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The article discusses adults on antidepressants for mild-to-moderate depression, and mentions yoga improves several aspects of quality of life.

The article discusses adults on antidepressants for mild-to-moderate depression, and mentions yoga improves several aspects of quality of life.

In April of 2006, a team of researchers consisting of both university and community partners from a Mi'kmaq reserve in Nova Scotia began the data-collection phase of a high school-based research study that had been two years in planning. The study examines the possible relationships between youth-reported childhood maltreatment, posttraumatic stress disorder (PTSD) symptoms, depressive symptoms, alcohol misuse, and resiliency factors. The aim of the research study is to provide information about adolescent alcohol misuse that is of practical benefit to community-based service providers, and capable of making a scholarly contribution to the scientific study of the relations of anxiety/mood symptoms and addictive behaviours. The primary aim of this paper is to present both the context from which the project grew, and the steps involved in conducting research with our school partners and the community service providers. A secondary aim is to present some of the preliminary data from the study, with a specific focus on resiliency. [ABSTRACT FROM AUTHOR]

This study aims to explore the potential psychological and cognitive advantages for older individuals engaged in hard martial arts (HMA), through a comprehensive scoping review of literature up to 2023. Specifically, it examines the extent of changes in cognition, mental state, and quality of life among elderly participants of HMA. Inclusion criteria were studies conducted on healthy persons who were over 50 years of age. Only papers published in the English language were included. The search was undertaken in electronic databases and sources of grey literature. Thirteen papers with a total of 514 participants met the inclusion criteria. Improved cognition and decreased levels of anxiety and depression were emerging themes. Together, these factors contributed to the quality of life of participants. HMA was found to benefit cognitive abilities and psychological well-being, increasing quality of life more than traditional exercise alone. Findings suggested duration of training influenced change more than frequency. The limited number of studies exploring the effects of HMA on mental wellness and cognitive ability in older adults underscores the need for further research. The findings of this review suggest cognitive and quality of life improvements and reduced depression and anxiety in individuals engaging in HMA. This review serves as a foundation for soundly designed future research. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to an increase in mental health problems, such as depression. Depression is a major cause of disability and can affect anyone. It is important to seek early detection and treatment for depression, as it can have a significant impact on a person's life. This research paper explores the complex and multifaceted nature of human personality using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria and Eysenck's personality traits questionnaire. The paper then outlines the methodology employed, involving the collection of data through surveys consisting of 66 questions divided into six forms. DSM-5 criteria are applied to assess depressive episodes, while Eysenck's questionnaire evaluates personality traits. The paper's approach involves two stages: predicting personality traits and estimating stress levels. Multiple machine learning models are utilized for these predictions. The results of this study highlight the effectiveness of the Support Vector Machine (SVM) classifier, which consistently outperforms other models, achieving impressive accuracy in predicting both personality traits, such as extroversion and neuroticism, and stress levels. Notably, SVM demonstrates its prowess with an accuracy of 91.43% in predicting stress levels. [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) is characterized by low mood, loss of interest and even suicidal ideation. Electroencephalogram based diagnosis of a variety of neurological conditions has been conducted in recent years using modern neurocomputing and deep learning approaches. Privacy is a high concern for medical data over distributed training scenarios. Hence, this paper aims to develop an EEG-based privacy-preserved system to identify MDD through the Federated Learning (FL) approach. In this study, training for 3-channel EEG-based MDD screening is introduced over FL using deep learning (DL) algorithms such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and One Dimensional Convolutional Neural Network (1D-CNN). A privacy-preserving solution for MDD patients utilizing FL is advantageous because clinical data is sensitive and most people do not want to share their personal information. Hence, the analysis of the comparative results for multiple applicable strategies including Independent and Identically Disturbed (IID) data, non-IID data, and algorithms provide proof that FL can be used to train DL models. The MODMA dataset of 3-channel EEG with 26 MDD and 29 non-MDD individuals is used in this paper. Future study on MDD has several potential areas of use and these FL approaches can also be coupled with new technologies. [ABSTRACT FROM AUTHOR]

The article presents abstracts of research related to pharmacy in the U.S. which include the assessment of basic medication management knowledge for individuals with diabetes and hypertension in an indigent care setting, assessment of pharmacists' confidence in Ohio in giving pharmaceutical care to children in a community setting and assessment of patient perceptions related to antidepressants and depression in the community pharmacy practice.

Background: Children and adolescents with Autism Spectrum Disorder (ASD) often experience symptoms of various mental disorders along with the characteristics that define ASD. High rates of several psychiatric disorders have been reported in people with ASD such as anxiety, depression, cognitive problems, emotional regulation difficulties and related behavioral problems can occur in children of all ages with ASD. There are many treatment programs that can help autistic persons cope with these symptoms. Cognitive and Behavioral Therapy (CBT), Information and Communication Technology (ICT) and more are treatment programs that can help people with autism recognize and manage their symptoms. Aim: This paper examines through bibliographic sources of the last 15 years the possible mental disorders that a child or adolescent with ASD may experience, as well as the therapeutic interventions that can help to manage them. Methodology: For the present bibliographic research, 15 scientific articles from English journals were used. The databases from which the scientific articles were found were PubMed, PsycINFO, MEDLINE, and Google Scholar. Results: According to the results of various studies, children and adolescents with autism show various symptoms of psychological disorders such as Anxiety Disorders, Depression and Obsessive-Compulsive Disorder. The combination of CBT and ICT can help people with autism recognize and manage their symptoms. Discussion: The various symptoms of disorders that children and adolescents with autism experience can have a major impact on their family, their daily life, their schooling, and their future work. It is of the utmost importance that these children enter into a treatment program in order to better manage and treat their symptoms. The support of the school is also very important. [ABSTRACT FROM AUTHOR]

Conceptual papers typically focus on proposing new relationships among constructs; the purpose is thus to develop logical and complete arguments about these associations rather than testing them empirically. The current article is the conceptual paper; however, the aim of the present study is to examine the relationship between loneliness, cognitive distortions, resilience, family support and depression among older people. Purposive sampling technique will be used to collect data from 300 participants form Pakistan. Cognitive distortion scale, UCLA loneliness scale, family support scale, Connor Davidson resilience scale and beck depression inventory will be used as an assessment measure in the current study to check the level of loneliness, cognitive distortions, resilience, family support and depression. SPSS-27 software will be used for the statistical analysis in the present study. Pearson product moment correlation analysis, reliability analysis, independent sample t-test and hierarchal regression analysis will be used in the current study. The authors conclude that higher level of loneliness and cognitive distortions will lead to higher level of depression. Policy makers, social workers and organizations that wish to jointly address mental health and performance at work would benefit from reducing depression by enhancing resilience and importance of family support. [ABSTRACT FROM AUTHOR]

