Using wearable technology to detect prescription opioid self-administration

Appropriate monitoring of opioid use in patients with pain conditions is paramount, yet it remains a very challenging task. The current work examined the use of a wearable sensor to detect self-administration of opioids after dental surgery using machine learning. Participants were recruited from an oral and maxillofacial surgery clinic. Participants were 46 adult patients (26 female) receiving opioids after dental surgery. Participants wore Empatica E4 sensors during the period they self-administered opioids. The E4 collected physiological parameters including accelerometer x-, y-, and z-axes, heart rate, and electrodermal activity. Four machine learning models provided validation accuracies greater than 80%, but the bagged-tree model provided the highest combination of validation accuracy (83.7%) and area under the receiver operating characteristic curve (0.92). The trained model had a validation sensitivity of 82%, a specificity of 85%, a positive predictive value of 85%, and a negative predictive value of 83%. A subsequent test of the trained model on withheld data had a sensitivity of 81%, a specificity of 88%, a positive predictive value of 87%, and a negative predictive value of 82%. Results from training and testing model of machine learning indicated that opioid self-administration could be identified with reasonable accuracy, leading to considerable possibilities of the use of wearable technology to advance prevention and treatment

Impulsivity and Attention Deficit-Hyperactivity Disorder: Subtype Classification Using the UPPS Impulsive Behavior Scale

This study examined the classification accuracy of the UPPS Impulsive Behavior Scale (UPPS) in discriminating several attention deficit/hyperactivity disorder (ADHD) subtypes, including predominantly inattentive type (ADHD/I), combined type (ADHD/C), and combined type with behavioral problems (ADHD/ODD), between each other and a non-ADHD control group using logistic regression analyses. The sample consisted of 88 children ranging in age from 9.0 years to 12.8 years, with a mean of 10.9 years. Children were predominantly male (74%) and Caucasian (86%) and in grades 3–7. Results indicated that the UPPS performed well in classifying ADHD subtypes relative to traditional diagnostic measures. In addition, analyses indicated that differences in symptoms between subtypes can be explained by specific pathways to impulsivity. Implications for the assessment of ADHD and conceptual issues are discussed

Transtorno do Déficit de Atenção/Hiperatividade: o que nos informa a investigação dimensional? Attention Deficit/Hyperactivity Disorder: what does the dimensional research inform us?

Investiga-se o Transtorno do Déficit de Atenção/Hiperatividade (TDAH) em contexto escolar adotando a perspectiva dimensional. Participaram 107 crianças com idade média de 12,37 anos. Utilizou-se a Escala de TDAH - versão para professores, uma versão adaptada para pais e tarefas computadorizadas de processamento cognitivo. A prevalência de Hiperatividade pelo relato de professores e pais foi condizente com aquela apontada pela literatura (4,7%). O Déficit de Atenção, avaliado por pais e professores, correlacionou com todas as medidas de processamento de informação. Hiperatividade e Comportamento Anti-social correlacionaram com as medidas de memória de trabalho quando a primeira foi avaliada pelos professores e a segunda pelos pais, refletindo baixa concordância entre o relato de informantes a respeito das dimensões comportamentais do TDAH. Entretanto, as respostas dos professores parecem estar mais de acordo com a literatura. Conclui-se que a perspectiva dimensional de investigação pode fornecer informações relevantes sobre as características demográficas e cognitivas do TDAH.<br>This research investigates the Attention Deficit/Hyperactivity Disorder (ADHD) in a school context from a dimensional perspective. It took part of the study 107 children with mean age of 12,37 years. It was used the ADHD's Scale - teacher's version and an adapted form for parents in addition to computer tasks to measure cognitive processing. The prevalence of Hyperactivity, according to teachers and parent's report, was consistent with the literature (4,7%). Attention deficit correlated with all measures of cognitive processing. Hyperactivity and Antisocial Behavior correlated with working memory measures only when the first was assessed by teachers and the second was assessed by parents. In this sense, it was found low concordance between informants regarding ADHD behavior dimensions. However, teacher's report seems to be according with contemporary literature. It was concluded that dimensional investigation can provides relevant information related to demographic and cognitive characteristic of ADHD

Both Trait and State Mindfulness Predict Lower Aggressiveness via Anger Rumination: a Multilevel Mediation Analysis

Trait mindfulness, or the capacity for nonjudgmental, present-centered attention, predicts lower aggression in cross-sectional samples, an effect mediated by reduced anger rumination. Experimental work also implicates state mindfulness (i.e., fluctuations around one's typical mindfulness) in aggression. Despite evidence that both trait and state mindfulness predict lower aggression, their relative impact and their mechanisms remain unclear. Higher trait mindfulness and state increases in mindfulness facets may reduce aggression-related outcomes by (1) limiting the intensity of anger, or (2) limiting rumination on anger experiences. The present study tests two hypotheses: First, that both trait and state mindfulness contribute unique variance to lower aggressiveness, and second, that the impact of both trait and state mindfulness on aggressiveness will be uniquely partially mediated by both anger intensity and anger rumination. 86 participants completed trait measures of mindfulness, anger intensity, and anger rumination, then completed diaries for 35 days assessing mindfulness, anger intensity, anger rumination, anger expression, and self-reported and behavioral aggressiveness. Using multilevel zero-inflated regression, we examined unique contributions of trait and state mindfulness facets to daily anger expression and aggressiveness. We also examined the mediating roles of anger intensity and anger rumination at both trait and state levels. Mindfulness facets predicted anger expression and aggressiveness indirectly through anger rumination after controlling for indirect pathways through anger intensity. Individuals with high or fluctuating aggression may benefit from mindfulness training to reduce both intensity of and rumination on anger