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Richter M, Mota S, Hater L, Bratek R, Goltermann J, Barkhau C, Gruber M, Repple J, Storck M, Blitz R, Grotegerd D, Masuhr O, Jaeger U, Baune BT, Dugas M, Walter M, Dannlowski U, Buhlmann U, Back M, Opel N. Narcissistic dimensions and depressive symptoms in patients across mental disorders in cognitive behavioural therapy and in psychoanalytic interactional therapy in Germany: a prospective cohort study. Lancet Psychiatry 2023; 10:955-965. [PMID: 37844592 DOI: 10.1016/s2215-0366(23)00293-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Narcissistic personality traits have been theorised to negatively affect depressive symptoms, therapeutic alliance, and treatment outcome, even in the absence of narcissistic personality disorder. We aimed to examine how the dimensional narcissistic facets of admiration and rivalry affect depressive symptoms across treatment modalities in two transdiagnostic samples. METHODS We did a naturalistic, observational prospective cohort study in two independent adult samples in Germany: one sample pooled from an inpatient psychiatric clinic and an outpatient treatment service offering cognitive behavioural treatment (CBT), and one sample from an inpatient clinic providing psychoanalytic interactional therapy (PIT). Inpatients treated with CBT had an affective or psychotic disorder. For the other two sites, data from all service users were collected. We examined the effect of core narcissism and its facets admiration and rivalry, measured by Narcissistic Admiration and Rivalry Questionnaire-short version, on depressive symptoms, measured by Beck's Depression Inventory and Patient Health Questionnaire-Depression Scale, at baseline and after treatment in patients treated with CBT and PIT. Primary analyses were regression models, predicting baseline and post-treatment depression severity from core narcissism and its facets. Mediation analysis was done in the outpatient CBT group for the effect of the therapeutic alliance on the association between narcissism and depression severity after treatment. FINDINGS The sample included 2371 patients (1423 [60·0%] female and 948 [40·0%] male; mean age 33·13 years [SD 13·19; range 18-81), with 517 inpatients and 1052 outpatients in the CBT group, and 802 inpatients in the PIT group. Ethnicity data were not collected. Mean treatment duration was 300 days (SD 319) for CBT and 67 days (SD 26) for PIT. Core narcissism did not predict depression severity before treatment in either group, but narcissistic rivalry was associated with higher depressive symptom load at baseline (β 2·47 [95% CI 1·78 to 3·12] for CBT and 1·05 [0·54 to 1·55] for PIT) and narcissistic admiration showed the opposite effect (-2·02 [-2·62 to -1·41] for CBT and -0·64 [-1·11 to -0·17] for PIT). Poorer treatment response was predicted by core narcissism (β 0·79 [0·10 to 1·47]) and narcissistic rivalry (0·89 [0·19 to 1·58]) in CBT, whereas admiration showed no effect. No effect of narcissism on treatment outcome was discernible in PIT. Therapeutic alliance mediated the effect of narcissism on post-treatment depression severity in the outpatient CBT sample. INTERPRETATION As narcissism affects depression severity before and after treatment with CBT across psychiatric disorders, even in the absence of narcissistic personality disorder, the inclusion of dimensional assessments of narcissism should be considered in future research and clinical routines. The relevance of the therapeutic alliance and therapeutic strategy could be used to guide treatment approaches. FUNDING IZKF Münster. TRANSLATION For the German translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Maike Richter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany.