While the ICH E9(R1) Addendum on "Estimands and Sensitivity Analysis in Clinical Trials" was released in late 2019, the widespread implementation of defining and reporting estimands across clinical trials is still in progress and the engagement of non-statistical functions in this process is also in progress. Case studies are sought after, especially those with documented clinical and regulatory feedback. This paper describes an interdisciplinary process for implementing the estimand framework, devised by the Estimands and Missing Data Working Group (a group with clinical, statistical, and regulatory representation) of the International Society for CNS Clinical Trials and Methodology. This process is illustrated by specific examples using various types of hypothetical trials evaluating a treatment for major depressive disorder. Each of the estimand examples follows the same template and features all steps of the proposed process, including identifying the trial stakeholder(s), the decisions they need to make about the investigated treatment in their specific role and the questions that would support their decision making. Each of the five strategies for handling intercurrent events are addressed in at least one example; the featured endpoints are also diverse, including continuous, binary and time to event. Several examples are presented that include specifications for a potential trial design, key trial implementation elements needed to address the estimand, and main and sensitivity estimator specifications. Ultimately this paper highlights the need to incorporate multi-disciplinary collaborations into implementing the ICH E9(R1) framework. [ABSTRACT FROM AUTHOR]

Objective: This review summarizes evidence pertaining to the impact of the COVID-19 pandemic on the psychological health of children and adolescents with autism spectrum disorder (ASD). Materials and Methods: An electronic search was conducted using four major databases: PubMed, ScienceDirect, Web of Science, and Google Scholar. Using an umbrella methodology, the reference lists of relevant papers were reviewed, and citation searches were conducted. The study included articles written in English between January 2020 and March 2021 that focused on the psychological health of autistic children and adolescents. Results: All eight studies included in the final review were cross-sectional. Three of the eight studies were conducted in Italy, two in Turkey, and one study each in Portugal, Spain, and the United Kingdom, with a total of 1,407 participants. All studies used a mixture of standardized and non-standardized questionnaires to collect data. The total number of patients were 1407 at a mean age of 9.53 (SD = 2.96) years. Seven studies report gender; male 74.7% (657/880) and female 25.3% (223/880). The finding showed that behavioral issues in children and adolescents with ASD have significantly increased; 521 (51.9 percent) of the 1004 individuals with ASD presented with behavioral changes, including conduct problems, emotional problems, aggression, and hyperactivity. Some studies also found increased anxiety and difficulties managing emotions. Only one study reported clinical stabilization in children with ASD during COVID-19. Finally, 82.7% of families and caregivers of children with ASD (544 out of 658) faced challenges during COVID-19. Conclusion: Although the studies in this review suggest a general worsening of ASD children's clinical status, it remains difficult to draw definitive conclusions at this moment, with newer COVID-19 variants on the rise worldwide. During this difficult pandemic period, caregivers, families, and healthcare professionals are recommended to pay more attention to the ASD patients' health and care needs. [ABSTRACT FROM AUTHOR]

Background: Major international guidelines recommend mental health screening during the perinatal period. However, substantial barriers to screening have been reported by pregnant and postpartum women and perinatal care providers. E-screening offers benefits that may address implementation challenges.Objective: The primary objective of this randomized controlled trial was to evaluate the feasibility and acceptability of Web-based mental health e-screening compared with paper-based screening among pregnant women. A secondary objective was to identify factors associated with women's preferences for e-screening and disclosure of mental health concerns.Methods: Pregnant women recruited from community and hospital-based antenatal clinics and hospital-based prenatal classes were computer-randomized to a fully automated Web-based e-screening intervention group or a paper-based control group. Women were eligible if they spoke or read English, were willing to be randomized to e-screening, and were willing to participate in a follow-up diagnostic interview. The intervention group completed the Antenatal Psychosocial Health Assessment and the Edinburgh Postnatal Depression Scale on a tablet computer, while controls completed them on paper. All women completed self-report baseline questions and were telephoned 1 week after randomization by a blinded research assistant for a MINI International Neuropsychiatric Interview. Renker and Tonkin's tool of feasibility and acceptability of computerized screening was used to assess the feasibility and acceptability of e-screening compared with paper-based screening. Intention-to-treat analysis was used. To identify factors associated with preference for e-screening and disclosure, variables associated with each outcome at P<.20 were simultaneously entered into final multivariable models to estimate adjusted odds ratios (AORs) and 95% CIs.Results: Of the 675 eligible women approached, 636 agreed to participate (participation rate 94.2%) and were randomized to the intervention (n=305) or control (n=331) groups. There were no significant baseline differences between groups. More women in the e-screening group strongly or somewhat agreed that they would like to use a tablet for answering questions on emotional health (57.9%, 175/302 vs 37.2%, 121/325) and would prefer using a tablet to paper (46.0%, 139/302 vs 29.2%, 95/325), compared with women in the paper-based screening group. There were no differences between groups in women's disclosure of emotional health concerns (94.1%, 284/302 vs 90.2%, 293/325). Women in the e-screening group consistently reported the features of e-screening more favorably than controls (more private or confidential, less impersonal, less time-consuming). In the multivariable models, being in the e-screening group was significantly associated with preferring e-screening (AOR 2.29, 95% CI 1.66-3.17), while no factors were significantly associated with disclosure.Conclusions: The findings suggest that mental health e-screening is feasible and acceptable to pregnant women.Trial Registration: Clinicaltrials.gov NCT01899534; https://clinicaltrials.gov/ct2/show/NCT01899534 (Archived by WebCite at http://www.webcitation.org/6ntWg1yWb). [ABSTRACT FROM AUTHOR]

Depression is an increasingly common problem that often goes undiagnosed. The aim of this paper was to determine whether an analysis of tweets can serve as a proxy for assessing depression levels in the society. The work considered keyword-based sentiment analysis, which was enhanced to exclude informational tweets about depression or about recovery. The results demonstrated the words used in the posts most often and the emotional polarity of the tweets. A schedule of user activity was mapped out and trends related to daily activity of users were analyzed. It was observed that the identified X (Twitter) activity related to depression corresponded well with reports on persons with depression and statistics related to suicidal deaths. Therefore, it could be construed that people with undiagnosed depression express their feelings in social media more often, looking, in this way, for help with their emotional problems. [ABSTRACT FROM AUTHOR]

