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European Journal of Investigation in... Jun 2024Today, many individuals read the daily news from social media platforms. Research has shown that news with negative valence might influence the well-being of...
Today, many individuals read the daily news from social media platforms. Research has shown that news with negative valence might influence the well-being of individuals. Existing research that examined the impact of headlines on individuals' well-being has primarily focused on examining the positive or negative polarity of words used in the headlines. In the present study, we adopt a different approach and ask participants to categorize the headlines themselves based on the emotions they experienced while reading them and how their choice impacts their well-being. A total of 306 participants were presented with 40 headlines from main news sites that were considered popular based on the number of public reactions. Participants had to rate their emotional experience of the headlines following five emotional states (i.e., happiness, anger, sadness, fear, and interest). Emotion regulation strategies and resilience were also measured. In line with our hypotheses, we found that participants reported experiencing negative emotions more intensively while reading the headlines. Emotion regulation was not found to influence the emotional states of individuals, whereas resilience did. These findings highlight that individuals can experience heightened emotions without reading the entire news story. This effect was observed regardless of the headline's emotional valence (i.e., positive, negative, or neutral). Furthermore, our study highlights the critical role of interest as a factor in news consumption. Interest significantly affects individuals' engagement and reactions to headlines, regardless of valence. The findings underscore the complex interplay between headline content and reader engagement and stress the need for further research into how headlines are presented to protect individuals from potential emotional costs.
PubMed: 38921075
DOI: 10.3390/ejihpe14060109 -
Behavioral Sciences (Basel, Switzerland) May 2024With the rapid development of society and the deteriorating natural environment, there has been an increase in public emergencies. This study aimed to explore how...
With the rapid development of society and the deteriorating natural environment, there has been an increase in public emergencies. This study aimed to explore how sadness and fear in the context of public emergencies influence moral judgments. This research first induced feelings of sadness and fear by using videos about public emergencies and music, and then used moral scenarios from the CNI model (C parameter: sensitivity to consequences; N parameter: sensitivity to norms; I parameter: general preference for inaction) to assess participants' moral thinking. In Study 1, participants were divided into a sadness group and a neutral group, while in Study 2, participants were divided into a fear group and a neutral group. During the experiment, participants were exposed to different videos related to public emergencies to induce the corresponding emotions, and emotional music was continuously played throughout the entire experiment. Participants were then asked to answer questions requiring moral judgments. The results showed that based on the CNI model, sadness induced in the context of public emergencies significantly increased the C parameter, without affecting the N or I parameters. Fear increased the I parameter, without affecting the C or I parameters. That is, sadness and fear induced in the context of a public emergency can influence moral judgments. Specifically, sadness increases individuals' sensitivity to consequences and fear increases the general preference for inaction in moral judgments.
PubMed: 38920800
DOI: 10.3390/bs14060468 -
Behavior Research Methods Jun 2024EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer...
EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-'action'-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses.
PubMed: 38918315
DOI: 10.3758/s13428-024-02433-0 -
Quality of Life Research : An... Jun 2024This study aimed to produce a patient-centered understanding of mental health symptoms of people with the post-COVID-19 syndrome (PCS).
OBJECTIVES
This study aimed to produce a patient-centered understanding of mental health symptoms of people with the post-COVID-19 syndrome (PCS).
METHODS
A cross-sectional analysis of 414 participants in a longitudinal study was carried out involving people who self-identified as having symptoms of PCS. People were asked to name their most frequent and most bothersome mental health symptoms affected by PCS using the structure of the Patient Generated Index (PGI). The text threads from the PGI were grouped into topics using BERTopic analysis.
RESULTS
20 topics were identified from 818 text threads referring to PCS mental health symptoms. 35% of threads were identified as relating to anxiety, discussed in terms of five topics: generalized/social anxiety, fear/worry, post-traumatic stress, panic, and nervous. 29% of threads were identified as relating to low mood, represented by five topics: depression, discouragement, emotional distress, sadness, and loneliness. A cognitive domain (22% of threads) was covered by four topics referring to concentration, memory, brain fog, and mental fatigue. Topics related to frustration, anger, irritability. and mood swings (7%) were considered as one domain and there were separate topics related to motivation, insomnia, and isolation.
CONCLUSIONS
This novel method of digital transformation of unstructured text data uncovered different ways in which people think about classical mental health domains. This information could be used to evaluate whether existing measures cover the content identified by people with PCS, to initiate a clinical conversation, or to justify the development of a new measure of the mental health impact of PCS.
PubMed: 38916660
DOI: 10.1007/s11136-024-03719-8 -
Medical Humanities Jun 2024Public health approaches to palliative care are internationally endorsed for their potential to improve the social determinants of dying such as energy costs, transport...
