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Sleep Fragmentation

Sleep fragmentation is a condition characterized by frequent awakenings or disruptions in the sleep cycle, leading to poor sleep quality and daytime fatigue.
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Most cited protocols related to «Sleep Fragmentation»

We further examined the genes within genome-wide significant loci using gene-based pathway and tissue enrichment analyses45 (link),47 (link),69 (link). Gene-based analysis was performed using PASCAL, which estimated a combined association P value from the summary statistics of multiple SNPs in a gene45 (link). Pathway and ontology enrichment analyses were performed using FUMA69 (link) and EnrichR47 (link). Tissue enrichment analysis was performed using MAGMA46 (link) in FUMA, which controlled for gene size. Pathway and tissue enrichment analyses were also performed on genes within loci belonging to sleep propensity and sleep fragmentation clusters separately.
We constructed a weighted GRS comprising the 42 significant sleepiness loci and tested for associations with other self-reported sleep traits (sleep duration, long sleep duration, short sleep duration, insomnia, chronotype, and day naps), and 7-day accelerometry traits in the UK Biobank. Weighted GRS analyses were performed by summing the products or risk allele count multiplied by the effect estimate reported in the primary GWAS of self-reported daytime sleepiness using R package gds (https://cran.r-project.org/web/packages/gds/gds.pdf). We also tested the GRSs of reported loci for insomnia, sleep duration, short sleep, long sleep, day naps, chronotype, restless legs syndrome (RLS), narcolepsy, and coffee consumption associated with self-reported daytime sleepiness using the same approach. The SNPs selected for each trait include 57 genome-wide significant loci for frequent insomnia49 (link); 78, 27, and 8 loci for sleep duration, long sleep, and short sleep, respectively59 (link); 348 loci for chronotype67 (link); 125 loci for daytime napping; 20 genome-wide significant loci for RLS48 (link); 8 non-HLA suggestive significant loci (P < 10−4) in a narcolepsy case–control study of European Americans51 , and 8 loci for coffee consumption50 (link).
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Publication 2019
Accelerometry Alleles Chronotype Coffee Europeans Genes Genetic Loci Genome Genome-Wide Association Study N-(4-aminophenethyl)spiroperidol Narcolepsy Restless Legs Syndrome Single Nucleotide Polymorphism Sleep Sleep Fragmentation Sleeplessness Somnolence Tissues
Essentially, a sleep detection method is a function that classifies the sleep state of a patient. Most sleep detection methods such as wrist actigraphy or mobile apps consider a binary function, where the state can be classified as Awake/Sleep. More sophisticated methods consider a ternary function: Awake/NREM/REM. And, finally, the most advanced methods, such as polysomnography—often used as the gold standard—consider a quinquenary function: Awake/N1/N2/N3/REM. Hence, any method can produce a two-dimensional chart where the X-axis is Time, and the Y-axis is the State of the Patient. In the particular case of polysomnography, the Y-axis has five possible values; thus, it can determine the sleep stage of the patient at any time, and study the transitions occurring between the states. Of course, a sleep study such as a polysomnography often produces much more complementary information that can be used, e.g., to diagnose sleep diseases. Among the information reported by a polysomnography we find oxygen saturations, limb movements, apneas, respiratory events by body position, etc. The interested reader is referred to Robertson, Marshall & Carno (2014) , Pandi-Perumal, Spence & BaHammam (2014) and Armon et al. (2016) for information about sleep study reports and their interpretation and usage.
The information that is common to the majority of sleep detection methods is the one that refers to a binary state classification (i.e., Awake/Sleep), because this is achieved by the basic methods, and subsumed by the advanced methods. Table 1 defines the basic parameters that can be collected by a binary state classification method. In grey, we show the primary data that should be collected by the sleep detection device and, in white, we show the most important parameters that can be derived from the primary data.
These parameters are particularly useful to determine the kind of sleep of patients, and each single parameter is relevant for a different sleep disorder or disease. For instance, the sleep onset, sleep latency, and total sleep time are essential to diagnose patients with insomnia. Similarly, an excess in the awakening and arousal indices suggests increased sleep fragmentation. In addition to the number of sleep states that they are able to detect, a sleep detection method can be classified according to other functional and operational characteristics, such as their underlying technology, which in turn directly affects their precision and validity.
In Fig. 2, we present a taxonomy of sleep detection methods. They all can be classified into two main groups according to whether they need medical assistance (Medical Assistance) or not (Self-Assessment). In this respect, there are methods that have been classified as not requiring medical assistance, such as Questionnaires and Sleep Diaries, even though their interpretation should be normally done by a professional. However, in the current state of the art there are many systems such as mobile apps that provide custom sleep questionnaires and produce reports without medical assistance. Hence, they are classified as Self-Assessment. They both deserve a deep discussion and will be explained separately in ‘Medical Assistance Methods’ and ‘Self-Assessment Methods’, respectively.
Self-Assessment methods include subjective methods such as questionnaires and sleep diaries (the figure lists some instances), and objective methods based on hardware sensors, which in turn can be classified as Contact devices or Contactless devices, depending on whether they need to be in contact with the patient’s body during sleep. Those devices that are based on the echo produced by signals can be further classified into Sonar, Radar, and Lidar devices. All of them will be explained in a dedicated section.
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Publication 2018
Actigraphy Apnea Arousal Diagnosis ECHO protocol Epistropheus Gold Human Body Hypersomnia Medical Devices Movement Oxygen Saturation Patients Polysomnography Respiratory Rate Self-Assessment Sleep Sleep Disorders Sleep Fragmentation Sleeplessness Sleep Stages Wrist

