Empathy is a critical ingredient in motivational interviewing (MI) and in psychotherapy generally. It is typically defined as the ability to experience and understand the feelings of another. Basic science indicates that empathy is related to the development of synchrony in dyads. However, in clinical research, empathy has proved difficult to operationalize and measure, and has mostly relied on the felt sense of observers, clients, or therapists. We extracted estimates of therapist and standardized patient (SP) vocally encoded arousal (mean fundamental frequency; mean f0) in 89 MI sessions with high and low empathy ratings from independent observers. We hypothesized (a) therapist and SP mean f0 would be correlated and (b) the correlation of therapist and SP mean f0 would be greater in sessions with high empathy as compared with low. On the basis of a multivariate mixed model, the correlation between therapist and SP mean f0 was large (r = .71) and close to 0 in randomly assigned therapist–SP dyads (r = −.08). The association was higher in sessions with high empathy ratings (r = .80) than in sessions with low ratings (r = .36). There was strong evidence for vocal synchrony in clinical dyads as well as for the association of synchrony with empathy ratings, illustrating the relevance of basic psychological processes to clinical interactions. These findings provide initial evidence for an objective and nonobtrusive method for assessing therapist performance. Novel indicators of therapist empathy may have implications for the study of MI process as well as the training of therapists generally.

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This study estimates pretreatment-posttreatment effect size benchmarks for the treatment of major depression in adults that may be useful in evaluating psychotherapy effectiveness in clinical practice. Treatment efficacy benchmarks for major depression were derived for 3 different types of outcome measures: the Hamilton Rating Scale for Depression (M. A. Hamilton, 1960A. Hamilton, , 1967, the Beck Depression Inventory (A. T. Beck, 1978;A. T. Beck & R. A. Steer, 1987), and an aggregation of low reactivity-low specificity measures. These benchmarks were further refined for 3 conditions: treatment completers, intent-to-treat samples, and natural history (wait-list) conditions. The study confirmed significant effects of outcome measure reactivity and specificity on the pretreatment-posttreatment effect sizes. The authors provide practical guidance in using these benchmarks to assess treatment effectiveness in clinical settings.

This preliminary study evaluated the effectiveness of psychotherapy treatment for adult clinical depression provided in a natural setting by benchmarking the clinical outcomes in a managed care environment against effect size estimates observed in published clinical trials. Overall results suggest that effect size estimates of effectiveness in a managed care context were comparable to effect size estimates of efficacy observed in clinical trials. Relative to the 1-tailed 95th-percentile critical effect size estimates, effectiveness of treatment provided in this setting was observed to be between 80% (patients with comorbidity and without antidepressants) and 112% (patients without comorbidity concurrently on antidepressants) as compared to the benchmarks. Because the nature of the treatments delivered in the managed care environment were unknown, it was not possible to make conclusions about treatments. However, while replications are warranted, concerns that psychotherapy delivered in a naturalistic setting is inferior to treatments delivered in clinical trials appear unjustified.

benchmarking, efficacy and effectiveness, meta-analysis, psychotherapy, clinical practice,

Psychophysiologists often use repeated measures analysis of variance (RMANOVA) and multivariate analysis of variance (MANOVA) to analyze data collected in repeated measures research designs. ANOVA and MANOVA are nomothetic approaches that focus on group means. Newer multilevel modeling techniques are more informative than ANOVA because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study. This article was written as an introduction to growth curve modeling for applied researchers. A growth model is defined that can be used in place of RMANOVAs and MANOVAs for single-group and mixed repeated measures designs. The model is expanded to test and control for the effects of baseline levels of physiological activity on stimulus-specific responses. Practical, conceptual, and statistical advantages of growth curve modeling are discussed.

The effects of a physical (pressing the toes to the floor) and a mental (counting backward by sevens) countermeasure on the concealed knowledge test (CKT) were examined in a mock crime experiment with 40 subjects. Some knowledgeable subjects were informed about the nature of the CKT and were trained in the use of a countermeasure, whereas others remained uninformed. All subjects were offered a monetary reward if they could produce a truthful outcome. Subjects were tested using standard field techniques and instrumentation. The physical and, to a lesser extent, the mental countermeasures reduced the accuracy of the CKT. These results clearly demonstrate that the CKT has no special immunity to the effects of countermeasures.

This study pools household travel and built environment data from 15 diverse US regions to produce travel models with more external validity than any to date. It uses a large number of consistently defined built environmental variables to predict five household travel outcomes – car trips, walk trips, bike trips, transit trips and vehicle miles travelled (VMT). It employs multilevel modelling to account for the dependence of households in the same region on shared regional characteristics and estimates ‘hurdle’ models to account for the excess number of zero values in the distributions of dependent variables such as household transit trips. It tests built environment variables for three different buffer widths around household locations to see which scale best explains travel behaviour. The resulting models are appropriate for post-processing outputs of conventional travel demand models, and for sketch planning applications in traffic impact analysis, climate action planning and health impact assessment.