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Kato M, Kikuchi T, Watanabe K, Sumiyoshi T, Moriguchi Y, Oudin Åström D, Christensen MC. Goal Attainment Scaling for Depression: Validation of the Japanese GAS-D Tool in Patients with Major Depressive Disorder. Neuropsychiatr Dis Treat 2024; 20:49-60. [PMID: 38239870 PMCID: PMC10796153 DOI: 10.2147/ndt.s441382] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/11/2024] [Indexed: 01/22/2024] Open
Abstract
Purpose Goal attainment scaling (GAS) has been proposed as a person-centric, semi-quantitative measure that assimilates achievement of individually set goals into a single standardized "goal attainment score" that can be compared at the population level. We aimed to examine the reliability and validity of the Japanese version of the GAS for depression (GAS-D) tool in assessing goal attainment in people living with major depressive disorder (MDD). Patients and Methods This was a prespecified analysis of a prospective, 24-week, multicenter, observational cohort study of employed Japanese outpatients with MDD initiating treatment with vortioxetine according to the Japanese label (JRCT1031210200). Participants were assessed using the Japanese version of the GAS-D and other clinical rating scales at baseline and Weeks 8, 12 and 24. Results Goal attainment was significantly associated with symptom severity as assessed by the Montgomery-Åsberg Depression Rating Scale (MADRS) scale, confirming convergent validity. In particular, GAS-D scores were significantly related to MADRS total score at Weeks 12 and 24, indicating that improvements in overall symptom severity with vortioxetine treatment were likely to be reflected in the achievement of individualized treatment goals. With an intraclass correlation coefficient of 0.67 (95% CI 0.45-0.82), the GAS-D also showed moderate test-retest reliability between Weeks 8 and 12 while proving independent of demographic characteristics. Conclusion The results of this open-label study support the use of the GAS-D as a valid and sensitive outcome measure in the assessment of treatment response in MDD.
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Affiliation(s)
- Masaki Kato
- Department of Neuropsychiatry, Kansai Medical University, Osaka, Japan
| | - Toshiaki Kikuchi
- Department of Neuropsychiatry, Keio University School of Medicine, Department of Neuropsychiatry, Tokyo, Japan
| | - Koichiro Watanabe
- Department of Neuropsychiatry, Kyorin University School of Medicine, Tokyo, Japan
| | - Tomiki Sumiyoshi
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
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Tran I, Gellner AK. Long-term effects of chronic stress models in adult mice. J Neural Transm (Vienna) 2023; 130:1133-1151. [PMID: 36786896 PMCID: PMC10460743 DOI: 10.1007/s00702-023-02598-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 01/28/2023] [Indexed: 02/15/2023]
Abstract
Neuropsychiatric disorders, such as major depression, anxiety disorders, and post-traumatic stress disorder, tend to be long-term conditions in whose development and maintenance stress are central pathogenic factors. Translational mouse models are widely used in neuropsychiatric research, exploiting social and non-social stressors to investigate the mechanisms underlying their detrimental effects. However, most studies focus on the short-term consequences of chronic stress, whereas only a few are interested in the long-term course. This is counterintuitive given the human conditions that preclinical models are designed to mimic. In this review, we have summarized the limited work to date on long-term effects of chronic stress in mice models. First, the different models are presented and a definition of short- vs. long-term sequelae is proposed. On this basis, behavioral, endocrine, and vegetative effects are addressed before examining data on cellular and molecular alterations in the brain. Finally, future directions for research on the long-term effects of stress are discussed.
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Affiliation(s)
- Inès Tran
- Institute of Physiology II, Medical Faculty, University of Bonn, Bonn, Germany
| | - Anne-Kathrin Gellner
- Institute of Physiology II, Medical Faculty, University of Bonn, Bonn, Germany.
- Department of Psychiatry and Psychotherapy, University Hospital Bonn, Bonn, Germany.