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Publication 2013
Anabolism Chronic Pain Hyperalgesia Pain Polysomnography Sleep Sleep Fragmentation Sleeplessness
Characteristics known to be related to activity rhythms or cognitive function were summarized using means and SDs for continuous data and counts and percentages for categorical data. We compared characteristics among categories of amplitude and acrophase using ANOVA for continuous covariates that were normally distributed, Kruskal-Wallis tests for skewed continuous data, and chi-square tests for categorical data.
To determine the relationship between circadian activity rhythms and incident dementia and MCI, we used logistic regressionto estimate odds ratios (ORs) and 95% confidence intervals(CIs). To identify potential confounders, we considered a list of predictors thought to be associated with circadian activity rhythms and cognition, based on biological plausibility or previous studies. Variables that were significantly related (p<0.10) to at least one activity rhythm predictor measure were included in the final multivariate analyses (age, clinic site, race, education, BMI, walking for exercise, functional status, depression score, benzodiazepine use, antidepressant use, sleep medication use, alcohol use, caffeine use, smoking, self-reported health status, and prior medical conditions). Women determined as having dementia were not included in the logistic models with the outcome of MCI. Sensitivity models were performed further adjusting the multivariate models by sleep efficiency to examine if the association of circadian activity rhythms and incident dementia or MCI was driven by underlying sleep fragmentation. Statistical analysis was performed using the statistical software program SAS version 9.2 (SAS Institute, Inc., Cary, NC).
Publication 2011
Antidepressive Agents Benzodiazepines Biopharmaceuticals Caffeine Circadian Rhythms Cognition Hypersensitivity neuro-oncological ventral antigen 2, human Pharmaceutical Preparations Presenile Dementia Sleep Sleep Fragmentation Woman

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Publication 2009
Actigraphy BAD protein, human Decompression Sickness EPOCH protocol Movement Sleep Sleep Fragmentation Sleep Starts Wrist

Most recents protocols related to «Sleep Fragmentation»

