Exploring personalized psychotherapy for depression: A system dynamics approach
Open accessAbstract
Depressive disorders are the leading contributor to medical disability, yet only 22% of depressed patients receive adequate treatment in a given year. Response to treatment varies widely among individuals with depression, and poor response to one treatment does not signal poor response to others. In fact, half of patients who do not recover from a first-line psychotherapy will recover from a second option. Attempts to personalize psychotherapy to patient characteristics have produced better outcomes than usual care, but research on personalized psychotherapy is still in its infancy. The present study explores a new method for personalizing psychotherapy for depression through simulation modeling. In this study, we developed a system dynamics simulation model of depression based on one of the major mechanisms of depression in the literature and investigated the trend of depressive symptoms under different conditions and treatments. Our simulation outputs show the importance of individualized services with appropriate timing, and reveal a new method for personalizing psychotherapy to heterogeneous individuals. Future research is needed to expand the model to include additional mechanisms of depression.
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A major limitation of homogenously assigned (non-personalized) behavioral intervention studies is the heterogeneity across individuals of the specific lifestyle factors most closely linked to their depression.
“Yet, a major limitation of these homogenously assigned (non-personalized) behavioral intervention studies is the heterogeneity across individuals of the specific lifestyle factors most closely linked to their depression.”
From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [19]
The Introduction and Discussion (page numbers unavailable) describe widely varying responses to psychotherapy and support tailoring treatment to heterogeneous patients. The paper examines differences in stressors and rumination, but does not identify variation in specific lifestyle factors as a major limitation of uniformly assigned behavioral-intervention studies. (LLM, conf=0.84)
Experimental — accuracy not yet measuredA single lifestyle treatment domain is unlikely to be universally beneficial for depression.
“Consequently, a single lifestyle treatment domain is unlikely to be universally beneficial, and these studies fail to account for individual differences that may favor one behavioral health solution over another [17–19].”
From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [19]
The Introduction (page not provided) says responses to depression treatment vary widely and that poor response to one treatment does not predict poor response to another. This supports the general point that one treatment will not benefit everyone, but the paper addresses psychotherapy, not lifestyle treatment domains. (LLM, conf=0.85)
Experimental — accuracy not yet measuredHomogenously assigned behavioral intervention studies fail to account for individual differences that may favor one behavioral health solution over another.
“Consequently, a single lifestyle treatment domain is unlikely to be universally beneficial, and these studies fail to account for individual differences that may favor one behavioral health solution over another [17–19].”
From Personalized machine learning guided intervention for optimizing lifestyle behaviors in depression: a pilot study (2026) · cites this paper as [19]
The Introduction says responses to depression treatment vary widely and that a poor response to one treatment does not predict a poor response to another. The Discussion supports selecting psychotherapy for heterogeneous patients. The paper does not, however, establish that the behavioral-intervention studies described in the citing sentence assigned treatments homogeneously or failed to account for individual differences across lifestyle treatment domains. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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“Consequently, a single lifestyle treatment domain is unlikely to be universally bene fi cial, and these studies fail to account for individual differences that may favor one behavioral health solution over another [17 – 19].”
- Authors
- Wittenborn, A. K., & Hosseinichimeh, N.
- Affiliations
- Michigan State University (US)
- Virginia Tech (US)
- Corresponding author
- Wittenborn, Andrea K.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2022-10-27
- Volume
- 17
- Issue
- 10
- Pages
- e0276441
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC9612473
- OpenAlex
- W4307441202
- Cited by
- 9
- References
- 41
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 2 · 2023: 3
- FWCI
- 1.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- VTechWorks (Virginia Tech) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of Mental Health (R21MH100515)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
- Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
- Keywords
- depression, personalized psychotherapy, system dynamics modeling, treatment response heterogeneity, depressive symptoms, treatment-resistant depression, psychotherapy outcome
- MeSH terms
- Depression, Humans, Psychotherapy, Research Design, Treatment Outcome
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