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Overnight sleep staging is an important part of the diagnosis of various sleep disorders. Polysomnography is the gold standard for sleep staging, but less-obtrusive sensing modalities are of emerging interest. Here, we developed and validated an algorithm to perform "proxy" sleep staging using cardiac and respiratory signals derived from a chest-worn accelerometer. We collected data in two sleep centers, using a chest-worn accelerometer in combination with full PSG. A total of 323 participants were analyzed, aged 13–83 years, with BMI 18–47 kg/m2. We derived cardiac and respiratory features from the accelerometer and then applied a previously developed method for automatic cardio-respiratory sleep staging. We compared the estimated sleep stages against those derived from PSG and determined performance. Epoch-by-epoch agreement with four-class scoring (Wake, REM, N1+N2, N3) reached a Cohen's kappa coefficient of agreement of 0.68 and an accuracy of 80.8%. For Wake vs. Sleep classification, an accuracy of 93.3% was obtained, with a sensitivity of 78.7% and a specificity of 96.6%. We showed that cardiorespiratory signals obtained from a chest-worn accelerometer can be used to estimate sleep stages among a population that is diverse in age, BMI, and prevalence of sleep disorders. This opens up the path towards various clinical applications in sleep medicine. [ABSTRACT FROM AUTHOR]

Purpose: Little is known about cognitive complaints (self-reported problems in cognitive functioning) in patients with Obstructive Sleep Apnea (OSA). We compared the prevalence and severity of cognitive complaints in patients with untreated OSA to patients with neurological and respiratory diseases. We also studied risk factors for cognitive complaints across these diseases, including OSA. Methods: We used a convenience sample to compare untreated OSA patients (N = 86) to patients with stroke (N = 166), primary brain tumor (N = 197) and chronic obstructive pulmonary disease (COPD, N = 204) on cognitive complaints (Cognitive Failure Questionnaire, CFQ), anxiety and depression (Hospital Anxiety and Depression Scale, HADS) and cognitive impairments using neuropsychological tests. We combined all patient groups (OSA, stroke, brain tumor and COPD) and studied potential risk factors (demographic variables, anxiety, depression and cognitive impairments) for cognitive complaints across all patient groups using regression analysis. Results: The prevalence of cognitive complaints was higher in OSA patients and complaints of forgetfulness and distractibility were more severe compared to stroke and primary brain tumor patients, but similar to or lower than COPD patients. Regression analysis for the combined sample of all patient groups showed that cognitive complaints were most strongly associated with symptoms of anxiety and depression. Conclusion: A high rate of OSA reported clinically significant cognitive complaints, comparable to other respiratory and neurological patients. Symptoms of anxiety and depression are important risk factors for cognitive complaints in patients with various neurological and respiratory diseases. Future studies should examine the relation between anxiety, depression and cognitive complaints in patients with OSA. [ABSTRACT FROM AUTHOR]

Summary: Non‐rapid eye movement parasomnia disorders, also called disorders of arousal, are characterized by abnormal nocturnal behaviours, such as confusional arousals or sleep walking. Their pathophysiology is not yet fully understood, and objective diagnostic criteria are lacking. It is known, however, that behavioural episodes occur mostly in the beginning of the night, after an increase in slow‐wave activity during slow‐wave sleep. A better understanding of the prospect of such episodes may lead to new insights in the underlying mechanisms and eventually facilitate objective diagnosis. We investigated temporal dynamics of transitions from slow‐wave sleep of 52 patients and 79 controls. Within the patient group, behavioural and non‐behavioural N3 awakenings were distinguished. Patients showed a higher probability to wake up after an N3 bout ended than controls, and this probability increased with N3 bout duration. Bouts longer than 15 min resulted in an awakening in 73% and 34% of the time in patients and controls, respectively. Behavioural episodes reduced over sleep cycles due to a reduction in N3 sleep and a reducing ratio between behavioural and non‐behavioural awakenings. In the first two cycles, N3 bouts prior to non‐behavioural awakenings were significantly shorter than N3 bouts advancing behavioural awakenings in patients, and N3 awakenings in controls. Our findings provide insights in the timing and prospect of both behavioural and non‐behavioural awakenings from N3, which may result in prediction and potentially prevention of behavioural episodes. This work, moreover, leads to a more complete characterization of a prototypical hypnogram of parasomnias, which could facilitate diagnosis. [ABSTRACT FROM AUTHOR]

Summary: Excessive daytime sleepiness is the core symptom of central disorders of hypersomnolence (CDH) and can directly impair driving performance. Sleepiness is reflected in relative alterations in distal and proximal skin temperature. Therefore, we examined the predictive value of skin temperature on driving performance. Distal and proximal skin temperature and their gradient (DPG) were continuously measured in 44 participants with narcolepsy type 1, narcolepsy type 2 or idiopathic hypersomnia during a standardised 1‐h driving test. Driving performance was defined as the standard deviation of lateral position (SDLP) per 5 km segment (equivalent to 3 min of driving). Distal and proximal skin temperature and DPG measurements were averaged over each segment and changes over segments were calculated. Mixed‐effect model analyses showed a strong, quadratic association between proximal skin temperature and SDLP (p < 0.001) and a linear association between DPG and SDLP (p < 0.021). Proximal skin temperature changes over 3 to 15 min were predictive for SDLP. Moreover, SDLP increased over time (0.34 cm/segment, p < 0.001) and was higher in men than in women (3.50 cm, p = 0.012). We conclude that proximal skin temperature is a promising predictor for real‐time assessment of driving performance in people with CDH. [ABSTRACT FROM AUTHOR]

Purpose: Comorbid insomnia often occurs in patients with obstructive sleep apnea (OSA), referred to as COMISA. Cortical arousals manifest as a common feature in both OSA and insomnia, often accompanied by elevated heart rate (HR). Our objective was to evaluate the heart rate response to nocturnal cortical arousals in patients with COMISA and patients with OSA alone. Methods: We analyzed data from patients with COMISA and from patients with OSA matched for apnea-hypopnea index. Sleep staging and analysis of respiratory events and cortical arousals were performed using the Philips Somnolyzer automatic scoring system. Beat-by-beat HR was analyzed from the onset of the cortical arousal to 30 heartbeats afterwards. HR responses were divided into peak and recovery phases. Cortical arousals were separately evaluated according to subtype (related to respiratory events and spontaneous) and duration (3–6 s, 6–10 s, 10–15 s). Results: A total of 72 patients with COMISA and 72 patients with OSA were included in this study. There were no overall group differences in the number of cortical arousals with and without autonomic activation. No significant differences were found for spontaneous cortical arousals. The OSA group had more cortical arousals related to respiratory events (21.0 [14.8–30.0] vs 16.0 [9.0–27.0], p = 0.016). However, the COMISA group had longer cortical arousals (7.2 [6.4–7.8] vs 6.7 [6.2–7.7] s, p = 0.024) and the HR recovery phase was prolonged (52.5 [30.8–82.5] vs 40.0 [21.8–55.5] beats/min, p = 0.017). Both the peak and the recovery phase for longer cortical arousals with a duration of 10–15 s were significantly higher in patients with COMISA compared to patients with OSA (47.0 [27.0–97.5] vs 34.0 [21.0–71.0] beats/min, p = 0.032 and 87.0 [47.0–132.0] vs 71.0 [43.0–103.5] beats/min, p = 0.049, respectively). Conclusions: The HR recovery phase after cortical arousals related to respiratory events is prolonged in patients with COMISA compared to patients with OSA alone. This response could be indicative of the insomnia component in COMISA. [ABSTRACT FROM AUTHOR]

