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Abstract Background Women with premenstrual syndrome (PMS) and premenstrual dysphoric disorder (PMDD) typically experience a range of psychological and physiological symptoms that negatively affect their quality of life. Disruption in biological rhythms, including alterations of the sleep–wake cycle, have been implicated in PMS/PMDD, though literature is still growing to substantiate these findings. The objective of this study is to systematically review the available literature on biological rhythms disruption in PMS/PMDD. Methods A literature search was conducted on four databases (Pubmed, Embase, Medline, and Web of Science) on December 3rd, 2021. This search yielded a total of 575 articles that assessed the relationship between biological rhythms and PMS/PMDD/premenstrual symptoms. Results After the exclusion of irrelevant articles and hand-searching references, 25 articles were included in this systematic review. Some studies showed that women with PMS/PMDD present lower melatonin levels, elevated nighttime core body temperature, and worse subjective perception of sleep quality when compared to women without PMS/PMDD. Other biological rhythms parameters showed either no differences between groups (wrist actimetry) or conflicting results (objective sleep parameters, cortisol, prolactin, and thyroid stimulating hormone). Conclusion Current research demonstrates that women with PMS/PMDD experience lower melatonin levels, higher body temperature, and worse subjective perception of sleep quality. This review outlines some possible mechanisms behind these findings and proposes recommendations for future research. This systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD42020149921.

Abstract Introduction The Iowa-Netherlands Comparison Orientation Measure (INCOM) was developed to measure individual differences in social comparison orientation and has been widely used in research and various different settings. Objectives The aim of this study was to adapt the online version of the INCOM and to evaluate its psychometric parameters when applied to a Brazilian population of university students. Methods The procedures were divided into two steps: step 1 – cross-cultural adaptation and analysis of content validity, and step 2 – assessment of psychometric characteristics. Step 1 comprised the processes of translation, evaluation by an expert committee, evaluation by the target population, and back-translation. For step 2, 1,065 university students were recruited and then factor analysis, analysis of reliability, and analysis of validity based on external measures were performed. Results The adaptation process yielded satisfactory results, including good indicators of content validity. Exploratory factor analysis revealed a two-dimensional structure and adequate factor loadings, except for item 11, which was excluded from the final version. Additionally, the final version of the scale had adequate fit indices (χ2 = 148.45, degrees of freedom [df] = 26; p < 0.001; root mean square error of approximation [RMSEA] = 0.06; comparative fit index [CFI] = 0.99; and Tucker-Lewis index [TLI] = 0.98). Evidence of reliability (Cronbach’s alpha = 0.83) was observed and there were positive correlations with negative affect (r = 0.36) and negative correlations with positive affect and self-esteem (r = -0.15; r = -0.41, respectively). Conclusion The Brazilian version of the INCOM presents satisfactory psychometric parameters and can thus be used to measure social comparison orientation.

Bipolar disorder (BD) impacts over 40 million people around the world, often manifesting in early adulthood and substantially impacting the quality of life and functioning of individuals. Although early interventions are associated with a better prognosis, the early detection of BD is challenging given the high degree of similarity with other psychiatric conditions, including major depressive disorder, which corroborates the high rates of misdiagnosis. Further, BD has a chronic, relapsing course, and the majority of patients will go on to experience mood relapses despite pharmacological treatment. Digital technologies present promising results to augment early detection of symptoms and enhance BD treatment. In this editorial, we will discuss current findings on the use of digital technologies in the field of BD, while debating the challenges associated with their implementation in clinical practice and the future directions.

