Introduction: Adequate knowledge of diabetes is essential for effective self-management and glycemic control. Emerging trends in digital platforms, video-based education offer a scalable and accessible approach to patient learning. However, evidence on the effectiveness of validated YouTube-based diabetes education in the Malaysian population remains limited. We aimed to evaluate the impact of a newly developed, tri-language YouTube diabetes education programme on clinical and knowledge outcomes. Methodology: In this multicentre study, patients with diabetes mellitus were randomly assigned to intervention (YouTube-based education) and control groups (standard care). The intervention consisted of newly developed and validated short educational videos on YouTube regarding diabetes, complications and lifestyle changes, delivered weekly via WhatsApp over 9 weeks. Videos were available in English, Malay and Chinese. The control group received standard care and counseling. Demographic data, hemoglobin A1c (HbA1c), and knowledge scores were collected at baseline and 3 months post-intervention. Results: A total of 109 patients (62.4% female; mean age 53.2 years; mean diabetes duration 16.3 years) were enrolled in the study, with similar baseline characteristics between groups. The intervention group demonstrated significantly greater improvement in glycemic control compared to the control group (HbA1c: −1.22 ± 1.36% vs. −0.24 ± 1.05%; p <0.001). While knowledge scores improved in both groups, the increase was significantly greater in the intervention group (2.66 vs. 0.80; p = 0.001). Notably, improvement in knowledge score predicted HbA1c reduction (b = 0.091; 95% CI 0.01, 0.172; p = 0.028). Conclusion The tri-language YouTube-based diabetes education programme significantly improved both glycemic control and patients’ knowledge. This accessible, scalable video-based education represents an effective strategy to enhance diabetes selfmanagement, offering flexible, on-demand learning for diverse populations.
Social Media ; Diabetes Mellitus

BACKGROUND AND OBJECTIVE: Social anxiety arising from intensive social media usage (SMU) among adolescents and youth has gained extensive attention in recent years due to its negative influence on mental health and academic performance. In spite of that, there is a dearth regarding the etiology of SMU-related social anxiety. This study aims to further clarify the influence of introversion personality on SMU-related social anxiety and the mechanism underlying such an association and provide a new perspective for developing effective intervention strategies for the highly prevailing SMU-related anxiety among Chinese college students. METHODS: A cohort of 979 college students (266 males and 713 females) aged 20.90 ± 1.91 years was enrolled in this cross-sectional study. Four measures including the "extroversion" domain of Eysenck Personality Questionnaire Revised, Short Scale (EPQ-R-S E), Interaction Anxiousness Scale (IAS), Mobile Phone Addiction Index (MPAI), and Social Anxiety Scale for Social Media Users (SAS-SMU) were used to evaluate the influence of introversion personality on SMU-related social anxiety that was potentially mediated sequentially by interaction anxiousness and mobile phone addiction. Hayes PROCESS was used for correlation and mediation analysis. RESULTS: Interaction anxiousness (indirect effect = -1.331, 95% CI : -1.559 - -1.122) partially mediated the association between introversion personality and SMU-related social anxiety. Besides, a sequential mediation of interaction anxiousness and mobile phone addiction in the link between introversion personality and SMU-related social anxiety was revealed (indirect effect = -0.308, 95% CI : -0.404 - -0.220). No significant mediating effect was found with mobile phone addiction in the association between introversion personality and SMU-related social anxiety. CONCLUSION: Targeting interaction anxiousness and mobile phone addiction may represent an efficient strategy alleviating SMU-related social anxiety among Chinese college students with introversion personality.
Humans ; Male ; Female ; Social Media ; Students/psychology* ; Anxiety/psychology* ; Young Adult ; Cross-Sectional Studies ; Universities ; Behavior, Addictive/psychology* ; Cell Phone ; Adolescent ; Introversion, Psychological ; China ; Surveys and Questionnaires ; Internet Addiction Disorder/psychology*

