The importance of assessing personality traits and disorders in clinical trials of major depressive disorder

Husain, Muhammad Ishrat; Carvalho, André F.

doi:10.1590/1516-4446-2019-0645

Cited by 4 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, most studies addressing the group Other Professions did not provide details about the professions included, qualifications, or whether the participants were healthcare providers or healthcare managers. Having more information about the context of these workers is relevant, especially in the context of low-and-middle-income countries, and this is one of this study's contributions ( 21 ).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Risk and Protective Factors for the Mental Health of Brazilian Healthcare Workers in the Frontline of COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

The objective was to compare the mental health indicators of health workers providing care to individuals with COVID-19 in Brazil, considering sociodemographic and occupational variables and the risk perception of contamination by the Sars-CoV-2 of workers from different professions, identifying risk and protective factors. A sample of 916 health workers was assessed: physicians, nursing workers, and workers from other professions (psychologists, physical therapists, nutritionists, speech therapists, occupational therapists, dentists, pharmacists, and social workers). REDCAP software was used to collect data online, using standardized instruments to assess anxiety, depression, posttraumatic stress, and insomnia, and one questionnaire addressed risk and protective variables. Statistical techniques for comparing groups were used along with logistic regression analysis. The results revealed that all the groups presented indicators of significant mental health problems (>36%), especially the nursing group. A larger percentage of participants, regardless of the profession, presented a high rate of insomnia disorders, while posttraumatic stress was the least expressive. Occupational variables stand out as risk factors for mental health, with specificities among the different groups. A protective factor for all the groups was having positive professional prospects. The protective factors for the physicians group included support provided by co-workers, being older and a man, while being satisfied with physical protective measures implemented by the employing institution was a protective factor for the groups composed of nursing workers and other professionals. These findings are relevant for devising mental health care strategies.

show abstract

Section: Discussionmentioning

confidence: 99%

“…Studies conducted in low-and-middle-income countries ( 21 ), such as Brazil, are opportune because specific sociocultural aspects can be identified and assessed, shedding light on risk and protective factors.…”

Section: Introductionmentioning

confidence: 99%

Risk and Protective Factors for the Mental Health of Brazilian Healthcare Workers in the Frontline of COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Personality traits have a key role for both diagnosis and prognosis of four major mental disorders. Although the significance of the influence of personality traits over treatment outcome on the individual patient level is debatable, the value of personality trait assessment in clinical trials is unobjectionable, if could be done effortlessly [45]. As they rely on the individuals subjective views on their own personality, the non-objective nature of the current assessment methodologies hinder their use on the individual level to guide clinical practice.…”

Section: Medical Perspective On Personality Traitsmentioning

confidence: 99%

“…We use random temporal sampling of 45 frames (RTS-45), which corresponds to 1.5 seconds, during training, and non-overlapping sliding window of the same size during test and validation. Window size is chosen among the values [30,45,60] through validation error. Finally, the last fully connected layer of the network is replaced with a linear regression layer and L1 loss is utilized.…”

Section: Modelingmentioning

confidence: 99%

Multimodal analysis of personality traits on videos of self-presentation and induced behavior

Giritlioğlu

Mandira

Yılmaz

et al. 2020

J Multimodal User Interfaces

View full text Add to dashboard Cite

Personality analysis is an important area of research in several fields, including psychology, psychiatry, and neuroscience. With the recent dramatic improvements in machine learning, it has also become a popular research area in computer science. While the current computational methods are able to interpret behavioral cues (e.g., facial expressions, gesture, and voice) to estimate the level of (apparent) personality traits, accessible assessment tools are still substandard for practical use, not to mention the need for fast and accurate methods for such analyses. In this study, we present multimodal deep architectures to estimate the Big Five personality traits from (temporal) audio-visual cues and transcribed speech. Furthermore, for a detailed analysis of personality traits, we have collected a new audio-visual dataset, namely: Self-presentation and Induced Behavior Archive for Personality Analysis (SIAP). In contrast to the available datasets, SIAP introduces recordings of induced behavior in addition to self-presentation (speech) videos. With thorough experiments on SIAP and ChaLearn LAP First Impressions datasets, we systematically assess the reliability of different behavioral modalities and their combined use. Furthermore, we investigate the characteristics and discriminative power of induced behavior for personality analysis, showing that the induced behavior indeed includes signs of personality traits.

show abstract

“…Mula and Sander (2019) reported that it is difficult to treat patients with depression and comorbid epilepsy. In addition to the characteristics of patients such as the course of the disease, severity of symptoms, simultaneous use of drugs, comorbidities (including mental and physical), and socioeconomic factors, personality characteristics and disorders affect the therapeutic effects on MDD (Husain & Carvalho, 2020). The therapeutic effects on MDD with personality disorder (PD) was worse than that of MDD alone, and all treatments except for electroconvulsive therapy showed that the risk and adverse outcomes in patients with a PD with MDD was twice as high as those without a PD (Newton-Howes et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Clinical traits of patients with major depressive disorder with comorbid borderline personality disorder based on propensity score matching

Tong

Shi

et al. 2020

Depression and Anxiety

View full text Add to dashboard Cite

Background: Major depressive disorder (MDD) with comorbid borderline personality disorder (BPD) makes the clinical symptoms of patients more complex and more difficult to treat, so more attention should be paid to the recognition of their clinical features. This study investigated the differences between patients with MDD with and without BPD in clinical traits. Methods: Propensity score matching was used to analyze the retrospective patients' data from August 2012 to September 2019. Altogether, 1381 patients with MDD were enrolled; 38 patients with MDD were matched to compare demographic data, and scores on the Hamilton Depression Scale, Hamilton Anxiety Scale (HAMA), Self-Rating Depression Scale (SDS), Modified Overt Aggression Scale (MOAS), and the frequency of nonsuicidal self-harm (NSSH). Results: Compared to patients with MDD without BPD, the age of onset of patients with MDD with comorbid BPD was significantly earlier (t = 3.25, p = .00). The scores of HAMA (t = −2.28, p = .03), SDS (t = 9.31, p = .00), MOAS (t = −13.67, p = .00), verbal aggression (t = −3.79, p = .00), aggression against objects (t = −2.84, p = .00), aggression against others (t = −6.70, p = .00), and aggression against self (t = −9.22, p = .00) were significantly higher in patients with MDD with comorbid BPD. Moreover, the frequency of NHSS in these patients was significantly higher (χ 2 = 20.13, p = .00). MOAS was an independent influencing factor in these (odds ratio = 7.38, p = .00).

show abstract

The importance of assessing personality traits and disorders in clinical trials of major depressive disorder

Cited by 4 publications

References 7 publications

Risk and Protective Factors for the Mental Health of Brazilian Healthcare Workers in the Frontline of COVID-19 Pandemic

Risk and Protective Factors for the Mental Health of Brazilian Healthcare Workers in the Frontline of COVID-19 Pandemic

Multimodal analysis of personality traits on videos of self-presentation and induced behavior

Clinical traits of patients with major depressive disorder with comorbid borderline personality disorder based on propensity score matching

Contact Info

Product

Resources

About