Starting at 0800 (lights on), mice were exposed to 1, 2, 6, 12, or 24 h (n=110; all groups, n = 10) of ASF, which involves a sweeping bar that moves horizontally across the modified cage every 120 sec, simulating the rate of SF in patients with severe sleep apnea25 (Fig. 1). For the non-sleep fragmentation (NSF) control mice, subjects were housed in SF chambers, but no sweeping bar movements occurred. The NSF groups matched collection times of ASF mice (1, 2, 6, 12, or 24 h; all groups, n = 10). Both ASF and NSF groups were compared to a baseline group (time = 0) of mice collected at 0800 (n = 10).
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Publication Preprint 2023
Light Mice, House Movement Patients Sleep Sleep Fragmentation
Male adult C57BL/6J mice between 8–12 weeks of age were used in this study (n= 110; Jackson Laboratory, Bar Harbor, ME). Mice were given food and water ad libitum and housed under standard rodent colony conditions (lights on: 0800–2000 h, 21°C ± 1°C) at Western Kentucky University. Acute sleep fragmentation (ASF) experiments were performed using automated sleep fragmentation chambers (Lafayette Instrument Company; Lafayette, IN; model 80390) with a thin layer of corn bedding as previously described and each chamber contained no more than five mice23 (link). These chambers ensure that mice are subjected to sleep fragmentation and not absolute sleep deprivation16 (link). Mice were acclimated to the sleep fragmentation (SF) chambers for 48 h before the commencement of experiments to minimize carryover effects from the different cage environments24 (link). This study was conducted under the approval of the Institutional Animal Care and Use Committee at Western Kentucky University (#19–11), and procedures followed the National Institutes of Health’s “Guide for the Use and Care of Laboratory Animals” and ARRIVE guidelines.
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Publication Preprint 2023
Adult Animals, Laboratory Corns Food Institutional Animal Care and Use Committees Light Males Mice, House Mice, Inbred C57BL Rodent Sleep Sleep Fragmentation
The electrodes used for recording the electroencephalographic signal were prepared by assembling an insulated ultra-thin stainless-steel wire (0.3 mm diameter, A-M Systems, Inc.) with a stainless-steel miniature screw (diameter 1.2 mm, P1 Technologies), soldered to a connector for the electronic circuitry. The recording electrodes were put in contact with the dura mater in order to obtain an ipsilateral fronto-parietal EEG signal (referential derivation). The frontal screws (one intended to recording and one to anchor the system) were positioned ± 1.2 mm from the interhemispheric fissure and + 1.2 from Bregma. The parietal screws (one recording and one used as common reference) were placed ± 1.2 mm from the interhemispheric fissure and + 1.2 from Lambda. A pair of insulated ultra-thin stainless-steel wire (0.3 mm diameter, A-M Systems, Inc.) was inserted in the posterior nuchal muscle to record the electromyographic (EMG) signal [20 (link)]. During the entire procedure of implantation of the electrodes for EEG and EMG recording, the animal was deeply anesthetized with 3% isoflurane (gaseous anesthetic for veterinary use), mixed with O2 (2 L/min) and N2O (1 L/min), and kept on a heated support to avoid hypothermia. The whole device was firmly attached to the skull by covering it with dental cement. At the end of the surgical procedure a subcutaneous dose of ketoprofen 10 mg/kg was administered. The mice underwent the sleep fragmentation protocol 10 days after surgery, in order to allow adequate recovery time and post-surgery adaptation.
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Publication 2023
Acclimatization Anesthetic Gases Animals Cranium Dental Cements Dura Mater Electroencephalography Estrus Isoflurane Ketoprofen Medical Devices Mus Muscle, Back Operative Surgical Procedures Sleep Fragmentation Stainless Steel
Two-month-old wild type (wt) (total mice = 22) and 5xFAD mice (total mice = 22) and 6-month-old 5xFAD mice (total mice = 8) were positioned on a time-controlled tilting platforms (Stuart Scientific Platform Rocker STR6) connected to a time relay (Mini Asymmetrical Cycle Timer, AC / DC 12-240V GRT8-S2, Regun) able to regulate their activation according to a pattern of 3 min OFF/10 s ON. The mice were divided into two groups: the first group (n = 11 for 2-months old and n = 4 for 6-months old mice) underwent sleep fragmentation for 30 days all day long (24 h), while the second one (n = 11 for 2-months old and n = 4 for 6-months old mice) was kept in cages under the same environmental conditions as fragmented mice, but in the absence of a time-controlled tilting platform, for the same length of time. In order to evaluate the effect of the protocol on sleep–wake cycle, an electroencephalography (EEG) and electromyographic (EMG) recording was performed on three animals per group (wild type n = 3 and 5xFAD n = 3) for 8 days (4 days in normal sleep conditions and 4 during sleep fragmentation). Only the EEG data from the last day were considered, as we preferred the day when the mouse was most likely to show adaptation to the chosen fragmentation system. Each recording was analyzed considering the 24-h day on the basis of the light/dark cycles imposed by the enclosure (8.00 a.m.–8.00 p.m.).
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Publication 2023
Acclimatization Animals Electroencephalography Mus Sleep Sleep Disorders Sleep Fragmentation
Two-months-old no carrier male mice (control mice) and 2- and 6-month-old male B6SJL-Tg(APPSwFlLon, PSEN1 ∗ M146L ∗ L286V)6799Vas/Mmjax (5xFAD) mice were used for a sleep fragmentation protocol. Experimental procedures involving the use of live animals have been carried out by the guidelines established by the European Community Directive 86/609/EEC (November 24, 1986), Italian Ministry of Health and the University of Turin institutional guidelines on animal welfare (law 116/92 on Care and Protection of living animals undergoing experimental or other scientific procedures; authorization number: 470/2021-PR). Moreover, the Ethical Committee of the University of Turin approved this type of study. The animals were maintained under 12-h light/dark cycles and were provided with water and food ‘‘ad libitum’’ (standard mouse chow 4RF25-GLP, Mucedola srl, Settimo Milanese, Italy). Specifically, all the procedures were carried out in order to minimize the pain and distress in the animals and we used the fewest number of animals required to obtain statistically significant data.
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Publication 2023
Animals Food Males Mice, House Pain PSEN1 protein, human Sleep Fragmentation

