Cited by 149Psychotherapy Techniques and ApplicationsMental Health Research TopicsChaos, Complexity, and Education

The DOI matches its Crossref record; 128 of the 128 references it cites were found in a registry or an index.

A complex systems approach to the study of change in psychotherapy

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Hayes, Adele M., Andrews, Leigh A.·2020·BMC Medicine

Abstract

BACKGROUND: A growing body of research highlights the limitations of traditional methods for studying the process of change in psychotherapy. The science of complex systems offers a useful paradigm for studying patterns of psychopathology and the development of more functional patterns in psychotherapy. Some basic principles of change are presented from subdisciplines of complexity science that are particularly relevant to psychotherapy: dynamical systems theory, synergetics, and network theory. Two early warning signs of system transition that have been identified across sciences (critical fluctuations and critical slowing) are also described. The network destabilization and transition (NDT) model of therapeutic change is presented as a conceptual framework to import these principles to psychotherapy research and to suggest future research directions. DISCUSSION: A complex systems approach has a number of implications for psychotherapy research. We describe important design considerations, targets for research, and analytic tools that can be used to conduct this type of research. CONCLUSIONS: A complex systems approach to psychotherapy research is both viable and necessary to more fully capture the dynamics of human change processes. Research to date suggests that the process of change in psychotherapy can be nonlinear and that periods of increased variability and critical slowing might be early warning signals of transition in psychotherapy, as they are in other systems in nature. Psychotherapy research has been limited by small samples and infrequent assessment, but ambulatory and electronic methods now allow researchers to more fully realize the potential of concepts and methods from complexity science.

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Crossref · 10.1186/s12916-020-01662-2

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Fig. 1 Network destabilization and transition (NDT) model. Attractor landscapes are depicted with the solid line and ball, which represents the state of the system. Pathological and more healthy networks are depicted with nodes of cognition (C), emotion (E), behavior (B), and physiological responses (P) and associated feedback loops. Larger nodes (circles) are stronger, and thicker lines represent stronger connections. Panel 1 depicts a well-established pathological attractor with a hypothetical network that is strongly interconnected and maintained by amplifying feedback loops. Panel 2 depicts a pathological attractor that is less strong. The adaptive attractor in panel 2 is more developed than in panel 1 and provides an alternative for the ball to enter when the pathological network is activated and destabilized. With repeated activation, exercise, and amplifying feedback loops (panel 3), the healthy attractor becomes stronger than the pathological attractor
Fig. 1 Network destabilization and transition (NDT) model. Attractor landscapes are depicted with the solid line and ball, which represents the state of the system. Pathological and more healthy networks are depicted with nodes of cognition (C), emotion (E), behavior (B), and physiological responses (P) and associated feedback loops. Larger nodes (circles) are stronger, and thicker lines represent stronger connections. Panel 1 depicts a well-established pathological attractor with a hypothetical network that is strongly interconnected and maintained by amplifying feedback loops. Panel 2 depicts a pathological attractor that is less strong. The adaptive attractor in panel 2 is more developed than in panel 1 and provides an alternative for the ball to enter when the pathological network is activated and destabilized. With repeated activation, exercise, and amplifying feedback loops (panel 3), the healthy attractor becomes stronger than the pathological attractorCC BY · PubMed Central

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Research works printed but not linked: 2

  1. Schiepek G, Aichhorn W, Schöller H. Monitoring change dynamics - a nonlinear approach to psychotherapy feedback. Chaos Complex Lett. 2017;11(3):355–75.
  2. Ruwaard J, Kooistra L, Thong M. Early warning signs of depression. In: Ecological momentary assessment in mental health research: a practical introduction, with examples in R. 1st ed. Amsterdam: APH Mental Health; 2018.

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Metadata

Authors
Hayes, A. M., & Andrews, L. A.
Affiliations
University of Delaware (US)
Corresponding author
Hayes, Adele M.
Venue
BMC Medicine
Published
2020-07-14
Volume
18
Issue
1
Pages
197-197
ISSN
1741-7015 (ISSN-L)
OpenAlex
W3042416448
Cited by
149
References
128
Citations by year
2026: 25 · 2025: 31 · 2024: 25 · 2023: 33 · 2022: 22 · 2021: 11 · 2020: 2
FWCI
6.81
Type
journal article
Language
en
Open access
Locations
  • BMC Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Foundation for the National Institutes of Health (R21-MH062662, R01-MH086558)
Topics
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
  • Social Sciences › Education › Chaos, Complexity, and Education
Keywords
psychotherapeutic change, complex systems, dynamical systems theory, synergetics, network theory, critical slowing down, critical fluctuations, early warning indicators, system transition, nonlinear dynamics, psychopathology, ambulatory assessment, digital assessment
MeSH terms
Delivery of Health Care, Feedback, Humans, Psychotherapy

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