Baseline Insomnia Research Articles

Insomnia symptoms predict systemic inflammation in women, but not in men with inflammatory bowel disease.

Insomnia has been suggested as a potential modulator of systemic inflammation. However, few studies have examined the longitudinal association between insomnia and inflammation as well as the role of sex differences, despite accumulating evidence of the vulnerability of women to immune consequences of disturbed sleep. In this study, we tested the association between self-reported insomnia symptoms and serum C-reactive protein, a marker of systemic inflammation, at 1-year follow-up, in 54 outpatients with inflammatory bowel disease (52.81 ± 16.09, 40.7% women). Insomnia symptoms were measured using the Insomnia Severity Index. After controlling for baseline inflammation and health variables, longitudinal moderated regression analysis showed that baseline insomnia symptoms predicted C-reactive protein levels at follow-up in women (β = 0.416, p = 0.014), but not in men (β = -0.179, p = 0.212). Results were not influenced by sex differences in insomnia severity or C-reactive protein levels. This study suggests insomnia symptoms may partially influence systemic inflammation in women with inflammatory bowel disease. Sex-specific psychological, immune and neuroendocrine pathways linking sleep to inflammation should be further elucidated.

Changes in insomnia and binge-eating symptom severity before and after treatment of eating disorders in individuals with non-low-weight binge-spectrum disorders.

Although emerging research suggests insomnia is a significant problem among people with eating disorders, little is known about how insomnia symptoms may serve as risk factors for eating disorder symptoms, treatment outcome, and relapse. University students with non-low-weight eating disorders (N = 89) completed a mobile guided self-help cognitive behavioural intervention for eating disorders. Insomnia and binge-eating symptoms were assessed at pre-treatment and end-of-treatment using the Insomnia Severity Index and Eating Pathology Symptoms Inventory. Multiply imputed datasets were used to test associations between insomnia and binge eating before, during and after treatment. Insomnia was positively associated with binge-eating symptoms prior to treatment (r = 0.47). Baseline insomnia symptoms were associated with binge-eating symptoms at end-of-treatment (est = 0.269). Eating disorder treatment modestly reduced insomnia (d = -0.38); however, about half of people with clinically significant insomnia prior to treatment remained symptomatic. Insomnia symptoms and binge-eating symptoms did not change concurrently during treatment. Overall, results support an association between insomnia and binge eating. Although eating disorder treatment may impact insomnia, many individuals remained symptomatic for sleep problems, suggesting the need for follow-up treatments, such as cognitive behavioural therapy for insomnia, or the development of integrated insomnia-eating disorder treatments. Future studies are needed to examine causal links between insomnia and eating disorder symptoms, and test if insomnia predicts relapse after eating disorder treatment.

Insomnia as a predictor of treatment outcomes in adolescents receiving concentrated exposure treatment for OCD

BackgroundResearch suggests that individuals with obsessive-compulsive disorder (OCD) frequently experience insomnia. Some previous studies have suggested that insomnia may predict treatment outcomes, but the evidence is limited, especially for adolescents. This study examined the prevalence of insomnia in an adolescent OCD patient sample, explored the correlation between OCD and insomnia, and tested whether levels of insomnia at baseline predict outcomes for adolescent patients receiving the Bergen 4-Day Treatment (B4DT) for OCD.MethodsForty-three adolescent OCD patients who received B4DT were selected for this study. Treatment outcome was quantified as change in Children Yale-Brown Obsessive Compulsive Scale (CY-BOCS) scores across time from pre- to posttreatment and 3-month follow-up. Insomnia symptoms were measured by the Bergen Insomnia Scale (BIS). Linear mixed models were used to examine the relationship between the BIS and changes in CY-BOCS scores. We controlled for symptoms of general anxiety disorder measured by the GAD-7 and depression symptoms measured by the PHQ-9.ResultsIn this sample, 68.4% of the patients scored above the cutoff for insomnia on the BIS. There was a moderate correlation between baseline CY-BOCS and BIS that did not reach statistical significance (r = .32, p = .051). High BIS scores before treatment were significantly associated with poorer treatment outcomes, as measured by changes in CY-BOCS over time (p = .002). The association between baseline insomnia and change in OCD symptoms remained significant (p = .033) while controlling for GAD-7 and PHQ-9.ConclusionInsomnia is common among adolescents with OCD, and these data suggest that these patients may be at increased risk for poor treatment outcomes. Future research to explore mechanisms and adjunctive treatments is warranted.Trial registrationThe study was approved by the Regional Committee for Medical and Health Research Ethics of Northern Norway (REK Nord: 2023/606482).

