Does emotional reasoning change during cognitive behavioural therapy for anxiety?

© 2015 Swedish Association for Behaviour Therapy. Abstract: Emotional reasoning refers to the use of subjective emotions, rather than objective evidence, to form conclusions about oneself and the world. It is a key interpretative bias in cognitive models of anxiety disorders and appears to be especially evident in individuals with anxiety disorders. However, the amenability of emotional reasoning to change during treatment has not yet been investigated. We sought to determine whether emotional reasoning tendencies change during a course of routine cognitive-behavioural therapy (CBT). Emotional reasoning tendencies were assessed in 36 individuals with a primary anxiety disorder who were seeking treatment at an outpatient clinic. Changes in anxiety and depressive symptoms as well as emotional reasoning tendencies after 12 sessions of CBT were examined in 25 individuals for whom there was complete data. Emotional reasoning tendencies were evident at pretreatment assessment. Although anxiety and depressive symptoms decreased during CBT, only one of six emotional reasoning interpretative styles (pertaining to conclusions that one is incompetent) changed significantly during the course of therapy. Attrition rates were high and there was not enough information regarding the extent to which therapy specifically focused on addressing emotional reasoning tendencies. Individuals seeking treatment for anxiety disorders appear to engage in emotional reasoning, however routine individual CBT does not appear to result in changes in emotional reasoning tendencies

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy

Australian Sphingidae – DNA Barcodes Challenge Current Species Boundaries and Distributions

© 2014 Rougerie et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The attached file is the published version of the article

Affect and mental health across the lifespan during a year of the COVID-19 pandemic: The role of emotion regulation strategies and mental flexibility

Online First Publication, May 18, 2023. OnlinePublDuring the COVID-19 pandemic, there has been a rise in common mental health problems compared to prepandemic levels, especially in young people. Understanding the factors that place young people at risk is critical to guide the response to increased mental health problems. Here we examine whether age-related differences in mental flexibility and frequency of use of emotion regulation strategies partially account for the poorer affect and increased mental health problems reported by younger people during the pandemic. Participants (N= 2,367; 11–100 years) from Australia, the UK, and US were surveyed thrice at 3-month intervals between May 2020 and April 2021. Participants completed measures of emotion regulation, mental flexibility, affect, and mental health. Younger age was associated with less positive (b=0.008, p,.001) and more negative (b=−0.015, p,.001) affect across the first year of the pandemic. Maladaptive emotion regulation partially accounted for age-related variance in negative affect (β=−0.013, p=.020), whereby younger age was associated with more frequent use of maladaptive emotion regulation strategies, which, in turn, was associated with more negative affect at our third assessment point. More frequent use of adaptive emotion regulation strategies, and in turn, changes in negative affect from our first to our third assessment, partially accounted for age-related variance in mental health problems (β= 0.007, p=.023). Our findings add to the growing literature demonstrating the vulnerability of younger people during the COVID-19 pandemic and suggest that emotion regulation may be a promising target for intervention.Savannah Minihan, Annabel Songco, Elaine Fox, Cecile D. Ladouceur, Louise Mewton, Michelle Moulds, Jennifer H. Pfeifer, Anne-Laura Van Harmelen, and Susanne Schweize

The effect of intolerance of uncertainty on anxiety and depression, and their symptom networks, during the COVID-19 pandemic

Individuals vary in their ability to tolerate uncertainty. High intolerance of uncertainty (the tendency to react nega‑ tively to uncertain situations) is a known risk factor for mental health problems. In the current study we examined the degree to which intolerance of uncertainty predicted depression and anxiety symptoms and their interrelations across the frst year of the COVID-19 pandemic. We examined these associations across three time points (May 2020 – April 2021) in an international sample of adults (N=2087, Mean age=41.13) from three countries (UK, USA, Australia) with varying degrees of COVID-19 risk. We found that individuals with high and moderate levels of intolerance of uncertainty reported reductions in depression and anxiety symptoms over time. However, symptom levels remained signifcantly elevated compared to individuals with low intolerance of uncertainty. Individuals with low intolerance of uncertainty had low and stable levels of depression and anxiety across the course of the study. Network analyses further revealed that the relationships between depression and anxiety symptoms became stronger over time among individuals with high intolerance of uncertainty and identified that feeling afraid showed the strongest association with intolerance of uncertainty. Our findings are consistent with previous work identifying intolerance of uncertainty as an important risk factor for mental health problems, especially in times marked by actual health, economic and social uncertainty. The results highlight the need to explore ways to foster resilience among individuals who struggle to tolerate uncertainty, as ongoing and future geopolitical, climate and health threats will likely lead to continued exposure to significant uncertainty.Jack L. Andrews, Meiwei Li, Savannah Minihan, Annabel Songco, Elaine Fox, Cecile D. Ladouceur, Louise Mewton, Michelle Moulds, Jennifer H. Pfeifer, Anne, Laura Van Harmelen, and Susanne Schweize