Abstract Objective This systematic review aims to describe the relationship between psychological resilience and mood disorders. Methods This is a systematic review and meta-analysis. The following databases were searched on November 6, 2020: PubMed, PsycINFO, and Embase. Results Twenty-three articles were included and the majority of the studies included (95.7%) showed that psychological resilience has a positive impact in mood disorders. Our meta-analysis showed that individuals with bipolar disorder presented significantly lower levels of psychological resilience compared to controls (standardized mean difference [SDM]: -0.99 [95% confidence interval {95%CI}: -1.13 to -0.85], p < 0.001). In addition, individuals with depression had significantly lower levels of psychological resilience compared to controls (SDM: -0.71 [95%CI -0.81 to -0.61], p < 0.001). Conclusion Our results showed that individuals with mood disorders are less resilient than individuals without mood disorders. Our findings reinforce the importance of investigating interventions that may help to improve psychological resilience considering its positive impact in the context of mood disorders.

Abstract Objectives To describe the theoretical procedures employed in the process of cross-cultural adaptation (CCA) for Brazil of the Child Mania Rating Scale - Parent Version (CMRS-P). Methods Seven steps were carried out: (1) translations and synthesis; (2) Committee of Judges-I; (3) grammatical review; (4) Committee of Judges-II; (5) semantic analysis (pre-test); (6) back-translation; and (7) discussion with the authors of the original instrument. Participants were two professional translators, 14 experts, a grammar proofreader, and 21 parents/guardians, representatives of the target population. The results were analyzed in terms of the percentage of agreement between evaluators and the content validity coefficient (CVC) and by analysis of comments and suggestions. Results Grammatical and cultural adjustments were made, in addition to substitution and/or inclusion of words and examples. Adequacy agreement indexes exceeding 86% were achieved and the CVC result for the total scale was excellent (0.95). The pre-test indicated good acceptance and understanding by participants. Conclusion The proposed version proved to be promising for use in the Brazilian context, although further psychometric studies are still needed to prove the scale’s validity and reliability.

Abstract Although reducing criminal outcomes in individuals with mental illness have long been a priority for governments worldwide, there is still a lack of objective and highly accurate tools that can predict these events at an individual level. Predictive machine learning models may provide a unique opportunity to identify those at the highest risk of criminal activity and facilitate personalized rehabilitation strategies. Therefore, this systematic review and meta-analysis aims to describe the diagnostic accuracy of studies using machine learning techniques to predict criminal and violent outcomes in psychiatry. We performed meta-analyses using the mada, meta, and dmetatools packages in R to predict criminal and violent outcomes in psychiatric patients (n = 2428) (Registration Number: CRD42019127169) by searching PubMed, Scopus, and Web of Science for articles published in any language up to April 2022. Twenty studies were included in the systematic review. Overall, studies used single-nucleotide polymorphisms, text analysis, psychometric scales, hospital records, and resting-state regional cerebral blood flow to build predictive models. Of the studies described in the systematic review, nine were included in the present meta-analysis. The area under the curve (AUC) for predicting violent and criminal outcomes in psychiatry was 0.816 (95% Confidence Interval (CI): 70.57–88.15), with a partial AUC of 0.773, and average sensitivity of 73.33% (95% CI: 64.09–79.63), and average specificity of 72.90% (95% CI: 63.98–79.66), respectively. Furthermore, the pooled accuracy across models was 71.45% (95% CI: 60.88–83.86), with a tau squared (τ2) of 0.0424 (95% CI: 0.0184–0.1553). Based on available evidence, we suggest that prospective models include evidence-based risk factors identified in prior actuarial models. Moreover, there is a need for a greater emphasis on identifying biological features and incorporating novel variables which have not been explored in prior literature. Furthermore, available models remain preliminary, and prospective validation with independent datasets, and across cultures, will be required prior to clinical implementation. Nonetheless, predictive machine learning models hold promise in providing clinicians and researchers with actionable tools to improve how we prevent, detect, or intervene in relevant crime and violent-related outcomes in psychiatry.

The ethics of generative artificial intelligence (AI) use in scientific manuscript content creation has become a serious matter of concern in the scientific publishing community. Generative AI has computationally become capable of elaborating research questions; refining programming code; generating text in scientific language; and generating images, graphics, or figures. However, this technology should be used with caution. In this editorial, we outline the current state of editorial policies on generative AI or chatbot use in authorship, peer review, and editorial processing of scientific and scholarly manuscripts. Additionally, we provide JMIR Publications’ editorial policies on these issues. We further detail JMIR Publications’ approach to the applications of AI in the editorial process for manuscripts in review in a JMIR Publications journal.

