Obsessive–compulsive disorder (OCD) is characterized by obsessions (intrusive, anxiety-provoking thoughts) and compulsions (repetitive behaviors that temporarily relieve this anxiety). Emerging evidence suggests OCD patients exhibit impaired cognitive flexibility (CF), which may be amenable to targeted training. This study evaluated the feasibility of integrating Virtual Reality-based Cognitive Training (VRCT) into an inpatient group cognitive-behavioral therapy (CBT) program for OCD, with exploratory cognitive outcomes as a secondary aim. Seventeen inpatients with OCD (ICD-10 F42; ICD-11 6B20) completed a VRCT program alongside standard CBT. We assessed feasibility via adherence rates, task completion, and subjective experience (presence, cybersickness; IPQ, SUS, SSQ). Pre- and post-training assessments included neuropsychological tests of cognitive flexibility (Trail Making Test, Stroop Task, Verbal Fluency, Go/No-Go, Berg’s Card Sorting Test), self-report measure of cognitive flexibility (CFI), and an assessment of OCD symptom severity (Yale–Brown Obsessive Compulsive Scale, Y-BOCS). VRCT integration into the inpatient CBT program was feasible and well-tolerated, with high adherence rates, minimal cybersickness, and positive subjective reception. In the exploratory effectivity analysis, nominal improvements were observed in processing speed (TMT A) and interference control (Stroop Task), but neither remained significant after correction for multiple comparisons. Self-reported cognitive flexibility (CFI) showed no significant change.CFI scores correlated poorly with neuropsychological measures overall, with only isolated subscale-level associations. Baseline Stroop interference, semantic fluency, and Go/No-Go accuracy were moderately associated with early VR training performance, though these associations weakened over subsequent sessions. A consistent within-session pattern emerged: performance dipped at session onset before recovering. VRCT can be feasibly and acceptably integrated into inpatient CBT for OCD. Exploratory effectivity analysis yielded no significant results.
This study explored relationships between attitudes toward artificial intelligence (AI), self-reported AI literacy, self-regulated learning (SRL), transactional distance (TD), and metacognitive awareness (MA) in large language model (LLM)-supported hybrid programming education. Participants were 239 undergraduates studying at one department of a Turkish state university; 188 (78.7
This research examines how perceptions of Responsible AI relate to the intention to adopt AI chatbots for personal healthcare management, extending the Technology Acceptance Model (TAM) through knowledge management factors and consumer innovativeness. The research used a cross-sectional, scenario-based online survey in Vietnam and China, with respondents recruited by non-probability sampling and asked about pre-adoption intention towards a hypothetical healthcare chatbot. Structural equation modeling was conducted in SmartPLS 4.0 on data from 476 respondents (295 Vietnam, 181 China), the majority of whom were young adults, including a high proportion of students. Knowledge acquisition, perceived ease of use, and consumer innovativeness were significantly associated with adoption intention, whereas knowledge sharing and knowledge application were not significant in either country. Specific indirect effects were not evaluated or interpreted as evidence of mediation. A two-sided permutation MGA with 5,000 permutations identified two nominal differences among fourteen between-country comparisons: Responsible AI was more strongly associated with perceived ease of use (Δβ = 0.160, p = 0.044) and knowledge sharing (Δβ = 0.170, p = 0.006) in the Vietnamese sample. These unadjusted differences are interpreted as exploratory findings in the present samples. The between-group difference for perceived usefulness → intention to adopt was not statistically significant, despite the different within-group significance results. These findings offer theoretical contributions to the understanding of intention to adopt AI chatbots in healthcare contexts, highlighting the relevance of Responsible AI and knowledge management alongside established technology-acceptance beliefs.
The proposed structural model in the study investigates the antecedents and consequences of problematic Instagram use among undergraduate students. Thereby, this study aims to understand this phenomenon by proposing a structural model that examines the factors associated with problematic Instagram use. The model includes Instagram usage motivations, technology usage behaviors, age, and gender as antecedent variables. Additionally, the study seeks to explore the relationship of problematic Instagram use with psychological well-being and online vigilance. A data collection instrument was applied to 311 university students. Most notably, problematic Instagram use was not significantly associated with psychological well-being, despite its strong and significant association with online vigilance. Age showed a significant negative association with problematic Instagram use, whereas gender was not significantly associated with it. Social media application usage frequency was significantly associated with problematic Instagram use, while daily time spent on Instagram, ICTs, and social media was not. Additionally, Instagram use motivations, including popularity and prestige, enjoyment, following others, and escape from problems, were significantly associated with problematic Instagram use.
The exponential increase in digital media platforms and food influencer content has played an important role in shaping young people eating behavior. However, there is limited research about the underlying psychological and behavioral processes in this context. This research explores the impact of digital media consumption and food influencer content on young adults eating behaviors through mediation factors. The data for this research were collected from 401 participants (aged 18–29 years; 53.4
