bluuebunny/crossref_metadata_2025_split huggingface dataset:
349 Cognitive Behavioral Therapy for Insomnia in Patients with Chronic Pain - A Systematic Review and Meta-Analysis
Authors: Selvanathan Janannii , Pham Chi , Nagappa Mahesh , Peng Philip , Englesakis Marina , Espie Colin , Morin Charles , Chung Frances 
Published: May 2021

Abstract

Introduction
Patients with chronic non-cancer pain often report insomnia as a significant comorbidity. Cognitive behavioral therapy for insomnia (CBT-I) is recommended as the first line of treatment for insomnia, and several randomized controlled trials (RCTs) have examined the efficacy of CBT-I on various health outcomes in patients with comorbid insomnia and chronic non-cancer pain. We conducted a systematic review and meta-analysis on the effectiveness of CBT-I on sleep, pain, depression, anxiety and fatigue in adults with comorbid insomnia and chronic non-cancer pain.

Methods
A systematic search was conducted using ten electronic databases. The duration of the search was set between database inception to April 2020. Included studies must be RCTs assessing the effects of CBT-I on at least patient-reported sleep outcomes in adults with chronic non-cancer pain. Quality of the studies was assessed using the Cochrane risk of bias assessment and Yates quality rating scale. Continuous data were extracted and summarized using standard mean difference (SMD) with 95% confidence intervals (CIs).

Results
The literature search resulted in 7,772 articles, of which 14 RCTs met the inclusion criteria. Twelve of these articles were included in the meta-analysis. The meta-analysis comprised 762 participants. CBT-I demonstrated a large significant effect on patient-reported sleep (SMD = 0.87, 95% CI [0.55–1.20], p &lt; 0.00001) at post-treatment and final follow-up (up to 9 months) (0.59 [0.31–0.86], p &lt; 0.0001); and moderate effects on pain (SMD = 0.20 [0.06, 0.34], p = 0.006) and depression (0.44 [0.09–0.79], p= 0.01) at post-treatment. The probability of improving sleep and pain following CBT-I at post-treatment was 81% and 58%, respectively. The probability of improving sleep and pain at final follow-up was 73% and 57%, respectively. There were no statistically significant effects on anxiety and fatigue.

Conclusion
This systematic review and meta-analysis showed that CBT-I is effective for improving sleep in adults with comorbid insomnia and chronic non-cancer pain. Further, CBT-I may lead to short-term moderate improvements in pain and depression. However, there is a need for further RCTs with adequate power, longer follow-up periods, CBT for both insomnia and pain, and consistent scoring systems for assessing patient outcomes.
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350 Opioid Use and Abuse are Associated with Use of Sedative Hypnotic Medications
Authors: Tubbs Andrew , Naps Michelle , Grandner Michael , Rivera Louis 
Published: May 2021

Abstract

Introduction
The Department of Health and Human Services recently reported that 10.3 million people misused opioid drugs in 2018. Recent research attributed 21% of the deaths from opioid overdose to benzodiazepines. The overdose data and clinical experience show that opioid misusers commonly complain of insomnia and use hypnotic medications to self-medicate their sleep disturbance. At the same time, it remains unclear from a scientific perspective whether those who use/abuse opioids are more likely to use drugs in the sedative-hypnotic medication category. Consequently, the present study explores the relationship between comorbid use of opioids and sedative-hypnotic medications.

Methods
We extracted data from the 2015–2018 waves of the National Survey on Drug Use and Health (N=171,766). The primary outcome was the use of sedative-hypnotic medications, either in the z-class (zaleplon, zolpidem, eszopiclone) or sedating benzodiazepines (temazepam, flurazepam, triazolam). The primary exposures were prescription use of an opioid or abuse of an opioid (i.e., use of an illegal opioid such as heroin or misuse of a prescription opioid). Covariates included age, sex, race, income, education, and predicted mental illness category (none, mild, moderate, severe). Exposures were balanced on covariates using inverse probability of treatment weighting. Sequential binomial logistic regression estimated the association between opioid use/abuse and sedative-hypnotic use after adjusting for covariates.

