Nicholas T. Van Dam

Self-compassion is a better predictor than mindfulness of symptom severity and quality of life in mixed anxiety and depression

Mindfulness has received considerable attention as a correlate of psychological well-being and potential mechanism for the success of mindfulness-based interventions (MBIs). Despite a common emphasis of mindfulness, at least in name, among MBIs, mindfulness proves difficult to assess, warranting consideration of other common components. Self-compassion, an important construct that relates to many of the theoretical and practical components of MBIs, may be an important predictor of psychological health. The present study compared ability of the Self-Compassion Scale (SCS) and the Mindful Attention Awareness Scale (MAAS) to predict anxiety, depression, worry, and quality of life in a large community sample seeking self-help for anxious distress (N = 504). Multivariate and univariate analyses showed that self-compassion is a robust predictor of symptom severity and quality of life, accounting for as much as ten times more unique variance in the dependent variables than mindfulness. Of particular predictive utility are the self-judgment and isolation subscales of the SCS. These findings suggest that self-compassion is a robust and important predictor of psychological health that may be an important component of MBIs for anxiety and depression.

Validation of the Center for Epidemiologic Studies Depression Scale—Revised (CESD-R): Pragmatic depression assessment in the general population

Depression has a huge societal impact, making accurate measurement paramount. While there are several available measures, the Center for Epidemiological Studies Depression Scale (CESD) is a popular assessment tool that has wide applicability in the general population. In order to reflect modern diagnostic criteria and improve upon psychometric limitations of its predecessor, the Center for Epidemiologic Studies Depression Scale--Revised (CESD-R) was recently created, but has yet to be publicized. This study explored psychometric properties of the CESD-R across a large community sample (N=7389) and smaller student sample (N=245). A newly proposed algorithmic classification method yielded base-rates of depression consistent with epidemiological results. Factor analysis suggested a unidimensional factor structure, but important utility for two separate symptom clusters. The CESD-R exhibited good psychometric properties, including high internal consistency, strong factor loadings, and theoretically consistent convergent and divergent validity with anxiety, schizotypy, and positive and negative affect. Results suggest the CESD-R is an accurate and valid measure of depression in the general population with advantages such as free distribution and an atheoretical basis.

Mind the Hype: A Critical Evaluation and Prescriptive Agenda for Research on Mindfulness and Meditation

During the past two decades, mindfulness meditation has gone from being a fringe topic of scientific investigation to being an occasional replacement for psychotherapy, tool of corporate well-being, widely implemented educational practice, and “key to building more resilient soldiers.” Yet the mindfulness movement and empirical evidence supporting it have not gone without criticism. Misinformation and poor methodology associated with past studies of mindfulness may lead public consumers to be harmed, misled, and disappointed. Addressing such concerns, the present article discusses the difficulties of defining mindfulness, delineates the proper scope of research into mindfulness practices, and explicates crucial methodological issues for interpreting results from investigations of mindfulness. For doing so, the authors draw on their diverse areas of expertise to review the present state of mindfulness research, comprehensively summarizing what we do and do not know, while providing a prescriptive agenda for contemplative science, with a particular focus on assessment, mindfulness training, possible adverse effects, and intersection with brain imaging. Our goals are to inform interested scientists, the news media, and the public, to minimize harm, curb poor research practices, and staunch the flow of misinformation about the benefits, costs, and future prospects of mindfulness meditation.

Pulmonary function in cannabis users: Support for a clinical trial of the vaporizer.

BACKGROUND Debates about cannabis policy often mention respiratory symptoms as a negative consequence of use. The cannabis vaporizer, a machine that heats the plant to release cannabinoids in a mist without smoke and other respiratory irritants, appears to have the potential to minimize respiratory complaints. METHODS Twenty frequent cannabis users (uninterested in treatment) reporting at least two respiratory symptoms completed subjective ratings of respiratory symptoms and spirometry measures prior to and following 1 months use of a cannabis vaporizer in a pre/post-design. Outcome measures included self-reported severity of nine respiratory symptoms as well as spirometry measures, including the maximum amount of air exhaled in 1s (forced expiratory volume; FEV1) and maximum total lung volume (forced vital capacity; FVC). RESULTS The 12 participants who did not develop a respiratory illness during the trial significantly improved respiratory symptoms (t(11)=6.22, p=0.000065, d=3.75) and FVC, t(11)=2.90, p=0.007, d=1.75. FEV1 improved but not significantly t(11)=1.77, p=0.053, d=1.07. CONCLUSIONS These preliminary data reveal meaningful improvements in respiratory function, suggesting that a randomized clinical trial of the cannabis vaporizer is warranted. The vaporizer has potential for the administration of medical cannabis and as a harm reduction technique.

Establishing a trait anxiety threshold that signals likelihood of anxiety disorders

Abstract Evidence suggests that the State Trait Inventory for Cognitive and Somatic Anxiety (STICSA) may be a more pure measure of anxiety than other commonly used scales. Further, the STICSA has excellent psychometric properties in both clinical and nonclinical samples. The present study aimed to extend the utility of the STICSA – Trait version by identifying a cut-off score that could differentiate a group of clinically diagnosed anxiety disorder patients (n=398) from a group of student controls (n =439). Two receiver operating characteristic curve analyses indicated cut-off scores of 43 (sensitivity=.73, specificity=.74, classification accuracy=.74) and 40 (sensitivity=.80, specificity=.67, classification accuracy=.73), respectively. In a large community sample (n =6685), a score of 43 identified 11.5% of individuals as probable cases of clinical anxiety, while a score of 40 identified 17.0% of individuals as probable cases of clinical anxiety. As a result of differences in sensitivity and specificity, the present findings suggest a cut-off score of 43 is optimal to identify probable cases of clinical anxiety, while a cut-off score of 40 is optimal to screen for the possible presence of anxiety disorders.

