The first-night effect and the consistency of short sleep in insomnia disorder.

The nature and degree of objective sleep impairments in insomnia disorder remain unclear. This issue is complicated further by potential changes in sleep architecture on the first compared with subsequent nights in the laboratory. Evidence regarding differential first-night effects in people with insomnia disorder and controls is mixed. Here, we aimed to further characterize insomnia- and night-related differences in sleep architecture. A comprehensive set of 26 sleep variables was derived from two consecutive nights of polysomnography in 61 age-matched patients with insomnia and 61 good sleeper controls. People with insomnia expressed consistently poorer sleep than controls on several variables during both nights. While poorer sleep during the first night was observed in both groups, there were qualitative differences regarding the specific sleep variables expressing a first-night effect. Short sleep (total sleep time < 6 hr) was more likely during the first night and in insomnia, although approximately 40% of patients with insomnia presenting with short sleep on night 1 no longer met this criterion on night 2, which is important given the notion of short-sleeping insomnia as a robust subtype.

A novel approach for constructing personalized networks from longitudinal perceived causal relations.

Personalized networks of psychological symptoms aim to advance therapy by identifying treatment targets for specific patients. Statistical relations in such networks can be estimated from intensive longitudinal data, but their causal interpretation is limited by strong statistical assumptions. An alternative is to create networks from patient perceptions, which comes with other limitations such as retrospective bias. We introduce the Longitudinal Perceived Causal Problem Networks (L-PECAN) approach to address both these concerns. 20 participants screening positive for depression completed 4 weeks day of brief daily assessments of perceived symptom interactions. Quality criteria of this new method are introduced, answering questions such as "Which symptoms should be included in networks?", "How many datapoints need to be collected to achieve stable networks?", and "Does the network change over time?". Accordingly, about 40% of respondents achieved stable networks and only few respondents exhibited network structure that changed during the assessment period. The method was time-efficient (on average 7.4 min per day), and well received. Overall, L-PECAN addresses several of the prevailing issues found in statistical networks and therefore provides a clinically meaningful method for personalization.

Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications.

Background: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain networks to parse the heterogeneity of depressive symptomatology in a large adolescent sample. Methods: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1,317 adolescents (52.49% female, mean±SD age=18.5±0.72). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS symptom/item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual symptom scores revealed associations between cortical thickness measures and specific symptoms, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor.=-0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). Limitations: This cross-sectional study included participants who were relatively healthy and relied on the self-reported assessment of depression symptoms. Conclusions: This study showcases the utility of network models in parsing heterogeneity in depressive symptoms, linking individual symptoms to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Differential impact of preventive cognitive therapy while tapering antidepressants versus maintenance antidepressant treatment on affect fluctuations and individual affect networks and impact on relapse: a secondary analysis of a randomised controlled trial.

