The UK has among the highest mortality rates in Europe for young people with asthma, with inadequate patient education cited as a contributing factor. This literature review aimed to explore the efficacy of video-based resources for educating young people about their chronic long-term condition. Ten studies were reviewed, most of which were randomised controlled trials, and seven of which were conducted in outpatient settings in the US by the same research collaborative. The findings show that, although video-based educational interventions improved participants' knowledge about their condition, these were not superior to other educational approaches. Further, no consistent improvement in medicine adherence, disease control or quality of life was demonstrated. Video-based education for young people appears to be most effective when used to support healthcare professional-led education rather than as a standalone intervention. Nurses and other healthcare professionals should incorporate structured education into consultations with young people and use a range of resources to ensure that their differing needs are met.

Paediatric obstructive sleep apnoea (OSA) is clinically significant yet difficult to diagnose, as children poorly tolerate sensor-based polysomnography. Acoustic monitoring provides a non-invasive alternative for home-based OSA screening, but limited paediatric data hinders the development of robust deep learning approaches. This paper proposes a transfer learning framework that adapts acoustic models pretrained on adult sleep data to paediatric OSA detection, incorporating SpO2-based desaturation patterns to enhance model training. Using a large adult sleep dataset (157 nights) and a smaller paediatric dataset (15 nights), we systematically evaluate (i) single- versus multi-task learning, (ii) encoder freezing versus full fine-tuning, and (iii) the impact of delaying SpO2 labels to better align them with the acoustics and capture physiologically meaningful features. Results show that fine-tuning with SpO2 integration consistently improves paediatric OSA detection compared with baseline models without adaptation. These findings demonstrate the feasibility of transfer learning for home-based OSA screening in children and offer its potential clinical value for early diagnosis.

This paper proposes a deep learning approach for contactless detection of sleep apnoea using pulse and blood oxygen saturation (SPO2) data. Three convolutional neural network architectures are adopted for apnoea classification purposes by fusing different features of the available time series signals. A conventional convolutional neural network (CNN), a CNN with a support vector machine (CNN-SVM), and a CNN combined with a recurrent neural network (CNN-RNN) are compared. The RNN includes Gated Recurrent Units (GRU) and Bidirectional GRU (BiGRU). The CNN is utilised to extract features, whilst the SVM and RNN are used for classification. In addition, we compare two different fusion methods, signal-level and feature-level fusion. The performance is validated and evaluated on a public dataset obtain from St. Vincent University Hospital. The results show that the concatenation of SPO2 and pulse signal at the signal level enhances the classification performance compared to using the individual signal. In addition, the classification sensitivity with signal-level fusion is higher than that with feature-level fusion. Overall, the proposed CNN-RNN with GRU (CNN-GRU) architecture gives the best performance with an accuracy of 85.4%, a sensitivity of 61.5%, a specificity of 91.9%, an $F_{1}$ score of 0.64, and a $\kappa$ score of 0.551 with a dropout rate of 0.5 and a 20-second overlap. The results demonstrate that the proposed deep learning approach offers a promising solution for non-invasive detection of sleep apnoea using affordable physiological signals.

Asthma deaths amongst young people in the United Kingdom are amongst the highest in the world. However, it is widely acknowledged that effective self-management can improve asthma control and ultimately outcomes. Good quality patient education is key to this effective self-management, and this is especially true when young people are preparing for transition to adult services. The Respiratory Research Team at Sheffield Children's NHS Foundation Trust has developed an educational resource to support healthcare professionals with the delivery of this education. This article considers the framework adopted, content creation, the hosting of videos, the review process for draft materials, promoting the resource and finally, assessing its impact. In conclusion the challenges, relating to the adoption of the materials in clinical practice and to patient education as a tool for the empowerment of patients and improvement of asthma outcome, are addressed.

The respiration rate (RR) is an important vital sign for early detection of health deterioration in critically unwell patients. Its current measurement has limitations, relying on visual counting of chest movements. The design of a new RR measurement device utilizing a self-heating thermistor is described. The thermistor is integrated into a hand-held air chamber with a funnel attachment to sensitively detect respiratory airflow. The exhaled respiratory airflow reduces the temperature of the thermistor that is kept at a preset temperature, and its temperature recovers during inhalation. A microcontroller provides signal processing, while its display screen shows the respiratory signal and RR. The device was evaluated on 27 healthy adult volunteers, with a mean age of 32.8 years (standard deviation of 8.6 years). The RR measurements from the device were compared with the visual counting of chest movements, and the contact method of inductance plethysmography that was implemented using a commercial device (SOMNOtouch™ RESP). Statistical analysis, e.g., correlations were performed. The RR measurements from the new device and SOMNOtouch™ RESP, averaged across the 27 participants, were 14.6 breaths per minute (bpm) and 14.0 bpm, respectively. The device has a robust operation, is easy to use, and provides an objective measure of the RR in a noncontact manner.