Dementia is an umbrella term used to describe a group of symptoms characterised by behavioural changes, loss of cognitive and social functioning brought about by progressive neurological disorders. It is estimated that around one million people live with a dementia in the UK, with that figure set to rise to 1.2 million by the year 2040. We are learning more about the risk factors for developing dementia over the life course. This paper discusses the non-modifiable and modifiable risk factors for dementia and considers health promotion and health education activities that can be used by community nurses. [ABSTRACT FROM AUTHOR]

• Digital mental health interventions are a promising way to disseminate evidence-supported therapy for depression. • Research supports the efficacy of digital cognitive-behavioral interventions for depression. • There is a need to make digital mental health interventions for depression more appealing. • Entertaining forms of digitally-based CBT hold promise as a method to increase treatment engagement. This paper provides an overview of the of digital mental health (DMHI) interventions for depression. The paper begins with a description of the clinical context and services needs for persons experiencing depressed mood. It is well-known that there is a large gap between the availability of evidence-supported psychotherapy for depression and the large number of people who would likely benefit from it. DHMIs based on a cognitive-behavioral (CBT) model have shown substantial promise as a method to deliver tested-effective treatment to large numbers of people experiencing depression. The article continues with a review of clinical research evaluating DMHIs for depression with a special emphasis on CBT. The article also reviews both the strengths and challenges associated with the clinical use of DHMI for depression. Next, the article continues with a description of a newly-developed DHMI for depression that uses an entertaining approach to deliver well-established CBT strategies. Finally, the paper concludes with a discussion of the need for further research and development of DHMIs for persons experiencing depression. [ABSTRACT FROM AUTHOR]

This paper presents the effect of the local low-pressure region in the atmosphere and rain on aerodynamic characteristics of medium altitude long endurance unmanned aerial vehicle (MALE-UAV) configuration during cruise/loiter. Computations are performed using CFD++, a commercial CFD software suite. A large low-pressure depression past the MALE UAV (symmetrically and asymmetrically) with pressure 10 - 15 % lower than the free stream pressure and a widespread rainfall type with a rainfall rate of 1195 mm/hr., are considered for CFD simulation. A large low pressure that spans the whole MALE-UAV results in a decrement in both lift and drag, but does not affect the yawing and rolling moments significantly. However, a low-pressure region that engulfs only one-half of MALE UAV causes sudden/abrupt changes in rolling and yawing moments. The effect of rain causes a significant decrease in a lift at higher alpha, accompanied by a decrease in stall angle of 2 degrees, and a significant increase in drag. From the study, a Standard Operating Procedure (SOP) was adopted to fly UAVs in adverse weather effects, such that the aircraft can be operated with a velocity higher than 1.3Vstall and at a power setting not less than 75% of max power capacity. [ABSTRACT FROM AUTHOR]

Depression is a mental disease affecting 5% of the population, and its prevalence is increasing. Depression is characterized by feelings of worthlessness, hopelessness, disinterest in enjoyable activities, and sadness, which can result in suicidal thoughts. Traditional approaches to recognizing depression have relied on manually crafted techniques to extract facial expressions, which have their limitations. To address these limitations, this paper proposes using convolutional neural networks (CNNs) as a practical approach for depression recognition. The proposed model in this study involves an eight-step process that includes input data, preprocessing, rescaling, model training, multi-classified results, selecting emotions based on accuracy, retraining the model, and finally, multi-classified results to determine the percentage of depression with greater accuracy. [ABSTRACT FROM AUTHOR]

Depression is one of the leading forms of mental health issues encountered by individuals of diverse age groups today worldwide. Like any other mental health concerns, depression too poses diagnostic challenges for medical practitioners and clinical experts, given obvious social reservations and lack of awareness and acceptance in the society. Since long researchers have been looking for methods to identify symptoms of depression among individuals from their speech and responses, by utilizing automation systems and computers. In this paper, we propose an audio based depression detection method, which relies on neural networks for audio spectrogram based feature extraction as well as classification between speech/response patterns of depressed vs. non-depressed persons. We adopt a multi-modal approach in our work, by combining Mel-Frequency Cepstral Coefficients (MFCC) features, as well as Spectrogram features extracted from an audio file, by a novel CNN network. Our CNN model demonstrates optimized residual blocks and the "glorot uniform" kernel initializer. The proposed method's performance is assessed in both multi-modal and multi-feature trials. We show our results on standard benchmark datasets DAIC-WOZ and MODMA, which provide repositories of questionnaire and patient responses, relevant in identification of depressive symptoms. We have also tested our model on standard emotion recognition audio dataset, RAVDESS. The proposed model achieves detection accuracy of over 90% in DAIC-WOZ and MODMA, and over 85% in RAVDESS, which is proven to surpass the present state-of-the-art. • We introduce a novel deep neural network for depression detection through audio signals. • We used a multi-modal concept considering mfcc and spectrogram to detect the depression. • This paper shows optimized residual block to extract the detailed information to enhance the performance of the model. • Our model gets better results on the three benchmark datasets. [ABSTRACT FROM AUTHOR]

For most of 2022, I was challenged by depression. One of its effects was to derail action-taking skills in my personal and professional life, leading to a sense of paralysis. This paper documents the narrative therapy skills and knowledge that helped me to move out from under depression’s dark cloud and shows how I applied learnings from my personal experience to my work with clients, including those also dealing with depression. [ABSTRACT FROM AUTHOR]

Depression is a health disorder that affects the population, regardless of their age or social status. The World Health Organization (WHO), considers it the greatest generator of incapacity worldwide. Depression increases the possibility of suicide, being the latter, the second trigger of death in people between fifteen and twenty-nine years of age. It negatively impacts different levels of the person: family, work and school and affects its ability to face daily life, aggravated preexisting medical conditions. Young or re-tired person and pregnant or postpartum women, are the groups most vulnerable to suffering from depressive disorder. In this paper, we apply two different classification techniques, namely: Bidirectional ***Encoder Representations from Transformers (BERT) and Support Vector Machines (SVM) in order to identify depression patterns in the Distress Analysis Interview Corpus DAICWOZ. We compare the models obtained and determine their robustness, using performance metrics. The results show that the approach BERT has good performance over the SVM model, reaching an accuracy of almost 90%. [ABSTRACT FROM AUTHOR]