A Longitudinal Examination between Chronotype and Insomnia in Youths: A Cross-Lagged Panel Analysis.

Adolescence and young adulthood are transitional periods associated with significant changes and challenges, leading to a heightened vulnerability to sleep disturbances and mental health difficulties. This stage is often associated with an increased preference for eveningness, manifested as a later chronotype. The current study aimed to investigate the directionality of the association between chronotype, based on an individual's sleep-wake behaviour, and insomnia in young people using a two-wave panel design with a 12-month interval. A total of 370 participants aged 15-24 (mean age: 21.0 ± 2.0, 72.7% female) were recruited from local secondary schools and universities. Insomnia symptoms were assessed using the Insomnia Severity Index, while chronotype was measured using the Munich Chronotype Questionnaire. Temporal associations were analysed using a series of cross-lagged panel models. The best fitting and most parsimonious model indicated that a later chronotype at baseline predicts more severe insomnia symptoms at the 12-month follow-up after accounting for autoregressive effects. However, the opposite causal model, where baseline insomnia symptoms predicted the chronotype at the 12-month follow-up, was not supported. These findings suggest that a late chronotype may be a potential risk factor for the development of insomnia in young people, emphasising the importance of considering circadian factors in the prevention and treatment of sleep disturbances among this population.

Effect of sleep quality on repetitive transcranial magnetic stimulation outcomes in depression.

While repetitive transcranial magnetic stimulation (rTMS) is effective for 50-60% of those treatment-resistant depression, it is critical to identify predictors of response for optimal patient selection to improve therapy. Insomnia is a known symptom of depression that is both correlated with depression severity and associated with poor antidepressant response. Therefore, understanding this relationship may open new opportunities for the optimization of rTMS treatment. We aimed to explore whether baseline sleep quality, specifically insomnia, is associated with rTMS outcomes in a naturalistic sample of 975 patients (age 18-90; 63.9% F) receiving a standard course of rTMS treatment from two outpatient TMS clinics located within psychiatric hospitals in the United States. One site additionally collected information on concurrent medication use on 350 patients; among these, we examined whether pharmacological treatment of insomnia affected TMS treatment response. Depression was measured using the 30-item Inventory of Depressive Symptomology Self Report (IDS-SR) in site one and an abbreviated 16-item Quick Inventory of Depressive Symptomology (QIDS) derived from the IDS-SR in site two. Sleep disturbances were measured using three insomnia-related questions. Multilevel logistic regression was used to determine whether baseline insomnia scores were associated with TMS treatment outcome. Upon dichotomous categorization of the sample by insomnia and sleep-medication use, depression and sleep scores were analyzed across time using mixed repeated measures ANOVA. We found that sleep quality improves after TMS (p<.001) and correlates with improvement in non-insomnia related depression symptoms (r= .318, p<.001). We found that among those who had significant insomnia at baseline, those not using sleep medications had significantly worse post-treatment IDS-SR scores compared to those using sleep medications (p=. 021) despite no difference in final insomnia score. Together, our results suggest that while baseline insomnia is not associated with TMS effectiveness, treating insomnia may affect the trajectory of TMS therapy. Future prospective studies are needed to examine the effect of insomnia treatment alongside TMS for depression.

