Advertisement BMC Psychologyvolume 13, Article number: 7 (2025) Cite this article Metrics details In recent years, the adoption of artificial intelligence (AI) has become increasingly relevant in various sectors, including higher education. This study investigates the psychosocial factors influencing AI adoption among Peruvian university students and uses an extended UTAUT2 model to examine various constructs that may impact AI acceptance and use. This study employed a quantitative approach with a survey-based design. A total of 482 students from public and private universities in Peru participated in the research. The study utilized partial least squares structural equation modeling (PLS-SEM) to analyze the data and test the hypothesized relationships between the constructs. The findings revealed that three out of the six hypothesized factors significantly influenced AI adoption among Peruvian university students. Performance expectancy (β = 0.274), social influence (β = 0.355), and AI learning self-efficacy (β = 0.431) were found to have significant positive effects on AI adoption. In contrast to expectations, ethical awareness, perceived playfulness, AI readiness and AI anxiety did not have significant impacts on AI appropriation in this context. This study highlights the importance of practical benefits, the social context, and self-confidence in the adoption of AI within Peruvian higher education. These findings contribute to the understanding of AI adoption in diverse educational settings and provide a framework for developing effective AI implementation strategies in higher education institutions. The results can guide universities and policymakers in creating targeted approaches to enhance AI adoption and integration in academic environments, focusing on demonstrating the practical value of AI, leveraging social networks, and building students’ confidence in their ability to learn and use AI technologies. Peer Review reports Artificial intelligence (AI) is significantly transforming higher education on a global scale, revolutionizing traditional educational processes and offering new learning opportunities. AI applications in this field range from task automation to personalized learning experiences, which impact teaching, learning, and organizational management [1, 2]. Key AI applications in higher education include adaptive learning systems, personalized learning experiences, and intelligent virtual environments [1, 3]. These advances are shifting traditional educational paradigms, moving the focus from conventional classrooms to AI-enhanced learning environments that can improve student outcomes and optimize administrative tasks [4, 5]. However, the integration of AI into higher education is not without challenges. Ethical concerns arise regarding data privacy, algorithmic bias, and the possibility that AI could replace educators [1, 6, 7]. Furthermore, issues surrounding global inequality and the unequal distribution of AI resources have also been raised [2, 4, 8]. Despite these challenges, AI’s potential to democratize and personalize learning, address persistent educational problems, and reduce overall education costs is significant [8, 9]. Nevertheless, it is crucial to develop comprehensive ethical guidelines to ensure that AI implementation in higher education aligns with the fundamental values of academic integrity and social responsibility. The adoption of AI in higher education is influenced by a complex interplay of psychosocial factors, as revealed by recent literature. Studies have identified perceptions of utility and effectiveness as key determinants [9, 10], along with ethical and privacy considerations, which may act as barriers to their implementation [10, 11]. The impact of AI on students’ psychological well-being presents a duality, offering personalized support but also generating potential stress and anxiety [12]. Learning outcomes and cost effectiveness emerge as influential factors in student acceptance [11], whereas the adaptability and personalization of AI tools are considered pivotal drivers [13]. Cognitive trust in educational AI, shaped by transparency, reliability, and ethical considerations, plays a significant role in its adoption [14]. Contextual factors such as performance expectations, effort expectations, social influence, and facilitating conditions also impact the intention to use and user behavior [15, 16]. Students’ attitudes and perceptions toward these technologies significantly affect their acceptance and use [15]. Importantly, these factors interact in complex ways, and their relevance may vary depending on the educational and cultural context [17], highlighting the need for more extensive research to fully understand the dynamics of AI adoption in diverse university settings. Nevertheless, despite various studies on the psychosocial factors involved in university students’ adoption of artificial intelligence (AI), significant knowledge gaps remain. A study with Peruvian university professors revealed a considerable gap in knowledge about AI and its educational application [18], suggesting that this lack of knowledge may extend to students, potentially limiting their ability to use AI tools effectively. While research in other contexts has identified factors such as epistemic capability, enabling environments, and psychological attitudes as critical for AI adoption [19], a comprehensive understanding of the interaction and variability of these factors in different cultural and educational contexts is lacking. Moreover, although challenges such as a lack of technical knowledge, privacy concerns, and unequal access to AI resources have been recognized, further exploration is needed to understand their influence on long-term adoption. The educational implications and impact on students’ psychological well-being, which range from personalized support to the generation of stress [12], represent another area where current knowledge is limited. Thus, this gap underscores the need for more exhaustive and longitudinal studies addressing the complexity of psychosocial factors in the adoption of AI by university students in diverse global contexts. To address these knowledge gaps, this study employs the UTAUT2 model as its theoretical foundation, extending it to incorporate psychological and social factors specific to AI adoption in higher education [15, 16]. The UTAUT2 model was selected because it integrates hedonic motivation and social influence, making it particularly suitable for understanding the complex psychological dynamics of AI adoption among students [10, 11]. The model’s flexibility enables the integration of context-specific constructs, such as SL and EA, which are crucial in educational settings [12, 13]. The comprehensive framework of UTAUT2 facilitates the examination of both individual psychological factors and social influences, providing a more nuanced understanding of AI adoption behavior [14, 15]. Research has demonstrated the model’s strong explanatory power across different cultural contexts and technologies [16, 17], making it well suited for studying AI adoption in the Peruvian higher education context. The extension of UTAUT2 in this study to include psychological constructs such as AI anxiety and EA addresses the gaps in understanding how emotional and ethical considerations influence AI adoption [10, 12], whereas the inclusion of SI factors acknowledges the collective nature of technology adoption in educational settings [15, 16]. This adapted theoretical framework enables a comprehensive examination of how psychological readiness, social dynamics, and ethical considerations interact to influence AI adoption among university students [17, 18]. In this study, the following constructs are considered: performance expectancy (PE), social influence (SI), perceived playfulness (PP), ethical awareness (EA), AI learning self-efficacy (SL), AI readiness and AI anxiety (AAN), and AI appropriation (AIO). The aim of the present study is to analyze the factors influencing the adoption of AI by university students in the Peruvian context. The general research question guiding this study is as follows: What psychosocial factors influence the appropriation of artificial intelligence among Peruvian university students? The specific research questions are as follows: (1) How does PE influence AIO among college students? (2) How does SI influence AIO in college students? (3) How does PP influence AIO in university students? (4) How does EA influence AIO among university students? (5) How does self-efficacy in learning AI influence AIO among college students? (6) How does AAN influence AIO in college students? From a theoretical perspective, this study is justified by the existence of significant knowledge gaps in understanding how university students adopt AI in diverse educational contexts. From a theoretical perspective, this study is justified by the existence of significant knowledge gaps in understanding how university students adopt AI in diverse educational contexts. While previous research has identified factors such as epistemic capability, enabling environments, and psychological attitudes as critical for AI adoption, a comprehensive understanding of the interaction and variability of these factors in different cultural and educational contexts, particularly in the Peruvian context, is lacking. This research contributes to filling that theoretical gap by providing a completer and more contextualized conceptual framework. In practical terms, this study is justified by its potential to inform and improve AI implementation strategies in Peruvian higher education. Identifying the psychosocial factors that influence students’ adoption of AI would allow educational institutions to develop more effective approaches to integrate these technologies, addressing challenges such as the lack of technical knowledge, privacy concerns, and unequal access to AI resources. Additionally, the findings could guide the design of more effective training and support programs for students and faculty. From a social perspective, this research is justified by its potential to contribute to the democratization and personalization of learning in Peruvian higher education. Understanding the psychosocial factors involved in AI adoption could help reduce inequalities in access to and use of these technologies, promoting a more equitable education that meets individual student needs. Moreover, by addressing the educational implications and impact on students’ psychological well-being, this study could contribute to a more ethical and responsible use of AI in higher education, aligned with the values of academic integrity and social responsibility. This study makes several significant contributions to the field of AI adoption in higher education. First, it provides a theoretical contribution by extending the UTAUT2 model through the incorporation of new variables specifically tailored to AI adoption in Peruvian higher education, such as SL and EA. This extension enhances our understanding of technology acceptance models in emerging educational contexts. Second, the study offers a methodological contribution by developing and validating a comprehensive instrument to measure AIO among university students, which can be adapted and used in similar educational settings. Third, it makes a practical contribution by providing empirical evidence that can guide policymakers and educational administrators in developing effective strategies for AI implementation in higher education institutions, particularly in the context of developing countries. Finally, this research contributes to the growing body of knowledge about psychosocial factors influencing AI adoption by offering insights from a Latin American perspective, thus adding a unique cultural dimension to the literature predominantly focused on Western and Asian contexts. This multicultural perspective enriches our understanding of how cultural and contextual factors may moderate the adoption of advanced technologies in educational settings. Personalized learning has emerged as one of the most promising applications of AI in higher education. Research indicates that AI facilitates the creation of customized learning pathways, significantly enhancing student engagement and academic outcomes [20, 21] [22]. supports this view, noting that adapting content to individual student needs represents a paradigm shift in pedagogy. However [23], cautions against overreliance on technology, emphasizing that human interaction remains essential to the educational process. In the realm of administration, AI involves streamlining processes within higher education institutions [24]. reported that automating administrative tasks through AI improves operational efficiency, enabling educators to devote more time to teaching and research [25]. echoes this sentiment, highlighting the transformative potential of AI for educational management. However [9], warns that unequal access to these technologies could exacerbate existing disparities between resource-rich and resource-poor institutions. AI has also had a notable effect on global access and equity in higher education [19]. demonstrated how AI helps to overcome geographical barriers, democratizing access to high-quality education [26]. further emphasized AI’s potential to foster educational inclusion. Nevertheless [27], highlights the risk of new forms of digital exclusion if disparities in access to technology are not adequately addressed. AI-driven teaching innovations are reshaping classroom dynamics [28]. illustrates how adaptive learning systems and smart classrooms create more interactive learning environments [29]. supports this view, stressing AI’s capacity to develop critical 21st-century skills. However [30], argues that AI integration must be balanced with traditional methods to preserve the valuable aspects of human interaction in education. The implementation of AI in higher education presents significant challenges, particularly with respect to ethics and academic integrity [28]. underscores the need for accuracy, fairness, and transparency in AI algorithms. Similarly [31], stressed the importance of fostering AI literacy among students and educators. On the other hand [32], argues that the benefits of AI in detecting plagiarism and promoting academic integrity outweigh the potential risks. To navigate these challenges and maximize benefits, scholars recommend a balanced approach [20]. advocated updating curricula and providing AI literacy training [33]. also emphasized the importance of preparing educators and students for an AI-driven future. However [27], advised caution, calling for more research on the long-term effects of AI in higher education. In conclusion, AI’s integration into higher education presents a unique combination of opportunities and challenges. While researchers such as [20] and [24] underscore the transformative potential of AI, others, including [9] and [27], highlight the associated risks. The prevailing consensus suggests that successful AI integration will depend on a balanced strategy that maximizes benefits while mitigating risks, ensuring that AI serves to enhance the quality and accessibility of higher education globally. PE refers to the degree to which individuals believe that using AI technology will enhance their academic performance and productivity [34]. In educational contexts, PE has been identified as a fundamental driver of technology acceptance, as students evaluate the potential benefits of AI tools in improving their learning outcomes and academic efficiency [35]. SI represents the extent to which students perceive that important others, including peers, professors, and educational institutions, believe that they should use AI technologies [35, 36]. The social environment within educational institutions plays a crucial role in shaping students’ attitudes and behaviors toward AI adoption [10]. PP, also known as hedonic motivation, reflects the fun, enjoyment, or pleasure derived from using AI technology [36]. In educational settings, the playful aspects of technology interaction can significantly influence students’ willingness to engage with and adopt AI tools for learning purposes [15]. EA encompasses students’ understanding and consciousness of the ethical implications and responsibilities associated with AI use in academic contexts [10]. This construct has gained importance as educational institutions grapple with questions of academic integrity and responsible AI use [15, 16]. SL refers to students’ confidence in their ability to learn and effectively use AI technologies [37]. This construct is particularly relevant in educational settings, as it influences students’ persistence and effort in mastering new AI tools [34, 35]. AANs represent two interrelated aspects: preparedness to adopt AI technologies and concerns or apprehensions about their use [37]. Readiness reflects students’ psychological and technical preparedness to integrate AI into their learning practices, whereas anxiety captures the emotional and psychological barriers that might hinder adoption [10, 15]. AIO represents the extent to which students incorporate and integrate AI technologies into their educational practices [38]. This construct goes beyond mere acceptance to encompass how students actively adapt and utilize AI tools to support their learning objectives [34, 38]. These constructs interact dynamically within the educational environment, influencing how students adopt and integrate AI technologies into their academic practices [15, 16]. Understanding these interactions is crucial for developing effective strategies to promote successful AI integration in higher education [37, 38]. PE has been shown to significantly influence AIO among university students, as evidenced by several recent studies. Research has revealed that PE positively affects students’ willingness to accept AI-assisted learning environments [34], which is a clear indicator of AIO. Additionally, PE significantly influences students’ behavioral intentions to use generative AI products [39], another crucial aspect of AIO. This relationship is reinforced by findings that PE is an important predictor of the acceptance and use of AI tools in higher education [40], directly aligning with the concept of AIO. The strength of the relationship between PE and AIO has been confirmed across various contexts, including a study on the use of ChatGPT among university students, which demonstrated that PE significantly influences students’ behavioral intentions to use this AI tool [16]. From a broader perspective, research has extended the UTAUT2 model to include ethical factors, finding that PE has a positive influence on students’ behavioral intentions to use generative AI products [39]. These consistent findings across multiple studies and contexts [16, 34, 39, 40] provide robust support for the hypothesis that PE significantly influences AIO among university students, although it is important to note that the studies covered different AI applications and contexts. Therefore, we propose the following: PE significantly influences AIO in college students. A study involving South Indian university students revealed that SI has a significant direct positive effect on the intention to use AI-enabled job application processes [41]. This finding suggests that the social environment of college students can strongly influence their willingness to adopt and appropriate AI technologies, which is a key aspect of AIO. The impact of SI on AIO is further supported by research demonstrating how students adapt their behaviors in response to AI interactions. A mixed-methods study with young individuals revealed that participants adjusted their behaviors to complement different types of AI teammates [42]. This behavioral adaptation highlights the powerful role of SI in shaping how students interact with and appropriate AI technologies. Moreover, the influence of social factors on AIO is evident in the context of AI-based education. Research in the U.S. highlighted the crucial role of perceived social presence in AI instructors, showing that students with high expectations for instructor nonverbal immediate behaviors demonstrate more positive perceptions of AI-based education when they experience stronger social presence in their AI instructor [43]. This finding underscores the importance of social cues and expectations in students’ acceptance and appropriation of AI in educational contexts. The relationship between SI and AIO is also reflected in broader STEM integration. Longitudinal research with students from historically overrepresented groups in STEM has revealed that interactions with SI agents, such as faculty mentor support and research engagement, promote integration into the STEM community through the development of science identity and community values [44]. This suggests that SI can play a significant role in shaping students’ engagement with and appropriation of advanced technologies, including AI. Additionally, research on the factors influencing academicians’ intentions to continue using AI-based chatbots at Indian universities has highlighted the nuanced role of peer networks in shaping adoption [15]. This finding further supports the idea that SI significantly affects how college students appropriate and integrate AI technologies into their academic lives. While these studies [15, 30, 41, 42, 44] provide strong support for the hypothesis that SI significantly influences AIO among college students, they cover various applications and contexts of AI. However, the consistency of findings across multiple studies and contexts offers robust support for the proposed hypothesis. SI significantly influences AIO in college students. PP (PP) has emerged as a significant factor in AIO among university students. The integration of PP into the UTAUT model for AI-based learning platforms underscores its importance in understanding students’ behavioral intentions toward AI technologies [45]. This inclusion suggests that the playful aspects of interactions with AI can significantly influence how students appropriate and engage with AI tools in their learning environments. Research has shown that incorporating game elements to increase PP positively impacts student participation. Specifically, the use of badges has demonstrated a positive relationship with PP [46], indicating that enhancing the playful aspects of AI technologies could lead to greater appropriation and engagement among university students. Moreover, gender differences have been observed in the effects of playfulness on students’ attitudes toward technology use, with playfulness directly influencing female students’ attitudes [47]. This finding highlights the nuanced role of PP in AI appropriation, suggesting that its impact may vary across different demographic groups within the student population. The importance of PP in AIO is further reinforced by research on AI-based education. A study highlighted the crucial role of perceived social presence in AI instructors, indicating that students with high expectations of nonverbal immediacy behaviors from their instructors have more positive perceptions of AI-based education [43]. While this study does not directly measure PP, it suggests that the interactive and engaging aspects of AI technologies, which are closely related to PPs, can significantly influence students’ perceptions and, by extension, their appropriation of AI in educational contexts. On the basis of this evidence, although further direct research on the specific relationship between PP and AIO in university students is needed, the following hypothesis is proposed: PP significantly influences AIO in university students. EA has been shown to significantly influence AIO among university students, as evidenced by several recent studies. Research has revealed that university students’ EA significantly affects their behavioral intentions and actual use of generative AI products [39]. This finding suggests that understanding the ethical implications of AI plays a crucial role in how students adopt and appropriate these technologies. Furthermore, EA can positively influence students’ intentions to use generative AI products, although it may also heighten their perceptions of ethical risk [39]. This duality underscores the complexity of the relationship between EA and AI appropriation, indicating that greater awareness may both encourage and moderate AI use, depending on individual ethical evaluations. The importance of EA in AIO is reinforced by studies on AI literacy programs. One study showed that an AI literacy program successfully improved participants’ EA, emphasizing the importance of ethical considerations in AI education [48]. This finding suggests that AI ethics education can directly influence how students perceive and appropriate AI technologies. Exposure to ethical instruction and internship experiences has also been shown to influence communication students’ ethical perceptions, including awareness of AI-related ethical issues [49]. Moreover, an explicit-reflective online learning module significantly improved graduate science and engineering students’ knowledge of AI ethics and their ability to identify and articulate ethical issues in AI [50]. These results indicate that ethical education can have a direct effect on students’ understanding and, consequently, their appropriation of AI. Concerns about privacy, ethics, social factors, and academic resources significantly influence AI adoption among university students [10], underscoring the importance of EA in the AIO process. Furthermore, the ethical challenges associated with AI and the Internet of Things pose risks to privacy and data security, highlighting the need for education on ethical issues related to technology [51]. On the basis of this evidence, although further direct research on the specific relationship between EA and AIO in university students is needed, the following hypothesis is proposed: EA significantly influences AIO among university students. SL has been shown to significantly influence AIO among university students, as evidenced by several recent studies. Research indicates that self-efficacy in AI can positively influence students’ attitudes toward AI and their actual use of AI tools [37, 52]. This finding suggests that students’ confidence in their ability to learn and use AI plays a crucial role in how they adopt and appropriate these technologies. Additionally, AI self-efficacy has been observed to have an indirect positive effect on the learning effectiveness of AI-based technological applications [53]. This finding indicates that students with higher self-efficacy tend to make better use of AI tools in their learning processes, likely leading to greater appropriation of these technologies. The importance of self-efficacy in AIO is reinforced by studies demonstrating that self-efficacy in learning positively affects learning intentions in the AI context [52, 54]. This relationship suggests that students with greater confidence in their AI learning abilities are more likely to engage with these technologies, leading to greater appropriation. Factors such as attitudes toward AI learning, confidence in AI learning, and subjective norms have been shown to significantly influence students’ intentions to learn AI [55]. These findings underscore the complexity of the relationship between self-efficacy and AI appropriation, indicating that multiple interrelated factors contribute to the appropriation process. In the context of translation technologies, a study demonstrated a positive prediction of perceived ease of use and enjoyment from computer self-efficacy, which increased students’ attitudes and behavioral intentions to use translation technologies [38]. While this study focuses on translation technologies, it provides relevant insights into how self-efficacy may influence the adoption and appropriation of AI-based technologies in general. Importantly, the influence of self-efficacy may vary by gender and learning environment. One study revealed that the impact of campus learning environments on self-directed learning self-efficacy was more significant for men than for women [56], suggesting the need to consider contextual factors when examining the relationship between SL and AIO. On the basis of this evidence, although further direct research on the specific relationship between SL and AIO in university students is needed, the following hypothesis is proposed: SL significantly influences AIO in college students. AANs have been shown to significantly influence AIO among university students, as evidenced by several recent studies. Research indicates that AI readiness, confidence, and the perceived relevance of AI positively influence students’ willingness to learn about AI [57]. This finding suggests that the level of readiness and perception of AI importance play key roles in how students approach and potentially appropriate these technologies. However, the relationship between AI anxiety and appropriation is more complex. One study revealed that AI learning anxiety negatively affects learning motivation, whereas job replacement anxiety related to AI has a positive effect on extrinsic motivation [52]. This finding indicates that different types of AI-related anxiety can influence students’ willingness to engage with AI in distinct ways. The importance of AI readiness and anxiety in AIO is further reinforced by a study on medical students, which revealed an inverse relationship between AI readiness and anxiety [58]. This finding underscores the importance of increasing students’ preparedness for AI applications and reducing their anxieties as a means of facilitating AI appropriation. Additionally, AI