BackgroundDepression is a burdensome, recurring mental health disorder with high prevalence. Even in developed countries, patients have to wait for several months to receive treatment. In many parts of the world there is only one mental health professional for over 200 people. Smartphones are ubiquitous and have a large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of depressive symptoms and providing context-sensitive intervention support.ObjectiveThe objective of this study is 2-fold, first to explore the detection of daily-life behavior based on sensor information to identify subjects with a clinically meaningful depression level, second to explore the potential of context sensitive intervention delivery to provide in-situ support for people with depressive symptoms.MethodsA total of 126 adults (age 20-57) were recruited to use the smartphone app Mobile Sensing and Support (MOSS), collecting context-sensitive sensor information and providing just-in-time interventions derived from cognitive behavior therapy. Real-time learning-systems were deployed to adapt to each subject’s preferences to optimize recommendations with respect to time, location, and personal preference. Biweekly, participants were asked to complete a self-reported depression survey (PHQ-9) to track symptom progression. Wilcoxon tests were conducted to compare scores before and after intervention. Correlation analysis was used to test the relationship between adherence and change in PHQ-9. One hundred twenty features were constructed based on smartphone usage and sensors including accelerometer, Wifi, and global positioning systems (GPS). Machine-learning models used these features to infer behavior and context for PHQ-9 level prediction and tailored intervention delivery.ResultsA total of 36 subjects used MOSS for ≥2 weeks. For subjects with clinical depression (PHQ-9≥11) at baseline and adherence ≥8 weeks (n=12), a significant drop in PHQ-9 was observed (P=.01). This group showed a negative trend between adherence and change in PHQ-9 scores (rho=−.498, P=.099). Binary classification performance for biweekly PHQ-9 samples (n=143), with a cutoff of PHQ-9≥11, based on Random Forest and Support Vector Machine leave-one-out cross validation resulted in 60.1% and 59.1% accuracy, respectively.ConclusionsProxies for social and physical behavior derived from smartphone sensor data was successfully deployed to deliver context-sensitive and personalized interventions to people with depressive symptoms. Subjects who used the app for an extended period of time showed significant reduction in self-reported symptom severity. Nonlinear classification models trained on features extracted from smartphone sensor data including Wifi, accelerometer, GPS, and phone use, demonstrated a proof of concept for the detection of depression superior to random classification. While findings of effectiveness must be reproduced in a RCT to proof causation, they pave the way for a new generation of digital heal...

Mindfulness an attentive non-judgmental focus on present experiences is increasingly incorporated in psychotherapeutic treatments as a skill fostering emotion regulation. Neurobiological mechanisms of actively induced emotion regulation are associated with prefrontally mediated downregulation of, for instance, the amygdala. We were interested in neurobiological correlates of a short mindfulness instruction during emotional arousal. Using functional magnetic resonance imaging, we investigated effects of a short mindfulness intervention during the cued expectation and perception of negative and potentially negative pictures (50% probability) in 24 healthy individuals compared to 22 controls. The mindfulness intervention was associated with increased activations in prefrontal regions during the expectation of negative and potentially negative pictures compared to controls. During the perception of negative stimuli, reduced activation was identified in regions involved in emotion processing (amygdala, parahippocampal gyrus). Prefrontal and right insular activations when expecting negative pictures correlated negatively with trait mindfulness, suggesting that more mindful individuals required less regulatory resources to attenuate emotional arousal. Our findings suggest emotion regulatory effects of a short mindfulness intervention on a neurobiological level.

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Our results strongly indicate that in-patients with obsessive-compulsive hoarding respond poorly to CBT.

Background: Previous studies have found a strong association between dissociation and obsessive-compulsive disorder (OCD). The purpose of the present study was to evaluate whether dissociation is a predictor of cognitive behavior therapy (CBT) outcome in patients with OCD. Methods: Fifty-two patients with OCD were assessed using the Dissociative Experience Scale (DES), the Yale-Brown Obsessive-Compulsive Scale and the Beck Depression Inventory. CBT lasted on average 9.5 weeks and included exposure therapy. Results: Patients who dropped out due to noncompliance had higher baseline DES scores and depression scores compared to the 43 patients (83%) who completed the study. Significant OCD symptom reduction at posttreatment was observed in study completers with a large effect size (d = 1.7). More severe OCD symptoms at posttreatment were associated with higher DES scores at baseline, and treatment nonresponders had significantly higher baseline DES scores compared to responders. These associations with outcome were mainly due to the DES subfactor absorption-imaginative involvement. In regression analyses, higher absorption-imaginative involvement scores at baseline predicted poorer CBT outcome, even after controlling for depressive symptoms, comorbid axis I disorders and concomitant psychotropic drugs. Conclusions: Results from this preliminary study suggest that higher levels of dissociation (particularly absorption-imaginative involvement) in patients with OCD might predict poorer CBT outcome. If our results can be replicated, treatment outcome might be improved by additional interventions for those patients with OCD who indicate high levels of dissociation, for example by using interventions aimed at improving coping with emotionally stressful situations.

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Social anxiety disorder (SAD) is the second leading anxiety disorder. On the functional neurobiological level, specific brain regions involved in the processing of anxiety-laden stimuli and in emotion regulation have been shown to be hyperactive and hyper-responsive in SAD such as amygdala, insula and orbito- and prefrontal cortex. On the level of brain structure, prior studies on anatomical differences in SAD resulted in mixed and partially contradictory findings. Based on previous functional and anatomical models of SAD, this study examined cortical thickness in structural magnetic resonance imaging data of 46 patients with SAD without comorbidities (except for depressed episode in one patient) compared with 46 matched healthy controls in a region of interest-analysis and in whole-brain. In a theory-driven ROI-analysis, cortical thickness was increased in SAD in left insula, right anterior cingulate and right temporal pole. Furthermore, the whole-brain analysis revealed increased thickness in right dorsolateral prefrontal and right parietal cortex. This study detected no regions of decreased cortical thickness or brain volume in SAD. From the perspective of brain networks, these findings are in line with prior functional differences in salience networks and frontoparietal networks associated with executive-controlling and attentional functions.