scholarly journals Modeling sleep onset misperception in insomnia

SLEEP ◽  
2020 ◽  
Vol 43 (8) ◽  
Author(s):  
Lieke W A Hermans ◽  
Merel M van Gilst ◽  
Marta Regis ◽  
Leonie C E van den Heuvel ◽  
Hanneke Langen ◽  
...  
Keyword(s):  
Sleep Onset ◽  
Model Performance ◽  
Minimum Length ◽  
Model Parameters ◽  
Sleep Onset Latency ◽  
Sleep State ◽  
Data Set ◽  
Sleep Diaries ◽  
The Difference ◽  
Mean Square Errors

Abstract Objectives To extend and validate a previously suggested model of the influence of uninterrupted sleep bouts on sleep onset misperception in a large independent data set. Methods Polysomnograms and sleep diaries of 139 insomnia patients and 92 controls were included. We modeled subjective sleep onset as the start of the first uninterrupted sleep fragment longer than Ls minutes, where parameter Ls reflects the minimum length of a sleep fragment required to be perceived as sleep. We compared the so-defined sleep onset latency (SOL) for various values of Ls. Model parameters were compared between groups, and across insomnia subgroups with respect to sleep onset misperception, medication use, age, and sex. Next, we extended the model to incorporate the length of wake fragments. Model performance was assessed by calculating root mean square errors (RMSEs) of the difference between estimated and perceived SOL. Results Participants with insomnia needed a median of 34 minutes of undisturbed sleep to perceive sleep onset, while healthy controls needed 22 minutes (Mann–Whitney U = 4426, p < 0.001). Similar statistically significant differences were found between sleep onset misperceivers and non-misperceivers (median 40 vs. 20 minutes, Mann–Whitney U = 984.5, p < 0.001). Model outcomes were similar across other subgroups. Extended models including wake bout lengths resulted in only marginal improvements of model outcome. Conclusions Patients with insomnia, particularly sleep misperceivers, need larger continuous sleep bouts to perceive sleep onset. The modeling approach yields a parameter for which we coin the term Sleep Fragment Perception Index, providing a useful measure to further characterize sleep state misperception.

Polysomnographic Characteristics of the Patients Having Chronic Insomnia and Obstructive Sleep Apnea: Evidence for Paradoxical Insomnia and Comorbid Insomnia with OSA (COMISA)

2021 ◽  
pp. 025371762110483
Author(s):  
Kaustav Kundu ◽  
Gaurav Sharma ◽  
Lokesh Saini ◽  
Ravi Gupta
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Sleep Onset ◽  
Chronic Insomnia ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Sleep State ◽  
Comorbid Insomnia ◽  
Obstructive Sleep ◽  
The Difference

Background: Sleep state misperception (SSM) is seen among patients with obstructive sleep apnea (OSA) as well as those having insomnia. Moreover, OSA and insomnia can also be comorbid. This study aims at finding the proportion of SSM and “Comorbid Insomnia with OSA” (COMISA) among patients of OSA and chronic insomnia. Macroachitecture of sleep was also compared across groups. Methods: This study utilized the retrospective laboratory and medical records of two groups of patients: chronic insomnia and OSA. Sleep disorders were diagnosed according to standard criteria. Daytime sleepiness was examined using the Epworth Sleepiness Scale. Diagnosis of SSM was based on the difference between subjective and objective sleep onset latency (Subjective SOL > 1.5 × Objective SOL). Results: Sixteen adult subjects were included in each group. Based on the difference between subjective and objective sleep onset latency, SSM was reported by 62.5% subjects of chronic insomnia and 56.25% subjects having OSA (OR = 1.29; 95% CI = 0.31–5.33; P = 0.79). The proportion of COMISA in subjects with chronic insomnia was 18% and among subjects with OSA, it was 43%. Effect size for the proportion was calculated as odds ratio (33.96; 95% CI = 7.48–154.01; P < 0.0002). Thus, the odds for COMISA were higher among subjects with OSA than those with chronic Insomnia. The three groups (OSA, COMISA and Chronic Insomnia) were comparable with regard to the macro-architecture of sleep. Conclusion: SSM is common among subjects with OSA and chronic insomnia. COMISA was commoner among patients with OSA compared to those with chronic insomnia. Macro-architecture of sleep is comparable among groups.