anxiety has been reported to negatively predict the actual use of AI tools among L2 university students [37]. This result indicates the practical impact of anxiety on engagement with AI resources, suggesting that lower anxiety levels could lead to greater AI appropriation (Fig. 1). Importantly, gender differences exist in AI readiness and anxiety. Compared with female students, male students reported greater confidence, perceived relevance, and readiness for AI [57]. This suggests the need to consider demographic factors when examining the relationship between AAN and AIO. On the basis of this evidence, although further direct research on the specific relationships among AI readiness, AI anxiety, and AIO in university students is needed, the following hypothesis is proposed: AANs significantly influence AIO in college students. Proposed research model. Note: Performance expectancy = PE; SI = Social influence; PP = Perceived playfulness; EA = Ethical awareness; AI learning self-efficacy = SL; AAN = AI readiness and AI anxiety; AIO = AI appropriation To test the research hypotheses, the researchers conducted an empirical assessment on the basis of the work of [59]. A survey was administered to university students with experience using AI models to gather data on their perceptions and attitudes toward these technologies. The study involved 482 university students from public and private institutions in Peru. The sample was selected through nonprobabilistic convenience sampling, as described by [60]. This method allowed for the inclusion of participants who were available and willing to voluntarily contribute to the research. Although this type of sampling does not guarantee full representativeness of the university student population, it was appropriate for this exploratory study, whose primary goal was to identify preliminary trends and patterns rather than to make broad generalizations. According to Tables 1 and 58.3% of the participants were male (281), whereas 41.7% were female (201). In terms of age distribution, the largest group was in the 29–33 years age range, accounting for 38.07% (183 participants), followed by the 24–28 years age range, accounting for 31.8% (153 participants). The majority of the respondents were from public universities (62.33%, 300 participants), whereas 37.7% (182 participants) were from private institutions. In terms of educational level, postgraduate students predominated, representing 59.3% (286 participants) of the sample. They were followed by independent researchers, who made up 33.3% (161 participants) of the sample. With respect to the duration of AI use, most participants (35.2%, 170 participants) reported having used AI for 1–2 months, followed by those with 3–5 months of experience (24.9%, 120 participants). A self-developed instrument was used to collect the data for the study (see supplementary file). This instrument is based on the literature and the constructs of the UTAUT2 model, which also incorporates concepts of perceived ethics and academic integrity. The questionnaire was developed through Google Forms and was structured in three main sections. The first section contained informed consent, provided detailed information about the study and ensured the anonymity of the participants. A branching question was included to confirm voluntary participation. The second section collected sociodemographic data, including age, gender, type of university, education level, and duration of AI usage. The third section included evaluation items designed to measure the constructs of the proposed model. This section included a total of 37 items distributed across the following constructs: PE, SI, PP, EA, SL, AAN, and AIO. All the items were measured via a 5-point Likert scale, where 1 represented “strongly disagree” and 5 “strongly agree.” The survey was conducted over a six-month period, from October 2023 to March 2024. The researchers obtained permission from universities and higher education institutions (HEIs) to distribute the survey online. The form was shared via email and distributed through student WhatsApp groups. For the analysis of the collected data, the researchers followed a systematic process that included several steps: Initially, data cleaning was performed via Microsoft Excel, and missing values and incomplete surveys were removed to ensure the quality of the information. Subsequently, descriptive statistical techniques were applied to create the sociodemographic results table, providing an overview of the sample’s characteristics. A confirmatory factor analysis (CFA) was conducted to evaluate the main reliability and validity indicators of the measurement model. In this process, indicators such as factor loadings and average variance extracted (AVE) were used. Additionally, to assess internal consistency reliability, Cronbach’s alpha and composite reliability (CR) (rho_a and rho_c) were employed. Discriminant validity was evaluated via the [61] and the heterotrait‒monotrait ratio (HTMT) criterion, ensuring adequate distinction between the model’s constructs. Finally, partial least squares structural equation modeling (PLS‒SEM) was used to test the research hypotheses. This analysis was carried out via SmartPLS software. Partial least squares structural equation modeling (PLS-SEM) was used in this study. Consequently, a confirmatory factor analysis (CFA) was carried out to confirm the convergent validity of the measurement model. Table 2 presents the factor loadings of the items, and according to [62], acceptable factor loadings higher than 0.70 are met; moreover, all the measured constructs present average variance extracted (AVE) values that exceed the threshold of 0.50, as proposed by [63]. On the other hand, the standard deviation of the items is between 0.008 and 0.092, which indicates that it seems that the items are not excessively dispersed with respect to their means. Table 3 shows the results of the reliability and discriminant validity tests. To evaluate the reliability of the constructs, the values of Cronbach’s alpha coefficient (α) and composite reliability (CR) (rho_a and rho_c) were used. Taking into account the criteria of [63, 66], values higher than 0.70 are considered adequate; as shown in Table 3, all the constructs exceeded this threshold. Furthermore, the values of the coefficient of determination (R²) suggest that AAN, EA, PE, PP, and SI explain 60.1% of the AIO variation. Discriminant validity was calculated via the criterion of [61], which states that a measurement model presents discriminant validity when the square root of the average variance extracted (AVE) (numbers on the diagonal) should be greater than the correlations with other constructs (numbers outside the diagonal in the same row and column). As shown in Table 3, all the constructs meet this criterion. On the other hand, the heterotrait‒monotrait criterion (HTMT) was used, in which all the evaluated constructs have values below the threshold of 0.85 [67], suggesting that the measurement instrument has discriminant validity. The goodness-of-fit indices of a measurement model are a relevant measure for determining convergent validity [68]; therefore, these criteria provide a reference for researchers to determine to what extent the values obtained fit well with the expected values [62]. Table 4 presents the values of the goodness-of-fit indices of the measurement model, where the standardized root mean square residual (SRMR) presented a value of 0.078, satisfying the threshold proposed by [69]. The value of Chi-square/gl (χ2/df) shows that the model has an acceptable fit because it has an adequate value between 1 and 3, as proposed by [70]. Finally, the value of the normalized fit index (NFI) is 0.923, which satisfies the threshold of [70]. Table 5; Fig. 2 present the main results of the analysis using standardized path coefficients (β), along with p values and confidence intervals for β. Path analysis allows for the determination of β values between the relationships of the exogenous and endogenous variables in the study, as well as the direction of these effects [62, 71]. Hypothesis 3 (H3) revealed a significant effect between PE and AIO, with a path coefficient of β = 0.274* and a p value of p > 0.006*, indicating that PE influences AIO among university students. Hypothesis 5 (H5) revealed a significant effect between SI and AIO, with a path coefficient of β = 0.335* and a p value of p > 0.002***, demonstrating that SI determines AIO in university students. Hypothesis 6 (H6) shows a significant effect between SL and AIO, with a path coefficient of β = 0.431* and a p value of p > 0.029*, indicating that self-efficacy in learning AI influences AIO in university students. Finally, Hypotheses 1, 2, and 4 did not have significant effects and were thus rejected. Resolved research model. Note: At the intersections of the relationship lines are the path coefficients on the left and the p values on the right (inside the parentheses) The objective of the study was to first test hypothesis H3, which posited that PE significantly influences AIO in university students. This hypothesis was accepted. This finding aligns with previous research that has demonstrated the importance of PE in adopting AI technologies in educational settings. For example, studies by [34] and [39] have shown that PE positively affects students’ willingness to accept AI-assisted learning environments and use generative AI products. This is further supported by research highlighting PE as a critical predictor of AI acceptance in higher education [15, 16, 40]. The results of the present study reinforce this trend, suggesting that Peruvian university students are more likely to appropriate AI when they perceive that it will improve their academic performance. Hypothesis H5, which proposed that SI significantly influences AIO in university students, was also accepted. This result is consistent with previous research highlighting the crucial role of SI in the adoption of AI technologies. For example, the study by [41] on university students in southern India revealed a direct positive effect of SI on the intention to use AI-enabled job application processes. Similarly [42], demonstrated how students adapt their behaviors in response to AI interactions, underscoring the powerful role of social influence. This is further supported by studies showing how social networks and peer influence shape AI adoption patterns [15, 35, 36]. The findings of this study suggest that the social environment of Peruvian university students plays a significant role in their decision to appropriate AI technologies. Hypothesis H6, which posited that SL significantly influences AIO in university students, was also accepted. This result is in line with those of previous studies demonstrating the importance of self-efficacy in adopting AI technologies. For example [37, 52], reported that AI self-efficacy can positively influence students’ attitudes toward AI and their actual use of AI tools. The findings of this study suggest that Peruvian university students with greater confidence in their ability to learn and use AI are more likely to appropriate these technologies. On the other hand, hypotheses H1, H2, and H4 did not have significant effects and were rejected. Hypothesis H1, which proposed that AANs significantly influence AIO, was not supported by the data. This result contrasts with those of previous studies, such as [57], which reported that AI readiness positively influences students’ willingness to learn about AI. Other studies have shown how AANs can affect learning motivation and actual AI tool usage [37, 52, 58]. The lack of significance in this study may suggest that, in the Peruvian context, other factors carry more weight in AIO than in readiness and anxiety. Hypothesis H2, which posited that EAs significantly influence AIO, was also rejected. This result differs from those of studies such as that of [39], who reported that university students’ EA significantly influences their behavioral intentions and actual use of generative AI products. Previous research has emphasized the importance of ethical considerations in AI adoption [10, 11, 48, 49]. The absence of a significant effect in this study could indicate that, for Peruvian university students, ethical considerations are not a determining factor in their decision to appropriate AI or that these considerations are overshadowed by more immediate factors. Hypothesis H4, which proposed that PP significantly influences AIO, was rejected. This finding contrasts with studies such as [45], which emphasize the importance of playful aspects in the adoption of AI technologies. Additional research has demonstrated the role of PP in technology adoption [46, 47]. The lack of a significant effect in this study may suggest that, for Peruvian university students, AIO is more driven by utilitarian factors than by hedonic factors. In summary, these results offer a nuanced view of the factors influencing AIO among Peruvian university students. While PE, social influence, and self-efficacy in learning AI emerge as key factors, other aspects, such as AI readiness and anxiety, EA, and PP, appear to play a less prominent role in this specific context. These findings underscore the importance of considering contextual and cultural factors when studying the adoption of AI technologies in educational environments. The findings of this study offer significant theoretical and practical implications for AI adoption in higher education. From a theoretical perspective, this research extends the UTAUT2 model by incorporating psychological constructs specific to AI adoption in educational contexts, contributing to the growing body of literature on technology acceptance models [72, 73]. The study validates the importance of self-efficacy as a crucial factor in technology adoption, aligning with recent research on planned behavior in educational settings [74, 75]. Furthermore, the findings regarding the interplay between performance expectancy and social influence provide new insights into how these factors operate in non-Western educational contexts [76, 79]. The integration of ethical awareness and AI anxiety into the model represents a significant theoretical contribution, particularly in the context of emerging AI technologies such as ChatGPT [76, 77]. This extension of the UTAUT2 model provides a more comprehensive framework for understanding technology adoption in educational settings, especially in developing countries [73, 79]. The study also contributes to the theoretical understanding of how cultural and contextual factors moderate the relationship between psychological factors and technology adoption [78, 79]. From a practical standpoint, the findings provide valuable insights for educational institutions implementing AI technologies. The strong influence of performance expectancy suggests