Готові списки джерел за темами / Sleep

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Добірка наукової літератури з теми "Sleep"

Автор: Grafiati
Опубліковано: 4 червня 2021
Оновлено: 9 червня 2025

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Статті в журналах з теми "Sleep"

1

Manners, J., E. Kemps, B. Lechat, N. Stuart, A. Guyett, S. Proctor, D. Eckert, P. Catcheside, and H. Scott. "O007 Validation of an under-mattress sleep tracker to estimate sleep and wake during day and night sleep opportunities." Sleep Advances 4, Supplement_1 (October 1, 2023): A3. http://dx.doi.org/10.1093/sleepadvances/zpad035.007.

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Abstract Introduction Consumer sleep trackers are useful for tracking daily sleep, particularly in shift-workers, yet large, multi-night validations against direct sleep measures are lacking. Conducted as one of the largest sleep tracker validation studies to date, this study evaluated performance of an under-mattress sensor (Withings Sleep Analyzer [WSA]) to classify sleep and wake versus polysomnography (PSG) in a multi-night laboratory protocol with both day and night sleep opportunities. Methods 27 healthy sleepers attended the laboratory twice, for 8 consecutive days (48% male, mean[SD] age=27[9]years). Participants underwent simultaneous PSG and WSA recordings for the initial night-time sleep (22:00-07:00) and 6 subsequent daytime sleeps (10:00-19:00) during simulated night-shift work. Epoch-by-epoch analysis of the 252 nights tested accuracy, sensitivity, and specificity for the first night-time sleep opportunity versus subsequent daytime sleeps. Sleep duration estimates were compared between WSA and PSG. Results Across all recordings, the WSA showed 83% sleep-wake classification accuracy, 87% sensitivity to sleep and 25% specificity to wake. The WSA significantly overestimated sleep duration versus PSG (25[65]minutes, p<0.05). Accuracy and specificity were higher for night versus day sleeps (88% and 38% versus 81% and 18% respectively, all p<0.05), while sensitivity (90% versus 88%) did not significantly differ. Discussion The WSA was moderately accurate compared to PSG, and comparable to other movement-based sleep trackers. The WSA was more accurate at classifying sleep and wake during night sleep opportunities compared to daytime sleeps. This differential performance is likely due to poorer sleep quality commonly observed during daytime sleep that is harder to accurately classify.
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2

Raghu, Ram Chowdary Velevela. "Deep Learning in Sleep Medicine: Evaluating Resnet50 and Inceptionv3 for Apnea Detection." Recent Trends in Data Science Technology 1, no. 1 (March 29, 2025): 16–25. https://doi.org/10.5281/zenodo.15104461.

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<em>Obstructive sleep apnea (OSA) is a medical condition in which the airway becomes obstructed regularly, and resulting in sleep disruption. Sleep apnea is a breathing condition in which a person stops breathing repeatedly while sleeping. OSA is usually diagnosed by an expensive procedure that requires the patient to remain in the hospital overnight. However, this method is expensive, inconvenient, and time consuming. Snoring, poor night sleeps due to choking or gasping, and waking up unrefreshed are all common symptoms of OSA. In this paper, we proposed the two deep pre-trained convolutional neural network models ResNet50 and InceptionV3 for predicting obstructive sleep apnea. We consider the 2-D facial images of different subjects of various conditions such as sleepy, normal, happy and sad images. Even with the small amount of data using transfer learning, InceptionV3&nbsp;had a good average&nbsp;accuracy of around 91% for OSA prediction and ResNet50&nbsp;had a good average accuracy of around 82%.</em>
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3

Lenker, Kristina, Susan Calhoun, Julio Fernandez-Mendoza, and Susan Mayes. "1044 Relationship Between Core Autism Symptoms and Sleep Disturbances in Youth with Autism: A Latent Class Analysis." SLEEP 47, Supplement_1 (April 20, 2024): A449. http://dx.doi.org/10.1093/sleep/zsae067.01044.

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Abstract Introduction Previous studies have used cluster analysis to clarify diagnostic heterogeneity of autism, but have been limited to identifying subgroups of autism on the basis of core symptoms rather than sleep problems. The present study examined the relationship between core autism symptoms and sleep problems in children and adolescents with autism spectrum disorder (ASD). Methods 1466 patients (1–18y, M=6.4±3.6; IQs of 8–146, M=88.9±27.25; 81.2% male, 89.0% white) diagnosed with ASD. Dimension reduction via principal component analysis (PCA) was performed on the 10 sleep items from the Pediatric Behavior Scale. Latent class analyses (LCA) was used to determine phenotypes characterized by core ASD symptoms (social interaction, perseveration, somatosensory disturbance, atypical communication/ development, mood, and selective attention/safety awareness), and sleep dimensions accounting for age, gender, IQ, and medication use. Results Three clusters with distinguishable sleep factors were retained from the PCA, with eigenvalues &amp;gt;1, with adequate goodness-of-fit (TLI=0.91;RMSEA=0.075); disturbed sleep (trouble falling asleep, restless sleep, wakes often, nightmares, and talks/walks/or cries in sleep), insufficient sleep (sleeps less, wakes early), and, hypersomnolence (drowsy/sleepy, sluggish/slow-moving, sleeps more). LCA revealed a 3-class (AIC=29164.00;BIC=29594.00;sBIC=29334.00) and 4-class model (AIC=28986.00;BIC=29489.00;sBIC=29184.00), with adequate fit. Based on IC measures, the 4-class model yielded the best model fit, with acceptable entropy (0.856). Based on conceptual grounds and considering all model fit statistics, the 4-class model was chosen. Using Class 3 (N=708;50.7%) as the reference group, Class 1 (N=71;5.1%) was categorized by more problems with social interactions, disturbed sleep, hypersomnolence, older age and increased medication use. Class 2 (N=367;26.3%) was categorized by less severe autism symptoms, particularly problems with selective attention/safety awareness, overall sleep problems, older age and higher IQ. Class 4 (N=251;18.0%) was categorized by more problems with perseveration and somatosensory disturbance, disturbed and insufficient sleep, younger age and increased medication use. Gender was not a significant covariate. Conclusion Using LCA we found four distinct patterns of core autism symptoms and sleep problems differing in terms of age, IQ, and medication use. This study provides strong evidence for phenotyping and targeting sleep as a standard part of therapeutic intervention in individuals with autism. Support (if any) Department of Health and Human Services, Health Resources and Services Administration, R4349152
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4