scholarly journals The Cycle of Daily Stress and Sleep: Sleep Measurement Matters

2020 ◽  
Author(s):  
Danica C Slavish ◽  
Justin Asbee ◽  
Kirti Veeramachaneni ◽  
Brett A Messman ◽  
Bella Scott ◽  
...  
Keyword(s):  
Sleep Disturbances ◽  
Multilevel Models ◽  
Single Channel ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Daily Stress ◽  
Sleep Diaries ◽  
Sleep Measurement

Abstract Background Disturbed sleep can be a cause and a consequence of elevated stress. Yet intensive longitudinal studies have revealed that sleep assessed via diaries and actigraphy is inconsistently associated with daily stress. Purpose We expanded this research by examining daily associations between sleep and stress using a threefold approach to assess sleep: sleep diaries, actigraphy, and ambulatory single-channel electroencephalography (EEG). Methods Participants were 80 adults (mean age = 32.65 years, 63% female) who completed 7 days of stressor and sleep assessments. Multilevel models were used to examine bidirectional associations between occurrence and severity of daily stress with diary-, actigraphy-, and EEG-determined sleep parameters (e.g., total sleep time [TST], sleep efficiency, and sleep onset latency, and wake after sleep onset [WASO]). Results Participants reported at least one stressor 37% of days. Days with a stressor were associated with a 14.4-min reduction in actigraphy-determined TST (β = −0.24, p = 0.030), but not with other actigraphy, diary, or EEG sleep measures. Nights with greater sleep diary-determined WASO were associated with greater next-day stressor severity (β = 0.01, p = 0.026); no other diary, actigraphy, or EEG sleep measures were associated with next-day stressor occurrence or severity. Conclusions Daily stress and sleep disturbances occurred in a bidirectional fashion, though specific results varied by sleep measurement technique and sleep parameter. Together, our results highlight that the type of sleep measurement matters for examining associations with daily stress. We urge future researchers to treat sleep diaries, actigraphy, and EEG as complementary—not redundant—sleep measurement approaches.

scholarly journals Distilling Deep Structural Facial Relationships for FAU Intensity Estimation

2021 ◽  
Author(s):  
Yingruo Fan ◽  
Jacqueline CK Lam ◽  
Victor On Kwok Li
Keyword(s):  
Structural Characteristics ◽  
Model Performance ◽  
Training Model ◽  
Model Parameters ◽  
Intensity Estimation ◽  
Structural Relationships ◽  
Proposed Model ◽  
Comparable Performance ◽  
Knowledge Distillation ◽  
The Difference

<div> <div> <div> <p>Facial emotions are expressed through a combination of facial muscle movements, namely, the Facial Action Units (FAUs). FAU intensity estimation aims to estimate the intensity of a set of structurally dependent FAUs. Contrary to the existing works that focus on improving FAU intensity estimation, this study investigates how knowledge distillation (KD) incorporated into a training model can improve FAU intensity estimation efficiency while achieving the same level of performance. Given the intrinsic structural characteristics of FAU, it is desirable to distill deep structural relationships, namely, DSR-FAU, using heatmap regression. Our methodology is as follows: First, a feature map-level distillation loss was applied to ensure that the student network and the teacher network share similar feature distributions. Second, the region-wise and channel-wise relationship distillation loss functions were introduced to penalize the difference in structural relationships. Specifically, the region-wise relationship can be represented by the structural correlations across the facial features, whereas the channel-wise relationship is represented by the implicit FAU co-occurrence dependencies. Third, we compared the model performance of DSR-FAU with the state-of-the-art models, based on two benchmarking datasets. Our proposed model achieves comparable performance with other baseline models, though requiring a lower number of model parameters and lower computation complexities. </p> </div> </div> </div>

scholarly journals Incorporating Robustness to Imaging Physics into Radiomic Feature Selection for Breast Cancer Risk Estimation

