In search of lost sleep: Secular trends in the sleep time of school-aged children and adolescents

a Health and Use of Time (HUT) Group, University of South Australia, GPO Box 2471, Adelaide SA 5000, Australiab Sansom Institute for Health Research, University of South Australia, GPO Box 2471, Adelaide SA 5000, Australiac School of Health Sciences, University of South Australia, GPO Box 2471, Adelaide SA 5000, AustraliaReceived 7 January 2011. Revised 18 March 2011. Accepted 18 March 2011. Available online 25 May 2011.View full text Sleep deficits are associated with a wide range of detrimental physical and mental health outcomes. There is concern that children are not getting enough sleep, and that sleep duration has been declining. However, evidence is sparse.

A systematic review of world literature was conducted to locate studies reporting the sleep duration of children aged 5–18 years. Monte Carlo simulation was used to generate pseudodata from summary data, which were combined with raw data and analysed by linear regression of sleep duration on year of measurement at the age × sex × day type × country level.

Data were available on 690,747 children from 20 countries, dating from 1905 to 2008. From these data, 641 regressions were derived. The sample-weighted median rate of change was -0.75 min nightly per year, indicating a decrease of more than 1 h per night over the study period. Rates of change were negative across age, sex and day type categories, but varied according to region, with Europe, the USA, Canada and Asia showing decreases and Australia, the UK and Scandinavia showing increases.

Over the last 103 years, there have been consistent rapid declines in the sleep duration of children and adolescents.

prs.rt("abs_end");Sleep duration; Children; Adolescents; Trends

Figures and tables from this article:

Fig. 1. PRISMA flowchart for the search.

View Within ArticleFig. 2. Funnel plots of changes in sleep duration (Y-axis, min/year) against the span of years for each regression, and the total sample size for each regression (X-axes). The dashed line is the sample-weighted median rate of change (-0.75 min/year).

View Within ArticleFig. 3. Box plots showing sample-weighted rates of change for age (Fig. 3a), sex (Fig. 3b) and day type (Fig. 3c) sub-groups. The dashed line is the sample-weighted median rate of change (-0.75 min/year). k = number of regressions assessed; SD = standard deviation; IQR = interquartile range.

View Within ArticleFig. 4. Box plots showing sample-weighted rates of change for different regions. The dashed line is the sample-weighted median rate of change (-0.75 min/year). k = number of regressions assessed; SD = standard deviation; IQR = interquartile range.

View Within ArticleFig. 5. Box plots showing sample-weighted rates of change for different year periods. The dashed line is the sample-weighted median rate of change (-0.75 min/year). k = number of regressions assessed; SD = standard deviation; IQR = interquartile range.

View Within ArticleTable 1. Search strategy used for each database.

View Within ArticleTable 2. Rates of change (minutes per day per year) in sleep duration according to sex, age, day type and geographical location.

Significant differences were found across age groups (with the exception of 13–15 and 16–18 year-old age categories), sexes, regions and between different day types (P < 0.05).k = number of regressions assessed; n = sample size; SD = standard deviation; IQR = interquartile range.

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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In search of lost sleep: Secular trends in the sleep time of school-aged children and adolescents

a Health and Use of Time (HUT) Group, University of South Australia, GPO Box 2471, Adelaide SA 5000, Australiab Sansom Institute for Health Research, University of South Australia, GPO Box 2471, Adelaide SA 5000, Australiac School of Health Sciences, University of South Australia, GPO Box 2471, Adelaide SA 5000, AustraliaReceived 7 January 2011. Revised 18 March 2011. Accepted 18 March 2011. Available online 25 May 2011.View full text Sleep deficits are associated with a wide range of detrimental physical and mental health outcomes. There is concern that children are not getting enough sleep, and that sleep duration has been declining. However, evidence is sparse.

A systematic review of world literature was conducted to locate studies reporting the sleep duration of children aged 5–18 years. Monte Carlo simulation was used to generate pseudodata from summary data, which were combined with raw data and analysed by linear regression of sleep duration on year of measurement at the age × sex × day type × country level.

Data were available on 690,747 children from 20 countries, dating from 1905 to 2008. From these data, 641 regressions were derived. The sample-weighted median rate of change was -0.75 min nightly per year, indicating a decrease of more than 1 h per night over the study period. Rates of change were negative across age, sex and day type categories, but varied according to region, with Europe, the USA, Canada and Asia showing decreases and Australia, the UK and Scandinavia showing increases.

Over the last 103 years, there have been consistent rapid declines in the sleep duration of children and adolescents.

prs.rt("abs_end");Sleep duration; Children; Adolescents; Trends

Figures and tables from this article:

Fig. 1. PRISMA flowchart for the search.

View Within ArticleFig. 2. Funnel plots of changes in sleep duration (Y-axis, min/year) against the span of years for each regression, and the total sample size for each regression (X-axes). The dashed line is the sample-weighted median rate of change (-0.75 min/year).