Background: There is an urgent need to better understand and prevent relapse in major depressive disorder (MDD). We explored the differential impact of various MDD relapse prevention strategies (pharmacological and/or psychological) on affect fluctuations and individual affect networks in a randomised setting, and their predictive value for relapse. Methods: We did a secondary analysis using experience sampling methodology (ESM) data from individuals with remitted recurrent depression that was collected alongside a randomised controlled trial that ran in the Netherlands, comparing: (I) tapering antidepressants while receiving preventive cognitive therapy (PCT), (II) combining antidepressants with PCT, or (III) continuing antidepressants without PCT, for the prevention of depressive relapse, as well as ESM data from 11 healthy controls. Participants had multiple past depressive episodes, but were remitted for at least 8 weeks and on antidepressants for at least six months. Exclusion criteria were: current (hypo)mania, current alcohol or drug abuse, anxiety disorder that required treatment, psychological treatment more than twice per month, a diagnosis of organic brain damage, or a history of bipolar disorder or psychosis. Fluctuations (within-person variance, root mean square of successive differences, autocorrelation) in negative and positive affect were calculated. Changes in individual affect networks during treatment were modelled using time-varying vector autoregression, both with and without applying regularisation. We explored whether affect fluctuations or changes in affect networks over time differed between treatment conditions or relapse outcomes, and predicted relapse during 2-year follow-up. This ESM study was registered at ISRCTN registry, ISRCTN15472145. Findings: Between Jan 1, 2014, and Jan 31, 2015, 72 study participants were recruited, 42 of whom were included in the analyses. We found no indication that affect fluctuations differed between treatment groups, nor that they predicted relapse. We observed large individual differences in affect network structure across participants (irrespective of treatment or relapse status) and in healthy controls. We found no indication of group-level differences in how much networks changed over time, nor that changes in networks over time predicted time to relapse (regularised models: hazard ratios [HR] 1063, 95% CI <0.0001->10 000, p = 0.65; non-regularised models: HR 2.54, 95% CI 0.23-28.7, p = 0.45) or occurrence of relapse (regularised models: odds ratios [OR] 22.84, 95% CI <0.0001->10 000, p = 0.90; non-regularised models: OR 7.57, 95% CI 0.07-3709.54, p = 0.44) during complete follow-up. Interpretation: Our findings should be interpreted with caution, given the exploratory nature of this study and wide confidence intervals. While group-level differences in affect dynamics cannot be ruled out due to low statistical power, visual inspection of individual affect networks also revealed no meaningful patterns in relation to MDD relapse. More studies are needed to assess whether affect dynamics as informed by ESM may predict relapse or guide personalisation of MDD relapse prevention in daily practice. Funding: The Netherlands Organisation for Health Research and Development, Dutch Research Council, University of Amsterdam.

The association of self-reported awake bruxism and sleep bruxism with temporomandibular pain and dysfunction in adult patients with temporomandibular disorders.

OBJECTIVE: To investigate the association of the severity of temporomandibular disorders (TMD) pain and dysfunction with the frequency of self-reported awake bruxism (AB), sleep bruxism (SB), and stress in an adult TMD-patient population. MATERIALS AND METHODS: This cross-sectional study included 237 TMD patients based on the Diagnostic Criteria for TMD. Age, sex, frequency of self-reported AB and SB, and stress were included as independent variables. TMD pain and TMD dysfunction were included as dependent variables in regression analyses. Univariate and multivariable linear regression analyses were used to predict TMD pain and TMD dysfunction in two separate models. Finally, network analysis was performed to investigate the associations between all variables. RESULTS: In the univariate analyses, TMD pain was significantly associated with self-reported AB-frequent (unstandardized coefficient (B) = 3.196, 95%CI 1.198-5.195, p = 0.002). TMD dysfunction was significantly associated with AB-frequent (B = 2.208, 95%CI 0.177-4.238, p = 0.033) and SB-sometimes (B = 1.698, 95%CI 0.001-3.394, p = 0.050). In the multivariable analyses, TMD pain was significantly associated with TMD dysfunction (B = 0.370, p < 0.001), stress (B=0.102, p < 0.001). TMD dysfunction was significantly associated with TMD pain (B = 0.410, p < 0.001) only. Network analysis showed that TMD pain is a bridge factor between AB, stress, and TMD dysfunction. CONCLUSIONS: TMD pain is directly associated with AB, stress, and TMD dysfunction, while TMD dysfunction is only associated with TMD pain. CLINICAL RELEVANCE: Reducing pain may improve pain-related dysfunction, and the management of AB and stress may improve TMD pain and dysfunction, and vice versa.

Network analysis of sleep bruxism in the EPISONO adult general population.

Sleep bruxism (SB) has been associated with biological and psychosocial factors. The assessment of SB includes self-report, clinical evaluation, and polysomnography. This study aimed to investigate the associations of self-reported SB with other sleep disorders and demographic, psychological, and lifestyle factors in the adult general population, and to investigate whether self-reported SB and polysomnographically (PSG) confirmed SB provide similar outcomes in terms of their associated factors. We recruited 915 adults from the general population in Sao Paulo, Brazil. All participants underwent a one-night PSG recording and answered questions about sex, age, BMI, insomnia, OSA risk, anxiety, depression, average caffeine consumption, smoking frequency, and alcohol consumption frequency. We investigated the link between SB and the other variables in univariate, multivariate, and network models, and we repeated each model once with self-reported SB and once with PSG-confirmed SB. Self-reported SB was only significantly associated with sex (p = 0.042), anxiety (p = 0.002), and depression (p = 0.03) in the univariate analysis, and was associated with insomnia in the univariate (p < 0.001) and multivariate (ß = 1.054, 95%CI 1.018-1.092, p = 0.003) analyses. Network analysis showed that self-reported SB had a direct positive edge to insomnia, while PSG-confirmed SB was not significantly associated with any of the other variables. Thus, sleep bruxism was positively associated with insomnia only when self-reported, while PSG-confirmed SB was not associated with any of the included factors.