Summary: Automatic estimation of sleep structure is an important aspect in moving sleep monitoring from clinical laboratories to people's homes. However, the transition to more portable systems should not happen at the expense of important physiological signals, such as respiration. Here, we propose the use of cardiorespiratory signals obtained by a suprasternal pressure (SSP) sensor to estimate sleep stages. The sensor is already used for diagnosis of sleep‐disordered breathing (SDB) conditions, but besides respiratory effort it can detect cardiac vibrations transmitted through the trachea. We collected the SSP sensor signal in 100 adults (57 male) undergoing clinical polysomnography for suspected sleep disorders, including sleep apnea syndrome, insomnia, and movement disorders. Here, we separate respiratory effort and cardiac activity related signals, then input these into a neural network trained to estimate sleep stages. Using the original mixed signal the results show a moderate agreement with manual scoring, with a Cohen's kappa of 0.53 in Wake/N1–N2/N3/rapid eye movement sleep discrimination and 0.62 in Wake/Sleep. We demonstrate that decoupling the two signals and using the cardiac signal to estimate the instantaneous heart rate improves the process considerably, reaching an agreement of 0.63 and 0.71. Our proposed method achieves high accuracy, specificity, and sensitivity across different sleep staging tasks. We also compare the total sleep time calculated with our method against manual scoring, with an average error of −1.83 min but a relatively large confidence interval of ±55 min. Compact systems that employ the SSP sensor information‐rich signal may enable new ways of clinical assessments, such as night‐to‐night variability in obstructive sleep apnea and other sleep disorders. [ABSTRACT FROM AUTHOR]

Excessive daytime sleepiness is a common symptom of sleep disorders. Despite its prevalence, it remains difficult to define, detect, and address. The difficulties surrounding sleepiness have been linked to an ambiguous conceptualization, a large variety of scales and measures, and the overlap with other constructs, such as fatigue. The present study aims to investigate patients' descriptions of sleepiness-related daytime complaints and their phenomenology. We performed semi-directed interviews with patients diagnosed with obstructive sleep apnea (N = 15) or narcolepsy (N = 5). The interviewers took care of utilizing the participants' terminology when describing daytime complaints related to their sleep disorder. Various aspects of the daytime complaints were investigated, such as their description and temporality. The transcribed content was thematically analyzed using an eclectic coding system, yielding five themes. The participants used different interchangeable descriptors (tired, sleepy, fatigued, exhausted) to express their daytime complaints. They enriched their description with indexes of magnitude (ranging from 'not especially' to 'most gigantic, extreme'), oppositions to other states (using antipodes like energy, alertness, wakefulness, or rest), and indications of fluctuations over the day. Interestingly, the participants often used metaphors to express their experiences and their struggles. The lived experiences of the patients were found to not always align with common self-reported monitoring tools of sleepiness and to relate only in part with current conceptions. In practice, it is important to probe daytime complaints, such as daytime sleepiness, with a broader consideration, for example, by exploring antipodes, consequences, and time-of-day fluctuations. [ABSTRACT FROM AUTHOR]

Introduction: Automated sleep staging using deep learning models typically requires training on hundreds of sleep recordings, and pre-training on public databases is therefore common practice. However, suboptimal sleep stage performance may occur from mismatches between source and target datasets, such as differences in population characteristics (e.g., an unrepresented sleep disorder) or sensors (e.g., alternative channel locations for wearable EEG). Methods: We investigated three strategies for training an automated single-channel EEG sleep stager: pre-training (i.e., training on the original source dataset), training-from-scratch (i.e., training on the new target dataset), and fine-tuning (i.e., training on the original source dataset, fine-tuning on the new target dataset). As source dataset, we used the F3-M2 channel of healthy subjects (N = 94). Performance of the different training strategies was evaluated using Cohen's Kappa (k) in eight smaller target datasets consisting of healthy subjects (N = 60), patients with obstructive sleep apnea (OSA, N = 60), insomnia (N = 60), and REM sleep behavioral disorder (RBD, N = 22), combined with two EEG channels, F3-M2 and F3-F4. Results: No differences in performance between the training strategies was observed in the age-matched F3-M2 datasets, with an average performance across strategies of κ = .83 in healthy, κ = .77 in insomnia, and κ = .74 in OSA subjects. However, in the RBD set, where data availability was limited, fine-tuning was the preferred method (κ = .67), with an average increase in κ of .15 to pre-training and training-from-scratch. In the presence of channel mismatches, targeted training is required, either through training-from-scratch or fine-tuning, increasing performance with κ= .17 on average. Discussion: We found that, when channel and/or population mismatches cause suboptimal sleep staging performance, a fine-tuning approach can yield similar to superior performance compared to building a model from scratch, while requiring a smaller sample size. In contrast to insomnia and OSA, RBD data contains characteristics, either inherent to the pathology or age-related, which apparently demand targeted training. [ABSTRACT FROM AUTHOR]

The classification of sleep state in preterm infants, particularly in distinguishing between active sleep (AS) and quiet sleep (QS), has been investigated using cardiorespiratory information such as electrocardiography (ECG) and respiratory signals. However, accurately differentiating between AS and wake remains challenging; therefore, there is a pressing need to include additional information to further enhance the classification performance. To address the challenge, this study explores the effectiveness of incorporating video-based actigraphy analysis alongside cardiorespiratory signals for classifying the sleep states of preterm infants. The study enrolled eight preterm infants, and a total of 91 features were extracted from ECG, respiratory signals, and video-based actigraphy. By employing an extremely randomized trees (ET) algorithm and leave-one-subject-out cross-validation, a kappa score of 0.33 was achieved for the classification of AS, QS, and wake using cardiorespiratory features only. The kappa score significantly improved to 0.39 when incorporating eight video-based actigraphy features. Furthermore, the classification performance of AS and wake also improved, showing a kappa score increase of 0.21. These suggest that combining video-based actigraphy with cardiorespiratory signals can potentially enhance the performance of sleep-state classification in preterm infants. In addition, we highlighted the distinct strengths and limitations of video-based actigraphy and cardiorespiratory data in classifying specific sleep states. [ABSTRACT FROM AUTHOR]

Introduction: The apnea-hypopnea index (AHI), defined as the number of apneas and hypopneas per hour of sleep, is still used as an important index to assess sleep disordered breathing (SDB) severity, where hypopneas are confirmed by the presence of an oxygen desaturation or an arousal. Ambulatory polygraphy without neurological signals, often referred to as home sleep apnea testing (HSAT), can potentially underestimate the severity of sleep disordered breathing (SDB) as sleep and arousals are not assessed. We aim to improve the diagnostic accuracy of HSATs by extracting surrogate sleep and arousal information derived from autonomic nervous system activity with artificial intelligence. Methods: We used polysomnographic (PSG) recordings from 245 subjects (148 with simultaneously recorded HSATs) to develop and validate a new algorithm to detect autonomic arousals using artificial intelligence. A clinically validated auto-scoring algorithm (Somnolyzer) scored respiratory events, cortical arousals, and sleep stages in PSGs, and provided respiratory events and sleep stages from cardio-respiratory signals in HSATs. In a four-fold cross validation of the newly developed algorithm, we evaluated the accuracy of the estimated arousal index and HSAT-derived surrogates for the AHI. Results: The agreement between the autonomic and cortical arousal index was moderate to good with an intraclass correlation coefficient of 0.73. When using thresholds of 5, 15, and 30 to categorize SDB into none, mild, moderate, and severe, the addition of sleep and arousal information significantly improved the classification accuracy from 70.2% (Cohen’s κ = 0.58) to 80.4% (κ = 0.72), with a significant reduction of patients where the severity category was underestimated from 18.8% to 7.3%. Discussion: Extracting sleep and arousal information from autonomic nervous system activity can improve the diagnostic accuracy of HSATs by significantly reducing the probability of underestimating SDB severity without compromising specificity. [ABSTRACT FROM AUTHOR]

