic process of ADHD. Additionally, more research is needed focusing on sleep architecture and sleep hygiene, and on the development of a sleep intervention for youths with ADHD. Support (if any):

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.023
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.023
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0150.026
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.333
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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