Associations between signs of sleep bruxism and insomnia: A polysomnographic study.

Sleep bruxism (SB) is a repetitive jaw-muscle activity characterised by clenching or grinding of the teeth and/or by bracing or thrusting of the mandible. Sleep bruxism has been linked with insomnia symptoms. Moreover, it has been suggested that there is a positive association between distress and the occurrence of sleep bruxism. However, the occurrence of sleep bruxism and its association with distress have not been studied in patients with insomnia. Therefore, we hypothesised that: (1) the occurrence of sleep bruxism is higher in patients with insomnia than in healthy controls; and (2) the occurrence of sleep bruxism in insomnia patients with moderate to high distress (IMHD) is higher than that in insomnia patients with slight distress (ISD). A total of 44 controls (34 females, 10 males, mean ± SD age = 46.8 ± 14.4 years) and 42 participants with insomnia (35 females, 7 males, mean ± SD age = 51.3 ± 12.1 years) were enrolled in this study. Among 42 participants with insomnia, 20 participants were subtyped as IMHD, 17 participants as ISD. Another five participants were not subtyped due to insufficient information. Group differences in rhythmic masticatory muscle activity (RMMA), a biomarker of sleep bruxism, were evaluated with Mann-Whitney U tests. The medians and interquartile ranges of the RMMA indices were 0.8|1.8|3.3 in controls, 1.1|1.6|2.3 in IMHD and 1.2|1.9|2.9 in ISD. There was no significant difference in the RMMA index, neither between participants with insomnia and controls (P = 0.514) nor between IMHD versus ISD (P = 0.270). The occurrence of RMMA indicators of possible sleep bruxism is not significantly different between individuals with insomnia and controls, nor between IMHD versus ISD.

Reporting standards for psychological network analyses in cross-sectional data.

Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Practical behavioural solutions to COVID-19: Changing the role of behavioural science in crises.

For a very long time in the COVID-19 crisis, behavioural change leading to physical distancing behaviour was the only tool at our disposal to mitigate virus spread. In this large-scale naturalistic experimental study we show how we can use behavioural science to find ways to promote the desired physical distancing behaviour. During seven days in a supermarket we implemented different behavioural interventions: (i) rewarding customers for keeping distance; (i) providing signage to guide customers; and (iii) altering shopping cart regulations. We asked customers to wear a tag that measured distances to other tags using ultra-wide band at 1Hz. In total N = 4, 232 customers participated in the study. We compared the number of contacts (< 1.5 m, corresponding to Dutch regulations) between customers using state-of-the-art contact network analyses. We found that rewarding customers and providing signage increased physical distancing, whereas shopping cart regulations did not impact physical distancing. Rewarding customers moreover reduced the duration of remaining contacts between customers. These results demonstrate the feasibility to conduct large-scale behavioural experiments that can provide guidelines for policy. While the COVID-19 crisis unequivocally demonstrates the importance of behaviour and behavioural change, behaviour is integral to many crises, like the trading of mortgages in the financial crisis or the consuming of goods in the climate crisis. We argue that by acknowledging the role of behaviour in crises, and redefining this role in terms of the desired behaviour and necessary behavioural change, behavioural science can open up new solutions to crises and inform policy. We believe that we should start taking advantage of these opportunities.

The network of psychosocial health in middle-aged and older adults during the first COVID-19 lockdown.