View Within ArticleFig. 3. Box plots showing sample-weighted rates of change for age (Fig. 3a), sex (Fig. 3b) and day type (Fig. 3c) sub-groups. The dashed line is the sample-weighted median rate of change (-0.75 min/year). k = number of regressions assessed; SD = standard deviation; IQR = interquartile range.

View Within ArticleFig. 4. Box plots showing sample-weighted rates of change for different regions. The dashed line is the sample-weighted median rate of change (-0.75 min/year). k = number of regressions assessed; SD = standard deviation; IQR = interquartile range.

View Within ArticleFig. 5. Box plots showing sample-weighted rates of change for different year periods. The dashed line is the sample-weighted median rate of change (-0.75 min/year). k = number of regressions assessed; SD = standard deviation; IQR = interquartile range.

View Within ArticleTable 1. Search strategy used for each database.

View Within ArticleTable 2. Rates of change (minutes per day per year) in sleep duration according to sex, age, day type and geographical location.

Significant differences were found across age groups (with the exception of 13–15 and 16–18 year-old age categories), sexes, regions and between different day types (P < 0.05).k = number of regressions assessed; n = sample size; SD = standard deviation; IQR = interquartile range.

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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Sleep in special needs children: The challenge

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Sleep in special needs children: The challenge

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Sleep-dependent memory consolidation in patients with sleep disorders

Sleep can improve the off-line memory consolidation of new items of declarative and non-declarative information in healthy subjects, whereas acute sleep loss, as well as sleep restriction and fragmentation, impair consolidation. This suggests that, by modifying the amount and/or architecture of sleep, chronic sleep disorders may also lead to a lower gain in off-line consolidation, which in turn may be responsible for the varying levels of impaired performance at memory tasks usually observed in sleep-disordered patients.

The experimental studies conducted to date have shown specific impairments of sleep-dependent consolidation overall for verbal and visual declarative information in patients with primary insomnia, for verbal declarative information in patients with obstructive sleep apnoeas, and for visual procedural skills in patients with narcolepsy-cataplexy.

These findings corroborate the hypothesis that impaired consolidation is a consequence of the chronically altered organization of sleep. Moreover, they raise several novel questions as to: a) the reversibility of consolidation impairment in the case of effective treatment, b) the possible negative influence of altered prior sleep also on the encoding of new information, and c) the relationships between altered sleep and memory impairment in patients with other (medical, psychiatric or neurological) diseases associated with quantitative and/or qualitative changes of sleep architecture.

Table 1. Methodological characteristics and results of the experimental studies on memory consolidation during sleep in patients with chronic sleep disorders.

Abbreviations: DM = declarative memory; NC = narcolepsy with cataplexy; NDM = non declarative memory; OSA = obstructive sleep apnoea; PI = primary insomnia; REM = rapid eye movement (sleep); REMD = REM density; SE = sleep efficiency; SWS = slow wave sleep; SPT = sleep period time; SFI = sleep fragmentation index; SOA= stimulus onset asynchrony; TST = total sleep time; WASO = wake after sleep onset.

View Within Article

Copyright © 2012 Elsevier Ltd. All rights reserved.

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Sleep-dependent memory consolidation in patients with sleep disorders

Sleep can improve the off-line memory consolidation of new items of declarative and non-declarative information in healthy subjects, whereas acute sleep loss, as well as sleep restriction and fragmentation, impair consolidation. This suggests that, by modifying the amount and/or architecture of sleep, chronic sleep disorders may also lead to a lower gain in off-line consolidation, which in turn may be responsible for the varying levels of impaired performance at memory tasks usually observed in sleep-disordered patients.

The experimental studies conducted to date have shown specific impairments of sleep-dependent consolidation overall for verbal and visual declarative information in patients with primary insomnia, for verbal declarative information in patients with obstructive sleep apnoeas, and for visual procedural skills in patients with narcolepsy-cataplexy.

These findings corroborate the hypothesis that impaired consolidation is a consequence of the chronically altered organization of sleep. Moreover, they raise several novel questions as to: a) the reversibility of consolidation impairment in the case of effective treatment, b) the possible negative influence of altered prior sleep also on the encoding of new information, and c) the relationships between altered sleep and memory impairment in patients with other (medical, psychiatric or neurological) diseases associated with quantitative and/or qualitative changes of sleep architecture.

Table 1. Methodological characteristics and results of the experimental studies on memory consolidation during sleep in patients with chronic sleep disorders.

Abbreviations: DM = declarative memory; NC = narcolepsy with cataplexy; NDM = non declarative memory; OSA = obstructive sleep apnoea; PI = primary insomnia; REM = rapid eye movement (sleep); REMD = REM density; SE = sleep efficiency; SWS = slow wave sleep; SPT = sleep period time; SFI = sleep fragmentation index; SOA= stimulus onset asynchrony; TST = total sleep time; WASO = wake after sleep onset.

