Cognitive conflicts in major depression : Between desired change and personal coherence

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposesThe notion of intrapsychic conflict has been present in psychopathology for more than a century within different theoretical orientations. However, internal conflicts have not received enough empirical attention, nor has their importance in depression been fully elaborated. This study is based on the notion of cognitive conflict, understood as implicative dilemma (ID), and on a new way of identifying these conflicts by means of the Repertory Grid Technique. Our aim was to explore the relevance of cognitive conflicts among depressive patientsPeer reviewedFinal Published versio

Standard and Innovative Strategies in Cognitive Behavior Therapy

Cognitive-behavioral therapy (CBT) is the fastest growing and the best empirically validated psychotherapeutic approach. Written by international experts, this book intends to bring CBT to as many mental health professionals as possible. Section 1 introduces basic and conceptual aspects. The reader is informed on how to assess and restructure cognitions, focusing on automatic thoughts and underlying assumptions as well as the main techniques developed to modify core beliefs. Section 2 of this book covers the cognitive therapy of some important psychiatric disorders, providing reviews of the recent developments of CBT for depression, bipolar disorder and obsessive-compulsive disorder. It also provides the latest advances in the CBT for somatoform disorders as well as a new learning model of body dysmorphic disorder. Two chapters on addiction close this book, providing a thorough review of the recent phenomenon of Internet addiction and its treatment, concluding with the CBT for substance abuse

Enhancing treatment decision-making in bipolar II disorder: Development and evaluation of a treatment decision-aid for patients and their families

Background/Aims. A diagnosis of bipolar II disorder (BPII) is commonly accompanied by a need to make complex treatment decisions. As most research on treatment efficacy relates to bipolar I disorder, BPII treatment decisions have less evidence to support available treatment options and are more finely balanced in terms of benefits and side-effects. Yet, there is currently no resource to support patients with BPII (and their families) to make informed, preference-based treatment decisions. Patient decision-aids (DAs) are interventions designed to facilitate this process. This thesis aimed to develop and evaluate the first known DA for patients considering BPII treatment options. Methods. The thesis comprised four phases: i) a systematic literature review (Chapter 2); ii) qualitative interviews with patients with BPII (n=28), their families (n=13), and clinicians (n=20) (Chapters 3 – 5); iii) development of a DA according to International Patient Decision-Aid Standards (Chapter 6); iv) pilot of the DA to obtain evidence on its acceptability, feasibility, safety, and potential usefulness within a sample of potential end-users (30 patients with BPII, and 10 families; Chapter 7). Results. Phases i) and ii) identified key informational and decision-support priorities for patients with BPII and their families, as well as clinician-endorsed strategies for addressing barriers to treatment decision-making. These priorities and strategies then informed the content, format and delivery of the DA. Pilot data indicated that potential end-users strongly endorsed the DA, and provided preliminary evidence to support DA-related improvements in treatment decision-making. Conclusion. This innovative and comprehensive program of research culminated in the development and evaluation of a world-first DA for patients deciding on BPII treatment. The final DA has the potential to facilitate informed treatment decisions, which are both evidence-based and consistent with patient preferences

Religious or Spiritual Experiences and Bipolar Disorder:A Case Study from the Perspective of Dialogical Self Theory

In this article, a case study will be presented of a person with bipolar I disorder, who struggles to interpret his religious experiences and how they are related to the disorder. The analysis builds on a larger study into religious experiences within the context of bipolar disorder (BD). In this previous study, medical and religious explanatory models for religious experiences related to BD often appeared to go hand in hand in patients who have had such experiences. In this case study, the various ‘voices’ in the interpretation process over time will be examined from the perspective of the dialogical self theory of Hubert Hermans, in order to explore the psychological dimension of this process. The case study demonstrates that a ‘both religious and pathological’ explanatory model for religious experiences consists of a rich and changing variety of I‐positions that fluctuate depending on mood episode. Structured reflection from a spiritual and from a medical perspective over the course of several years helped this person to allow space for different dialoguing ‘voices’, which—in this case—led to a more balanced attitude towards such experiences and less pathological derailment. The systematic reflection on religious experiences by the person in the case study was mainly conducted without help of mental health care professionals and was not derived from a DST perspective. It could be argued, however, that DST could be used as a helpful instrument for the exploration of both medical and spiritual ‘voices’ in the interpretation of religious experiences in both clinical practice by hospital chaplains and by other professionals

Machine learning and big data analytics in bipolar disorder:A position paper from the International Society for Bipolar Disorders Big Data Task Force

Objectives The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. Method A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. Results The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. Conclusion Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.Peer reviewe

A Mental Health Professional Opinion on Family Involvement During the Treatment of Severe Mental Illness: A Multiple Case Study

While severe mental illness (SMI) is extensively studied, less attention focuses on the relationship between people with SMI and their families and the impact this may have on treatment. Bronfenbrenner\u27s (1979) ecological systems theory conceptualized the treatment and recovery of people with SMI and their families. This study proposes that if family members getting social support contribute to a person\u27s treatment plan, the individual\u27s treatment outcome will improve rather than the individual enduring the treatment process individually. By applying ecological systems theory, mental health professionals and policymakers can better grasp the systemic nature of individuals, their family members, and surrounding external circumstances. Future research implications and recommendations aim to increase knowledge and awareness of family involvement during SMI treatment approaches