Sleep Quality and Urinary Incontinence in Prostate Cancer Patients: A Data Analytics Approach with the ASCAPE Dataset.

The ASCAPE project aims to improve the health-related quality of life of cancer patients using artificial intelligence (AI)-driven solutions. The current study employs a comprehensive dataset to evaluate sleep and urinary incontinence, thus enabling the development of personalized interventions. This study focuses on prostate cancer patients eligible for curative treatment with surgery. Forty-two participants were enrolled following their diagnosis and were followed up at baseline and 3, 6, 9, and 12 months after surgical treatment. The data collection process involved a combination of standardized questionnaires and wearable devices, providing a holistic view of patients' QoL and health outcomes. The dataset is systematically organized and stored in a centralized database, with advanced statistical and AI techniques being employed to reveal correlations, patterns, and predictive markers that can ultimately lead to implementing personalized intervention strategies, ultimately enhancing patient QoL outcomes. The correlation analysis between sleep quality and urinary symptoms post-surgery revealed a moderate positive correlation between baseline insomnia and baseline urinary symptoms (r = 0.407, p = 0.011), a positive correlation between baseline insomnia and urinary symptoms at 3 months (r = 0.321, p = 0.049), and significant correlations between insomnia at 12 months and urinary symptoms at 3 months (r = 0.396, p = 0.014) and at 6 months (r = 0.384, p = 0.017). Furthermore, modeling the relationship between baseline insomnia and baseline urinary symptoms showed that baseline insomnia is significantly associated with baseline urinary symptoms (coef = 0.222, p = 0.036). The investigation of sleep quality and urinary incontinence via data analysis through the ASCAPE project suggests that better sleep quality could improve urinary disorders.

The role of psychiatric symptoms, sociodemographic factors, and baseline sleep variables on pediatric insomnia treatment outcomes in a clinically referred population.

The current study aimed to examine clinically relevant psychiatric and sociodemographic predictors of insomnia treatment outcomes in pediatric patients clinically referred for insomnia. Pediatric patients (n = 1,428; ages 1.5-18 years) presenting for insomnia evaluation in a medical/sleep center-based behavioral sleep clinic were followed for treatment as clinically indicated. According to patient age, parents/patients completed validated measures of insomnia severity, psychiatric symptoms, and sociodemographic measures. Patients were also categorized by treatment outcome status (ie, not recommended to follow-up after initial evaluation and treatment session, successful treatment completion, lost to follow-up after initial evaluation and treatment session, and early termination) according to the clinically indicated treatment recommended and dose of treatment received. Youth had elevated scores on psychiatric screening indexes and affective problems were highest for all age groups. Other comorbid sleep disorders were present in nearly 25% of patients with insomnia and use of sleep aids (melatonin or hypnotics) was commonplace. Baseline insomnia severity significantly predicted sleep treatment trajectories and posttreatment insomnia severity with large effects for all age groups. Other clinically relevant predictors of insomnia treatment outcomes included medication use and externalizing mental health concerns in younger patients and internalizing mental health concerns and chronological age in older patients. Lack of treatment follow-up and premature treatment termination was observed for patients with the worst insomnia symptoms at time of initial evaluation. Pediatric health providers delivering insomnia treatment should take a developmentally sensitive approach that is proactive with regards to managing treatment barriers that are likely influenced by severity of insomnia and comorbid mental health concerns. Van Dyk TR, Simmons DM, Durracio K, Becker SP, Byars KC. The role of psychiatric symptoms, sociodemographic factors, and baseline sleep variables on pediatric insomnia treatment outcomes in a clinically referred population. J Clin Sleep Med. 2024;20(11):1727-1738.

Relationship of Nocturnal Insomnia Symptoms and Outcomes After Hypoglossal Nerve Stimulation.

