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Journal of Sleep Research Aug 2019Actigraphy is increasingly used in practice and research studies because of its relative low cost and decreased subject burden. How multiple nights of at-home actigraphy...
Actigraphy is increasingly used in practice and research studies because of its relative low cost and decreased subject burden. How multiple nights of at-home actigraphy compare to one independent night of in-laboratory polysomnography (PSG) has not been examined in people with insomnia. Using event markers (MARK) to set time in bed (TIB) compared to automatic program analysis (AUTO) has not been systematically evaluated. Subjects (n = 30) meeting DSM-5 criteria for insomnia and in-laboratory PSG sleep efficiency (SE) of <85% were studied. Subjects were free of psychiatric, sleep or circadian disorders, other chronic conditions and medications that effect sleep. Subjects had an in-laboratory PSG, then were sent home for 7 nights with Philips Actiwatch Spectrum Plus. Data were analysed using Philips Actiware version 6. Using the mean of seven nights, TIB, total sleep time (TST), SE, sleep-onset latency (SOL) and wake after sleep onset (WASO) were examined. Compared to PSG, AUTO showed longer TIB and TST and less WASO. MARK only differed from PSG with decreased WASO. Differences between the PSG night and the following night at home were found, with better sleep on the first night home. Actigraphy in people with insomnia over seven nights is a valid indicator of sleep compared to an independent in-laboratory PSG. Event markers increased the validity of actigraphy, showing no difference in TIB, TST, SE and SOL. AUTO was representative of SE and SOL. Increased SE and TST without increased TIB suggests possible compensatory sleep the first at night home after in-laboratory PSG.
Topics: Actigraphy; Female; Humans; Male; Middle Aged; Polysomnography; Sleep Initiation and Maintenance Disorders
PubMed: 30941838
DOI: 10.1111/jsr.12854 -
Journal of Medical Internet Research Sep 2022American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without... (Review)
Review
BACKGROUND
American Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.
OBJECTIVE
We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.
METHODS
We searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.
RESULTS
Our search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.
CONCLUSIONS
Although high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.
TRIAL REGISTRATION
PROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339.
Topics: Adult; Bayes Theorem; Humans; Machine Learning; Neural Networks, Computer; Polysomnography; Sleep Apnea, Obstructive
PubMed: 36178720
DOI: 10.2196/39452 -
Journal of Clinical Sleep Medicine :... Nov 2017Perfectionism has been suggested to represent a predisposing factor for poor sleep. However, previous studies have relied on self-reported measures. The association...
STUDY OBJECTIVES
Perfectionism has been suggested to represent a predisposing factor for poor sleep. However, previous studies have relied on self-reported measures. The association between perfectionism and poor sleep measured by polysomnography (PSG) warrants further investigation.
METHODS
The current retrospective exploratory study used the Frost Multidimensional Perfectionism Scale and PSG in an unselected sample of 334 consecutive sleep laboratory patients (140 males, 194 females, 44.6 ± 15.9 years). Data were analyzed using linear regression analyses.
RESULTS
High levels of perfectionism were associated with PSG-determined markers of poor sleep in the first sleep laboratory night. The total Frost Multidimensional Perfectionism Scale score was significantly associated with the number of nocturnal awakenings in the first sleep laboratory night. The subscales "concern over mistakes" and "personal standards" of perfectionism were significantly associated with markers of poor sleep. In contrast, there were only a few associations between perfectionism and PSG variables of the second sleep laboratory night.
CONCLUSIONS
This pattern of results suggests that high levels of perfectionism may predispose individuals to sleep disturbances in the context of acute stressors. Thus, the influence of perfectionism on poor sleep should be further investigated to improve treatment.
Topics: Adult; Female; Humans; Male; Perfectionism; Polysomnography; Retrospective Studies; Sleep Initiation and Maintenance Disorders
PubMed: 28992830
DOI: 10.5664/jcsm.6806 -
NeuroImage Oct 2023Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep...