A new prototype device to monitor breathing in children diagnosed with central sleep apnoea (CSA) was developed. CSA is caused by the failure of central nervous system signals to the respiratory muscles and results in intermittent breathing pauses during sleep. Children diagnosed with CSA require home respiration monitoring during sleep. Apnoea monitors initiate an audio alarm when the breath-to-breath respiration interval exceeds a preset time. This allows the child’s parents to attend to the child to ensure safety. The article describes the development of the monitor’s hardware, software, and evaluation. Features of the device include the detection of abnormal respiratory pauses and the generation of an associated alarm, the ability to record the respiratory signal and its storage using an on-board disk, miniaturised hardware, child-friendliness, cost-effectiveness, and ease of use. The device was evaluated on 10 healthy adult volunteers with a mean age of 46.6 years (and a standard deviation of 14.4 years). The participants randomly intentionally paused their breathing during the recording. The device detected and provided an alarm when the respiratory pauses exceeded the preset time. The respiration rates determined from the device closely matched the values from a commercial respiration monitor. The study indicated the peak-detection method of the respiration rate measurement is more robust than the zero-crossing method.

Sleep deprivation has a serious impact on physical and mental health. Children with neurodevelopmental disorders are frequently affected by chronic insomnia, defined as difficulty in either initiating sleep, maintaining sleep continuity or poor sleep quality which can lead to long-term detrimental effects on behaviour, learning and development.Interventions to address chronic insomnia in children include both pharmacological and non-pharmacological approaches. While some children unequivocally benefit from pharmacological treatment, recommendations suggest an intervention based on cognitive-behavioural techniques involving a thorough assessment of the child's sleep pattern, environment and psychosocial factors supporting the child to learn to self-soothe as first-line treatment. Evidence from sleep clinics delivered by trained community practitioners supports the efficacy of an intensive programme, whereby education, practical advice and follow-up support were key factors; however, these services are inconsistently resourced. In practice, sleep support interventions range from verbal advice given in clinics to healthy sleep leaflets to tailored and non-tailored parent-directed interventions. Delivery models include promotion of safe sleep within a wider health promotion context and targeted early intervention within sleep clinics delivered in health and community services or by the third sector but evidence for each model is lacking.We describe a comprehensive whole systems city-wide model of sleep support, ranging from awareness raising, universal settings, targeted support for complex situations to specialist support, delivered according to complexity and breadth of need. By building capacity and quality assurance into the existing workforce, the service has been sustainable and has continued to develop since its initial implementation in 2017. With increasing access to specialist sleep services across the UK, this model could become a widely generalisable approach for delivery of sleep services to children in the UK and lead to improved outcomes in those with severe sleep deprivation.

Noninvasive respiratory support delivered through a face mask has become a cornerstone treatment for adults and children with acute or chronic respiratory failure. However, an imperfect mask fit by using commercially available interfaces is frequently encountered, which may result in patient discomfort and treatment inefficiency or failure. To overcome this challenge, over the past decade, increasing attention has been given to the development of personalized face masks, which are custom-made to address the specific facial dimensions of an individual patient. With this scoping review, we aim to provide a comprehensive overview of the current advances and gaps in knowledge with regard to the personalization masks for CPAP and NIV. We performed a systematic search of the literature and identified and summarized a total of 23 studies. Most studies included were involved in the development of nasal masks. Studies that targeted adult respiratory care mainly focused on chronic (home) ventilation and included some clinical testing in a relevant subject population. In contrast, pediatric studies focused mostly on respiratory support in the acute setting, whereas testing was limited to bench or case studies only. Most studies were positive with regard to the performance (ie, comfort, level of air leak, and mask pressure applied to the skin) of personalized masks in bench testing or in human, healthy or patient, subjects. Advances in the field of 3-dimensional scanning and soft material printing were identified, but important gaps in knowledge remain. In particular, more insight into cushion materials, headgear design, clinical feasibility, and cost-effectiveness is needed before definite recommendations can be made with regard to implementation of large-scale clinical programs that personalize noninvasive respiratory support masks for adults and children.

Background. Insomnia is a prevalent sleep disorder characterized by difficulties in initiating sleep or experiencing non-restorative sleep. It is a multifaceted condition that impacts both the quantity and quality of an individual’s sleep. Recent advancements in machine learning (ML), and deep learning (DL) have enabled automated sleep analysis using physiological signals. This has led to the development of technologies for more accurate detection of various sleep disorders, including insomnia. This paper explores the algorithms and techniques for automatic insomnia detection. Methods. We followed the recommendations given in the Preferred Reporting Items for systematic reviews and meta-analyses (PRISMA) during our process of content discovery. Our review encompasses research papers published between 2015 and 2023, with a specific emphasis on automating the identification of insomnia. From a selection of well-regarded journals, we included more than 30 publications dedicated to insomnia detection. In our analysis, we assessed the performance of various methods for detecting insomnia, considering different datasets and physiological signals. A common thread across all the papers we reviewed was the utilization of artificial intelligence (AI) models, trained and tested using annotated physiological signals. Upon closer examination, we identified the utilization of 15 distinct algorithms for this detection task. Results. The major goal of this research is to conduct a thorough study to categorize, compare, and assess the key traits of automated systems for identifying insomnia. Our analysis offers complete and in-depth information. The essential components under investigation in the automated technique include the data input source, objective, ML and DL network, training framework, and references to databases. We classified pertinent research studies based on ML and DL model perspectives, considering factors like learning structure and input data types. Conclusion. Based on our review of the studies featured in this paper, we have identified a notable research gap in the current methods for identifying insomnia and opportunities for future advancements in the automation of insomnia detection. While the current techniques have shown promising results, there is still room for improvement in terms of accuracy and reliability. Future developments in technology and machine learning algorithms could help address these limitations and enable more effective and efficient identification of insomnia.