Item Response Theory Analysis of the Five Facet Mindfulness Questionnaire and its Short Forms.

OBJECTIVES: The Five Facet Mindfulness Questionnaire (FFMQ) is a self-report measure of mindfulness with forms of several different lengths, including the FFMQ-39, FFMQ-24, and FFMQ-15. We use item response theory analysis to directly compare the functioning of these three forms. METHODS: Data were drawn from a non-clinical Amazon Mechanical Turk study (N = 522) and studies of aftercare treatment of individuals with substance use disorders (combined N = 454). The item and test functioning of the three FFMQ forms were studied and compared. RESULTS: All 39 items were strongly related to the facet latent variables, and the items discriminated over a similar range of the latent mindfulness constructs. Items provided more information in the low to medium range of latent mindfulness than in the high range. Scores in three of the five FFMQ-39 facets were unreliable when measuring individuals in the high range of latent mindfulness, resulting from ceiling effects in item responses. Reliability in the high range of mindfulness was further reduced in the FFMQ-24 and FFMQ-15, such that short forms may be ill-suited for applications that require reliable measurement in the high range. CONCLUSIONS: Results suggest the existing FFMQ item pool cannot be reduced without negatively affecting either overall reliability or the span of mindfulness over which reliability is assessed. Conditional test reliability curves and item functioning parameters can aid investigators in tailoring their choice of FFMQ form to the reliability they hope to achieve and to the range of latent mindfulness over which they must reliably measure.

Applying novel technologies and methods to inform the ontology of self-regulation.

Self-regulation is a broad construct representing the general ability to recruit cognitive, motivational and emotional resources to achieve long-term goals. This construct has been implicated in a host of health-risk behaviors, and is a promising target for fostering beneficial behavior change. Despite its clear importance, the behavioral, psychological and neural components of self-regulation remain poorly understood, which contributes to theoretical inconsistencies and hinders maximally effective intervention development. We outline a research program that seeks to define a neuropsychological ontology of self-regulation, articulating the cognitive components that compose self-regulation, their relationships, and their associated measurements. The ontology will be informed by two large-scale approaches to assessing individual differences: first purely behaviorally using data collected via Amazon's Mechanical Turk, then coupled with neuroimaging data collected from a separate population. To validate the ontology and demonstrate its utility, we will then use it to contextualize health risk behaviors in two exemplar behavioral groups: overweight/obese adults who binge eat and smokers. After identifying ontological targets that precipitate maladaptive behavior, we will craft interventions that engage these targets. If successful, this work will provide a structured, holistic account of self-regulation in the form of an explicit ontology, which will better clarify the pattern of deficits related to maladaptive health behavior, and provide direction for more effective behavior change interventions.

Identifying treatment response subgroups for adolescent cannabis use.

INTRODUCTION: Outpatient treatments for adolescent substance use demonstrate clinically meaningful reductions in substance use, but effect sizes are often low, relapse rates are high, and response to treatment is heterogeneous across participants. The present study utilized cluster analysis to identify subgroups of treatment response among adolescents from three randomized clinical trials evaluating behavioral treatments for substance use. METHODS: Analyses were performed on a sample of 194 adolescents (average age=15.8, 81.4% male) who reported cannabis use during the past 30days or had a cannabis-positive urine test. Clustering was based on percent days cannabis use at 5 time periods (intake, end of treatment, 3, 6, and 9months post-treatment). Participants in the identified subgroups were then compared across a number of variables not involved in the clustering (e.g., substance use, demographics, and psychopathology) to test for predictors of cluster membership. RESULTS: Four clusters were identified based on statistical indices and visual inspection of the resulting cluster profiles: Low Use Responders (n=109, low baseline level, sustained decrease); High Use Responders (n=45, high baseline level, sustained decrease); Relapsers (n=25, medium baseline level, decrease, rapid increase post-treatment); and Non-Responders (n=15; consistently high level of use). Cannabis dependence, mean cannabis uses per day, and socioeconomic status were predictive of cluster membership. CONCLUSIONS: Cluster analysis empirically identified different patterns of treatment response over time for adolescent outpatients. Investigating homogenous subgroups of participants provides insight into study outcomes, and variables associated with clusters have potential utility to identify participants that may benefit from more intensive treatment.