Editorial: Sleep, vigilance & disruptive behaviors
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".