In patients undergoing hypoglossal nerve stimulation (HGNS), we examined the Insomnia Severity Index (ISI) to understand how baseline sleep onset insomnia (SOI), sleep maintenance insomnia (SMI), and early morning awakening (EMA) affected postsurgical outcomes. Observational. Multicenter registry. We included patients from the Adherence and Outcomes of Upper Airway Stimulation for Obstructive Sleep Apnea International Registry (ADHERE) with a baseline ISI from 2020 to 2023. Regression analysis examined the association of ISI question scores for SOI, SMI, and EMA and outcomes: Apnea-Hypopnea Index (AHI) reduction, device usage, changes in the Epworth Sleepiness Scale (ESS) and overall ISI score, final visit (FV) completion, and satisfaction. No relationship was noted between insomnia subtypes and AHI reduction or FV completion. In the subgroup of patients with baseline moderate/severe insomnia, patients with major impairment for SOI used their device 64 min/day longer than those with minimal impairment. Amongall patients, those with baseline major impairment for SOI had a 2.3 points greater improvement in ISI from baseline to FV compared to patients with minimal impairment, while patients with baseline major impairment for SMI had a 2.0 and 3.5 points greater improvement in the ESS and ISI than those with minimal impairment. Patients with EMA and moderate/severe baseline insomnia had decreased odds of being satisfied after surgery. In ADHERE, nocturnal symptoms of insomnia did not limit HGNS efficacy or therapy use. Conversely, those with worse insomnia subtype impairments at baseline had improved outcomes related to adherence, sleepiness, and insomnia at the FV.

Longitudinal study of insomnia, suicidal ideation, and psychopathology in schizophrenia

ObjectiveInsomnia is a common comorbidity in schizophrenia. Increasing cross-sectional evidence suggests an association between insomnia and suicidal ideation (SI) and symptom severity in schizophrenia. We investigated longitudinal associations over 3 months between insomnia, suicidal ideation, and symptom severity in a group of patients with chronic schizophrenia. MethodWe performed a secondary analysis of data from n = 305 participants from the Preventing Relapse Oral Antipsychotics Compared to Injectables Evaluating Efficacy (PROACTIVE) schizophrenia trial using regression models. ResultsThe prevalence of moderate-to-severe insomnia was 17.7 % at baseline and 13.6 % at 3 months, respectively. The prevalence of SI was 22 % at baseline and 22.5 % at 3 months. After controlling for potential confounders, improved SI from baseline to 3 months was associated with both baseline moderate-to-severe insomnia (OR = 3.81, 95 % CI 1.11–13.12, p = 0.034) and improvement in insomnia (OR = 3.50, 95 % CI 1.23–9.92, p = 0.013). Worsening SI from baseline to 3 months was associated with worsening insomnia (OR = 3.50, 95 % CI 1.23–9.92, p = 0.013), but not baseline insomnia. Improvement in BPRS total score from baseline to 3 months was associated with improvement in insomnia (β = 0.17, p = 0.029), but not baseline insomnia. ConclusionInsomnia is common in patients with chronic schizophrenia and insomnia showed significant associations with SI and psychopathology. Clinicians should consider insomnia when assessing suicide risk in patients with schizophrenia.

The Potential Usefulness of Peripheral Somatosensory Stimulation in Improving Sleep Quality in Patients with Insomnia