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5497
Author(s):  
Raymond J. Acciavatti ◽  
Eric A. Cohen ◽  
Omid Haji Maghsoudi ◽  
Aimilia Gastounioti ◽  
Lauren Pantalone ◽  
...  
Keyword(s):  
Risk Estimation ◽  
Characteristic Curve ◽  
Model Performance ◽  
Image Sharpness ◽  
Data Set ◽  
Cancer Occurrence ◽  
Measure Model ◽  
Left And Right ◽  
The Difference ◽  
One Year

Digital mammography has seen an explosion in the number of radiomic features used for risk-assessment modeling. However, having more features is not necessarily beneficial, as some features may be overly sensitive to imaging physics (contrast, noise, and image sharpness). To measure the effects of imaging physics, we analyzed the feature variation across imaging acquisition settings (kV, mAs) using an anthropomorphic phantom. We also analyzed the intra-woman variation (IWV), a measure of how much a feature varies between breasts with similar parenchymal patterns—a woman’s left and right breasts. From 341 features, we identified “robust” features that minimized the effects of imaging physics and IWV. We also investigated whether robust features offered better case-control classification in an independent data set of 575 images, all with an overall BI-RADS® assessment of 1 (negative) or 2 (benign); 115 images (cases) were of women who developed cancer at least one year after that screening image, matched to 460 controls. We modeled cancer occurrence via logistic regression, using cross‑validated area under the receiver-operating-characteristic curve (AUC) to measure model performance. Models using features from the most-robust quartile of features yielded an AUC = 0.59, versus 0.54 for the least-robust, with p < 0.005 for the difference among the quartiles.

scholarly journals 0524 Web-Delivered CBT for Insomnia Intervention Improves Sleep Among Adults with Insomnia and Depressive Symptoms

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A200-A201
Author(s):  
P J Batterham ◽  
H Christensen ◽  
F P Thorndike ◽  
L M Ritterband ◽  
R Gerwien ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Sleep Quality ◽  
Large Scale ◽  
Behavioral Therapy ◽  
Controlled Trial ◽  
Sleep Onset ◽  
Sleep Diary ◽  
Sleep Onset Latency ◽  
Sleep Diaries

Abstract Introduction Cognitive behavioral therapy for insomnia (CBT-I) is the first line recommended treatment for adults with chronic insomnia. In a prior randomized controlled trial (RCT), data showed web-delivered CBT-I (SHUTi) reduced insomnia severity as well as symptoms of depression, among adults with insomnia and elevated depressive symptoms. The present study aimed to further evaluate the effectiveness of web CBT-I to improve sleep outcomes as measured by prospectively entered sleep diaries in this same sample. Methods A large-scale RCT (N=1149) of Australian adults with insomnia and depressive symptoms compared a 9-week, web CBT-I therapeutic with an attention-matched web program at baseline, posttest and 6-, 12-, and 18-month follow-ups. Although depression outcomes have been presented previously, the online sleep-diary derived variables have not yet been presented, including sleep-onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), number of awakenings, sleep quality, and total sleep time (TST). Sleep diaries were entered online for 10 days at each assessment period. Results Data showed web CBT-I participants demonstrated greater reductions from baseline to posttest compared with control for the following sleep variables: SOL (LS mean difference [95% CI]=-22.3 min [-29.2, -15.3]; p&lt;.0001), WASO (-17.8 min [-23.4, -12.3]; p&lt;.0001), and number of awakenings (-0.38 [-0.68, -0.09]; p=.0113). Web CBT-I also showed greater improvements in SE (9.18% [7.25%, 11.10%]; p&lt;.0001) and sleep quality (0.41 [0.30, 0.53]; p&lt;.0001) from baseline to posttest compared with control. TST was not significantly different between groups at posttest or 6-month follow-up, although it improved over baseline at 12 (18.73 min [7.39, 30.07]; p=.0013) and 18 months (23.76 min [9.15, 38.36]; p=.0015) relative to control. All other significant sleep treatment effects were maintained in the treatment arm at 6, 12, and 18-month follow-up. Conclusion Data showed web CBT-I produced lasting improvements in sleep outcomes among adults with insomnia and elevated depressive symptoms. Support Clinical trial ACTRN12611000121965 was funded by the Australian National Health and Medical Research Council. The statistical analysis described here was funded by Pear Therapeutics, Inc and conducted by Provonix.