Results
Opioid use and abuse varied by age, sex, race, education, and income (all p &lt; 0.001). When adjusted for age, sex, and race (Model 1), sedative benzodiazepine use was more common among opioid users (OR 4.4 [4.04–4.79] and opioid abusers (OR 11.9 [9.72–14.5]). The use of z-class drugs was also more prevalent in opioid users (OR 3.69 [3.48–3.89]) and abusers (OR 7.74 [6.97–8.60]). Further adjusting for income and education (Model 2) and mental illness category (Model 3) attenuated but did not eliminate these associations.

Conclusion
Individuals who use or abuse opioids are significantly more likely to receive a sedative-hypnotic medication, a finding that is of concern and one that also suggests that sleep disturbance is common in this population. Further research is needed to determine the underlying nature and prevalence of sleep continuity disturbances in this population.

Support (if any)
VA grant IK2CX000855 and I01 CX001957 (S.C.).
370 Depressive symptoms in the context of Cognitive-Behavioral Therapy for Insomnia, a long-term follow-up study
Authors: Sforza Marco , Salibba Andrea , Scarpellino Alessandro , Galbiati Andrea , Zucconi Marco , Casoni Francesca , Ferini-Strambi Luigi , Castronovo Vincenza 
Published: May 2021

Abstract

Introduction
Cognitive-Behavioral Therapy for Insomnia (CBT-I) is considered the first-choice treatment for Insomnia Disease (ID). The bi-directional causal relationship between insomnia and depression is recognized. Aim of our study is to investigate the role of depressive symptoms in predicting CBT-I outcomes, and the effectiveness of the treatment both on insomnia and depression.

Methods
77 ID patients (mean age 38.2±10.4 years, 69.2% females) underwent 7-sessions group CBT-I and were assessed pre- (T0) post- (T1) and at long-term after CBT-I (T2=7.6±1.6 years after treatment). The primary outcomes are Insomnia Severity Index (ISI) and Sleep Diary parameters. The secondary outcome is Beck Depression Inventory-II (BDI). Patients were divided in two groups according to BDI baseline score (≥14): depressive (D) vs non-depressive (ND).

Results
All patients showed significant improvements at ISI score at T1 that were maintained at T2 (T0=16.2±4.8 vs T1=8.2±4.5 vs T2=10.0±6.1;p&lt;0.001). Also Sleep Diary parameters (sleep latency, wake after sleep onset and sleep efficiency) showed significant improvement at T1 (p&lt;0.001). Moreover all patients showed improvements of depressive symptoms at T1 that were maintained at T2 (T0=10.8±6.8 vs T1=6.2±5.5 vs T2=8.2±6.6; p&lt;0.001). Indeed, if 29.3% if ID patients at T0 presented clinically significant depressive symptoms (BDI≥14), only 9.7% at T1 and 20.5% at T2. Nevertheless, we found an interaction between ISI along time (T0-T1-T2) and D vs ND group membership (ISI_TREAT*BDI_BL_GROUP Sig=p&lt;0.05). In other words, group D patients at baseline showed a worsening of insomnia symptoms at the long-term evaluation (T2).

Conclusion
CBT-I showed improvements both in insomnia and in depressive symptoms at the end of treatment that are maintained at long-term (7.6yrs after treatment). Nevertheless, clinically significant depressive symptoms at the baseline predicted a worsening of insomnia at the long-term evaluation. This could suggest the need of a more frequent follow-up evaluation of CBT-I efficacy in those patients presenting depressive symptoms at the baseline.

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None
382 Measuring Daytime Sleepiness in Insomnia Disorder
Authors: Manber Rachel , Simpson Norah , Dietch Jessica , Tutek Joshua , Tully Isabelle 
Published: May 2021

Abstract

Introduction
The EPWORTH Sleepiness Scale (ESS) is correlated with clinical parameters among patients with obstructive sleep apnea. However, its clinical relevance among patients with insomnia disorder is not clear. These patients often do not report daytime sleepiness nor have abnormal MSLT scores, but many do experience sleepiness in the evening. As a result, the ESS may not be the most appropriate measure for assessing daytime sleepiness among insomnia patients. This study aimed to evaluate clinical correlates of ESS among patients with insomnia. It also examined correlates of two items from the PROMIS item bank that specifically probe daytime sleepiness.