Background Insomnia and other sleep disorders represent a major cause of disability and impaired productivity. We evaluated the impact of peripheral somatosensory stimulation (PSS) on sleep quality in 12 patients with varying degrees of insomnia. Methods Twelve adult patients underwent daily PSS therapy for a 4-week period and were evaluated using the Insomnia Severity Index at baseline (prior to initiation of therapy) and then at the conclusion of the treatments. All data were obtained through a self-reported 7-question survey evaluating overall severity of insomnia symptoms and the impact of sleep patterns on satisfaction with sleep, daily functioning, and overall quality of life. Changes from baseline insomnia scores were analyzed using cumulative link mixed models (CLMMs). Results Seven men and five women completed one month of PSS therapy. Mean age was 55.1 (range 29 to 80 years). No adverse events were described by the patients. The average total change from baseline score was -10.3 points (baseline: 16.5 vs. week 4: 6.2). Patients had statistically significant improvements for every individual survey question by week 4. The median composite score was improved from baseline, with an overall median score of 2 (IQR: 1.25 – 2.75, min-max: 1-4) at baseline compared to 0.5 (IQR: 0 – 0.25, min-max: 0-2) by week 4 (MD = -1 95% CI: , p &lt; 0.001), signaling typically moderate insomnia at baseline vs. typically minimal to no symptoms by week 4. The predicted probability of obtaining the best outcome (score=0) was 9% at baseline vs. 53% by week 4. The overall cumulative odds ratio was 11.9 (p &lt; 0.001), suggesting that on average, the odds of moving from one score to a lower (improved) score at week 4 compared to the baseline are approximately 12 times higher than moving to a neutral or worse score. Conclusions PSS stimulation appeared to have a significantly favorable effect on sleep quality in this group of patients. Symptoms related to ability to fall asleep, remain asleep, and overall quality of sleep were all improved with PSS therapy. We suggest that further investigation into the potential usefulness of PSS therapy in patients with sleep disorders is warranted.

Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia.

The standard of care for military personnel with insomnia is cognitive behavioral therapy for insomnia (CBT-I). However, only a minority seeking insomnia treatment receive CBT-I, and little reliable guidance exists to identify those most likely to respond. As a step toward personalized care, we present results of a machine learning (ML) model to predict CBT-I response. Administrative data were examined for n = 1,449 nondeployed US Army soldiers treated for insomnia with CBT-I who had moderate-severe baseline Insomnia Severity Index (ISI) scores and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble ML model was developed in a 70% training sample to predict clinically significant ISI improvement (reduction of at least 2 standard deviations on the baseline ISI distribution). Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample. 19.8% of patients had clinically significant ISI improvement. Model area under the receiver operating characteristic curve (standard error) was 0.60 (0.03). The 20% of test-sample patients with the highest probabilities of improvement were twice as likely to have clinically significant improvement compared with the remaining 80% (36.5% vs 15.7%; χ21 = 9.2, P = .002). Nearly 85% of prediction accuracy was due to 10 variables, the most important of which were baseline insomnia severity and baseline suicidal ideation. Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment. Parallel models will be needed for alternative treatments before such a system is of optimal value. Gabbay FH, Wynn GH, Georg MW, etal. Toward personalized care for insomnia in the US Army: a machine learning model to predict response to cognitive behavioral therapy for insomnia. J Clin Sleep Med. 2024;20(6):921-931.

Acupuncture for patients with insomnia and predictors of treatment response: a chart review.

Acupuncture is a potentially effective non-pharmacological treatment for insomnia. We observed the responses of patients with insomnia to acupuncture in routine clinical practice. In addition, we explored patient characteristics that might affect the treatment response to acupuncture for insomnia. Medical records of patients with insomnia in a Korean medicine clinic with baseline Insomnia Severity Index (ISI) scores ⩾8 and Pittsburgh Sleep Quality Index (PSQI) scores ⩾5 were reviewed. Acupuncture was applied at ST43, GB41, ST41, SI5, HT3, KI10, HT7 and ST3, for 1-2 months. The ISI and PSQI were measured monthly to assess insomnia severity. The effect of acupuncture over time was analyzed using a multilevel linear model for repeated measures. In addition, logistic regression was used to explore predictors of treatment response. A total of 91 patients with insomnia aged 59.2 ± 12.5 years (mean ± standard deviation (SD)) (90.1% female) were included in the analysis. After the acupuncture treatment, ISI scores were significantly reduced by -3.75 (95% confidence interval (CI) = -4.99, -2.50) and -4.69 (95% CI = -6.22, -3.16) after the first and second month, respectively. The PSQI global scores also improved, and sleep duration showed a tendency to increase by 0.35 h (95% CI = -0.17, 0.86) after acupuncture treatment. Three cases of mild fatigue were reported. In addition, higher baseline pain/discomfort predicted a greater likelihood of response after acupuncture treatment (odds ratio (OR) = 1.66, 95% CI = 1.10, 2.60). In a real-world setting, the insomnia of outpatients in a clinic was slightly alleviated after acupuncture treatment. These findings require validation by randomized controlled trials.