Objective The importance of a healthy lifestyle in preventing morbidity and mortality is well-established. The COVID-19 pandemic brought about significant lifestyle changes globally, but the extent of these changes in the Brazilian population remains unclear. The objective of this study was to evaluate changes in lifestyle among the Brazilian general population during the first year of the pandemic.Design Three consecutive anonymous web surveys were carried out: survey 1 (S1)—April 2020, S2—August 2020 and S3—January 2021.Setting Brazil.Participants The study included 19 257 (S1), 1590 (S2) and 859 (S3) participants from the general population, who were ≥18 years, of both sexes, with access to the internet, self-reporting living in Brazil and who agreed to participate after reading the informed consent.Primary outcome Lifestyle changes were assessed using the Short Multidimensional Instrument for Lifestyle Evaluation—Confinement (SMILE-C). The SMILE-C assesses lifestyle across multiple domains including diet, substance use, physical activity, stress management, restorative sleep, social support and environmental exposures. We used a combination of bootstrapping and linear fixed-effect modelling to estimate pairwise mean differences of SMILE-C scores overall and by domain between surveys.Results In all the surveys, participants were mostly women and with a high education level. Mean SMILE-C scores were 186.4 (S1), 187.4 (S2) and 190.5 (S3), indicating a better lifestyle in S3 as compared with S1. The pairwise mean differences of the overall SMILE-C scores were statistically significant (p

The study aimed to assess whether sleep quality is associated with global functioning and cognition in subjects that recently converted to bipolar disorder (BD), subjects without mood episodes at follow-up and subjects with a recurrent depressive episode in a sample of depressed adults. Significantly worse scores were found in global functioning, sleep quality, and cognitive complaints in the BD and recurrent depression groups when compared to the group without mood episode at follow-up. The findings also showed a significant association between functioning and cognitive complaints with sleep quality in all groups. The cross-sectional design prevents the inference of the causal relationship between sleep quality and measures of cognition and functioning. The authors reinforce the need to follow-up in order to improve functional and cognitive outcomes, notably with patients with BD, who may suffer in addition to damage caused by sleep alterations, also with neuroprogression in the long term.

Abstract Introduction Bipolar disorder (BD) has a high heritability rate. Current studies have been dedicated to identifying prodromes of BD in the offspring of parents with BD (BO) and the sleep patterns of these individuals have been considered important factors. Objective To describe changes in sleep parameters among offspring of parents with BD when compared to offspring of controls and to identify if changes in parameters and quality of sleep predict the onset of BD among these individuals. Methods PubMed, PsycINFO, and Embase were systematically searched with no year or language restrictions, up to August 18, 2020. We searched for a combination of the following search items (“sleep*”) AND (“bipolar disorder*” OR “mania” OR “hypomania” OR “bipolar depression”) AND (“ultra-high risk” OR “high risk” OR “offspring” OR “first degree relatives”). Results A total of 10 studies were included in the systematic review and 4 studies were included in the meta-analysis. Our meta-analysis showed that the BO had greater daytime sleepiness as compared to the offspring of control parents. The systematic review indicated that shorter sleep duration, sleep disorders, and other related features can differentiate the two groups. Finally, some sleep patterns such as decreased sleep, difficulty falling asleep, and overall sleep problems might be predictors for the development of BD. Conclusion Results from the meta-analysis indicated that BO had greater daytime sleepiness. Qualitative results showed that the offspring of parents with BD have an increased likelihood of experiencing an adverse sleep pattern.

