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Cited by 157Anxiety, Depression, Psychometrics, Treatment, Cognitive ProcessesPsychotherapy Techniques and ApplicationsPersonality Disorders and Psychopathology

Open trial of a personalized modular treatment for mood and anxiety

Open access
Fisher, Aaron J., Bosley, Hannah G., Fernandez, Katya C., Reeves, Jonathan W., Soyster, Peter D., Diamond, Allison E., Barkin, Jonathan·2019·Behaviour Research and Therapy
DOI 10.1016/j.brat.2019.01.010
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Abstract

Psychosocial treatments for mood and anxiety disorders are generally effective, however, a number of treated individuals fail to demonstrate clinically-significant change. Consistent with the decades-old aim to identify 'what works for whom,' personalized and precision treatments have become a recent area of interest in medicine and psychology. The present study followed the recommendations of Fisher (2015) to employ a personalized modular model of cognitive-behavioral therapy. Employing the algorithms provided by Fernandez, Fisher, and Chi (2017), the present study collected intensive repeated measures data prior to therapy in order to perform person-specific factor analysis and dynamic factor modeling. The results of these analyses were then used to generated personalized modular treatment plans on a person-by-person basis. Thirty-two participants completed therapy. The average number of sessions was 10.38. Hedges g's for the Hamilton Rating Scale for Depression (HRSD) and Hamilton Anxiety Rating Scale (HARS) were 2.33 and 1.62, respectively. The change per unit time was g = .24/session for the HRSD and g = 0.17/session for the HARS. The current open trial provides promising data in support of personalization, modularization, and idiographic research paradigms.

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  • The identification of which active elements work for whom could improve outcomes via development of personalized treatment protocols, where individuals receive a specific suite of active elements suited to their needs.

    “The identification of which active elements work for whom could improve outcomes via develop­ ment of personalized treatment protocols, where individuals receive a specific suite of active elements suited to their needs (Fisher et al., 2019; Rubel, Fisher, Husen, & Lutz, 2018).”

    From Measuring the active elements of cognitive-behavioral therapies (2023) · cites this paper as Fisher et al., 2019

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Authors
Fisher, A. J., Bosley, H. G., Fernandez, K. C., Reeves, J. W., Soyster, P. D., Diamond, A. E., & Barkin, J.
Affiliations
  • University of California, Berkeley (US)
  • Center for Creative Leadership (US)
Corresponding author
Fisher, Aaron J.
ORCID
Venue
Behaviour Research and Therapy
Publisher
Elsevier BV
Published
2019-05
Volume
116
Pages
69-79
ISSN
0005-7967 (ISSN-L)
DOI
10.1016/j.brat.2019.01.010
OpenAlex
W2915885068
Cited by
156
References
63
Citations by year
2026: 12 · 2025: 16 · 2024: 30 · 2023: 17 · 2022: 22 · 2021: 23 · 2020: 27 · 2019: 9
FWCI
23.78
Type
journal article
Language
en
Open access
Licence
Locations
  • Behaviour Research and Therapy (publishedVersion)
  • PubMed (publishedVersion)
  • PsyArXiv (OSF Preprints) (submittedVersion, public-domain)
  • PsyArXiv (OSF Preprints) (submittedVersion)
  • OSF Preprints (OSF Preprints) (submittedVersion)
Topics
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
  • Social Sciences › Psychology › Clinical Psychology › Psychotherapy Techniques and Applications
  • Social Sciences › Psychology › Clinical Psychology › Personality Disorders and Psychopathology
Keywords
personalized treatment, mood disorders, anxiety disorders, depression, anxiety, treatment response prediction, idiographic research
MeSH terms
Adolescent, Adult, Algorithms, Anxiety Disorders, Major Depressive Disorder, Female, Humans, Male, Middle Aged, Cognitive Behavioral Therapy, Young Adult, Precision Medicine

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