BACKGROUND: Heated tobacco products (HTPs) are widely used in Japan, following cigarettes, but their health effects remain unclear. HTPs are often considered a less harmful alternative to cigarettes and are commonly used by adults with asthma, even though smoking is one of the most obvious and treatable factors in asthma. We aimed to elucidate the association between HTP use and asthma symptoms in adults with asthma. METHODS: A total of 3,787 individuals with asthma were extracted from the data in the Japan COVID-19 and Society Internet Survey 2023, an ongoing longitudinal internet-based cohort study conducted by a nationwide internet research company in Japan. They were categorized into three groups (never, past, and current smokers) based on cigarette use. The association between HTP use and worsening of asthma symptoms within the previous 2 months in each group was analyzed using univariate and multivariate logistic regression analyses. Both exposure and outcomes were assessed by self-reporting. RESULTS: Among the participants, 2,470 (65.2%) were never smokers, 845 (22.3%) were past smokers, and 472 (12.5%) were current smokers. Overall, the proportion of HTP users was 429 (11.3%), and worsened asthma symptoms were observed in 400 (10.6%) individuals. The total proportion of HTP users and worsened asthma symptoms was 70 (2.8%) and 259 (10.5%) among never smokers, 180 (21.3%) and 72 (8.5%) among past smokers, and 179 (37.9%) and 69 (14.6%) among current smokers. After adjusting for confounders, the odds ratio (OR) was 3.25 (95% confidence interval [CI] 1.86-5.68, p < 0.001), 1.47 (95% CI 0.93-2.34, p = 0.1), and 2.23 (95% CI 1.46-3.43, p < 0.001) for never, past, and current cigarette smokers with HTP use, respectively, where never smokers without HTP use were set as the standard. CONCLUSION The use of HTPs, not only cigarette smoking, was associated with worsening of asthma symptoms in adults with asthma. Therefore, people need to understand the harmful effects of HTPs on asthma symptoms.
Humans ; Japan/epidemiology* ; Asthma/etiology* ; Male ; Female ; Middle Aged ; Adult ; Aged ; Tobacco Products/adverse effects* ; Internet ; Surveys and Questionnaires ; Young Adult ; Hot Temperature ; Longitudinal Studies

OBJECTIVE: This study aims to address the configuration and efficiency issues in the use of digital subtraction angiography (DSA) equipment through the practical implementation of a rationalization platform based on the Internet of Things (IoT). METHODS: By employing IoT and data integration technologies, the deep integration of DSA equipment operational data with clinical data was achieved to construct a knowledge base for rational use of DSA equipment. Simultaneously, a knowledge base was developed using software engineering techniques to visually display data analysis results. RESULTS: Through thorough data analysis, an imbalance in DSA usage between the southern and northern hospital campuses was identified. Addressing this issue, optimizations were implemented based on the data analysis results, which ultimately yielded significant effects. These adjustments not only effectively alleviated the pressure on DSA equipment usage in the southern campus, but also increased equipment utilization in the northern district (the average daily working hours have increased from 4.64 h to 7.19 h), shortened patient appointment wait time (the appointment duration in the southern campus decreased by 21.86% year-on-year, while the appointment duration in the northern campus decreased by 20.51% year-on-year). CONCLUSION Through the practical implementation of a DSA rationalization platform based on IoT, this study not only successfully explored methods for rational DSA usage but also provided valuable reference for the rational management of medical equipment.
Internet of Things ; Angiography, Digital Subtraction/instrumentation* ; Humans ; Software

Medical equipment, as an important indicator of smart hospital evaluation, plays a vital role in hospital operations. To ensure the safe and efficient operation of medical equipment, a reasonable performance evaluation system is indispensable. This study introduces a platform based on Internet of Things (IoT) technology that connects medical devices and collects data, achieving standardized and structured data processing, and supporting online operational supervision. Through the Delphi method, a performance evaluation system for large medical equipment is constructed, including 4 primary indicators and 22 secondary indicators. DICOM data acquisition devices are used to achieve functions such as efficiency analysis, benefit analysis, usage evaluation, and decision-making support for medical equipment. The study is still in its early stages, and in the future, it is expected to integrate more types of equipment, achieve rational resource allocation, and significantly impact decision-making for the development of public hospitals.
Internet of Things ; Delphi Technique

Medical equipment management plays a crucial role in enhancing the quality and efficiency of healthcare services. However, traditional management approaches are increasingly inadequate to meet the growing demands of modern healthcare. As intelligent operation and maintenance (O&M) models based on big data, the Internet of Things (IoT), and artificial intelligence (AI) technologies develop, it is imperative to explore their application in medical equipment management. This paper reviews the technical overview of intelligent O&M and discusses the algorithms and challenges of intelligent O&M models based on different technologies. It also proposes issues that need improvement in intelligent O&M models, aiming to provide valuable references for the future development of medical equipment management.
Artificial Intelligence ; Algorithms ; Internet of Things ; Equipment and Supplies ; Big Data