that institutions should focus on demonstrating the tangible benefits of AI tools in improving academic outcomes [75, 76]. The significance of social influence highlights the importance of creating supportive peer networks and fostering a positive institutional culture toward AI adoption [77, 78]. Educational administrators and policymakers can use these findings to develop more effective AI implementation strategies. The study suggests that initiatives should focus on building students’ self-efficacy through targeted training programs while addressing anxiety concerns [75, 77]. The findings regarding ethical awareness indicate the need for comprehensive guidelines and educational programs on responsible AI use [76, 78]. Additionally, the study provides practical recommendations for addressing cultural and contextual factors in AI implementation. Institutions in similar cultural contexts can benefit from understanding how these factors influence adoption patterns [79]. The findings suggest that successful AI implementation requires a balanced approach that considers both technological and psychosocial factors [75, 78]. The present study has methodological limitations that should be considered when interpreting its findings. The cross-sectional nature of the data collection restricts causal inferences about the relationships between variables [72, 73]. The use of convenience sampling in Peruvian universities may limit the generalizability of findings to other educational contexts [75, 76]. Additionally, the study focused exclusively on student perspectives, without considering the views of educators and administrators, who play crucial roles in AI implementation [77]. These limitations present opportunities for future research directions. Longitudinal studies could provide deeper insights into how AI appropriation evolves over time and how different factors influence adoption patterns at various stages [74, 78]. Cross-cultural comparative studies could help identify universal and context-specific factors in AI adoption across different educational settings [76, 79]. Research incorporating multiple stakeholder perspectives, including those of educators, administrators, and technical staff, would offer a more comprehensive understanding of AI adoption in higher education [77, 78]. Future studies could examine the moderating effects of demographic variables and explore how factors such as academic discipline, previous technology experience, and cultural background influence AI appropriation patterns [75, 79]. An investigation of the relationship between AI appropriation and academic performance outcomes would provide valuable insights for educational policy and practice [76, 78]. Additionally, research exploring the interaction between institutional support systems and individual adoption factors could enhance the understanding of how organizational contexts influence AI appropriation [77, 79]. This research on the factors influencing the appropriation of AI among Peruvian university students reveals three key determinants in the adoption process. PE emerges as a crucial predictor, demonstrating that Peruvian students prioritize tangible benefits in their academic performance when adopting AI technologies. SI proves to be a significant factor, reflecting the collectivist nature of Peruvian culture and its influence on technology adoption decisions. Additionally, SL stands out as a critical determinant, highlighting how students’ confidence in their ability to learn and use AI technologies affects their adoption patterns. The nonsignificant effects of EA, PP, and AAN on AI appropriation provide important insights into the context-specific nature of technology adoption in Peruvian higher education. These findings challenge assumptions about universal adoption factors and emphasize the need for culturally adapted implementation strategies. The study’s methodological approach, which uses an extended UTAUT2 model, offers a refined framework for understanding AI adoption in educational settings. This adaptation, which incorporates context-specific constructs, contributes to the evolution of technology acceptance models in diverse educational environments. As AI continues to transform the educational landscape, this research provides evidence-based insights for developing effective AI implementation strategies in higher education, particularly in contexts similar to Peru’s educational environment. 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Universidad Nacional de Trujillo, Trujillo, Perú Benicio Gonzalo Acosta-Enriquez & Karina Saavedra Tirado Universidad Tecnológica del Perú, Lima, Perú María de los Ángeles Guzmán Valle Universidad César Vallejo, Trujillo, Perú Marco Arbulú Ballesteros, Julie Catherine Arbulú Castillo, Carmen Graciela Arbulu Perez Vargas, Isaac Saavedra Torres & Pedro Manuel Silva León You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar Conceptualization: Benicio Acosta-Enriquez, María de los Ángeles Guzmán Valle, Karina Saavedra Tirado; Methodology: Julie Catherine Arbulú Castillo, Carmen Graciela Arbulu Perez Vargas; Formal analysis: Marco Arbulú Ballasteros, Benicio Acosta-Enriquez; Writing – preparation of the original draft: Isaac Saavedra Torres, Isaac Saavedra Torres; Writing – revision and editing: María de los Ángeles Guzmán Valle, Karina Saavedra Tirado, Julie Catherine Arbulú Castillo. All the authors have read and approved the final manuscript. Correspondence to María de los Ángeles Guzmán Valle. The research that led to these results was approved by the Ethics Committee of the Universidad Nacional de Trujillo and by the University Council by resolution N° 1762–2023/UNT. Informed consent was obtained from all participants included in the study; if participants were under 18 years of age, parents or legal guardians provided informed consent. The intervention was conducted in accordance with the Declaration of Helsinki. Not applicable. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Below is the link to the electronic supplementary material. 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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions Acosta-Enriquez, B.G., Guzmán Valle, M., Arbulú Ballesteros, M. et al. What is the influence of psychosocial factors on artificial intelligence appropriation in college students?. BMC Psychol13, 7 (2025). https://doi.org/10.1186/s40359-024-02328-x Download citation Received: Accepted: Published: DOI: https://doi.org/10.1186/s40359-024-02328-x Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 15, Article number: 786 (2025) Cite this article Metrics details Given the significant prevalence of adverse childhood experiences (ACEs) and their detrimental impact on mental health, this study examines the relationship between attachment anxiety, attachment avoidance, and complex post-traumatic stress disorder (CPTSD) among college students with ACEs, emphasizing the mediating role of self-compassion (SC). A sample of 32,388 students from Kunming, China completed a survey including the Revised Adverse Childhood Experiences Questionnaire (ACEQ-R), the Adult Attachment Scale (AAS), the International Trauma Questionnaire (ITQ), and the Self-Compassion Scale-Short Form (SCS-SF). Among the participants, 3,896 reported at least one ACE. Data were analyzed using structural equation modeling (SEM) to explore the proposed mediation model. Results revealed that both attachment anxiety and avoidance positively influenced CPTSD symptoms while negatively affecting SC. SC negatively influenced CPTSD symptoms, acting as a significant mediator. The mediating effect of SC was stronger for disturbances in self-organization (DSO) than for post-traumatic stress disorder (PTSD) symptoms. These findings underscore the importance of fostering SC in interventions aimed at mitigating the influence of attachment anxiety and avoidance on CPTSD among college students with ACEs. Adverse childhood experiences (ACEs) have been thoroughly documented in extensive research to have detrimental consequences on both physical and mental health outcomes1. Research indicates that experiencing interpersonal trauma during childhood hinders a child’s ability to manage emotions and establish secure, healthy attachments2,3,4. These impairments are partly due to the adverse effects on the development of neurobiological systems involved in emotional responses, stress reactions, and reward processing5. Consequently, these fundamental psychobiological functions are intricately linked to the emergence of long-term issues, particularly complex post-traumatic stress disorder (CPTSD)6,7. The WHO defined CPTSD as an independent diagnostic category in the latest edition of ICD-118. This landmark decision aimed to accurately identify and treat individuals experiencing sustained and repeated trauma. The reliability and validity of CPTSD as a diagnosis have been supported by numerous studies9,10,11,12. CPTSD shares some symptom clusters with post-traumatic stress disorder (PTSD), with additional clusters collectively referred to as disturbances in self-organization (DSO)9,13. DSO encompasses affective dysregulation, negative self-concept, and disturbed relationships, which are commonly linked to individuals exposed to prolonged, repeated, or multiple traumatic experiences14 and signify a depletion of emotional, psychological, and social resources under conditions of sustained adversity15. Exposing individuals to prolonged and repeated ACEs significantly increases the likelihood of developing CPTSD symptoms16. Among Chinese college students, the prevalence of ACEs is particularly high, with studies highlighting significant exposure rates and their detrimental impact on mental health and behavior16,17,18,19. Given the high prevalence of ACEs, investigating the mechanisms underlying PTSD and DSO symptoms among this population is meaningful. According to Bowlby (1973/1982)20,21 and Ainsworth (1991)22, children internalize early attachment patterns with primary caregivers, forming internal working models (IWMs) of self and others that become prototypes for later relationships23. Attachment styles in adult relationships often develop from early attachment patterns with primary caregivers, making them particularly vulnerable to disruption by ACEs24. Consequently, ACEs can lead to insecure attachment styles, commonly reflected in attachment anxiety (excessive fear of abandonment), where individuals may value their partner but hold a negative self-view, or attachment avoidance (discomfort with closeness), where they maintain a positive self-view but hold a negative view of closeness with others25,26. Insecure attachment styles, marked by heightened attachment anxiety and/or avoidance, are associated with difficulties in maintaining a positive self-view or perception of others, lower self-worth, emotional regulation challenges, and diminished interpersonal satisfaction and overall well-being27. Studies have highlighted the importance of attachment styles for PTSD symptoms23,28. For instance, one study found that attachment anxiety and avoidance significantly increased the risk of lifetime PTSD29. Another study found that attachment anxiety significantly predicted the likelihood of CPTSD symptoms, while attachment avoidance did not30. Additionally, research has distinguished between PTSD and DSO symptoms, finding that insecure attachment styles showed stronger associations with DSO symptoms2. Attachment styles, reflecting internalized evaluations of self and others, impact self-worth and self-evaluation, manifesting in self-compassion(SC)31. Insecure attachment styles can reduce SC by fostering negative self-perceptions and relational patterns32. Attachment anxiety often leads to self-criticism, hindering SC33, and attachment avoidance involves defense mechanisms that deter vulnerability, reducing SC34. According to Neff and McGehee (2010)32, individuals with attachment anxiety often exhibit doubts about their worthiness of others’ care and may rely heavily on external validation, which can limit their ability to cultivate self-compassion. Similarly, those with avoidant attachment tend to downplay the importance of support in relationships, exhibit distrust in others, and struggle with self-worth, leading to a weaker foundation for self-acceptance and reduced self-compassion. Secure attachment style supports a stronger ability to engage in self-compassion. In contrast, individuals with attachment anxiety and avoidance often struggle with self-compassion due to unmet emotional needs and defensive relational strategies35. Research by Gilbert (2011)36 suggested that capacity for compassion is rooted in the attachment system, which shapes how individuals perceive themselves and interact with others. Secure attachment styles typically associate with a greater capacity for compassion, as individuals develop positive IWMs from nurturing relationships20,36. Conversely, anxious and avoidant attachment styles can hinder this capacity. Social mentality theory suggests that humans have evolved specialized systems to manage social interactions and build relationships, which later directed toward the self. These systems, shaped by early attachment experiences, guide how individuals relate to both others and themselves37. For those with insecure attachment styles, these systems may become maladaptive. Fear of SC arises when individuals associate compassion with vulnerability, rejection, or emotional pain based on past negative experiences36,37. And the fear of SC, which is often seen in individuals with insecure attachment styles, is linked to self-coldness, self-criticism, and greater psychological distress33,38. Early life experiences play a crucial role in shaping attachment styles and the capacity for self-compassion. Exposure to ACEs can influence how individuals perceive and relate to themselves. Neglect by early caregivers may contribute to the development of attachment anxiety and avoidance, fostering beliefs that one is unworthy of consistent care and that others are unreliable in meeting their needs31. Insecure attachment styles can hinder the development of self-compassion and reinforce negative beliefs about oneself and relationships that arise from adverse relational experiences in childhood. Over time, these beliefs may become internalized, shaping an individual’s self-view and interactions with the world39. Numerous studies have documented varying associations between SC and attachment styles, highlighting the varying predictive abilities of attachment anxiety and avoidance on33,34,40. Joeng et al. (2017)33 examined the mediating roles of self-compassion and fear of self-compassion in the relationship between attachment anxiety/avoidance and emotional distress, finding that self-compassion can act as a protective buffer, reducing distress, while fear of self-compassion amplifies it. Similarly, Mackintosh