Bipolar affective disorder is a chronic mental disorder, characterized by mood swings alternating between depression and manic or hypomanic episodes. Unfortunately, in some patients pharmacological treatment is not effective, and a certain group of patients shows treatment resistance. Therefore, other treatment methods are sought after, including a change in diet. The most promising is the ketogenic diet. In the presented case study of a male patient, thanks to the introduction of the ketogenic diet, full remission of the disease was achieved, doses of lamotrigine were reduced and quetiapine was completely discontinued. Previously, neither lamotrigine monotherapy nor combined treatment with quetiapine achieved euthymia. The effects of the diet may be related to, among others, the influence on ionic channels and increase in blood acidity (similar to the use of mood stabilizers), increase in gamma-aminobutyric acid (GABA) concentration, modulation of GABAA receptors, effects on the concentration of catecholamines, blocking of AMPA receptors by medium-chain fatty acids, with significant share of omega-3 fatty acids, reduction in insulin levels, and changes in the gut microbiota. The ketogenic diet influences glutamate metabolism and nerve cell metabolism, which uses ketone bodies as energy sources. Ketosis can also stimulate biogenesis of mitochondria, improve brain metabolism, act as a neuroprotective factor, as well as increase glutathione synthesis and reduce oxidative stress. Due to the limited size of the present study, literature review includes selected papers published in the last two decades in the PubMed and Google Scholar scientific literature databases, in English and Polish, with the following key words: ketogenic diet, bipolar affective disorder, depression, schizophrenia. [ABSTRACT FROM AUTHOR]

Depression is a common issue among elderly people in both developing and developed countries. Existing research indicates that cooking with solid fuels has a negative impact on the mental health of middle-aged and elderly people (aged 45 and older). However, the potential role of the residential environment in this process is not yet clear. Clarifying this issue may help identify effective interventions to improve public health for elderly people. This study aimed to explore the association between cooking with solid fuels and depressive symptoms, as well as the potential mediating role of the residential environment in this relationship. This study utilized cross-sectional data from the China Health and Retirement Longitudinal Study (CHARLS) for 2020, involving approximately 19,000 respondents aged 45 years and older. Propensity score matching (PSM) was used to explore the association between cooking with solid fuels and depressive symptoms. Additionally, a range of potential covariates were adjusted, and the Sobel test was applied to assess the potential mediating effect of the residential environment on this relationship. According to the fully adjusted model, cooking with solid fuels was significantly associated with an increased risk of depressive symptoms in middle-aged and older adults (β = 0.315, P < 0.001), and this finding was confirmed through robustness tests using different propensity score matching methods. Heterogeneity analysis revealed that this association was particularly significant among men (β = 0.318, P < 0.001), those aged 60–74 (β = 0.347, P < 0.001), and individuals with a middle school education (β = 0.353, P < 0.001). Mediation effect analysis revealed that indoor cleanliness (β = 0.0090, P < 0.001), indoor broadband coverage (β = 0.0077, P < 0.001), and the installation of indoor air purifiers (β = 0.0010, P < 0.1) mediated the relationships between cooking with solid fuels and depressive symptoms. Given the growing attention given to improving indoor environments and enhancing mental health, the findings of this paper highlight that improving indoor cleanliness, increasing broadband coverage indoors, and installing air purifiers can effectively intervene in and prevent depressive symptoms caused by cooking with solid fuels. • Cooking with solid fuels was significantly associated with an increased risk of depressive symptoms in middle-aged and older adults, and this finding was confirmed through robustness tests using different propensity score matching methods. • Heterogeneity analysis revealed that this association was particularly significant among men, those aged 60 to 74, and individuals with a middle school education. • Mediation effect analysis revealed that indoor cleanliness, indoor broadband coverage, and the installation of indoor air purifiers mediated the relationships between cooking with solid fuels and depressive symptoms. [ABSTRACT FROM AUTHOR]

[Display omitted] • Analyzing brain networks of MDD patients during rTMS treatment using EEG signals recorded at multiple time points (before treatment, after 3rd, 6th, and 10th sessions. • Constructing brain networks using partial transfer entropy, a multivariate, nonlinear, and model-free connectivity measure. • Exploring the relationship between topological measures of brain networks and depression severity scores, providing insights into how network characteristics are associated with treatment outcomes. • Investigating the ability of brain network metrics derived from EEG signals at different time points to predict treatment response. Major depressive disorder (MDD) is a common and debilitating mental illness. One of the MDD treatments is Repetitive transcranial magnetic stimulation (rTMS) which has shown promise in treating MDD but predicting individual patient response remains a challenge. In this paper, we analyzed EEG signals recorded at four different time points including baseline, and after 3rd, 6th, and 10th rTMS sessions in 18 MDD patients receiving rTMS treatment. Brain networks were constructed using partial transfer entropy of EEG signals in four frequency bands. Graph theory metrics were extracted from these networks, and their correlations with patients' depression levels during treatment were assessed. Furthermore, the ability of these networks' metrics obtained from EEG data of four separate time points in discerning between treatment responders and non-responders to treatment was assessed by classification analysis. Results showed a high correlation between depression severity and certain network metrics, such as node betweenness centrality, diameter, and local efficiency in the delta band, as well as global and local efficiency in alpha frequency bands. Furthermore, based on the results, brain network metrics derived from EEG signals collected at second week of rTMS treatment can predict treatment response with an accuracy of 94.44%. This study investigates the relation between brain network metrics and treatment outcomes for MDD and suggests that analyzing topological changes in brain networks may be a useful approach for predicting patient response to rTMS treatment. [ABSTRACT FROM AUTHOR]

Dementia is an umbrella term used to describe a group of symptoms characterised by behavioural changes, loss of cognitive and social functioning brought about by progressive neurological disorders. There are estimated to be 850,000 people living with dementia in the UK and estimates indicate that this will increase to one million people by 2025 and two million by 2051. Left undiagnosed, dementia can have an insidious and devastating impact on the outcomes for patients and their families. However, we know more about its causes and some of the factors that may increase a person's risk of developing the condition. This paper is the first in a series relating to dementia that will follow two families through their progression with dementia and considers the recognition and initial cognitive tests that can be used in a primary care setting. Each of the papers in the series will build upon our understanding of both families, as they face different issues and scenarios over the life course of the dementia. [ABSTRACT FROM AUTHOR]

