We investigated the impact of mindfulness training (MT) on working memory capacity (WMC) and affective experience. WMC is used in managing cognitive demands and regulating emotions. Yet, persistent and intensive demands, such as those experienced during high-stress intervals, may deplete WMC and lead to cognitive failures and emotional disturbances. We hypothesized that MT may mitigate these deleterious effects by bolstering WMC. We recruited 2 military cohorts during the high-stress predeployment interval and provided MT to 1 (MT, n ϭ 31) but not the other group (military control group, MC, n ϭ 17). The MT group attended an 8-week MT course and logged the amount of out-of-class time spent practicing formal MT exercises. The operation span task was used to index WMC at 2 testing sessions before and after the MT course. Although WMC remained stable over time in civilians (n ϭ 12), it degraded in the MC group. In the MT group, WMC decreased over time in those with low MT practice time, but increased in those with high practice time. Higher MT practice time also corresponded to lower levels of negative affect and higher levels of positive affect (indexed by the Positive and Negative Affect Schedule). The relationship between practice time and negative, but not positive, affect was mediated by WMC, indicating that MT-related improvements in WMC may support some but not all of MT's salutary effects. Nonetheless, these findings suggest that sufficient MT practice may protect against functional impairments associated with high-stress contexts.

BackgroundAdvances in personalized medicine require the identification of variables that predict differential response to treatments as well as the development and refinement of methods to transform predictive information into actionable recommendations.ObjectiveTo illustrate and test a new method for integrating predictive information to aid in treatment selection, using data from a randomized treatment comparison.MethodData from a trial of antidepressant medications (N = 104) versus cognitive behavioral therapy (N = 50) for Major Depressive Disorder were used to produce predictions of post-treatment scores on the Hamilton Rating Scale for Depression (HRSD) in each of the two treatments for each of the 154 patients. The patient's own data were not used in the models that yielded these predictions. Five pre-randomization variables that predicted differential response (marital status, employment status, life events, comorbid personality disorder, and prior medication trials) were included in regression models, permitting the calculation of each patient's Personalized Advantage Index (PAI), in HRSD units.ResultsFor 60% of the sample a clinically meaningful advantage (PAI≥3) was predicted for one of the treatments, relative to the other. When these patients were divided into those randomly assigned to their “Optimal” treatment versus those assigned to their “Non-optimal” treatment, outcomes in the former group were superior (d = 0.58, 95% CI .17—1.01).ConclusionsThis approach to treatment selection, implemented in the context of two equally effective treatments, yielded effects that, if obtained prospectively, would rival those routinely observed in comparisons of active versus control treatments.

This study attempted to replicate an earlier study (R. J. DeRubeis & M. Feeley, 1990) of the prediction of symptom change from process variables in cognitive therapy for depressed outpatients. Measures of in-session therapist behavior and therapist-patient interactions were correlated with prior and subsequent symptom change. One of the positive findings was confirmed, but the other received only marginal support. A "concrete" subset of theory-specified therapist actions, measured early in treatment, predicted subsequent change in depression. The therapeutic alliance was predicted by prior symptom change in 1 of the 2 later assessments, but only at a trend level. Several negative findings were similar to those obtained in the earlier study. Specifically, the alliance, an "abstract" subset of theory-specified therapist actions, and facilitative conditions did not predict subsequent change. Implications for causal inferences in psychotherapy process research are discussed.

R. Baron and D. A. Kenny’s (1986) paper introducing mediation analysis has been cited over 9,000 times, but concerns have been expressed about how this method is used. The authors review past and recent methodological literature and make recommendations for how to address 3 main issues: association, temporal order, and the no omitted variables assumption. The authors briefly visit the topics of reliability and the confirmatory–exploratory distinction. In addition, to provide a sense of the extent to which the earlier literature had been absorbed into practice, the authors examined a sample of 50 articles from 2002 citing R. Baron and D. A. Kenny and containing at least 1 mediation analysis via ordinary least squares regression. A substantial proportion of these articles included problematic reporting; as of 2002, there appeared to be room for improvement in conducting such mediation analyses. Future literature reviews will demonstrate the extent to which the situation has improved.