Instantaneous heart rate (IHR) has been investigated for sleep applications, such as sleep apnea detection and sleep staging. To ensure the comfort of the patient during sleep, it is desirable for IHR to be measured in a contact-free fashion. In this work, we use speckle vibrometry (SV) to perform on-skin and on-textile IHR monitoring in a sleep setting. Minute motions on the laser-illuminated surface can be captured by a defocused camera, enabling the detection of cardiac motions even on textiles. We investigate supine, lateral, and prone sleeping positions. Based on Bland–Altman analysis between SV cardiac measurements and electrocardiogram (ECG), with respect to each position, we achieve the best limits of agreement with ECG values of [−8.65, 7.79] bpm, [−9.79, 9.25] bpm, and [−10.81, 10.23] bpm, respectively. The results indicate the potential of using speckle vibrometry as a contact-free monitoring method for instantaneous heart rate in a setting where the participant is allowed to rest in a spontaneous position while covered by textile layers. [ABSTRACT FROM AUTHOR]

Background: Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals. Methods: We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI. Results: Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman's correlation = 0.922), and SDB severity classification (Cohen's kappa of 0.62 was obtained based on AHI). Conclusion: Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task. [ABSTRACT FROM AUTHOR]

This study describes a computationally efficient algorithm for 4-class sleep staging based on cardiac activity and body movements. Using an accelerometer to calculate gross body movements and a reflective photoplethysmographic (PPG) sensor to determine interbeat intervals and a corresponding instantaneous heart rate signal, a neural network was trained to classify between wake, combined N1 and N2, N3 and REM sleep in epochs of 30 s. The classifier was validated on a hold-out set by comparing the output against manually scored sleep stages based on polysomnography (PSG). In addition, the execution time was compared with that of a previously developed heart rate variability (HRV) feature-based sleep staging algorithm. With a median epoch-per-epoch κ of 0.638 and accuracy of 77.8% the algorithm achieved an equivalent performance when compared to the previously developed HRV-based approach, but with a 50-times faster execution time. This shows how a neural network, without leveraging any a priori knowledge of the domain, can automatically "discover" a suitable mapping between cardiac activity and body movements, and sleep stages, even in patients with different sleep pathologies. In addition to the high performance, the reduced complexity of the algorithm makes practical implementation feasible, opening up new avenues in sleep diagnostics. [ABSTRACT FROM AUTHOR]

Objective. The accurate detection of respiratory effort during polysomnography is a critical element in the diagnosis of sleep-disordered breathing conditions such as sleep apnea. Unfortunately, the sensors currently used to estimate respiratory effort are either indirect and ignore upper airway dynamics or are too obtrusive for patients. One promising alternative is the suprasternal notch pressure (SSP) sensor: a small element placed on the skin in the notch above the sternum within an airtight capsule that detects pressure swings in the trachea. Besides providing information on respiratory effort, the sensor is sensitive to small cardiac oscillations caused by pressure perturbations in the carotid arteries or the trachea. While current clinical research considers these as redundant noise, they may contain physiologically relevant information. Approach. We propose a method to separate the signal generated by cardiac activity from the one caused by breathing activity. Using only information available from the SSP sensor, we estimate the heart rate and track its variations, then use a set of tuned filters to process the original signal in the frequency domain and reconstruct the cardiac signal. We also include an overview of the technical and physiological factors that may affect the quality of heart rate estimation. The output of our method is then used as a reference to remove the cardiac signal from the original SSP pressure signal, to also optimize the assessment of respiratory activity. We provide a qualitative comparison against methods based on filters with fixed frequency cutoffs. Main results. In comparison with electrocardiography (ECG)-derived heart rate, we achieve an agreement error of 0.06 ± 5.09 bpm, with minimal bias drift across the measurement range, and only 6.36% of the estimates larger than 10 bpm. Significance. Together with qualitative improvements in the characterization of respiratory effort, this opens the development of novel portable clinical devices for the detection and assessment of sleep disordered breathing. [ABSTRACT FROM AUTHOR]

Introduction: Excessive daytime sleepiness (EDS) associated with narcolepsy or obstructive sleep apnea (OSA) can impair vigilance/attention. Solriamfetol, a dopamine/norepinephrine reuptake inhibitor, is approved to treat EDS associated with narcolepsy (75–150 mg/day) or OSA (37.5–150 mg/day). The analysis reported here explored the use of the Sleep, Activity, Fatigue, and Task Effectiveness (SAFTE) model (used in transport industries to model performance based on accumulated sleep and circadian variability) as a substitute for healthy controls using psychomotor vigilance task (PVT) data collected during clinical studies. Methods: Data were analyzed from two phase 2 studies of solriamfetol in adults with OSA (NCT02806895, EudraCT 2015-003930-28) or narcolepsy (NCT02806908, EudraCT 2015-003931-36). Participants were randomly assigned 1:1 to solriamfetol 150 mg/day (3 days) followed by 300 mg/day (4 days), or placebo (7 days), then crossed over to the other treatment. Actual task effectiveness scores were calculated from average PVT inverse reaction time (pre-dose; 2 h post-dose; 6 h post-dose). Actigraphy-derived sleep intervals were used in SAFTE to determine modeled healthy control task effectiveness scores. Results: In participants with OSA (N = 31) on placebo or solriamfetol, actual and modeled healthy control task effectiveness did not differ at any time point. In participants with narcolepsy (N = 20) on placebo, actual task effectiveness at 2 h post-dose was lower than modeled healthy control task effectiveness (nominal P = 0.03), a difference not present with solriamfetol. There was no main effect of solriamfetol on actual or modeled healthy control task effectiveness across time points. Conclusion: This study represents a novel application of the SAFTE biomathematical model to approximate healthy controls in sleep disorder research and provides valuable lessons that may optimize future research. Future studies should perform a priori power analyses for model-tested outcomes and use sleep measures that capture sleep fragmentation characteristic of sleep disorders for sleep input (e.g., total sleep time rather than time in bed). Trial Registration: NCT02806895, EudraCT 2015-003930-28: A Randomized, Double-Blind, Placebo-Controlled, Crossover On-Road Driving Study Assessing the Effect of JZP-110 on Driving Performance in Subjects With Excessive Sleepiness Due to Obstructive Sleep Apnea. NCT02806908, EudraCT 2015-003931-36: A Randomized, Double-Blind, Placebo-Controlled, Crossover On-Road Driving Study Assessing the Effect of JZP-110 on Driving Performance in Subjects With Excessive Sleepiness Due to Narcolepsy. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the impact of solriamfetol, a dopamine and norepinephrine reuptake inhibitor, on on‐the‐road driving performance in participants with narcolepsy. Methods: In this randomised, double‐blind, placebo‐controlled, crossover study, driving performance during a 1 h on‐road driving test was assessed at 2 and 6 h post‐dose following 7 days of treatment with solriamfetol (150 mg/day for 3 days, followed by 300 mg/day for 4 days) or placebo. The primary endpoint was standard deviation of lateral position (SDLP) at 2 h post‐dose. Results: The study included 24 participants (54% male; mean age, 40 years); 22 had evaluable SDLP data. At 2 h post‐dose, median SDLP was significantly lower (improved) with solriamfetol compared with placebo (19.08 vs. 20.46 cm [median difference, −1.9 cm], p = 0.002). Four participants on solriamfetol and 7 on placebo had incomplete driving tests. At 6 h post‐dose, median SDLP was not statistically significantly different with solriamfetol compared with placebo (19.59 vs. 19.78 cm [median difference, −1.1 cm], p = 0.125). Three participants on solriamfetol and 10 on placebo had incomplete driving tests. Common adverse events (≥5%) included headache, decreased appetite, and somnolence. Conclusions: Solriamfetol 300 mg/day improved on‐the‐road driving performance, at 2 h post‐administration in participants with narcolepsy. [ABSTRACT FROM AUTHOR]

