BMC Psychology最新文献

Background: Adverse childhood experiences (ACE) are highly prevalent, with long-lasting effects on physical and mental health that vary among individuals across their lifespan. Notably, ACE is associated with depression and anxiety disorders, which are major public health concerns. However, the factors involved in the detrimental effects of ACE on women in the perinatal period, a time of heightened vulnerability to physical and mental health outcomes for both mothers and their offspring, require further investigation.

Methods: To investigate the role of perceived stress in the association between ACE and maternal depressive and anxiety symptoms across the perinatal period, we included 138 pregnant women recruited for a prospective longitudinal study that examined the effects of increased maternal prenatal stress on pregnancy during the COVID-19 pandemic. Participants self-reported adverse childhood experiences and serial measures of perceived stress, anxiety, and depressive symptoms. Linear mixed-effects (LME) models were employed to assess the association between ACE, perceived stress, anxiety, and depressive symptoms and to determine an interaction effect between ACE and perceived stress on state anxiety, trait anxiety, and depressive symptoms.

Results: On average, participants were 34 years of age and at 26 weeks of gestation (gestational age; GA). We found a significant interaction between ACE and perceived stress on trait anxiety (β = 0.06, SE = 0.01, p < .001) and depressive symptoms (β = 0.07, SE = 0.01, p < .001) in the perinatal period. Perceived stress moderated the association between ACE and trait anxiety symptoms in the third trimester (β = 0.07, SE = 0.02, p < .001). The association between ACE and depressive symptoms was moderated by perceived stress in the first trimester (β = 0.23, SE = 0.09, p < .05), second trimester (β = 0.09, SE = 0.03, p < .05), and third trimester (β = 0.04, SE = 0.02, p < .05).

Conclusion: These findings underscore the importance of assessing ACE as a risk factor when evaluating maternal mental health to identify individuals susceptible to depressive and anxiety disorders in the perinatal period. Moreover, multiple mental health screenings in the perinatal period reduce the risk of missed opportunities for intervention and preventative actions.

As global carbon emissions continue to rise and climate targets grow increasingly urgent, promoting voluntary low-carbon behaviors has become a critical component of climate policy. Among various behavior-oriented instruments, Personal Carbon Trading (PCT) offers a promising yet underutilized strategy for incentivizing individual emissions reduction, though public participation remains limited due to psychological and communicative barriers. This study investigates how message framing (gain vs. loss) influences individuals' willingness to participate in PCT, and whether this effect is mediated by climate change anxiety (CCA) and moderated by two psychological traits: felt responsibility (FR) and green self-efficacy (GSE). Drawing upon Prospect Theory, Norm Activation Theory, and Self-Efficacy Theory, we conducted an online experiment with randomized stimuli and analyzed the responses of 788 participants. The results show that loss-framed messages significantly enhance voluntary engagement intention (VEI) towards PCT compared to gain-framed messages. Moreover, CCA partially mediates the relationship between message framing and VEI, confirming the emotional pathway through which message framing operates. Importantly, both FR and GSE moderate these effects, such that loss framing is more effective among individuals with higher levels of responsibility or efficacy. These findings advance theoretical understanding of the framing-emotion-intention pathway while offering evidence-based strategies for designing personalized environmental communications that promote voluntary participation in PCT.

Background: With the rapid advancements in emotion processing algorithm within artificial intelligence, it is essential to explore the evolving relationships between humans and robots. This exploration can prepare society for the future widespread application of social robots and address the new social dynamics involving AI agents. Human-robot empathy emerges as a crucial avenue for exploring the emotional connections between humans and robots. The purpose of this study was to investigate the impact of users' perceptions of robots' minds on human-robot empathy.

Methods: This study manipulated perceived theory of mind (ToM) in robots through human-robot interaction scenarios, utilizing four experiments to assess the effects of perceived ToM in robots-categorized as cognitive ToM (cToM) in Experiments 1a and 2a, and affective ToM (aToM) in Experiments 1b and 2b-on human-robot empathy, including pain empathy and empathic concern. Experiments 1a and 1b examined the influence of perceived ToM in robots on human-robot empathy within classic ToM scenarios, while Experiments 2a and 2b were conducted within real service contexts, further investigating the mediating role of users' mind perceptions of robots.

Findings: First, perceiving a robot with high aToM significantly enhanced users' pain empathy and empathic concern towards robots, with the experience dimension of mind perception potentially serving as an indirect-only mediator in this relationship. Second, in real home service scenarios in Experiment 2, while the total effect of high cToM on empathic concern was not statistically significant after multiple comparisons correction, mediation analysis revealed a significant negative direct effect alongside a positive indirect effect through agency. This pattern suggests that perceiving high cToM may simultaneously inhibit empathic concern directly while potentially fostering it through enhanced agency perception.