The objective of this research is to compare the diagnosis and treatment of Major Depressive Disorder (MDD) and Psychopathy. Despite how dangerous psychopaths are, and how similar the condition is to the common mental illness MDD, there is no medication and limited behavioral therapy available. The two have similar imbalances in the same neurotransmitters, dopamine and serotonin, yet there is a glaring difference in the amount of treatment available. Psychopaths have tendencies to commit violent crimes with no motivation or remorse, so this paper will explore why there are no treatments available as well as ways research aims to change that. The key role of the neurotransmitters dopamine and serotonin will be discussed. Additionally, the use of Artificial Intelligence (AI) in the field of psychology and medicine is also a relatively new one, so the benefits of implementing it will also be discussed. [ABSTRACT FROM AUTHOR]

This paper provides examples of defining estimands in real-world scenarios following ICH E9(R1) guidelines. Detailed discussions on choosing the estimands and estimators can be found in our companion papers. Three scenarios of increasing complexity are illustrated. The first example is a proof-of-concept trial in major depressive disorder where the estimand is chosen to support the sponsor decision on whether to continue development. The second and third examples are confirmatory trials in severe asthma and rheumatoid arthritis respectively. We discuss the intercurrent events expected during each trial and how they can be handled so as to be consistent with the study objectives. The estimands discussed in these examples are not the only acceptable choices for their respective scenarios. The intent is to illustrate the key concepts rather than focus on specific choices. Emphasis is placed on following a study development process where estimands link the study objectives with data collection and analysis in a coherent manner, thereby avoiding disconnect between objectives, estimands, and analyses. [ABSTRACT FROM AUTHOR]

Introduction: Evidence suggests group walking in natural environments is more beneficial to the general population's mental health than walking indoors, in urban environments, and alone. Such 'green walking groups' have been suggested as an occupational therapy intervention that could be suitable for adults with mental health problems. However, there have been no reviews of the mental health outcomes of participating in green walking groups for this population. Method: A mixed-methods literature review was conducted. A range of databases was systematically searched electronically. Papers that met pre-defined inclusion criteria were selected, critically appraised, and qualitative and quantitative data were extracted. Thematic analysis was used to identify key qualitative outcomes. Findings: Six papers were included and eight mental health outcomes identified. The evidence suggests participants can experience connections with other people, connections with nature, and a sense of freedom. There is some limited evidence to support improvements to mood, self-esteem, reflection on life tasks, and symptoms of depression, with mixed evidence for experiencing a sense of achievement. Conclusion: This review can be used to build the evidence base for the link between occupation and mental health, and inform the clinical decision-making of occupational therapists, who are well-placed to design and implement green walking groups. [ABSTRACT FROM AUTHOR]

Objective: This paper sought to review the impact of depression in patients with comorbid medical problems, the importance of bridging the gap between inpatient and outpatient care for medical inpatients with depression (especially for organizations that treat patients in both settings), and the elements necessary to implement a pilot for an outpatient Collaborative Care Management program for patients with depression following medical admissions. Taken into account is the presence of new billing mechanisms and potential cost offsets. Methods: The literature referenced in this paper was identified through a search of online databases, including PubMed and Google Scholar. The data used to analyze cost were drawn from national, publicly available sources, such as the Kaiser Family Foundation, Bureau of Labor Statistics, and the Organisation for Economic Cooperation and Development. Results: Collaborative care is an evidence- based intervention for depression that can aid with successful transition of care as patients move from the inpatient to the outpatient setting. It can be considered cost-effective when treating a panel of patients that falls below the recommended caseload for a single case manager (i.e., 19-46 billed encounters, depending on the payer mix), particularly when considering the savings from a reduced length of stay associated with well-controlled depressive symptoms. Conclusion: Organizations should consider implementing collaborative care management for patients with depression to improve depression outcomes, reduce costs, and prepare themselves for a health financing environment that rewards value. [ABSTRACT FROM AUTHOR]

Analyzing human behaviors using computer-based approaches is a new dimension of digital humanity. It integrates computing with healthcare, psychology, and social media. Like other healthcare and medical-related problems, anxiety and depressive disorders are common in Pakistan. In the existing era, this disease is not only affecting people of all ages but the impact of depression is also witnessed on social media communications. On social media, anyone could not guess about the mental conditions of a user through his/ her tweets or comments. Especially, the prediction of depressed unknown users through his behavior on social media is a hard task. Computer science and the medical domain can eliminate such healthcare-related issues. In this paper, a system has been proposed to detect and analyze the behavior of a user on social media. The proposed system comprises of an algorithm that performs sentimental analysis of users’ tweets and comments and a web-based platform to share best practices and success stories of users who have recovered from the disease. The system has been tested on real-time data obtained from tweeter using Application Programming Interface (API). Top trends have been focused to obtain the data and the proposed system successfully detected the users under depression. After working on the huge dataset, the proposed system will be a good contribution to humanity. [ABSTRACT FROM AUTHOR]

Background/purpose: Suicide is a huge global health burden. High suicide rates with a low prevalence of major depressive disorder were reported in East Asia. This study aimed to investigate the prevalence of suicidal behaviors in relation to the demographic characteristics and major depressive disorder in Taiwan.Methods: This study was based on the Taiwan Psychiatric Morbidity Survey, conducted between 2003 and 2005, a survey of common psychiatric disorders in a nationally representative sample of non-institutionalized civilians aged 18 or above. Demographic data, major depressive disorder, and suicidal behaviors were ascertained by a face-to-face interview using the paper version of the World Mental Health Survey Composite International Diagnostic Interview.Results: According to the total sample of 10,135 participants, the lifetime prevalence of suicidal ideation, plans and attempts was 7.52% (S.E = 0.46%), 1.31% (S.E. = 0.16%) and 1.29% (S.E. = 0.16%), respectively. Among suicide ideators, the conditional probability of making a suicide plan was 17.39% (S.E. = 1.92%), and a suicide attempt 17.16% (S.E. = 2.15%). Age ≤ 40, female sex, and major depressive disorder were related to a higher risk of suicidal behaviors in the general population; the former two were associated with further developing suicide attempts and the latter one developing plans among ideators.Conclusion: Despite low prevalence, major depressive disorder remained a significant risk factor for suicidal behaviors in Taiwan. [ABSTRACT FROM AUTHOR]