Background: The identification of predictors of recurrence and persistence of depressive episodes in major depressive disorder (MDD) can be important to inform clinicians and collaborate to clinical decisions. Objective: The aim of the present study is to predict recurrent or persistent depressive episodes, in addition to predicting severe recurrent or persistent depressive episodes using a machine learning method. Methods: This is a prospective cohort study with three years of follow-up. Individuals diagnosed with MDD in the first phase of the study (2012–2015) were evaluated in the second phase (2012–2015). The sociodemographic, clinical, comorbid disorders and substance use variables were used as predictors in all predictive models. Initially, the first model predicted recurrence/persistence, including subjects of any severity of depression level. The second model predicted recurrence/persistence depression as the first model, although it was trained with severely depressed subjects and those without indicative for depression. The third model predicted severe depression among depressed patients. Results: Area under the curve (AUC) values ranged from 0.65 to 0.81, and accuracies ranged from 62% to 71%. Psychiatric comorbidities, substance abuse/dependence, and family medical history were important features in all three models. Limitation: The time between baseline and the second phase of the study was approximately three years, making it difficult to detect depressive symptoms during this time frame. Also, age at depression onset and number of episodes were not included in the model due to the large number of missing data. Conclusions: In conclusion, this study adds new information that can help health professionals both in their clinical practice and in public services.

Studies suggest that personality disorders can predict mood disorders. The present study aims to assess whether personality traits in individuals with major depressive disorder (MDD) can predict the conversion to bipolar disorder (BD). This was a prospective cohort study conducted in two waves. In the first wave, 585 subjects were diagnosed with MDD; in the second wave, all of them were reevaluated in a three-year follow-up. Personality traits were evaluated, in the first phase, using the Millon Clinical Multiaxial Inventory (MCMI). MDD and BD diagnosis was performed by trained psychologists using a clinical structured interview based on diagnostics criteria of DSM IV, the Mini International Neuropsychiatric Interview, Plus version (MINI-PLUS). During the second wave, 468 individuals were reevaluated. Diagnostic conversion rate from MDD to BD was 12.4%. Higher mean scores in Antissocial, Borderline personality traits and in the sum of all Cluster B disorders were found among individuals who had converted to BD. In addition, individuals who converted to BD had lowest scores in obsessive-compulsive personality traits. Our findings suggest that Cluster B personality disorders can be considered as predictors of diagnostics conversion from MDD to BD. Also, it seems that obsessive-compulsive traits were lower among those individuals who have converted to BD.

There is some clinical evidence indicating that obesity may change the trajectory of individuals with Bipolar Disorder (BD) to having more mixed features, however, to the best of our knowledge, there are no systematic reviews investigating this relationship. Thus, this systematic review is aimed at describing the association between obesity and mixed features in BD, specifically whether obesity changes the trajectory of patients with BD to having a greater frequency of mixed features. A literature search was conducted on October 22, 2021, using the following databases: PubMed, PsycINFO, and EMBASE. A total of 2 studies were included in this systematic review. Both studies described the frequency of mixed features in a population of patients with BD with and without the presence of obesity. Among individuals with BD, patients who were obese had a significantly greater probability of having mixed features than patients who were not obese. The prevalence of manic versus depressive or mixed mood states was significantly associated with obesity, since a higher percentage of patients who had BD and were obese presented with depressive or mixed mood states compared to patients who had BD and were not obese. Patients that are diagnosed with BD and present with obesity have a greater prevalence of presenting mixed features, which has been linked to worse clinical outcomes. More studies are needed to definitively determine the association between obesity and mixed features, and therefore, implement interventions to improve prognosis in patients with BD.

In this report, we aim to assess the interaction of bipolar disorder and major depressive disorder with the evolution of social roles, economic classification, and substance misuse in emerging adults. This is a longitudinal population-based study (n = 231 at baseline), in which participants were reassessed at a mean of 5 years after baseline. A structured clinical interview was used to diagnose the participants with bipolar disorder and major depression; a control group without mood disorders was included. Men with mood disorders were less likely to be married in the beginning of the study and less likely to work in the follow-up. Women with major depression were less likely to study and more likely to be in a lower economic class at the beginning of the study. In comparison, women with bipolar disorder were less likely to live with their parents and more likely to live with their children in the first wave of the study. Substance misuse was more likely in people with mood disorders, especially in men, and women with bipolar disorder had the highest likelihood in the follow-up. Albeit longitudinal analyses were limited by a possibly insufficient sample size and mediating mechanisms for change, such as stigma, were not explored, the study suggests sex-related specificities regarding the change in social roles and substance use in people with mood disorders. Emerging adults, especially those with mood disorders, are in a period of change and instability and at a greater risk for substance use and abuse.