et al. (2018)34 investigated self-compassion, attachment, and interpersonal difficulties among clinical patients with anxiety and depression, revealing that attachment anxiety and avoidance are associated with reduced self-compassion. Research also supports the idea that self-compassion serves as a vital pathway linking attachment anxiety and avoidance to psychological distress40. Collectively, these studies underscore self-compassion’s essential role in mitigating the negative impact of attachment anxiety and avoidance on mental health. Recent studies highlight the significant impact of SC on CPTSD. Research indicates that SC significantly influences CPTSD symptoms6,14. Studies confirmed that SC is negatively related to PTSD symptomatology, particularly the avoidance cluster of symptoms41. There is tentative evidence that SC-based interventions potentially reduce PTSD symptoms41. Research on the impact of SC on DSO symptoms is relatively sparse. However, existing studies indicate that SC significantly helps reduce DSO symptoms6,14. Notably, the effects of SC are more substantial on DSO than on PTSD6. Similar to attachment security, SC offers an inner safe haven for individuals to find refuge and recover during distress, and a secure base from which they can explore the world and connect with others42. Studies suggest that attachment styles can evolve later in life through corrective experiences with new attachment Fig. 43. It can be posited that SC is one result of these corrective experiences42,44. ACEs can lead to insecure attachment styles, which may play a role in the development of CPTSD symptoms. Previous research has demonstrated that both attachment anxiety and avoidance are associated with increased vulnerability to PTSD and DSO symptoms. However, the potential mediating role of SC in these relationships has not been thoroughly investigated. Multiple studies have demonstrated that SC mediates the links between attachment styles and various mental health outcomes such as mental health31, depression40, anxiety33, interpersonal difficulties34, and well-being32,45. These findings suggest that SC could be a crucial mechanism through which attachment styles impact psychological functioning. Importantly, these findings note that SC may offer a more accessible focus for intervention than attempting to modify deeply ingrained attachment styles, making it a valuable area for clinical and therapeutic strategies32. This underscores the necessity and value of further investigating SC’s mediating role in the relationship between attachment styles and CPTSD symptoms, contributing to the understanding and development of effective intervention approaches. The present study develops a mediation model to examine how attachment anxiety and avoidance influence CPTSD symptoms through SC in Chinese college students with ACEs. We hypothesize that attachment anxiety and avoidance are correlated with the presence of PTSD and DSO symptoms. Additionally, we propose that SC plays a significant mediating role in the relationships between them. Furthermore, we hypothesize that the strength of this relationship is more pronounced in DSO symptoms compared to PTSD symptoms. Data for this study were collected between September and October 2023 at universities in Kunming, China. Informed consent was obtained from all participants/legal guardians with an assent from the participant. After obtaining informed consent, students were invited to complete the survey via online platform. A total of 32,388 students completed the questionnaires, of which 32,247 provided valid data. Among the participants, 3896 reported at least one ACE. The final sample consisted of 2326 males (59.7%) and 1570 females (40.3%), with ages ranging from 15 to 27 (M ± SD = 21.05 ± 1.486). The ACEQ-R, originally developed by the Centers for Disease Control and Prevention and later revised by multiple countries, is a widely used instrument that includes 10 questions to assess the relationship between ACEs and adult health and social outcomes, covering various aspects such as physical abuse, emotional abuse, physical neglect, and family dysfunction46. Each question is answered with either 0 (no) or 1 (yes). The questionnaire has been translated into Chinese and validated for using among college students47. The AAS included 18 items that could measure the two dimensions of attachment anxiety and avoidance48. Each item is measured on a 5-point Likert scale from 1 (not at all) to 5 (very much). A high score in that dimension indicated a high level of attachment anxiety or avoidance. AAS has been validated in Chinese college students49. The Cronbach α coefficient for attachment anxiety and avoidance in this study was 0.858 and 0.759, respectively. The ITQ was used to measure CPTSD. The ITQ assesses PTSD through three symptom clusters (re-experiencing in the here and now, avoidance and sense of current threat) and DSO through three symptom clusters (affective dysregulation, negative self-concept, disturbances in relationships)50. ITQ adopts a five-point Likert scoring from 0 (Not at all) to 4 (Extremely). Higher scores, obtained by summing up individual item scores, indicate more severe symptoms. The Chinese translation of ITQ has been validated in prior research, demonstrating strong psychometric properties51. In this study, the PTSD subscale had a Cronbach’s α coefficient of 0.893, and the DSO subscale had a Cronbach’s α coefficient of 0.891. SC was assessed using the SCS-SF, a widely used tool for measuring individuals’ SC52. The SCS-SF is highly correlated with its full version and has been validated for use in the Chinese college students53. The scale consists of 12 items, rated on a five-point Likert scale from 1 (Almost never) to 5 (Almost always). In this study, the Cronbach’s α coefficient was 0.863 for SC. Descriptive statistics and correlation analyses were conducted in SPSS 29.0. The structural equation model (SEM) was used in Amos 29.0 to examine the relationships between attachment anxiety, attachment avoidance, PTSD, and DSO symptoms. Bootstrap analysis with 5000 replicates was employed to determine the 95% confidence intervals for the mediating effects of SC in the SEM. The research was performed in accordance with relevant guidelines/regulations, and approval was obtained from the ethics committee of Kunming University of Science and Technology (Approval No: KMUST-MEC-149). Informed consent was obtained from all participants with an assent from the participant. This study has been performed in accordance with the Declaration of Helsinki. Among the 3896 college students who reported experiencing at least one ACE, our analysis revealed that the most frequently reported experience was parental separation or divorce, with 38.53% (n = 1501) of participants indicating exposure. Other commonly reported ACEs included domestic violence (33.50%, n = 1305), physical neglect (32.65%, n = 1272), and physical abuse (32.16%, n = 1253). Additionally, 22.43% (n = 874) of participants reported household substance abuse. Other notable ACEs included emotional neglect (18.81%, n = 733), having an incarcerated household member (17.07%, n = 665), and sexual assault (15.86%, n = 618). Less frequent ACEs in this sample were emotional abuse (12.04%, n = 469) and household mental illness (10.63%, n = 414). Table 1 presents the descriptive statistics and correlations (N = 3896). In this study of college students with ACEs, the mean score for attachment anxiety was 2.73 (SD = 0.871), while the mean score for attachment avoidance was 2.65 (SD = 0.535). For comparison, a study conducted among a large sample of Chinese young adults reported mean scores of 2.43 for attachment anxiety and 2.50 for attachment avoidance54, indicating slightly higher levels in our sample. SC in this study had a mean score of 40.34 (SD = 7.928), which is comparable to the reported average of approximately 39.0 in nonclinical university student populations55. The mean scores for PTSD and DSO were 3.64 (SD = 3.972) and 4.73 (SD = 4.586), respectively. Previous research on CPTSD among nonclinical Chinese college students showed mean PTSD and DSO scores of 0.96 and 0.2056, respectively, which are considerably lower than those found in the current study. However, in studies focusing on Chinese college students with childhood adversity, the mean scores for PTSD and DSO were 4.39 and 3.1157, respectively. The correlation analysis revealed significant relationships among the study variables. Attachment anxiety was positively correlated with attachment avoidance, PTSD and DSO, and negatively correlated with SC. Similarly, attachment avoidance was positively correlated with PTSD and DSO, and negatively correlated with SC. SC showed a negative correlation with both PTSD and DSO. PTSD and DSO were positively correlated. Figure 1 illustrates the standardized coefficients of the structural model (N = 3896). The fit indices indicated that the model fit the data well, with acceptable values for various indices (χ2/df = 7.867, RMSEA = 0.030, GFI = 0.973, AGFI = 0.956, CFI = 0.978, NFI = 0.974, TLI = 0.969). The model demonstrates that attachment anxiety (β = − 0.464, p < 0.001) and avoidance (β = − 0.403, p < 0.001) are significantly and negatively associated with SC. SC significantly and negatively influences both PTSD (β = − 0.641, p < 0.001) and DSO (β = − 0.802, p < 0.001). The paths to DSO had higher effect size. Moreover, attachment avoidance has a small but significant direct positive effect on DSO (β = 0.070, p < 0.001). The direct paths from attachment anxiety to PTSD and DSO, as well as from attachment avoidance to PTSD, were not significant, as shown by the dotted lines in Fig. 1. As illustrated in Table 2, the 95% bootstrap confidence intervals for the mediating effects of SC between the relationships of attachment anxiety and avoidance with PTSD and DSO symptoms did not include zero among college students, indicating significance. The mediation model (***p < 0.001, PTSD = post-traumatic stress disorder, DSO disturbances in self-organization). This study examined the influence of attachment anxiety and avoidance on CPTSD symptoms. We found that both attachment anxiety and avoidance significantly influence the presence of PTSD and DSO symptoms through SC among college students with ACEs. SC demonstrated stronger mediating effects in the relationships between attachment anxiety and avoidance with DSO symptoms than with PTSD symptoms. According to attachment theory, attachment styles represent internalized assessments and expectations of oneself and important others21. Insecure attachment styles can affect how individuals relate to themselves, influencing their self-worth and self-evaluation, which is reflected in the construct of SC58. From the perspective of social mentality theory, individuals utilize evolved systems for interacting with others to form a relationship with themselves. Social mentalities guide individuals in establishing specific roles in their interactions with others37,59. These mentalities are triggered not only in relation to others but also within the individual’s own internal dynamics60,61. For example, an individual might employ the same nurturing behaviors towards themselves that they would towards a loved one, fostering SC and care. This internalization process underscores the importance of self-directed compassion, where individuals apply the same empathetic and supportive attitudes towards themselves as they do towards others61. Thus, it is suggested that early attachment experiences may influence SC, which in turn correlates with mental health58. From the perspective of childhood experiences, receiving adequate care allows a child to feel comforted and supported, leading to the internalization of positive self-perceptions and relational patterns. In contrast, ACEs can lead individuals to feel unworthy of support and perceive others as unreliable, fostering negative self-perceptions and maladaptive relational patterns62. Recent systematic reviews have also documented the association between ACEs and a range of mental health and relational difficulties, reinforcing the notion that childhood adversities significantly shape mental health trajectories and attachment styles63,64. This suggests that early attachment experiences can have on the development of SC and subsequent mental health outcomes56. Recurring interactions with attachment figures who were untrustworthy or insufficiently responsive during childhood may predispose individuals to attachment anxiety and avoidance, fostering self-associations characterized by insecurity and perceived unworthiness of affection65,66. These findings help explain the observed impact of attachment anxiety and avoidance on SC and CPTSD among college students with ACEs. Our findings align with prior studies that attachment anxiety and avoidance significantly influence the level of SC33,45,67. Childhood experiences, including the formation of attachment to security figures, are crucial pathways for developing high levels of SC, and the relationship between SC and emotion regulation31. Consistent with our findings, previous studies demonstrate that attachment anxiety and avoidance have significant but varying effects on SC33,34,40. Individuals experiencing attachment anxiety often develop a pessimistic self-perception, engaging in self-criticism rather than self-care45,68. Their inclination to seek external validation and attention hinders their ability to rely on internal resources to foster SC31. Contrary to the core of SC, is the capacity to wholeheartedly embrace one’s own pain or distress, care for oneself during difficult times69, those with attachment avoidance exhibit heightened defense mechanisms that deter vulnerability70. Consequently, those with high attachment avoidance often set strict expectations for themselves, opting to depend on their own abilities instead of seeking support from others, leading to reduced SC45,71,72. Align with existing research, our results suggesting that SC significantly influences CPTSD symptoms, serving as an adaptive resource for coping with both traumatic experiences and distressing situations6,14,41. As a result, individuals with high levels of self-compassion are less likely to CPTSD symptoms in response to ACEs6. Individuals with high SC tend to be kind to themselves during adversity, which aids in trauma recovery and reduces PTSD symptoms42,73. Our results indicate that, although SC significantly impacts both PTSD and DSO, its effect is more pronounced for DSO among college students with ACEs. This finding is consistent with prior research14, which indicates that SC may be particularly important in predicting DSO symptoms compared to PTSD symptoms, especially regarding negative self-concept. It has been argued that this negative self-concept might be more responsive to compassion focused interventions74. SC