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Purpose The purpose of this study was to identify the following two research questions for paternal depression through a meta-analysis of relevant qualitative studies: 1. How has paternal depression been screened for by professionals? 2. What are the coping strategies /support available for paternal depression and the challenges in providing strategies/ support for paternal depression? Methods Relevant articles were identified using the following databases: CINAHL, MEDLINE, and Google Scholar. The search keywords used were 'support' AND 'postpartum depression of father' OR 'paternal depression' OR 'mental health of fathers' AND 'qualitative study' in the database. There were 32 qualitative articles retrieved from the database and through hand searching, of which 5 articles were included in the analysis. Meta-ethnography were utilised in this study. All analysed papers were scored and guaranteed by the Critical Appraisal Skills Program (CASP; 0-10 points) as valuable qualitative studies. The analysis was performed using NVivo 12 for Windows. Results In the present meta-synthesis, the Patient Health Questionnaire -9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), the Patient Health Questionnaire -15 (PHQ-15), and the List of Threatening Events were used to identify depression in fathers. Additionally, one study used the six criteria of the definition of postnatal depression to identify paternal depressive symptoms. As a result of the meta-synthesis, the following eight metaphors were extracted from all analyses articles: 'Triggers of paternal depression'; 'Awareness of paternal depression'; 'The Impact of paternal depression'; 'Coping'; 'Lack/unhelpful of information resources'; 'Barriers to seeking help'; 'Reasons for needing supports'; and 'Helps for paternal depression'. Paternal depression due to a sequence of triggering events and the perceived symptoms varied. Once fathers realized they had depression, they attempted to cope with it. However, there were inadequate therapies and information to cope with depression. Moreover, embarrassment of seeking help due to being male was also a barrier to coping with depression. In contrast, the responsibility to protect the family motivated them to acknowledge their depression and seek social support and professional help. Conclusion Anxiety and general depression scales were used to screen for paternal depression, and no measures for paternal depression were not used. Men who were aware of paternal depression tried to cope with it; however, it is possible that support for paternal depression was not sufficiently available and that masculinity may also be a barrier to seeking help for depression. On the other hand, the responsibility of protecting their families motivated fathers to be proactive in seeking help to overcome depression. [ABSTRACT FROM AUTHOR]

The aim of this study was to test whether adding assessment of functional impairment to symptoms of specific mental disorders would have any effect on estimated prevalence of mental disorders in a non-clinical sample of university students in Iceland. A self-report measure was designed to assess the subjective functional impairment of anxiety, depression and stress in students' everyday life. Measures were administered on paper to 671 participants. We hypothesized that taking functional impairment into account would yield lower prevalence rates than using only specific symptoms measures. The results suggests that the addition of functional impairment measure lends a context to the results of the symptom-specific measures and can provide a more accurate estimation of mental health problems of university students than symptoms self-report measures alone. [ABSTRACT FROM AUTHOR]

• In the task of depression detection, this paper proposes a multi-channel topological analysis algorithm based on a multiscale dynamic graph convolutional network and spiking neural network. • To address the complexity issue arising from topological information and channel features, we introduce dynamic graph convolution for spatiotemporal attributes aggregation. • To circumvent the resource consumption of graph computation, we develop multiple diffusion branches with varying receptive fields, enabling parallel acquisition of multiscale topological information. • We enhance the model's representation of multichannel topology by integrating EEG data under both positive and negative stimuli. As the global public health risk intensifies, the number of patients with depression is increasing. Since the current clinical scale assessments may be influenced by subjective patient factors and physician diagnostic experience, we need to explore objective biomarkers from complex electroencephalographic (EEG). Addressing the multichannel topology characteristic of depression, we innovatively propose a lightweight depression detection method based on multiscale dynamic graph convolutional networks and spiking neural networks. By constructing a foundational detection framework utilizing a spiking neural network, the model processes information in the form of discrete spikes and highly fits biological neuron mechanisms. To handle the complexity issue arising from topological information and channel features, we introduce dynamic graph convolution for effective spatiotemporal attribute aggregation. Moreover, to circumvent the costly resource consumption associated with graph computation, we design multiple diffusion branches with different receptive field levels, and obtain multiscale topological information in parallel. By strengthening the learning of neighboring node information, the framework is optimized. Additionally, the integration of EEG under both positive and negative stimulation significantly improves the model's representation of multichannel topology. Our method achieves a classification accuracy of 90.51%, and improves the detection efficiency without neglecting multichannel structural relationships. In addition, after visualizing the network output features, it shows that patients with depression exhibit distinct frontal and temporal EEG abnormalities compared to healthy controls. [ABSTRACT FROM AUTHOR]

This paper discusses relevant research on structured therapy techniques used in the course of cognitive behavioral therapy (CBT) that are helpful in treating older adults with depressive disorders. These findings are compared and contrasted with clinical observations pertinent to the identification of moderator/mediator and other contextual factors critical to the efficacy of CBT for the treatment of this population. While some of these techniques may be viewed as a specific type of intervention in their own right (e.g., Behavioral Activation and Lifeskills Approach), their underlying theory and specific operations are consistent with the underpinnings of other cognitive and behavioral strategies and may be frequently juxtaposed within a CBT framework, depending on the nature of the problem and the specific available resources. Several common issues identified as being problematic for clinicians new to clinical work with older adults are highlighted, and useful information on how to adapt/modify traditional CBT approaches to augment treatment outcome with older adults is provided. Clinicians who use CBT will be familiar with most components discussed, but one novel augmentation towards the development of an age-appropriate format of CBT, termed Lifeskills Approach, is included. In this approach, clinicians are encouraged to identify and incorporate evidence of prior successful coping strategies to challenges that occur across the lifespan. This approach values and respects how clients have overcome aversive life experiences to facilitate attentional deployment away from a narrative of failure to one of resilience and self-acceptance, thereby down-regulating emotional distress. An important consideration discussed is how to use behavioral activation effectively, particularly with persons who have mild cognitive impairment (MCI) or are in the early stages of dementia. • CBT is an effective treatment for older adults with various types of depressive disorders. • A number of specific recommendations to enhance effectiveness of CBT with older adults are provided. • Behavioral activation is described in detail and recommended as an excellent starting point for CBT with depressed older adults. • Cognitive reappraisal, when appropriate, may help prevent future depressive episodes and may be associated with longer-term maintenance of gains. • A novel approach involving recognition and application of Lifeskills is presented. • Although there is a solid research base on the efficacy of CBT for late life depression, several topics for needed research in the future are delineated. [ABSTRACT FROM AUTHOR]

