Cognitive behavioural therapy for insomnia in inpatient psychiatric care: a systematic review.

Insomnia is highly prevalent among patients with psychiatric disorders. According to current guidelines, cognitive behavioural therapy for insomnia (CBT-I) represents the first-line treatment for chronic insomnia, also for patients with psychiatric comorbidity. While recent studies have demonstrated that CBT-I not only improves insomnia but also other health outcomes in patients with psychiatric disorders and comorbid insomnia in outpatient settings, the level of implementation and treatment potential of CBT-I in inpatient psychiatry is less clear. The objective of this systematic review is to present and discuss studies that have adapted CBT-I for inpatient psychiatric care. PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PsycINFO, were searched until June 2023. A total of 10 studies were identified, with the majority being non-randomised trials without comparison groups and small sample sizes. With preliminary character, studies report feasibility and potential efficacy in inpatient settings. Together, this review identifies a paucity of studies on CBT-I or derivates in inpatient psychiatry. Despite challenging in this setting, studies adapting CBT-I to the needs of severely ill patients and hospital settings might have the potential to improve mental health care

SLEEPexpert App – A Mobile Application to Support Insomnia Treatment for Patients with Severe Psychiatric Disorders

Cognitive behavior therapy for insomnia (CBT-I) is the first-line treatment for patients with insomnia disorder, including patients with severe mental disorders and comorbid insomnia. However, CBT-I is not sufficiently implemented in acute psychiatry settings. To make this treatment more accessible, we are currently adapting CBT-I to the needs of patients with severe psychiatric disorders in the form of a treatment program entitled SLEEPexpert. A core element of SLEEPexpert is keeping a sleep diary and restricting time in bed to increase sleep pressure. Here, we present a mobile application which supports the implementation of SLEEPexpert. The app is kept very simple, specifically designed for the target user group, and offers four main functionalities: entering information into the sleep diary, calculating the sleep efficiency and adapting the sleep window, delivering information on sleep and sleep disorders and accessing the recorded data in the sleep diary. Currently, we are preparing a usability test for the app aiming at fixing usability issues before running a clinical trial to assess the efficacy of this mHealth intervention

Co-ordination of brain and heart oscillations during non-rapid eye movement sleep

Oscillatory activities of the brain and heart show a strong variation across wakefulness and sleep. Separate lines of research indicate that non‐rapid eye movement (NREM) sleep is characterised by electroencephalographic slow oscillations (SO), sleep spindles, and phase–amplitude coupling of these oscillations (SO–spindle coupling), as well as an increase in high‐frequency heart rate variability (HF‐HRV), reflecting enhanced parasympathetic activity. The present study aimed to investigate further the potential coordination between brain and heart oscillations during NREM sleep. Data were derived from one sleep laboratory night with polysomnographic monitoring in 45 healthy participants (22 male, 23 female; mean age 37 years). The associations between the strength (modulation index [MI]) and phase direction of SO–spindle coupling (circular measure) and HF‐HRV during NREM sleep were investigated using linear modelling. First, a significant SO–spindle coupling (MI) was observed for all participants during NREM sleep, with spindle peaks preferentially occurring during the SO upstate (phase direction). Second, linear model analyses of NREM sleep showed a significant relationship between the MI and HF‐HRV (F = 20.1, r (2) = 0.30, p < 0.001) and a tentative circular‐linear correlation between phase direction and HF‐HRV (F = 3.07, r (2) = 0.12, p = 0.056). We demonstrated a co‐ordination between SO–spindle phase–amplitude coupling and HF‐HRV during NREM sleep, presumably related to parallel central nervous and peripheral vegetative arousal systems regulation. Further investigating the fine‐graded co‐ordination of brain and heart oscillations might improve our understanding of the links between sleep and cardiovascular health

Cognitive behavioral therapy for insomnia in patients with mental disorders and comorbid insomnia: A systematic review and meta-analysis.

Almost 70% of patients with mental disorders report sleep difficulties and 30% fulfill the criteria for insomnia disorder. Cognitive behavioral therapy for insomnia (CBT-I) is the first-line treatment for insomnia according to current treatment guidelines. Despite this circumstance, insomnia is frequently treated only pharmacologically especially in patients with mental disorders. The aim of the present meta-analysis was to quantify the effects of CBT-I in patients with mental disorders and comorbid insomnia on two outcome parameters: the severity of insomnia and mental health. The databases PubMed, CINHAL (Ebsco) und PsycINFO (Ovid) were searched for randomized controlled trials on adult patients with comorbid insomnia and any mental disorder comparing CBT-I to placebo, waitlist or treatment as usual using self-rating questionnaires as outcomes for either insomnia or mental health or both. The search resulted in 1994 records after duplicate removal of which 22 fulfilled the inclusion criteria and were included for the meta-analysis. The comorbidities were depression (eight studies, 491 patients), post-traumatic stress disorder (PTSD, four studies, 216 patients), alcohol dependency (three studies, 79 patients), bipolar disorder (one study, 58 patients), psychosis (one study, 50 patients) and mixed comorbidities within one study (five studies, 189 patients). The effect sizes for the reduction of insomnia severity post treatment were 0.5 (confidence interval, CI, 0.3-0.8) for patients with depression, 1.5 (CI 1.0-1.9) for patients with PTSD, 1.4 (CI 0.9-1.9) for patients with alcohol dependency, 1.2 (CI 0.8-1.7) for patients with psychosis/bipolar disorder, and 0.8 (CI 0.1-1.6) for patients with mixed comorbidities. Effect sizes for the reduction of insomnia severity were moderate to large at follow-up. Regarding the effects on comorbid symptom severity, effect sizes directly after treatment were 0.5 (CI 0.1-0.8) for depression, 1.3 (CI 0.6-1.9) for PTSD, 0.9 (CI 0.3-1.4) for alcohol dependency in only one study, 0.3 (CI -0.1 - 0.7, insignificant) for psychosis/bipolar, and 0.8 (CI 0.1-1.5) for mixed comorbidities. There were no significant effects on comorbid symptoms at follow-up. Together, these significant, stable medium to large effects indicate that CBT-I is an effective treatment for patients with insomnia and a comorbid mental disorder, especially depression, PTSD and alcohol dependency. CBT-I is also an effective add-on treatment with the aim of improving mental health in patients with depression, PTSD, and symptom severity in outpatients with mixed diagnoses. Thus, in patients with mental disorders and comorbid insomnia, given the many side effects of medication, CBT-I should be considered as a first-line treatment