Polysomnography (PSG) remains the gold standard for sleep monitoring but is obtrusive in nature. Advances in camera sensor technology and data analysis techniques enable contactless monitoring of heart rate variability (HRV). In turn, this may allow remote assessment of sleep stages, as different HRV metrics indirectly reflect the expression of sleep stages. We evaluated a camera-based remote photoplethysmography (PPG) setup to perform automated classification of sleep stages in near darkness. Based on the contactless measurement of pulse rate variability, we use a previously developed HRV-based algorithm for 3 and 4-class sleep stage classification. Performance was evaluated on data of 46 healthy participants obtained from simultaneous overnight recording of PSG and camera-based remote PPG. To validate the results and for benchmarking purposes, the same algorithm was used to classify sleep stages based on the corresponding ECG data. Compared to manually scored PSG, the remote PPG-based algorithm achieved moderate agreement on both 3 class (Wake–N1/N2/N3–REM) and 4 class (Wake–N1/N2–N3–REM) classification, with average κ of 0.58 and 0.49 and accuracy of 81% and 68%, respectively. This is in range with other performance metrics reported on sensing technologies for wearable sleep staging, showing the potential of video-based non-contact sleep staging. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the impact of solriamfetol, a dopamine and norepinephrine reuptake inhibitor, on on‐the‐road driving in participants with excessive daytime sleepiness (EDS) associated with obstructive sleep apnoea (OSA). Methods: Eligible participants were aged 21–75 years with OSA and EDS (Maintenance of Wakefulness Test mean sleep latency <30 minutes and Epworth Sleepiness Scale score ≥10). Participants were randomised 1:1 to solriamfetol (150 mg/day [3 days], then 300 mg/day [4 days]) or placebo for 7 days, before crossover to the other treatment paradigm. On Day 7 of each period, standardised on‐road driving tests occurred (2 and 6 hours postdose). Standard deviation of lateral position (SDLP) was the primary endpoint. Results: Solriamfetol significantly reduced SDLP at 2 (n = 34; least squares mean difference, –1.1 cm; 95% CI, –1.85, –0.32; p = 0.006) and 6 hours postdose (n = 32; least squares mean difference, –0.8 cm; 95% CI, –1.58, –0.03; p = 0.043). Two hours postdose, 4 placebo‐treated and 1 solriamfetol‐treated participants had incomplete driving tests; 6 hours postdose, 7 and 3 participants, respectively, had incomplete tests. Common treatment‐emergent adverse events included headache, nausea, and insomnia. Conclusions: Solriamfetol 300 mg/day significantly improved on‐the‐road driving performance in participants with EDS associated with OSA. [ABSTRACT FROM AUTHOR]

Summary: Patients with narcolepsy or idiopathic hypersomnia (IH) are at increased risk of driving accidents. Both excessive daytime sleepiness, i.e. unwanted sleep episodes during the day, and disturbed vigilance are core features of these disorders. We tested on‐the‐road driving performance of patients with narcolepsy or IH coming in for a routine driving fitness evaluation and examined: (1) correlations between driving performance and the Maintenance of Wakefulness Test (MWT), Sustained Attention to Response Task (SART) and Psychomotor Vigilance Test (PVT) as objective tests; (2) the predictive power of the MWT and SART for increased risk of impaired driving; (3) the best set of objective predictors for increased risk of impaired driving. Participants were 44 patients (aged 18–75 years) with narcolepsy type 1 (NT1), type 2 (NT2) or IH. They completed the MWT, SART, PVT, a subjective sleepiness questionnaire, and a standardised on‐the‐road driving test. The standard deviation of the lateral position (SDLP) was used as outcome measure of driving performance. The MWT had low correlation with the SDLP (ρ = −0.41 to −0.49, p < 0.01). The SART and PVT had low correlations with SDLP (ρ = 0.30 and ρ = 0.39, respectively, both p < 0.05). The predictive power of MWT for an increased risk of impaired driving was significant, but low (area under the curve = 0.273, p = 0.012), and non‐significant for SART. We conclude that in our present group, none of the tests had adequate ability to predict impaired driving, questioning their use for clinical driving fitness evaluation in narcolepsy and IH. Real‐time monitoring of sleepiness while driving seems more promising in these patients. [ABSTRACT FROM AUTHOR]

Background: The reported prevalence of insomnia symptoms in people with intellectual disabilities varies greatly, possibly due to the lack of a common definition. This article provides an overview of the different definitions used and formulates key points for a general definition. Methods: A literature search was performed. An overview of the definitions used was given and compared to the third edition of the international classification of sleep disorders. Results: The search yielded 16 studies. No uniform definition was used. Terminology and cut‐off points of insomnia symptoms differed. Insomnia symptoms were mostly described as night‐time problems. A minority of studies incorporated daytime consequences. Conclusion: An insomnia disorder entails more than merely night‐time complaints and should include daytime consequences. A general definition is warranted. This definition should focus on night‐time and daytime insomnia symptoms, incorporate subjective features, and discuss the use of objective measurements and influence of environmental circumstances. [ABSTRACT FROM AUTHOR]

The core symptoms of narcolepsy such as excessive daytime sleepiness and cataplexy are well known. However, there is mounting evidence for a much broader symptom spectrum, including psychiatric symptoms. Disordered sleep has previously been linked with dissociative symptoms, which may imply that patients with narcolepsy are more prone to develop such symptoms. To investigate the frequency of dissociative symptoms in adult patients with narcolepsy type 1 compared to population controls. In a retrospective case control study, sixty adult patients fulfilling the criteria for narcolepsy type 1 and 120 matched population control subjects received a structured interview using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) to assess dissociative symptoms and disorders. A majority of narcolepsy patients reported dissociative symptoms, and even fulfilled the DSM-IV-TR criteria of a dissociative disorder (62% vs 1% in controls, p <.001). Most frequently reported symptoms were "dissociative amnesia" (37% vs 1%, p <.001) and "dissociative disorder of voluntary movement" (32% vs 1%, p <.001). Dissociative symptoms are strikingly prevalent in adult patients with narcolepsy type 1. Although a formal diagnosis of dissociation disorder should not be made as the symptoms can be explained by narcolepsy as an underlying condition, the findings do illustrate the extent and severity of the dissociative symptoms. As for the pathophysiological mechanism, there may be symptom overlap between narcolepsy and dissociation disorder. However, there may also be a more direct link between disrupted sleep and dissociative symptoms. In either case, the high frequency of occurrence of dissociative symptoms should result in an active inquiry by doctors, to improve therapeutic management and guidance. [ABSTRACT FROM AUTHOR]

Summary: Rest–activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long‐term monitoring of rest–activity patterns is typically performed with diaries or actigraphy. Here, we propose an unobtrusive method to obtain rest–activity patterns using smartphone keyboard activity. The present study investigated whether this proposed method reliably estimates rest and activity timing compared to daily self‐reports within healthy participants. First‐year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours and completed the Consensus Sleep Diary for 1 week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results revealed high correlations between these markers and user‐reported onset and offset of resting period (r ranged from 0.74 to 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R2 ranged from 0.60 to 0.66). This indicates that smartphone keyboard activity can be used to estimate rest–activity patterns. In addition, effects of chronotype and type of day were investigated. Implementing this method in longitudinal studies would allow for long‐term monitoring of (disturbances to) rest–activity patterns, without user burden or additional costly devices. It could be particularly interesting to replicate these findings in studies amongst clinical populations with sleep‐related problems, or in populations for whom disturbances in rest–activity patterns are secondary complaints, such as neurological disorders. [ABSTRACT FROM AUTHOR]

Objective: Parkinson's disease is a common, age-related, neurodegenerative disease, affecting gait and other motor functions. Technological developments in consumer imaging are starting to provide high-quality, affordable tools for home-based diagnosis and monitoring. This pilot study aims to investigate whether a consumer depth camera can capture changes in gait features of Parkinson's patients. The dataset consisted of 19 patients (tested in both a practically defined OFF phase and ON phase) and 8 controls, who performed the "Timed-Up-and-Go" test multiple times while being recorded with the Microsoft Kinect V2 sensor. Camera-derived features were step length, average walking speed and mediolateral sway. Motor signs were assessed clinically using the Movement Disorder Society Unified Parkinson's Disease Rating Scale. Results: We found significant group differences between patients and controls for step length and average walking speed, showing the ability to detect Parkinson's features. However, there were no differences between the ON and OFF medication state, so further developments are needed to allow for detection of small intra-individual changes in symptom severity. [ABSTRACT FROM AUTHOR]