Copyright of Salud Uninorte is the property of Fundacion Universidad del Norte and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Contributions / Prilozi (1857-9345) is the property of Sciendo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today, online education is becoming more important. The effectiveness of online education has been measured by student satisfaction and the possibility of substituting offline education. This study proposes a plan to increase the effectiveness of education in a new form by using online information. Education is the process of socializing and growing learners. Representative negative emotions experienced by learners are stress, anxiety, and depression (SAD). A reduction in SAD will promote student growth and improve educational outcomes. This paper considers online information by dividing it into online educational information support (OEDIS) and online emotional information support (OEMIS). We demonstrate that OEDIS reduces SAD, and OEMIS reduces stress and anxiety. By providing online information, negative emotions can be reduced, and educational outcomes can be improved. This study suggests a new role for online information support, such as emotional change in individuals and solving psychological problems. Online information support goes beyond knowledge transfer and can be used in various fields, such as online education that promotes human growth and positive change, and even healthcare. [ABSTRACT FROM AUTHOR]

There are currently 15 million people in England who have a long-term condition, which is defined as one which currently has no cure other than drugs and symptomatic management (Bennett et al, 2012). At present, the UK population is affected by the Covid-19 pandemic, and those with a long-term condition have been advised to self-isolate to prevent being infected by the virus (Department of Health [DH], 2020). This paper explores some of the effects of stress and anxiety with reference to Covid-19, as well as how the pandemic has affected the community nurse's role. [ABSTRACT FROM AUTHOR]

Depression diagnosis is easily affected by subjective consciousness.It is of great significance to study objective and accurate identification methods. Electroencephalogram (EEG) can reflect brain activity and working state. Therefore, this paper aims to explore features with significant differences based on brain functional connectivity to improve the accuracy of depression recognition. We propose a Functional Connection Feature Selection based on Fuzzy Label (FLFCFS), it calculates the correlation between electrode pairs through the phase lag index (PLI), constructing a functional connection matrix. The cluster center is initialized with the same number as the actual category, and the local distance from the sample to the cluster center is calculated to determine its membership degree, serving as the fuzzy label. And a sparse regression model is employed to select the most related features associated with the fuzzy label. Finally, the top ranked feature subset is selected and input into support vector machine (SVM) for depression recognition. The experimental results show that FLFCFS effectively improves the recognition accuracy, reaching 92.59%, and obtains the highest classification performance. Our method makes full use of the semantic information implied in category markers, it effectively guides feature selection to obtain discriminant feature subsets, enhancing the accuracy of depression recognition. [ABSTRACT FROM AUTHOR]

Depression can induce a range of physiological effects, leading to notable distinctions in the acoustic characteristics exhibited by individuals with depression as opposed to those without. Designing efficient algorithms to accurately identify depression through speech poses a formidable challenge. In this paper, we propose the Multi-Feature Deep Supervised Voiceprint Adversarial Network (MFDS-VAN) for audio-based depression recognition. The MFDS-VAN assimilates extracted acoustic features and the audio waveform, subsequently generating predictions regarding the depression score. In order to attain more robust and discriminative spatial–temporal features associated with depression, the Encoding Network module merges long-term and short-term acoustic features with the unprocessed audio waveform, while the Regression Network module enables the prediction of the depression score. The Deep Supervised Regression algorithm is designed by combining GE2E clustering and Huber regression for better network optimization. Furthermore, to enhance the representation of the MFDS-VAN while diminishing the influence of individual voiceprint information, we propose the Voiceprint Adversarial Network. Experimental results conducted on AVEC 2013, AVEC 2014, and AVEC 2017 datasets demonstrate that the MFDS-VAN significantly enhances robustness and performance in speech-based depression recognition. Our model achieves competitive results when compared to recent audio-based methodologies. • The MFDS-VAN uses various features to robustly capture depression information. • EN module uses attention-equipped sub-modules feature extraction and fusing. • DSR algorithm improves convergence, accuracy, and robustness through optimization. • VAN boosts vocal features and reducing the impact of individual voiceprints. [ABSTRACT FROM AUTHOR]

Depression is a common mental disorder that causes sadness and loss of interest. It affects 350 million people in the world and its most severe state can lead to suicide. Many technologies are being used to aid the depression treatment and gamification has been used as an approach to improve adherence and engagement in the treatment. This systematic study aimed at identifying how gamification and serious games have been applied to support the treatment of depression, what technologies are being used currently and what gaps are still left unexplored. Eight scientific repositories were used to search for papers in the area of depression and a filter process was used to remove bias. As a result of this search and filter process, 28 works were completely reviewed, analyzed and categorized in this paper. In the reviewed papers the technologies found for treatment of depression were mobile, computer, wearables and web applications. These technologies are applied in gamification, serious games, virtual reality and speech analysis. Some papers used Cognitive Behavioral Therapy as an intervention and other papers used gamification as a way to promote engagement and adherence to treatment. [ABSTRACT FROM AUTHOR]

This review explores how the experience of living with environmental contamination is related to residents' mental health and psychological wellbeing. MEDLINE, PsycINFO, Scopus and Web of Science were searched for peer-reviewed literature reporting relevant original empirical data published before 1 April 2021. The search identified 40 papers for full review. Of these, 25 articles examined how living with environmental contamination influenced pre-clinical mental health symptoms, including depression, anxiety and schizophrenia, 17 reported on emotions, such as worry and concern, and seven considered associations with clinical mental health disorders, such as major depressive disorder. Most articles (n = 38) identified some statistically significant or anecdotal evidence of an association between mental health and the experience of living with environmental contamination. Through the critical interpretive synthesis of our review, the factors associated with mental health and wellbeing outcomes in the included papers were thematically organised into five categories: intrinsic, extrinsic (sociodemographic and personal), social, environmental, and regulatory. The conceptual framework contributes to our understanding of how environmental contamination impacts mental health and wellbeing, which may assist in preventing poor mental health outcomes in contaminated neighbourhoods. • Reviewed how the experience of living with environmental contamination relates to residents' mental health. • Screened 1970 articles, with 40 meeting the study inclusion criteria. • Most articles identified evidence of mental health being adversely affected by the experience of living contamination. • Constructed a socioecological framework of the factors associated with mental health outcomes. [ABSTRACT FROM AUTHOR]