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Is obstructive sleep apnea associated with cortisol levels? A systematic review of the research evidence

a San Diego State University & University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego, UCSD Mail Code 0804, La Jolla, CA, United Statesb Department of Psychiatry, University of California, San Diego, CA, United StatesReceived 8 March 2011. Revised 21 May 2011. Accepted 23 May 2011. Available online 30 July 2011.View full text The pathophysiology of obstructive sleep apnea (OSA) has been associated with dysregulation of the hypothalamic pituitary adrenal (HPA) axis; however a relationship between OSA and altered cortisol levels has not been conclusively established. We conducted a systematic review using the PRISMA Guidelines based on comprehensive database searches for 1) studies of OSA patients compared to controls in whom cortisol was measured and 2) studies of OSA patients treated with continuous positive airway pressure (CPAP) in whom cortisol was measured pre and post treatment. Five electronic databases were searched along with the reference lists of retrieved studies. The primary outcomes were 1) differences in cortisol between OSA and control subjects and 2) differences in cortisol pre-post CPAP treatment. Sampling methodology, sample timing and exclusion criteria were evaluated. Fifteen studies met the inclusion criteria. Heterogeneity of studies precluded statistical pooling. One study identified differences in cortisol between OSA patients and controls. Two studies showed statistically significant differences in cortisol levels pre-post CPAP. The majority of studies were limited by assessment of cortisol at a single time point. The available studies do not provide clear evidence that OSA is associated with alterations in cortisol levels or that treatment with CPAP changes cortisol levels. Methodological concerns such as infrequent sampling, failure to match comparison groups on demographic factors known to impact cortisol levels (age, body mass index; BMI), and inconsistent control of variables known to influence HPA function may have limited the results.

prs.rt("abs_end");Obstructive sleep apnea; Cortisol; Continuous positive airway pressure; Systematic review

Figures and tables from this article:

Fig. 1. PRISMA trial flow used to identify studies for detailed analysis of cortisol in 1) patients with obstructive sleep apnea and healthy controls and 2) patients with obstructive sleep apnea before and after treatment with continuous positive airway pressure. AHI = Apnea hypopnea index; CPAP = Continuous positive airway pressure.

View Within ArticleTable 1. The 7 included studies of cortisol in patients with OSA versus controls.

Na = No information; OSA = Obstructive sleep apnea; BMI = Body mass index; AHI = Apnea hypopnea index; EDS = Excessive daytime sleepiness; w = with; wo = without.

View Within ArticleTable 2. The 8 included studies of cortisol in patients with OSA treated with CPAP.

Na = No information; OSA = Obstructive sleep apnea; BMI = Body mass index; AHI = Apnea hypopnea index; EDS = Excessive daytime sleepiness; SE = Standard error of the mean; w = with; wo = without.

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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Is obstructive sleep apnea associated with cortisol levels? A systematic review of the research evidence

a San Diego State University & University of California, San Diego, Joint Doctoral Program in Clinical Psychology, San Diego, UCSD Mail Code 0804, La Jolla, CA, United Statesb Department of Psychiatry, University of California, San Diego, CA, United StatesReceived 8 March 2011. Revised 21 May 2011. Accepted 23 May 2011. Available online 30 July 2011.View full text The pathophysiology of obstructive sleep apnea (OSA) has been associated with dysregulation of the hypothalamic pituitary adrenal (HPA) axis; however a relationship between OSA and altered cortisol levels has not been conclusively established. We conducted a systematic review using the PRISMA Guidelines based on comprehensive database searches for 1) studies of OSA patients compared to controls in whom cortisol was measured and 2) studies of OSA patients treated with continuous positive airway pressure (CPAP) in whom cortisol was measured pre and post treatment. Five electronic databases were searched along with the reference lists of retrieved studies. The primary outcomes were 1) differences in cortisol between OSA and control subjects and 2) differences in cortisol pre-post CPAP treatment. Sampling methodology, sample timing and exclusion criteria were evaluated. Fifteen studies met the inclusion criteria. Heterogeneity of studies precluded statistical pooling. One study identified differences in cortisol between OSA patients and controls. Two studies showed statistically significant differences in cortisol levels pre-post CPAP. The majority of studies were limited by assessment of cortisol at a single time point. The available studies do not provide clear evidence that OSA is associated with alterations in cortisol levels or that treatment with CPAP changes cortisol levels. Methodological concerns such as infrequent sampling, failure to match comparison groups on demographic factors known to impact cortisol levels (age, body mass index; BMI), and inconsistent control of variables known to influence HPA function may have limited the results.

prs.rt("abs_end");Obstructive sleep apnea; Cortisol; Continuous positive airway pressure; Systematic review

Figures and tables from this article:

Fig. 1. PRISMA trial flow used to identify studies for detailed analysis of cortisol in 1) patients with obstructive sleep apnea and healthy controls and 2) patients with obstructive sleep apnea before and after treatment with continuous positive airway pressure. AHI = Apnea hypopnea index; CPAP = Continuous positive airway pressure.

