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OBJECTIVE: To conduct a systematic review to describe cognitive abilities in bipolar disorder (BD) in comparison to cognitive abilities in mild cognitive impairment (MCI) and dementia. METHODS: A literature search was performed with no year or language restrictions. The search yielded 1,461 articles, with 1,261 remaining after removal of duplicates, five of which were suitable for the systematic review: two for the comparison between BD and MCI and three comparing BD and dementia. RESULTS: Analyses from our systematic review showed that euthymic individuals with BD present impairments in cognitive domains such as attention and executive functioning, motor skills, conceptual thinking, and visuo-spatial abilities that are equally severe as or more severe than the impairments observed in individuals with MCI. In contrast, studies comparing BD and dementia indicated that Alzheimer's disease (AD) dementia and behavioral variant frontotemporal dementia (bvFTD) both showed greater cognitive deficits than BD during euthymia, whereas BD during a mood episode demonstrated higher cognitive impairments than bvFTD. CONCLUSION: Findings from our systematic review suggest that cognitive impairments in euthymic BD fall into a range between the impairments seen in MCI and those seen in dementia. More studies are needed to analyze these comparisons, while also focusing on comparing different clinical stages of BD with MCI and dementia to analyze the progression of the clinical course and cognitive dysfunction in BD.

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INTRODUCTION: Recent research has suggested an increase in the global prevalence of psychiatric symptoms during the COVID-19 pandemic. This study aimed to assess whether lifestyle behaviors can predict the presence of depression and anxiety in the Brazilian general population, using a model developed in Spain. METHODS: A web survey was conducted during April-May 2020, which included the Short Multidimensional Inventory Lifestyle Evaluation (SMILE) scale, assessing lifestyle behaviors during the COVID-19 pandemic. Depression and anxiety were examined using the PHQ-2 and the GAD-7, respectively. Elastic net, random forest, and gradient tree boosting were used to develop predictive models. Each technique used a subset of the Spanish sample to train the models, which were then tested internally (vs. the remainder of the Spanish sample) and externally (vs. the full Brazilian sample), evaluating their effectiveness. RESULTS: The study sample included 22,562 individuals (19,069 from Brazil, and 3,493 from Spain). The models developed performed similarly and were equally effective in predicting depression and anxiety in both tests, with internal test AUC-ROC values of 0.85 (depression) and 0.86 (anxiety), and external test AUC-ROC values of 0.85 (depression) and 0.84 (anxiety). Meaning of life was the strongest predictor of depression, while sleep quality was the strongest predictor of anxiety during the COVID-19 epidemic. CONCLUSIONS: Specific lifestyle behaviors during the early COVID-19 epidemic successfully predicted the presence of depression and anxiety in a large Brazilian sample using machine learning models developed on a Spanish sample. Targeted interventions focused on promoting healthier lifestyles are encouraged.

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The aim of this paper is to analyse the factors associated with the dropout from brief psychotherapy for adults with major depressive disorder (MDD) treated at a mental health outpatient clinic. This is a randomized clinical trial with two models of psychotherapy: cognitive behavioural therapy (CBT) and supportive expressive dynamic psychotherapy (SEDP). MDD and anxiety disorders were evaluated through the Mini International Neuropsychiatric Interview-Plus. The personality disorders were evaluated by the Millon Clinical Multiaxial Inventory-III. The severity of depressive symptoms was measured using the Beck Depression Inventory-II and resilience through Resilience Scale. Of the 215 participants, 41.9% abandoned psychotherapy (n = 90), and, of these, 54.4% (n = 49) abandoned after the fourth session. The proportion of psychotherapy dropout was higher among those with nonwhite skin colour, belonging to economic classes C and D, who had children and whose depressive symptoms were moderate. Presence of obsessive-compulsive personality trait was protective against dropout. The damage caused by this abrupt interruption is evident for all those involved in the psychotherapeutic process, so the clinician should pay attention to the predictors found in this study in order to develop strategies that promote therapeutic adherence.

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Abstract Introduction Recent research has suggested an increase in the global prevalence of psychiatric symptoms during the COVID-19 pandemic. This study aimed to assess whether lifestyle behaviors can predict the presence of depression and anxiety in the Brazilian general population, using a model developed in Spain. Methods A web survey was conducted during April-May 2020, which included the Short Multidimensional Inventory Lifestyle Evaluation (SMILE) scale, assessing lifestyle behaviors during the COVID-19 pandemic. Depression and anxiety were examined using the PHQ-2 and the GAD-7, respectively. Elastic net, random forest, and gradient tree boosting were used to develop predictive models. Each technique used a subset of the Spanish sample to train the models, which were then tested internally (vs. the remainder of the Spanish sample) and externally (vs. the full Brazilian sample), evaluating their effectiveness. Results The study sample included 22,562 individuals (19,069 from Brazil, and 3,493 from Spain). The models developed performed similarly and were equally effective in predicting depression and anxiety in both tests, with internal test AUC-ROC values of 0.85 (depression) and 0.86 (anxiety), and external test AUC-ROC values of 0.85 (depression) and 0.84 (anxiety). Meaning of life was the strongest predictor of depression, while sleep quality was the strongest predictor of anxiety during the COVID-19 epidemic. Conclusions Specific lifestyle behaviors during the early COVID-19 epidemic successfully predicted the presence of depression and anxiety in a large Brazilian sample using machine learning models developed on a Spanish sample. Targeted interventions focused on promoting healthier lifestyles are encouraged.

