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Enregistrement W4384943617 · doi:10.3389/fpsyt.2023.1230825

Editorial: Sleep, vigilance & disruptive behaviors

2023· editorial· en· W4384943617 sur OpenAlexaffabout
O. Ipsiroglu, Gerhard Klösch, Rosalia Silvestri, Susan McCabe, Georg Dorffner, Thomas C. Wetter, Luci Wiggs

Notice bibliographique

RevueFrontiers in Psychiatry · 2023
Typeeditorial
Langueen
DomainePsychology
ThématiqueSleep and related disorders
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésVigilance (psychology)PsychologySleep (system call)Sleep deprivationMedicinePsychiatryCognitive psychologyCognitionComputer science

Résumé

récupéré en direct d'OpenAlex

From the five review articles, the first one revisits the concept of vigilance as an indicator of sleep disturbances. Since the introduction of the vigilance concept by Head, many different aspects were analysed; however, the underlying context for gathering insight into the interplay between sleep and daytime behaviours, reflecting both physical and cognitive performance, as well as sleep quality and quantity, was hardly ever investigated. (4) Similarly, the second review article explores a universal screening strategy for sleep health in the community, utilising social-ecological considerations, as the cultural context of the current (medicalized) approach does not acknowledge the myriad of presentations and possible root causes of sleep disturbances (5). The third review examines the functional links between thermoregulation for maintaining thermoneutrality and sleep in children with chronic health conditions; as circadian patterns of sleep-wake are dependent on patterns of body temperature changes (6). The forth review and meta-analysis investigates the efficacy of eye masks and earplugs in intensive care units as an intervention for promoting sleep health (7). The fifth review investigates the root causes of most hypermotor restlessness, such as central iron deficiency and its exacerbation by vitamin D deficiency (8). While the first two reviews focus on screening and how to integrate a sleep screening in a tier service model (4,5), the latter three reviews demonstrate how with minimal consideration, sleep health can be promoted in the various facets of modern medicine and mirrors the need for a holistic approach to sleep and sleep health and how harmonisation of first line treatment options could improve sleep health (6)(7)(8).The second block consists of six articles investigating the associations between sleep and behavioural patterns, e.g., such as ADHD, utilising big and small data. Vigilance regulation disturbances in the wake state play a key role in the development of mental health disorders. Hyperactivity in ADHD is an attempt to increase low vigilance level via external stimulation in order to avoid drowsiness -this common hypothesis led to analysis of resting-state EEGs in children diagnosed with ADHD or depression (9). The study, using longitudinal 'big' data from the national Korean registry, suggests that addressing underlying sleep disturbances, rather than sleep duration is most important in predicting and preventing young children's adjustment problems. Further, more attention should be paid to maternal depressive symptoms in preschooler years as much as during the postpartum period for better child adjustment outcomes (10). The next two studies utilise small data and a qualitative approach. While one study explores disruptive behaviours of adolescents with Down syndrome in a summer school setting, including the link to probable familial RLS, relieved by hours of physical activity, this qualitative study reveals that disruptive behaviours of children with intellectual disabilities have different connotations depending on guiding contextual frameworks (11). Finally, yet importantly, in a qualitative study, parental challenges in sourcing effective sleep solutions for their child with cerebral palsy is explored. Sleep may be a low priority for parents or clinicians, as other health problems take precedence (12). The RLS prevalence in hospitalised psychiatric patients, a multicenter adult study from Germany and Switzerland rounds this picture. Clinically significant RLS had almost five times higher prevalence in psychiatric patients and more than three quarters were diagnosed with RLS for the first time, which speaks for a RLSscreening (13).Canadian sleep clinic, demonstrates that special attention to probable RLS induced insomnia should be given as early as the triaging process at the community level (14). Note that RLS, even familial RLS, is an underestimated clinical sleep/wake-behavioural diagnosis, where there is no need for a sleep laboratory based diagnostics -instead, relying solely on naturalistic observations and exploration, including structured history taking and blood work, steps which should become essential in assessments of insomnia (8,15).The third block analyses exposure to sleep/wake behaviours and timing, in relation to digital media. In a large study from the United Kingdom, the relationship between smartphone addiction and sleep quality in young adults was investigated demonstrating that 39 % of young adults reported smartphone addiction. Smartphone addiction was associated with poor sleep, independent of duration of usage, indicating that length of time should not be used as a proxy for harmful usage (16). Data from South-Germany, a region with high social-economic status, show that the actual exposure to digital media may start already in 12-month-old infants; a proportion of 10 % of 1-year-old children were already regularly exposed to digital media (17). Given the warnings of the American Academy of Pediatrics and national guidelines, which recommend no digital media use at all under the age of 18 months, the question could be around how this rate might fluctuate in varied regions with different social-economic status. Explorations for understanding sleep-wake behaviours in late chronotype adolescents show that with increasing lateness, the likelihood of experiencing poor sleep quality and mood disorders increases (18). However, as dim light melatonin onset did not predict bedtime, this data indicates that the factors contributing to a late chronotype are versatile, complex, a nd understanding needs exploration that is more individual. Again, this article proves our leitmotif that naturalistic observations and exploration will open up new perspectives to typical "disruptive" adolescent behaviours.Reading these articles, as an editorial team, we have been thinking about critical issues for our field. Sleep is an important public health issue. Yet, the current emphasis is on clinical sleep medicine as a Western-centric urban sub-specialty, where we have not implemented a universal screening concept for sleep health and the knowledge regarding pattern recognition (see Head's vigilance concept and Hoffman's disruptive behaviours) is often overlooked, or even unknown. Partners in the community, such as public health nurses, occupational therapists, psychologists, general practitioners, internists, psychiatrists and even paediatricians and child and adolescent psychiatrists lack basic sleep health training and knowledge. Thus, we all unanimously agree to advocate for establishing sleep as a priority on the national public health agenda. We suggest 'HumanRight2Sleep' or 'ChildRight2Sleep' as the communication motto for overcoming a checklist based daytime focus. A rights-based approach to sleep disturbances may support us to review things from a patient rather than professional subs-specialist perspective, and move the agenda further.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,018
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,034
Score d'incertitude au seuil0,113

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,018
Méta-épidémiologie (sens strict)0,0040,001
Méta-épidémiologie (sens large)0,0050,004
Bibliométrie0,0060,003
Études des sciences et des technologies0,0020,002
Communication savante0,0060,004
Science ouverte0,0050,002
Intégrité de la recherche0,0120,008
Charge utile insuffisante (le modèle a refusé de juger)0,0340,015

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,298
Écart entre enseignants0,292 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2023
Routes d'admission2
Résumé présentoui

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