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      Negative affect variability differs between anxiety and depression on social media

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          Abstract

          Objective

          Negative affect variability is associated with increased symptoms of internalizing psychopathology (i.e., depression, anxiety). The Contrast Avoidance Model (CAM) suggests that individuals with anxiety avoid negative emotional shifts by maintaining pathological worry. Recent evidence also suggests that the CAM can be applied to major depression and social phobia, both characterized by negative affect changes. Here, we compare negative affect variability between individuals with a variety of anxiety and depression diagnoses by measuring the levels and degree of change in the sentiment of their online communications.

          Method

          Participants were 1,853 individuals on Twitter who reported that they had been clinically diagnosed with an anxiety disorder ( A cohort, n = 896) or a depressive disorder ( D cohort, n = 957). Mean negative affect (NA) and negative affect variability were calculated using the Valence Aware Dictionary for Sentiment Reasoning (VADER), an accurate sentiment analysis tool that scores text in terms of its negative affect content.

          Results

          Findings showed differences in negative affect variability between the D and A cohort, with higher levels of NA variability in the D cohort than the A cohort, U = 367210, p < .001, r = 0.14, d = 0.25. Furthermore, we found that A and D cohorts had different average NA, with the D cohort showing higher NA overall, U = 377368, p < .001, r = 0.12, d = 0.21.

          Limitations

          Our sample is limited to individuals who disclosed their diagnoses online, which may involve bias due to self-selection and stigma. Our sentiment analysis of online text may not completely capture all nuances of individual affect.

          Conclusions

          Individuals with depression diagnoses showed a higher degree of negative affect variability compared to individuals with anxiety disorders. Our findings support the idea that negative affect variability can be measured using computational approaches on large-scale social media data and that social media data can be used to study naturally occurring mental health effects at scale.

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          Most cited references56

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          Development and validation of brief measures of positive and negative affect: The PANAS scales.

          In recent studies of the structure of affect, positive and negative affect have consistently emerged as two dominant and relatively independent dimensions. A number of mood scales have been created to measure these factors; however, many existing measures are inadequate, showing low reliability or poor convergent or discriminant validity. To fill the need for reliable and valid Positive Affect and Negative Affect scales that are also brief and easy to administer, we developed two 10-item mood scales that comprise the Positive and Negative Affect Schedule (PANAS). The scales are shown to be highly internally consistent, largely uncorrelated, and stable at appropriate levels over a 2-month time period. Normative data and factorial and external evidence of convergent and discriminant validity for the scales are also presented.
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            The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R).

            Uncertainties exist about prevalence and correlates of major depressive disorder (MDD). To present nationally representative data on prevalence and correlates of MDD by Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria, and on study patterns and correlates of treatment and treatment adequacy from the recently completed National Comorbidity Survey Replication (NCS-R). Face-to-face household survey conducted from February 2001 to December 2002. The 48 contiguous United States. Household residents ages 18 years or older (N = 9090) who responded to the NCS-R survey. Prevalence and correlates of MDD using the World Health Organization's (WHO) Composite International Diagnostic Interview (CIDI), 12-month severity with the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR), the Sheehan Disability Scale (SDS), and the WHO disability assessment scale (WHO-DAS). Clinical reinterviews used the Structured Clinical Interview for DSM-IV. The prevalence of CIDI MDD for lifetime was 16.2% (95% confidence interval [CI], 15.1-17.3) (32.6-35.1 million US adults) and for 12-month was 6.6% (95% CI, 5.9-7.3) (13.1-14.2 million US adults). Virtually all CIDI 12-month cases were independently classified as clinically significant using the QIDS-SR, with 10.4% mild, 38.6% moderate, 38.0% severe, and 12.9% very severe. Mean episode duration was 16 weeks (95% CI, 15.1-17.3). Role impairment as measured by SDS was substantial as indicated by 59.3% of 12-month cases with severe or very severe role impairment. Most lifetime (72.1%) and 12-month (78.5%) cases had comorbid CIDI/DSM-IV disorders, with MDD only rarely primary. Although 51.6% (95% CI, 46.1-57.2) of 12-month cases received health care treatment for MDD, treatment was adequate in only 41.9% (95% CI, 35.9-47.9) of these cases, resulting in 21.7% (95% CI, 18.1-25.2) of 12-month MDD being adequately treated. Sociodemographic correlates of treatment were far less numerous than those of prevalence. Major depressive disorder is a common disorder, widely distributed in the population, and usually associated with substantial symptom severity and role impairment. While the recent increase in treatment is encouraging, inadequate treatment is a serious concern. Emphasis on screening and expansion of treatment needs to be accompanied by a parallel emphasis on treatment quality improvement.
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              A meta-analysis of heart rate variability and neuroimaging studies: implications for heart rate variability as a marker of stress and health.

              The intimate connection between the brain and the heart was enunciated by Claude Bernard over 150 years ago. In our neurovisceral integration model we have tried to build on this pioneering work. In the present paper we further elaborate our model and update it with recent results. Specifically, we performed a meta-analysis of recent neuroimaging studies on the relationship between heart rate variability and regional cerebral blood flow. We identified a number of regions, including the amygdala and ventromedial prefrontal cortex, in which significant associations across studies were found. We further propose that the default response to uncertainty is the threat response and may be related to the well known negativity bias. Heart rate variability may provide an index of how strongly 'top-down' appraisals, mediated by cortical-subcortical pathways, shape brainstem activity and autonomic responses in the body. If the default response to uncertainty is the threat response, as we propose here, contextual information represented in 'appraisal' systems may be necessary to overcome this bias during daily life. Thus, HRV may serve as a proxy for 'vertical integration' of the brain mechanisms that guide flexible control over behavior with peripheral physiology, and as such provides an important window into understanding stress and health. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2024
                21 February 2024
                : 19
                : 2
                : e0272107
                Affiliations
                [1 ] Center for Social and Biomedical Complexity, Indiana University Bloomington, Bloomington, IN, United States of America
                [2 ] Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, United States of America
                [3 ] Department of Advanced Computing Sciences, Maastricht University, Maastricht, NL, United States of America
                [4 ] Department of Applied Health Science, School of Public Health, Indiana University Bloomington, Bloomington, IN, United States of America
                [5 ] Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States of America
                Universidad Diego Portales, CHILE
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-8852-7602
                https://orcid.org/0000-0002-7186-7344
                Article
                PONE-D-22-19578
                10.1371/journal.pone.0272107
                10881019
                38381769
                305b80e6-cdd9-4aee-865f-aecdaa2cead2
                © 2024 Rutter et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 12 July 2022
                : 23 October 2023
                Page count
                Figures: 1, Tables: 3, Pages: 14
                Funding
                The author(s) received no specific funding for this work.
                Categories
                Research Article
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mood Disorders
                Depression
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Twitter
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Twitter
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Neuropsychiatric Disorders
                Anxiety Disorders
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Neuroses
                Anxiety Disorders
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Biology and Life Sciences
                Psychology
                Emotions
                Social Sciences
                Psychology
                Emotions
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Neuropsychiatric Disorders
                Anxiety Disorders
                Social Anxiety Disorder
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Neuroses
                Anxiety Disorders
                Social Anxiety Disorder
                Medicine and Health Sciences
                Mental Health and Psychiatry
                Mood Disorders
                Seasonal Affective Disorder
                Custom metadata
                The code and related data of this study are available on GitHub at https://github.com/mctenthij/affect_var.

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