Abstract
IntroductionThere is growing evidence that mental disorders behave like complex dynamic systems. Complex dynamic systems theory states that a slower recovery from small perturbations indicates a loss of resilience of a system. This study is the first to test whether the speed of recovery of affect states from small daily life perturbations predicts changes in psychopathological symptoms over 1 year in a group of adolescents at increased risk for mental disorders.MethodsWe used data from 157 adolescents from the TWINSSCAN study. Course of psychopathology was operationalized as the 1-year change in the Symptom Checklist-90 sum score. Two groups were defined: one with stable and one with increasing symptom levels. Time-series data on momentary daily affect and daily unpleasant events were collected 10 times a day for 6 days at baseline.We modeled the time-lagged effect of daily unpleasant events on negative and positive affect after each unpleasant event experienced, to examine at which time point the impact of the events is no longer detectable.ResultsThere was a significant difference between groups in the effect of unpleasant events on negative affect 90 min after the events were reported. Stratified by group, in the Increase group, the effect of unpleasant events on both negative (B = 0.05, p < 0.01) and positive affect (B = − 0. 08, p < 0.01) was still detectable 90 min after the events, whereas in the Stable group this was not the case.ConclusionFindings cautiously suggest that adolescents who develop more symptoms in the following year may display a slower affect recovery from daily perturbations at baseline. This supports the notion that mental health may behave according to the laws of a complex dynamic system. Future research needs to examine whether these dynamic indicators of system resilience may prove valuable for personalized risk assessment in this field.
Highlights
There is growing evidence that mental disorders behave like complex dynamic systems
A promising approach to obtain accurate risk estimations comes from the theory of complex systems. Examples of such complex systems are ecosystems, which are known to make shifts from a forest state to a swamp state, or the financial market, which can experience a sudden collapse [3, 4]. Such changes are results of numerous mechanistic interactions, complex systems theory states that the stability of a system, i.e., how hard it is for a large change to occur, can be quantified in one characteristic: an index of resilience
Indicators of critical slowing down” (CSD) have been shown to predict-critical transitions as well as gradual change in various sorts of complex systems, whether they are financial markets, oceans, climate, or brain activity [3, 5, 6]. If these principles work for psychopathology as well, we can assume that higher instability in the system, and lower resilience, means that it is more difficult to remain in a current healthy state and that this is related to, on average, higher levels of symptoms in the near future in this group of people
Summary
There is growing evidence that mental disorders behave like complex dynamic systems. Complex dynamic systems theory states that a slower recovery from small perturbations indicates a loss of resilience of a system. Indicators of CSD have been shown to predict (non)-critical transitions as well as gradual change in various sorts of complex systems, whether they are financial markets, oceans, climate, or brain activity [3, 5, 6] If these principles work for psychopathology as well, we can assume that higher instability in the system (in this case, mental health), and lower resilience, means that it is more difficult to remain in a current healthy state and that this is related to, on average, higher levels of symptoms in the near future in this group of people
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