ISI-3: evaluation of a brief screening tool for insomnia

Insomnia symptoms and neurofunctional correlates among adults receiving buprenorphine for opioid use disorder

OBJECTIVES

Insomnia symptoms are negatively related to opioid use disorder (OUD) treatment outcomes, possibly reflecting the influence of sleep on neurofunctional domains implicated in addiction. Moreover, the intersection between OUD recovery and sleep represents an area well-suited for the development of novel, personalized treatment strategies. This study assessed the prevalence of clinically significant insomnia symptoms and characterized its neurofunctional correlates among a clinical sample of adults with OUD receiving buprenorphine.

METHODS

Adults (N = 129) receiving buprenorphine for OUD from an outpatient clinic participated in a cross-sectional survey. Participants completed an abbreviated version of NIDA's Phenotyping Assessment Battery, which assessed 6 neurofunctional domains: sleep, negative emotionality, metacognition, interoception, cognition, and reward. Bivariate descriptive statistics compared those with evidence of clinically significant insomnia symptoms (Insomnia Severity Index [ISI] score of ≥11) to those with minimal evidence of clinically significant insomnia symptoms (ISI score of ≤10) across each of the neurofunctional domains.

RESULTS

Roughly 60% of participants reported clinically significant insomnia symptoms (ISI score of ≥11). Experiencing clinically significant insomnia symptoms was associated with reporting greater levels of depression, anxiety, post-traumatic stress, stress intolerance, unhelpful metacognition, and interoceptive awareness (ps<0.05). Participants with evidence of clinically significant insomnia were more likely to report that poor sleep was interfering with their OUD treatment and that improved sleep would assist with their treatment (ps<0.05).

CONCLUSIONS

Insomnia was prevalent among adults receiving buprenorphine for OUD. Insomnia was associated with neurofunctional performance, which may impact OUD treatment trajectories. Our findings indicate potential targets in the development of personalized treatment plans for patients with co-morbid insomnia and OUD. To inform the development of novel treatment strategies, more research is needed to understand the potential mechanistic links between sleep disturbances and substance use.

Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach

BACKGROUND

The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clinical settings. In this study, we aimed to develop a data-driven shortened version of the ISI that accurately predicts the severity level of insomnia disorder.

METHODS

We collected a sample of 800 responses from the EMBRAIN survey system. Based on the responses, seven items were grouped based on the similarity of their response using exploratory factor analysis (EFA). The most representative item within each group was selected by using eXtreme Gradient Boosting (XGBoost).

RESULTS

Based on the selected three key items, maintenance of sleep, interference with daily function, and concerns about sleep problems, we developed a data-driven shortened questionnaire of ISI, ISI-3 m (machine learning). ISI-3 m achieved the highest coefficient of determination (R 2 = 0.910 ) for the ISI score prediction task and the accuracy of 0.965, precision of 0.841, and recall of 0.838 for the multiclass-classification task, outperforming four previous versions of the shortened ISI.

CONCLUSION

As ISI-3 m is a highly accurate shortened version of the ISI, it allows clinicians to efficiently screen for insomnia and observe variations in the condition throughout the treatment process. Furthermore, the framework based on the combination of EFA and XGBoost developed in this study can be utilized to develop data-driven shortened versions of the other questionnaires.

Insomnia among coronavirus disease 2019 survivors: A single-center cross-sectional study

Since the coronavirus disease 2019 (COVID-19) epidemic, insomnia has become one of the longer COVID-19 symptoms. This study aimed to investigate insomnia among COVID-19 survivors and explore the occurrence and influencing factors of insomnia. A cross-sectional study was performed from December 2022 to February 2023 through an online questionnaire star survey with 8 questions. The insomnia severity index scale (ISI) was used to assess the severity of insomnia. Univariate analysis was used to analyze the factors related to COVID-19 infection. A total of 564 participants (183 males and 381 females) were surveyed in the present study. The prevalence of insomnia was 63.12%. Among these insomnia patients, there were 202 (35.82%) with sub-threshold symptoms, 116 (20.57%) with moderate symptoms, and 38 (6.74%) with severe symptoms. Univariate analysis indicated that there were statistically significant differences in the prevalence of insomnia among COVID-19 survivors of different ages, occupations, and educational levels (P < .05). Of the 356 insomnia patients, 185 (51.97%) did not take any measures against insomnia, while those who took drugs only, physical exercise only, drugs and physical exercise, and other measures were 90 (25.28%), 42 (11.80%), 17 (4.78%), and 22 (6.18%), respectively. Additionally, of the 107 insomnia patients with drug therapy, 17 (15.89%) took estazolam, 16 (14.95%) took alprazolam, 39 (36.45%) took zopiclone, and 35 (32.71%) took other drugs to improve insomnia symptoms. The prevalence of insomnia symptoms remains high among COVID-19 survivors in China. Education level and occupation may be the influencing factors. Unfortunately, most patients with insomnia do not take corresponding treatment measures.

