tunepig047

These novel findings are discussed in the context of improving CT for depression among patients with greater lifetime history of SLE and require replication before clinical application.One potential factor that could influence how individuals with at least moderate symptoms of depression cope with upsetting events in their daily lives is the beliefs that these individuals hold about whether emotions are malleable or fixed. The current study adopted an experience sampling approach to examine how the beliefs about emotion's malleability related to daily positive and negative affect and daily emotion regulation efforts among individuals with at least moderate symptoms of depression (N = 84). Results demonstrated that individuals having at least moderate symptoms of depression who held more malleable beliefs about emotions reported decreased negative affect both overall during the day and specifically in response to daily upsetting events. https://www.selleckchem.com/products/snx-2112.html Additionally, these individuals who held more malleable beliefs about their emotions also reported more daily use of cognitive reappraisal to regulate their emotions in response to upsetting daily events. Results from the current study extend previous work examining the relationship between emotion malleability beliefs, emotional experiences, and emotion regulation to examine these relationships in people who are moderately depressed as they navigate the emotional landscape of their daily lives.This study examined the function of hoarding behaviors and the relations between hoarding and a series of cognitive and affective processes in the moment using ecological momentary assessment. A matched-groups design was used to compare college students with higher hoarding symptoms (n = 31) and matched controls (n = 29). The two groups did not differ in what function they reported acquiring served, and positive automatic reinforcement was the most commonly reported function in both groups. Engaging in hoarding-relevant behaviors did not predict change in positive or negative affect when controlling for previous affect. Emotional reactivity and experiential avoidance in the moment were both elevated in the higher hoarding group compared to controls, while momentary mindfulness and negative affect differentiation were lower. Overall, these findings support the importance of emotion regulation processes in hoarding. They also suggest individuals may not be successfully regulating affect in the moment with hoarding behaviors, despite efforts to do so. It may be useful to evaluate processes such as striving for positive affect in hoarding disorder in the future.Cognitive-behavioral conjoint therapy for posttraumatic stress disorder (CBCT for PTSD; Monson & Fredman, 2012) is efficacious in improving PTSD symptoms and relationship adjustment among couples with PTSD. However, there is a need for more efficient delivery formats to maximize engagement and retention and to achieve faster outcomes in multiple domains. This nonrandomized trial was designed to pilot an abbreviated, intensive, multi-couple group version of CBCT for PTSD (AIM-CBCT for PTSD) delivered over a single weekend for 24 couples that included an active-duty service member or veteran with PTSD who had deployed in support of combat operations following September 11, 2001. All couples completed treatment. Assessments conducted by clinical evaluators 1 and 3 months after the intervention revealed significant reductions in clinician-rated PTSD symptoms (ds = -0.77 and -0.98, respectively) and in patients' self-reported symptoms of PTSD (ds = -0.73 and -1.17, respectively), depression (ds = -0.60 and -0.75, respectively), anxiety (ds = -0.63 and -0.73, respectively), and anger (ds = -0.45 and -0.60, respectively), relative to baseline. By 3-month follow-up, partners reported significant reductions in patients' PTSD symptoms (d = -0.56), as well as significant improvements in their own depressive symptoms (d = -0.47), anxiety (d = -0.60), and relationship satisfaction (d = 0.53), relative to baseline. Delivering CBCT for PTSD through an abbreviated, intensive multi-couple group format may be an efficient strategy for improving patient, partner, and relational well-being in military and veteran couples with PTSD.Presleep cognitive intrusions about next-day activities, or proprioceptive and environmental stimuli, are thought to trigger insomnia in neurocognitive models. Recent research showed that intrusive cognitions at bedtime may interact with sleep in influencing next-day emotional functioning; their effects on cognitive functioning, however, is largely unknown. We tested the effects of presleep cognitive intrusions on subjective sleep and next-day cognitive performance in 80 participants, either with chronic insomnia or good sleepers. Presleep intrusions were inspected using a validated questionnaire and sleep was assessed with a sleep diary. Cognitive functioning the following morning was measured using a task-switching paradigm assessing executive functions. Structural equation modeling with manifest variables (i.e., path analysis) shows that presleep cognitive intrusions predicted increased sleep onset latency and wake after sleep onset, and lowered sleep efficiency. Moreover, task-switching accuracy was independently predicted by presleep cognitive intrusions in the previous night in those with insomnia but not in controls, beyond the effects of trait anxiety, task-switching components, and previous night's sleep. Findings confirm detrimental effects of presleep intrusions on sleep continuity and suggest the presence of links between presleep conscious activity and next-day executive performance in patients with insomnia, with the need to better elucidate potential mediators.Machine learning is increasingly used in mental health research and has the potential to advance our understanding of how to characterize, predict, and treat mental disorders and associated adverse health outcomes (e.g., suicidal behavior). Machine learning offers new tools to overcome challenges for which traditional statistical methods are not well-suited. This paper provides an overview of machine learning with a specific focus on supervised learning (i.e., methods that are designed to predict or classify an outcome of interest). Several common supervised learning methods are described, along with applied examples from the published literature. We also provide an overview of supervised learning model building, validation, and performance evaluation. Finally, challenges in creating robust and generalizable machine learning algorithms are discussed.