Depression is a global health problem; as an illness it may affect any person, independently from age, job or social condition. University students are immersed in environments of high levels of stress and responsibility, which may cause depressive symptoms. This is the context from which the relevance of the work described in this paper derives: a pilot study that was developed during the first stages of a research project aimed at assessing the efficacy of a digital audiovisual psychoeducational intervention (called DEEP) on depressive symptoms and depressive disorders in Portuguese university students. The pilot study was developed with a group of 12 Portuguese university students and included a survey by questionnaire applied to assess literacy in depression in pre- and post-intervention stages. The audiovisual content was shared on a private group on an instant messaging application for smartphones. Pre- and post-intervention results show that participants' ability to recognize depressive symptoms, identify causes and adequate treatment options for depression was higher after watching the audiovisual content of DEEP intervention. Our work has led us to conclude that, when combined with digital resources, psychoeducational interventions arouse more interest in participants and motivation to new knowledge enhancing mental health literacy. [ABSTRACT FROM AUTHOR]

The current paper presents an example of conducting outpatient CBT with an emphasis on principle-informed conceptualization and interventions rather than using specific, manualized protocols. The case illustration highlights the manner in which the therapist conceptualizes and prioritizes the patient's problems, targeting key areas of patient dysfunction that cut across diagnostic areas of concern. The patient ("Sue," a single, American graduate student with Chinese heritage) presents with multiple interacting problems, including severe depression with habitual suicidal thinking, a history of sexual trauma and ongoing sense of vulnerability to harm, chronic anxiety with related avoidance, and occasional purging as a means by which to regulate affect. Cross-cultural issues factor into understanding the patient, solidifying the therapeutic relationship, and negotiating a treatment plan. The course of treatment described herein includes illustrations of the therapist's efforts to keep the patient objectively safe while also promoting her sense of subjective safety; conceptualizing the historical, environmental, and intra-personal variables pertinent to the development and maintenance of the patient's problems; incorporating cross-cultural considerations in promoting the therapeutic relationship and collaborating on goals; and measuring and positively reinforcing the patient's improvements in functioning, including promoting her personal strengths and areas of potential growth. Further, interdisciplinary matters (e.g., professional consulting and collaboration) are also addressed with regard to the patient's concurrent pharmacotherapy with another mental health-care provider. • It is impractical to adhere to a manualized approach with complex, comorbid cases. • Principle-informed CBT allows for comprehensive conceptualization and intervention. • Assessment of dysfunctional patient processes assists conceptualization beyond diagnoses. • Process-based interventions can have multiple applications in a given case. • Cross-cultural factors impact the therapeutic relationship and interventional decision-making. [ABSTRACT FROM AUTHOR]

Each year 800,000 people worldwide die from completed suicide. For every person who suicides, many more have attempted to end their lives. The impact of suicide is not only devastating for patients and their families, friends, and acquaintances, but it remains one of the most shocking events that can happen to clinicians who work with mentally distressed and vulnerable patients. Certain sources of published information suggest that a rational and essentially riskfree orthomolecular strategy involving specific orthomolecules might mitigate suicide risk if used on a timely basis by clinicians instead of waiting for higher levels of evidence while potentially-suicidal patients may be deteriorating. Orthomolecules refer to substances found naturally or normally in the human body, such as amino acids, essential fatty acids, hormones, minerals, and vitamins. In this paper, the author reviews published information and builds a case for specific orthomolecular interventions that could be offered to patients that have attempted suicide and/or to patients vulnerable to suicide. While this paper does not exhaustively review all of the available evidence, selected publications associate suicide risk and suicide with cholesterol, omega-3 essential fatty acids, kynurenine pathway modulators, 25-hydroxyvitamin D3 levels, and lithium levels in drinking water. The author comments on the evidence and advances a clinical strategy that could help to prevent suicide among vulnerable patients. [ABSTRACT FROM AUTHOR]

Depression has become a major health and economic burden worldwide. Electroencephalography (EEG) data has been used by a growing number of researchers to study depression. EEG-based functional connectivity (FC) features have emerged since they can account for the relationships between different brain regions. In this paper, the time–frequency analysis technique is introduced into the construction of the FC matrix. Specifically, instead of directly building the FC matrix from the EEG signals, the intrinsic time-scale decomposition (ITD) method is employed to mine the time–frequency information, and then the Pearson correlation is used to measure the FC between channels. The results show the significant differences in the FC networks between different groups. Furthermore, the graph-based adaptive least absolute shrinkage and selection operator model (GA-LASSO) is proposed in this paper to learn the discriminative features from the FC matrix, which is mainly achieved by adding both the adaptive L 1 and graph regularized terms to the original least absolute shrinkage and selection operator (LASSO) model. The advantages of GA-LASSO come from the processing of discriminative weights of different features, and the connections between features by graph topology. In addition, the effectiveness of the proposed strategy of depression detection is validated on the open dataset MODMA, as well as the self-collected dataset called EDRA. The experimental results show that the current study sheds new light on the pathological mechanism of subclinical depression and suggests that EEG resting-state FC analysis may identify potentially effective biomarkers for its clinical diagnosis. • A novel feature learning model, called GA-LASSO, is proposed for depression detection. • GA-LASSO improves LASSO by both the graph and adaptive L 1 regularization terms. • The ITD method is used to extract the time–frequency information from the EEG data. • To pay attention to mental health of the young, a new dataset called EDRA is presented. [ABSTRACT FROM AUTHOR]

Maternal health and wellbeing is crucial to ensure the best outcomes for babies and children. Pregnancy and the perinatal period are particularly vulnerable times for mothers and children. Almost 1 in 5 mothers will experience some form of depression or anxiety during the perinatal period and other presentations, such as postpartum psychosis are specific to this period. Healthcare professionals, including paediatricians, whose clinical practice is devoted to the care of children often feel inadequately prepared to deal with these issues. Perinatal mental illnesses can feel daunting with anxiety about treatment impacting on the developing fetus, neonate or the breastfeeding infant. This short paper covers some common perinatal psychiatric presentations and aspects of treatment relevant for paediatricians. [ABSTRACT FROM AUTHOR]

Hypnosis and biofeedback have demonstrated efficacy for chronic pain management. However, using hypnosis and biofeedback together may have additive or synergistic effects, resulting in better outcomes than if either are provided alone. In this paper we present a case study which explores the potential benefits of using hypnosis and biofeedback together with cognitive behavioral therapy (CBT) for improving chronic pain management in a patient with Postural Orthostatic Tachycardic Syndrome (POTS) and its co-occurring anxiety, depression, insomnia, attention-deficit/hyperactivity disorder (ADHD) and Dissociative Identity Disorder (DID) symptoms. [ABSTRACT FROM AUTHOR]

Copyright of Revista Colombiana de Psiquiatria is the property of Asociacion Colombiana de Psiquiatria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)