Åkerstedt, Torbjörn, Ken Hume, David Minors, and Jim Waterhouse. "Experimental separation of time of day and homeostatic influences on sleep." American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 274, no. 4 (April 1, 1998): R1162—R1168. http://dx.doi.org/10.1152/ajpregu.1998.274.4.r1162.

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The purpose of the present study was to evaluate the simultaneous effects on sleep of prior time awake (PRW) and time of day (TOD). Eight male subjects spent 13 days in an isolated sleep lab and had three 8-h baseline sleeps and then 18 4-h sleeps, distributed to provide three sleeps starting at 2400, 0400, 0800, 1200, 1600, and 2000. The three sleeps were preceded by 4, 8, and 12 h of PRW, respectively. ANOVA showed that TST and subjective sleepiness increased with PRW and with closeness to the trough of the circadian rhythm of rectal temperature, whereas sleep latency showed the opposite pattern, and rapid eye movement sleep (REM) latency strongly decreased with PRW and with closeness to the trough. Slow-wave sleep (SWS) increased with PRW, whereas SWS latency and final time awake decreased. REM sleep increased with closeness to the circadian trough, and time awake decreased. Multiple-regression analysis showed that REM latency was closely related to increased SWS in the first sleep cycle, reduced SWS latency, and increased PRW [a short PRW before sleep at noon yielded an extremely short (14 min) REM latency]. Sleep latency and final time awake showed almost exactly the same relationship to TOD and PRW. It is concluded that both homeostatic and circadian influences simultaneously affect sleep, that REM latency is very sensitive to the need for SWS, and that the circadian acrophase strongly interferes with sleep. It should be emphasized that the conclusions should not be extrapolated to longer (&gt;12 h) wake spans.
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5

Honda, Takato, Tomoyuki Fujiyama, Chika Miyoshi, Aya Ikkyu, Noriko Hotta-Hirashima, Satomi Kanno, Seiya Mizuno, et al. "A single phosphorylation site of SIK3 regulates daily sleep amounts and sleep need in mice." Proceedings of the National Academy of Sciences 115, no. 41 (September 25, 2018): 10458–63. http://dx.doi.org/10.1073/pnas.1810823115.

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Анотація:
Sleep is an evolutionally conserved behavior from vertebrates to invertebrates. The molecular mechanisms that determine daily sleep amounts and the neuronal substrates for homeostatic sleep need remain unknown. Through a large-scale forward genetic screen of sleep behaviors in mice, we previously demonstrated that the Sleepy mutant allele of the Sik3 protein kinase gene markedly increases daily nonrapid-eye movement sleep (NREMS) amounts and sleep need. The Sleepy mutation deletes the in-frame exon 13 encoding a peptide stretch encompassing S551, a known PKA recognition site in SIK3. Here, we demonstrate that single amino acid changes at SIK3 S551 (S551A and S551D) reproduce the hypersomnia phenotype of the Sleepy mutant mice. These mice exhibit increased NREMS amounts and inherently increased sleep need, the latter demonstrated by increased duration of individual NREMS episodes and higher EEG slow-wave activity during NREMS. At the molecular level, deletion or mutation at SIK3 S551 reduces PKA recognition and abolishes 14-3-3 binding. Our results suggest that the evolutionally conserved S551 of SIK3 mediates, together with PKA and 14-3-3, the intracellular signaling crucial for the regulation of daily sleep amounts and sleep need at the organismal level.
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6

Gopichandran, V., S. Suganya, and K. P. Misra. "Sleepy Heart and Hearty Sleep: Sleep Disorders and Cardiovascular Diseases." Indian Journal of Sleep Medicine 1, no. 3 (2006): 141–44. http://dx.doi.org/10.5005/ijsm-1-3-141.

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7

Bouloukaki, Izolde, Theofilos Vouis, Antonios Velidakis, Violeta Moniaki, Eleni Mavroudi, George Stathakis, Michail Fanaridis, and Sophia Schiza. "Polysomnography Differences Between Sleepy and Non-Sleepy Obstructive Sleep Apnea (OSA) Patients." Healthcare 13, no. 5 (February 22, 2025): 478. https://doi.org/10.3390/healthcare13050478.