Methods
We used baseline data from 141 participants with insomnia disorder (mean age = 63.1y [SD 8.2]; 72% female) in the RESTING study (RCT of the Effectiveness of Stepped-Care Sleep Therapy In General Practice). Clinical measures: the Insomnia Severity Index (ISI), the Geriatric Depression Scale (GDS), and the Morningness Eveningness Questionnaire (MEQ). ESS&gt;10 defined excessive sleepiness. Two PROMIS items assessed frequency of being sleepy and of having trouble staying awake during the day, with ratings of ‘quite a bit’ and ‘very much’ classified as excessively sleepy.

Results
The ESS did not correlate with any of the clinical variables (ISI, GDS, MEQ). Correlations with these three variables with the PROMIS Sleepiness were .36, .22, and -.24 and with the PROMIS Excessive Sleepiness .28, .31, and -.17. Of the 38 participants with ESS&gt;10, 80% were not excessively sleepy per PROMIS. PROMIS Excessive Sleepiness classified only 9 participants as excessively sleepy, of which 7 (78%) were not classified by the ESS as excessively sleepy.

Conclusion
These results suggest that the ESS might not be a clinically relevant measure among people with insomnia disorder and that the PROMIS items may better capture sleepiness and excessive sleepiness during the daytime in this patient group. The divergence of the two measures might relate to whether instructions specify daytime as the period assessed, since people with insomnia may be more likely to experience evening, rather than daytime, sleepiness. It might also relate to differences in item wording (i.e., struggling to stay awake [PROMIS] versus likelihood of falling asleep [ESS]).

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1R01AG057500
396 Ethnicity modifies the association between central sleep apnea and atrial fibrillation in older men: Kuakini HAAS and Mr.OS
Authors: Anzai Tagayasu , Grandinetti Andrew , Katz Alan , Hurwitz Eric , Wu Yan Yan , Masaki Kamal 
Published: May 2021

Abstract

Introduction
Several studies indicated there is an association between central sleep apnea (CSA) and atrial fibrillation (AF) in older populations. However, few studies assessed the impact of ethnicity on the association. We assessed the hypothesis that ethnicity modifies the association between CSA and AF in older men.

Methods
We did a cross-sectional analysis using two population studies of Japanese-American (JA) and White-American (WA) men. The Kuakini Honolulu-Asia Aging Study (HAAS) is a longitudinal cohort study of JA men living in Hawaii. Sleep data were collected between 1999–2000. The Osteoporotic Fractures in Men (Mr.OS) Sleep Study was conducted between 2003–2005 on the continental U.S. The majority of Mr.OS participants were WA. We selected 79–90 year old males, who had overnight polysomnography from both studies. Total participants were 690 JA and 871 WA men. Obstructive apnea-hypopnea index (OAHI) was the measure of the number of obstructive apneas and hypopneas with &gt;4% oxygen desaturation. Additionally, the central apnea index (CAI) was the measure of the number of central apneas. Obstructive sleep apnea (OSA) was categorized as none (OAHI &lt;5), mild (OAHI 5–14), moderate (OAHI 15–29), and severe (OAHI&gt;=30). CSA was defined by CAI&gt;=5. Cheyne-Stokes breathing (CSB) was defined as a minimum consecutive 5–10 minute period of a crescendo-decrescendo respiratory pattern associated with CSA. A board-certified physician confirmed AF by single lead electrocardiography of polysomnography.

Results
The prevalence of AF was 5.7% in JA and 9.1% in WA. The prevalence of CSA and CSB in WA were higher than in JA (11.5% vs 6.5% and 5.7% vs 3.3%, respectively). Conversely, the prevalence of severe OSA in JA (20.7%) was higher than in WA (11.8%). In multivariable-adjusted logistic regression models, CSA was associated with higher odds of AF, and the association was stronger in JA [Odds Ratio (OR)=4.77, 95% confidence interval (CI): 1.95–11.64] than in WA (OR=2.05, 95% CI: 1.07–3.94). CSB showed similar trends as CSA. In contrast, the severity of OSA was not significantly associated with AF in either ethnicity.