Impact of deep transcranial magnetic stimulation on insomnia outcomes in patients with treatment-resistant depression: a retrospective study.

Chopra A, Singal P, Kodya S. Impact of deep transcranial magnetic stimulation on insomnia outcomes in patients with treatment-resistant depression: a retrospective study. J Clin Sleep Med. 2024;20(5):813-815.

Defining and empirically testing a two-stage build-maintain model of cognitive behavioural therapy for insomnia

Cognitive Behavioural Therapy for Insomnia is the front-line intervention for Insomnia Disorder. However, few studies have reported data on how CBT-I implementation evolves over the course of therapy or the time course of client response. This is due, in part, to no formal framework for defining stages of CBT-I. Here, we follow Spielman's original conceptualisation of Sleep Restriction Therapy (SRT) to report a two-stage model for defining stages of CBT-I, based on progression through the phases of SRT. Using data from a prior randomized clinical trial, we show the model helps identify patterns of treatment progression, which are in turn associated with baseline insomnia severity. The reported model is parsimonious and easy to implement and it holds potential for further examining and understanding a variety of CBT-I processes currently not fully understood, such as adherence and the optimal order of various CBT-I treatment components.

Sleep Disorders and Cognitive Aging Among Cognitively Impaired Versus Unimpaired Older Adults.

Sleep disorders often predict or co-occur with cognitive decline. Yet, little is known about how the relationship unfolds among older adults at risk for cognitive decline. To examine the associations of sleep disorders with cognitive decline in older adults with unimpaired cognition or impaired cognition (mild cognitive impairment and dementia). A total of 5,822 participants (Mage = 70) of the National Alzheimer's Coordinating Center database with unimpaired or impaired cognition were followed for 3 subsequent waves. Four types of clinician-diagnosed sleep disorders were reported: sleep apnea, hyposomnia/insomnia, REM sleep behavior disorder, or "other." Cognition over time was measured by the Montreal Cognitive Assessment (MoCA) or an estimate of general cognitive ability (GCA) derived from scores based on 12 neuropsychological tests. Growth curve models were estimated adjusting for covariates. In participants with impaired cognition, baseline sleep apnea was related to better baseline MoCA performance (b = 0.65, 95% confidence interval [95% CI] = [0.07, 1.23]) and less decline in GCA over time (b = 0.06, 95% CI = [0.001, 0.12]). Baseline insomnia was related to better baseline MoCA (b = 1.54, 95% CI = [0.88, 2.21]) and less decline in MoCA over time (b = 0.56, 95% CI = [0.20, 0.92]). Furthermore, having more sleep disorders (across the 4 types) at baseline predicted better baseline MoCA and GCA, and less decline in MoCA and GCA over time. These results were only found in those with impaired cognition and generally consistent when using self-reported symptoms of sleep apnea or insomnia. Participants with sleep disorder diagnoses may have better access to healthcare, which may help maintain cognition through improved sleep.