Case Series

2016 ◽  
Vol 21 (2) ◽  
pp. 168-179 ◽  
Author(s):  
Jayachandran Vetrayan ◽  
Suhana Othman ◽  
Smily Jesu Priya Victor Paulraj
Keyword(s):  
Positive Reinforcement ◽  
Intervention Program ◽  
Sleep Problems ◽  
Sleep Onset ◽  
Case Series ◽  
Objective Measure ◽  
Sleep Onset Latency ◽  
Behavioral Strategies ◽  
Sleep Diaries ◽  
Children’S Sleep

Objective: To assess the effectiveness and feasibility of behavioral sleep intervention for medicated children with ADHD. Method: Six medicated children (five boys, one girl; aged 6-12 years) with ADHD participated in a 4-week sleep intervention program. The main behavioral strategies used were Faded Bedtime With Response Cost (FBRC) and positive reinforcement. Within a case-series design, objective measure (Sleep Disturbance Scale for Children [SDSC]) and subjective measure (sleep diaries) were used to record changes in children’s sleep. Results: For all six children, significant decrease was found in the severity of children’s sleep problems (based on SDSC data). Bedtime resistance and mean sleep onset latency were reduced following the 4-week intervention program according to sleep diaries data. Gains were generally maintained at the follow-up. Parents perceived the intervention as being helpful. Conclusion: Based on the initial data, this intervention shows promise as an effective and feasible treatment.

scholarly journals Sleep disturbances in patients with Addison's disease

2003 ◽  
Vol 148 (4) ◽  
pp. 449-456 ◽  
Author(s):  
K Lovas ◽  
ES Husebye ◽  
F Holsten ◽  
B Bjorvatn
Keyword(s):  
Sleep Disturbances ◽  
Sleep Onset ◽  
Addison’S Disease ◽  
Early Morning ◽  
Addison's Disease ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Sleep Diaries ◽  
Falling Asleep ◽  
Pituitary Adrenal Axis

OBJECTIVE: The standard replacement therapy in Addison's disease does not restore normal nocturnal levels of the hormones of the hypothalamic-pituitary-adrenal axis. The aim of the study was to describe the prevalence and characteristics of sleep disturbances in patients with Addison's disease. METHODS: Sixty patients completed a self-administered sleep questionnaire and the Epworth Sleepiness Scale (ESS) questionnaire. Activity-based monitoring (actigraph recordings) and sleep diaries were obtained from eight patients. RESULTS: Thirty-four percent reported weekly sleep disturbances (difficulties falling asleep in 13%; repeated awakenings in 14%; early morning awakenings in 20%). The sleep need was 8.21 h (s.d. 1.34; range 6-14 h), and sleep onset latency was 29 min (s.d. 29, range 2-150 min). Forty percent of the patients were tired during daily activities more than once a week, but the scores of the ESS were 6.0 (s.d. 3.5), which is not higher than normal. The actigraph recordings showed higher sleep efficiency than the subjective recordings. CONCLUSION: We did not identify specific sleep disturbances which were characteristic for patients with Addison's disease. Patients with Addison's disease have increased daytime fatigue, but no more daytime sleepiness than normal.

Sleep Related Beliefs and their Association with Alcohol Relapse Following Residential Alcohol Detoxification Treatment

2013 ◽  
Vol 42 (5) ◽  
pp. 593-604 ◽  
Author(s):  
Neil Smith ◽  
Robert Hill ◽  
Jane Marshall ◽  
Francis Keaney ◽  
Shamil Wanigaratne
Keyword(s):  
Alcohol Dependence ◽  
Sleep Problems ◽  
Sleep Onset ◽  
Poor Sleep ◽  
Inpatient Setting ◽  
Sleep Onset Latency ◽  
Dysfunctional Beliefs ◽  
Sleep Diaries ◽  
Relapse Rates ◽  
Dysfunctional Beliefs About Sleep

