Individual variation in temporal relationships between stress and functional somatic symptoms

Autor: Christopher R Burton, Anne van Gils, Karin A.M. Janssens, Elisabeth H. Bos, Robert A. Schoevers, Judith G. M. Rosmalen
Přispěvatelé: Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Perceptual and Cognitive Neuroscience (PCN), Life Course Epidemiology (LCE)
Jazyk: angličtina
Rok vydání: 2014
Předmět:
Zdroj: Journal of Psychosomatic Research, 77(1), 34-39. PERGAMON-ELSEVIER SCIENCE LTD
ISSN: 0022-3999
Popis: OBJECTIVE: Medically unexplained or functional somatic symptoms (FSSs) constitute a major health problem because of their high prevalence and the suffering and disability they cause. Psychosocial stress is widely believed to be a precipitating or perpetuating factor, yet there is little empirical evidence to support this notion. Prior studies mainly focused on comparing groups, which has resulted in the obscuring of temporal complexity and individual differences. The aim of this study is to elucidate the relationship between stress and FSSs over time within individual patients.METHODS: Twenty patients (17 females, ages 29-59) with multiple, persistent FSSs were included in the study. They used electronic diaries to report stress and FSSs twice daily over the course of 12 weeks. For each individual data set, Vector autoregressive (VAR) modelling was used to investigate possible associations between daily average stress and FSSs scores.RESULTS: In six subjects (30%), an increase in stress was followed by an increase in one or more FSSs. In three subjects (15%), an increase in FSSs was followed by an increase in stress. Additionally, negative and mixed associations were found. Only two subjects (10%) showed no cross-lagged association between stress and FSSs in either direction. We did not find specific types of symptoms to be more stress-related than others.CONCLUSION: Although stress does not seem to be a universal predictor of FSSs, an increase in stress precedes an increase in symptoms in some individuals. Identifying these individuals using time-series analysis might contribute to a more patient-tailored treatment.
Databáze: OpenAIRE