Content analysis of insomnia questionnaires: A step to better evaluate the complex and multifaceted construct of insomnia disorder

Insomnia disorder is a mental disorder that includes various types of symptoms (e.g., insomnia initiating, worries, mood disturbances) and impairments (e.g., distress related to sleep alterations). Self-report questionnaires are the most common method for assessing insomnia but no systematic quantified analysis of their content and overlap has been carried out. We used content analysis and a visualization method to better identify the different types of clinical manifestations that are investigated by nine commonly used insomnia questionnaires for adults and the Jaccard index to quantify the degree to which they overlap. Content analysis found and visualized 16 different clinical manifestations classified into five dimensions ("Insomnia symptoms", "Insomnia-related symptoms", "Daytime symptoms", "Insomnia-related impairments", "Sleep behaviors"). The average Jaccard Index was 0.409 (moderate overlap in content). There is a lack of distinction between symptoms and impairments, and the assessment of sleep duration and hyperarousal symptoms remains overlooked. This preliminary analysis makes it possible to visualize the content of each of the nine questionnaires and to select the most appropriate questionnaire based on the issue to be addressed. Suggestions are made regarding the development of future questionnaires to better distinguish symptoms and impairments, and the different phenotypes of insomnia disorder.

What complex factors influence sleep quality in college students? PLS-SEM vs. fsQCA

Introduction

Sleep quality has a significant impact on the health-related quality of life, particularly among college students. This study proposes a framework for identifying factors that influence college students' sleep quality, including stress, self-control, bedtime habits, and neighborhood environment.

Methods

The study employed a cross-sectional analytical approach on a convenience sample of 255 medical students from a private university in China during the 2021/2022 academic year, of which 80.39% (205) were women. Two complementary methodologies, partial least squares-structural equation modeling (PLS-SEM), and fuzzy sets qualitative comparative analysis (fsQCA), were utilized in the study.

Results

The results of the PLS-SEM analysis suggest that Stress and Self-control act as mediating variables in the model, with Bedtime habits and Neighborhood environment influencing sleep quality through these variables. Additionally, the fsQCA analysis reveals that Bedtime habits and Neighborhood environment can combine with Stress and Self-control, respectively, to influence sleep quality.

Discussion

These findings provide insight into how multiple factors, such as Stress, Self-control, Bedtime habits, and Neighborhood environment, can impact college students' sleep quality, and can be used to develop intervention programs aimed at improving it. Moreover, the use of both methodologies enables the expansion of new methodological approaches that can be applied to different contexts.

A Novel Screening, Brief Intervention, and Referral to Treatment (SBIRT) Based Model for Mental Health in Occupational Health Implemented on Smartphone and Web-Based Platforms: Development Study With Results From an Epidemiologic Survey

BACKGROUND

While the importance of mental health is well-recognized in the field of occupational health, implementation of effective strategies in the workplace has been limited by gaps in infrastructure, program comprehensiveness, coverage, and adherence. The authors developed a Screening, Brief Intervention, and Referral to Treatment (SBIRT) model based occupational mental health intervention, and implemented in a web-based format with a smartphone application.

METHODS

The SBIRT-based intervention was developed by a multidisciplinary team, including occupational health physicians, nurses, psychiatrists, and software developers. The following mental health areas were included, based on outcomes of an epidemiological survey conducted: insomnia, depression, anxiety, problematic alcohol use, and suicidal risk. The viability of the two-step evaluation process utilizing a combination of the brief version and the full-length version of the questionnaire was examined using responses from the survey. The intervention was adjusted according to the survey results and expert opinions.

RESULTS

The epidemiological survey included 346 employees who completed the long-form version of mental health scales. These data were the used to confirm the diagnostic value of using a combination of short-form and long-form version of the scales for screening in the SBIRT model. The model uses a smartphone application for screening, provision of psychoeducation, and for surveillance. The universal methods of the model ensure it can be implemented by all occupational managers, regardless of their specialization in mental health. In addition to the two-step screening procedure to identify employees at-risk for mental health problems, the model includes a stepped care approach, based on risk stratification, to promote mental health education, management, and follow-up for continuous care.

CONCLUSION

The SBIRT model-based intervention provides an easy-to-implement approach for the management of mental health in the workplace. Further studies are required to examine the effectiveness and feasibility of the model.

