Complexity in psychological self-ratings: implications for research and practice

• Published in: BMC medicine Vol. 18; no. 1; pp. 317 - 16
• Main Authors: Olthof, Merlijn, Hasselman, Fred, Lichtwarck-Aschoff, Anna
• Format: Journal Article
• Language: English
• Published: London BioMed Central 08.10.2020; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Analysis; Autocorrelation functions; Biomedicine; Complex system; Complex systems; Complexity; Complexity in Mental Health Research: Theory; Data points; Dependence; Divergence; Dynamical systems; Ecological momentary assessment; Empirical Contributions; Experience sampling method; Female; Humans; Initial conditions; Long term memory; Male; Markers; Mathematical models; Medicine; Medicine & Public Health; Memory; Mental disorders; Mental health; Method; Moving targets; Patients; Phase transitions; Physiology; Precision medicine; Psychological research; Psychopathology; Psychopathology - methods; Random variables; Rank tests; Ratings; Ratings & rankings; Research Article; Research Design; Science; Statistical analysis; Time series; Trajectory analysis; Ecological momentary assessment; Experience sampling method; Complex system; Mental health; Time series; Psychopathology; Personalized medicine; Complexity
• ISSN: 1741-7015; 1741-7015
• DOI: 10.1186/s12916-020-01727-2

Background Psychopathology research is changing focus from group-based “disease models” to a personalized approach inspired by complex systems theories. This approach, which has already produced novel and valuable insights into the complex nature of psychopathology, often relies on repeated self-ratings of individual patients. So far, it has been unknown whether such self-ratings, the presumed observables of the individual patient as a complex system, actually display complex dynamics. We examine this basic assumption of a complex systems approach to psychopathology by testing repeated self-ratings for three markers of complexity: memory , the presence of (time-varying) short- and long-range temporal correlations; regime shifts , transitions between different dynamic regimes; and sensitive dependence on initial conditions , also known as the “butterfly effect,” the divergence of initially similar trajectories. Methods We analyzed repeated self-ratings (1476 time points) from a single patient for the three markers of complexity using Bartels rank test, (partial) autocorrelation functions, time-varying autoregression, a non-stationarity test, change point analysis, and the Sugihara-May algorithm. Results Self-ratings concerning psychological states (e.g., the item “I feel down”) exhibited all complexity markers: time-varying short- and long-term memory, multiple regime shifts, and sensitive dependence on initial conditions. Unexpectedly, self-ratings concerning physical sensations (e.g., the item “I am hungry”) exhibited less complex dynamics and their behavior was more similar to random variables. Conclusions Psychological self-ratings display complex dynamics. The presence of complexity in repeated self-ratings means that we have to acknowledge that (1) repeated self-ratings yield a complex pattern of data and not a set of (nearly) independent data points, (2) humans are “moving targets” whose self-ratings display non-stationary change processes including regime shifts, and (3) long-term prediction of individual trajectories may be fundamentally impossible. These findings point to a limitation of popular statistical time series models whose assumptions are violated by the presence of these complexity markers. We conclude that a complex systems approach to mental health should appreciate complexity as a fundamental aspect of psychopathology research by adopting the models and methods of complexity science. Promising first steps in this direction, such as research on real-time process monitoring, short-term prediction, and just-in-time interventions, are discussed.