Search Results (36)

The rapid progress of generative artificial intelligence (AI) has sparked growing concerns regarding its misuse, privacy risks, and ethical issues. This study investigates the interplay between Generative AI Abuse Anxiety, trust, perceived usefulness, acceptance, and the intention to use it. Using variance-based partial least squares (PLS-SEM), we analyze 318 valid survey responses. The findings reveal that Generative AI Abuse Anxiety negatively impacts trust, perceived usefulness, acceptance, and the intention to use generative AI. Additionally, different subdimensions of trust play significant roles in influencing users’ technology acceptance and intention to use it, though the specific mechanisms differ. This research extends the applicability of the technology acceptance model to the generative AI context and enriches the multidimensional framework of trust studies. Full article

This study reviews how technology-based interventions have been designed and implemented to promote lung cancer screening (LCS), support shared decision-making, and enhance patient engagement. A systematic search of six databases in February 2025 identified 28 eligible studies published between 2014 and 2025. Most interventions were home-based and self-guided, including videos, websites, mobile apps, telehealth, and patient portal messages. Common features included risk calculators, multimedia content, simplified navigation, and integration with electronic medical records. These tools aim to raise awareness, improve informed decision-making, and support smoking cessation. While 82% of studies reported positive effects on knowledge and decision-making confidence, only some showed an increased screening uptake. Key barriers included limited internet access, low digital literacy, provider time constraints, fear or anxiety, and concerns about radiation or cost. Despite these challenges, digital tools show promise in advancing LCS promotion. Their effectiveness, however, depends on thoughtful design, integration into clinical workflows, and equitable access. Future work should address structural and contextual challenges to scale digital health solutions and reduce disparities in screening participation. This review identifies both the potential and limitations of current interventions and offers guidance for enhancing impact through targeted, accessible, and user-informed approaches. Full article

Exposure to fine particulate matter (PM2.5) has become an increasing public health concern, particularly in urban areas facing severe air pollution. In response, individuals are increasingly turning to real-time tracking systems and self-monitoring tools. This study aimed to examine the association between PM2.5 risk perception, self-monitoring behaviors, and anxiety levels in the general population of Thailand. A cross-sectional survey was conducted during the dry season using an online questionnaire, which included the 7-item Generalized Anxiety Disorder (GAD-7) scale. A total of 921 participants residing in Bangkok and Chiang Mai were included. Binary logistic regression analysis, adjusted for sex, age, marital status, monthly income, and years of residence, revealed a significant association between anxiety and perceived health risks of PM2.5 exposure (OR = 1.09; 95% CI: 1.06–1.13). Daily self-monitoring of air quality over the past two weeks was also significantly linked to higher anxiety levels compared to non-monitoring individuals: OR = 1.92 (95% CI: 1.11–3.33) for websites, OR = 1.65 (95% CI: 1.01–2.72) for mobile apps, OR = 1.72 (95% CI: 1.12–2.64) for air purifiers, and OR = 3.34 (95% CI: 1.77–6.31) for air quality detectors. Monitoring 4–6 days per week using apps and air detectors was similarly associated with increased anxiety (OR = 1.64 and 2.30, respectively). Heightened perception of PM2.5 health risks and frequent self-monitoring behaviors are associated with increased anxiety among urban residents in Thailand. Public health interventions should consider implementing targeted alert systems during high-pollution periods and prioritize strategies to reduce PM2.5 emissions to alleviate public anxiety. Full article

Parkinson’s disease (PD) is a common movement disorder affecting adults. People diagnosed with PD can have a multitude of physical (motor) symptoms, including tremors, and rigidness, and psychological (non-motor) symptoms, including anxiety and depression. These symptoms dramatically affect daily living activities, including dressing oneself, preparing meals, and speaking and writing. Background/Objectives: To determine the symptom similarities and differences among PD patients, a method referred to as cluster analysis can be applied to patient data. This method can separate patients who differ by symptom presence while grouping patients with disease similarities. Previous PD cluster analysis studies provided patient groups that were defined by their age and disease duration—both numerical values—and excluded categorical values, such as patient gender, family history of the disease, and symptom presence. In addition, patient age and disease duration were limited in range in previous studies, providing a patient group that was too similar to divide into distinct clusters. Methods: This study utilized a decision tree cluster analysis method applied to categorical symptom data from PD patients. The applied cluster method automatically determines the number of clusters, reducing estimation errors, as many cluster analysis methods require the end user to estimate the number of clusters prior to applying cluster analysis. A post analysis of additional categorical and numerical variables was conducted, and this provided a means to describe the PD patient clusters in terms of gender, family history of PD, median age, disease duration, and symptom presence. The patient dataset utilized was accessed from the Parkinson’s Progression Markers Initiative (PPMI) website. Results and Conclusions: The cluster analysis results provided a means to describe seven PD patient subtypes based on motor symptom presence, with the largest PD patient cluster containing half of the patient sample, and these individuals had three of the motor symptoms present: bradykinesia, rigidity, and tremors. Full article

