Telephone-delivered cognitive-behavioral therapy for pain management among older military veterans: a randomized trial.

This study investigated the effectiveness of telephone-delivered cognitive-behavioral therapy (T-CBT) in the management of chronic pain with older military veterans enrolled in VA primary-care clinics. We conducted a randomized clinical trial comparing T-CBT with telephone-delivered pain education (T-EDU). A total of 98 military veterans with chronic pain were enrolled in the study and randomized into one of two treatment conditions. Study participants were recruited from primary-care clinics at an urban VA medical center and affiliated VA community-based outpatient clinics (CBOCs). Pain management outcomes were measured at midtreatment (10 weeks), posttreatment (20 weeks), 3-month follow-up (32 weeks), and 6-month follow-up (46 weeks). No significant differences were found between the two treatment groups on any of the outcome measures. Both treatment groups reported small but significant increases in level of physical and mental health, and reductions in pain and depressive symptoms. Improvements in all primary outcome measures were mediated by reductions in catastrophizing. Telephone-delivered CBT and EDU warrant further study as easily accessible interventions for rural-living older individuals with chronic pain

Emotion Regulation in the Prisoner’s Dilemma: Effects of Reappraisal on Behavioral Measures and Cardiovascular Measures of Challenge and Threat

The current study examines cooperation and cardiovascular responses in individuals that were defected on by their opponent in the first round of an iterated Prisoner’s Dilemma. In this scenario, participants were either primed with the emotion regulation strategy of reappraisal or no emotion regulation strategy, and their opponent either expressed an amused smile or a polite smile after the results were presented. We found that cooperation behavior decreased in the no emotion regulation group when the opponent expressed an amused smile compared to a polite smile. In the cardiovascular measures, we found significant differences between the emotion regulation conditions using the biopsychosocial (BPS) model of challenge and threat. However, the cardiovascular measures of participants instructed with the reappraisal strategy were only weakly comparable with a threat state of the BPS model, which involves decreased blood flow and perception of greater task demands than resources to cope with those demands. Conversely, the cardiovascular measures of participants without an emotion regulation were only weakly comparable with a challenge state of the BPS model, which involves increased blood flow and perception of having enough or more resources to cope with task demands

AVEC 2017--Real-life depression, and affect recognition workshop and challenge

The Audio/Visual Emotion Challenge and Workshop (AVEC 2017) “Real-life depression, and affect” will be the seventh competition event aimed at comparison of multimedia processing and machine learning methods for automatic audiovisual depression and emotion analysis, with all participants competing under strictly the same conditions. .e goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the depression and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of the various approaches to depression and emotion recognition from real-life data. .is paper presents the novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline system on the two proposed tasks: dimensional emotion recognition (time and value-continuous), and dimensional depression estimation (value-continuous)

The Relationship Between Task-induced Stress, Vocal Changes, and Physiological State During a Dyadic Team Task

It is commonly known that a relationship exists between the human voice and various emotional states. Past studies have demonstrated changes in a number of vocal features, such as fundamental frequency f0 and peakSlope, as a result of varying emotional state. These voice characteristics have been shown to relate to emotional load, vocal tension, and, in particular, stress. Although much research exists in the domain of voice analysis, few studies have assessed the relationship between stress and changes in the voice during a dyadic team interaction. The aim of the present study was to investigate the multimodal interplay between speech and physiology during a high-workload, high-stress team task. Specifically, we studied task-induced effects on participants' vocal signals, specifically, the f0 and peakSlope features, as well as participants' physiology, through cardiovascular measures. Further, we assessed the relationship between physiological states related to stress and changes in the speaker's voice. We recruited participants with the specific goal of working together to diffuse a simulated bomb. Half of our sample participated in an "Ice Breaker" scenario, during which they were allowed to converse and familiarize themselves with their teammate prior to the task, while the other half of the sample served as our "Control". Fundamental frequency (f0), peakSlope, physiological state, and subjective stress were measured during the task. Results indicated that f0 and peakSlope significantly increased from the beginning to the end of each task trial, and were highest in the last trial, which indicates an increase in emotional load and vocal tension. Finally, cardiovascular measures of stress indicated that the vocal and emotional load of speakers towards the end of the task mirrored a physiological state of psychological "threat"

NADiA - Towards Neural Network Driven Virtual Human Conversation Agents

Advances in artificial intelligence and machine learning - in particular neural networks - have given rise to a new generation of virtual assistants and chatbots. Within this work, we describe the motivation and architecture of NADiA - Neurally Animated Dialog Agent - which leverages both the user's verbal input and facial expressions for multi-modal conversation. NADiA combines a neural language model that generates conversational responses, a convolutional neural network for facial expression analysis, and virtual human technology that is deployed on a mobile phone

NADiA -Towards Neural Network Driven Virtual Human Conversation Agents

International audienceAdvances in artificial intelligence and machine learning-in particular neural networks-have given rise to a new generation of virtual assistants and chatbots. Within this work, we describe the motivation and architecture of NADiA-Neurally Animated Dialog Agent-which leverages both the user's verbal input and facial expressions for multi-modal conversation. NADiA combines a neural language model that generates conversational responses, a convolutional neural network for facial expression analysis, and virtual human technology that is deployed on a mobile phone

Machine learning for semi-automated scoping reviews

Scoping reviews are a type of research synthesis that aim to map the literature on a particular topic or research area. Though originally intended to provide a quick overview of a field of research, scoping review teams have been overwhelmed in recent years by a deluge of available research literature. This work presents the interdisciplinary development of a semi-automated scoping review methodology aimed at increasing the objectivity and speed of discovery in scoping reviews as well as the scalability of the scoping review process to datasets with tens of thousands of publications. To this end we leverage modern representation learning algorithms based on transformer models and established clustering methods to discover evidence maps, key themes within the data, knowledge gaps within the literature, and assess the feasibility of follow-on systematic reviews within a certain topic. To demonstrate the wide applicability of this methodology, we apply the here proposed semi-automated method to two separate datasets, a Virtual Human dataset with more than 30,000 peer-reviewed academic articles and a smaller Self-Avatar dataset with less than 500 peer-reviewed articles. To enable collaboration, we provide full access to analyzed datasets, keyword and author word clouds, as well as interactive evidence maps

AVEC 2017--Real-life depression, and affect recognition workshop and challenge

The Audio/Visual Emotion Challenge and Workshop (AVEC 2017) “Real-life depression, and affect” will be the seventh competition event aimed at comparison of multimedia processing and machine learning methods for automatic audiovisual depression and emotion analysis, with all participants competing under strictly the same conditions. .e goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the depression and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of the various approaches to depression and emotion recognition from real-life data. .is paper presents the novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline system on the two proposed tasks: dimensional emotion recognition (time and value-continuous), and dimensional depression estimation (value-continuous)