Public health approaches to palliative care are internationally endorsed for their potential to improve the social determinants of dying such as energy costs, transport and housing. Enhancing public understanding of inequities in end of life experiences, which exist even in economically advanced countries, is vital if the value of public health approaches are to be endorsed and invested in. Visual exhibitions have a strong tradition of raising awareness and influencing public health discourse. The UK-based Cost of Dying exhibition (April-August 2023) presented real examples of how financial hardship and deprivation intersect with end of life experience through professional portraits, photovoice imagery taken by individuals at the end of their lives, and digital stories co-produced with bereaved relatives. Three iterations of the exhibition were displayed at public venues and a health conference. Evaluation methods comprised anonymous feedback cards (n=208), panel discussions and social media reactions. Thematic analysis was used to identify themes within the feedback. The emotional resonance of the exhibition was a key theme, with attendees expressing sadness, anger, empathy and hope. Visitors found the exhibition thought-provoking and expressed that it countered existing stereotypes about what it means to experience financial hardship at the end of life. The exhibition spurred calls for change, with some attendees questioning in what capacity they could help. Individuals with expertise in end of life care reported that the imagery validated their professional experiences. In conclusion, the Cost of Dying exhibition made visible the struggles endured by individuals confronting financial hardship and material deprivation at the end of life. Such exhibitions can challenge the traditional view of dying as a swift process taking place sequestered in institutions, revealing that it often unfolds over time and individuals may continue to live at home in the community, struggling with unmet needs and unresponsive state services.
PubMed: 38914458
DOI: 10.1136/medhum-2024-012950 -
Emotion (Washington, D.C.) Jun 2024Nostalgia is a mixed emotion, often evoked by music. This study sought to conceptually replicate and extend Barrett et al.'s (see record 2010-09991-008) pioneering work...
Nostalgia is a mixed emotion, often evoked by music. This study sought to conceptually replicate and extend Barrett et al.'s (see record 2010-09991-008) pioneering work exploring music-evoked nostalgia, where the authors identified person- and context-level predictors of the experience of nostalgia in music. In a sample of 582 adults across the United States, we identified self-selected nostalgic and musically matched nonnostalgic, familiar songs for each individual, using an online survey in 2021. The participants listened to music and indicated feelings of valence and arousal, followed by assessments of affect (Positive and Negative Affect Schedule, Short Form) and personality (Ten-Item Personality Inventory, Brief Affective Neuroscience Personality Scales, and Southampton Nostalgia Scale). Nostalgic songs were rated higher in valence and arousal than familiar, nonnostalgic control songs, and higher in mixed valence in some metrics. Individuals with higher trait-level Trait Nostalgia reported higher nostalgia ratings across nostalgic and control songs. Interactions between context- and person-level factors indicated that personality influenced the felt valence and arousal profile of music-evoked nostalgia, distinct from familiar control music. While some personality types found nostalgic music to make them feel more aroused and positive (those high in care, trait nostalgia, anger), others felt more negative while listening (those high in sadness). Last, we extend the personality profile of a highly nostalgic person; trait-level Trait Nostalgia was associated with care, play, agreeableness, extraversion, and neuroticism. We demonstrate affective and person-level contributors to music-evoked nostalgia observed in Barrett et al.'s (2010) hold even when controlling for familiarity and musical features. We provide novel insights on complex interactions supporting this emotion, in a larger and more diverse sample with personalized stimuli. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
PubMed: 38913707
DOI: 10.1037/emo0001389 -
Scientific Reports Jun 2024Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify...
Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limited range of expressions. This study expands the use of deep learning for facial emotion recognition (FER) based on Emognition dataset that includes ten target emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness, and neutral. A series of data preprocessing was carried out to convert video data into images and augment the data. This study proposes Convolutional Neural Network (CNN) models built through two approaches, which are transfer learning (fine-tuned) with pre-trained models of Inception-V3 and MobileNet-V2 and building from scratch using the Taguchi method to find robust combination of hyperparameters setting. The proposed model demonstrated favorable performance over a series of experimental processes with an accuracy and an average F1-score of 96% and 0.95, respectively, on the test data.
Topics: Humans; Emotions; Neural Networks, Computer; Facial Expression; Deep Learning; Image Processing, Computer-Assisted; Facial Recognition; Female; Male
PubMed: 38910179
DOI: 10.1038/s41598-024-65276-x -
European Neuropsychopharmacology : the... Jun 2024Social dysfunction represents one of the most common signs of neuropsychiatric disorders, such as Schizophrenia (SZ) and Alzheimer's disease (AD). Perturbed...