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Background/Objectives: Factors underlying excessive daytime sleepiness (EDS) in obstructive sleep apnea (OSA) are not fully understood. We investigated whether polysomnography (PSG) parameters differed between non-sleepy and sleepy (based on the Epworth Sleepiness Scale (ESS)) OSA patients with the same disease severity, which may play a role in the presence of EDS. Methods: A total of 1307 patients, without cardiovascular, metabolic, respiratory, or inflammatory comorbidities, diagnosed with OSA (apnea–hypopnea index (AHI) ≥ 5 per hour of sleep) with type 1 PSG were included. Based on the AHI, patients were classified into mild- (AHI 5–14.9, n = 236), moderate- (AHI 15–29.9, n = 367), and severe-OSA (AHI ≥ 30, n = 704) groups. These groups were further divided into two subgroups based on the ESS, the most convenient and widely used tool to assess excessive daytime sleepiness: sleepy (ESS &gt; 10) and non-sleepy (ESS ≤ 10). PSG data were compared between groups, and multivariable logistic regression was used to identify differences after adjustment for confounders. Results: For the entire population, male sex, younger age, obesity, depression, increased wakefulness after sleep onset (WASO), the arousal index, shorter sleep latency, and all indices of OSA severity (AHI, oxygen desaturation index, mean and lowest resting room air pulse oximetry (SpO2), and sleep time with oxygen saturation &lt; 90% (TST90)) were significantly associated with EDS. The arousal index consistently showed a strong association with EDS across all OSA severity groups. Moderate-OSA sleepy patients were younger, with shorter sleep latency and increased indices of OSA severity, excluding the AHI. Severe-OSA sleepy patients were younger, males, and obese; had depression, decreased slow-wave sleep (SWS) and sleep latency, and increased WASO; and presented an increase in all indices of OSA severity. Conclusions: Our results suggest that male sex, younger age, obesity, the presence of depression, WASO, lower sleep efficiency, the arousal index, and all indices of OSA severity may account for the presence or absence of EDS in OSA patients and could be useful for exploring the underlying pathophysiological mechanisms for precision medicine.
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8

Parekh, Ankit, Bresne Castillo, Do Hyung Kim, Kathleen Black, Rafael de la Hoz, David Rapoport, Jag Sunderram, and Indu Ayappa. "401 Clinical Phenotypes of Obstructive Sleep Apnea in World Trade Center Responders." Sleep 44, Supplement_2 (May 1, 2021): A159—A160. http://dx.doi.org/10.1093/sleep/zsab072.400.

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Abstract Introduction The heterogeneity of symptoms in obstructive sleep apnea (OSA) patients has been recently formalized into 3 distinct clusters: Sleepy, Disturbed Sleep, and Minimally Symptomatic. Our previous data showed that OSA is highly prevalent (&amp;gt;75%) in World Trade Center (WTC) responders, and positive airway pressure (PAP) treatment adherence is very poor (&amp;lt;20%). To better understand the heterogeneity of OSA in the WTC cohort, here we sought to examine the distribution of these distinct clinical phenotypes. Methods 643 subjects with no history of OSA or reported loud and frequent snoring before 9/11/2001 from the WTC health program clinical centers at Rutgers RWJMS, New Jersey, NYU School of Medicine, and Icahn School Medicine at Mount Sinai, New York underwent 2 nights of home sleep testing using the ARES unicorder (SleepMed, Inc., West Palm Beach, FL, USA). Epworth Sleepiness Scale (ESS), sleep onset insomnia, and sleep maintenance insomnia were assessed with questionnaires. OSA was defined as (AHI4%&amp;gt;=5 or RDI&amp;gt;=15/hr). The three clusters were defined as 1) Sleepy (ESS&amp;gt;10 and/or sleep onset/maintenance insomnia); 2) Disturbed Sleep (not sleepy (ESS&amp;lt;=10) and sleep onset/maintenance insomnia); and 3) Minimally Symptomatic (not sleepy (ESS&amp;lt;=10) and no sleep onset/maintenance insomnia). Distribution of clusters in the WTC cohort was compared to published data from the Sleep Apnea Global Interdisciplinary Consortium (SAGIC) and the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Results Among the subjects diagnosed with OSA (N 440; AHI4%=13(15); RDI =28(19); median(iqr); 81% men; age, 33–87 years; BMI, 27.4±3.7 kg/m2), the distribution of clinical phenotypes was 31.4% sleepy, 48.9% disturbed sleep, and 19.7% minimally symptomatic, and did not differ between OSA severity groups. In comparison to SAGIC and HCHS/SOL, the WTC cohort exhibited significantly increased prevalence of the disturbed sleep phenotype (WTC vs SAGIC: 48.9% vs. 19.8%, □2=54.9; p&amp;lt;0.001; WTC vs. HCHS/SOL: 48.9% vs. 38.1%, □2=26.1, p&amp;lt;0.001). Conclusion The predominant clinical phenotype of OSA in the WTC cohort is disturbed sleep (insomnia) and its prevalence is significantly greater than what has been observed in other large OSA cohorts. These findings may help explain the poor adherence to PAP treatment observed in the WTC cohort. Support (if any) NIOSH U01OH01415; AASM Foundation 233-BS-20.
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Dr.A.Shaji, George. "Envisioning a Sleep-Friendly India: Tackling the Drivers and Repercussions of a National Sleep Deficit Crisis." Partners Universal Multidisciplinary Research Journal (PUMRJ) 02, no. 02 (March 25, 2025): 50–58. https://doi.org/10.5281/zenodo.15067142.