P058 Dissecting Sleep Restriction: Defining and Empirically testing a two-stage Build-Maintain model of Sleep Restriction Therapy for Insomnia

Abstract Introduction Sleep Restriction Therapy (SRT) is one of the core components of CBT for Insomnia (CBT-I). However, there is little empirical evidence regarding how SRT implementation evolves over the course of therapy. Here, we propose a conceptual model for defining stages of SRT and assess the applicability of the model in a recently completed clinical trial. Methods Our proposed two-stage Build-Maintain model of SRT is: 1) The Build stage starts with SRT implementation. The primary goal is building homeostatic pressure until average weekly sleep efficiency (SE) is high enough to increase time-in-bed (e.g., 85% or 90%). 2) The Maintain stage starts with the first increase of time-in-bed after the target SE is reached. The goal is to maintain sleep pressure sufficiently high that one can maintain SE in the desired range. We assessed the model in 83 adults (age=48.3±14.8 years; 50F) undergoing CBT-I in a RCT. SRT was introduced in week 2. We explored the extent to which age, sex, psychiatric comorbidities, use of hypnotics, and baseline insomnia severity predicted progression through the SRT stages. Results Participants were in the Build stage 2.1±1.3 weeks (41.8% were in Build for only one week; 84% for ≤3 weeks). Higher baseline ISI (r=.238,p=.036) and lower baseline SE (r=-.365,p=.001) predicted spending more weeks in the Build stage before transitioning to the Maintain stage. Discussion The two-stage Build-Maintain model of SRT may inform: a) setting empirically-based expectations for client progress; b) examining adherence to CBT-I; c) evaluating shortened versions of CBT-I.

Up all night: the effect of the COVID-19 pandemic on insomnia among college students

Objective: To investigate COVID-19’s impact on sleep, specifically insomnia, in college students. Participants: Students from a private university were eligible if they registered for Refresh, an online sleep-health program. Methods: A pre-intervention survey was distributed and assessed insomnia and COVID-19 factors using the Insomnia Severity Index (ISI) and a COVID-19 questionnaire. Baseline ISI scores from students enrolled in 2020–2021 were compared to scores from students enrolled in 2018–2019 using a two-sample t-test. Associations between insomnia and the COVID-19 factors were determined using chi-square tests. Results: There was no significant difference in baseline ISI scores when comparing 2018–2019 scores to those collected during the pandemic, p = 0.274. There was a statistically significant association between having insomnia and a student’s learning location, p = 0.006, as well as disturbances in sleep caused by the pandemic, p = 0.026. Conclusion: Our results indicate that the COVID-19 pandemic did not worsen baseline insomnia.

Does treating insomnia with digital cognitive behavioural therapy (Sleepio) mediate improvements in anxiety for those with insomnia and comorbid anxiety? An analysis using individual participant data from two large randomised controlled trials

BackgroundConsiderable comorbidity exists between insomnia and anxiety, and evidence shows that the benefits of CBT for insomnia extend to anxiety. Using data from two large trials of digital CBT (dCBT) for insomnia, we evaluated whether improving sleep is an effective treatment target to reduce both insomnia and anxiety symptoms in individuals with insomnia and clinically significant anxiety. MethodsThis was a controlled sub-analysis combining individual participant data from two previous randomised controlled trials of dCBT for insomnia (Sleepio). Participants (N = 2172) with insomnia disorder and clinically significant anxiety symptoms were included in this sub-analysis and received either dCBT or control (usual care or sleep hygiene education). Assessments were evaluated at baseline, post-intervention (week 8 or 10), and follow-up (week 22 or 24). Mediation was evaluated using structural equation models. ResultsdCBT for insomnia was superior to control at reducing both insomnia (Hedges' g range = 0.77–0.81; both p < 0.001) and anxiety symptoms (Hedges' g range = 0.39–0.44; both p < 0.001) at all time points. Baseline insomnia symptoms moderated the effects of dCBT on insomnia, however no variables moderated treatment effects on anxiety. Reductions in anxiety symptoms at follow-up were mediated by improvements in sleep at post-intervention (% mediated = 84 %), suggesting a causal pathway. LimitationsParticipants did not have a formal anxiety disorder diagnosis and so the effects of dCBT for insomnia on anxiety may differ by anxiety disorder. ConclusionsAddressing sleep using dCBT for insomnia may serve as a treatment target from which to improve anxiety in individuals with insomnia and clinically significant comorbid anxiety. Clinical trial registrationsDigital Insomnia therapy to Assist your Life as well as your Sleep (DIALS) - ISRCTN60530898 http://www.isrctn.com/ISRCTN60530898.Oxford Access for Students Improving Sleep (OASIS) - ISRCTN61272251 http://www.isrctn.com/ISRCTN61272251.

