Cyberpsychology, behavior and social networking最新文献

Older adults, particularly impacted by the pandemic, are anticipated to face considerable challenges to their cognitive and psychological health. Various cognitive training methods have been proposed to improve their cognitive decline through regular practice. Immersive virtual reality (IVR) offers a promising avenue to bolster cognitive and mental health care accessibility for older adults and has shown potential in augmenting traditional cognitive training methods. This systematic review aims to assess the efficacy of IVR-based cognitive training on improving cognitive impairments and depressive symptoms among older adults, irrespective of their level of cognitive decline. Systematic searches were conducted across MEDLINE-PubMed, EMBASE, and Web of Science databases from inception to December 2022. Only peer-reviewed articles reporting results from randomized controlled trials and pilot studies, focusing on the effects of VR-based cognitive training on adults aged 55 or older, were deemed eligible. Using the Cochrane Handbook for Systematic Reviews of Interventions, the risk of bias was appraised for each selected study. Out of 3,835 studies identified, 9 met the inclusion criteria. The qualitative analyses concluded that IVR cognitive training interventions enhanced certain cognitive functions and well-being among older adults, notably those with mild cognitive impairments. These interventions were particularly effective in boosting memory, visuospatial abilities, and measures related to depressive symptoms and overall well-being. However, enhancements were less pronounced for executive functions, attention, and global cognition. Based on these findings, we propose a set of tailored, inclusive, and evidence-based recommendations to adapt emerging technologies to better accommodate the needs of older adults.

Prior research has investigated the link between social media use (SMU) and negative well-being. However, the connection with positive well-being has not been extensively studied, leading to a situation where there are inconsistent and inconclusive findings. This study fills this gap by examining the correlation between excessive and problematic SMU and subjective as well as psychological well-being (PWB). We conducted a systematic search across databases such as PubMed, Scopus, and Web of Science, and gray literature sources such as Research Gate and ProQuest, yielding 51 relevant studies for meta-analysis, encompassing a sample size of 680,506 individuals. Employing the Newcastle-Ottawa Scale, we assessed study quality, whereas statistical analysis was executed using R Studio. Excessive SMU showed no significant association with subjective (ES = 0.003, 95% confidence interval [95% CI]: -0.08, 0.09; p = 0.94, I² = 95.8%, k =16) and PWB (ES = 0.16, 95% CI: -0.15, 0.45; p = 0.26, I² = 98%, k = 7). Conversely, problematic SMU showed a negative correlation with subjective (ES = -0.14, 95% CI: -0.20, -0.09; p = 0.00, I² = 93.3%, k = 25) and PWB (ES = -0.19, 95% CI: -0.31, -0.06; p = 0.01, I² = 95%, k = 5), with two outliers removed. No publication bias was detected. Subgroup analysis highlighted effects of "sampling method" (p < 0.05), "study quality" (p < 0.05), "developmental status" (p < 0.05), "forms of social media" (p < 0.05), and "type of population" (p < 0.01) on the estimated pooled effect sizes. Although univariate meta-regression showed the effects of "% of Internet users" (p < 0.05) and "male%" (p < 0.05), and multivariate meta-regression showed the combined effect of moderators only on the relationship between problematic SMU and subjective well-being.

Aim: Identify and systematically review cohort studies examining the association between cyberbullying (CB) and suicidal ideation, attempted suicide, self-harm, and nonsuicidal self-injury (NSSI). Methods: Systematic literature review following PRISMA guidelines. Searches for relevant literature were conducted in Scopus, Web of Science, PubMed, PsyINFO, CINAHL, Embase, and Cochrane up until July 2023. Cohort studies were included that provided information regarding the association between CB and different dimensions pertaining to self-injurious thoughts and behaviors (SITB) separately. Gathered information included data on study characteristics, sample characteristics, prevalence, and associations between CB and variables pertaining to SITB. Findings were synthesized, and grouping studies as a function of the outcome variable was analyzed. Results: A total of 19 studies were included. Suicidal ideation was analyzed in 11 studies, with the majority (n = 8) reporting statistically significant positive associations (aOR = 1.88 [1.08, 3.29], β = 0.08 to 0.47). Only a single study conducted independent analysis of attempted suicide, finding a statistically significant association (aOR 1.88 [1.08, 3.29]). Outcomes regarding the self-harm variable were equivocal, with only two out of five studies finding a statistically significant association and one of these, which conducted a gender differentiated analysis, only finding a significant association in females. Studies that considered the NSSI dimension were also inconclusive, with only a limited number of studies (n = 5) producing contradictory outcomes. Conclusion: There is a need to continue exploring the relationship between CB and different dimensions within the spectrum of SITB through longitudinal studies. It is recommended that analyses adopt a new perspective in which short-term follow-up is prioritized or individualized follow-up periods are considered, given the rapidly changing nature of suicidal tendencies. It would be interesting to analyze the frequency, persistence, or severity of CB events and broaden research to include all age ranges. Special attention should also be given to potential gender differences and possible mediators or moderators.

While artificial Intelligence (AI) has made significant advancements, the seeming absence of its emotional ability has hindered effective communication with humans. This study explores how ChatGPT (ChatGPT-3.5 Mar 23, 2023 Version) represents affective responses to emotional narratives and compare these responses to human responses. Thirty-four participants read affect-eliciting short stories and rated their emotional responses and 10 recorded ChatGPT sessions generated responses to the stories. Classification analyses revealed the successful identification of affective categories of stories, valence, and arousal within and across sessions for ChatGPT. Classification analyses revealed the successful identification of affective categories of stories, valence, and arousal within and across sessions for ChatGPT. Classification accuracies predicting affective categories of stories, valence, and arousal of humans based on the affective ratings of ChatGPT and vice versa were not significant, indicating differences in the way the affective states were represented., indicating differences in the way the affective states were represented. These findings suggested that ChatGPT can distinguish emotional states and generate affective responses consistently, but there are differences in how the affective states are represented between ChatGPT and humans. Understanding these mechanisms is crucial for improving emotional interactions with AI.