Need for Affect, Problematic Social Media Use and the Mediating Role of Fear of Missing Out in European and Arab Samples.

Purpose: The growing awareness and concern about the excessive use of social media have led to an increasing number of studies investigating the underlying factors contributing to this behavior. In the literature, it is discussed that problematic social media use (PSMU) can impact individuals' mental health and well-being. Drawing on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study aimed to examine the association between the need for affect (affect approach and affect avoidance) and PSMU (operationalized via the social media disorder scale), as well as the mediating role of fear of missing out (FoMO) in that relation. Participants and Methods: Data were collected via an online survey from 513 participants in European and Arabic countries. Regression and mediation analyses were conducted to explore the relationships between affect approach, affect avoidance, FoMO, and PSMU. Results: Regression analysis results indicated that both affect approach and affect avoidance as part of the need for affect construct significantly predicted PSMU in both cultural contexts. Mediation analysis showed that FoMO partially mediated the relationship between affect approach and PSMU in the Arab sample but not in the European sample. Beyond this, FoMO partially mediated the relationship between affect avoidance and PSMU in both cultural samples. Conclusion: The present study indicates that managing emotions could be an effective strategy to combat PSMU. In line with this and against the background of the data business model behind social media companies, we deem it to be of importance to minimize triggers related to FoMO in the design of social media platforms (for example, push notifications). This might be particularly relevant for individuals with a high inclination towards affect approach and affect avoidance.

On the association between personality, fear of missing out (FoMO) and problematic social media use tendencies in European and Arabian samples.

Recent meta-analytical evidence indicates a mild association between higher neuroticism and lower conscientiousness scores and a tendency towards problematic social media use (PSMU). However, fear of missing out (FoMO) has emerged as a critical variable in understanding the positive link between neuroticism and PSMU. Given the replication crisis in psychology, this study aimed to reinvestigate personality-PSMU associations and, crucially, the less-studied FoMO mediation effect. To ensure generalizability of the findings, we recruited two diverse samples with European and Arabian backgrounds. The results revealed a significant total effect of neuroticism on PSMU for both cultural groups, with the European sample demonstrating a fully mediated effect via FoMO, whereas the Arab sample showed a partially mediated effect via FoMO, along with a significant direct effect. This study contributes to the existing literature by highlighting the importance of FoMO as a mediator between neuroticism and PSMU and some minor potential cultural differences in this association.

Effectiveness of Using Augmented Reality for Training in the Medical Professions: Meta-analysis.

BACKGROUND: Augmented reality (AR) is an interactive technology that uses persuasive digital data and real-world surroundings to expand the user's reality, wherein objects are produced by various computer applications. It constitutes a novel advancement in medical care, education, and training. OBJECTIVE: The aim of this work was to assess how effective AR is in training medical students when compared to other educational methods in terms of skills, knowledge, confidence, performance time, and satisfaction. METHODS: We performed a meta-analysis on the effectiveness of AR in medical training that was constructed by using the Cochrane methodology. A web-based literature search was performed by using the Cochrane Library, Web of Science, PubMed, and Embase databases to find studies that recorded the effect of AR in medical training up to April 2021. The quality of the selected studies was assessed by following the Cochrane criteria for risk of bias evaluations. RESULTS: In total, 13 studies with a total of 654 participants were included in the meta-analysis. The findings showed that using AR in training can improve participants' performance time (I2=99.9%; P<.001), confidence (I2=97.7%; P=.02), and satisfaction (I2=99.8%; P=.006) more than what occurs under control conditions. Further, AR did not have any effect on the participants' knowledge (I2=99.4%; P=.90) and skills (I2=97.5%; P=.10). The meta-regression plot shows that there has been an increase in the number of articles discussing AR over the years and that there is no publication bias in the studies used for the meta-analysis. CONCLUSIONS: The findings of this work suggest that AR can effectively improve performance time, satisfaction, and confidence in medical training but is not very effective in areas such as knowledge and skill. Therefore, more AR technologies should be implemented in the field of medical training and education. However, to confirm these findings, more meticulous research with more participants is needed.

Emotion Self-Regulation in Neurotic Students: A Pilot Mindfulness-Based Intervention to Assess Its Effectiveness through Brain Signals and Behavioral Data.

Neuroticism has recently received increased attention in the psychology field due to the finding of high implications of neuroticism on an individual's life and broader public health. This study aims to investigate the effect of a brief 6-week breathing-based mindfulness intervention (BMI) on undergraduate neurotic students' emotion regulation. We acquired data of their psychological states, physiological changes, and electroencephalogram (EEG), before and after BMI, in resting states and tasks. Through behavioral analysis, we found the students' anxiety and stress levels significantly reduced after BMI, with p-values of 0.013 and 0.027, respectively. Furthermore, a significant difference between students in emotion regulation strategy, that is, suppression, was also shown. The EEG analysis demonstrated significant differences between students before and after MI in resting states and tasks. Fp1 and O2 channels were identified as the most significant channels in evaluating the effect of BMI. The potential of these channels for classifying (single-channel-based) before and after BMI conditions during eyes-opened and eyes-closed baseline trials were displayed by a good performance in terms of accuracy (~77%), sensitivity (76-80%), specificity (73-77%), and area-under-the-curve (AUC) (0.66-0.8) obtained by k-nearest neighbor (KNN) and support vector machine (SVM) algorithms. Mindfulness can thus improve the self-regulation of the emotional state of neurotic students based on the psychometric and electrophysiological analyses conducted in this study.

A Hybrid EMD-Wavelet EEG Feature Extraction Method for the Classification of Students' Interest in the Mathematics Classroom.

Situational interest (SI) is one of the promising states that can improve student's learning and increase the acquired knowledge. Electroencephalogram- (EEG-) based detection of SI could assist in understanding SI neuroscientific causes that, as a result, could explain the SI role in student's learning. In this study, 26 participants were selected based on questionnaires to participate in the mathematics classroom experiment. SI and personal interest (PI) questionnaires along with knowledge tests were undertaken to measure student's interest and knowledge levels. A hybrid method combining empirical mode decomposition (EMD) and wavelet transform was developed and employed for feature extraction. The proposed method showed significant difference using the multivariate analysis of variance (MANOVA) test and consistently outperformed other methods in the classification performance using weighted k-nearest neighbours (wkNN). The high classification accuracy of 85.7% with the sensitivity of 81.8% and specificity of 90% revealed that brain oscillation patterns of high SI students are somewhat different than students with low or no SI. In addition, the result suggests that the delta rhythm could have a significant effect on cognitive processing.

Machine Learning to Differentiate Between Positive and Negative Emotions Using Pupil Diameter.

Pupil diameter (PD) has been suggested as a reliable parameter for identifying an individual's emotional state. In this paper, we introduce a learning machine technique to detect and differentiate between positive and negative emotions. We presented 30 participants with positive and negative sound stimuli and recorded pupillary responses. The results showed a significant increase in pupil dilation during the processing of negative and positive sound stimuli with greater increase for negative stimuli. We also found a more sustained dilation for negative compared to positive stimuli at the end of the trial, which was utilized to differentiate between positive and negative emotions using a machine learning approach which gave an accuracy of 96.5% with sensitivity of 97.93% and specificity of 98%. The obtained results were validated using another dataset designed for a different study and which was recorded while 30 participants processed word pairs with positive and negative emotions.