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Meadows R, Hine C, Suddaby E. Conversational agents and the making of mental health recovery. Digit Health 2020; 6:2055207620966170. [PMID: 33282335 PMCID: PMC7683843 DOI: 10.1177/2055207620966170] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 09/22/2020] [Indexed: 11/26/2022] Open
Abstract
Background Artificial intelligence (AI) is said to be “transforming mental health”. AI-based technologies and technique are now considered to have uses in almost every domain of mental health care: including decision-making, assessment and healthcare management. What remains underexplored is whether/how mental health recovery is situated within these discussions and practices. Method Taking conversational agents as our point of departure, we explore the ways official online materials explain and make sense of chatbots, their imagined functionality and value for (potential) users. We focus on three chatbots for mental health: Woebot, Wysa and Tess. Findings “Recovery” is largely missing as an overt focus across materials. However, analysis does reveal themes that speak to the struggles over practice, expertise and evidence that the concept of recovery articulates. We discuss these under the headings “troubled clinical responsibility”, “extended virtue of (technological) self-care” and “altered ontologies and psychopathologies of time”. Conclusions Ultimately, we argue that alongside more traditional forms of recovery, chatbots may be shaped by, and shaping, an increasingly individualised form of a “personal recovery imperative”.
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Affiliation(s)
- Robert Meadows
- Department of Sociology, University of Surrey, Guildford, Surrey, UK
| | - Christine Hine
- Department of Sociology, University of Surrey, Guildford, Surrey, UK
| | - Eleanor Suddaby
- Department of Sociology, University of Surrey, Guildford, Surrey, UK
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Rush AJ, Aaronson ST, Demyttenaere K. Difficult-to-treat depression: A clinical and research roadmap for when remission is elusive. Aust N Z J Psychiatry 2019; 53:109-118. [PMID: 30378447 DOI: 10.1177/0004867418808585] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVES The report considers the pros and cons of the most commonly used conceptual model that forms the basis for most clinical practice guidelines for depression. This model promotes the attainment of sustained symptom remission as the treatment goal based on its well-established prognostic and functional importance. Sustained remission is very unlikely, however, after multiple treatment attempts. Our current model propels many clinicians to continue to change or add treatments despite little chance for remission or full functional restoration and despite the increasing risk of more adverse events from polypharmacy. An alternative 'difficult-to-treat depression' model is presented and considered. It accepts that the treatment aims for some depressed patients may shift to optimal symptom control rather than remission. When difficult-to-treat depression is suspected, the many treatable causes of persistent depression must be assessed and addressed (given the importance of remission when attainable) before difficult-to-treat depression can be ascribed. The clinical and research implications of the difficult-to-treat depression model are discussed. CONCLUSION Suspected difficult-to-treat depression provides a practical basis for considering when to conduct a comprehensive evaluation. Once difficult-to-treat depression is confirmed, treatment may better focus on optimal disease management (symptom control and functional improvement).