The hierarchy of coupled sleep oscillations reverses with aging in humans.

A well-orchestrated coupling hierarchy of slow waves and spindles during slow wave sleep supports memory consolidation. In old age, duration of slow wave sleep and number of coupling events decreases. The coupling hierarchy deteriorates, predicting memory loss and brain atrophy. Here, we investigate the dynamics of this physiological change in slow wave-spindle coupling in a frontocentral electroencephalography position in a large sample (N=340, 237 female, 103 male) spanning most of the human lifespan (ages 15-83). We find that, instead of changing abruptly, spindles gradually shift from being driven by-, to driving slow waves with age, reversing the coupling hierarchy typically seen in younger brains. Reversal was stronger the lower the slow wave frequency, and starts around midlife (∼age 40-48), with an established reversed hierarchy at age 56-83. Notably, coupling strength remains unaffected by age. In older adults, deteriorating slow wave-spindle coupling, measured using phase slope index (PSI) and number of coupling events, is associated with blood plasma glial fibrillary acidic protein (GFAP) levels, a marker for astrocyte activation. Data-driven models suggest decreased sleep time and higher age lead to fewer coupling events, paralleled by increased astrocyte activation. Counterintuitively, astrocyte activation is associated with a back-shift of the coupling hierarchy (PSI) towards a "younger" status along with increased coupling occurrence and strength, potentially suggesting compensatory processes. As the changes in coupling hierarchy occur gradually starting at midlife, we suggest there exists a sizable window of opportunity for early interventions to counteract undesirable trajectories associated with neurodegeneration.Significance StatementEvidence accumulates that sleep disturbances and cognitive decline are bi-directionally and causally linked forming a vicious cycle. Improving sleep quality could break this cycle. One marker for sleep quality is a clear hierarchical structure of sleep oscillations. Previous studies showed that sleep oscillations decouple in old age. Here, we show that, rather, the hierarchical structure gradually shifts across the human lifespan and reverses in old age, while coupling strength remains unchanged. This shift is associated with markers for astrocyte activation in old age. The shifting hierarchy resembles brain maturation, plateau, and wear processes. This study furthers our comprehension of this important neurophysiological process and its dynamic evolution across the human lifespan

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Acceptance and Commitment Therapy, Combined with Bedtime Restriction, versus Cognitive Behavioral Therapy for Insomnia: A Randomized Controlled Pilot Trial.

INTRODUCTION Cognitive behavioral therapy for insomnia (CBT-I) is the current first-line treatment for insomnia. However, rates of nonresponse and nonremission are high and effects on quality of life are only small to moderate, indicating a need for novel treatment developments. We propose that Acceptance and Commitment Therapy (ACT) addresses core pathophysiological pathways of insomnia. ACT therefore has the potential to improve treatment efficacy when combined with bedtime restriction, the most effective component of CBT-I. The aim of this study was to compare the efficacy of ACT for insomnia combined with bedtime restriction (ACT-I) and CBT-I in improving insomnia severity and sleep-related quality of life. METHODS Sixty-three patients with insomnia disorder (mean age 52 years, 65% female, 35% male) were randomly assigned to receive either ACT-I or CBT-I in a group format. The primary outcomes were insomnia severity (Insomnia Severity Index) and sleep-related quality of life (Glasgow Sleep Impact Index). Outcomes were assessed before randomization (T0), directly after treatment (T1), and at 6-month follow-up (T2). RESULTS The results indicated significant, large pre-to-post improvements in both groups, for both primary and secondary outcomes. Improvements were maintained at the 6-month follow-up. However, there was no significant group by time interactions in linear mixed models, indicating an absence of differential efficacy. On a subjective treatment satisfaction scale, patients in the ACT-I group indicated significantly greater satisfaction with their improvement of several aspects of health including their energy level and work productivity. CONCLUSIONS The results suggest that ACT-I is feasible and effective, but not more effective than CBT-I for the improvement of insomnia severity and sleep-related quality of life. Future studies are needed to assess whether ACT-I is noninferior to CBT-I and to shed light on mechanisms of change in both treatments