mediates the relationship between attachment anxiety, attachment avoidance, and CPTSD. SC, similar to attachment security, provides an inner safe base where individuals can seek refuge and recover during times of distress, connect with others to recover21. Individuals with higher attachment anxiety or avoidance tend to have lower levels of SC, which increases their vulnerability to CPTSD symptoms. Our findings suggest that the influence of attachment anxiety and avoidance on CPTSD is largely mediated by SC. Our findings indicate that the pathway from attachment anxiety and avoidance through SC to DSO symptoms exhibited a stronger effect size than the pathway to PTSD symptoms. This aligns with previous research highlighting differences in symptom manifestation and underlying mechanisms between PTSD and DSO. Specifically, PTSD symptoms are more closely associated with emotions such as fear and anxiety75. In contrast, DSO is often linked to prolonged and repeated adverse experiences13. Additionally, research exploring distinct contributing factors for PTSD and DSO has found that interpersonal trauma during childhood and a tendency toward insecure attachment have a more pronounced impact on DSO compared to PTSD2,76.The results also reveal a minor direct effect of attachment avoidance on DSO, aligning with prior research indicating that avoidant attachment consistent with relational dysregulation in DSO2. Although attachment anxiety and avoidance influence CPTSD through SC, some studies suggest that SC can also act as a buffer, reducing the negative impact of attachment insecurities on psychological outcomes by promoting adaptive coping mechanisms and emotional resilience31. It serves as a protective factor against the impact of traumatic stress by helping individuals better utilize social support, thereby buffering the adverse effects of traumatic experiences77. Insecure attachment styles have been shown to associated with lower compassion, potentially increasing vulnerability to mental health challenges78. SC, which reflects a caring and compassionate attitude toward oneself, can serve as a protective mechanism, enabling individuals with insecure attachment styles to better cope with the emotional effects of ACEs. Clinical applications suggest that enhancing SC and attachment security may be instrumental in improving psychological well-being for those with insecure attachment styles. Attachment-Based Compassion Therapy (ABCT)79 is one such intervention that integrates attachment theory with compassion practices. Evidence indicates that ABCT can effectively increase secure attachment and SC, while decreasing psychological distress80. Additionally, interventions like secure attachment priming71 and SC priming81 have shown promise in making SC exercises more approachable for individuals with attachment anxiety or avoidance82. Our study, along with these previous researches, highlights the value of attachment-focused interventions and suggests that fostering SC within this framework can enhance the social and emotional resilience needed to overcome the long-term effects of ACEs. Our findings specifically confirmed the mediating role of SC in the relationships between attachment anxiety, attachment avoidance, and both PTSD and DSO symptoms, highlighting SC’s importance in alleviating CPTSD symptoms and supporting its therapeutic potential. In a therapeutic context, for self-compassion to be effective in treating complex PTSD, it should be integrated within a comprehensive, interpersonal, and attachment-based framework that takes early experiences into account42. For individuals with attachment anxiety, therapeutic work on SC can gradually foster a sense of self-soothing, a crucial skill that anxious individuals may lack due to dependence on others for emotional reassurance42. In these cases, therapists can use SC-based interventions to help individuals recognize and attend to their own needs with compassion, reinforcing instances where they have effectively responded to their needs. Over time, this approach may empower individuals to rely less on external validation and more on self-support. For individuals with attachment avoidance, early experiences often lead to a mistrust of both self and others in times of distress. They may benefit from SC as a means of addressing deep-seated feelings of shame and worthlessness, which are common in CPTSD and often rooted in ACEs83. SC-based therapy is inherently attachment-oriented, as it taps into foundational experiences of care, love, and validation that may have been absent in early life. This approach can help individuals gradually build an internal secure base, thereby enhancing their resilience against the effects of trauma84,85. Our study makes original contributions by exploring the relationships between attachment styles, SC, and CPTSD symptoms in college students with ACEs. Specifically, we developed a mediation model to examine how SC links attachment anxiety and avoidance with CPTSD symptoms, demonstrating a stronger mediating effect on DSO symptoms compared to PTSD symptoms. This approach enriches the existing literature, positioning SC not only as a mediating factor but also as a potential therapeutic target to mitigate the effects of attachment insecurities that stem from ACEs. Our findings show that SC has a more substantial impact on DSO symptoms, underscoring its role in self-regulation, self-concept, and relational difficulties. By identifying SC as an accessible target for intervention, our research suggests that fostering SC may offer a more adaptable focus in therapeutic settings than altering entrenched attachment styles. Clinical applications, such as ABCT and SC priming, could promote resilience and reduce psychological distress in individuals with insecure attachment styles. The study’s implications for both theory and practice are significant, as our findings support attachment theory and social mentality theory, illustrating how insecure attachment styles, shaped by early adversity, can diminish SC, and elevate vulnerability to CPTSD symptoms. By clarifying the differential influence of attachment anxiety and avoidance on DSO and PTSD symptoms, this study suggests that integrating SC-focused interventions within an attachment-based therapeutic framework could effectively address CPTSD symptoms in individuals with ACEs. Our study highlights the importance of SC as a protective mechanism, opening new directions for interventions aimed at enhancing mental health outcomes in college students with ACEs. Several limitations should be acknowledged. First, this study adopted a retrospective method to measure the ACEs which may result in biased information in college students. Second, this study relied on only self-report scale to assess CPTSD symptoms. Future studies might collect multiple-informant reported data to measure CPTSD more accurately in college students. Third, while we selected a mediating model to explore SC as a pathway through which attachment anxiety and avoidance might impact CPTSD, this approach may limit certain interpretations. Specifically, SC could plausibly act as a moderating factor, buffering the negative effects of ACEs on mental health. Examining SC as a moderator might have provided additional insights into its potential protective role, particularly in individuals with ACEs. Additionally, we did not differentiate among specific categories of ACEs in our analyses, which could have provided a more nuanced understanding of how different types of adversity uniquely impact attachment styles, self-compassion, and CPTSD symptoms. Future research could examine these categories separately to yield more tailored insights. Fourth, the cross-sectional design limits the ability to draw causal conclusions. While we examined SC as a mediator in this model, evidence suggests a bidirectional relationship between attachment styles and SC67, or even a causal relationship opposite to our hypothesis86, which could potentially alter the dynamics of this model. Studies implies that SC may co-develop with attachment styles, where both influence each other over time rather than following a unidirectional pathway87,88. The directionality of these relationships cannot be definitively established without longitudinal data. Given these considerations, future longitudinal studies could provide a more comprehensive understanding of the reciprocal influences between attachment styles and SC. Lastly, these findings are based on college students, and therefore it may not be possible to generalize these to other samples. This study examined the significant mediating role of SC in the relationship between attachment anxiety, attachment avoidance, and CPTSD symptoms among college students with ACEs. Our findings indicate that insecure attachment styles are linked to higher levels of CPTSD symptoms, with SC serving as a significant mediator. Higher SC levels are associated with reduced PTSD and DSO symptoms, particularly the latter. These results emphasize the importance of fostering SC to mitigate the negative mental health outcomes associated with insecure attachment styles in college students with ACEs. 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Students Mental Health Education and Counseling Center, Kunming University of Science and Technology, Kunming, 650500, China Yu Peng Faculty of Social Sciences and Liberal Arts, UCSI University, 56000, Kuala Lumpur, Malaysia Yu Peng & Zahari Ishak You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Y.P. The first draft of the manuscript was written by Y.P. and all authors commented on previous versions of the manuscript. Z.I. provided critical edits. All authors discussed the results and contributed to the final manuscript. All authors read and approved the final manuscript. Correspondence to Zahari Ishak. The authors declare no competing interests. We confirm that the research was performed in accordance with relevant guidelines/regulations, and approval was obtained from the ethics committee of Kunming University of Science and Technology (Approval No: KMUST-MEC-149). Informed consent was obtained from all participants with an assent from the participant. This study has been performed in accordance with the Declaration of Helsinki. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. 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Sci Rep15, 786 (2025). https://doi.org/10.1038/s41598-024-84947-3 Download citation Received: Accepted: Published: DOI: https://doi.org/10.1038/s41598-024-84947-3 Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article.
Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Advertisement Scientific Reportsvolume 15, Article number: 837 (2025) Cite this article Metrics details Benign paroxysmal vertigo (BPV) is a common cause of dizziness, and some patients are comorbid with psychiatric disorders such as depression, requiring intervention with antidepressants. However, the causal association between BPV, depression and antidepressants has not been clearly established. We used two-sample bidirectional Mendelian randomization (MR) to analyze the causal association between BPV, depression, and antidepressants. From a Finnish database, 43,280 patients with depression and 329,192 controls, and 106,785 patients with antidepressants and 88,536 controls were selected. Independent single nucleotide polymorphisms (SNPs) for depression and antidepressants were used as instrumental variables (IVs) with genomic significance (p < 5 × 10–8). Similarly, genome-wide association study (GWAS) data for BPV were selected from a Finnish database consisting of 8280 cases and 359,094 controls. Afterwards, a two-sample MR study was performed using R’s Two Sample MR and MR-PRESSO software packages. The multiplicity and heterogeneity of the data, as well as the effect of individual SNPs on the results were investigated. The main statistical analyses were weighted median, weighted mode, MR-Egger and weighted inverse variance weighting (IVW) for random effects. Finally, we identified associations between BPV, antidepressants and depression. Four outliers (rs3773087, rs4619804, rs62099231, rs7192848) were found to be associated with depression. After removing the outliers, the statistics showed no heterogeneity (p > 0.05) and horizontal pleiotropy (p > 0.05). Antidepressants were also found to have a random effect IVW (β = 0.440; p = 9.692 × 10–6; OR = 1.553; 95% CI 1.278–1.887). The inverse MR random effects IVW results showed a causal association between BPV and antidepressants (β = 0.051; p = 0.045; OR = 1.052; 95% CI 1.001–1.1066). In conclusion, there was a significant causal association between antidepressants and BPV at the genetic level. Clinicians should pay attention to patients with BPV combined with depressive disorders and develop timely interventions. Dizziness, as a common disabling symptom, its impact on an individual’s quality of life and socio-economic impact cannot be ignored. According to statistics, the prevalence of dizziness is about 20–30%1, and especially in patients with vertigo2, suffering from tremendous life stress. The most prevalent peripheral vestibular dysfunction injury among the vertigo types is benign paroxysmal vertigo (BPV), which is typified by sporadic episodes of vertigo, nausea, vomiting, and an unsteadiness sensation brought on by head movements3. BPV is associated with abnormal movement of otolith particles, and otoliths and calcium carbonate particles exiting the elliptical capsule into one side of the semicircular canals are the main etiologic factor in BPV. BPV has a lifetime prevalence of 2.4% and can affect people at any stage in life4. The incidence and progression of BPV have been linked to a number of variables, including old age, head trauma, vitamin D inadequacy, osteoporosis, and psychosocial problems5. The complex interaction of these factors makes the prevention and treatment of BPV more complicated. Vertigo caused by BPV can lead to anxiety and depression in patients and even affect their sleep, thus forming a vicious circle. Manual reduction is a non-invasive maneuver designed to reset displaced otoliths in the inner ear and is the most effective treatment for BPV6. After manual reduction treatment, some BPV patients still experience dizziness, which means they may have anxiety or depression and require medication. Residual symptoms such as dizziness and depression are more common in BPV patients treated with antidepressants7. There is still no consensus on the use of antidepressants for BPV with depression. The causal relationship between BPV and depressive disorders as well as between BPV and antidepressant treatment remains unclear. However, the causal relationship between BPV and depressive disorders and whether depression after the onset of BPV is treated with depression medications remain unclear8. Mendelian randomization (MR) uses genetic