OBJECTIVE: This study aims to develop a regional collaborative hierarchical diagnosis and treatment model based on the "Internet+" approach, to address issues such as the uneven distribution of transcranial magnetic stimulation (TMS) treatment resources, information silos, and low patient accessibility in regional medical institutions. METHODS: This model establishes standardized business and information protocols, creating a real-time TMS treatment resource database, develops a regional TMS treatment management platform, and integrates with the Xiamen Health Medical Cloud Platform for collaborative operation. RESULTS: This model enables the internal communication of TMS treatment information within hospitals and sharing across medical institutions, optimizing the rational allocation of TMS treatment resources. CONCLUSION The model effectively optimizes the allocation of TMS treatment resources, significantly enhances the accessibility and quality of medical services, provides valuable insights for hierarchical diagnosis and treatment of other therapeutic models, and contributes to the development of a more organized and efficient hierarchical diagnosis and treatment system.
Transcranial Magnetic Stimulation ; Humans ; Internet

OBJECTIVES: We propose a multimodal model integrating social media text and image data for automated assessment of psychological stress in college students to support the development of intelligent mental health services in higher education institutions. METHODS: Based on deep learning technology, we designed an evaluation framework comprising a text sentiment modeling module, an image sentiment modeling module, and a multimodal fusion prediction module. Text sentiment features were extracted using Bi-LSTM, and image semantic cues were extracted via U-Net. A feature concatenation strategy was used to enable cross-modal semantic collaboration to achieve automatic identification of 3 psychological stress levels: mild, moderate, and severe. We constructed a multimodal annotated dataset using social platform data from 1577 students across multiple universities in Guangdong Province. After data cleaning, 252 samples were randomly selected for model training and testing. RESULTS: In the 3-classification task, the model demonstrated outstanding performance on the test set, and achieved an accuracy of 92.86% and an F1 score of 0.9276, exhibiting excellent stability and consistency. Confusion matrix analysis further revealed the model's ability to effectively distinguish between different pressure levels. CONCLUSIONS The multimodal psychological stress assessment model developed in this study effectively integrates unstructured social behavior data to enhance the scientific rigor and practical applicability of psychological state recognition, and thus provides support for developing intelligent psychological service systems.
Humans ; Stress, Psychological/diagnosis* ; Students/psychology* ; Universities ; Social Media ; Deep Learning

Hospitals worldwide recognise the importance of data and digital transformation in healthcare. We traced a smart hospital's data-driven journey to build an artificial intelligence and digital ecosystem (AIDE) to achieve healthcare excellence. We measured the impact of data and digital transformation on patient care and hospital operations, identifying key success factors, challenges, and opportunities. The use of data analytics and data science, robotic process automation, AI, cloud computing, Medical Internet of Things and robotics were stand-out areas for a hospital's data-driven journey. In the future, the adoption of a robust AI governance framework, enterprise risk management system, AI assurance and AI literacy are critical for success. Hospitals must adopt a digital-ready, digital-first strategy to build a thriving healthcare system and innovate care for tomorrow.
Artificial Intelligence ; Humans ; Delivery of Health Care ; Hospitals ; Cloud Computing ; Robotics ; Internet of Things ; Data Science

BACKGROUND AND OBJECTIVES

One of the effects of the COVID-19 pandemic on medical education is an increased awareness and use of social media (SocMed) to facilitate learning. However, literature on the use of SocMed in medical education has focused primarily on educator-led teaching activities. Our study aimed to describe SocMed initiatives that were student-led, particularly for information dissemination and peer collaborative learning, and to elicit perceptions of medical students towards such activities.

An online survey on SocMed usage in medical education was sent to all first- and second-year medical students at the University of the Philippines Manila College of Medicine from October to December 2021. The questionnaire collected data on demographics, SocMed habits and preferences, and perceived advantages and disadvantages of SocMed. Descriptive statistics were calculated while the free-text responses were grouped into prominent themes and summarized.

We received a total of 258 responses (71%) out of 361 eligible participants. Overall, 74% found SocMed platforms to be very and extremely helpful; 88% recommended its continued use. The most popular SocMed platforms for different tasks were as follows: Discord for independent study groups and for conducting peer tutoring sessions; Facebook Messenger for reading reminders; Telegram for reading announcements related to academics and administrative requirements, and for accessing material provided by classmates and professors.

The high uptake of SocMed among medical students may be attributed to its accessibility and costefficiency. The use of a particular SocMed platform was dependent on the students’ needs and the platform's features. Students tended to use multiple SocMed platforms that complemented one another. SocMed also had disadvantages, such as the potential to distract from academic work and to become a source of fatigue. Educators must engage with students to understand how SocMed platforms can be integrated into medical education, whether in the physical or virtual learning environment.