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Sleep is a fundamental human need, yet many people struggle to get adequate, good quality sleep. This article examines the sleep landscape in India, where studies show a majority of adults fail to get the recommended 7-9 hours of sleep per night. It delves into the reasons behind India's sleep deficit, including frequent nighttime awakenings, irregular sleep schedules, and external disturbances. The consequences of chronic sleep loss are explored, from impaired cognitive functioning to increased risk of medical conditions like heart disease and diabetes. The article discusses why sleep remains elusive for so many, despite a booming sleep industry worth billions. It suggests shifting the focus from trying to achieve an unrealistic "ideal" amount of sleep to understanding personal sleep needs and addressing the root lifestyle factors disrupting sleep. If sleep is not approached as a high-pressure challenge but accepted as a vital need, people may worry less about sleep and, ironically, sleep better.
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Åkerstedt, Torbjörn, Ken Hume, David Minors, and Jim Waterhouse. "The Subjective Meaning of Good Sleep, An Intraindividual Approach Using the Karolinska Sleep Diary." Perceptual and Motor Skills 79, no. 1 (August 1994): 287–96. http://dx.doi.org/10.2466/pms.1994.79.1.287.

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The present experiment used an intraindividual design to investigate the meaning and measurement of “good sleep.” Each of 16 subjects slept in an isolation unit according to a schedule (15 sleeps) designed to give variable quality of sleep. Self-rated sleep measures (from the Karolinska Sleep Diary) were obtained after each sleep and subjected to intraindividual regression analyses across time. Most subjective sleep measures showed a strong covariation across conditions. Subjective quality of sleep mainly involved variables of sleep continuity, in particular, perceived calmness of sleep and sleep efficiency. “Sleep quality,” “calm sleep,” “ease of falling asleep,” and ability to “sleep throughout” the time allotted strongly covaried and formed an index of sleep quality. Self-rated ease of awakening deviated from the general pattern and was associated with poor sleep quality. So was reported dreaming (related to awakenings). It was concluded that most subjective sleep measures tend to covary across conditions and that “good sleep” is mainly a question of sleep continuity.
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Більше джерел

Дисертації з теми "Sleep"

1

Sahlin, Carin. "Sleep apnea and sleep : diagnostic aspects." Doctoral thesis, Umeå : Umeå universitet, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-18959.

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2

Eugene, Nicole Christina. "POTENT SLEEP: THE CULTURAL POLITICS OF SLEEP." Connect to this title online, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=bgsu1151208257.

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3

Altaf, Quratul-ain. "Sleep in patients with type 2 diabetes : the impact of sleep apnoea, sleep duration, and sleep quality on clinical outcomes." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8270/.

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Introduction: Type 2 Diabetes (T2DM) and sleep-related disorders share common risk factors such as obesity; but the interrelationships between T2DM and sleep disorders are not well examined. Aims: In this thesis I aimed to assess: 1. The longitudinal impact of obstructive sleep apnoea (OSA) on micro vascular complications in patient with T2DM. 2. The relationship between sleep quality, sleep duration and adiposity in patients with T2DM Methods: To examine the first aim, I utilized the data collected from a previous project that examined the cross-sectional associations between OSA and micro vascular complications in patients with T2DM and followed up the study participants longitudinally using 1-2-1 interviews and electronic health records. For aim 2, I conducted a crosssectional study in patients with young-onset T2DM who were recruited from Heart of England NHS Foundation Trust and primary care. Result: For Aim 1: Depending on the micro vascular outcome examined, we had approximately 200 patients in the analysis. Patients were followed up for 2.5 years for renal outcomes, and 4-4.5 years for retinopathy and neuropathy outcomes. The prevalence of OSA was 63%. I found that baseline OSA was significantly associated with greater decline of eGFR and greater progression to pre-proliferative and proliferative retinopathy. I also found that OSA was associated with progression to a combined outcome of foot insensitivity or diabetic foot ulceration but this was a non-significant trend (p=0.06). In addition, I found that patients who received and were compliant with continuous positive airway pressure (CPAP) treatment (delivered during routine care) had improvements in heart rate variability parameters by study end. For Aim 2: Poor sleep quality and shorter sleep duration were associated with increased total body fat% after adjustment for potential confounders. Conclusion: I found that OSA plays an important role in the progression of micro vascular complications in patients with T2DM. Whether treatment with CPAP has a favourable impact on micro vascular complications is currently being examined in a randomized controlled trial. I also found that sleep duration and quality are associated with increased adiposity. The direction of this relationship need to be examined in longitudinal studies and interventional trials.
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4

Žakevičius, Martynas. "Sleep and the sense of rest: relation between sleep fragmentation and subjective sleep quality." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2012. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2012~D_20121001_093350-31580.