Medium- and Long-Term Effects of Insomnia Severity and Circadian Preference on Pain and Emotional Distress Among Individuals With Chronic Pain

Studies have identified insomnia as having significant influence on chronic pain. A rising body of research has also underscored the association between eveningness and chronic pain. However, co-assessment of insomnia and eveningness in the context of chronic pain adjustment has been limited. The present study sought to investigate the effects of insomnia and eveningness on pain severity, pain interference, and emotional distress (ie, depressive and anxiety symptoms) over nearly 2 years among adults with chronic pain in the U.S. Adults with chronic pain (N = 884) were surveyed 3 times via Amazon’s MTurk online crowdsourcing platform: baseline, 9-month follow-up, and 21-month follow-up. Path analysis was conducted to examine the effects of baseline insomnia severity (Insomnia Severity Index) and eveningness (Morningness and Eveningness Questionnaire), as well as their moderating effects on outcomes. Controlling for select sociodemographic variables and baseline outcome levels, greater insomnia severity at baseline was associated with worsening of all of the pain-related outcomes at 9-month follow-up, and pain interreference and emotional distress at 21-month follow-up. We did not find evidence that evening types are at a higher risk of experiencing worsening pain-related outcomes over time compared to morning and intermediate types. There were also no significant insomnia severity and eveningness moderation effects on any outcome. Our findings suggest that insomnia is a more robust predictor of changes in pain-related outcomes as compared to eveningness. Treatment of insomnia can be important in chronic pain management. Future studies should evaluate the role of circadian misalignment on pain using more accurate biobehavioral makers. PerspectiveThis study examined the effects of insomnia and eveningness on pain and emotional distress in a large sample of individuals with chronic pain. Insomnia severity is a stronger predictor of changes in pain and emotional distress than eveningness, highlighting insomnia as an important clinical target for chronic pain management.

Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy.

Although many military personnel with insomnia are treated with prescription medication, little reliable guidance exists to identify patients most likely to respond. As a first step toward personalized care for insomnia, we present results of a machine-learning model to predict response to insomnia medication. The sample comprised n = 4,738 nondeployed US Army soldiers treated with insomnia medication and followed 6-12 weeks after initiating treatment. All patients had moderate-severe baseline scores on the Insomnia Severity Index (ISI) and completed 1 or more follow-up ISIs 6-12 weeks after baseline. An ensemble machine-learning model was developed in a 70% training sample to predict clinically significant ISI improvement, defined as reduction of at least 2 standard deviations on the baseline ISI distribution. Predictors included a wide range of military administrative and baseline clinical variables. Model accuracy was evaluated in the remaining 30% test sample. 21.3% of patients had clinically significant ISI improvement. Model test sample area under the receiver operating characteristic curve (standard error) was 0.63 (0.02). Among the 30% of patients with the highest predicted probabilities of improvement, 32.5.% had clinically significant symptom improvement vs 16.6% in the 70% sample predicted to be least likely to improve (χ21 = 37.1, P < .001). More than 75% of prediction accuracy was due to 10 variables, the most important of which was baseline insomnia severity. Pending replication, the model could be used as part of a patient-centered decision-making process for insomnia treatment, but parallel models will be needed for alternative treatments before such a system is of optimal value. Gabbay FH, Wynn GH, Georg MW, etal. Toward personalized care for insomnia in the US Army: development of a machine-learning model to predict response to pharmacotherapy. J Clin Sleep Med. 2023;19(8):1399-1410.