Literature DB >> 23502433

Less structured movement patterns predict severity of positive syndrome, excitement, and disorganization.

Sebastian Walther1, Fabian Ramseyer, Helge Horn, Werner Strik, Wolfgang Tschacher.   

Abstract

Disorganized behavior is a key symptom of schizophrenia. The objective assessment of disorganized behavior is particularly challenging. Actigraphy has enabled the objective assessment of motor behavior in various settings. Reduced motor activity was associated with negative syndrome scores, but simple motor activity analyses were not informative on other symptom dimensions. The analysis of movement patterns, however, could be more informative for assessing schizophrenia symptom dimensions. Here, we use time series analyses on actigraphic data of 100 schizophrenia spectrum disorder patients. Actigraphy recording intervals were set at 2 s. Data from 2 defined 60-min periods were analyzed, and partial autocorrelations of the actigraphy time series indicated predictability of movements in each individual. Increased positive syndrome scores were associated with reduced predictability of movements but not with the overall amount of movement. Negative syndrome scores were associated with low activity levels but unrelated with predictability of movement. The factors disorganization and excitement were related to movement predictability but emotional distress was not. Thus, the predictability of objectively assessed motor behavior may be a marker of positive symptoms and disorganized behavior. This behavior could become relevant for translational research.

Entities:  

Keywords:  actigraphy; behavior; negative symptoms; positive symptoms; schizophrenia; time series analysis

Mesh:

Year:  2013        PMID: 23502433      PMCID: PMC3984503          DOI: 10.1093/schbul/sbt038

Source DB:  PubMed          Journal:  Schizophr Bull        ISSN: 0586-7614            Impact factor:   9.306


  36 in total

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8.  Motion energy analysis reveals altered body movement in youth at risk for psychosis.

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9.  Modelling and forecasting Positive and Negative Syndrome Scale scores to achieve remission using time series analysis.

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