Objective: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions, and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate, and control the RSA. These methods are also compared, and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep. Methods: A simulation model is used to create a dataset of heart rate variability, and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in real-life applications, regression models trained on the simulated data are used to map the estimates to the same measurement scale. Results, and conclusion: RSA estimates based on cross entropy, time-frequency coherence, and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly. Significance: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing, and newly proposed RSA estimates. It is freely accessible online. [ABSTRACT FROM AUTHOR]

Polysomnography (PSG) is the current gold standard for the diagnosis of sleep disorders. However, this multi-parametric sleep monitoring tool also has some drawbacks, e.g. it limits the patient's mobility during the night and it requires the patient to come to a specialized sleep clinic or hospital to attach the sensors. Unobtrusive techniques for the detection of sleep disorders such as sleep apnea are therefore gaining increasing interest. Remote photoplethysmography using video is a technique which enables contactless detection of hemodynamic information. Promising results in near-infrared have been reported for the monitoring of sleep-relevant physiological parameters pulse rate, respiration and blood oxygen saturation. In this study we validate a contactless monitoring system on eight patients with a high likelihood of relevant obstructive sleep apnea, which are enrolled for a sleep study at a specialized sleep center. The dataset includes 46.5 hours of video recordings, full polysomnography and metadata. The camera can detect pulse and respiratory rate within 2 beats/breaths per minute accuracy 92% and 91% of the time, respectively. Estimated blood oxygen values are within 4 percentage points of the finger-oximeter 89% of the time. These results demonstrate the potential of a camera as a convenient diagnostic tool for sleep apnea, and sleep disorders in general. [ABSTRACT FROM AUTHOR]

Objective. Measurement of respiratory rate and effort is useful in various applications, such as the diagnosis of sleep apnea and early detection of patient deterioration in medical conditions, such as infections. A chest-worn accelerometer may be an easy and non-intrusive method, provided it is accurate and robust. We investigate the use of a novel method that can perform under realistic sleeping conditions such as variable sensor positions and body posture. Approach. Twenty subjects (aged 46–65 years) wore an accelerometer on the chest and a respiratory impedance plethysmography band as a reference. The subjects underwent an experimental protocol lasting approximately 90 min, under various postures and with different sensor positions. We used a novel, constrained, and recursive form of principal component analysis (PCA) to estimate the respiratory effort signal robustly. To obtain an estimate for the respiratory rate, first, multiple estimates were aggregated into a single frequency. Subsequently, a quality index was determined, such that unreliable estimates could be identified, and a trade-off could be made between coverage (percentage of time that the quality index is above a threshold) and limits of agreement. Main results. Results were determined over all recorded data, including changes in sensor position and posture. For respiratory effort, it was found that recursive and constrained computation of PCA reduced the estimation error significantly. For respiratory rate, a relation between coverage and limits of agreement was determined. If a minimum coverage of 80% was required, the limits of agreement could be kept below 1.45 breaths per minute. If the limits of agreement were constrained to 0.2 breaths per minute, a mean coverage of 5% was still attainable. Significance. We have shown that chest-worn accelerometery can be a robust and accurate method for measurement of respiratory features under realistic conditions. [ABSTRACT FROM AUTHOR]

Acute total sleep deprivation and partial sleep deprivation have negative impacts on cognitive performance. Studies in subjects who regularly experience sleep loss, however, are rare and often restricted to examination of internal sleeping disorders. To address this issue, we set up a pilot study to explore the effects of a week characterized by sleep disruption on cognitive functioning, using a case–control setting in a maritime pilot group with chronic exposure to intermittent extrinsic, work‐related sleep disruption. Twenty maritime pilots (aged 30–50 years) were compared to sex‐ and education‐matched controls with normal sleep behaviour, from the same age range. We assessed subjective and objective cognitive function, including attention, psychomotor speed, memory and executive function using the Cambridge Neuropsychological Test Automated Battery (CANTAB). Although we were able to confirm poorer sleep in maritime pilots and subjective complaints in some cognitive domains, we did not find objective cognitive deficits in the maritime pilot group compared to controls without sleep disruption. This could suggest that in this group of healthy, young maritime pilots, exposure to sleep disruption resulted in some subjective cognitive complaints, but objective deficits of cognitive function were not detected in comparison with a non‐pilot control group. However, given the small sample size, the absence of an effect does not exclude the possibility that sleep disruption could result in cognitive deficits in general. Therefore, our findings have to be confirmed in future prospective studies with a larger sample size and matched controls, regarding age, education and work history. [ABSTRACT FROM AUTHOR]

Rationale: The mechanisms underlying impaired sleep quality in insomnia are not fully known, but an important role for sleep fragmentation has been proposed. Objectives: The aim of this study is to explore potential mechanisms of sleep fragmentation influencing alterations of perceived sleep quality. Methods: We analyzed polysomnography (PSG) recordings from a double-blind crossover study with zopiclone 7.5 mg and placebo, in elderly participants with insomnia complaints and age-matched healthy controls. We compared survival dynamics of sleep and wake across group and treatment. Subsequently, we used a previously proposed model to estimate the amount of sleep onset latency (SOL) misperception from PSG-defined sleep fragmentation. Self-reported and model-estimated amount of SOL misperception were compared across group and treatment, as well as model prediction errors. Results: In the zopiclone night, the average segment length of NREM sleep was increased (group F = 1.16, p = 0.32; treatment F = 8.89, p< 0.01; group x treatment F = 0.44, p = 0.65), while the segment length of wake was decreased (group F = 1.48, p = 0.23; treatment F = 11.49, p< 0.01; group x treatment F = 0.36, p = 0.70). The self-reported and model-estimated amount of SOL misperception were lower during the zopiclone night (self-reported group F = 6.08, p< 0.01, treatment F = 10.8, p< 0.01, group x treatment F = 2.49, p = 0.09; model-estimated F = 1.70, p = 0.19, treatment F = 16.1, p< 0.001, group x treatment F = 0.60, p = 0.55). The prediction error was not altered (group F = 1.62, p = 0.20; treatment F = 0.20, p = 0.65; group x treatment F = 1.01, p = 0.37). Conclusions: Impaired subjective sleep quality is associated with decreased NREM stability, together with increased stability of wake. Furthermore, we conclude that zopiclone-induced changes in SOL misperception can be largely attributed to predictable changes of sleep architecture. [ABSTRACT FROM AUTHOR]