View Within ArticleTable 1. The 7 included studies of cortisol in patients with OSA versus controls.

Na = No information; OSA = Obstructive sleep apnea; BMI = Body mass index; AHI = Apnea hypopnea index; EDS = Excessive daytime sleepiness; w = with; wo = without.

View Within ArticleTable 2. The 8 included studies of cortisol in patients with OSA treated with CPAP.

Na = No information; OSA = Obstructive sleep apnea; BMI = Body mass index; AHI = Apnea hypopnea index; EDS = Excessive daytime sleepiness; SE = Standard error of the mean; w = with; wo = without.

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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Sleep disturbance interventions for oncology patients: Steps forward and issues arising

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Sleep disturbance interventions for oncology patients: Steps forward and issues arising

Note to users: Corrected proofs are Articles in Press that contain the authors' corrections. Final citation details, e.g., volume/issue number, publication year and page numbers, still need to be added and the text might change before final publication.

Although corrected proofs do not have all bibliographic details available yet, they can already be cited using the year of online publication and the DOI , as follows: author(s), article title, journal (year), DOI. Please consult the journal's reference style for the exact appearance of these elements, abbreviation of journal names and use of punctuation.

When the final article is assigned to an issue of the journal, the Article in Press version will be removed and the final version will appear in the associated published issue of the journal. The date the article was first made available online will be carried over.

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Secular trends in adult sleep duration: A systematic review

Little evidence exists to support the common assertion that adult sleep duration has declined. We investigated secular trends in sleep duration over the past 40 years through a systematic review.

Systematic search of 5 electronic databases was conducted to identify repeat cross-sectional studies of sleep duration in community-dwelling adults using comparable sampling frames and measures over time. We also attempted to access unpublished or semi-published data sources in the form of government reports, theses and conference proceedings. No studies were excluded based on language or publication date. The search identified 278 potential reports, from which twelve relevant studies were identified for review.

The 12 studies described data from 15 countries from the 1960s until the 2000s. Self-reported average sleep duration of adults had increased in 7 countries: Bulgaria, Poland, Canada, France, Britain, Korea and the Netherlands (range: 0.1–1.7 min per night each year) and had decreased in 6 countries: Japan, Russia, Finland, Germany, Belgium and Austria (range: 0.1–0.6 min per night each year). Inconsistent results were found for the United States and Sweden.

There has not been a consistent decrease in the self-reported sleep duration of adults from the 1960s to 2000s. However, it is unclear whether the proportions of very short and very long sleepers have increased over the same period, which may be of greater relevance for public health.

Table 1. Literature search strategy and number of results for each database.

View Within ArticleTable 2. Summary of included results by country (some studies have multiple results).

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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Secular trends in adult sleep duration: A systematic review

Little evidence exists to support the common assertion that adult sleep duration has declined. We investigated secular trends in sleep duration over the past 40 years through a systematic review.

Systematic search of 5 electronic databases was conducted to identify repeat cross-sectional studies of sleep duration in community-dwelling adults using comparable sampling frames and measures over time. We also attempted to access unpublished or semi-published data sources in the form of government reports, theses and conference proceedings. No studies were excluded based on language or publication date. The search identified 278 potential reports, from which twelve relevant studies were identified for review.

The 12 studies described data from 15 countries from the 1960s until the 2000s. Self-reported average sleep duration of adults had increased in 7 countries: Bulgaria, Poland, Canada, France, Britain, Korea and the Netherlands (range: 0.1–1.7 min per night each year) and had decreased in 6 countries: Japan, Russia, Finland, Germany, Belgium and Austria (range: 0.1–0.6 min per night each year). Inconsistent results were found for the United States and Sweden.

There has not been a consistent decrease in the self-reported sleep duration of adults from the 1960s to 2000s. However, it is unclear whether the proportions of very short and very long sleepers have increased over the same period, which may be of greater relevance for public health.

Table 1. Literature search strategy and number of results for each database.

View Within ArticleTable 2. Summary of included results by country (some studies have multiple results).

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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Sleep scoring using artificial neural networks

Marina Ronzhinaa, , , Oto Janoušeka, d, , Jana Kolárováa, e, , Marie Novákováb, g, , Petr Honzíkc, h, , Ivo Provazníka, f, a Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech Republicb Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, Brno 62500, Czech Republicc Department of Control and Instrumentation, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech RepublicReceived 18 March 2011. Revised 30 June 2011. Accepted 30 June 2011. Available online 24 October 2011.View full text Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved – next to other classification methods – by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.

prs.rt("abs_end");Polysomnographic data; Sleep scoring; Features extraction; Artificial neural networks

Figures and tables from this article:

Fig. 1. Schematic representation. (a) Single neuron with vector input. (b) One-layer network with m neurons.