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Sleep disorders are one of the most common medical complaints today. There is a growing interest in sleep medicine, attitude of doctors and society is changing and knowledge about sleep and its disorders is increasing. One of the most tedious and understudied sleep problems is non-restorative sleep. Researchers are still debating about what determines persons rest sense after the sleep. A lot of attention recently is paid for sleep integrity and a role of sleep fragmentation for the rest sense. It is thought that sleep fragmentation with short arousals could have effect on the sleeps restorative function. The aim of our study was to analyze sleep structure and sleep quality through sleep cycles, phases and stages and to evaluate structure’s relationship with subjective sense of rest after the sleep without paying attention to the type of insomnia. We have analysed three types of arousals (behavioural, vegetative and microarousals) and their dynamics during the night, in different sleep cycles and stages. Subjective sleep quality was evaluated using the Pittsburgh sleep quality index. Work results showed that for the subjective sense of rest after the sleep it is more important the stability of sleep in the initial than in the last sleep cycles. Sleep stage and arousal type regardless of sleep cycle are significant factors for the arousal index values and that increase of all arousal indices in NREM 2 stage (especially increase of microarousal index) has strongest impact for... [to full text]<br>Miego sutrikimai yra vienas labiausiai paplitusių nūdienos medicininių nusiskundimų. Paskutiniu metu miego medicina domimasi vis labiau, keičiasi gydytojų ir visuomenės supratimas apie miegą, jo sutrikimus, ligas ir negalavimus, susijusius su miegu. Vienas iš svarbesnių ir labai varginančių sutrikimų yra miegas be poilsio jausmo. Mokslininkai iki šiol diskutuoja, kas lemia poilsio jausmą po miego. Pastaruoju metu nemažai dėmesio skiriama miego vientisumo ir suskaldymo reikšmės poilsio jausmui tyrimams. Manoma, kad miego fragmentacija – miego suskaidymas dažnais trumpais nubudimais – mažina jo atstatomąją vertę, ypač smegenims. Pagrindinis darbo tikslas buvo išnagrinėti miego struktūrą ir kokybę per miego ciklus, fazes ir stadijas bei įvertinti miego struktūros ryšį su poilsio jausmu po miego nepriklausomai nuo nemigos tipo. Buvo nagrinėjami trijų tipų – elgesiniai, vegetaciniai ir žieviniai mikro – nubudimai ir jų dinamika nakties metu, skirtinguose miego cikluose ir stadijose. Subjektyviai vertinama miego kokybė buvo analizuojama pasitelkus Pitsburgo miego kokybės indeksą. Darbo rezultatai parodė, kad miego stabilumas nakties pradžioje turi didesnę įtaką subjektyviam miego kokybės jausmui negu miegas nakties pabaigoje. Nubudimų indekso dydžiui reikšmingos įtakos turi miego stadija ir nubudimo tipas, nepriklausomai nuo miego ciklo, o didžiausią įtaką poilsio jausmui turi visų tipų nubudimų, ypač žievinių mikronubudimų, indeksų padidėjimas antroje lėtojo miego stadijoje.
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5

Alkhanaizi, Walaa(Walaa M. ). "Fascia : a sleep mask for conducting sleep studies." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/130196.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020<br>Cataloged from student-submitted PDF of thesis. Date of graduation confirmed by MIT Registrar Office. "May 2020."<br>Includes bibliographical references (pages 51-53).<br>In this thesis, I discuss the importance of sleep and therefore the study of sleep. I highlight limitations with existing methodologies to conduct sleep studies and collect sleep data, and present a solution to overcome current limitations by providing better mechanisms for sensing during sleep in the wild. This document details the thought process of every aspect of design and development of the progress made on the project so far. First, I present the motivation for the project and provide general background. Second, I discuss the physiological signals that sleep studies monitor and their relationship to sleep. If the reader is familiar with these physiological signals and how they are relevant to sleep studies, they should feel free to skip that section. Next, I provide an overview of some existing alternatives in the market and discuss why they do not satisfy the purpose of in-the-wild sleep studies. Next, I detail the design of the device, physically and on the system level. Then, I go into a detailed description of the components of the device in hardware, firmware, and software. I include a brief description of some of the efforts made in the code to make it easier to debug while developing. Lastly, I discuss what work was completed, and what work remains to be done. I close with a full list of the tasks remaining and some implementation concerns. There is a glossary near the end of the document of terms and acronyms I use throughout the thesis. Feel free to consult it should any confusion arise regarding the meaning of words used. The document ends with a list of appendices starting with a complete usage guide for the system in its current state. The other appendices include copies of all the firmware and software code, and circuit and PCB designs.<br>by Walaa Alkhanaizi.<br>M. Eng.<br>M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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6

Dhand, Neal. "Sleep shift /." Online version of thesis, 2009. http://hdl.handle.net/1850/9881.

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7

Aeschbach, Daniel. "Dynamics of the human sleep electroencephalogram : effects of hypnotics, sleep deprivation, and habitual sleep length /." [S.l.] : [s.n.], 1995. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=11177.

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8

Sethi, Kevin J. "A Comparison of the Pittsburgh Sleep Quality Index, a New Sleep Questionnaire, and Sleep Diaries." Thesis, University of North Texas, 2012. https://digital.library.unt.edu/ark:/67531/metadc177254/.