Background: Isolated rapid eye movement sleep behavior disorder is known to be prodromal for alpha‐synucleinopathies, such as Parkinson's disease (PD) and dementia with Lewy bodies. The [18F]fluorodeoxyglucose‐positron emission tomography (PET)–based PD‐related brain pattern can be used to monitor disease progression. Objective: We longitudinally investigated PD‐related brain pattern expression changes in 20 subjects with isolated rapid eye movement sleep behavior disorder to investigate whether this may be a suitable technique to study prodromal PD progression in these patients and to identify potential phenoconverters. Methods: Subjects underwent two [18F]fluorodeoxyglucose‐PET brain scans ~3.7 years apart, along with baseline and repeated motor, cognitive, and olfactory testing within roughly the same time frame. Results: At baseline, 8 of 20 (40%) subjects significantly expressed the PD‐related brain pattern (with z scores above the receiver operating characteristic–determined threshold). At follow‐up, six additional subjects exhibited significant PD‐related brain pattern expression (70% in total). PD‐related brain pattern expression increased in all subjects (P = 0.00008). Four subjects (20%), all with significant baseline PD‐related brain pattern expression, phenoconverted to clinical PD. Conclusions: Suprathreshold PD‐related brain pattern expression and greater score rate of change may signify greater shorter‐term risk for phenoconversion. Our results support the use of serial PD‐related brain pattern expression measurements as a prodromal PD progression biomarker in patients with isolated rapid eye movement sleep behavior disorder. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Study objectives: While poor sleep quality has been related to increased risk of Alzheimer's disease, long-time shift workers (maritime pilots) did not manifest evidence of early Alzheimer's disease in a recent study. We explored two hypotheses of possible compensatory mechanisms for sleep disruption: Increased efficiency in generating deep sleep during workweeks (model 1) and rebound sleep during rest weeks (model 2). Methods: We used data from ten male maritime pilots (mean age: 51.6±2.4 years) with a history of approximately 18 years of irregular shift work. Subjective sleep quality was assessed with the Pittsburgh Sleep Quality Index (PSQI). A single lead EEG-device was used to investigate sleep in the home/work environment, quantifying total sleep time (TST), deep sleep time (DST), and deep sleep time percentage (DST%). Using multilevel models, we studied the sleep architecture of maritime pilots over time, at the transition of a workweek to a rest week. Results: Maritime pilots reported worse sleep quality in workweeks compared to rest weeks (PSQI = 8.2±2.2 vs. 3.9±2.0; p<0.001). Model 1 showed a trend towards an increase in DST% of 0.6% per day during the workweek (p = 0.08). Model 2 did not display an increase in DST% in the rest week (p = 0.87). Conclusions: Our findings indicated that increased efficiency in generating deep sleep during workweeks is a more likely compensatory mechanism for sleep disruption in the maritime pilot cohort than rebound sleep during rest weeks. Compensatory mechanisms for poor sleep quality might mitigate sleep disruption-related risk of developing Alzheimer's disease. These results should be used as a starting point for future studies including larger, more diverse populations of shift workers. [ABSTRACT FROM AUTHOR]

Objective: Studies have shown an increased cardiovascular risk in obstructive sleep apnea (OSA) patients. In order to prioritize treatment of high risk patients, there is a need for improved cardiovascular OSA phenotyping. This study investigates the use of oxygen saturation (SpO $_{2}$) parameters for cardiovascular risk assessment of OSA patients. To this end, a novel multilevel interval coded scoring (mICS) algorithm is proposed. Methods: The study includes SpO $_{2}$ recordings from 1987 overnight polysomnographies, of which 974 are from patients suspected to have OSA, 931 from the general population based Sleep Heart Health Study and 83 from healthy controls. The minimal SpO $_{2}$ value, SpO $_{2}$ upslope and amplitude ratio of desaturation over resaturation are extracted for all oxygen desaturations and averaged per patient. These three SpO $_{2}$ parameters are used together with patient demographics to develop a mICS model to predict the probability that a patient had a cardiovascular condition, or had already experienced a cardiovascular event, at the time of the polysomnography. Results: Including the SpO $_{2}$ parameters in the mICS together with age and BMI improves the model's performance by 2.7% and leads to a test area under the curve (AUC) of 69.5% for the detection of any cardiovascular comorbidity. Moreover, an increase in AUC of 5% was obtained for the detection of cardiovascular events, resulting in an AUC of 93.5%. Conclusions: This study shows that parameters based on SpO $_{2}$ and the mICS model are useful to predict the cardiovascular comorbidity status of OSA patients. Significance: The proposed model could be used to assist in prioritizing OSA patients for treatment. [ABSTRACT FROM AUTHOR]

A large part of the worldwide population suffers from obstructive sleep apnea (OSA), a disorder impairing the restorative function of sleep and constituting a risk factor for several cardiovascular pathologies. The standard diagnostic metric to define OSA is the apnea–hypopnea index (AHI), typically obtained by manually annotating polysomnographic recordings. However, this clinical procedure cannot be employed for screening and for long-term monitoring of OSA due to its obtrusiveness and cost. Here, we propose an automatic unobtrusive AHI estimation method fully based on wrist-worn reflective photoplethysmography (rPPG), employing a deep learning model exploiting cardiorespiratory and sleep information extracted from the rPPG signal trained with 250 recordings. We tested our method with an independent set of 188 heterogeneously disordered clinical recordings and we found it estimates the AHI with a good agreement to the gold standard polysomnography reference (correlation = 0.61, estimation error = 3±10 events/h). The estimated AHI was shown to reliably assess OSA severity (weighted Cohen's kappa = 0.51) and screen for OSA (ROC–AUC = 0.84/0.86/0.85 for mild/moderate/severe OSA). These findings suggest that wrist-worn rPPG measurements that can be implemented in wearables such as smartwatches, have the potential to complement standard OSA diagnostic techniques by allowing unobtrusive sleep and respiratory monitoring. [ABSTRACT FROM AUTHOR]

Objective: Respiratory activity is an essential parameter to monitor healthy and disordered sleep, and unobtrusive measurement methods have important clinical applications in diagnostics of sleep-related breathing disorders. We propose a respiratory activity surrogate extracted from wrist-worn reflective photoplethysmography validated on a heterogeneous dataset of 389 sleep recordings. Approach: The surrogate was extracted by interpolating the amplitude of the PPG pulses after evaluation of pulse morphological quality. Subsequent multistep post-processing was applied to remove parts of the surrogate with low quality and high motion levels. In addition to standard respiration rate performance, we evaluated the similarity between surrogate respiratory activity and reference inductance plethysmography of the thorax, using Spearman's correlations and spectral coherence, and assessed the influence of PPG signal quality, motion levels, sleep stages and obstructive sleep apnea. Main results: Prior to post-processing, the surrogate already had a strong similarity with the reference (correlation = 0.54, coherence = 0.81), and reached respiration rate estimation performance in line with the literature (estimation error = 0.76± 2.11 breaths/min). Detrimental effects of low PPG quality, high motion levels and sleep-dependent physiological phenomena were significantly mitigated by the proposed post-processing steps (correlation = 0.62, coherence = 0.88, estimation error = 0.53± 1.85 breaths/min). Significance: Wrist-worn PPG can be used to extract respiratory activity, thus allowing respiration monitoring in real-world sleep medicine applications using (consumer) wearable devices. [ABSTRACT FROM AUTHOR]

Insomnia, i.e., difficulties initiating and/or maintaining sleep, is one of the most common sleep disorders. To study underlying mechanisms for insomnia, we studied autonomic activity changes around sleep onset in participants without clinical insomnia but with varying problems with initiating or maintaining sleep quantified as increased sleep onset latency (SOL) and wake after sleep onset (WASO), respectively. Polysomnography and electrocardiography were simultaneously recorded in 176 participants during a single night. Cardiac autonomic activity was assessed using frequency domain analysis of RR intervals and results show that the normalized spectral power in the low frequency band (LF nu ) after sleep onset was significantly higher in participants with long SOL compared to participants with short SOL. Furthermore, the normalized spectral power in the high frequency band (HF nu ) was significantly lower in participants with long SOL as compared to participants with short SOL over 3 time periods (first 10 min in bed intending to sleep, 10 min before, and 10 min after sleep onset). These results suggest that participants with long SOL are more aroused in all three examined time periods when compared to participants with short SOL, especially for young adults (20–40 years). As there is no clear consensus on the cutoff for an increased WASO, we used a data-driven approach to explore different cutoffs to define short WASO and long WASO groups. LF nu , HF nu , and LF / HF differed between the long and the short WASO groups. A higher LF nu and LF / HF and a lower HF nu was observed in participants with long WASO for most cutoffs. The highest effect size was found using the cutoff of 66 min. Our findings suggest that autonomic cardiac activity has predictive value with respect to sleep characteristics pertaining to sleep onset and maintenance. [ABSTRACT FROM AUTHOR]

