The Role of Emotion Regulation Strategies for Sexual Function and Mental Health: A Cluster Analytical Approach.

We investigated distinct profiles in emotion regulation strategies (reappraisal and suppression) and their associations with sexual function and mental health. The online survey sample consisted of 5436 adult participants. The gender stratified cluster analysis resulted in a four-cluster solution for both men and women. Better sexual function and mental health scores were found for participants with high cognitive reappraisal and low expressive suppression. High expressive suppression was associated with higher anxiety and depression and worse sexual function. Sexological care should take into account the assessment of emotion regulation abilities and emotion regulation training interventions to support reappraisal strategies.

Gaming passion contributes to the definition and identification of problematic gaming.

Even if for most people playing video games is a healthy leisure activity, a minority of vulnerable users present an excessive use associated to negative consequences (e.g., psychosocial maladjustment, sleep interference) and functional impairment. The current study first aims to identify psychological factors that contribute to discriminate highly involved (but healthy) gamers from problematic gamers. For that purpose, we used a cluster analysis approach to identify different groups of gamers based on their profiles of passion towards gaming (using the Dualistic Model of Passion). Another objective of the present study is to explore, using supervised machine-learning, how gaming disorder symptoms, assessed within the substance use disorder framework (e.g., tolerance, withdrawal), might be linked to harmonious and/or an obsessive passion for gaming. Three distinct clusters of gamers were identified based on their passion profiles, including risky gamers, engaged gamers, and casual gamers. Supervised machine-learning algorithms identified that specific gaming disorder symptoms (salience, mood modification, tolerance, low level of conflict) were predominantly related to harmonious passion, whereas others (withdrawal, high level of conflict, relapse) were more directly related to obsessive passion. Our results support the relevance of person-centered approaches to the treatment of problematic gaming.

Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning.

BACKGROUND: Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multidimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome," which is associated with health anxiety, problematic internet use, and obsessive-compulsive symptoms. Cyberchondria is not included in the International Classification of Diseases 11th Revision or the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and its defining features, etiological mechanisms, and assessment continue to be debated. OBJECTIVE: This study aims to investigate changes in the severity of cyberchondria during the COVID-19 pandemic and identify the predictors of cyberchondria at this time. METHODS: Data collection started on May 4, 2020, and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the study took place, French-speaking countries in Europe (France, Switzerland, Belgium, and Luxembourg) all implemented lockdown or semilockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level, and country of residence) and information about socioeconomic circumstances during the first lockdown (eg, economic situation, housing, and employment status) and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18-77 (mean 33.29, SD 12.88) years, with females constituting the majority (416/725, 57.4%). RESULTS: The results showed that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and compulsion increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). In addition, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic. CONCLUSIONS: These findings provide evidence of the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. In addition, they are consistent with a theoretical model of cyberchondria during the COVID-19 pandemic proposed in 2020. These findings have implications for the conceptualization and future assessment of cyberchondria.

The psychological syndrome associated with Long-COVID: A study protocol.

Introduction: Chronic post-viral syndromes, including Long-COVID, are characterized by a range of persistent symptoms that occur following a viral infection. Psychological symptoms are prevalent in Long-COVID patients and can have a significant impact on their quality of life. However, the specific patterns of psychological symptoms, their associations with physical symptoms, and the factors predicting their severity remain poorly understood. Aims: This study aims to explore and systematically assess psychological symptoms in Long-COVID, to identify syndrome clusters based on these symptoms, to examine their relationship with physical symptoms, and to investigate the influence of pandemic-related variables. Methods: Descriptive, cross-sectional study with data collected through an online questionnaire across several EU countries, from February 2020 to December 2022. Participants were recruited using public relations, the social media and information campaigns directed at the public and health professionals using snowball sampling. Results: The findings will allow to phenotype Long-COVID related psychological symptom clusters based on self-reports. facilitating improved assessment and treatment approaches. Conclusions: The results will provide important knowledge for the public health management of the public healh management of Long COVID. The findings will contribute to a better understanding of the psychological symptoms associated with Long-COVID and the development of specific treatment guidelines for psychological burden associated with Long-COVID, thereby supporting management strategies to combat the after-effects of the COVID-19 pandemic, enhancing their overall well-being and quality of life.