Background: Often lacking immediate access to care providers, pregnant individuals frequently turn to web-based sources for information to address their evolving physical and mental health needs. Social media has gained increasing prominence as a source of news and information despite privacy concerns and unique risks posed to the pregnant population.
Objectives: This study investigated the extent to which patients may be willing to disclose personal health information to social media companies in exchange for more personalized health content.
Methods: We designed and deployed an electronic survey to pregnant individuals worldwide electronically in 2023. We used the classical Internet Users' Information Privacy Concerns (IUIPC) model to examine how privacy concerns modulate pregnant individuals' behaviors and beliefs regarding risk and trust when using social media for health purposes. Results were analyzed using partial least squares structural equation modeling.
Results: Among 317 respondents who initiated the survey, 84% (265/317) of the respondents remained in the study, providing complete responses. Among them, 54.7% (145/265) indicated willingness to provide their personalized health information for receiving personalized health content via social media, while 26% (69/265) were uncertain and 19.3% (51/265) were opposed. Our estimated IUIPC model results are statistically significant and qualitatively align with the classic IUIPC model for the general population, which was previously found in an e-commerce context. The structural model revealed that privacy concerns (IUIPC) negatively affected trusting beliefs (β=-0.408; P<.001) and positively influenced risk beliefs (β=0.442; P<.001). Trusting beliefs negatively impacted risk beliefs (β=-o.362; P<.001) and positively affected the intention to disclose personal health information (β=o.266; P<.001). Risk beliefs negatively influenced the intention to disclose (β=-0.281; P<.001). The model explained 41.5% of the variance in the intention to disclose personal health information (R²=0.415). In parallel with pregnant individuals' willingness to share, we find that they have heightened privacy concerns and their use of social media for information seeking is largely impacted by their trust in the platforms. This heightened concern significantly affects both their trusting beliefs, making them less inclined to trust social media companies, and their risk beliefs, leading them to perceive greater risks in sharing personal health information. However, within this population, an increase in trust toward social media companies leads to a more substantial decrease in perceived risks than what has been previously observed in the general population.
Conclusions: We find that more than half of the pregnant individuals are open to sharing their personal health information to receive personalized content about health via social media, although they have more privacy concerns than the general population. This study emphasizes the need for policy regarding the protection of health data on social media for the pregnant population and beyond.
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http://dx.doi.org/10.2196/60862 | DOI Listing |
J Med Internet Res
March 2025
Inverness College, University of the Highlands and Islands, Inverness, GB.
Background: Artificial intelligence (AI) is rapidly transforming healthcare, offering significant advancements in patient care, clinical workflows, and nursing education. While AI has the potential to enhance health outcomes and operational efficiency, its integration into nursing practice and education raises critical ethical, social, and educational challenges that must be addressed to ensure responsible and equitable adoption.
Objective: This umbrella review aims to evaluate the integration of AI into nursing practice and education, with a focus on ethical and social implications, and to propose evidence-based recommendations to support the responsible and effective adoption of AI technologies in nursing.
Int J Pharm Pract
March 2025
Griffith University School of Pharmacy and Medical Science, 1 Parklands Drive, Southport, QLD 4215, Australia.
Objective: This study explored community pharmacists' experiences and perceptions of information transfer from Queensland health hospitals for patients during transitions of care and the current utilization of electronic medical records for accessing patient information.
Methods: Qualitative methodology was used involving in-depth semi-structured interviews with community pharmacists to explore their experiences and perceptions with information transfer during patients' transitions of care. Purposive sampling was used to ensure the participation of community pharmacists who had experience with the medication management of patients discharged from Queensland health hospitals.
Wearable Technol
February 2025
Department of Human Centered Design, Cornell University, Ithaca, NY, USA.
Real-time measurement of head rotation, a primary human body movement, offers potential advantages in rehabilitating head or neck motor disorders, promoting seamless human-robot interaction, and tracking the lateral glance of children with autism spectrum disorder for effective intervention. However, existing options such as cameras capturing the entire face or skin-attached sensors have limitations concerning privacy, safety, and/or usability. This research introduces a novel method that employs a battery-free RFID tag-based wearable sensor for monitoring head orientation, as a substitute for the existing options like camera.
View Article and Find Full Text PDFInt Nurs Rev
March 2025
Geriatric Nursing Department, Faculty of Nursing, Cairo University, Cairo, Egypt.
Aim: This study explored the ethical boundaries and data-sharing practices in artificial intelligence (AI)-enhanced nursing from the perspective of Arab nurses.
Background: Although AI offers advancements in clinical decision-making and operational efficiency, it also presents challenges such as ethical dilemmas, data privacy concerns, and technical issues. These challenges are being addressed through continuous education, the development of robust ethical guidelines, and the implementation of transparent data-sharing practices METHODS: A qualitative approach was employed, adhering to the Standards for Reporting Qualitative Research (SRQR) guidelines.
Medicine (Baltimore)
March 2025
Department of Biomedical and Laboratory Science, Africa University, Mutare, Zimbabwe.
The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profiles, clinical laboratory results, and imaging data, these technologies enhance diagnostic accuracy, enable robust prognostic modeling, and support personalized therapeutic interventions. Advanced ML algorithms, such as neural networks and ensemble learning, facilitate the discovery of novel biomarkers and refine risk stratification for hematological disorders, including leukemias, lymphomas, and coagulopathies.
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