Meta-analysis of psychological interventions for chronic low back pain.

The purpose of this meta-analysis of randomized controlled trials was to evaluate the efficacy of psychological interventions for adults with noncancerous chronic low back pain (CLBP). The authors updated and expanded upon prior meta-analyses by using broad definitions of CLBP and psychological intervention, a broad data search strategy, and state-of-the-art data analysis techniques. All relevant controlled clinical trials meeting the inclusion criteria were identified primarily through a computer-aided literature search. Two independent reviewers screened abstracts and articles for inclusion criteria and extracted relevant data. Cohen's d effect sizes were calculated by using a random effects model. Outcomes included pain intensity, emotional functioning, physical functioning (pain interference or pain-specific disability, health-related quality of life), participant ratings of global improvement, health care utilization, health care provider visits, pain medications, and employment/disability compensation status. A total of 205 effect sizes from 22 studies were pooled in 34 analyses. Positive effects of psychological interventions, contrasted with various control groups, were noted for pain intensity, pain-related interference, health-related quality of life, and depression. Cognitive-behavioral and self-regulatory treatments were specifically found to be efficacious. Multidisciplinary approaches that included a psychological component, when compared with active control conditions, were also noted to have positive short-term effects on pain interference and positive long-term effects on return to work. The results demonstrated positive effects of psychological interventions for CLBP. The rigor of the methods used, as well as the results that reflect mild to moderate heterogeneity and minimal publication bias, suggest confidence in the conclusions of this review.     

Multiattribute Decision Making in Context: A Dynamic Neural Network Methodology

A theoretical structure for multiattribute decision making is presented, based on a dynamical system for interactions in a neural network incorporating affective and rational variables. This enables modeling of problems that elude two prevailing economic decision theories: subjective expected utility theory and prospect theory. The network is unlike some that fit economic data by choosing optimal weights or coefficients within a predetermined mathematical framework. Rather, the framework itself is based on principles used elsewhere to model many other cognitive and behavioral data, in a manner approximating how humans perform behavioral functions. Different, interconnected modules within the network encode (a) attributes of objects among which choices are made, (b) object categories, (c) and goals of the decision maker. An example is utilized to simulate the actual consumer choice between old and new versions of Coca-Cola. Potential applications are also discussed to market decisions involving negotiations between participants, such as international petroleum traders.