Objective: To evaluate the impact of defense mechanisms at baseline on depressive symptoms after brief psychotherapies and after 6-months of follow-up among depressed patients with and without cluster B personality disorders (PDs). Methods: This quasi-experimental study nested within a randomized clinical trial included a clinical sample of adults (18-60 years) diagnosed with major depressive disorder using the Mini-International Neuropsychiatric Interview. The Millon Clinical Multiaxial Inventory-III was applied to assess PD, the Defense Style Questionnaire 40 was used to analyze defense mechanisms, and the Beck Depression Inventory was used to measure the severity of depressive symptoms. Adjusted analysis was performed by linear regression. Results: The final sample consisted of 177 patients diagnosed with major depressive disorder, of whom 39.5% had cluster B PDs. Immature defenses at baseline significantly predicted the persistence of depressive symptoms at post-intervention and at 6-months of follow-up only in patients with PDs. Conclusion: In depressed patients with cluster B PDs, immature defenses predicted a poor response to brief therapies. The assessment of immature defenses at baseline can help identify patients at greater risk of poor therapeutic results and enable more appropriate treatment choices.

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.

This study aimed to assess the association between maternal diagnosis of Bipolar Disorder (BD) and the biological rhythms of their offspring, and describe the synchronization of biological rhythms among mothers and their offspring. This study included 59 mothers with a diagnosis of BD and 59 mothers who have not been diagnosed with BD at the first stage of the study, as well as their offspring aged 9–11 years. The mothers' biological rhythms were assessed using the Biological Rhythm Interview of Assessment in Neuropsychiatry (BRIAN) and the offspring's biological rhythms were evaluated using the same assessment tool in the kids' version. The offspring of mothers with BD showed a significantly higher disruption of overall biological rhythm when compared to the offspring of mothers without BD. The mothers with BD and their offspring showed a higher disruption of biological rhythms in all the BRIAN/BRIAN-k domains when compared to the mothers without BD and their offspring. Additionally, we identified a positive correlation between the disruption of biological rhythms in mothers and the disruption of biological rhythms in their offspring. It is likely that they synced their own rhythm with the disrupted rhythm of their mothers.

Objectives: Previous studies have estimated the 30-day prevalence of alcohol use to be approximately 21% among youth in Brazil, despite the legal drinking age of 18 years. The present study aimed to determine the prevalence of underage drinking and its associated factors among adolescents in Brazil. Methods: The 3rd National Survey on Drug Use by the Brazilian Population (III Levantamento Nacional sobre o Uso de Drogas pela População Brasileira) is a nationwide, multi-stage, probability-sample household survey. Herein, youth between the ages of 12-17 years were included. Lifetime and 12-month alcohol use prevalence were estimated. Factors associated with 12-month alcohol use were evaluated through multivariate analysis considering survey weights and design. Results: Overall, 628 youth were interviewed. Estimated lifetime and 12-month alcohol use were 34.3% (standard error [SE] = 1.9) and 22.2% (SE = 1.7), respectively. Factors associated with 12-month drinking were: other/no religion vs. Christianity; living in rural vs. urban areas; self-reported diagnosis of depression vs. no self-reported depression; lifetime tobacco use vs. no history of tobacco use; and any illicit drug use vs. no history of illicit drug use. Conclusion: Considering that alcohol use is a major risk factor for early death among Brazilian youth, our findings highlight the importance of preventative measures to reduce underage drinking.