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van Bronswijk SC, Howard J, Lorenzo-Luaces L. Data-driven personalized medicine approaches to cognitive-behavioral therapy allocation in a large sample: A reanalysis of the ENRICHED study. J Affect Disord 2024; 356:115-121. [PMID: 38582129 DOI: 10.1016/j.jad.2024.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Although effective treatments for common mental health problems are available, individual responses to treatments are difficult to predict. Treatment efficacy could be optimized by targeting interventions using individual predictions of treatment outcomes. The aim of this study was to develop a prediction algorithm using data from one of the largest randomized controlled trials on psychological interventions for common mental health problems. METHODS This is a secondary analysis of the Enhancing Recovery in Coronary Heart Disease study investigating the effectiveness of cognitive behavioral therapy (CBT) and care as usual (CAU) for depression and low perceived social support following acute myocardial infarction. 2481 participants were randomly assigned to CBT and CAU. Baseline social-demographics, depression characteristics, comorbid symptoms, and stress and adversity measures were used to build an algorithm predicting post-treatment depression severity using elastic net regularization. Performance and generalizability of this algorithm were determined in a hold-out sample (n = 1203). RESULTS Treatment matching based on predictions in the hold-out sample resulted in inconsistent and small effects (d = 0.15), that were more pronounced for individuals matched to CBT (d = 0.22). We identified a small subgroup of individuals for which CBT did not appear more efficacious than CAU. LIMITATIONS Limitations are a poorly defined CAU condition, a low-severity sample, specific exclusion criteria and unavailability of certain baseline variables. CONCLUSIONS Small matching effects are likely a realistic representation of the performance and generalizability of multivariable prediction algorithms based on clinical measures. Results indicate that future work and new approaches are needed.
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Affiliation(s)
- Suzanne Catharina van Bronswijk
- Department of Psychiatry and Psychology, Maastricht University Medical Center, Maastricht, the Netherlands; Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | | | - Lorenzo Lorenzo-Luaces
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
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Effectiveness of ACT-based intervention in compliance with the model for sustainable mental health: A cluster randomized control trial in a group of older adults. JOURNAL OF CONTEXTUAL BEHAVIORAL SCIENCE 2023. [DOI: 10.1016/j.jcbs.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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Bohlmeijer E, Westerhof G. The Model for Sustainable Mental Health: Future Directions for Integrating Positive Psychology Into Mental Health Care. Front Psychol 2021; 12:747999. [PMID: 34744925 PMCID: PMC8566941 DOI: 10.3389/fpsyg.2021.747999] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 09/13/2021] [Indexed: 11/13/2022] Open
Abstract
This position paper proposes a model for systematic integration of positive psychology interventions (PPIs) in mental healthcare. On the one hand, PPIs can contribute to the decrease of dysfunctional processes underlying mental illness. This evidence is at the core of the new domains of positive clinical psychology and positive psychiatry. On the other hand, a growing number of studies demonstrate that mental health is not merely the absence of mental illness. Mental wellbeing represents a related but separate dimension of mental health. Mental wellbeing reduces the risk of future incidence of mental illness and is highly valued by people receiving psychological treatment as an important aspect of personal and complete recovery and personal growth. This makes mental wellbeing a vital outcome of mental healthcare. PPIs can directly increase mental wellbeing. The model of sustainable mental health is presented integrating the science of positive psychology and mental wellbeing into mental healthcare. This heuristic model can guide both practitioners and researchers in developing, implementing, and evaluating a more balanced, both complaint- and strength-oriented, treatment approach. The role of gratitude interventions is discussed as an example of applying the model. Also, three potential modalities for implementing PPIs as positive psychotherapy in treatment are as: positive psychotherapy as primary treatment, as combinatorial treatment, and as intervention for personal recovery of people with severe or persistent mental disorder. Finally, we argue that longitudinal studies are needed to substantiate the model and the processes involved.