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Self-report retrospective estimates of sleep behaviors are not as accurate as prospective estimates from sleep diaries, but are more practical for epidemiological studies. Therefore, it is important to evaluate the validity of retrospective measures and improve upon them. The current study compared sleep diaries to two self-report retrospective measures of sleep, the commonly used Pittsburgh Sleep Quality Index (PSQI) and a newly developed sleep questionnaire (SQ), which assessed weekday and weekend sleep separately. It was hypothesized that the new measure would be more accurate than the PSQI because it accounts for variability in sleep throughout the week. The relative accuracy of the PSQI and SQ in obtaining estimates of total sleep time (TST), sleep efficiency (SE), and sleep onset latency (SOL) was examined by comparing their mean differences from, and correlations with, estimates obtained by the sleep diaries. Correlations of the PSQI and SQ with the sleep diaries were moderate, with the SQ having significantly stronger correlations on the parameters of TST, SE, and sleep quality ratings. The SQ also had significantly smaller mean differences from sleep diaries on SOL and SE. The overall pattern of results indicated that the SQ performs better than the PSQI when compared to sleep diaries.
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Reyner, Louise Ann. "Sleep, sleep disturbance and daytime sleepiness in normal subjects." Thesis, Loughborough University, 1995. https://dspace.lboro.ac.uk/2134/27108.

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The concept of sleep disturbance is rather vague. Many people claim to suffer from sleep disturbance, but yet find it hard to describe exactly what they mean by the label in subjective terms. Sleep researchers have a similar problem, it is difficult to describe what is meant by sleep disturbance either in an objective or a subjective way, and harder still to relate sleep disturbance to sleepiness shown the following day.
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HAFEZI, AUTRI NICOLE. "SLEEP REACTIVITY AND PARASYMPATHETIC CONTROL WHEN RETURNING TO SLEEP." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/612960.

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Insomnia is among the most common health problems and is associated with lower parasympathetic control. Sleep reactivity is associated with development of chronic insomnia. The purpose of this study was to examine whether sleep reactivity is associated with parasympathetic control in response to a sleep-relevant stressor. Parasympathetic control was operationalized using respiratory sinus arrhythmia (RSA). Sleep reactivity was operationalized as a score of 14 or higher on the Ford Insomnia Response to Stress Test (FIRST). Participants were 33 healthy young adults required to adhere to a fixed eight-hour sleep schedule for three nights before an in-laboratory sleep study. Physiological signals were recorded for two five-minute baseline periods of resting wakefulness prior to lights out. Participants were woken after the first five minutes of contiguous N2 sleep in the third NREM period and kept awake for 15 minutes, then allowed to return to sleep. In a multiple linear regression, the interaction between baseline RSA and sleep reactivity predicted RSA when returning to sleep. Individuals with high sleep reactivity had relatively low parasympathetic control when returning to sleep. People with high sleep reactivity may benefit from interventions to increase parasympathetic control during awakenings from sleep.
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Книги з теми "Sleep"

1

Brown, Margaret Wise. Sleep tight, sleepy bears. Bath, UK: Parragon, 2012.

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Brown, Margaret Wise. Sleep tight, sleepy bears. Bath, UK: Parragon, 2012.

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Brown, Margaret Wise. Sleep tight, sleepy bears. New York: Parragon Inc, 2016.

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4

Auerbach, Jessica. Sleep, baby, sleep. London: Headline Book Publishing, 1994.

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5

ill, Van Lieshout Maria, ed. Sleep, baby, sleep. New York: Philomel Books, 2009.

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6

Auerbach, Jessica. Sleep, baby, sleep. New York: Fawcett Crest, 1995.

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7

illustrator, Van Lieshout Maria, ed. Sleep, baby, sleep. New York: Philomel Books, 2009.

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8

Casey, Crisenbery, ed. Sleep angel sleep. Herndon, Va: Mascot Books, 2011.

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9

Lader, Malcolm, Daniel P. Cardinali, and S. R. Pandi-Perumal. Sleep and Sleep Disorders. Boston, MA: Springer US, 2006. http://dx.doi.org/10.1007/0-387-27682-3.

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Wright, Maureen. Sleep, Big Bear, sleep! New York: Marshall Cavendish, 2009.

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Частини книг з теми "Sleep"

1

Kales, Joyce D., Michael Carvell, and Anthony Kales. "Sleep and Sleep Disorders." In Geriatric Medicine, 562–78. New York, NY: Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4757-2093-8_43.

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Nofzinger, Eric A., and Charles F. Reynolds. "Sleep and Sleep Disorders." In Geriatric Medicine, 823–40. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2705-0_54.

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3

Parati, Gianfranco, Carolina Lombardi, Krzysztof Narkiewicz, Jacek Wolf, and Juan Eugenio Ochoa. "Sleep Disturbances/Sleep Apnea." In Updates in Hypertension and Cardiovascular Protection, 259–87. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59918-2_18.

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4

Goodlin-Jones, Beth L., Melissa M. Burnham, and Thomas F. Anders. "Sleep and Sleep Disturbances." In Handbook of Developmental Psychopathology, 309–25. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4163-9_17.

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Dai, David, Kevin J. Eng, and Cathy A. Alessi. "Sleep and Sleep Disorders." In Geriatric Medicine, 1251–65. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-030-74720-6_94.

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Dai, David, Kevin J. Eng, and Cathy A. Alessi. "Sleep and Sleep Disorders." In Geriatric Medicine, 1–15. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-01782-8_94-1.

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Roehrs, Timothy, and Thomas Roth. "Sleep and Sleep Disorders." In Drug Abuse and Addiction in Medical Illness, 375–84. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3375-0_30.

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Nussbaumer-Ochsner, Yvonne, and Konrad E. Bloch. "Sleep." In High Altitude, 325–39. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8772-2_17.