Conclusion
Ethnicity modifies the association between CSA and AF. In older JA and WA men, screening for CSA might be important to prevent AF.
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403 Comparative Study of Wireless Sensors Versus Type III Home Sleep Apnea Test for Home-based Diagnosis of Obstructive Sleep Apnea
Authors: Lee Jong Yoon , Kim Donghyun , Blake Stefanida , Kalluri Lakshmi , Walter Jessica , Davies Charles , Zee Phyllis , Xu Shuai , Power Thomas 
Published: May 2021

Abstract

Introduction
More than 22 million Americans are estimated to have obstructive sleep apnea (OSA), though this disease remains perpetually underdiagnosed. Undiagnosed OSA contributes to poor clinical outcomes, large healthcare costs and an economic burden in excess of $150 billion dollars annually. While polysomnography (PSG) is considered the gold standard to diagnose OSA home sleep apnea testing (HSAT) is now used for most patient cohorts. Wireless sensors may offer a lower cost and less burdensome approach to home testing than traditional HSAT.

Methods
We performed a fully remote, national, single-arm, open-label, prospective clinical study to evaluate the performance of a wireless, two sensor experimental system (ANNETM One, Sibel Health) against a Type III HST system. A total of 154 individuals completed screening with 62 screening in as high risk for OSA using the STOP-BANG questionnaire. Ultimately 60 participants were enrolled, and 46 completed a successful home testing night wearing both the commercially available HSAT (Philips Alice NightOne Home Sleep, Koninklijke Philips N.V) and the wireless experimental system. A board-certified sleep medicine physician determined the apnea-hypopnea index (AHI) for the HSAT defined by American Academy of Sleep Medicine v2.6 guidelines. Two study investigators, blinded to the HSAT results, scored the experimental system to determine AHI based on similar guidelines. An independent study investigator conducted the final analysis of comparative performance. Participants completed a psychometric survey of their preferences, experience, and usability of the two testing systems.

Results
We demonstrated a high level of agreement between the HSAT and experimental system for AHI (r2=0.81, p&lt;0.0001). The sensitivity and specificity of the experimental system to diagnose moderate and severe OSA (AHI&gt;15) was 85% and 95%, respectively. The experimental system had a significantly higher Systems Usability Scale score compared to HSAT (61 vs 48, p&lt;0.0001) and more than 85% of participants preferred the experimental system.

Conclusion
This study provides compelling evidence that the experimental system was highly acceptable and comparable to a currently used HSAT. Continued innovation in reliable, cost-effective, low profile technologies will be critical to address the unmet needs of sleep diagnostic testing.

Support (if any)
Anthem and Sibel Inc.
412 Prevalence of pulmonary hypertension in patients referred for sleep apnea diagnostics
Authors: Elfimova Eugeniya , Mikhailova Oxana , Danilov Nikolai , Litvin Alexander , Pevzner Alexander , Chazova Irina 
Published: May 2021

Abstract

Introduction
The aim to evaluate the prevalence of pulmonary hypertension according to echocardiography in patients referred for sleep apnea diagnostics.

Methods
We included 145 patients referred to Sleep laboratory for sleep apnea diagnostics. Mean age 63,8 ± 10,4 years, BMI 34,0 ± 5,7 kg/m2, AHI 31,3 ± 20,3/h, ODI 3% 28,2 ± 19,5/h, min SpO2 77,4 ± 9,8%, systolic pulmonary artery pressure (systolic PAP) 25,9 ± 16,4 mmHg. All patients underwent cardiorespiratory and respiratory diagnostics for sleep apnea and echocardiography.

Results
From the random sample of patients referred to Sleep laboratory 14,5% (21) had systolic PAP &gt; 40 mmHg (by echocardiography). Patients with higher levels of systolic PAP (Systolic PAP, mmHg 49,9 [43,6; 56,2] vs 20,7 [19,9; 23,5],p=0.000) had more severe OSA (AHI 35,7 [27,1; 44,3] vs 26,6 [22,6; 30,6], p = 0.029, ODI 3%, /h 35,8 [25,1; 46,4] vs 23,8 [19,8; 27,8], p= 0.017) and were more obese (BMI 37,1 [33,8; 40,4] vs 33,4 [32,4; 34,5], p=0.024). Prevalence of AHI &gt; 30 /h was 62% in group with systolic PAP &gt; 40 mmHg vs 23% in the group with systolic PAP &lt; 40 mmHg. We observed differences in echocardiography, in group with systolic PAP &gt; 40 mmHg: left atrium (4.6 ± 0,5 vs 4,2 ± 0,4 cm, p=0.012), left atrium volume (94.0 ± 23.6 vs 71.7 ± 16.5 ml, p=0.001) and right atrium area (24.5 ± 4.9 vs 18.4 ± 3.8cm2, p=0.000) were higher. Though ejection fraction (58.2 ± 3.8 vs 59.0 ± 3.8%, p=0.268), interventricular septum thickness (1,13 ± 0,2 vs 1,06 ± 0,3 cm, p=0,654) and left ventricular posterior wall thickness (1,05 ± 0,08 vs 1,00 ± 0,13 cm, p=0,117) didn’t differ. In terms of excessive daytime sleepiness, snoring and nocturia groups didn’t differ, as well as for the prevalence of arterial hypertension, coronary artery disease, chronic heart failure, diabetes mellitus and chronic obstructive pulmonary disease.