PURPOSE: Psychosocial health problems, such as social isolation, loneliness, depression and anxiety, have gained attention during the COVID-19 pandemic and are commonly co-occurring. We investigated the network of psychosocial health constructs during the COVID-19 pandemic. METHODS: This study included 4553 participants (mean age: 68.6 ± 11.2 years, 56% women) from the prospective Rotterdam Study, who filled out a questionnaire between April and July 2020, the time of the first COVID-19 wave in the Netherlands. Psychosocial health constructs included were depressive symptoms (Center for Epidemiological Studies Depression scale), anxiety symptoms (Hospital Anxiety and Depression scale), loneliness (University of California, Los Angeles loneliness scale), social connectedness (five items) and pandemic-related worry (five items). We estimated mixed graphical models to assess the network of items of these constructs and whether age and sex affected the network structure. RESULTS: Within the network of psychosocial constructs, a higher depressive symptoms score was particularly associated with items of loneliness and social connectedness, whereas overall anxiety was particularly associated with items of pandemic-related worry. Between people from different sex and age, the network structure significantly altered. CONCLUSION: This study demonstrates that within the same network of psychosocial health constructs, depressive symptom score is particularly associated with loneliness and social connectedness, whereas anxiety symptom score is associated with pandemic-related worry during the first COVID-19 lockdown. Our results support that psychosocial constructs should be considered in conjunction with one another in prevention and treatment efforts in clinical care, and that these efforts need to be tailored to specific demographic groups.

Insomnia disorder: State of the science and challenges for the future.

Insomnia disorder comprises symptoms during night and day that strongly affect quality of life and wellbeing. Prolonged sleep latency, difficulties to maintain sleep and early morning wakening characterize sleep complaints, whereas fatigue, reduced attention, impaired cognitive functioning, irritability, anxiety and low mood are key daytime impairments. Insomnia disorder is well acknowledged in all relevant diagnostic systems: Diagnostic and Statistical Manual of the American Psychiatric Association, 5th revision, International Classification of Sleep Disorders, 3rd version, and International Classification of Diseases, 11th revision. Insomnia disorder as a chronic condition is frequent (up to 10% of the adult population, with a preponderance of females), and signifies an important and independent risk factor for physical and, especially, mental health. Insomnia disorder diagnosis primarily rests on self-report. Objective measures like actigraphy or polysomnography are not (yet) part of the routine diagnostic canon, but play an important role in research. Disease concepts of insomnia range from cognitive-behavioural models to (epi-) genetics and psychoneurobiological approaches. The latter is derived from knowledge about basic sleep-wake regulation and encompass theories like rapid eye movement sleep instability/restless rapid eye movement sleep. Cognitive-behavioural models of insomnia led to the conceptualization of cognitive-behavioural therapy for insomnia, which is now considered as first-line treatment for insomnia worldwide. Future research strategies will include the combination of experimental paradigms with neuroimaging and may benefit from more attention to dysfunctional overnight alleviation of distress in insomnia. With respect to therapy, cognitive-behavioural therapy for insomnia merits widespread implementation, and digital cognitive-behavioural therapy may assist delivery along treatment guidelines. However, given the still considerable proportion of patients responding insufficiently to cognitive-behavioural therapy for insomnia, fundamental studies are highly necessary to better understand the brain and behavioural mechanisms underlying insomnia. Mediators and moderators of treatment response/non-response and the associated development of tailored and novel interventions also require investigation. Recent studies suggest that treatment of insomnia may prove to add significantly as a preventive strategy to combat the global burden of mental disorders.

A network analysis of self-reported sleep bruxism in the Netherlands sleep registry: its associations with insomnia and several demographic, psychological, and life-style factors.

OBJECTIVES: To investigate the association between self-reported sleep bruxism and insomnia and their potential risk factors (eg, depression and anxiety), and to construct a network model with all these factors. METHODS: We recruited 2251 participants from the Netherlands Sleep Registry. All participants completed questionnaires on self-reported sleep bruxism, insomnia, depression, anxiety, smoking frequency, and alcohol and caffeine consumption. The associations between self-reported sleep bruxism and other variables were analyzed by univariate analysis, multivariate logistic regression, and network analysis. RESULTS: Although univariate analysis showed that there was a positive association between sleep bruxism and insomnia (P < 0.001), this association disappeared in the multivariate logistic regression model (P = 0.258). However, multivariate logistic regression did show an association between self-reported sleep bruxism and anxiety (OR = 1.087, 95% CI 1.041-1.134). The network model showed that there was no direct link between self-reported sleep bruxism and insomnia. However, there was an indirect link between self-reported sleep bruxism and insomnia via anxiety. CONCLUSIONS: Although self-reported sleep bruxism has no direct association with insomnia, anxiety is a bridging factor between these variables.

