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

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

The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People's Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
Intensive Care Units ; Internet of Things ; Humans ; Internet ; Data Collection

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*

Internet of Things (IoT) technology plays an important role in smart healthcare. This paper discusses IoT solution for emergency medical devices in hospitals. Based on the cloud-edge-device architecture, different medical devices were connected; Streaming data were parsed, distributed, and computed at the edge nodes; Data were stored, analyzed and visualized in the cloud nodes. The IoT system has been working steadily for nearly 20 months since it run in the emergency department in January 2021. Through preliminary analysis with collected data, IoT performance testing and development of early warning model, the feasibility and reliability of the in-hospital emergency medical devices IoT was verified, which can collect data for a long time on a large scale and support the development and deployment of machine learning models. The paper ends with an outlook on medical device data exchange and wireless transmission in the IoT of emergency medical devices, the connection of emergency equipment inside and outside the hospital, and the next step of analyzing IoT data to develop emergency intelligent IoT applications.
Internet of Things ; Reproducibility of Results ; Internet ; Machine Learning ; Technology

Introduction: This study determined the relationship of personal factors and gaming factors with Internet Gaming Disorder (IGD). It aimed to provide information on the association of IGD with depression, anxiety, stress and both depression and anxiety among adolescent gamers. Methods: This was an analytic cross-sectional study among 560 14-18-year-old adolescents from two public high schools in Pasay City that used the Internet Gaming Disorder Scale–Short-Form (IGDS9-SF), Depression, Anxiety and Stress Scale-21 items (DASS-21) and gamer profile questionnaires. Results: The prevalence of Internet Gaming Disorder (IGD) was low at 1.1%, while moderate depression was observed in 67%, stress in 46.8%, both depression and anxiety in 64.8%, and high anxiety in 89.3% among adolescents. Using multiple logistic regression to control the effects of possible confounders, the association of IGD with depression (OR 0.971, 95% CI 0.085-11.084, p-value .981), anxiety (OR 6.0x107, p-value .999), stress (OR 6.135, 95% CI 0.373-100.991, p-value .204) and both depression and anxiety (OR 1.027, 95% CI 0.089-11.846, p-value .983) were not statistically significant. Conclusion The odds of depression, anxiety, stress and both depression and anxiety were higher among those with IGD as compared to those without IGD. However, it is not statistically significant. This study recommended further validation of the new IGD definition in the local setting and longitudinal studies with a larger population to determine other factors associated with mental disorders.
Adolescent ; Internet ; Depression ; Anxiety