Zusammenfassung Die isolierte schlafbezogene Hypoventilation stellt die erste Stufe einer sich im Verlauf auch am Tage entwickelnden ventilatorischen Insuffizienz dar. Insbesondere bei Patient*Innen mit progredienten neuromuskulären Erkrankungen folgt regelhaft eine chronische hyperkapnische respiratorische Insuffizienz mit zunehmender Abhängigkeit von einer nichtinvasiven oder invasiven Langzeitbeatmung. Dieses Risiko besteht auch bei primär respiratorischen und chronisch progredient verlaufenden Erkrankungen, wie der chronisch obstruktiven Lungenerkrankung (COPD) – v. a. bei akuten Exazerbationen. In fortgeschrittenen Stadien resultiert bei einem Teil der Betroffenen die Notwendigkeit einer außerklinischen Intensivpflege (AKI). Eine intersektorale Vernetzung von Schlaf- und Beatmungsmedizin, häuslicher Versorgung und AKI ist essenziell, um den Übergang in hochintensive Versorgungsformen vorausschauend und patientenzentriert zu gestalten.

Abstract Background Frequent sleep disruption in preterm infants in neonatal intensive care units (NICU) is suspected to be associated with adverse neurodevelopmental outcomes. A continuous, noninvasive sleep monitoring solution could optimise care by aligning interventions with natural sleep–wake cycles. The algorithm for automatic sleep stage classification by Demme et al. [5] offers a promising approach, with an accuracy of 92.2 ± 0.01% compared to sleep stages measured in the sleep laboratory. Objective This study aims to evaluate the applicability of the sleep classification algorithm with regards to two main questions: (1) Does the algorithm’s performance vary with factors like pre-existing medical conditions or other patient characteristics in preterm infants? (2) How well does the algorithm capture sleep stage transitions compared to manual annotations? Materials and methods This study is a secondary analysis of data previously used by Demme et al. [5] to train models for automatic sleep stage classification in preterm infants. The models, which were trained on manual annotations of polysomnography (PSG) data, were applied to a diverse cohort of preterm infants. The performance was analysed in relation to medical conditions, sex, gestational age (GA), apnoea–hypopnea index (AHI), bodyweight at measurement, and Fleiss’ kappa (a measure of interrater variability). Sleep stage transition frequencies were compared between algorithmic and manual scoring. Results No significant performance differences could be observed with respect to any covariates, except for a reduction in performance among patients with low Fleiss’ kappa values, suggesting classification difficulties in cases in which manual raters have come to diverging assessments. Transition analysis revealed significant differences, likely due to the algorithm’s more sensitive ability to detect short sleep stage transitions. Conclusion The algorithm shows strong performance and generalisability. Despite some discrepancies in sleep stage transition detection, it appears suitable for noninvasive, continuous sleep monitoring in the NICU, supporting sleep-aware clinical care.

Zusammenfassung Hintergrund Die chronische Insomnie betrifft bis zu 10 % der Erwachsenen und bleibt in der Praxis häufig unzureichend diagnostiziert und behandelt. Bisher fehlt ein praxisnahes, strukturiertes Instrument, das die Diagnostik und Therapie der Insomnie effizient unterstützt. Zielsetzung Ziel war die Entwicklung einer Checkliste zur Unterstützung von Diagnostik, Differenzialdiagnostik und Therapie der Insomnie, die in der klinischen Praxis leicht anwendbar und leitlinienkonform ist. Material und Methode Eine interdisziplinäre Arbeitsgruppe entwickelte die Checkliste basierend auf einer umfassenden Literaturrecherche und aktuellen Leitlinien. Der Fokus lag auf einer praxisnahen Struktur mit Integration bewährter diagnostischer Instrumente. Ergebnisse Die ALiBABA-Checkliste umfasst sechs zentrale Bereiche: Art der Schlaflosigkeit, Liegezeiten, Befindlichkeit am Tag, Auslöser, bisherige Therapien und einen differenzialdiagnostischen Anhang. Sie integriert den Insomnia Severity Index (ISI) und praxisorientierte Screeningfragen zur Förderung der Patient:innenpartizipation und Differenzialdiagnostik. Schlussfolgerungen Die ALiBABA-Checkliste ist ein leicht anwendbares, praxisnahes Instrument, das auf aktuellen Leitlinien basiert und die Diagnostik und Behandlung von Insomnie erleichtern soll. Sie unterstützt die Zusammenarbeit zwischen Arzt und Patient und bietet Potenzial, durch eine personalisierte Therapie die Schlafqualität entscheidend zu verbessern.

Abstract Background Sleep problems are common in infancy, childhood, and adolescence, affecting up to 30% of individuals. Parents often seek help for such problems, for example by consulting their pediatrician. In addition to child and adolescent psychotherapists and specialists in sleep centers educated in classical cognitive behavioral therapy (CBT), treatments are also offered by sleep coaches on the internet. However, the quality and content of these offers remain unclear. Methods In this study, the websites of 153 pediatric sleep coaches in Germany, Austria, and Switzerland were screened with regard to aspects such as educational background, training, sleep-related services, screening and diagnostics, and types of treatment for sleep disturbances offered as well as regarding media presence. All authors were involved in this process. Results Of the 153 pediatric sleep coaches, 25.5% had a medical or psychological background, 7.3% a health-related background, 23.5% a pedagogical background, and 13.1% had other backgrounds; 35.9% offered no information about their background. Whereas 72.5% of the sleep coaches reported having participated in a sleep coach training, 27.5% did not mention any form of sleep-treatment education; 63.4% mentioned no sleep certification, but 32.7% reported some kind of certification. Coaches with a medical or health-related background were less likely to have participated in a sleep training. The addressed sleep problems were mostly related to insomnia. The target group was most often babies. As a screening or diagnostic procedure, only the sleep log was mentioned. Most sleep coaches offered single sessions. Costs for sessions ranged from 20 up to 199 €. A huge number of pediatric sleep coaches were active on social media. Conclusion This study provides a critical overview of pediatric sleep coaches in German-speaking countries. We hope that these preliminary observations will inspire additional research and discussion on this important topic, e.g., on educational processes or networking structures, as has been mentioned in the stepped-care model for childhood insomnia.