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Given the highly infectious nature of COVID-19, social distancing practices are key in stemming the spread of the virus. We aimed to assess the complex interplay among psychological factors, socio-demographic characteristics and social distancing behaviours within the framework of the widely used Capability, Opportunity, Motivation-Behaviour (COM-B) model. The present research employed network psychometrics on data collected during the first UK lockdown in April 2020 as part of the COVID-19 Psychological Research Consortium (C19PRC) Study. Using a network approach, we examined the predictions of psychological and demographic variables onto social distancing practices at two levels of analysis: macro and micro. Our findings revealed several factors that influenced social distancing behaviour during the first UK lockdown. The COM-B model was successful in predicting particular aspects of social-distancing via the influence of psychological capability and motivation at the macro-and micro-levels, respectively. Notably, demographic variables, such as education, income, and age, were directly and uniquely predictive of certain social distancing behaviours. Our findings reveal psychological factors that are key predictors of social distancing behaviour and also illustrate how demographic variables directly influence such behaviour. Our research has implications for the design of empirically-driven interventions to promote adherence to social distancing practices in this and future pandemics. Supplemental data for this article is available online at [ABSTRACT FROM AUTHOR]

Background: The current study argues that population prevalence estimates for mental health disorders, or changes in mean scores over time, may not adequately reflect the heterogeneity in mental health response to the COVID-19 pandemic within the population. Methods: The COVID-19 Psychological Research Consortium (C19PRC) Study is a longitudinal, nationally representative, online survey of UK adults. The current study analysed data from its first three waves of data collection: Wave 1 (March 2020, N = 2025), Wave 2 (April 2020, N = 1406) and Wave 3 (July 2020, N = 1166). Anxiety-depression was measured using the Patient Health Questionnaire Anxiety and Depression Scale (a composite measure of the PHQ-9 and GAD-7) and COVID-19-related posttraumatic stress disorder (PTSD) with the International Trauma Questionnaire. Changes in mental health outcomes were modelled across the three waves. Latent class growth analysis was used to identify subgroups of individuals with different trajectories of change in anxiety-depression and COVID-19 PTSD. Latent class membership was regressed on baseline characteristics. Results: Overall prevalence of anxiety-depression remained stable, while COVID-19 PTSD reduced between Waves 2 and 3. Heterogeneity in mental health response was found, and hypothesised classes reflecting (i) stability, (ii) improvement and (iii) deterioration in mental health were identified. Psychological factors were most likely to differentiate the improving, deteriorating and high-stable classes from the low-stable mental health trajectories. Conclusions: A low-stable profile characterised by little-to-no psychological distress ('resilient' class) was the most common trajectory for both anxiety-depression and COVID-19 PTSD. Monitoring these trajectories is necessary moving forward, in particular for the ~30% of individuals with increasing anxiety-depression levels. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) emergency has led to numerous attempts to assess the impact of the pandemic on population mental health. The findings indicate an increase in depression and anxiety but have been limited by the lack of specificity about which aspects of the pandemic (e.g. viral exposure or economic threats) have led to adverse mental health outcomes. Methods: Network analyses were conducted on data from wave 1 (N = 2025, recruited 23 March–28 March 2020) and wave 2 (N = 1406, recontacts 22 April–1 May 2020) of the COVID-19 Psychological Research Consortium Study, an online longitudinal survey of a representative sample of the UK adult population. Our models included depression (PHQ-9), generalized anxiety (GAD-7) and trauma symptoms (ITQ); and measures of COVID-specific anxiety, exposure to the virus in self and close others, as well as economic loss due to the pandemic. Results: A mixed graphical model at wave 1 identified a potential pathway from economic adversity to anxiety symptoms via COVID-specific anxiety. There was no association between viral exposure and symptoms. Ising network models using clinical cut-offs for symptom scores at each wave yielded similar findings, with the exception of a modest effect of viral exposure on trauma symptoms at wave 1 only. Anxiety and depression symptoms formed separate clusters at wave 1 but not wave 2. Conclusions: The psychological impact of the pandemic evolved in the early phase of lockdown. COVID-related anxiety may represent the mechanism through which economic consequences of the pandemic are associated with psychiatric symptoms. [ABSTRACT FROM AUTHOR]

Objective: While there is now strong evidence that psychological therapies can alter the activity of individual brain regions, their impact on the functional integration between regions has not yet been systematically evaluated. This area is important given that brain dysconnectivity has been implicated across almost all psychiatric disorders. Accordingly, we sought to establish connectivity predictors and mechanisms of effective psychological therapies. We further establish whether connectivity changes represent normalisation of disorder pathophysiology or compensatory changes. Method: We reviewed studies examining structural and functional connectivity longitudinally as either a predictor or outcome variable of successful psychological therapies across psychiatric disorders. Results: Fifteen studies met our inclusion criteria. All but three related to cognitive behavioural therapy. Of these, five assessed resting state, nine probed affective processing and one probed cognitive processing. Twelve studies reported evidence of functional connectivity as a significant predictor or outcome of cognitive behavioural therapy, with prefronto-limbic circuitry most commonly implicated. Only six studies included healthy participants, limiting direct inferences about normalisation as opposed to compensatory changes. Anxiety disorders were overrepresented, totalling 13 of the studies reviewed. No studies examined structural connectivity or utilised analyses allowing the directionality of functional connectivity to be inferred. Conclusion: While the evidence base is still in its infancy for other therapy approaches, there was clearer evidence that functional connectivity both predicts and is altered by cognitive behavioural therapy. Connections from prefrontal cortex appear especially key, perhaps given their role in cognitive appraisal of lower order affective, motivational and cognitive processes. A number of recommendations are made for this rapidly developing literature. [ABSTRACT FROM AUTHOR]