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Abstract Objective To conduct a systematic review to describe cognitive abilities in bipolar disorder (BD) in comparison to cognitive abilities in mild cognitive impairment (MCI) and dementia. Methods A literature search was performed with no year or language restrictions. The search yielded 1,461 articles, with 1,261 remaining after removal of duplicates, five of which were suitable for the systematic review: two for the comparison between BD and MCI and three comparing BD and dementia. Results Analyses from our systematic review showed that euthymic individuals with BD present impairments in cognitive domains such as attention and executive functioning, motor skills, conceptual thinking, and visuo-spatial abilities that are equally severe as or more severe than the impairments observed in individuals with MCI. In contrast, studies comparing BD and dementia indicated that Alzheimer's disease (AD) dementia and behavioral variant frontotemporal dementia (bvFTD) both showed greater cognitive deficits than BD during euthymia, whereas BD during a mood episode demonstrated higher cognitive impairments than bvFTD. Conclusion Findings from our systematic review suggest that cognitive impairments in euthymic BD fall into a range between the impairments seen in MCI and those seen in dementia. More studies are needed to analyze these comparisons, while also focusing on comparing different clinical stages of BD with MCI and dementia to analyze the progression of the clinical course and cognitive dysfunction in BD.PROSPERO registration ID: CRD42020150412

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OBJECTIVES: To assess the differences in sleep impairments in major depressive disorder (MDD) and individuals recently diagnosed with bipolar disorder (BD) across different mood stages. METHODS: This is a cross-sectional study corresponding to the second wave of a prospective clinical cohort of a sample of outpatients. The first wave included subjects diagnosed with MDD aged 18 to 60 years. Averaging 3 years after the first phase (second wave), conversion from MDD to BD diagnosis was assessed using the Mini International Neuropsychiatric Interview. The total sample was divided into four groups: euthymic MDD, MDD in a current episode, euthymic BD, and BD in a current mood episode. The sleep alterations were assessed using the Pittsburgh Sleep Quality Index. RESULTS: The sample included 468 subjects (261 euthymic MDD, 149 MDD currently depressed, 16 euthymic BD, and 42 BDs currently in a (hypo)manic or depressive episode). Euthymic BD differed from euthymic MDD only in the domains of sleep efficiency and sleep disturbances, showing lower sleep efficiency (PR 4.91 [95%CI 1.94 - 12.42]) and higher sleep disturbances (PR 3.38 [95%CI 1.32 - 8.67]) in subjects recently diagnosed with BD during euthymia. These differences remained significant after adjusting for the potential confounding factors. CONCLUSIONS: The findings point out the relevance of regular sleep assessments in individuals recently diagnosed with BD, since the differences in sleep quality observed could provide insights regarding prognosis, treatment, and the extent to which these individuals display significant subsyndromal symptomatology, even in the absence of a mood episode.

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Background: Lifestyle Medicine (LM) aims to address six main behavioral domains: diet/nutrition, substance use (SU), physical activity (PA), social relationships, stress management, and sleep. Digital Health Interventions (DHIs) have been used to improve these domains. However, there is no consensus on how to measure lifestyle and its intermediate outcomes aside from measuring each behavior separately. We aimed to describe (1) the most frequent lifestyle domains addressed by DHIs, (2) the most frequent outcomes used to measure lifestyle changes, and (3) the most frequent DHI delivery methods. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) Extension for Scoping Reviews. A literature search was conducted using MEDLINE, Cochrane Library, EMBASE, and Web of Science for publications since 2010. We included systematic reviews and meta-analyses of clinical trials using DHI to promote health, behavioral, or lifestyle change. Results: Overall, 954 records were identified, and 72 systematic reviews were included. Of those, 35 conducted meta-analyses, 58 addressed diet/nutrition, and 60 focused on PA. Only one systematic review evaluated all six lifestyle domains simultaneously; 1 systematic review evaluated five lifestyle domains; 5 systematic reviews evaluated 4 lifestyle domains; 14 systematic reviews evaluated 3 lifestyle domains; and the remaining 52 systematic reviews evaluated only one or two domains. The most frequently evaluated domains were diet/nutrition and PA. The most frequent DHI delivery methods were smartphone apps and websites. Discussion: The concept of lifestyle is still unclear and fragmented, making it hard to evaluate the complex interconnections of unhealthy behaviors, and their impact on health. Clarifying this concept, refining its operationalization, and defining the reporting guidelines should be considered as the current research priorities. DHIs have the potential to improve lifestyle at primary, secondary, and tertiary levels of prevention-but most of them are targeting clinical populations. Although important advances have been made to evaluate DHIs, some of their characteristics, such as the rate at which they become obsolete, will require innovative research designs to evaluate long-term outcomes in health.

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