Top products related to «Sleep Fragmentation»

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C57BL/6J mice are a widely used inbred mouse strain. They are a commonly used model organism in biomedical research.
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The sleep fragmentation chamber is a laboratory equipment designed to disrupt the normal sleep patterns of test subjects. It functions by introducing controlled environmental stimuli that interrupt the sleep cycle, enabling researchers to study the effects of sleep fragmentation on various physiological and psychological aspects.
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The Actiwatch 2 is a small, wearable device designed to monitor physical activity and sleep patterns. It features an accelerometer that detects movement and records data, which can be analyzed to provide insights into an individual's daily activity levels and sleep quality.
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The Alice 5 is a diagnostic equipment product from Philips. It is designed to perform various medical tests and evaluations. The core function of the Alice 5 is to assist healthcare professionals in diagnosing and monitoring patient conditions.
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The Actiwatch Spectrum is a wearable device designed to monitor activity and sleep patterns. It is a compact, lightweight, and waterproof device that can be worn on the wrist. The Actiwatch Spectrum collects data on movement, light exposure, and body position, which can be used to analyze sleep-wake cycles and activity levels.
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ActiLife 6 is a software platform developed by ActiGraph for the analysis and management of physical activity and sleep data collected using ActiGraph's wearable activity monitors. It provides tools for data processing, visualization, and reporting.
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ActiLife is a software application designed for the analysis and management of data collected from ActiGraph wearable activity monitors. The software provides tools for data processing, visualization, and reporting to support physical activity research and clinical studies.
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Actiware 6.0.9 is a software package designed for the analysis and interpretation of actigraphy data. It provides tools for the processing and visualization of data collected from actigraph devices.
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The Embla N7000 system is a comprehensive sleep diagnostic platform designed for clinical and research applications. It offers high-quality data acquisition and analysis capabilities for sleep studies.
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The Drosophila Activity Monitoring System (DAMS) is a laboratory equipment designed for the continuous monitoring of locomotor activity in Drosophila (fruit flies). The system records and analyzes the movement of individual flies within their enclosures, providing precise data on their activity patterns.

More about "Sleep Fragmentation"

Sleep disruption, Sleep disturbance, Nocturnal awakenings, Sleep quality impairment, Diurnal fatigue.
Researchers studying sleep fragmentation can leverage advanced technologies like PubCompare.ai's AI-driven platform to optimize their workflows and discover the most effective solutions.
This cutting-edge tool enables intelligent comparisons of the best protocols from literature, pre-prints, and patents, providing AI-driven insights to enhance studies on this important sleep disorder.
Utilizing PubCompare.ai's platform, scientists can explore a wide range of resources, including studies on C57BL/6J mice, which are commonly used in sleep fragmentation research.
The platform can also help researchers analyze data from various diagnostic equipment, such as the Actiwatch 2, Alice 5, Actiwatch Spectrum, and Embla N7000 system, as well as software like ActiLife 6 and Actiware 6.0.9.
By harnessing the power of AI and machine learning, PubCompare.ai's platform can optimize research workflows, identify the most effective protocols, and uncover the latest advancements in the field of sleep fragmentation.
Experiance the benefits of this cutting-edge technology to take your sleep disorder studies to new heights and gain a deeper understanding of this complex condition.