Cognitive behaviour therapy for common mental disorders in people with Multiple Sclerosis: A bench marking study

Background

Mental health problems such as depression and anxiety are common in Multiple Sclerosis (MS) and are often under treated.

Aims

This paper reports on the clinical effectiveness of a cognitive behaviour therapy service for common mental disorders in people with MS and compares it to previous randomised controlled trials (RCTs) of cognitive behaviour therapy (CBT) in this population.

Methods

49 patients were deemed appropriate for CBT and 29 accepted treatment. Assessments were completed at baseline and end of treatment and included the Hospital Anxiety & Depression Scale. Results in the form of a standardized effect of treatment were compared with five previous RCTs.

Results

The results from this clinical service indicated statistically significant outcomes with reductions in depression and anxiety. The uncontrolled effect size was large but inferior to those found in published RCTs.

Conclusions

Cognitive behaviour therapy is effective for people with MS in routine clinical practice. Possible limits on effectiveness include more liberal patient selection, lack of specificity in rating scales and heterogeneity of target problems. Given the high rates of distress in this population, routine psychological interventions within neurology services are justifiable. Future research should aim to maximise CBT in such settings.     

Coping and physical health outcomes: an overview

The aim of the present study was to investigate longitudinally the sensitivity of optimistic beliefs (positive outcome expectancies, efficacy expectancies, and optimistic bias) to disease-related stress (measured by disease duration, depressive symptoms, physical symptoms, physical impairment, and life-events) in Type 1 diabetes (n = 90) and multiple sclerosis (n = 90). First, we examined whether chronically ill patients were less optimistic than a healthy control group (n = 60), which showed that patients reported lower levels of optimistic bias but similar levels of the two other optimistic beliefs. Next, we analyzed the stability of the three types of optimistic beliefs across time (one year) for both disease groups, which demonstrated that optimistic bias in MS patients decreased. Finally, the impact of disease-related stressors on optimistic beliefs during a one-year period was determined, showing that all three optimistic beliefs decreased when patients reported depressive symptoms. Together, these findings show that positive outcome expectancies and efficacy expectancies were unaffected by being ill and fairly stable unless patients reported depressive symptoms while optimistic bias appeared to decrease as a result of being ill. Results are discussed in terms of the role of control and depression in optimistic bias.     

Treatment preference, engagement, and clinical improvement in pharmacotherapy versus psychotherapy for depression

Pharmacotherapy and psychotherapy are generally effective treatments for major depressive disorder (MDD); however, research suggests that patient preferences may influence outcomes. We examined the effects of treatment preference on attrition, therapeutic alliance, and change in depressive severity in a longitudinal randomized clinical trial comparing pharmacotherapy and psychotherapy. Prior to randomization, 106 individuals with MDD reported whether they preferred psychotherapy, antidepressant medication, or had no preference. A mismatch between preferred and actual treatment was associated with greater likelihood of attrition, fewer expected visits attended, and a less positive working alliance at session 2. There was a significant indirect effect of preference match on depression outcomes, primarily via effects of attendance. These findings highlight the importance of addressing patient preferences, particularly in regard to patient engagement, in the treatment of MDD.     

Interaction of knowledge-driven and data-driven processing in category learning

The present paper argues that category learning is both a data-driven and a knowledge-driven process. This is described in a generic model that distinguishes between categorical knowledge, conceptual knowledge, and implicit cognitive theories. The model assumes that each of these knowledge aspects may affect the process of category learning by affecting the way similarities between objects are perceived. This central assumption of the model is tested in two experiments. The first experiment shows that the presence or absence of prior categorical and conceptual knowledge affects the psychological stimulus space by changing the saliency of the stimulus dimensions. The second experiment uses these weights to predict the distribution of errors over the stimuli and the number of trials to criterion in category learning by other participants under the same knowledge conditions. We conclude that prior categorical and conceptual knowledge affect category learning by mediation of similarity perception, and discuss the implications of these results.     