View Within ArticleFig. 2. Transfer functions. (a) Log-sigmoid. (b) Tan-sigmoid. (c) Hard limit. (d) Linear.

View Within ArticleFig. 3. Extraction of the 4-elements features vector from EEG epoch. PSD – power spectral density, d, ?, a, ß – delta, theta, alpha and beta bands, respectively, drel, ?rel, arel, ßrel – relative power values for delta, theta, alpha and beta bands, respectively.

View Within ArticleTable 1. Summary of artificial neural network (ANN) based systems for sleep scoring. BP: backpropagation, EEG: electroencephalogram, EMG: electromyogram, EOG: electrooculogram (LEOG, REOG: left, right EOG, respectively), FC: fully connected, FT: Fourier transform, MLNN: multilayer neural network, MLP: multilayer perceptron, MT: movement time, RatP: ratio power, REM, rapid eye movement, RMS: root mean square, RP: relative power, RUM, LM: Rumelhart (gradient descent without momentum) and Levenberge-Marquardt learning algorithm, respectively, S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, SD: standard deviation, SOM: self-organizing map, SWS: slow wave sleep, TP: total power, W: wakefulness, WT: wavelet transform. Description of sleep stages is according to R&K and AASM.

View Within ArticleTable 2. Output neurons of proposed artificial neural network models. REM: rapid eye movement, S*: stage involving four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.

View Within ArticleTable 3. Results of sleep scoring obtained by proposed artificial neural network (ANN) models. EEG: electroencephalogram, REM: rapid eye movement, RP, relative power, S: stage involving the four stages of non-REM and REM sleep, S*: stage involving the four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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Sleep scoring using artificial neural networks

Marina Ronzhinaa, , , Oto Janoušeka, d, , Jana Kolárováa, e, , Marie Novákováb, g, , Petr Honzíkc, h, , Ivo Provazníka, f, a Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech Republicb Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 753/5, Brno 62500, Czech Republicc Department of Control and Instrumentation, Faculty of Electrical Engineering and Communication, Brno University of Technology, Kolejní 4, Brno 61200, Czech RepublicReceived 18 March 2011. Revised 30 June 2011. Accepted 30 June 2011. Available online 24 October 2011.View full text Rapid development of computer technologies leads to the intensive automation of many different processes traditionally performed by human experts. One of the spheres characterized by the introduction of new high intelligence technologies substituting analysis performed by humans is sleep scoring. This refers to the classification task and can be solved – next to other classification methods – by use of artificial neural networks (ANN). ANNs are parallel adaptive systems suitable for solving of non-linear problems. Using ANN for automatic sleep scoring is especially promising because of new ANN learning algorithms allowing faster classification without decreasing the performance. Both appropriate preparation of training data as well as selection of the ANN model make it possible to perform effective and correct recognizing of relevant sleep stages. Such an approach is highly topical, taking into consideration the fact that there is no automatic scorer utilizing ANN technology available at present.

prs.rt("abs_end");Polysomnographic data; Sleep scoring; Features extraction; Artificial neural networks

Figures and tables from this article:

Fig. 1. Schematic representation. (a) Single neuron with vector input. (b) One-layer network with m neurons.

View Within ArticleFig. 2. Transfer functions. (a) Log-sigmoid. (b) Tan-sigmoid. (c) Hard limit. (d) Linear.

View Within ArticleFig. 3. Extraction of the 4-elements features vector from EEG epoch. PSD – power spectral density, d, ?, a, ß – delta, theta, alpha and beta bands, respectively, drel, ?rel, arel, ßrel – relative power values for delta, theta, alpha and beta bands, respectively.

View Within ArticleTable 1. Summary of artificial neural network (ANN) based systems for sleep scoring. BP: backpropagation, EEG: electroencephalogram, EMG: electromyogram, EOG: electrooculogram (LEOG, REOG: left, right EOG, respectively), FC: fully connected, FT: Fourier transform, MLNN: multilayer neural network, MLP: multilayer perceptron, MT: movement time, RatP: ratio power, REM, rapid eye movement, RMS: root mean square, RP: relative power, RUM, LM: Rumelhart (gradient descent without momentum) and Levenberge-Marquardt learning algorithm, respectively, S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, SD: standard deviation, SOM: self-organizing map, SWS: slow wave sleep, TP: total power, W: wakefulness, WT: wavelet transform. Description of sleep stages is according to R&K and AASM.

View Within ArticleTable 2. Output neurons of proposed artificial neural network models. REM: rapid eye movement, S*: stage involving four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.