Introduction Polysomnography (PSG) is the primary tool for sleep monitoring and the diagnosis of sleep disorders. Recent advances in signal analysis make it possible to reveal more information from this rich data source. Furthermore, many innovative sleep monitoring techniques are being developed that are less obtrusive, easier to use over long time periods and in the home situation. Here, we describe the methods of the Sleep and Obstructive Sleep Apnoea Monitoring with Non-Invasive Applications (SOMNIA) project, yielding a database combining clinical PSG with advanced unobtrusive sleep monitoring modalities in a large cohort of patients with various sleep disorders. The SOMNIA database will facilitate the validation and assessment of the diagnostic value of the new techniques, as well as the development of additional indices and biomarkers derived from new and/or traditional sleep monitoring methods. Methods and analysis We aim to include at least 2100 subjects (both adults and children) with a variety of sleep disorders who undergo a PSG as part of standard clinical care in a dedicated sleep centre. Full-video PSG will be performed according to the standards of the American Academy of Sleep Medicine. Each recording will be supplemented with one or more new monitoring systems, including wrist-worn photoplethysmography and actigraphy, pressure sensing mattresses, multimicrophone recording of respiratory sounds including snoring, suprasternal pressure monitoring and multielectrode electromyography of the diaphragm. Ethics and dissemination The study was reviewed by the medical ethical committee of the Maxima Medical Center (Eindhoven, the Netherlands, File no: N16.074). All subjects provide informed consent before participation. The SOMNIA database is built to facilitate future research in sleep medicine. Data from the completed SOMNIA database will be made available for collaboration with researchers outside the institute. [ABSTRACT FROM AUTHOR]

Introduction: Recent evidence suggests that poor sleep is a risk factor that contributes to the development of Alzheimer's disease (AD). Most studies have focused on short-term effects of sleep deprivation on cognitive function, whereas longitudinal studies are limited to self-reported sleep and the risk of later-life dementia. Because sleep loss could be an early manifestation of neurodegenerative disease, reverse causality in these studies cannot be excluded. Objective: In this explorative, observational study, we investigated the effects of extended periods of extrinsically (work-related) caused sleep loss on later-life cognitive function, early dementia symptoms, and current sleep quality. Methods: We approached a community of retired male maritime pilots (approx. n = 500) through a newsletter. We investigated 50 respondents (mean age 71.7 years ± 7.7), with a history of >25 years of work on irregular schedules, which resulted in extended periods of sleep loss. Validated questionnaires on cognitive complaints (Cognitive Failure Questionnaire [CFQ]), early dementia symptoms (Early Dementia Questionnaire [EDQ]), current sleep quality (Pittsburgh Sleep Quality Index [PSQI] and sleep-wake diaries), quality of life (QoL, EQ-5D), and mood (Hospital Anxiety and Depression Scale [HADS]) were administered by a single investigator (J.T.), who also completed an observer rating of cognitive function. Results: Scores on the CFQ, EDQ, PSQI, EQ-5D, and HADS were within normal ranges adjusted for age, sex, and education. The observer rating was not indicative of cognitive decline. Conclusion: We found no evidence that long-term exposure to work-related sleep loss had resulted in cognitive decline or early dementia symptoms in this sample of retired maritime pilots. [ABSTRACT FROM AUTHOR]

Introduction Evidence indicates a bidirectional relationship between poor sleep and Alzheimer's disease (AD). While AD may lead to disruption of normal sleep, poor sleep in itself may play a causal role in the development of AD by influencing the production and/or clearance of the amyloid-beta (Aβ) protein. This led to the hypothesis that extended periods (>10 years) of sleep loss could lead to Aβ accumulation with subsequent cognitive AD-related decline. This manuscript describes the methodology of the SCHIP study, a cohort study in maritime pilots that aims at investigating the relationship between prolonged work-related sleep loss, cognitive function and amyloid accumulation among healthy middle-aged maritime pilots, to test the hypothesis that prolonged sleep loss increases the risk of AD-related cognitive decline. Methods Our study sample consists of a group of healthy middle-aged maritime pilots (n=20), who have been exposed to highly irregular work schedules for more than 15 years. The maritime pilots will be compared to a group of healthy, age and educationmatched controls (n=20) with normal sleep. Participants will complete 10 days of actigraphy (Actiwatch 2, Philips Respironics) combined with a sleep-wake diary. They will undergo one night of polysomnography, followed by comprehensive assessment of cognitive function. Additionally, participants will undergo amyloid positron emission tomography-CT to measure brain amyloid accumulation and MRI to investigate atrophy and vascular changes. Analysis All analyses will be performed using IBM SPSS V.20.0 (SPSS). We will perform independent samples t-tests to compare all outcome parameters. Ethics and dissemination The study protocol was approved by our institutional ethical review board (NL55712.091.16, file number 2016-2337) and will be performed according to Good Clinical Practice rules. Data and results will be published in 2020. [ABSTRACT FROM AUTHOR]

Background/Objective: Idiopathic REM sleep behavior disorder (iRBD) often precedes Parkinson's disease (PD) and other alpha-synucleinopathies. The aim of the study is to investigate brain glucose metabolism of patients with RBD and PD by means of a multidimensional scaling approach, using18F-FDG-PET as a biomarker of synaptic function. Methods: Thirty-six iRBD patients (64.1±6.5 y, 32 M), 72 PD patients, and 79 controls (65.6±9.4 y, 53 M) underwent brain 18F-FDG-PET. PD patients were divided according to the absence (PD, 32 subjects; 68.4±8.5 y, 15 M) or presence (PDRBD, 40 subjects; 71.8±6.6 y, 29 M) of RBD. 18F-FDG-PET scans were used to independently discriminate subjects belonging to four categories: controls (RBD no, PD no), iRBD (RBD yes, PD no), PD (RBD no, PD yes) and PDRBD (RBD yes, PD yes). Results: The discriminant analysis was moderately accurate in identifying the correct category. This is because the model mostly confounds iRBD and PD, thus the intermediate classes. Indeed, iRBD, PD and PDRBD were progressively located at increasing distance from controls and are ordered along a single dimension (principal coordinate analysis) indicating the presence of a single flux of variation encompassing both RBD and PD conditions. Conclusion: Data-driven approach to brain 18F-FDG-PET showed only moderate discrimination between iRBD and PD patients, highlighting brain glucose metabolism heterogeneity among such patients. iRBD should be considered as a marker of an ongoing condition that may be picked-up in different stages across patients and thus express different brain imaging features and likely different clinical trajectories. [ABSTRACT FROM AUTHOR]

Background: Idiopathic REM sleep behavior disorder is a prodromal stage of Parkinson's disease and dementia with Lewy bodies. Hyposmia, reduced dopamine transporter binding, and expression of the brain metabolic PD-related pattern were each associated with increased risk of conversion to PD. The objective of this study was to study the relationship between the PD-related pattern, dopamine transporter binding, and olfaction in idiopathic REM sleep behavior disorder.Methods: In this cross-sectional study, 21 idiopathic REM sleep behavior disorder subjects underwent 18 F-fluorodeoxyglucose PET, dopamine transporter imaging, and olfactory testing. For reference, we included 18 F-fluorodeoxyglucose PET data of 19 controls, 20 PD patients, and 22 patients with dementia with Lewy bodies. PD-related pattern expression z-scores were computed from all PET scans.Results: PD-related pattern expression was higher in idiopathic REM sleep behavior disorder subjects compared with controls (P = 0.048), but lower compared with PD (P = 0.001) and dementia with Lewy bodies (P < 0.0001). PD-related pattern expression was higher in idiopathic REM sleep behavior disorder subjects with hyposmia and in subjects with an abnormal dopamine transporter scan (P < 0.05, uncorrected).Conclusion: PD-related pattern expression, dopamine transporter binding, and olfaction may provide complementary information for predicting phenoconversion. © 2017 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. [ABSTRACT FROM AUTHOR]