Informational divergence emerged as a significant phenomenon during the COVID-19 health crisis. This period was characterized by information overload and changes in the communication of public health recommendations and policies by authorities and media outlets. This study examines the impact of such divergence on the population’s mental health, focusing on primary emotions expressed in comments across digital ecosystems. A media EMIC approach was used to analyze digital ecosystems during March and April 2020. Data were collected from Twitter, YouTube, Instagram, official press websites, and internet forums, yielding 3,456,387 communications. These were filtered to extract emotion-expressing content, resulting in 106,261 communications. Communications were categorized into primary emotions (anger, disgust, joy, fear, and sadness) using an exclusionary emotion assignment procedure. Analysis techniques included polarity and term frequency calculation, content analysis using Natural Language Understanding, emotion intensity measurement using IBM Watson Analytics, and data reliability assessment using the ISMA-OWA operator. The findings suggest that exposure to informational divergence from governments, health organizations, and media negatively affected mental health, evidenced by sadness, fear, disgust, and anger, which are associated with elevated levels of stress, anxiety, and information fatigue. In contrast, information perceived as reflecting coordination, support, and solidarity elicited positive emotional responses, particularly joy. Full article

Background/Objectives: The COVID-19 pandemic has had a profound impact on physical, mental, and social aspects of life worldwide. This study aimed to explore and compare differences in knowledge, awareness, behavior, and the psychological impact of the pandemic among students of biomedical faculties at the University of Zagreb. Methods: A cross-sectional study was conducted between 27 November 2020 and 19 January 2021 involving 518 students from the School of Dental Medicine, School of Medicine, Faculty of Pharmacy and Biochemistry, and Faculty of Veterinary Medicine. An anonymous online questionnaire was distributed, assessing participants’ knowledge about SARS-CoV-2, information sources, attitudes, and psychological responses. Data were analyzed using descriptive statistics and appropriate statistical tests. Results: The median knowledge score was 61.54%, with senior and female students demonstrating significantly more knowledge (p < 0.001 and p = 0.044, respectively). Students who consulted the scientific literature and official websites had higher knowledge levels (p < 0.001). Most participants used media and social networks for pandemic information, while scientific sources were underutilized. Psychological impacts were evident, with 46.3% expressing fear about the future and 25% reporting anxiety if they were to be infected. Additionally, those who engaged with the scientific literature were more likely to accept vaccination and showed lower levels of COVID-19 stigma. A majority (64.5%) believed that the media exaggerated the pandemic’s risks. Conclusions: Biomedical students demonstrated moderate knowledge about COVID-19, with a clear link between scientific literacy and more informed, less stigmatizing attitudes. This study underscores the importance of reliable information sources in shaping public health awareness and highlights the need for further education on COVID-19 symptoms and preventive measures. Full article

This article aims to find the determinants that affect patient satisfaction regarding the Mawiidi public portal in Moroccan public hospitals and assess its outpatient online booking system effectiveness using a model that integrates the Technology Acceptance Model (TAM) with the Information Systems Success Model (ISSM) while adopting a quantitative research methodology. The analysis was conducted using 348 self-administered questionnaires to analyze eight key constructs, such as information quality, patient satisfaction, perceived ease of use, and privacy protection, among others. The results of PLS-SEM verified six out of eleven hypotheses tested, which reflected that information quality has a positive influence on perceived ease of use, which again enhances patient satisfaction. The major factors influencing the satisfaction and trust of patients in online appointment scheduling systems at public hospitals are highlighted. Indeed, privacy protection enhances patient satisfaction and trust. Service quality positively affects satisfaction but to a lesser degree. Website-related anxiety impacts perceived ease of use, although it has a limited influence on satisfaction. Such findings can inform suggestions for the managers of hospitals and portal designers to increase user satisfaction. This study uses a model from the TAM and ISSM frameworks, including cultural and socioeconomic aspects that apply to Morocco’s healthcare context. Full article