| | - Simon Mota
- Department of Psychology, University of Münster, Münster, Germany
| | - Leonie Hater
- Department of Psychology, University of Münster, Münster, Germany
| | - Rebecca Bratek
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Carlotta Barkhau
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Marius Gruber
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, Goethe University Frankfurt, University Hospital, Frankfurt, Germany
| | - Jonathan Repple
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Psychosomatic Medicine, and Psychotherapy, Goethe University Frankfurt, University Hospital, Frankfurt, Germany
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Rogério Blitz
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Dominik Grotegerd
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | | | | | - Bernhard T Baune
- Department of Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne Parkville, VIC, Australia; Florey Institute of Neuroscience and Mental Health, University of Melbourne Parkville, VIC, Australia; Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Münster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany; German Center for Mental Health (DZPG), Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Münster, Germany
| | - Ulrike Buhlmann
- Department of Psychology, University of Münster, Münster, Germany
| | - Mitja Back
- Department of Psychology, University of Münster, Münster, Germany; Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Center for Intervention and Research on adaptive and maladaptive brain Circuits underlying mental health (C-I-R-C), Jena-Magdeburg-Halle, Germany; German Center for Mental Health (DZPG), Germany
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Koh ZH, Skues J, Murray G. Digital self-report instruments for repeated measurement of mental health in the general adult population: a protocol for a systematic review. BMJ Open 2023; 13:e065162. [PMID: 36693693 PMCID: PMC9884895 DOI: 10.1136/bmjopen-2022-065162] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
INTRODUCTION Digital technologies present tremendous opportunities for enabling long-term measurement of mental health in the general population. Emerging studies have established preliminary efficacy of collecting self-report data digitally. However, a key challenge when developing a new self-report instrument is navigating the abundance of existing instruments to select relevant constructs for measurements. This review is a precursor to developing a novel future integrated digital instrument for repeated measurements. We interrogate the literature as the first step towards optimal measurement of the multifaceted mental health concept, in the context of digital repeated measurement. This review aims to identify (1) digital self-report instruments administered repeatedly to measure the mental health of the general adult population; (2) their structure and format; (3) their psychometric properties; (4) their usage in empirical studies; and (5) the constructs these instruments were designed to measure (as characterised in the original publication), and the constructs the instruments have been used to measure in the identified empirical studies. METHODS AND ANALYSIS Five major electronic databases will be searched. Studies administering mental health instruments (in English) repeatedly to community dwellers in the general adult population are eligible. A reviewer will preliminarily screen for eligible studies. Then, two reviewers will independently screen the full text of the eligible articles and extract data. Both reviewers will resolve any disagreement through discussion or with a third reviewer. After the data extraction, a reviewer will manually search for the structure, format, psychometric properties and the original constructs these instruments were developed to measure. This review will synthesise the results in a narrative approach. The reporting in this review will be guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. ETHICS AND DISSEMINATION Ethical approval is not required as no data will be collected. Findings of the systematic review will be disseminated through peer-reviewed publications and conference presentations. PROSPERO REGISTRATION NUMBER CRD42022306547.
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Affiliation(s)
- Zhao Hui Koh
- Centre for Mental Health, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Jason Skues
- Department of Psychological Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Greg Murray
- Centre for Mental Health, Swinburne University of Technology, Hawthorn, Victoria, Australia
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Herpertz J, Richter MF, Barkhau C, Storck M, Blitz R, Steinmann LA, Goltermann J, Dannlowski U, Baune BT, Varghese J, Dugas M, Lencer R, Opel N. Symptom monitoring based on digital data collection during inpatient treatment of schizophrenia spectrum disorders - A feasibility study. Psychiatry Res 2022; 316:114773. [PMID: 35994863 DOI: 10.1016/j.psychres.2022.114773] [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] [Received: 05/15/2022] [Revised: 08/04/2022] [Accepted: 08/05/2022] [Indexed: 10/15/2022]
Abstract
Digital acquisition of patients' self-reports on individual risk factors and symptom severity represents a promising, cost-efficient, and increasingly prevalent approach for standardized data collection in psychiatric clinical routine. Yet, studies investigating digital data collection in patients with a schizophrenia spectrum disorder (PSSDs) are scarce. The objective of this study was to explore the feasibility of digitally acquired self-report assessments of risk and symptom profiles at the time of admission into inpatient treatment in an age-representative sample of hospitalized PSSDs. We investigated the required support, the data entry pace, and the subjective user experience. Findings were compared with those of patients with an affective disorder (PADs). Of 82 PSSDs who were eligible for inclusion, 59.8% (n=49) agreed to participate in the study, of whom 54.2% (n=26) could enter data without any assistance. Inclusion rates, drop-out rates, and subjective experience ratings did not differ between PSSDs and PADs. Patients reported high satisfaction with the assessment. PSSDs required more support and time for the data entry than PADs. Our results indicate that digital data collection is a feasible and well-received method in PSSDs. Future clinical and research efforts on digitized assessments in psychiatry should include PSSDs and offer support to reduce digital exclusion.