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Affiliation(s)
- Ernst Bohlmeijer
- University of Twente, Enschede, Netherlands.,Center for eHealth and well-being, Enschede, Netherlands
| | - Gerben Westerhof
- University of Twente, Enschede, Netherlands.,Center for eHealth and well-being, Enschede, Netherlands
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Nieto I, Vazquez C. Disentangling the mediating role of modifying interpretation bias on emotional distress using a novel cognitive bias modification program. J Anxiety Disord 2021; 83:102459. [PMID: 34358756 DOI: 10.1016/j.janxdis.2021.102459] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 07/12/2021] [Accepted: 07/26/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Negative interpretation bias is a potential risk factor for emotional disorders. In this study, we tested a clinically inspired 4-session online Cognitive Bias Modification-Interpretation (CBM-IClin) program to modify negative interpretation biases. METHODS We randomized one hundred and twenty-one volunteer young adults (Mean age = 21.6 years, SD = 3.5; 85 % women) with varying levels of emotional distress to either an experimental or waitlist control group. Mediation analyses were used to disentangle the associations between the intervention, changes in interpretation biases (assessed by both a self-report and an experimental task), and changes in measures of cognitive vulnerability and symptoms of depression and anxiety. RESULTS The results showed that the CBM-IClin could change negative interpretation biases. Also, it had a direct effect on the change in negative memory bias, an indirect effect on the change in depression symptoms via the change in interpretation bias, and both direct and indirect effects on the change in self-reported dysfunctional attitudes. LIMITATIONS The study included a non-clinical sample of participants and it did not control for some potential confounding factors (e.g., attentional disorders). Furthermore, participants' engagement during the sessions at home was not supervised. CONCLUSIONS The CBM-IClin is a potential tool to prevent and intervene in emotional disorders in young adults and could complement other traditional CBM procedures or clinical interventions.
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Affiliation(s)
- Inés Nieto
- Department of Clinical Psychology, School of Psychology, Complutense University of Madrid, Spain.
| | - Carmelo Vazquez
- Department of Clinical Psychology, School of Psychology, Complutense University of Madrid, Spain
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González-Blanch C, Muñoz-Navarro R, Medrano LA, Moriana JA, Ruiz-Rodríguez P, Cano-Vindel A. Moderators and predictors of treatment outcome in transdiagnostic group cognitive-behavioral therapy for primary care patients with emotional disorders. Depress Anxiety 2021; 38:757-767. [PMID: 34043853 DOI: 10.1002/da.23164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 03/06/2021] [Accepted: 04/17/2021] [Indexed: 11/11/2022] Open
Abstract
OBJECTIVE Transdiagnostic group cognitive behavior therapy (TD-GCBT) has shown to be efficacious in the treatment of emotional disorders in primary care. However, little is known about possible moderators or predictors of treatment outcome. We aimed to explore the potential predictors and moderators of outcome in a large multicentre randomized controlled trial comparing TD-GCBT plus treatment as usual (TAU) to TAU alone. METHOD Putative demographic and baseline clinical variables were examined using the PROCESS macro as potential predictors/moderators of depressive and anxiety symptoms at posttreatment and 1-year follow-up. RESULTS Analyses were based on a study completer sample of 1061 participants randomized to TD-CBT + TAU (n = 527) or TAU alone (n = 534), with 631 participants assessed at the posttreatment evaluation and 388 at the 1-year follow-up. Individuals working or with a partner among sociodemographic variables, and higher baseline comorbidities and more severity of symptoms among clinical variables obtained more benefits from adding TDCBT to TAU. Those taking medication before treatment obtained less benefits from the TD-GCBT than those without prescribed antidepressant medications, after controlling for baseline severity of symptoms. Overall, the moderating effect of clinical (but not sociodemographic) variables remained at 1-year follow-up. CONCLUSION Findings support largely the generalization of the TD-GCBT for emotional disorders in primary care to a variety of sociodemographic and clinical groups. However, TD-GCBT seems to work to a greater extent for those individuals with a more severe clinical profile. Providing TD-GCBT before prescribing antidepressant medication and while people are still working may enhance the effects of adding this psychological treatment to TAU in primary care.