Research in the field of psychological health care has confirmed that chronic homesickness can exacerbate emotional problems. However, there is a lack of diverse products in the design field that can effectively address the issue of alleviating homesickness. This study explores how interaction design can mitigate homesickness-related anxiety while delivering a good user experience, from an application-driven perspective. An inclusive design approach was adopted. Firstly, an online survey was conducted in China through social media platforms (WeChat and websites), and 323 responses were analyzed to examine Chinese preferences for sounds that alleviate homesickness and the influence of various characteristics on the perception of hometown sounds. Secondly, personas were created to capture user needs. Based on regional differences, local sound characteristics, and adaptation to various audience backgrounds, this study examined the effects of these sounds on alleviating homesickness across different scenarios and proposed a sound therapy design model. The practical application of this model was demonstrated through the HomeSeek app, which provides personalized therapeutic experiences. Finally, an application usability test was conducted. The findings indicated that 92.5% of participants were satisfied with the homesickness therapy experience, demonstrating its effectiveness in alleviating homesickness and providing strong support for the research model. Full article

The purpose of this study was to prove the use of content and sentiment analysis to understand public discourse on Nytimes.com around the coronavirus (2019-nCOV) pandemic. We examined the pandemic discourses in the article contents, news, expert opinions, and statements of official institutions with natural language processing methods. We analyzed how the mainstream media (Nytimes.com) sets the community agenda. As a method, the textual data for the research were collected with the Orange3 software text-mining tool via the Nytimes.com API, and content analysis was conducted with Leximancer software. The research data were divided into three categories (first, mid, and last) based on the date ranges determined during the pandemic. Using Leximancer concept maps tools, we explained concepts and their relationships by visualizing them to show pandemic discourse. We used VADER sentiment analysis to analyze the pandemic discourse. The results gave us the distance and proximity positions of themes related to Nytimes.com pandemic discourse, revealed according to their conceptual definitions. Additionally, we compared the performance of six machine learning algorithms on the task of text classification. Considering the findings, it is possible to conclude that in Nytimes.com (2019-nCOV) discourse, some concepts have changed on a regular basis while others have remained constant. The pandemic discourse focused on specific concepts that were seen to guide human behavior and presented content that may cause anxiety to readers of Nytimes.com. The results of the sentiment analysis supported these findings. Another result was that the findings showed us that the contents of the coronavirus (2019-nCOV) articles supported official policies. It can be concluded that regarding the coronavirus (2019-nCOV), which has caused profound societal changes and has results such as death, restrictions, and mask use, the discourse did not go beyond a total of 15 main themes and about 100 concepts. The content analysis of Nytimes.com reveals that it has behavioral effects, such as causing fear and anxiety in people. Considering the media dependency of society, this result is important. It can be said that the agenda-setting of society does not go beyond the traditional discourse due to the tendency of individuals to use newspapers and news websites to obtain information. Full article

The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website’s content as a technical resource and reference. Full article

Pregnant women presumably gather information about the coronavirus disease 2019 (COVID-19) from various sources. However, it is difficult for pregnant women who are not medical professionals to source the appropriate information because of the infodemic related to the COVID-19 pandemic. Therefore, the objective of our study was to investigate how pregnant women gathered information about COVID-19 and COVID-19 vaccination. To address this issue, we conducted an online questionnaire survey between 5 October and 22 November 2021, which was approved by the Ethics Committee of Nihon University School of Medicine. We received 4962 responses after excluding 1179 insufficient answers. Our study found that age, occupation, and infection-risk anxiety influenced the selection of media for obtaining information. Pregnant women who were older, medical professionals, public servants, or educators tended to rely on specialized medical websites, whereas housewives tended to use mass media, social media, and sources with uncertain scientific evidence. Additionally, the number of weeks of gestation and the method of conception (natural or assisted reproductive conception) affected the selection of media. The accessibility of COVID-19 information for pregnant women was determined by their social background and pregnancy status. We need to continue making efforts to ensure that appropriate information is readily available to pregnant women and their families. Full article