We identify difficulties researchers encounter in psychotherapy process-outcome investigations, and we describe several limitations of the popular “variance accounted for” approach to understanding the effects of psychotherapy. Using data simulations, we show how the expected correlation between an excellent measure of therapy quality and outcome would be surprisingly small (approximately .25) under conditions likely to be common in psychotherapy research. Even when we modeled conditions designed to increase the likelihood that strong process-outcome relationships would be observed, we found that the expected correlations were still only in the modest range (.38 – .51). We discuss the implications of our analysis for the interpretation of process-outcome findings as well as for design considerations in future investigations.

Background Naltrexone is approved for the treatment of alcohol dependence when used in conjunction with a psychosocial intervention. This study was undertaken to examine the impact of 3 types of psychosocial treatment combined with either naltrexone or placebo treatment on alcohol dependency over 24 weeks of treatment: (1) Cognitive-Behavioral Therapy (CBT) + medication clinic, (2) BRENDA (an intervention promoting pharmacotherapy) + medication clinic, and (3) a medication clinic model with limited therapeutic content. Methods Two hundred and forty alcohol-dependent subjects were enrolled in a 24-week double- blind placebo-controlled study of naltrexone (100 mg/d). Subjects were also randomly assigned to 1 of 3 psychosocial interventions. All patients were assessed for alcohol use, medication adherence, and adverse events at regularly scheduled research visits. Results There was a modest main treatment effect for the psychosocial condition favoring those subjects randomized to CBT. Intent-to-treat analyses suggested that there was no overall efficacy of naltrexone and no medication by psychosocial intervention interaction. There was a relatively low level of medication adherence (50% adhered) across conditions, and this was associated with poor outcome. Conclusions Results from this 24-week treatment study demonstrate the importance of the psychosocial component in the treatment of alcohol dependence. Moreover, results demonstrate a substantial association between medication adherence and treatment outcomes. The findings suggest that further research is needed to determine the appropriate use of pharmacotherapy in maximizing treatment response.

Objective: Survival time is an important type of outcome variable in treatment research. Currently, limited guidance is available regarding performing mediation analyses with survival outcomes, which generally do not have normally distributed errors, and contain unobserved (censored) events. We present considerations for choosing an approach, using a comparison of semi-parametric proportional hazards (PH) and fully parametric accelerated failure time (AFT) approaches for illustration.Method: We compare PH and AFT models and procedures in their integration into mediation models and review their ability to produce coefficients that estimate causal effects. Using simulation studies modeling Weibull-distributed survival times, we compare statistical properties of mediation analyses incorporating PH and AFT approaches (employing SAS procedures PHREG and LIFEREG, respectively) under varied data conditions, some including censoring. A simulated data set illustrates the findings.Results: AFT models integrate more easily than PH models into mediation models. Furthermore, mediation analyses incorporating LIFEREG produce coefficients that can estimate causal effects, and demonstrate superior statistical properties. Censoring introduces bias in the coefficient estimate representing the treatment effect on outcome—underestimation in LIFEREG, and overestimation in PHREG. With LIFEREG, this bias can be addressed using an alternative estimate obtained from combining other coefficients, whereas this is not possible with PHREG.Conclusions: When Weibull assumptions are not violated, there are compelling advantages to using LIFEREG over PHREG for mediation analyses involving survival-time outcomes. Irrespective of the procedures used, the interpretation of coefficients, effects of censoring on coefficient estimates, and statistical properties should be taken into account when reporting results.

Background-The authors examined the patterns of improvement in cognitive and vegetative symptoms of major depression in individuals treated with cognitive therapy (CT) or pharmacotherapy (PT).