Quantifying agent impacts on contact sequences in social interactions.

Human social behavior plays a crucial role in how pathogens like SARS-CoV-2 or fake news spread in a population. Social interactions determine the contact network among individuals, while spreading, requiring individual-to-individual transmission, takes place on top of the network. Studying the topological aspects of a contact network, therefore, not only has the potential of leading to valuable insights into how the behavior of individuals impacts spreading phenomena, but it may also open up possibilities for devising effective behavioral interventions. Because of the temporal nature of interactions-since the topology of the network, containing who is in contact with whom, when, for how long, and in which precise sequence, varies (rapidly) in time-analyzing them requires developing network methods and metrics that respect temporal variability, in contrast to those developed for static (i.e., time-invariant) networks. Here, by means of event mapping, we propose a method to quantify how quickly agents mingle by transforming temporal network data of agent contacts. We define a novel measure called contact sequence centrality, which quantifies the impact of an individual on the contact sequences, reflecting the individual's behavioral potential for spreading. Comparing contact sequence centrality across agents allows for ranking the impact of agents and identifying potential 'behavioral super-spreaders'. The method is applied to social interaction data collected at an art fair in Amsterdam. We relate the measure to the existing network metrics, both temporal and static, and find that (mostly at longer time scales) traditional metrics lose their resemblance to contact sequence centrality. Our work highlights the importance of accounting for the sequential nature of contacts when analyzing social interactions.

Treating Insomnia with High Risk of Depression Using Therapist-Guided Digital Cognitive, Behavioral, and Circadian Rhythm Support Interventions to Prevent Worsening of Depressive Symptoms: A Randomized Controlled Trial.

INTRODUCTION: The global disease burden of major depressive disorder urgently requires prevention in high-risk individuals, such as recently discovered insomnia subtypes. Previous studies targeting insomnia with fully automated eHealth interventions to prevent depression are inconclusive: dropout was high and likely biased, and depressive symptoms in untreated participants on average improved rather than worsened. OBJECTIVE: This randomized controlled trial aimed to efficiently prevent the worsening of depressive symptoms by selecting insomnia subtypes at high risk of depression for internet-based circadian rhythm support (CRS), cognitive behavioral therapy for insomnia (CBT-I), or their combination (CBT-I+CRS), with online therapist guidance to promote adherence. METHODS: Participants with an insomnia disorder subtype conveying an increased risk of depression (n = 132) were randomized to no treatment (NT), CRS, CBT-I, or CBT-I+CRS. The Inventory of Depressive Symptomatology - Self Report (IDS-SR) was self-administered at baseline and at four follow-ups spanning 1 year. RESULTS: Without treatment, depressive symptoms indeed worsened (d = 0.28, p = 0.041) in high-risk insomnia, but not in a reference group with low-risk insomnia. Therapist-guided CBT-I and CBT-I+CRS reduced IDS-SR ratings across all follow-up assessments (respectively, d = -0.80, p = 0.001; d = -0.95, p < 0.001). Only CBT-I+CRS reduced the 1-year incidence of clinically meaningful worsening (p = 0.002). Dropout during therapist-guided interventions was very low (8%) compared to previous automated interventions (57-62%). CONCLUSIONS: The findings tentatively suggest that the efficiency of population-wide preventive strategies could benefit from the possibility to select insomnia subtypes at high risk of developing depression for therapist-guided digital CBT-I+CRS. This treatment may provide effective long-term prevention of worsening of depressive symptoms. TRIAL REGISTRATION: the Netherlands Trial Register (NL7359).

Promoting physical distancing during COVID-19: a systematic approach to compare behavioral interventions.

