Evidence of reduced complexity in self-report data from patients with medically unexplained symptoms

Christopher Burton, Rachel A Heath, David Weller, Michael Sharpe

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Physical symptoms which cannot be adequately explained by organic disease are a common problem in all fields of medicine. Reduced complexity, shown using nonlinear dynamic analysis, has been found to be associated with a wide range of illnesses. These methods have been applied to short time series of mood but not to self-rated physical symptoms. We tested the hypothesis that self-reported medically unexplained physical symptoms display reduced complexity by measuring the approximate entropy of self-reported emotions and physical symptoms collected twice daily over 12 weeks and comparing the results with series-specific surrogate data. We found that approximate entropy (ApEn) was lower for actual data series than for surrogate data. There was no significant difference in entropy between different types of symptoms and no significant correlation between entropy and the diurnal variation of the data series. Future studies should concentrate on specific symptoms and conditions, and evaluate the effect of treatment on the entropy of symptom patterns.
Original languageEnglish
Pages (from-to)15-25
Number of pages11
JournalNonlinear dynamics, psychology, and life sciences
Volume14
Issue number1
Publication statusPublished - Jan 2010

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Approximate Entropy
Surrogate Data
Entropy
Series
Mood
Nonlinear Analysis
Dynamic Analysis
Medicine
Nonlinear Dynamics
Time series
Evaluate
Dynamic analysis
Range of data
Evidence
Emotion

Cite this

Evidence of reduced complexity in self-report data from patients with medically unexplained symptoms. / Burton, Christopher; Heath, Rachel A; Weller, David; Sharpe, Michael.

In: Nonlinear dynamics, psychology, and life sciences, Vol. 14, No. 1, 01.2010, p. 15-25.

Research output: Contribution to journalArticle

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