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Morgan, Michael M., MacDonald J. Christie, Luis De Lecea, Jason C. G. Halford, Josee E. Leysen, Warren H. Meck, Catalin V. Buhusi, et al. "Sleep." In Encyclopedia of Psychopharmacology, 1239. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_683.

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Di Bartolo, Christina A., and Maureen K. Braun. "Sleep." In Pediatrician's Guide to Discussing Research with Patients, 189–206. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49547-7_11.

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Тези доповідей конференцій з теми "Sleep"

1

Jeon, Sanghoon, Anand Paul, Haengju Lee, Yongsoon Bun, and Sang Hyuk Son. "SleePS: Sleep position tracking system for screening sleep quality by wristbands." In 2017 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2017. http://dx.doi.org/10.1109/smc.2017.8123110.

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Ali, Raja Mahamade, Monica Zolezzi, and Ahmed Awaisu. "Exploration of Sleep Quality and Sleep Hygiene among QU students." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0181.

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Sleep is an important component of healthy lifestyles. Worldwide reports suggest that one in every three adults suffers from insomnia. University students are vulnerable to insomnia due to their stressful lifestyle and inconsistent sleeping schedules, which contribute to poor, sleep hygiene. The purpose of this study is to explore the prevalence of sleeping problems among university students in Qatar and to investigate factors contributing to insomnia development. A cross-sectional survey utilizing two validated sleep questionnaires, the Pittsburgh sleep quality index (PSQI) and the sleep hygiene index (SHI), were administered to Qatar University (QU) students in either English or Arabic. An online survey was sent to all QU students through e-mail. Descriptive and inferential statistics were used to analyse and report the findings. A total of 2,062 students responded to this survey. Most of the respondents were females, Qataris, and the majority of them belonged to the colleges of Arts and Sciences, Business and Economics or Engineering. Around 25% of the participating students reported previous use of sleep aids. The findings indicated that the majority of the students had poor sleep quality (69.7%) and poor sleep hygiene (79%). A positive association was found between sleep quality and sleep hygiene (r = 0.39; p &lt; 0.0001). College distribution and marital status were shown to significantly influence sleep quality (p =0.031 and p=0.02 respectively). The regression analysis revealed that sleep hygiene had the greatest effect on sleep quality (accounting for 7% of the variance) and individuals with good sleep hygiene were 4 times more likely to have good sleep quality. The findings of this study suggest that poor sleep quality and inadequate sleep hygiene practices are common among university students in Qatar, both of which may have a negative impact on students’ academic performance which warrants further investigation in future studies.
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Dhamchatsoontree, Supakit, Chaiyapat Sirisin, Monika Proncharoensukkul, and Konlakorn Wongpatikaseree. "i-Sleep: Intelligent Sleep Detection System for Analyzing Sleep Behavior." In 2019 4th International Conference on Information Technology (InCIT). IEEE, 2019. http://dx.doi.org/10.1109/incit.2019.8912047.

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4

Almeida, Beatriz, Carlos Albuquerque, Madalena Cunha, and Anabela Antunes. "SLEEP QUALITY AND SLEEP HABITS IN STUDENTS." In International Conference on Education and New Developments. inScience Press, 2021. http://dx.doi.org/10.36315/2021end102.

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Introduction: The student stage is marked by many changes that will affect different aspects of young youth life, including changes in sleep patterns. Sleep is known to play an active role in the overall development of students, mainly because of its restorative functions and an insufficient number of hours of sleep can be associated with consequences on physical and mental health. Objectives: To analyze the factors that interfere with higher education students’ sleep quality. Methods: A systematic review of the literature was carried out drawing on a selection of articles published between 2012 and 2020, following the method proposed by the Joanna Briggs Institute and according to the Preferred reporting items for systematic reviews and meta-analyzes (PRISMA). This selection was carried out using PubMed, B-On and SCIELO search engines. The review was based on 5 articles whose methodological quality was found to be undeniable. Results: Results show that, on the whole, students suffer from a poor quality of sleep. This situation is commonly associated with factors such as being a higher education student worker, shift work, or caffeine, alcohol, and tobacco use, among others. Conclusions: In view of the outcomes, it became clear that higher education students need to be made more aware of the importance of sleep habits and daytime sleepiness, and to improve their health literacy. They need to be informed and trained in these areas so they may reduce or at least prevent certain risk behaviours that increasingly threaten their sleep quality and overall health.
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5

Brakmo, Lawrence S., Deborah A. Wallach, and Marc A. Viredaz. "Sleep." In the 2nd international conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/990064.990069.

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6

Cao, Hanfei. "Sleep Stage, Sleep Duration, Attention Span." In 2021 4th International Conference on Humanities Education and Social Sciences (ICHESS 2021). Paris, France: Atlantis Press, 2022. http://dx.doi.org/10.2991/assehr.k.211220.033.

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Ernst, Sharon, Daniel Krauss, Moritz Moss, Martin Vossiek, Bjoern Eskofier, and Matthias Berking. "Affect during sleep: Exploring the capabilities of Artificial Intelligence in Sleep Research." In International Medicine and Health Sciences Congress. Doha, Qatar: ECER, 2024. https://doi.org/10.53375/imhsc.2024.117.