In the wake of the COVID-19 pandemic, physical distancing behavior turned out to be key to mitigating the virus spread. Therefore, it is crucial that we understand how we can successfully alter our behavior and promote physical distancing. We present a framework to systematically assess the effectiveness of behavioral interventions to stimulate physical distancing. In addition, we demonstrate the feasibility of this framework in a large-scale natural experiment (N = 639) conducted during an art fair. In an experimental design, we varied interventions to evaluate the effect of face masks, walking directions, and immediate feedback on visitors' contacts. We represent visitors as nodes, and their contacts as links in a contact network. Subsequently, we used network modelling to test for differences in these contact networks. We find no evidence that face masks influence physical distancing, while unidirectional walking directions and buzzer feedback do positively impact physical distancing. This study offers a feasible way to optimize physical distancing interventions through scientific research. As such, the presented framework provides society with the means to directly evaluate interventions, so that policy can be based on evidence rather than conjecture.

Smart Distance Lab's art fair, experimental data on social distancing during the COVID-19 pandemic.

In the absence of a vaccine, social distancing behaviour is pivotal to mitigate COVID-19 virus spread. In this large-scale behavioural experiment, we gathered data during Smart Distance Lab: The Art Fair (n = 839) between August 28 and 30, 2020 in Amsterdam, the Netherlands. We varied walking directions (bidirectional, unidirectional, and no directions) and supplementary interventions (face mask and buzzer to alert visitors of 1.5 metres distance). We captured visitors' movements using cameras, registered their contacts (defined as within 1.5 metres) using wearable sensors, and assessed their attitudes toward COVID-19 as well as their experience during the event using questionnaires. We also registered environmental measures (e.g., humidity). In this paper, we describe this unprecedented, multi-modal experimental data set on social distancing, including psychological, behavioural, and environmental measures. The data set is available on figshare and in a MySQL database. It can be used to gain insight into (attitudes toward) behavioural interventions promoting social distancing, to calibrate pedestrian models, and to inform new studies on behavioural interventions.

Connecting brain and behavior in clinical neuroscience: A network approach.

In recent years, there has been an increase in applications of network science in many different fields. In clinical neuroscience and psychopathology, the developments and applications of network science have occurred mostly simultaneously, but without much collaboration between the two fields. The promise of integrating these network applications lies in a united framework to tackle one of the fundamental questions of our time: how to understand the link between brain and behavior. In the current overview, we bridge this gap by introducing conventions in both fields, highlighting similarities, and creating a common language that enables the exploitation of synergies. We provide research examples in autism research, as it accurately represents research lines in both network neuroscience and psychological networks. We integrate brain and behavior not only semantically, but also practically, by showcasing three methodological avenues that allow to combine networks of brain and behavioral data. As such, the current paper offers a stepping stone to further develop multi-modal networks and to integrate brain and behavior.

Symptom-specific effects of cognitive therapy and behavior therapy for insomnia: A network intervention analysis.

Objective: Cognitive therapy (CT) and behavior therapy (BT) are both effective for insomnia but are expected to work via different pathways. Empirically, little is known about their symptom-specific effects. Method: This was a secondary analysis of a randomized controlled trial of online treatment for insomnia disorder (N = 219, 72.9% female, mean age = 52.5 years, SD = 13.9). Participants were randomized to CT (n = 72), BT (n = 73), or wait-list (n = 74). Network Intervention Analysis was used to investigate the symptom-specific treatment effects of CT and BT throughout treatment (wait-list was excluded from the current study). The networks included the Insomnia Severity Index items and the sleep diary-based sleep efficiency and were estimated biweekly from Week 0 until Week 10. Results: Participants in the BT condition showed symptom-specific effects compared to CT on "sleep efficiency" (Week 4-8, post-test), "difficulty maintaining sleep" (Week 4), and "dissatisfaction with sleep" (post-test). Participants in the CT showed symptom-specific effects compared to BT on "interference with daily functioning" (Week 8, post-test), "difficulty initiating sleep", "early morning awakenings," and "worry about sleep" (all post-test). Conclusions: This is the first study that observed specific differential treatment effects for BT and CT throughout the course of their treatment. These effects were more pronounced for BT than for CT and were in line with the theoretical background of these treatments. We think the embedment of the theoretical background of CT and BT in empirical data is of major importance to guide further treatment development. (PsycInfo Database Record (c) 2021 APA, all rights reserved).