Conclusion: The findings demonstrate that perceived aToM in robots consistently enhances human-robot emotional interactions, while revealing a more complex dual-pathway mechanism for cToM effects. These results provide valuable insights into how distinct dimensions of mind perception shape human-robot relationships.

Background: With the integration of Generative AI (GenAI) technology in language educational settings, the impact of GenAI tools has garnered increasing attention. Yet extant research has predominantly examined GenAI's cognitive impacts on learners' linguistic abilities, leaving the empirical landscape of learners' emotional experiences largely uncharted. This study, drawing on Positive Psychology, investigated the impact of GenAI tools on Chinese EFL students' foreign language enjoyment (FLE), as well as the roles of three factors-gender, English proficiency, and GenAI usage duration-on students' FLE.

Methods: A mixed-methods design was applied. Ninety-eight Chinese EFL university students completed the survey and 10 participated in the interview after the 12-week intervention. SPSS 24.0 was employed to perform descriptive statistics, independent-samples t-tests, and one-way ANOVA in sequence on the survey data, and the interview data were manually coded using thematic analysis.

Results: Findings indicated that participants reported medium level of FLE, and the FLE-teacher dimension was slightly higher than FLE-private and FLE-atmosphere. Among three influential factors examined, although neither gender nor English proficiency significantly influenced participants' FLE statistically, male participants exhibited a marginally higher level of FLE compared to their female counterparts. Noticeably, participants with relatively lower English proficiency levels tended to experience a slightly higher degree of FLE than those with higher English proficiency levels. However, this subtle intergroup difference did not reach statistical significance. Moreover, the duration of GenAI tool usage is shown to statistically impact the participants' FLE level, with six-month duration serving as a threshold at which participants' FLE undergoes a positive qualitative shift.

Conclusions: Participants generally perceived that GenAI tools fostered an enjoyable learning environment, but they also expressed concerns about potential over-reliance on GenAI technology and the possible erosion of their critical thinking abilities. The study recommends encouraging students to use GenAI tools for more than six months, enhancing female students' digital competence through learning tasks requiring cooperation between boys and girls, and designing more process-oriented tasks that require reflective thinking and sustained student engagement to strengthen critical thinking skills.

Background: Loneliness is a critical mental health issue among university students, yet the mechanisms linking physical activity (PA) to loneliness remain underexplored. This study examines whether core self-evaluation (CSE) mediates the PA-loneliness relationship, and whether only-child status moderates this pathway.

Participants: Using random cluster sampling from a Chinese university, 558 valid questionnaires were collected.

Methods: This cross-sectional study collected self-reported data on PA levels, loneliness, and CSE among Chinese university students. The data underwent normality tests. Independent samples t-tests were used to examine differences in these variables by sex, grade, and only-child status. Pearson's correlation analyses were employed to assess the relationships among loneliness, PA, and CSE. We used the PROCESS macro for hierarchical regression analysis to test the mediation effect of CSE and the moderation effect of only-child status.

Results: PA was negatively correlated with loneliness (r = -.154, P < .001) and positively with CSE (r = .263, P < .001). CSE fully mediated the PA-loneliness relationship (indirect effect = -0.001, 95% CI [-0.0007, -0.0003]). Only-child status moderated the direct path, the interaction term between PA and only-child status had a significant effect on loneliness (B= -0.0006, P = .016), The model explained 35.5% of the variance in loneliness.

Conclusions: CSE serves as a complete mediator between PA and loneliness, and only-child status moderates this association. These findings highlight the importance of tailoring PA-based mental health interventions to students' family backgrounds.

Background: Mental health disorders affect 970 million people globally, yet treatment access remains limited by stigma and resource constraints. User profiling technology leverages AI and multi-source data to enable personalized mental healthcare.

Objective: This scoping review synthesizes evidence on user profiling applications in patient mental health management to inform evidence-based practice.

Methods: Following established scoping review methodology, we systematically searched seven databases (Wanfang, CNKI, PubMed, Embase, Web of Science, Scopus, Cochrane Library) from 1 January 1964 to 28 May 2025. Two researchers independently screened records against eligibility criteria and extracted data.

Results: Eighteen studies were included. User profiling construction encompassed three core phases: multidimensional data acquisition, feature extraction, and visualization. Applications spanned diverse populations including adolescents, young adults, and psychiatric patients, facilitating personalized intervention planning, resource allocation, and crisis management. Implementation demonstrated enhanced service accessibility, intervention precision, user experience optimization, and remote intervention efficacy.

Conclusion: User profiling technology delivers personalised mental health services through the integration of multidimensional data. This approach demonstrates significant advantages in enhancing intervention effectiveness, reducing healthcare costs, and improving user satisfaction, positioning it as a promising technology within the field of digital psychological interventions. However, its practical application still faces numerous challenges, including the long-term validation of current intervention outcomes and limitations in technological universality.