mutations as instrumental variables (IVs)9. MR is helpful in reducing confusion and the likelihood of drawing false conclusions about reverse causality because the postnatal environment has no effect on these mutations10. MR studies usually use publicly available genome-wide association studies (GWAS) to obtain large samples and to be able to identify additional causal pathways11. As a result, MR has emerged as a useful technique for etiological research investigating causality. In order to better understand the causal relationship among depression, antidepressants, and BPV and to offer a fresh viewpoint on the etiology of BPV, this study employed the MR approach to investigate the impact of depression and antidepressants on the etiology of BPV. From the Finnish database, summary GWAS data for depression and antidepressants were retrieved (r9.finngen.fi)12. The depression study included 372,472 individuals from Europe, including 43,280 cases and 329,192 controls, with a total of 20,170,115 single nucleotide polymorphisms (SNPs). The antidepressants study comprised 195,321 European individuals, 106,785 of whom were in the medication group and 88,536 of whom were not on medication. Together, these individuals carried a total of 20,160,506 SNPs. The International Classification of Diseases, Tenth Revision (ICD-10) codes F32 and F33 were used to define depression. The Finnish database’s greatest BPV GWAS summary data were chosen (r9.finngen.fi)12. A total of 367,374 individuals from Europe, including 8280 cases and 359,094 controls, were included in this GWAS database, with a total of 20,170,074 SNPs. The International Classification of Diseases, Tenth Revision, H81.8 code was used to define all cases (ICD-10). Depression and antidepressants were the subjects of instrumental variables to research. The three MR analysis assumptions were satisfied by the instrumental variables used in this study: they had a significant correlation with exposure factors and had no relationship with confounding variables. Through exposure factors, instrumental variables have an impact on results. Using a genome-wide significance threshold of p < 5 × 10–8, we first identified SNPs linked to depression and antidepressants. Strong LD-induced linkage disequilibrium between SNPs was eliminated by clumping distance = 10,000 kb and r2 < 0.001. We then excluded SNPs associated with BPV (p < 1 × 10−5). Next, we determined that female sex, hypertension, hyperlipidemia, diabetes mellitus, osteoporosis, and vitamin D deficiency were confounding factors linked to BPV5,13,14,15. LDlink database was used to exclude confounding factors16. The deletion of palindromic SNPs with intermediate allele frequencies ensured the accuracy of the findings. Simultaneously, SNPs with F-statistics > 10 were chosen as instrumental variables in order to guarantee a higher correlation between instrumental variables and exposure17. F = β2/SE2 was the formula used to compute the F-statistic18.R2 using the formula R2 = (2 × EAF × (1 − EAF) × β2) /[ (2 × EAF × (1 − EAF) × β2) + (2 × EAF × (1 − EAF) × N × SE2)]19. Ultimately, we were able to identify 15 SNPs linked to antidepressants and 9 SNPs linked to depression (Supplementary Tables 1 and 3). Before identifying the SNPs linked to depression, we eliminated one palindrome SNP (rs11756123) and five confounding SNPs (rs3773087, rs4619804, rs62099231, rs7192848, rs9324959). No SNP was linked to BPV. IVs were derived from these nine SNPs (F-statistic > 10). Before we discovered SNPs linked to antidepressants, we eliminated three palindromic SNPs (rs117661209, rs2011374, and rs805826). There were no SNPs linked to BPV after seven confounding SNPs (rs115493740, rs2011374, rs328301, rs59956089, rs805826, rs9270366, rs9845443) were eliminated; these 15 SNPs were used as IV (F statistic > 10). To assess the likelihood of reverse causality, we performed a reverse MR analysis. In order to investigate the causal relationship between BPV and depression and antidepressants, BPV was employed as an instrumental variable. When selecting IVs with a p < 5e-8, we loosened the selection criteria for SNPs to a p < 1e-5 because there were only a limited number of SNPs available for reverse MR20. In order to confirm that the included SNPs still met the IVs requirements, we also calculated the F-statistics for each SNP in the reverse MR analysis, as was previously mentioned. The specific analysis methods employed were identical to those for the two-sample MR. We took nine distinct genetic IVs for depression out of the combined GWAS data set. Independent genetic IV association with BPV GWAS was shown. (Supplementary Table 1). In all, 24 distinct genetic IVs were extracted from the pooled BPV GWAS data sets. An independent genetic IV association with depression (GWAS) was shown. (Supplementary Table 2). From the combined GWAS dataset for antidepressants, we isolated 15 distinct genetic IVs for each. An independent genetic IV association with BPV GWAS was shown. (Supplementary Table 3). The BPV GWAS pooled data set contained 27 distinct genetic IVs that we separately extracted. Showed an independent genetic IV association with the antidepressants GWAS. (Supplementary Table 4). Using the Two Sample MR and MR-PRESSO packages in R, version 4.3.1, we conducted two-sample MR analyses for depression, antidepressants, and BPV21. Horizontal pleiotropy was found using the MR-PRESSO method and the MR-Egger’s intercept test22, p > 0.05 showed that the GWAS of depression and antidepressants gene instrumental variables for BPV did not exhibit horizontal pleiotropy. To find heterogeneity in the MR analysis, Cochran Q statistics (MR-IVW) and Rucker Q statistics (MR-Egger) were employed, and p > 0.05 indicated no heterogeneity23. Outliers, which typically affect the heterogeneity of the data, were found in our MR analysis using the MR-PRESSO analysis distortion test. After eliminating outliers, we must reanalyze the data24. The causal relationship between depression, antidepressants, and BPV was examined using the MR-Egger, weighted median, IVW, simple model, and weighted model methods. The IVW results served as our primary foundation25. Auxiliary judgment was based on the MR-Egger, weighted median, simple pattern, and weighted pattern methods26. P < 0.05 was considered a statistically significant difference. We expressed each putative causal effect of depression, antidepressants, and BPV separately using “mr” and “mr_scatter_plot” in R27. To calculate the single SNP effect size of depression and antidepressants on BPV, “mr_forest_plot” was utilized28. To find out if a single SNP had an impact on the causal relationship between depression, antidepressants, and BPV, “mr_leaveoneout_plot” sensitivity analysis was employed29. In our study, we investigated the genetic interplay between depression, antidepression, and BPV using MR analysis. Initially, we identified four outliers (rs3773087, rs4619804, rs62099231, rs7192848) in the MR analysis between depression and BPV. However, no outliers were found in the analysis between antidepressants and BPV. When outliers were eliminated from the MR analysis of depression, antidepressants, and BPV, there was no longer any horizontal pleiotropy (p > 0.05) or heterogeneity (p > 0.05) (Tables 5–8 of Supplementary Material). We found no correlation between BPV and depression. Further MR analysis, conducted after excluding the outliers, did not reveal any gene-level causal relationship between depression and BPV. The results of the depression and BPV analyses were as follows: IVW (β = − 0.025; p = 0.846; OR = 0.976; 95% CI 0.763–1.249), MR-Egger (β = − 0.461; p = 0.587; OR = 0.630; 95% CI 0.130–3.050), simple model (β = − 0.224; p = 0.443; OR = 0.799; 95% CI 0.475–1.346), weighted model (β = − 0.215; p = 0.413; OR = 0.806; 95%CI 0.488–1.332), weighted median (β = − 0.146; p = 0.356; OR = 0.864; 95% CI 0.632–1.180). These findings suggest that there is no evidence of a causal relationship between depression and genetic alterations in BPV. By MR analysis, there was no genetic causal effect of BPV on depression. The following were the findings from the analyses of depression and BPV: IVW (β = 0.030; p = 0.230; OR = 1.030; 95% CI 0.981–1.082), MR-Egger (β = 0.004; p = 0.942; OR = 1.004; 95% CI 0.906–1.112), simple model (β = 0.086; p = 0.256; OR = 1.090; 95% CI 0.944–1.257), weighted model (β = 0.072; p = 0.295; OR = 1.075; 95% CI 0.943–1.226), weighted median (β = 0.044; p = 1.182; OR = 1.045; 95% CI 0.980–1.113). According to our analysis, there is no connection between depression and genetic variations in BPV. On the other hand, our MR analysis demonstrated a genetic causal effect of antidepressants on BPV. Antidepressants and BPV analysis showed that IVW (β = 0.440; p = 9.692 × 10–6; OR = 1.553; 95% CI 1.278–1.887), MR-Egger (β = 1.233; p = 3.132 × 10–1; OR = 3.432; 95% CI 0.345–34.088), simple model (β = 0.216; p = 3.726 × 10–1; OR = 1.241; 95% CI 0.781–1.972), weighted model (β = 0.203; p = 4.345 × 10–1; OR = 1.225; 95% CI 0.754–1.990), weighted median (β = 0.271; p = 4.930 × 10–2; OR = 1.311; 95% CI 1.004–1.771). Our analysis concluded that genetic changes are causal between antidepressants and BPV. Furthermore, our MR analysis revealed a genetic causal effect of BPV on antidepressants. The results of BPV and antidepressants analysis were as follows: IVW (β = 0.051; p = 0.045; OR = 1.052; 95% CI 1.001–1.106), MR-Egger (β = − 0.007; p = 0.903; OR = 0.993; 95% CI 0.894–1.104), simple model (β = 0.042; p = 0.448; OR = 1.042; 95% CI 0.937–1.160), weighted model (β = 0.046; p = 0.457; OR = 1.047; 95% CI 0.932–1.176), weighted median (β = 0.042; p = 0.178; OR = 1.042; 95% CI 0.984–1.104). Our analysis concluded that there is a causal relationship between genetic changes in BPV and the use of antidepressants. Lastly, our MR analysis did not detect any significant bias in the SNP effects for depression and antidepressants. Analyses using weighted median, IVW, simple mode, weighted mode, and MR-Egger methods demonstrated that the impact of antidepressants on depression did not influence BPV for any SNP variation (Fig. 1). The analysis of the single SNP effect size further demonstrated this point (Fig. 2). Simultaneously, our MR leave-one-out sensitivity analysis demonstrated that altering any depression or antidepressant SNP by deleting a particular SNP did not alter the outcomes (Fig. 3). Individual estimates about the putative causal effect of depression, antidepressants with BPV. (a) Depression and BPV. (b) BPV and depression. (c) Antidepressants and BPV. (d) BPV and antidepressants. IVW inverse variance weighted, MR Mendelian randomization, SNP single‐nucleotide polymorphism, BPV benign paroxysmal vertigo. Forest plot of depression, antidepressants associated with BPV. (a) Depression and BPV. (b) BPV and depression. (c) Antidepressants and BPV. (d) BPV and antidepressants. MR Mendelian randomization, BPV benign paroxysmal vertigo. MR leave‐one‐out sensitivity analysis for the effect of depression and antidepressants SNPs with BPV. (a) Depression and BPV. (b) BPV and depression. (c) Antidepressants and BPV. (d) BPV and antidepressants. MR Mendelian randomization, SNP single‐nucleotide polymorphism, BPV benign paroxysmal vertigo. To our knowledge, this is the first two-sample MR analysis of the association between BPV and antidepressant risk using a large genetic dataset. The MR results support a causal relationship between BPV and antidepressants. It provides ideas for the clinical management of patients with BPV combined with depression. We found no significant causal association between BPV and depression. However, there are different views on the relationship between depression and BPV. Many studies have found that depression levels are generally higher in BPV patients than in the general population30. Depressive disorders have been reported to produce somatic symptoms, including BPV31. In a population-based cohort study based on Taiwan, the risk of subsequent development of BPV was 1.55 times higher in patients with depressive disorder than in the general population32. A national cohort observational study from Korea showed that mood disorders increased the risk of BPV. In the subgroup analysis, the incidence of BPV in the emotional disorder group was significantly higher than that in the general population in all age groups and both genders33. This may be associated with proinflammatory cytokines playing an important role in mood disorders, altering serotonin levels and glutamate receptor activity34,35,36,37, and inducing oxidative stress in endothelial cells, mediating the recurrence of vasospasm, which can be displaced from the macular epithelium via otoliths, leading to BPV38. These studies suggest an epidemiological association between depression and BPV. Studies have found higher rates of comorbid psychiatric disorders in BPV patients39. The risk of depressive disorder is significantly increased in BPV patients. However, a meta-analysis of BPV and depressive disorder found that the association between BPV and depressive disorder was not statistically significant40. Our results suggest that there is no significant causal relationship between BPV and depression at the genetic level. The differences in the results of studies on the relationship between BPV and depression may be related to a variety of factors, including study design, patient population, and interventions. In the future, larger and rigorous clinical studies may be needed to explore the relationship between BPV and depression. Dizziness is a common side effect of many antidepressant treatments and may contribute to residual instability after BPV treatment41,42. The persistence of residual symptoms after manual reduction of BPV may be related to age, times of manual reduction, anxiety or depression, and other factors. Residual symptoms were more common in patients treated with antidepressants7. The unpredictability and uncontrollability of residual symptoms and vertigo attacks after BPV treatment may aggravate psychological distress and reduce the quality of life of patients. Some patients need antidepressant treatment to relieve the symptoms of depressive disorder after BPV7. However, there is no unified consensus on the use of antidepressants for BPV-comorbid depressive disorders. Our study found a significant causal association between antidepressants and BPV. Due to the high prevalence of comorbid psychiatric disorders in patients with BPV, this suggests that they may require antidepressants to help manage the mental health issues associated with vertigo. As a result, there is a potential increased demand for antidepressants. This may lead to a vicious circle between depressive disorder and vestibular dysfunction, which seriously affects the quality of life of patients. Therefore, in clinical work, we should pay more attention to the mental health of BPV patients, determine whether BPV patients are complicated with depression, and provide psychological counseling rather than direct use of antidepressant drugs. We examined the causal relationship between depression, antidepressants, and BPV onset. At the same time, we also examined the reverse causality between depression, antidepressants, and BPV. This study is not without limitations, though. First, the results of our study are only applicable to individuals with European ancestry, and this limits the generalizability of the findings. Thus, further research utilizing larger ethnic data sets might be required to evaluate the generalizability of our findings to other ethnic groups. Second, there is no guarantee that the SNPs will be specific in identifying particular symptoms and the severity of BPV. Furthermore, a prospective randomized controlled trial was not carried out in our study to confirm the effectiveness of antidepressant-focused interventions for the treatment of BPV. As consequently, the possible benefit of reducing antidepressant use in treating BPV-comorbid depressive disorders could not be determined by our research. Our findings are drawn from a genetic standpoint and are meant to provide clinicians with insight into potential treatment options. Using Mendelian randomization techniques, we have demonstrated a significant genetic causal association between antidepressants and BPV. This has prompted us to strengthen our focus on mental health issues in patients with BPV in our clinical practice, to assess psychiatric disorders at an early stage, and to work with mental health professionals to provide a more comprehensive treatment program, if necessary. In addition, the biological basis of this association should be studied in greater depth in the future to elucidate the underlying mechanisms. You can find all the datasets created and/or examined in this study in the FinnGen repository (https://r9.finngen.fi). The website (https://mrcieu.github.io/TwoSampleMR/articles/index.html) has the R code that has been analyzed. Pothier, D. D. et al. 