The simplification of the insomnia severity index and epworth sleepiness scale using machine learning models

Insomnia and excessive daytime sleepiness (EDS) are the most common complaints in sleep clinics, and the cost of healthcare services associated with them have also increased significantly. Though the brief questionnaires such as the Insomnia Severity Index (ISI) and Epworth Sleepiness Scale (ESS) can be useful to assess insomnia and EDS, there are some limitations to apply for large numbers of patients. As the researches using the Internet of Things technology become more common, the need for the simplification of sleep questionnaires has been also growing. We aimed to simplify ISI and ESS using machine learning algorithms and deep neural networks with attention models. The medical records of 1,241 patients who examined polysomnography for insomnia or EDS were analyzed. All patients are classified into five groups according to the severity of insomnia and EDS. To develop the model, six machine learning algorithms were firstly applied. After going through normalization, the process with the CNN+ Attention model was applied. We classified a group with an accuracy of 93% even with only the results of 6 items (ISI1a, ISI1b, ISI3, ISI5, ESS4, ESS7). We simplified the sleep questionnaires with maintaining high accuracy by using machine learning models.

Chaihu Longgu Muli Decoction for post-stroke insomnia: A protocol for systematic review and meta-analysis

BACKGROUND

Poststroke insomnia (PSI) is a frequent complication of stroke usually as a comorbidity of poststroke depression and mainly occurs within the first 6 months after stroke.[1] Addressing PSI to improve stroke prognosis is of great value. Herbal medicine like Chaihu Longgu Muli Decoction (CLMD), which is commonly considered to be a good treatment for depression and epilepsy, has the therapeutic potential on PSI; however, insufficient systematic reviews were conducted to testify its efficacy. Therefore, the objective of this paper is to provide reliable evidence of the efficacy and safety of CLMD on PSI and a foundation for further investigation.

METHODS

The literature of clinical randomized controlled trials (RCTs) regarding CLMD for PSI published before June of 2021 will be retrieved in the databases, and 2 investigators will be asked to collect and crosscheck the data independently. For the including studies, the quality evaluation on methodology will be assessed in the light of the Cochrane Handbook for Systematic Review of Interventions V.5.1.0 as well as the quality of evidence will be evaluated by the Grading of Recommendations Assessment, Development, and Evaluation. Besides, the assessment of heterogeneity and reporting bias, the sensitivity analysis and the subgroup analysis will be conducted. Stata 15 will be applied to analyze the above data.

RESULTS

The review will conduct a high-quality synthesis on present evidence of CLMD for PSI.

CONCLUSION

The conclusion of the study will indicate whether CLMD is effective and safe for PSI.

Validation of the Three-Item Insomnia Severity Index Short Form in Young Adult Cancer Survivors: Comparison with a Structured Diagnostic Interview

Chronic insomnia affects ∼25% of young adult cancer survivors (YACS) but is often overlooked in routine follow-up. A recently introduced three-item version of the Insomnia Severity Index (ISI-3) was compared with a diagnostic interview (SCID-5) in 250 YACS (ages 18-40) to evaluate its validity in this population. The ISI-3 had good discrimination compared with the SCID-5 (area under the receiver operating characteristic curve = 0.88). Although no ISI-3 cutoff met study criteria for both sensitivity (≥0.85) and specificity (≥0.75), an ISI-3 cutoff of ≥4 had high sensitivity (94%) and moderate specificity (70%), and is recommended as the first step in a two-step screening procedure.

Prevalence, Impact, and Trajectories of Sleep Disturbance in Cardiac Rehabilitation: A NARRATIVE REVIEW AND SUGGESTIONS FOR EVALUATION AND TREATMENT

OBJECTIVE

The aim of this review was to summarize literature examining the prevalence, impact, and trajectories of sleep disturbance in cardiac rehabilitation (CR) patients and discuss how CR programs may incorporate targeted evaluation and interventions to promote sleep health.

REVIEW METHODS

A narrative review of literature allowed for an examination of the prevalence of sleep disturbance in CR patients, the effects of sleep disturbance on CR outcomes, and trajectories of sleep disturbance in CR.

SUMMARY

Sleep disturbance is prevalent in CR patient populations and is related to clinical and functional outcomes. Sleep may be an important biobehavioral process to target in CR to improve important patient outcomes and achieve secondary prevention goals.

Pandemic and student mental health: mental health symptoms among university students and young adults after the first cycle of lockdown in the UK

BACKGROUND

Early COVID-19 research suggests a detrimental impact of the initial lockdown on young people's mental health.

AIMS

We investigated mental health among university students and young adults after the first UK lockdown and changes in symptoms over 6 months.