Background: The COVID-19 pandemic has caused changes in the lives of many university students around the globe, including students at Maria Curie-Skłodowska University in Lublin, Poland. Isolation, a sense of threat, and the transition to remote learning resulted in numerous, mainly psychological, negative consequences for students. The university aimed to provide students with effective assistance during the COVID-19 pandemic. The question now is whether it has succeeded or failed. This study demonstrates good practices in mental health support at Maria Curie-Skłodowska University during the pandemic and post-pandemic period. Methods: The study was conducted between October and December 2022. The case study method and purposive sampling were used in the study. A total of 19 participants took part in this study. Of the respondents, 16 were females, 3 were males. Ages ranged between 26 and 55 years. Results: Research has shown that the university provided students with various forms of mental health support. During the COVID-19 pandemic, in the 2020/2021 academic year, students and university staff were provided with pedagogical, psychological, and/or psychotherapeutic consultations. Among the main problems reported by students and staff were anxiety, lowered mood, depressive conditions, failure to cope with stress, relationship problems at university, a spectrum of pandemic-related problems, life crises, and discrimination related to sexual orientation. Support was provided via web platforms, social networking websites, and by phone, directly and free of charge. Conclusions: The impact of the pandemic has exposed strengths and weaknesses in the management of the mental health support system at the university. It also showed new needs and directions of support. The university has new goals; one of the greatest is preparing students for the challenges of the future. Full article

Digital devices play a significant role in the learning and living of children and adolescents, whose overuse or addiction has become a global concern. This scoping review seeks to synthesize existing studies to investigate relevant interventions and their effects on digital addiction in children (ages 0–18). To understand the latest advances, we have identified 17 studies published in international peer-reviewed journals between 2018–2022. The findings revealed that, first, most interventions for digital addiction in children and adolescents were cognitive–behavioral therapies (CBT) or CBT-based interventions, which could improve anxiety, depression, and related symptoms of digital addiction. Second, rather than directly targeting addictive behaviors, some family-based interventions aim to strengthen family functions and relationships. Finally, digital-based interventions, such as website-based, application-based, and virtual reality interventions, are promising in adolescent digital addiction interventions. However, these studies shared the same limitations: small sample sizes, short intervention durations, no control group, and nonrandomized assignments. The small sample size problem is difficult to solve by offline intervention. Meanwhile, online digital-based intervention is still in its infancy, resulting in limited generalizability of the findings and the inability to popularize digital intervention. Accordingly, future intervention studies should integrate various assessments and interventions to form an integrated platform to provide interventions for addicted children and adolescents worldwide. Full article

Dental anxiety (DA) is a prevalent public health issue. However, there is a lack of self-administered DA interventions. The aim of this study was to evaluate the short-term effects of web-based interventions aiming to reduce DA in adults in two European countries. A pretest posttest design was used. Tailor-made websites were developed in Lithuania and Norway. Volunteers who self-reported DA were invited to participate. DA levels measured by the Modified Dental Anxiety Scale (MDAS) were assessed at baseline and after two weeks via online questionnaires. The interventions were completed by 34 participants in Lithuania and 35 participants in Norway. In Lithuania, the median posttest MDAS score (9.5, IQR 5.25) decreased compared to the median pretest MDAS score (14.5, IQR 8; Z value = −4.246, p < 0.001). The same was found in Norway—the median posttest MDAS score (12, IQR 9) was lower compared to the median pretest MDAS score (15, IQR 7; Z value = −3.818, p < 0.001). The present study demonstrated that two tailor-made web-based interventions had the potential to reduce dental anxiety levels when assessed in the short term in Lithuania and Norway. Studies with more controlled designs assessing long-term outcomes are needed to validate the results of this pilot study also in other cultures. Full article

The objective of this study was to describe pregnancy- and birth-related experiences of postpartum women during the third wave of the COVID-19 pandemic and their association with mental health outcomes. An online questionnaire was distributed in five European countries (Belgium, The Netherlands, Norway, Switzerland, UK) between June and August 2021. Participants were recruited though social media platforms including pregnancy- and motherhood-related websites, pregnancy fora, and apps. Postpartum women were asked eleven specific questions about pregnancy- and birth-related changes and the presence of support during delivery. The Edinburgh Depression Scale was used to assess depressive and anxiety symptoms. Covariates included sociodemographics, health and reproductive characteristics, and COVID-19 status. Associations were estimated with logistic regression. The study included 1730 postpartum women. Frequent changes included the exclusion of the partner from pregnancy care appointments (83.2%), changed prenatal care settings (64.4%), and cancellation of hospital information visits (42.7%). Few women, however, were without support apart from medical staff during delivery (1.4%). The number of pregnancy- and birth-related changes was associated with each woman’s mental health status, as well as the type of change. Experiencing changes related to delivery and cancellation or reduction of prenatal examination was associated with a doubling in the odds of symptoms of major depression and anxiety postpartum. These findings highlight the importance of ensuring adequate maternity care for women’s mental health postpartum, as well as during a pandemic. Full article