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Clinical research demonstrates the bidirectional associations between affect and sleep: Major depressive disorder is a prevalent clinical disorder, characterized by alterations in affect and sleep. Conversely, sleep disturbances emerge as prodromal manifestations preceding the onset of the disorder. Despite these close links, few studies investigated depressed daytime affect and its bidirectional associations with sad affect at the time of sleep. This lack of evidence can be attributed to methodological constraints, challenging the assessment of affect during sleep. To address this research gap, the present study introduces an innovative method utilizing Machine Learning and Infrared technology for the unobtrusive assessment of affect during sleep. The objectives of the present proof of concept were as follows: First, to introduce a novel approach for measuring affect during sleep. Second, to determine the bidirectional associations of depressed affect at daytime and sad affect during sleep in a healthy sample. Sad affect during sleep was measured using three infrared cameras with adaptive night vision, recording 63 videos during sleep. Daytime depressed affect was measured by survey, data analysis included general linear modeling (GLM). In line with the first objective, sad affect during sleep was extracted from video-based facial expressions using Artificial Intelligence software frameworks open DBM and open Face (AiCure). In line with the second objective, GLM analysis yielded that higher levels of depressed affect at daytime predicted higher levels of sad affect during sleep significantly. The inverse association was not significant. Conclusionally, we present an innovative method for the unobtrusive measurement of affect during sleep. This method may be particularly advantageous for the early detection of depressed mood states in subclinical populations and samples undergoing transient depressed mood states. Clinical and general implications are discussed.
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Marques Grafino, Mónica, Richard Staats, A. Cristina Lutas, Teresa Pequito, Marco Pereira, Ana Ladeira, João Valença, and Sofia Tello Furtado. "Sleepy versus non-sleepy severe obstructive sleep apnea patients: Polysomnographic distinctions." In ERS International Congress 2020 abstracts. European Respiratory Society, 2020. http://dx.doi.org/10.1183/13993003.congress-2020.217.

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Tang, Xiaoying, Zhi Xu, Yanjun Li, and Shaojie Shan. "Macrostructural and microstructural sleep variables for distinguishing normal sleep from pathologic sleep." In 2016 IEEE International Conference on Mechatronics and Automation. IEEE, 2016. http://dx.doi.org/10.1109/icma.2016.7558869.

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Han, Hyonyoung, Jun Jo, Youngsung Son, and Junhee Park. "Smart sleep care system for quality sleep." In 2015 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2015. http://dx.doi.org/10.1109/ictc.2015.7354571.

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Звіти організацій з теми "Sleep"

1

Germain, Anne. Neurobiology of Sleep and Sleep Treatment Response in PTSD. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada525916.

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Lampkin, Cheryl. Quality Sleep: Why Is Getting Consistent Quality Sleep Important? Washington, DC: AARP Research, December 2024. https://doi.org/10.26419/res.00811.012.

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Kelly, Tamsin L., Mark R. Rosekind, and Paul Naitoh. Sleep Management Manual. Fort Belvoir, VA: Defense Technical Information Center, December 1994. http://dx.doi.org/10.21236/ada294586.

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4

Blanchflower, David, and Alex Bryson. Unemployment Disrupts Sleep. Cambridge, MA: National Bureau of Economic Research, September 2020. http://dx.doi.org/10.3386/w27814.

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Bunn, Sarah, and Lev Tankelevitch. Sleep and Health. Parliamentary Office of Science and Technology, September 2018. http://dx.doi.org/10.58248/pn585.

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A POSTnote that explains what is known about sleep and sleep disorders, the effects of poor sleep on performance, and on physical and mental health. It also describes the role of sleep in the context of public and occupational health, road safety, education and the consumer technology market.
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Germain, Anne. Neurobiology of Sleep and Sleep Treatments in PTSD (NOS-STIP). Fort Belvoir, VA: Defense Technical Information Center, October 2010. http://dx.doi.org/10.21236/ada537223.

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Birati, Yosefa, Orly Tonkikh, and Tamar Shochat. Sleep outcomes in cardiac rehabilitation: A scoping review protocol of sleep-focused and non-sleep-focused approaches. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2025. https://doi.org/10.37766/inplasy2025.5.0023.

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LeDuc, Patricia A., John A. Caldwell, and Jr. A Review of the Relationships Among Sleep, Sleep Deprivation, and Exercise. Fort Belvoir, VA: Defense Technical Information Center, April 1998. http://dx.doi.org/10.21236/ada342896.

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Donald R Powers, Donald R. Powers. Do Hummingbirds Actually Sleep? Experiment, January 2017. http://dx.doi.org/10.18258/8929.

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Macvean, M., E. Avdagic, G.-M. Sartore, and C. Wade. Sleep and settling interventions. The Sax Institute, June 2024. https://doi.org/10.57022/btxr9854.

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This Evidence Check aimed to evaluate the effectiveness of current sleep and settling interventions for infants and children aged 0–5 years in Australia. The findings from thirteen systematic reviews indicate that bedtime routines have strong evidence for improving child sleep and settling. Behavioural approaches show moderate evidence for improving sleep but inconsistent results for other outcomes. The review found limited evidence for specific practices within bedtime routines and behavioural approaches. There were also differences in terminology and definitions across sources. Recommendations should promote consistent bedtime routines and limit behavioural approaches, especially those based on extinction principles. Tailored advice for diverse family needs is essential.
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