The short form of the ÖMSPQ is appropriate for clinical and research purposes, since it is nearly as accurate as the longer version.

Many patients with musculoskeletal pain also suffer from a depressed mood. Catastrophizing is one process that may link depression and pain since it is a key concept in models of both problems. Earlier research has suggested that catastrophizing measures something above and beyond depression. This study tests the idea that if depressed mood and catastrophizing are separate entities then when one is absent the other should still contribute to poor outcome, and, when both are present there should be an additional adverse effect. To this end, a prospective design, with a built-in replication from two clinical samples of patients with sub-acute pain (one from Sweden, N=373; one from Australasia, N=259), was employed. Participants were classified as to having high/low scores on measures of depression and catastrophizing. Subsequently, these classifications were studied in relation to outcome variables cross-sectionally and at follow-up. Results showed a small to moderate correlation between catastrophizing and depression and that there are individuals with one, but not the other problem. Further, having one or the other of the entities was associated with current pain problems and outcome, while having both increased the associations substantially. The replication showed very similar results Our data demonstrate that pain catastrophizing and heightened depressed mood have an additive and adverse effect on the impact of pain, relative to either alone. It suggests that each should be assessed in the clinic and that future research should focus on treatments specifically designed to tackle both depressed mood and catastrophizing.

Background. The notion of the affective system as being composed of two dimensions led Archer and colleagues to the development of the affective profiles model. The model consists of four different profiles based on combinations of individuals’ experience of high/low positive and negative affect: self-fulfilling, low affective, high affective, and self-destructive. During the past 10 years, an increasing number of studies have used this person-centered model as the backdrop for the investigation of between and within individual differences in ill-being and well-being. The most common approach to this profiling is by dividing individuals’ scores of self-reported affect using the median of the population as reference for high/low splits. However, scores just-above and just-below the median might become high and low by arbitrariness, not by reality. Thus, it is plausible to criticize the validity of this variable-oriented approach. Our aim was to compare the median splits approach with a person-oriented approach, namely, cluster analysis.Method. The participants (N = 2, 225) were recruited through Amazons’ Mechanical Turk and asked to self-report affect using the Positive Affect Negative Affect Schedule. We compared the profiles’ homogeneity and Silhouette coefficients to discern differences in homogeneity and heterogeneity between approaches. We also conducted exact cell-wise analyses matching the profiles from both approaches and matching profiles and gender to investigate profiling agreement with respect to affectivity levels and affectivity and gender. All analyses were conducted using the ROPstat software.Results. The cluster approach (weighted average of cluster homogeneity coefficients = 0.62, Silhouette coefficients = 0.68) generated profiles with greater homogeneity and more distinctive from each other compared to the median splits approach (weighted average of cluster homogeneity coefficients = 0.75, Silhouette coefficients = 0.59). Most of the participants (n = 1,736, 78.0%) were allocated to the same profile (Rand Index = .83), however, 489 (21.98%) were allocated to different profiles depending on the approach. Both approaches allocated females and males similarly in three of the four profiles. Only the cluster analysis approach classified men significantly more often than chance to a self-fulfilling profile (type) and females less often than chance to this very same profile (antitype).Conclusions. Although the question whether one approach is more appropriate than the other is still without answer, the cluster method allocated individuals to profiles that are more in accordance with the conceptual basis of the model and also to expected gender differences. More importantly, regardless of the approach, our findings suggest that the model mirrors a complex and dynamic adaptive system.

Depression is a common and debilitating disorder in adolescence. Sleep disturbances and depression often co-occur with sleep disturbances frequently preceding depression. The current study investigated whether catastrophic worry, a potential cognitive vulnerability, mediates the relationship between adolescent sleep disturbances and depressive symptoms, as well as whether there are gender differences in this relationship. High school students, ages 16-18, n = 1,760, 49% girls, completed annual health surveys including reports of sleep disturbance, catastrophic worry, and depressive symptoms. Sleep disturbances predicted depressive symptoms 1-year later. Catastrophic worry partially mediated the relationship. Girls reported more sleep disturbances, depressive symptoms, and catastrophic worry relative to boys. The results, however, were similar regardless of gender. Sleep disturbances and catastrophic worry may provide school nurses, psychologists, teachers, and parents with non-gender specific early indicators of risk for depression. Several potentially important practical implications, including suggestions for intervention and prevention programs, are highlighted.

Cognitions pertaining to avoidant safety behaviors and catastrophizing are associated with symptom severity and overlap in co-morbid pain and sleep disorders. More research is needed to explore the importance of shared psychological processes and consequences when studying and treating ill health.

This paper presents a new method of knowledge gathering for decision support in image understanding based on information extracted from the dynamics of saccadic eye movements. The framework involves the construction of a generic image feature extraction library, from which the feature extractors that are most relevant to the visual assessment by domain experts are determined automatically through factor analysis. The dynamics of the visual search are analyzed by using the Markov model for providing training information to novices on how and where to look for image features. The validity of the framework has been evaluated in a clinical scenario whereby the pulmonary vascular distribution on Computed Tomography images was assessed by experienced radiologists as a potential indicator of heart failure. The performance of the system has been demonstrated by training four novices to follow the visual assessment behavior of two experienced observers. In all cases, the accuracy of the students improved from near random decision making (33%) to accuracies ranging from 50% to 68%.

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We estimated the number of possible dark personality profiles in a large population (N = 18,088) using a subtractive clustering method, which suggested three cluster or dark personality profiles: high malevolent, intermediate malevolent, and low malevolent or benevolent. While the three profiles differed significantly in each dark trait, there was a considerably large cluster overlap.