Human cognitive performance is a key function whose biological foundations have been partially revealed by genetic and brain imaging studies. The sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system and serves as another promising biomarker. We used data from MrOS, a large cohort of older men and cross-validated regularized regression to link sleep EEG features to cognitive performance in cross-sectional analyses. In independent validation samples 2.5-10% of variance in cognitive performance can be accounted for by sleep EEG features, depending on the covariates used. Demographic characteristics account for more covariance between sleep EEG and cognition than health variables, and consequently reduce this association by a greater degree, but even with the strictest covariate sets a statistically significant association is present. Sigma power in NREM and beta power in REM sleep were associated with better cognitive performance, while theta power in REM sleep was associated with worse performance, with no substantial effect of coherence and other sleep EEG metrics. Our findings show that cognitive performance is associated with the sleep EEG (r = 0.283), with the strongest effect ascribed to spindle-frequency activity. This association becomes weaker after adjusting for demographic (r = 0.186) and health variables (r = 0.155), but its resilience to covariate inclusion suggest that it also partially reflects trait-like differences in cognitive ability.
Topics: Male; Humans; Aged; Cross-Sectional Studies; Polysomnography; Sleep; Electroencephalography; Cognition
PubMed: 37574121
DOI: 10.1016/j.neuroimage.2023.120319 -
Journal of Clinical Sleep Medicine :... Jan 2018Sleep bruxism (SB) is common in children and is associated with somatic symptoms and sleep disturbance. Etiological theories posit the role of anxiety, suggesting youth...
STUDY OBJECTIVES
Sleep bruxism (SB) is common in children and is associated with somatic symptoms and sleep disturbance. Etiological theories posit the role of anxiety, suggesting youth with anxiety disorders may be at high risk for SB, but empirical data are lacking. Furthermore, parent report rather than polysomnography (PSG) has been used to examine SB-anxiety relationships in children. We examined rates of PSG-detected compared to parent-reported SB in children with generalized anxiety disorder (GAD) and healthy controls. Associations among SB, somatic complaints, and sleep disturbance were also examined.
METHODS
Thirty-one children, aged 7-11 years, completed 1 night of PSG monitoring and 7 daily reports of somatic symptoms. Bruxism events were scored during stage R sleep, stage N1 sleep, and stage N2 sleep.
RESULTS
Almost one-third of children showed evidence of SB based on PSG. No associations were identified between parent-reported and PSG-detected SB. Rates of SB did not differ between anxious and control groups, though children with GAD showed more tonic bruxisms during stage R sleep. Presence of SB predicted more muscle aches and stomach aches, and children with SB had more awake time after sleep onset than those without bruxism.
CONCLUSIONS
Results indicate poor concordance between PSG-detected and parent-reported SB in children, suggesting that parent report alone is not a reliable method for detection. The lack of association between SB and anxiety status suggests that stress sensitivity rather than anxiety may be predictive of SB. Associations between SB, somatic symptoms, and sleep disturbance are congruent with the broader literature.
Topics: Anxiety Disorders; Child; Female; Humans; Male; Medically Unexplained Symptoms; Polysomnography; Sleep Bruxism; Surveys and Questionnaires
PubMed: 29198292
DOI: 10.5664/jcsm.6872 -
Journal of Sleep Research Dec 2022This work aimed to evaluate if a contact-free radar sensor can be used to observe ultradian patterns in sleep physiology, by way of a data processing tool known as...
This work aimed to evaluate if a contact-free radar sensor can be used to observe ultradian patterns in sleep physiology, by way of a data processing tool known as Locomotor Inactivity During Sleep (LIDS). LIDS was designed as a simple transformation of actigraphy recordings of wrist movement, meant to emphasise and enhance the contrast between movement and non-movement and to reveal patterns of low residual activity during sleep that correlate with ultradian REM/NREM cycles. We adapted the LIDS transformation for a radar that detects body movements without direct contact with the subject and applied it to a dataset of simultaneous recordings with polysomnography, actigraphy, and radar from healthy young adults (n = 12, four nights of polysomnography per participant). Radar and actigraphy-derived LIDS signals were highly correlated with each other (r > 0.84), and the LIDS signals were highly correlated with reduced-resolution polysomnographic hypnograms (r >0.80, r >0.76). Single-harmonic cosine models were fitted to LIDS signals and hypnograms; significant differences were not found between their amplitude, period, and phase parameters. Mixed model analysis revealed similar slopes of decline per cycle for radar-LIDS, actigraphy-LIDS, and hypnograms. Our results indicate that the LIDS technique can be adapted to work with contact-free radar measurements of body movement; it may also be generalisable to data from other body movement sensors. This novel metric could aid in improving sleep monitoring in clinical and real-life settings, by providing a simple and transparent way to study ultradian dynamics of sleep using nothing more than easily obtainable movement data.