Background: Alcohol dependence is known to impact upon sleep, and poor sleep has been shown to affect relapse rates following treatment for alcohol dependence. Aims: The aim of this study was to investigate the association between sleep problems and relapse in dependent drinkers in an inpatient setting. This was done by studying sleep related cognitions in individuals undergoing medically assisted alcohol withdrawal. Method: Sleep and sleep-related cognitions data were collected for 71 individuals undergoing detoxification treatment. Sleep was measured using sleep diaries and actigraph motion monitors. Participants completed sleep-related cognition questionnaires and were subject to telephone follow-up interviews. The results were then used to predict relapse rates 4 weeks after discharge. Results: Longer sleep onset latency recorded on the unit predicted relapse at 4 weeks. Higher dysfunctional beliefs about sleep were found to be associated with lower relapse rates. Conclusions: This study suggests that some dysfunctional beliefs about sleep may support recovery following discharge from treatment. The study further supports the need for tailored cognitive-behavioural treatments for sleep difficulties in this population to reduce relapse rates.

scholarly journals Which method is more accurate? or errors have error bars

2017 ◽  
Author(s):  
Jan H Jensen
Keyword(s):  
Root Mean Square ◽  
Mean Square ◽  
Data Set ◽  
Error Bars ◽  
The Difference ◽  
Mean Square Errors

This document is my attempt at distilling some of the information in two papers published by Anthony Nicholls (J. Comput. Aided Mol. Des. 2014, 28, 887; ibid 2016, 30, 103). Anthony also very kindly provided some new equations, not found in the papers, in response to my questions. The paper describes how one determines whether the difference in accuracy of two methods in predicting some properties for the same data set is statistically significant using root-mean-square errors, mean absolute errors, mean errors, and Pearsons r values.

scholarly journals Bi-directional relations between stress and self-reported and actigraphy-assessed sleep: a daily intensive longitudinal study

SLEEP ◽  
2019 ◽  
Vol 43 (3) ◽  
Author(s):  
Yang Yap ◽  
Danica C Slavish ◽  
Daniel J Taylor ◽  
Bei Bei ◽  
Joshua F Wiley
Keyword(s):  
Multilevel Models ◽  
Sleep Onset ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Sleep Complaints ◽  
Sleep Quantity ◽  
Sleep Diaries ◽  
Directional Relations ◽  
Moderating Role

Abstract Study Objectives Stress is associated with poor and short sleep, but the temporal order of these variables remains unclear. This study examined the temporal and bi-directional associations between stress and sleep and explored the moderating role of baseline sleep complaints, using daily, intensive longitudinal designs. Methods Participants were 326 young adults (Mage = 23.24 ± 5.46), providing &gt;2,500 nights of sleep altogether. Prospective total sleep time (TST), sleep onset latency (SOL), wake after sleep onset (WASO), and sleep efficiency (SE) were measured using actigraphy and sleep diaries. Perceived stress was reported three times daily between: 11:00–15:00, 15:30–19:30, and 20:00–02:00. Sleep complaints were measured at baseline using the PROMIS sleep disturbance scale. Within- and between-person sleep and stress variables were tested using cross-lagged multilevel models. Results Controlling for covariates and lagged outcomes, within-person effects showed that higher evening stress predicted shorter actigraphic and self-reported TST (both p &lt; .01). Conversely, shorter actigraphic and self-reported TST predicted higher next-day stress (both p &lt; .001). Longer self-reported SOL and WASO (both p &lt; .001), as well as lower actigraphic (p &lt; .01) and self-reported SE (p &lt; .001), predicted higher next-day stress. Between-person effects emerged only for self-reported TST predicting stress (p &lt; .01). No significant results were found for the moderating role of baseline sleep complaints. Conclusions Results demonstrated bi-directional relations between stress and sleep quantity, and a consistent direction of worse sleep quantity and continuity predicting higher next-day stress. Results highlighted within-individual daily variation as being more important than between-individual differences when examining sleep and daytime functioning associations.

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