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Affiliation(s)
- César González-Blanch
- Mental Health Centre, University Hospital Marqués de Valdecilla-IDIVAL, Santander, Spain
| | - Roger Muñoz-Navarro
- Department of Psychology and Sociology, Faculty of Social and Human Sciences University of Zaragoza, Zaragoza, Spain
| | - Leonardo Adrián Medrano
- Research Secretariat, Faculty of Psychology, Universidad Empresarial Siglo 21, Córdoba, Argentina
| | - Juan Antonio Moriana
- Department of Psychology, Maimónides Institute for Research in Biomedicine of Cordoba-IMIBIC, Reina Sofía University Hospital, University of Córdoba, Córdoba, Spain
| | | | - Antonio Cano-Vindel
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Faculty of Psychology, Complutense University of Madrid, Madrid, Spain
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Senger K, Schröder A, Kleinstäuber M, Rubel JA, Rief W, Heider J. Predicting optimal treatment outcomes using the Personalized Advantage Index for patients with persistent somatic symptoms. Psychother Res 2021; 32:165-178. [PMID: 33910487 DOI: 10.1080/10503307.2021.1916120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Abstract
Because individual patients with persistent somatic symptoms (PSS) respond differently to treatments, a better understanding of the factors that predict therapy outcomes are of high importance. Aggregating a wide selection of information into the treatment-decision process is a challenge for clinicians. Using the Personalized Advantage Index (PAI) this study aims to deal with this. Methods: Data from a multicentre RCT comparing CBT (N = 128) versus CBT enriched with emotion regulation training (ENCERT) (N = 126) for patients diagnosed with somatic symptom disorder were used to identify based on two machine learning approaches predictors of therapy outcomes. The identified predictors were used to calculate the PAI. Results: Five treatment unspecific predictors (pre-treatment somatic symptom severity, depression, symptom disability, health-related quality of life, age) and five treatment specific moderators (global functioning, early childhood traumatic events, gender, health anxiety, emotion regulation skills) were identified. Individuals assigned to their PAI-indicated optimal treatment had significantly lower somatic symptom severity at the end of therapy compared to those randomised to their non-optimal condition. Conclusion: Allowing patients to choose a personalised treatment seems to be meaningful. This could help to improve outcomes for PSS and reduce its high costs to the health care system.
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Affiliation(s)
- Katharina Senger
- Department of Psychology, University of Koblenz-Landau, Landau, Germany
| | - Annette Schröder
- Department of Psychology, University of Koblenz-Landau, Landau, Germany
| | - Maria Kleinstäuber
- Department of Psychological Medicine, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand
| | - Julian A Rubel
- Department of Psychology, University of Giessen, Germany
| | - Winfried Rief
- Department of Clinical Psychology and Psychotherapy, Philipps University Marburg, Germany
| | - Jens Heider
- Department of Psychology, University of Koblenz-Landau, Landau, Germany
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Personalized Medicine and Cognitive Behavioral Therapies for Depression: Small Effects, Big Problems, and Bigger Data. Int J Cogn Ther 2020. [DOI: 10.1007/s41811-020-00094-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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van Bronswijk SC, Bruijniks SJE, Lorenzo-Luaces L, Derubeis RJ, Lemmens LHJM, Peeters FPML, Huibers MJH. Cross-trial prediction in psychotherapy: External validation of the Personalized Advantage Index using machine learning in two Dutch randomized trials comparing CBT versus IPT for depression. Psychother Res 2020; 31:78-91. [DOI: 10.1080/10503307.2020.1823029] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Suzanne C. van Bronswijk
- Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands
| | - Sanne J. E. Bruijniks
- Department of Clinical Psychology and Psychotherapy, University of Freiburg, Freiburg, Germany
- Department of Clinical Psychology, Amsterdam Public Health research institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | | | - Lotte H. J. M. Lemmens
- Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands
| | - Frenk P. M. L. Peeters
- Department of Clinical Psychological Science, Maastricht University, Maastricht, The Netherlands
| | - Marcus. J. H. Huibers
- Department of Clinical Psychology, Amsterdam Public Health research institute, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Department of Psychology, University of Pennsylvania, Philadelphia, USA
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Blanco I, Contreras A, Chaves C, Lopez-Gomez I, Hervas G, Vazquez C. Positive interventions in depression change the structure of well-being and psychological symptoms: A network analysis. THE JOURNAL OF POSITIVE PSYCHOLOGY 2020. [DOI: 10.1080/17439760.2020.1789696] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Ivan Blanco
- School of Psychology, Complutense University of Madrid, Madrid, Spain
- Department of Psychology, Cardenal Cisneros University Centre, Madrid, Spain
| | - Alba Contreras
- School of Psychology, Complutense University of Madrid, Madrid, Spain
| | - Covadonga Chaves
- School of Psychology, Complutense University of Madrid, Madrid, Spain
| | | | - Gonzalo Hervas
- School of Psychology, Complutense University of Madrid, Madrid, Spain
| | - Carmelo Vazquez
- School of Psychology, Complutense University of Madrid, Madrid, Spain
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Moskowitz JT, Addington EL, Cheung EO. Positive psychology and health: Well-being interventions in the context of illness. Gen Hosp Psychiatry 2019; 61:136-138. [PMID: 31757566 DOI: 10.1016/j.genhosppsych.2019.11.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 11/05/2019] [Indexed: 02/07/2023]
Affiliation(s)
- Judith T Moskowitz
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, United States of America.
| | - Elizabeth L Addington
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, United States of America
| | - Elaine O Cheung
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, United States of America
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