Topics: Young Adult; Humans; Radar; Sleep; Polysomnography; Actigraphy; Movement
PubMed: 35794011
DOI: 10.1111/jsr.13687 -
Journal of Medical Internet Research Jul 2023Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of... (Review)
Review
BACKGROUND
Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard.
OBJECTIVE
This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population.
METHODS
We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures.
RESULTS
We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events.
CONCLUSIONS
These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings.
TRIAL REGISTRATION
PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748.
Topics: Adult; Humans; Surveys and Questionnaires; Sleep Apnea, Obstructive; Sleep Apnea Syndromes; Sleep; Polysomnography
PubMed: 37494079
DOI: 10.2196/47735 -
Chest Dec 2015Pediatric OSA can result in significant neurocognitive, behavioral, cardiovascular, and metabolic morbidities. Prompt diagnosis and treatment are, therefore, of... (Review)
Review
Pediatric OSA can result in significant neurocognitive, behavioral, cardiovascular, and metabolic morbidities. Prompt diagnosis and treatment are, therefore, of paramount importance. The current gold standard for diagnosis of OSA in children is in-laboratory polysomnography (PSG). Home sleep apnea testing has been considered as an alternative as it is potentially more cost effective, convenient, and accessible. This review concentrates mainly on the use of type 2 and 3 portable monitoring devices. The current evidence on the feasibility and diagnostic accuracy of home testing in the diagnosis of pediatric OSA was examined. Overall, the evidence in children is limited. Feasibility studies that have been performed have on the whole shown good results, with several reporting > 90% of their home recordings as meeting predetermined quality criteria regarding signal artifact and minimum recording time. The limited data comparing type 2 studies with in-laboratory PSG have shown no significant differences in respiratory parameters. The results pertaining to diagnostic accuracy of type 3 home sleep apnea testing devices are conflicting. Although more research is needed, home testing with at least a type 3 portable monitor offers a viable alternative in the diagnosis of otherwise healthy children with moderate to severe OSA, particularly in settings where access to polysomnography is scarce or unavailable. Of note, since most studies have been performed in habitually snoring healthy children, home sleep apnea testing may not be applicable to children with other comorbid conditions. In particular, CO2 monitoring is important in children in whom there is concern regarding nocturnal hypoventilation, such as children with neuromuscular disease, underlying lung disease, or obesity hypoventilation, and most home testing devices do not include a transcutaneous or end-tidal CO2 channel.
Topics: Child; Diagnostic Equipment; Dimensional Measurement Accuracy; Home Care Services; Humans; Point-of-Care Testing; Polysomnography; Sleep Apnea, Obstructive
PubMed: 26270608
DOI: 10.1378/chest.15-1365 -
TheScientificWorldJournal Nov 2007Insomnia and sleep disturbance are associated with declines in health functioning, along with increases in mortality risk. Given the prominence of reported sleep... (Review)
Review
Insomnia and sleep disturbance are associated with declines in health functioning, along with increases in mortality risk. Given the prominence of reported sleep disturbance in cocaine-dependent subjects and persistence into recovery, understanding the nature and severity of these disturbances in this population may help to identify relevant pathways that contribute to the increased mortality in cocaine dependence. Polysomnography provides a means of objectively characterizing sleep and, in turn, sleep disturbances. Few studies have used polysomnography to evaluate sleep in cocaine-dependent persons, yet these studies have the potential to advance treatments that will ultimately reduce morbidity in cocaine-dependent subjects.
Topics: Acute Disease; Cocaine-Related Disorders; Humans; Polysomnography; Sleep Wake Disorders
PubMed: 17982595
DOI: 10.1100/tsw.2007.264 -
Journal of Clinical Sleep Medicine :... Jun 2013
Topics: Humans; Polysomnography; Sleep Wake Disorders
PubMed: 23772183
DOI: 10.5664/jcsm.2738