Visual target detection is impaired at event boundaries

Boundaries between meaningful events are key moments in comprehending human action. At these points, viewers may focus on the event's contents at the expense of other information. We tested whether visual detection was impaired at those moments perceivers judged to be boundaries between events. Short animated football clips were used as stimulus material, and event boundaries were imposed by having the ball change possession. In a first experiment, we found that possession changes were perceived to be event boundaries. In a second experiment, participants were asked to keep track of 4 of 10 players and to watch for 120 ms probes appearing either at an event boundary or a nonboundary. Probe detection was less accurate at event boundaries. This result suggests that the segmentation of ongoing activity into events corresponds with the regulation of attention over time.     

Contextual cueing in multiple object tracking

In this study, we examined whether visual context can be learned through a dynamic display and whether it can facilitate sustained attentional tracking by combining a multiple object tracking (MOT) task and a contextual cueing procedure. The trajectories of the targets and distractors in the MOT task were made invariant by repeatedly presenting them. The results revealed that when the targets were repeatedly displayed, tracking performance implicitly improved, and this effect was enhanced when the unattended distractors in the displays were also repeated. However, the repetition of the distractors alone did not produce any effect. Interestingly, when the targets and distractors were switched in a display in which the distractors had been previously repeated, the tracking performance was impaired as compared with that in the case of nonrepeated displays. We concluded that the contextual information in a dynamic display facilitates attentional tracking and that different types of contextual modulations occurred in MOT processes, such as facilitation for attended targets and inhibition for ignored distractors.     

Assessing complex problem-solving skills with multiple complex systems

In this paper we propose the multiple complex systems (MCS) approach for assessing domain-general complex problem-solving (CPS) skills and its processes knowledge acquisition and knowledge application. After defining the construct and the formal frameworks for describing complex problems, we emphasise some of the measurement issues inherent in assessing CPS skills with single tasks (i.e., fixed item difficulty, low or unknown reliability, and a large impact of random errors). With examples of the MicroDYN test and the MicroFIN test (two instances of the MCS approach), we show how to adequately score problem-solving skills by using multiple tasks. We discuss implications for problem-solving research and the assessment of CPS skills in general.     

A Comparison of Multilevel Imputation Schemes for Random Coefficient Models: Fully Conditional Specification and Joint Model Imputation with Random Covariance Matrices

Abstract

Literature addressing missing data handling for random coefficient models is particularly scant, and the few studies to date have focused on the fully conditional specification framework and “reverse random coefficient” imputation. Although it has not received much attention in the literature, a joint modeling strategy that uses random within-cluster covariance matrices to preserve cluster-specific associations is a promising alternative for random coefficient analyses. This study is apparently the first to directly compare these procedures. Analytic results suggest that both imputation procedures can introduce bias-inducing incompatibilities with a random coefficient analysis model. Problems with fully conditional specification result from an incorrect distributional assumption, whereas joint imputation uses an underparameterized model that assumes uncorrelated intercepts and slopes. Monte Carlo simulations suggest that biases from these issues are tolerable if the missing data rate is 10% or lower and the sample is composed of at least 30 clusters with 15 observations per group. Furthermore, fully conditional specification tends to be superior with intraclass correlations that are typical of crosssectional data (e.g., ICC?=?.10), whereas the joint model is preferable with high values typical of longitudinal designs (e.g., ICC?=?.50).     

Residual Normality Assumption and the Estimation of Multiple Membership Random Effects Models

While conventional hierarchical linear modeling is applicable to purely hierarchical data, a multiple membership random effects model (MMrem) is appropriate for nonpurely nested data wherein some lower-level units manifest mobility across higher-level units. Although a few recent studies have investigated the influence of cluster-level residual nonnormality on hierarchical linear modeling estimation for purely hierarchical data, no research has examined the statistical performance of an MMrem given residual non-normality. The purpose of the present study was to extend prior research on the influence of residual non-normality from purely nested data structures to multiple membership data structures. Employing a Monte Carlo simulation study, this research inquiry examined two-level MMrem parameter estimate biases and inferential errors. Simulation factors included the level-two residual distribution, sample sizes, intracluster correlation coefficient, and mobility rate. Results showed that estimates of fixed effect parameters and the level-one variance component were robust to level-two residual non-normality. The level-two variance component, however, was sensitive to level-two residual non-normality and sample size. Coverage rates of the 95% credible intervals deviated from the nominal value assumed when level-two residuals were non-normal. These findings can be useful in the application of an MMrem to account for the contextual effects of multiple higher-level units.