View Within ArticleTable 3. Results of sleep scoring obtained by proposed artificial neural network (ANN) models. EEG: electroencephalogram, REM: rapid eye movement, RP, relative power, S: stage involving the four stages of non-REM and REM sleep, S*: stage involving the four stages of non-REM sleep (other sleep stages are according to R&K), S1, S2, S3 and S4: see section “Polygraphic data and visual sleep scoring” for definitions, W: wakefulness.

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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Longitudinal associations between sleep duration and subsequent weight gain: A systematic review

a Doctoral Program in Population Health and Clinical Outcomes Research, Department of Preventive Medicine, HSC Level 3, Stony Brook University, Stony Brook, NY 11794-8338, USAb Department of Preventive Medicine, Graduate Program in Public Health, HSC Level 3, room 071, Stony Brook University, Stony Brook, NY 11794-8338, USAReceived 31 December 2010. Revised 19 May 2011. Accepted 23 May 2011. Available online 23 July 2011.View full text To systematically examine the relationship between sleep duration and subsequent weight gain in observational longitudinal human studies.

Systematic review of twenty longitudinal studies published from 2004–October 31, 2010.

While adult studies (n = 13) reported inconsistent results on the relationship between sleep duration and subsequent weight gain, studies with children (n = 7) more consistently reported a positive relationship between short sleep duration and weight gain.

While shorter sleep duration consistently predicts subsequent weight gain in children, the relationship is not clear in adults. We discuss possible limitations of the current studies: 1) the diminishing association between short sleep duration on weight gain over time after transition to short sleep, 2) lack of inclusion of appropriate confounding, mediating, and moderating variables (i.e., sleep complaints and sedentary behavior), and 3) measurement issues.

prs.rt("abs_end");Sleep; Obesity; Weight gain; Longitudinal studiesBMI, Body mass index; CDC, Centers for Disease Control and Prevention

Figures and tables from this article:

Fig. 1. Illustration of literature search.

View Within ArticleFig. 2. Patel & Hu Model2 with media use added.

View Within ArticleTable 1. Adult studies.

View Within ArticleTable 2. Adult Study Independent Variables.

View Within ArticleTable 3. Children Studies.

View Within ArticleTable 4. Children Study Independent Variables.

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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Longitudinal associations between sleep duration and subsequent weight gain: A systematic review

a Doctoral Program in Population Health and Clinical Outcomes Research, Department of Preventive Medicine, HSC Level 3, Stony Brook University, Stony Brook, NY 11794-8338, USAb Department of Preventive Medicine, Graduate Program in Public Health, HSC Level 3, room 071, Stony Brook University, Stony Brook, NY 11794-8338, USAReceived 31 December 2010. Revised 19 May 2011. Accepted 23 May 2011. Available online 23 July 2011.View full text To systematically examine the relationship between sleep duration and subsequent weight gain in observational longitudinal human studies.

Systematic review of twenty longitudinal studies published from 2004–October 31, 2010.

While adult studies (n = 13) reported inconsistent results on the relationship between sleep duration and subsequent weight gain, studies with children (n = 7) more consistently reported a positive relationship between short sleep duration and weight gain.

While shorter sleep duration consistently predicts subsequent weight gain in children, the relationship is not clear in adults. We discuss possible limitations of the current studies: 1) the diminishing association between short sleep duration on weight gain over time after transition to short sleep, 2) lack of inclusion of appropriate confounding, mediating, and moderating variables (i.e., sleep complaints and sedentary behavior), and 3) measurement issues.

prs.rt("abs_end");Sleep; Obesity; Weight gain; Longitudinal studiesBMI, Body mass index; CDC, Centers for Disease Control and Prevention

Figures and tables from this article:

Fig. 1. Illustration of literature search.

View Within ArticleFig. 2. Patel & Hu Model2 with media use added.

View Within ArticleTable 1. Adult studies.

View Within ArticleTable 2. Adult Study Independent Variables.

View Within ArticleTable 3. Children Studies.

View Within ArticleTable 4. Children Study Independent Variables.

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Does abnormal non-rapid eye movement sleep impair declarative memory consolidation? Disturbed thalamic functions in sleep and memory processing

a Yeshiva University: Ferkauf Graduate School of Psychology, Rousso Building, 1165 Morris Park Avenue, Bronx, NY 10461, United Statesb Department of Psychiatry and Psychotherapy, University Hospital Schleswig-Holstein, University of Kiel, GermanyReceived 10 July 2010. Revised 30 July 2011. Accepted 1 August 2011. Available online 1 September 2011.View full text Non-rapid eye movement (NREM) sleep has recently garnered support for its role in consolidating hippocampus-based declarative memories in humans. We provide a brief review of the latest research on NREM sleep activity and its association with declarative memory consolidation. Utilizing empirical findings from sleep studies on schizophrenia, Alzheimer’s disease, and fibromyalgia, we argue that a significant reduction of slow-wave sleep and sleep spindle activity contribute to the development of deficits in declarative memory consolidation along with concomitant sleep disturbances commonly experienced in the aforementioned disorders. A tentative model is introduced to describe the mediating role of the thalamocortical network in disruptions of both declarative memory consolidation and NREM sleep. The hope is to stimulate new research in further investigating the intimate link between these two very important functions.