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Department of Neurology, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, Guangxi, China Yayun Liao, Kejian Zhou, Lu Qin, Shan Deng, Hong Yang, Baohui Weng & Liya Pan Department of Rehabilitation, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China Zhiyan Guo You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar You can also search for this author in PubMedGoogle Scholar Liya Pan, Yayun Liao and Kejian Zhou designed the study; Yayun Liao and Kejian Zhou wrote the manuscript; Yayun Liao, Zhiyan Guo, Lu Qin, Shan Deng, Hong Yang and Baohui Weng performed the statistical analysis. Yayun Liao and Kejian Zhou are co-first authors. All authors agreed to the published version of the manuscript. Correspondence to Liya Pan. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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Clear skies. Low 27F. Winds light and variable.. Clear skies. Low 27F. Winds light and variable. Updated: January 4, 2025 @ 5:55 pm Clear skies. Low 27F. Winds light and variable.. Clear skies. Low 27F. Winds light and variable. Updated: January 4, 2025 @ 5:55 pm
Lexie Boone, a member of the Colonial Life Arena staff for more than 17 years, is the arena’s new general manager at the arena. He officially began begin his new duties Jan. 2. Since July 2015, Boone has served as Senior Assistant General Manager. He started working at Colonial Life Arena in January 2003 in Premium Services and Sales. In between his Colonial Life Arena stints, he was an Event Manager at Wells Fargo Center in Philadelphia, Pa. (Aug. 2005-Sept. 2006) and General Manager at UCF Arena in Orlando, Fla. (Jan. 2008-Dec. 2011). Boone has overseen the booking arrangements for all Colonial Life Arena events since 2012. Boone replaces Sid Kenyon, who had been the Colonial Life Arena General Manager since January 2015. Kenyon retired after serving many roles in his more than 47 years at the University of South Carolina. He will continue to teach classes in the school’s College of Hospitality, Retail and Sport Management (HRSM). “Lexie has been an outstanding contributor to the success of Colonial Life Arena,” said USC Athletics Director Ray Tanner. “He and Sid have made a fantastic team, and I have no doubt that the accomplishments will continue under Lexie’s direction. Lexie is a wonderful manager and leader.” Boone, a 2001 graduate of the University of Mississippi, and his wife Kim, have two children, a daughter, Cora, and a son, Battley. Kim is the Director of Events, Engaged Learning and Outreach for the USC Department of Sport and Entertainment Management. “This is an incredible honor and one that I take great pride in,” said Boone. “I’m very thankful and excited to have the opportunity to continue leading the Colonial Life Arena team as we strive to take our success to new heights.” Colonial Life Arena continues to be one of the highest financially grossing University-owned facilities in the nation. In 2022-23, the arena had its third highest grossing fiscal year in its history. Colonial Life Arena is operated by the University of South Carolina Athletics Department. It is the primary home to Gamecock men’s and women’s basketball and hosts many other events, including concerts, conferences, entertainment shows and graduations. Colonial Life Arena opened in 2002 and was named Colonial Center from 2003-08 and Colonial Life Arena since 2008. Colonial Life & Accident Insurance Company is based in Columbia, S.C. and is a sponsor of Gamecock Athletics. Your comment has been submitted.
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Latest AI Amazon Apps Biotech & Health Climate Cloud Computing Commerce Crypto Enterprise EVs Fintech Fundraising Gadgets Gaming Google Government & Policy Hardware Instagram Layoffs Media & Entertainment Meta Microsoft Privacy Robotics Security Social Space Startups TikTok Transportation Venture Events Startup Battlefield StrictlyVC Newsletters Podcasts Videos Partner Content TechCrunch Brand Studio Crunchboard Contact Us A Vietnamese cryptocurrency company Modern Tech launched an ICO for its Pincoin token, raising $660 million from approximately 32,000 people. The company first ran the Pincoin ICO, promising constant returns to investors, and then launched another token, iFan (a social network token for celebrities). Picoin investors first received cash from their investment and then the team began paying out rewards to Pincoin investors in iFan tokens. Then the team disappeared. This so-called exit scam could be that largest in recent memory and is also indicative of what’s to come in the ICO space. The team of seven Vietnamese nationals seem to have left the country while scammed investors massed outside the company’s old headquarters. From Tuoi Tre news: In fact, the real mastermind behind these projects is a team of seven Vietnamese nationals, who have held different conferences in Hanoi, Ho Chi Minh City and even remote areas to lure investors. Investors have been told that they would enjoy a profit rate of 48 percent a month from their initial investment, and recoup all investments after four months. People would also be rewarded with an eight percent commission for every new member they have introduced to the network. Pincoin was particularly unique in that it offered bonuses for bringing other people into the program, a tactic that might sound familiar. The scammers paid out in cash until January when they began sending iFan tokens. Then, last month, the team vacated their fancy offices leaving only an oddly well-made – if incomplete – website in its wake. Taking a closer look at the site we find a model of obfuscation. The mission – “The PIN Project is about building an online collaborative consumption platform for global community, base on principles of Sharing Economy, Blockchain Technology, and Crypto Currency” – seems on par with other pie-in-the-sky solutions but there is no mention of any founders or advisors and even their fancy, multi-lingual white paper, has no clear founder information. In short, the team paid a great deal for a very nice website and convinced thousands of people it was legitimate. According to Viet Bao, a team consisting of Bui Thi My Ngoc, Ho Phu Ty, Ho Xuan Van, Luong Huynh Quoc Huy, Luu Trong Tuan, Nguyen Duc Trong, Nguyen Trung Hieu, and Vu Huu Loi led Pincoin and iFan from zero to multi-millions in a few months while claiming they were representing products from Singapore and India. “To formalize the mode of operation, ifan and Pincoin authorized their company as legal representative in Vietnam with tax code 0314707223. Modern Tech then held the event in Ho Chi Minh City Minh and Hanoi to raise capital from investors,” Viet Bao reporter. One interesting bit of chicanery is this screen from the iFan page. Near the middle of the page we find information that the token is based on the Ethereum platform. The page shows the price and rating of the cryptocurrency, suggesting that the Ethereum is directly related to the iFan price. And this popped up when I visited the project’s white paper: Again we find that the current, unregulated, ICO market is the most interesting system for parting fools from their money in recent history. Topics Contributor Uber CEO Dara Khosrowshahi resigns from self-driving truck startup Aurora’s board
Share Disclosure: This article does not represent investment advice. The content and materials featured on this page are for educational purposes only. As Bitcoin’s rally nears $100k, the crypto market turns bullish, making now the perfect time to explore top altcoins like XRP, DOGE, SUI, and rising stars like Rollblock (RBLK). Bitcoin is on its way back up to $100k again, and the crypto market is turning bullish in response. Traditionally, where Bitcoin leads, altcoins follow, but what are the best altcoins to buy now? In this article, we’ll look at four different alts, from legacy crypto XRP to meme coin DOGE and see how they will perform as older altcoins. We’ll also take a look at newer cryptos, such as Rollblock (RBLK), whose presale has caught the attention of the crypto market at large. Looking at the SUI price, it could be that this relatively new crypto is enjoying its first bull market, too. Read on to examine the potential of these four altcoins. With the 2025 bull market starting up, savvy investors are seeking the best altcoins to buy now, and Rollblock’s successful and ongoing presale has captured their attention. This GambleFi platform combines cutting-edge features like Revenue Sharing and deflationary tokenomics, with everything you’d expect from an online gambling platform. Its presale has already raised $8 million, and with the token price soaring over 340%, it signals confidence in Rollblock’s potential to tap into the $500 billion online gambling industry. Investors aren’t merely attracted to price speculation but to the real-world utility of its token. RBLK token holders benefit from passive income through revenue sharing, and with a 30% APY for staking, it’s using the best of DeFi to attract long term investment. DOGE has faced a significant correction after its impressive Q4 rally. Despite this pullback of the DOGE price, the broader crypto bull market is gaining momentum, which is where meme coins like DOGE often thrive. As the expected 2025 bull market gains pace, DOGE could emerge as one of the best altcoins to buy now. SUI recently reached a new all-time high (ATH), and although a slight correction followed, the SUI price remains robust as sentiment among crypto investors grows bullish. SUI’s performance indicates a solid foundation, and analysts speculate the SUI price could continue its upward trajectory in 2025, especially given that it is SUI‘s first cycle. XRP surged an impressive 5x in November after strong links with the Trump administration. With the bull market of 2025 approaching, and of course, Trump taking office this month, analysts believe XRP could reach a new ATH, which would only be a 50% increase in the XRP price. Of course, nothing is clear from Trump, even though he seems to be pro crypto, XRP’s strong performance suggests that of all the legacy cryptos, it’s one of the best altcoins to buy now. With a new pro-crypto US government, this year is expected to be extremely bullish, so which altcoins to buy now? XRP ran wildly in November and looks well positioned with the government. The SUI price also looks primed to take off, and you should never write off DOGE in a bull market. But it’s the newest cryptos that bring in the biggest gains, and if Rollblock’s presale success is a signal, then 2025 could belong to RBLK. For more information, visit the Rollblock presale website and join the online community. Disclosure: This content is provided by a third party. crypto.news does not endorse any product mentioned on this page. Users must do their own research before taking any actions related to the company. Deep Dives Social media is finally breaking free from the shackles of Big Tech | Opinion From print to TikTok: PR perspectives on the end of media and influencers | Opinion China continues to tighten crypto regulations. Do other countries take notes? Crypto charts to watch for 2025: Bitcoin, Solana, and XRP What does 2025 have in store for crypto and digital assets? | Opinion Top five blockchain analytics trends for 2025 | Opinion Is there a crypto thaw in Russia and why? African crypto revolution continues as Ethiopia ranks among global BTC mining leaders Top 5 reasons why Ethereum could take off in 2025 Cardano co-founder explores Delimited continuations: What does it mean for Cardano? Get crypto market analysis and curated news delivered right to your inbox every week. You have successfully joined our subscriber list.
It was winner number three of 2025 for Tiz The Law on Saturday when Versus (3c Tiz The Law x Evita’s Sister, by Candy Ride) broke his maiden in his second start. Returning to the one mile on the turf distance he debuted at in December, Versus raced just a few lengths off the leader in the four path for much of the early running. Asked to get serious about his job in the middle of the far turn, he responded well to David Egan. The colt raced hard to the line to win by a neck. Versus is trained by Kelly Breen for Screen Door Stables and was bred by Fox Run Homestead. The Kentucky-bred is a half-brother to Practical Joke’s Grade III winner Money Supply and the stakes winning Sister Nation among four winners out of Evita’s Sister. The mare is also the granddam of stakes winner Notorious Gangster. Evita’s Sister is a two-time winning full sister to the Grade I winner Evita Argentina with Grade I winner Trippi also in the family. All three of Tiz The Law’s 2025 winners have been maiden winners, taking his overall total to 25.
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