Social dysfunction represents one of the most common signs of neuropsychiatric disorders, such as Schizophrenia (SZ) and Alzheimer's disease (AD). Perturbed socioaffective neural processing is crucially implicated in SZ/AD and generally linked to social dysfunction. Yet, transdiagnostic properties of social dysfunction and its neurobiological underpinnings remain unknown. As part of the European PRISM project, we examined whether social dysfunction maps onto shifts within socioaffective brain systems across SZ and AD patients. We probed coupling of social dysfunction with socioaffective neural processing, as indexed by an implicit facial emotional processing fMRI task, across SZ (N = 46), AD (N = 40) and two age-matched healthy control (HC) groups (N = 26 HC-younger and N = 27 HC-older). Behavioural (i.e., social withdrawal, interpersonal dysfunction, diminished prosocial or recreational activity) and subjective (i.e., feelings of loneliness) aspects of social dysfunction were assessed using the Social Functioning Scale and De Jong-Gierveld loneliness questionnaire, respectively. Across SZ/AD/HC participants, more severe behavioural social dysfunction related to hyperactivity within fronto-parieto-limbic brain systems in response to sad emotions (P = 0.0078), along with hypoactivity of these brain systems in response to happy emotions (P = 0.0418). Such relationships were not found for subjective experiences of social dysfunction. These effects were independent of diagnosis, and not confounded by clinical and sociodemographic factors. In conclusion, behavioural aspects of social dysfunction across SZ/AD/HC participants are associated with shifts within fronto-parieto-limbic brain systems. These findings pinpoint altered socioaffective neural processing as a putative marker for social dysfunction, and could aid personalized care initiatives grounded in social behaviour.
PubMed: 38909542
DOI: 10.1016/j.euroneuro.2024.05.004 -
Sante Publique (Vandoeuvre-les-Nancy,... 2024In France, 122 women were killed by their partner or ex-partner in 2021.
INTRODUCTION
In France, 122 women were killed by their partner or ex-partner in 2021.
PURPOSE OF THE RESEARCH
The principal objective of the AVIC-MG study, on women victims of domestic violence and their expectations of their general practitioner, was to observe whether the women in question, who visit specialist facilities for victims of domestic violence, would like to be questioned about domestic violence by their general practitioner (GP). The secondary objective was to describe this population of women and the characteristics of their GP visits during the last twelve months.
RESULTS
The study showed that more than 90 percent of these women had consulted a GP in the last twelve months and 65 percent of the mothers in the group had consulted a GP for their child(ren). The majority of these women (82 percent) wanted the GP to ask them about domestic violence. They had gone to the GP for specific reasons: fatigue, pain, psychological suffering (anxiety, sadness, difficulty sleeping).
CONCLUSION
The majority of women victims of domestic violence would like primary care practitioners to identify the abuse. Tools are available to help GPs with this complex identification, in particular the DECLICVIOLENCE.FR website.
Topics: Humans; Female; Adult; France; Domestic Violence; Middle Aged; General Practitioners; Young Adult; Adolescent
PubMed: 38906814
DOI: No ID Found -
Journal of Affective Disorders Jun 2024Dimensional frameworks of psychopathology call for multivariate approaches to map co-occurring disorders to index what symptoms emerge when and for whom. Ecological...
BACKGROUND
Dimensional frameworks of psychopathology call for multivariate approaches to map co-occurring disorders to index what symptoms emerge when and for whom. Ecological momentary assessment (EMA) offers a method for assessing and differentiating the dynamics of co-occurring symptoms with greater temporal granularity and naturalistic context. The present study used multivariate mixed effects location-scale modeling to characterize the time-varying dynamics of depressed mood and anxiety for women diagnosed with social anxiety disorder (SAD) and major depression (MDD).
METHODS
Women completed five daily EMA surveys over 30 days (150 EMA surveys/woman, T ≈ 5250 total observations) and two clinical diagnostic and retrospective self-report measures administered approximately two months apart.
RESULTS
There was evidence of same-symptom lagged effects (bs = 0.08-0.09), but not cross-symptom lagged effects (bs < 0.01) during EMA. Symptoms co-varied such that momentary spikes from one's typical level of anxiety were associated with increases in momentary depressed mood (b = 0.19) and greater variability of depressed mood (b = 0.06). Similarly, spikes from one's typical levels of depressed mood were associated with increases in momentary anxiety (b = 0.19). Furthermore, the presence and magnitude of effects demonstrated person-specific heterogeneity.
LIMITATIONS
Our findings are constrained to the dynamics of depressed and anxious mood among cisgender women with primary SAD and current or past MDD.
CONCLUSIONS
Findings from this work help to characterize how daily experiences of co-occurring mood and anxiety fluctuate and offer insight to aid the development of momentary, person-specific interventions designed to regulate symptom fluctuations.
PubMed: 38906224
DOI: 10.1016/j.jad.2024.06.064