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Affiliation(s)
- Julian Herpertz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Maike Frederike Richter
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Carlotta Barkhau
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Michael Storck
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Rogério Blitz
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Institute of Medical Informatics, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany
| | - Lavinia A Steinmann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Janik Goltermann
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Bernhard T Baune
- Department of Psychiatry, Melbourne Medical School, The University of Melbourne, Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne Parkville, Melbourne, Australia; Department of Psychiatry, University of Münster, Münster, Germany
| | - Julian Varghese
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, University of Münster, Münster, Germany; Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
| | - Rebekka Lencer
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Department of Psychiatry and Psychotherapy, University of Lübeck, Lübeck, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany; Interdisciplinary Centre for Clinical Research Münster, University of Münster, Münster, Germany; Department of Psychiatry, Jena University Hospital/Friedrich-Schiller-University Jena, Jena, Germany.
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Richesson RL, Marsolo KS, Douthit BJ, Staman K, Ho PM, Dailey D, Boyd AD, McTigue KM, Ezenwa MO, Schlaeger JM, Patil CL, Faurot KR, Tuzzio L, Larson EB, O'Brien EC, Zigler CK, Lakin JR, Pressman AR, Braciszewski JM, Grudzen C, Fiol GD. Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory. J Am Med Inform Assoc 2021; 28:2626-2640. [PMID: 34597383 PMCID: PMC8633608 DOI: 10.1093/jamia/ocab202] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/05/2021] [Accepted: 09/02/2021] [Indexed: 01/29/2023] Open
Abstract
OBJECTIVE We identified challenges and solutions to using electronic health record (EHR) systems for the design and conduct of pragmatic research. MATERIALS AND METHODS Since 2012, the Health Care Systems Research Collaboratory has served as the resource coordinating center for 21 pragmatic clinical trial demonstration projects. The EHR Core working group invited these demonstration projects to complete a written semistructured survey and used an inductive approach to review responses and identify EHR-related challenges and suggested EHR enhancements. RESULTS We received survey responses from 20 projects and identified 21 challenges that fell into 6 broad themes: (1) inadequate collection of patient-reported outcome data, (2) lack of structured data collection, (3) data standardization, (4) resources to support customization of EHRs, (5) difficulties aggregating data across sites, and (6) accessing EHR data. DISCUSSION Based on these findings, we formulated 6 prerequisites for PCTs that would enable the conduct of pragmatic research: (1) integrate the collection of patient-centered data into EHR systems, (2) facilitate structured research data collection by leveraging standard EHR functions, usable interfaces, and standard workflows, (3) support the creation of high-quality research data by using standards, (4) ensure adequate IT staff to support embedded research, (5) create aggregate, multidata type resources for multisite trials, and (6) create re-usable and automated queries. CONCLUSION We are hopeful our collection of specific EHR challenges and research needs will drive health system leaders, policymakers, and EHR designers to support these suggestions to improve our national capacity for generating real-world evidence.
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Affiliation(s)
- Rachel L Richesson
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Keith S Marsolo
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Brian J Douthit
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.,US Department of Veterans Affairs, Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - Karen Staman
- Duke Clinical Research Institute, Durham, North Carolina, USA
| | - P Michael Ho
- Department of Medicine, University of Colorado Medicine, Denver, Colorado, USA
| | - Dana Dailey
- Center for Health Sciences, St. Ambrose University, Davenport, Iowa and Department of Physical Therapy and Rehabilitation Science, University of Iowa, Iowa City, Iowa, USA
| | - Andrew D Boyd
- Department of Biomedical and Health Information Sciences University of Illinois Chicago, Chicago, Illinois, USA
| | - Kathleen M McTigue
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Miriam O Ezenwa
- Department of Biobehavioral Nursing Science, University of Florida, College of Nursing, Gainesville, Florida, USA
| | - Judith M Schlaeger
- Department of Human Development Nursing Science, University of Illinois Chicago, College of Nursing, Chicago, Illinois, USA
| | - Crystal L Patil
- Department of Human Development Nursing Science, University of Illinois Chicago, College of Nursing, Chicago, Illinois, USA
| | - Keturah R Faurot
- Department of Physical Medicine and Rehabilitation, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Leah Tuzzio
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Eric B Larson
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA
| | - Emily C O'Brien
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Christina K Zigler
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, USA
| | - Joshua R Lakin
- Palliative Medicine, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Alice R Pressman
- Center for Health Systems Research, Sutter Health Center for Health Systems Research, Walnut Creek, California, USA
| | - Jordan M Braciszewski
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA
| | - Corita Grudzen
- Department of Emergency Medicine, New York University School of Medicine, New York, New York, USA
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, USA
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