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Affiliation(s)
- Augustus John Rush
- 1 Duke-National University of Singapore Medical School, Singapore.,2 Department of Psychiatry & Behavioral Sciences, Duke University, School of Medicine, Durham, NC, USA.,3 Department of Psychiatry, Texas Tech University Health Sciences Center, Lubbock, TX, USA
| | - Scott T Aaronson
- 4 Clinical Research Programs, Sheppard Pratt Health System, Baltimore, MD, USA.,5 Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Koen Demyttenaere
- 6 University Psychiatric Centre, University of Leuven, Leuven, Belgium.,7 Faculty of Medicine, Department of Neurosciences, Research Group Psychiatry, University of Leuven, Leuven, Belgium
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6
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Servaas MN, Riese H, Renken RJ, Wichers M, Bastiaansen JA, Figueroa CA, Geugies H, Mocking RJT, Geerligs L, Marsman JBC, Aleman A, Schene AH, Schoevers RA, Ruhé HG. Associations Between Daily Affective Instability and Connectomics in Functional Subnetworks in Remitted Patients with Recurrent Major Depressive Disorder. Neuropsychopharmacology 2017; 42:2583-2592. [PMID: 28361870 PMCID: PMC5686496 DOI: 10.1038/npp.2017.65] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2016] [Revised: 03/22/2017] [Accepted: 03/24/2017] [Indexed: 12/12/2022]
Abstract
Remitted patients with major depressive disorder (rMDD) often report more fluctuations in mood as residual symptomatology. It is unclear how this affective instability is associated with information processing related to the default mode (DMS), salience/reward (SRS), and frontoparietal (FPS) subnetworks in rMDD patients at high risk of recurrence (rrMDD). Sixty-two unipolar, drug-free rrMDD patients (⩾2 MDD episodes) and 41 healthy controls (HCs) were recruited. We used experience sampling methodology to monitor mood/cognitions (10 times a day for 6 days) and calculated affective instability using the mean adjusted absolute successive difference. Subsequently, we collected resting-state functional magnetic resonance imaging data and performed graph theory to obtain network metrics of integration within (local efficiency) the DMS, SRS, and FPS, and between (participation coefficient) these subnetworks and others. In rrMDD patients compared with HCs, we found that affective instability was increased in most negative mood/cognition variables and that the DMS had less connections with other subnetworks. Furthermore, we found that rrMDD patients, who showed more instability in feeling down and irritated, had less connections between the SRS and other subnetworks and higher local efficiency coefficients in the FPS, respectively. In conclusion, rrMDD patients, compared with HCs, are less stable in their negative mood and these dynamics are related to differences in information processing within- and between-specific functional subnetworks. These results are a first step to gain a better understanding of how mood fluctuations in real life are represented in the brain and provide insights into the vulnerability profile of MDD.
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Affiliation(s)
- Michelle N Servaas
- Department of Psychiatry, Mood and Anxiety Disorders, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Psychiatry, University Medical Center Groningen, Interdisciplinary Center for Psychopathology and Emotion Regulation, University of Groningen, Groningen, The Netherlands
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, Neuroimaging Center, Groningen, The Netherlands
| | - Harriëtte Riese
- Department of Psychiatry, University Medical Center Groningen, Interdisciplinary Center for Psychopathology and Emotion Regulation, University of Groningen, Groningen, The Netherlands
| | - Remco J Renken
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, Neuroimaging Center, Groningen, The Netherlands
| | - Marieke Wichers
- Department of Psychiatry, University Medical Center Groningen, Interdisciplinary Center for Psychopathology and Emotion Regulation, University of Groningen, Groningen, The Netherlands
| | - Jojanneke A Bastiaansen
- Department of Psychiatry, University Medical Center Groningen, Interdisciplinary Center for Psychopathology and Emotion Regulation, University of Groningen, Groningen, The Netherlands
- Department of Education and Research, Friesland Mental Health Care Services, Leeuwarden, The Netherlands
| | - Caroline A Figueroa
- Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Hanneke Geugies
- Department of Psychiatry, Mood and Anxiety Disorders, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, Neuroimaging Center, Groningen, The Netherlands
| | - Roel JT Mocking
- Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Linda Geerligs
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jan-Bernard C Marsman
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, Neuroimaging Center, Groningen, The Netherlands
| | - André Aleman
- Department of Neuroscience, University of Groningen, University Medical Center Groningen, Neuroimaging Center, Groningen, The Netherlands
- Department of Psychology, University of Groningen, The Netherlands
| | - Aart H Schene
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Robert A Schoevers
- Department of Psychiatry, Mood and Anxiety Disorders, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Psychiatry, University Medical Center Groningen, Interdisciplinary Center for Psychopathology and Emotion Regulation, University of Groningen, Groningen, The Netherlands
| | - Henricus G Ruhé
- Department of Psychiatry, Mood and Anxiety Disorders, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- Department of Psychiatry, University Medical Center Groningen, Interdisciplinary Center for Psychopathology and Emotion Regulation, University of Groningen, Groningen, The Netherlands
- Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
- Department of Psychiatry, University of Oxford, Warnford Hospital, Oxford, UK
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