prs.rt("abs_end");NREM sleep; Sleep spindles; Slow-wave sleep; Declarative memory consolidation; Hippocampus; Thalamocortical network; Schizophrenia; Alzheimer’s disease; Fibromyalgia syndrome

Figures and tables from this article:

Fig. 1. During NREM sleep, abnormal thalamocortical structures may be unable to generate sufficient slow oscillations to drive the reactivation of hippocampal memory traces. These same structures may also be unable to facilitate normal spindle activity, preventing efficient declarative memory consolidation due to an absence in cortical plastic changes. Decreases in spindle activity lead to failure in inhibiting sensory information from reaching the neocortex. Thus, the individual is awakened and kept awake by sensory information, consequently experiencing disturbed NREM sleep.

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Does abnormal non-rapid eye movement sleep impair declarative memory consolidation? Disturbed thalamic functions in sleep and memory processing

a Yeshiva University: Ferkauf Graduate School of Psychology, Rousso Building, 1165 Morris Park Avenue, Bronx, NY 10461, United Statesb Department of Psychiatry and Psychotherapy, University Hospital Schleswig-Holstein, University of Kiel, GermanyReceived 10 July 2010. Revised 30 July 2011. Accepted 1 August 2011. Available online 1 September 2011.View full text Non-rapid eye movement (NREM) sleep has recently garnered support for its role in consolidating hippocampus-based declarative memories in humans. We provide a brief review of the latest research on NREM sleep activity and its association with declarative memory consolidation. Utilizing empirical findings from sleep studies on schizophrenia, Alzheimer’s disease, and fibromyalgia, we argue that a significant reduction of slow-wave sleep and sleep spindle activity contribute to the development of deficits in declarative memory consolidation along with concomitant sleep disturbances commonly experienced in the aforementioned disorders. A tentative model is introduced to describe the mediating role of the thalamocortical network in disruptions of both declarative memory consolidation and NREM sleep. The hope is to stimulate new research in further investigating the intimate link between these two very important functions.

prs.rt("abs_end");NREM sleep; Sleep spindles; Slow-wave sleep; Declarative memory consolidation; Hippocampus; Thalamocortical network; Schizophrenia; Alzheimer’s disease; Fibromyalgia syndrome

Figures and tables from this article:

Fig. 1. During NREM sleep, abnormal thalamocortical structures may be unable to generate sufficient slow oscillations to drive the reactivation of hippocampal memory traces. These same structures may also be unable to facilitate normal spindle activity, preventing efficient declarative memory consolidation due to an absence in cortical plastic changes. Decreases in spindle activity lead to failure in inhibiting sensory information from reaching the neocortex. Thus, the individual is awakened and kept awake by sensory information, consequently experiencing disturbed NREM sleep.

View Within ArticleCopyright © 2011 Elsevier Ltd. All rights reserved.

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View the original article here

Does abnormal non-rapid eye movement sleep impair declarative memory consolidation? Disturbed thalamic functions in sleep and memory processing

a Yeshiva University: Ferkauf Graduate School of Psychology, Rousso Building, 1165 Morris Park Avenue, Bronx, NY 10461, United Statesb Department of Psychiatry and Psychotherapy, University Hospital Schleswig-Holstein, University of Kiel, GermanyReceived 10 July 2010. Revised 30 July 2011. Accepted 1 August 2011. Available online 1 September 2011.View full text Non-rapid eye movement (NREM) sleep has recently garnered support for its role in consolidating hippocampus-based declarative memories in humans. We provide a brief review of the latest research on NREM sleep activity and its association with declarative memory consolidation. Utilizing empirical findings from sleep studies on schizophrenia, Alzheimer’s disease, and fibromyalgia, we argue that a significant reduction of slow-wave sleep and sleep spindle activity contribute to the development of deficits in declarative memory consolidation along with concomitant sleep disturbances commonly experienced in the aforementioned disorders. A tentative model is introduced to describe the mediating role of the thalamocortical network in disruptions of both declarative memory consolidation and NREM sleep. The hope is to stimulate new research in further investigating the intimate link between these two very important functions.

prs.rt("abs_end");NREM sleep; Sleep spindles; Slow-wave sleep; Declarative memory consolidation; Hippocampus; Thalamocortical network; Schizophrenia; Alzheimer’s disease; Fibromyalgia syndrome

Figures and tables from this article:

Fig. 1. During NREM sleep, abnormal thalamocortical structures may be unable to generate sufficient slow oscillations to drive the reactivation of hippocampal memory traces. These same structures may also be unable to facilitate normal spindle activity, preventing efficient declarative memory consolidation due to an absence in cortical plastic changes. Decreases in spindle activity lead to failure in inhibiting sensory information from reaching the neocortex. Thus, the individual is awakened and kept awake by sensory information, consequently experiencing disturbed NREM sleep.