Mind Your Words: Positive and Negative Items Create Method Effects on the Five Facet Mindfulness Questionnaire

Mindfulness, a construct that entails moment-to-moment effort to be aware of present experiences and positive attitudinal features, has become integrated into the sciences. The Five Facet Mindfulness Questionnaire (FFMQ), one popular measure of mindfulness, exhibits different responses to positively and negatively worded items in nonmeditating groups. The current study employed confirmatory factor analysis with a large undergraduate sample to examine the validity of a hierarchical mindfulness model and whether response patterns related to item wording arose from method effects. Results indicated that a correlated facets model better explained the data and that negative and positive wording constituted substantive method effects. This study suggests that the FFMQ measures components that may relate to, but do not seem to directly reflect, a latent variable of mindfulness. The authors recommend against the use of an FFMQ total score, favoring individual scale scores, and further examination of method effects in mindfulness scales.

Quantitative characterization of functional anatomical contributions to cognitive control under uncertainty

Although much evidence indicates that RT increases as a function of computational load in many cognitive tasks, quantification of changes in neural activity related to increasing demand of cognitive control has rarely been attempted. In this fMRI study, we used a majority function task to quantify the effect of computational load on brain activation, reflecting the mental processes instantiated by cognitive control under conditions of uncertainty. We found that the activation of the frontoparieto-cingulate system as well as the deactivation of the anticorrelated default mode network varied parametrically as a function of information uncertainty, estimated as entropy with an information theoretic model. The current findings suggest that activity changes in the dynamic networks of the brain (especially the frontoparieto-cingulate system) track with information uncertainty, rather than only conflict or other commonly proposed targets of cognitive control.

Polydrug use, cannabis, and psychosis-like symptoms.

To examine psychosis‐like symptoms in users of legal and illicit drugs.

Characteristics of clinically anxious versus non-anxious regular, heavy marijuana users.

Both the key mechanism of action for marijuana (the endocannabinoid system) and the symptoms associated with marijuana withdrawal suggest an important link to anxiety. Despite this link, there is a dearth of research on the characteristics of heavy marijuana users with clinical-level anxiety compared to those with heavy marijuana use alone. Over 10,000 participants (friends or affiliates of the National Organization for the Reform of Marijuana Laws) provided data via online survey. After careful, conservative screening, anxiety, other psychopathology, other drug use, and marijuana-related problems were examined in 2567 heavy marijuana users. Subsequently, 275 heavy users with clinical-level anxiety were compared to demographically-equivalent non-anxious heavy users on psychopathology, drug use, and cannabis-related problems. Among several psychological variables (including anxiety, depression, schizotypy, and impulsivity), anxiety was most strongly predictive of amount of marijuana used and marijuana-related problems. Group comparison (n=550 total) revealed that clinically anxious heavy users exhibited more use, more non-anxiety psychopathological symptoms, and a greater number and severity of marijuana-related problems than their non-anxious peers. The findings reveal that anxiety shows an important relation to marijuana use and related problems among regular, heavy users. Further examinations of common and unique factors predisposing individuals for anxiety and marijuana abuse appear warranted.

Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels

BACKGROUND Data-driven approaches can capture behavioral and biological variation currently unaccounted for by contemporary diagnostic categories, thereby enhancing the ability of neurobiological studies to characterize brain-behavior relationships. METHODS A community-ascertained sample of individuals (N = 347, 18-59 years of age) completed a battery of behavioral measures, psychiatric assessment, and resting-state functional magnetic resonance imaging in a cross-sectional design. Bootstrap-based exploratory factor analysis was applied to 49 phenotypic subscales from 10 measures. Hybrid hierarchical clustering was applied to resultant factor scores to identify nested groups. Adjacent groups were compared via independent samples t tests and chi-square tests of factor scores, syndrome scores, and psychiatric prevalence. Multivariate distance matrix regression examined functional connectome differences between adjacent groups. RESULTS Reduction yielded six factors, which explained 77.8% and 65.4% of the variance in exploratory and constrained exploratory models, respectively. Hybrid hierarchical clustering of these six factors identified two, four, and eight nested groups (i.e., phenotypic communities). At the highest clustering level, the algorithm differentiated functionally adaptive and maladaptive groups. At the middle clustering level, groups were separated by problem type (maladaptive groups; internalizing vs. externalizing problems) and behavioral type (adaptive groups; sensation-seeking vs. extraverted/emotionally stable). Unique phenotypic profiles were also evident at the lowest clustering level. Group comparisons exhibited significant differences in intrinsic functional connectivity at the highest clustering level in somatomotor, thalamic, basal ganglia, and limbic networks. CONCLUSIONS Data-driven approaches for identifying homogenous subgroups, spanning typical function to dysfunction, not only yielded clinically meaningful groups, but also captured behavioral and neurobiological variation among healthy individuals.