Conclusion
Pulmonary hypertension is frequently observed in patients with OSA and appears to be related to the severity of sleep apnea and obesity. PH should be considered in the regular clinical assessment of all patients with sleep apnea, especially with severe form.
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414 Deep Neural Networks: A Survey Tool for Obstructive Sleep Apnea Prediction
Authors: Taweesedt Pahnwat , Kim JungYoon , Park Jaehyun , Park Jangwoon , Sharma Munish , Surani Salim 
Published: May 2021

Abstract

Introduction
Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires.

Methods
Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models.

Results
Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77.

Conclusion
Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models.
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441 Influence Of Chronotype On CPAP Adherence
Authors: Adekolu Olurotimi , Xu Zhichao , Chu Jen-hwa , Kushida Clete , Yaggi Henry , Knauert Melissa , Zinchuk Andrey 
Published: May 2021

Abstract

Introduction
Continuous positive airway pressure therapy (CPAP) is an efficacious treatment for obstructive sleep apnea (OSA). However, despite interventions targeting established determinants of CPAP use, adherence to CPAP remains poor. An Individual’s chronotype can influence behavior including adherence to dietary changes and alcohol abstinence. We hypothesized that chronotype will be associated with CPAP adherence and aimed to identify the mechanisms of this association.

Methods
Secondary data analysis of the active CPAP arm (n=469) from the Apnea Positive Pressure Long-term Efficacy Study, a multicenter randomized controlled trial of CPAP therapy in a sleep clinic population was performed. The Morningness-Eveningness Questionnaire (MEQ) was used to identify three chronotype categories: Evening (MEQ score: 16–41), Neither (42–58) and Morning (59–86) types. CPAP adherence over 6-months (hours/night) was objectively measured using smart-card downloads. Linear mixed modelling evaluated the association between the chronotype and CPAP adherence, adjusting for confounders (e.g., age). Mechanisms of this association were examined by change in beta estimates for the chronotype with addition of a potential mediator (e.g., sleep duration).

Results
There were 206(44%), 38(8%) and 219(47%) Morning, Evening and Neither chronotype patients respectively. Evening types were youngest (48.0±13.4 vs. 50.3±11.3 and 56.3±11.4 years, p&lt;0.001) with highest body mass index (BMI, 34.9±10.5 vs. 31.2±10.5 and 32.7±10.5 kg/m2, p=0.006) and longest sleep on weekends (7.9±2.0 vs. 7.3±1.3 and 7.6±1.5 hours, p=0.017) compared to Morning and Neither types. A higher proportion of Evening types also reported symptoms of insomnia and fatigue (p-values 0.017 and 0.048 respectively). CPAP adherence, however, did not differ between Evening and Neither types (p=0.276). Compared to the Neither types, Morning types exhibited significantly higher CPAP adherence (β= 40mins/night, p=0.001) with a slight decrease after adjusting for age, sex, race, marital status, education, and OSA severity (β= 33mins/night, p=0.012). Sleep duration, insomnia, BMI and fatigue mediated a minimal proportion of this effect (0.7–3.7%).

Conclusion
In this cohort of sleep clinic patients, the Morning chronotype was associated with better CPAP adherence. Only a small proportion of this association was mediated by observed clinical differences between the chronotypes. Understanding the influence of chronotype on CPAP use may provide novel insight for improving OSA therapy effectiveness.
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