Irregular sleep/wake patterns are associated with reduced quality of life in post-treatment cancer patients: a study across three cancer cohorts.

Abstract Background: Cancer patients often describe poor sleep quality and sleep disruption as contributors to poor quality of life (QoL). In a cross-sectional study of post-treatment breast, endometrial and melanoma cancer patients, we used actigraphy to quantify sleep regularity using the sleep regularity index (SRI), and examined relationships with reported sleep symptoms and QoL. Methods: Participants were recruited post-primary treatment (35 diagnosed with breast cancer, 24 endometrial cancer and 29 melanoma) and wore an actigraphy device for up to 2 weeks and SRI was calculated. Self-report questionnaires for cancer-related QoL (European Organisation for Research and Treatment of Cancer EORTC (QLQ-C30)) were completed. Data were compared using Analysis of Variance or Chi-Square tests. Multivariate linear regression analysis was used to determine independent variable predictors for questionnaire-derived data. Results: Age distribution was similar between cohorts. Endometrial and breast cancer cohorts were predominantly female, as expected, and Body Mass Indexwas higher in the endometrial cancer cohort, followed by breast and melanoma. There were no differences between tumour groups in: total sleep time , sleep onset latency, bedtime, and SRI (breast 80.98.0, endometrial 80.312.2, melanoma 81.47.0) (all p>0.05). A higher SRI was associated with both better functional and symptom scores, including increased global QoL, better physical functioning, less sleepiness and fatigue, better sleep quality, and associated with less nausea/vomiting, dyspnea, and diarrhea (all p<0.05). Conclusions: In cancer patients post-treatment, greater sleep regularity is associated with increased global QoL, as well as better physical functioning and fewer cancer related symptoms. Improving sleep regularity may improve QoL for cancer patients.This contains questionnaire and actigraphy data from clinic patients with cancer

The challenge of COVID-19 has accelerated the use of new data-sharing technologies

The coronavirus disease 2019 (COVID-19) pandemic has vexed many healthcare providers, and the scale of disease and certain atypical characteristics have rattled confidence and inserted doubt into usual practices. As a result, uncertainty exists around optimal treatments and where to turn for answers

From CPAP to tailored therapy for obstructive sleep Apnoea

Abstract Obstructive Sleep Apnoea (OSA) is a common sleep disorder that is associated with daytime symptoms and a range of comorbidity and mortality. Continuous Positive Airway Pressure (CPAP) therapy is highly efficacious at preventing OSA when in use and has long been the standard treatment for newly diagnosed patients. However, CPAP therapy has well recognised limitations in real world effectiveness due to issues with patient acceptance and suboptimal usage. There is a clear need to enhance OSA treatment strategies and options. Although there are a range of alternative treatments (e.g. weight loss, oral appliances, positional devices, surgery, and emerging therapies such as sedatives and oxygen), generally there are individual differences in efficacy and often OSA will not be completely eliminated. There is increasing recognition that OSA is a heterogeneous disorder in terms of risk factors, clinical presentation, pathophysiology and comorbidity. Better characterisation of OSA heterogeneity will enable tailored approaches to therapy to ensure treatment effectiveness. Tools to elucidate individual anatomical and pathophysiological phenotypes in clinical practice are receiving attention. Additionally, recognising patient preferences, treatment enhancement strategies and broader assessment of treatment effectiveness are part of tailoring therapy at the individual level. This review provides a narrative of current treatment approaches and limitations and the future potential for individual tailoring to enhance treatment effectiveness

Development and validation of a computational finite element model of the rabbit upper airway : simulations of mandibular advancement and tracheal displacement

The mechanisms leading to upper airway (UA) collapse during sleep are complex and poorly understood. We have previously developed an anaesthetized rabbit model for studying UA physiology. Based on this body of physiological data, we aimed to develop and validate a two-dimensional (2D) computational finite element (FEM) of the passive rabbit UA and peripharyngeal tissues.Model geometry was reconstructed from a mid-sagittal CT image of a representative NZ White rabbit, which included major soft (tongue, soft palate, constrictor muscles), cartilaginous (epiglottis, thyroid cartilage) and bony pharyngeal tissues (mandible, hard palate, hyoid bone). Other UA muscles were modeled as linear elastic connections. Initial boundary and contact definitions were defined from anatomy and material properties derived from the literature. Model parameters were optimized to physiological data sets associated with mandibular advancement (MA) and caudal tracheal displacement (TD), including hyoid displacement, which featured with both applied loads. The model was then validated against independent data sets involving combined MA and TD. Model outputs included UA lumen geometry, peripharyngeal tissue displacement, stress and strain distributions.Simulated MA and TD resulted in UA enlargement and non-uniform increases in tissue displacement, stress and strain. Model predictions closely agreed with experimental data for individually applied MA, TD and their combination.We have developed and validated an FEM of the rabbit UA which predicts UA geometry and peripharyngeal tissue mechanical changes associated with interventions known to improve UA patency. The model has the potential to advance our understanding of UA physiology and peripharyngeal tissue mechanics.15 page(s