The development of a clinically relevant sleep modification protocol for youth with Type 1 diabetes.

Findings from type 2 diabetes research indicate that sleep is both a predictor of onset and a correlate of disease progression. However, the role sleep plays in glucose regulation and daytime functioning in youth with type 1 diabetes mellitus (T1DM) has not been systematically investigated. Nonetheless, preliminary findings have supported that various sleep parameters are strongly correlated to health-related and neurobehavioral outcomes in youth with T1DM. This suggests that improving sleep might reduce morbidity. A critical step in developing evidence-based guidelines regarding sleep in diabetes management is to first determine that sleep modification in natural settings is possible (i.e., instructing youth to have a healthy sleep opportunity leads to more total sleep time) and that an increased sleep duration impacts disease and psychosocial outcomes in these youth. This article describes the background, design, and feasibility of an ongoing randomized clinical trial that aims to examine if increasing sleep relative to youth's own sleep routines affects glucose control and daytime functioning

Sleep characteristics in type 1 diabetes and associations with glycemic control: systematic review and meta-analysis

AbstractObjectivesThe association between inadequate sleep and type 2 diabetes has garnered much attention, but little is known about sleep and type 1 diabetes (T1D). Our objectives were to conduct a systematic review and meta-analysis comparing sleep in persons with and without T1D, and to explore relationships between sleep and glycemic control in T1D.MethodsStudies were identified from Medline and Scopus. Studies reporting measures of sleep in T1D patients and controls, and/or associations between sleep and glycemic control, were selected.ResultsA total of 22 studies were eligible for the meta-analysis. Children with T1D had shorter sleep duration (mean difference [MD] = −26.4 minutes; 95% confidence interval [CI] = −35.4, −17.7) than controls. Adults with T1D reported poorer sleep quality (MD in standardized sleep quality score = 0.51; 95% CI = 0.33, 0.70), with higher scores reflecting worse sleep quality) than controls, but there was no difference in self-reported sleep duration. Adults with TID who reported sleeping >6 hours had lower hemoglobin A1c (HbA1c) levels than those sleeping ≤6 hours (MD = −0.24%; 95% CI = −0.47, −0.02), and participants reporting good sleep quality had lower HbA1c than those with poor sleep quality (MD = −0.19%; 95% CI = −0.30, −0.08). The estimated prevalence of obstructive sleep apnea (OSA) in adults with TID was 51.9% (95% CI = 31.2, 72.6). Patients with moderate-to-severe OSA had a trend toward higher HbA1c (MD = 0.39%, 95% CI = −0.08, 0.87).ConclusionT1D was associated with poorer sleep and high prevalence of OSA. Poor sleep quality, shorter sleep duration, and OSA were associated with suboptimal glycemic control in T1D patients

A Novel Artificial Neural Network Based Sleep-Disordered Breathing Screening Tool

Study Objectives: This study evaluated a novel artificial neural network (ANN) based sleep disordered breathing (SDB) screening tool incorporating nocturnal pulse oximetry with demographic, anatomic and clinical data. The tool was compatible with 6 categories of apnea hypopnea index (AHI) with 4% oxyhemoglobin desaturation threshold, ≥ 5/hour, 10/hour, 15/hour, 20/hour, 25/hour, and 30/hour. Methods: Using a general population dataset, the training set included 2,280 subjects, while the test set included 470 subjects. The input of this tool was a set of 22 variables. The tool had six multilayer perceptron (MLP) neural network models for each AHI threshold. Several criteria were explored to evaluate the accuracy of the tool: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and 95% confidence intervals (CI). Results: The AUCs were 0.904, 0.912, 0.913, 0.926, 0.930, and 0.954 respectively, with models of AHI ≥ 5/hour, 10/hour, 15/hour, 20/hour, 25/hour, and 30/hour thresholds. The sensitivities of all MLP neural network models were higher than 95%. The AHI ≥ 30/hour model had the maximum sensitivity: 98.31% (95% CI: 95.01% - 100%). Conclusions: The results of this study suggested that the ANN based SDB screening tool can be used to identify the presence or absence of SDB. Future validation should be performed in other populations to determine the practicability of this screening tool in sleep clinics and other at risk populations