Insomnia is a too general term for various subtypes that might have different etiologies and therefore require different types of treatment. In this explorative study we used cluster analysis to distinguish different phenotypes in 218 patients with insomnia, taking into account several factors including sleep variables and characteristics related to personality and psychiatric comorbidity. Three clusters emerged from the analysis. The ‘moderate insomnia with low psychopathology’-cluster was characterized by relatively normal personality traits, as well as normal levels of anxiety and depressive symptoms in the presence of moderate insomnia severity. The ‘severe insomnia with moderate psychopathology’-cluster showed relatively high scores on the Insomnia Severity Index and scores on the sleep log that were indicative for severe insomnia. Anxiety and depressive symptoms were slightly above the cut-off and they were characterized by below average self-sufficiency and less goal-directed behavior. The ‘early onset insomnia with high psychopathology’-cluster showed a much younger age and earlier insomnia onset than the other two groups. Anxiety and depressive symptoms were well above the cut-off score and the group consisted of a higher percentage of subjects with comorbid psychiatric disorders. This cluster showed a ‘typical psychiatric’ personality profile. Our findings stress the need for psychodiagnostic procedures next to a sleep-related diagnostic approach, especially in the younger insomnia patients. Specific treatment suggestions are given based on the three phenotypes. [ABSTRACT FROM AUTHOR]

Chronic insomnia is known to have a negative influence on quality of life (QOL). To date, most studies on chronic insomnia have focused on health-related aspects of QOL. General QOL, which is a different construct, has not been studied in detail. Moreover, it is not known which factors are associated with general QOL in insomnia, and whether the presence of mental disorders, a condition known as comorbid insomnia, affects these variables. The present study focused on identifying sleep and psychosocial variables that might be associated with general QOL in primary and comorbid insomnia. Personality traits, coping variables, anxiety and depressive symptoms, fatigue and subjective sleep variables were assessed in 218 consecutive well-characterized patients with primary and comorbid insomnia, referred to a third line centre for sleep medicine. In primary insomnia, higher extraversion and lower discrepancies in social support were associated with higher QOL. Surprisingly, insomnia severity was not significantly associated with QOL in this group. However, lower fatigue, which can be seen as an important daytime consequence of insomnia was correlated with higher QOL in patients with primary insomnia. In both insomnia groups, low anxiety and depressive symptoms and low fatigue were associated with higher general QOL. In contrast with the primary insomnia group, lower insomnia severity was correlated with higher QOL in patients with comorbid insomnia. These results stress the importance of assessing and treating daytime fatigue in insomnia. In primary insomnia, improving social support might be an important treatment goal. Furthermore, this study supports the concept that treatment of insomnia should not be neglected in patients with comorbid insomnia. Indeed, both insomnia and indices of psychiatric disease are strongly associated with general QOL in this condition. [ABSTRACT FROM AUTHOR]

This two-centre observational study of vigilance measurements assessed the feasibility of vigilance measurements on multiple days using the Sustained Attention to Response Task and the Psychomotor Vigilance Test with portable task equipment, and subsequently assessed the effect of sodium oxybate treatment on vigilance in patients with narcolepsy. Twenty-six patients with narcolepsy and 15 healthy controls were included. The study comprised two in-laboratory days for the Maintenance of Wakefulness Test and the Oxford Sleep Resistance test, followed by 7-day portable vigilance battery measurements. This procedure was repeated for patients with narcolepsy after at least 3 months of stable treatment with sodium oxybate. Patients with narcolepsy had a higher Sustained Attention to Response Task error count, lower Psychomotor Vigilance Test reciprocal reaction time, higher Oxford Sleep Resistance test omission error count adjusted for test duration (Oxford Sleep Resistance test OMIS/ MIN), and lower Oxford Sleep Resistance test and Maintenance of Wakefulness Test sleep latency compared with controls (all P < 0.01). Treatment with sodium oxybate was associated with a longer Maintenance of Wakefulness Test sleep latency ( P < 0.01), lower Oxford Sleep Resistance test OMIS/ MIN ( P = 0.01) and a lower Sustained Attention to Response Task error count ( P = 0.01) in patients with narcolepsy, but not with absolute changes in Oxford Sleep Resistance test sleep latency or Psychomotor Vigilance Test reciprocal reaction time. It was concluded that portable measurements of sustained attention as well as in-laboratory Oxford Sleep Resistance test and Maintenance of Wakefulness Test measurements revealed worse performance for narcoleptic patients compared with controls, and that sodium oxybate was associated with an improvement of sustained attention and a better resistance to sleep. [ABSTRACT FROM AUTHOR]

Background: Upon awaking, many Parkinson's patients experience an improved mobility, a phenomenon known as 'sleep benefit'. Despite the potential clinical relevance, no objective correlates of sleep benefit exist. The discrepancy between the patients' subjective experience of improvement in absence of objective changes is striking, and raises questions about the nature of sleep benefit. We aimed to clarify what patients reporting subjective sleep benefit, actually experience when waking up. Furthermore, we searched for factors associated with subjective sleep benefit. Methods: Using a standardized topic list, we interviewed 14 Parkinson patients with unambiguous subjective sleep benefit, selected from a larger questionnaire-based cohort. A grounded theory approach was used to analyse the data. Results: A subset of the participants described a temporary decrease in their Parkinson motor symptoms after sleep. Others did experience beneficial effects which were, however, non-specific for Parkinson's disease (e.g. feeling 'rested'). The last group misinterpreted the selection questionnaire and did not meet the definition of sleep benefit for various reasons. There were no general sleep-related factors that influenced the presence of sleep benefit. Factors mentioned to influence functioning at awakening were mostly stress related. Conclusions: The group of participants convincingly reporting sleep benefit in the selection questionnaire appeared to be very heterogeneous, with only a portion of them describing sleep benefit on motor symptoms. The group of participants actually experiencing motor sleep benefit may be much smaller than reported in the literature so far. Future studies should employ careful inclusion criteria, which could be based on our reported data. [ABSTRACT FROM AUTHOR]

Several studies have identified an association between PandemrixTM, an AS03 adjuvanted pandemic influenza A(H1N1) vaccine, and narcolepsy, a rare and under-diagnosed sleep disorder with a median onset-to-diagnosis interval of ten years. This paper reviews potential sources of bias in published studies and aims to provide, through simulation, methodological recommendations for assessment of vaccine safety signals. Our simulation study showed that in the absence of an association between the vaccine and the outcome, presence of detection bias and differential exposure misclassification could account for elevated risk estimates. These may play a major role, particularly in alert situations when observation times are limited and the disease has a long latency period. Estimates from the case-control design were less inflated than those from the cohort design when these biases were present. Overall, these simulations provide useful insights for the design and interpretation of future studies. [ABSTRACT FROM PUBLISHER]

Patients suffering from narcolepsy type 1 show altered skin temperatures, resembling the profile that is related to sleep onset in healthy controls. The aim of the present study is to investigate the effects of sodium oxybate, a widely used drug to treat narcolepsy, on the 24-h profiles of temperature and sleep-wakefulness in patients with narcolepsy and controls. Eight hypocretin-deficient male narcolepsy type 1 patients and eight healthy matched controls underwent temperature measurement of core body and proximal and distal skin twice, and the sleep-wake state for 24 h. After the baseline assessment, 2 × 3 g of sodium oxybate was administered for 5 nights, immediately followed by the second assessment. At baseline, daytime core body temperature and proximal skin temperature were significantly lower in patients with narcolepsy (core: 36.8 ± 0.05 °C versus 37.0 ± 0.05 °C, F = 8.31, P = 0.01; proximal: 33.4 ± 0.26 °C versus 34.3 ± 0.26 °C, F = 5.66, P = 0.03). In patients, sodium oxybate administration increased proximal skin temperature during the day ( F = 6.46, P = 0.04) to a level similar as in controls, but did not affect core body temperature, distal temperature or distal-proximal temperature gradient. Sodium oxybate administration normalised the predictive value of distal skin temperature and distal-proximal temperature gradient for the onset of daytime naps ( P < 0.01). In conclusion, sodium oxybate administration resulted in a partial normalisation of the skin temperature profile, by increasing daytime proximal skin temperature, and by strengthening the known relationship between skin temperature and daytime sleep propensity. These changes seem to be related to the clinical improvement induced by sodium oxybate treatment. A causal relationship is not proven. [ABSTRACT FROM AUTHOR]