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.
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.
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.
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.
The Netherlands Organisation for Health Research and Development, Dutch Research Council, University of Amsterdam.
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, posttest), 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.
A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of ...brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research.
While Acceptance and Commitment Therapy for Insomnia (ACT-I) has been proposed as a promising alternative to Cognitive Behavioral Therapy for Insomnia, its efficacy as a distinct alternative, without ...sleep restriction and stimulus control, remains largely unknown. In this protocol paper, we describe a randomized controlled trial that aims to test the efficacy of ACT-I as a stand-alone intervention for insomnia. Adults with insomnia (N = 80) will be randomly allocated to five individual sessions of ACT-I or a waitlist control group. The main objective is to assess whether ACT-I is superior to the control group in improving insomnia severity, alongside secondary outcomes including sleep diary measures, anxiety, depression, general well-being, and sleep-related quality of life. Additionally, we aim to explore potential mechanisms of ACT-I, including psychological (in)flexibility, sleep-related arousal, dysfunctional cognitions, and sleep-related safety behaviors. Both the treatment and waiting period span 7 weeks. Assessments take place at baseline (pre), after 4 weeks (mid), and after 8 weeks (post), followed by a 3- and 6-month follow-up for the ACT-I group. Treatment effects will be analyzed with mixed linear regression based on the intention-to-treat principle, and potential mechanisms will be explored with network intervention analysis. This study contributes to the understanding of ACT-I’s treatment effects and potential working mechanisms, informing clinical practice on whether ACT-I without sleep restriction or stimulus control could provide an adequate alternative treatment for insomnia. Trial registration number: NCT06336551.
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.
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.
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.
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