METHOD

In total, 895 university students and 547 young adults not in higher education completed an online survey at T1 (July-September 2020). A subset of 201 university students also completed a 6 month follow-up survey at T2 (January-March 2021). Anxiety, depression, insomnia, substance misuse and suicide risk were assessed.

RESULTS

At T1, approximately 40%, 25% and 33% of the participants reported moderate to severe anxiety and depression and substance misuse risk, clinically significant insomnia and suicidal risk. In participants reassessed at T2, reductions were observed in anxiety and depression but not in insomnia, substance misuse or suicidality. Student and non-student participants reported similar levels of mental health symptoms. Student status was not a significant marker of mental health symptoms, except for lower substance misuse risk.Cross-sectionally, greater symptoms across measures were consistently associated with younger age, pre-existing mental health conditions, being a carer, worse financial status, increased sleep irregularity and difficulty since lockdown. Longitudinally, T2 symptoms were consistently associated with worse financial status and increased difficulty sleeping at T1. However, these associations were attenuated when baseline mental health symptoms were adjusted for in the models.

CONCLUSIONS

Mental health symptoms were prevalent in a large proportion of young people after the first UK lockdown. Risk factors identified may help characterise high-risk groups for enhanced support and inform interventions.

Sleep dissatisfaction is a potential marker for nomophobia in adults

INTRODUCTION

NOMOPHOBIA is a term used to describe an anxiety disorder in which people fear being disconnected from their mobile phones. Strong associations between nomophobia and insomnia have previously been documented. However, there is no clear explanation for this relationship between the two disorders. The present study was designed to first determine the diagnostic precision of the Insomnia Severity Index (ISI) various components in detecting or classifying nomophobia; and second, examine the diagnostic performance of the identified ISI components in classifying nomophobia.

METHODS

From a previous study 549 participants completed demographic information, the Nomophobia Questionnaire (NMP-Q), and the ISI. The sample was divided into two parts so that each part represented the original sample, using a 40% (n = 209) allocation for sample 1 and 60% (n = 340) for sample 2. To determine common components between nomophobia and insomnia, an exploratory factor analysis was performed using sample 1 to determine the diagnostic precision of the ISI's various components in detecting or classifying nomophobia. A test of the ISI and a cut-off value (ISI-4 ≥2) was then conducted on Sample 2 to determine whether they would accurately identify significant nomophobia.

RESULTS

Sleep dissatisfaction was a common component of insomnia and nomophobia. Sleep dissatisfaction had excellent diagnostic accuracy in detecting individuals with nomophobia (sensitivity 75.13%, specificity 100%, Youden' index 0.75, area under curve 0.88).

CONCLUSION

Questioning patients sleep dissatisfaction may serve as a marker for both nomophobia and insomnia, both of which may demand more comprehensive evaluation.

The therapeutic effect of Taijiquan combined with acupoint pressing on the treatment of anxiety insomnia in college students: A study protocol for a randomized controlled trial

INTRODUCTION

Sleep health is an important part of health and has become a common concern of society. For anxiety insomnia, the commonly used clinical therapies have limitations. Alternative and complementary therapy is gradually rising and showing remarkable effect in clinical practice. This is the first study to evaluate the therapeutic effect of Taijiquan combined with acupoint pressing in the treatment of anxiety insomnia in college students and to compare the difference in intervention before and after sleep, to choose the best treatment time.

METHODS AND ANALYSIS

This is a multicenter, single-blind, randomized controlled trial. A total of 126 eligible subjects who have passed the psychological evaluation and met inclusion criteria by completing a psychometric scale will be randomly divided into treatment group A (treat before sleep), treatment group B (treat after sleep) and control group C (waiting list group) in a ratio of 1:1:1. All the three groups will receive regular psychological counseling during the trial, and the treatment groups will practice 24-style Taijiquan and do meridian acupuncture at Baihui (DU20), Shenting (DU24), Yintang (EX-HN3), Shenmen (HT7) and Sanyinjiao (SP6). This RCT includes a 2-week baseline period, a 12-week intervention period, and a 12-week follow-up period. The main results will be measured by changes in the Pittsburgh sleep quality index (PSQI) and Hamilton anxiety scale (HAMA). The secondary results will be measured by the generalized anxiety scale (GAD-7) and insomnia severity index (ISI). The safety of the intervention will be evaluated at each assessment. The statistical analysis of data will be carried out by SPSSV.26.0 software.

DISCUSSION

We expect this trial to explore the effectiveness of Taijiquan combined with acupoint pressing in the treatment of anxiety insomnia in college students and choose the best treatment time by comparison.

CLINICAL TRIAL REGISTRATION

[www.ClinicalTrials.gov], identifier [ChiCTR2200057003].