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Sleep in attention-deficit/hyperactivity disorder in children and adults: Past, present, and future

Sun Young Rosalia Yoona, b, , , , Umesh Jainb, e, , Colin Shapiroa, c, d, f, a Institute of Medical Sciences, University of Toronto, Canadab Child, Youth and Family Service, Centre for Addiction and Mental Health, 352-250 College Street, Toronto, ON, M5T 1R8, Canadac Division of Patient Based Clinical Research, Toronto Western Research Institute, Canadad Youthdale Child and Adolescent Sleep Centre, CanadaReceived 5 April 2011. Revised 1 July 2011. Accepted 5 July 2011. Available online 26 October 2011.View full text The understanding that sleep can give rise to, or exacerbate symptoms of attention-deficit/hyperactivity disorder (ADHD), and that good sleep hygiene improves attention and concentration tasks has sparked interest in the investigation of possible etiological relationships between sleep disorders and ADHD.

Studies indicate that 30% of children and 60–80% of adults with ADHD have symptoms of sleep disorders such as daytime sleepiness, insomnia, delayed sleep phase syndrome, fractured sleep, restless legs syndrome, and sleep disordered breathing. The range and diversity of findings by different researchers have posed challenges in establishing whether sleep disturbances are intrinsic to ADHD or whether disturbances occur due to co-morbid sleep disorders. As a result, understanding of the nature of the relationship between sleep disturbances/disorders and ADHD remains unclear.

In this review, we present a comprehensive and critical account of the research that has been carried out to investigate the association between sleep and ADHD, as well as discuss mechanisms that have been proposed to account for the elusive relationship between sleep disturbances, sleep disorders, and ADHD.

prs.rt("abs_end");Sleep architecture; Sleep disturbances; Sleep disordered breathing; Restless legs; Periodic limb movements; ADHD; Circadian cycle

Figures and tables from this article:

Table 1. Studies of sleep disturbances in children with ADHD with subjective methods.

ADHD = Attention-deficit/hyperactivity disorder, ADHD-C = ADHD of the combined subtype, ADHD-H/I = ADHD of the hyperactive/impulsive subtype, ADHD-I = ADHD of the inattentive subtype, BD = bipolar disorder, CD = conduct disorder, DEP = major depressive episode, C(P/T)RS-R:S = Conner’s (parent/teacher) rating scale-revised: short forms, GAD = generalized anxiety disorder, IQ = intelligence quotient, LD = learning disability, MPH = methylphenidate, OCD = obsessive compulsive disorder, ODD = oppositional defiant disorder, PTSD = post-traumatic stress disorder, SAD = separation anxiety disorder, SD = standard deviation.

View Within ArticleTable 2. Studies of sleep disturbances in children with ADHD with objective methods.

AHI = Apnea hypopnea index, BD = bipolar disorder, CD = conduct disorder, DEX = dextro-amphetamine, DLMO = dim light melatonin onset, GAD = generalized anxiety disorder, LD = learning disability, MD = major depression, MPH = methylphenidate, MSLT = multiple sleep latency test, ODD = oppositional defiant disorder, PLMI = periodic limb movement index, RDI = respiratory disturbance index, REM = rapid eye movement, S1 = stage 1 sleep, SAD = separation anxiety disorder, SDB = sleep disordered breathing, SE = sleep efficiency, SOL = sleep onset latency, SOT = sleep onset time,TSP = total sleep period.

View Within ArticleTable 3. Studies of sleep disturbances in adults with ADHD with subjective methods.

ADHD = attention-deficit/hyperactivity disorder, ADHD-C = ADHD of the combined subtype, ADHD-H/I = ADHD of the hyperactive/impulsive subtype, ADHD-I = ADHD of the inattentive subtype, ASRS = adult self report scale, CSM = composite scale of morningness, EDS = excessive daytime sleepiness, ESS - Epworth sleepiness scale, IH = idiopathic hypersomnia, GAD = generalized anxiety disorder, MDD = major depressive disorder, MPH = methylphenidate, OCD = obsessive compulsive disorder, PTSD = post-traumatic stress disorder.

View Within ArticleTable 4. Studies of sleep disturbances in adults with ADHD with objective methods.

DEX = dextro-amphetamine, BRD = brief recurrent depression, MDD = major depressive disorder, MPH = methylphenidate, PSG = polysomnography, REM = rapid eye movement, SE = sleep efficiency, SOL = sleep onset latency.

View Within ArticleCopyright © 2011 Elsevier Ltd. Published by Elsevier Ltd. All rights reserved.

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