BACKGROUND: Up to 25% of children and 5.6% of adults in the USA have atopic dermatitis (AD), with substantial impacts on quality of life. Effective control can be challenging despite therapy efforts. The emergence of information and communication technologies (ICT) in AD management prompted this study to assess its impact on self-management. We conducted a meta-analysis to assess outcomes from peer-reviewed clinical trials evaluating the effectiveness of teledermatology, mobile health (mHealth) apps, and electronic devices for managing AD.
METHODS: We searched PubMed, Web of Science, Scopus, and Embase for articles written in English and published until May 2023.
RESULTS: Twelve trials with 2424 participants were selected from 811 studies. A meta-analysis of 1038 individuals reported a mean difference (MD) of -1.57 [95% confidence interval (CI): -2.24, -0.91] for the Patient Oriented Eczema Measure (POEM). A meta-analysis of 495 individuals reported a Dermatology Life Quality Index (DLQI) MD of -0.59 [95% CI: -0.95, -0.23]. Despite heterogeneity (I2 = 47% and I2 = 74%), the impact was significant (P ≤ 0.001). SCORing Atopic Dermatitis (SCORAD) showed an insignificant MD of -0.12 (P = 0.91).
CONCLUSIONS: mHealth applications and telemonitoring show significant improvement in patients\' quality of life (DLQI) and self-management (POEM) but no significant impact on AD severity (SCORAD).

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented. The full IoT solution includes both edge devices worn by the workers in the field and a remote cloud IoT platform, which is responsible for storing and efficiently sharing the gathered data in accordance with regulations, ethics, and GDPR rules. Extended experiments conducted to validate the IoT components both in the laboratory and in the field proved the effectiveness of the proposed solution in monitoring the real-time status of workers in mines.

This article presents a dataset of activities associated with stress and boredom obtained through wearable sensors. Data was collected from 40 right-handed participants aged 20 to 25, evenly split between males and females. Each individual wore a smart device on their dominant arm\'s wrists to facilitate the capture of data. This dataset covers five activities associated with stress and boredom, namely, smoking, eating, nail biting, face touching, and staying still. These activities were selected for their potential psychological implications and captured in an uncontrolled environment to mimic real-life scenarios. The data provides a unique resource for developing machine learning models aimed at recognizing these behaviors, which could lead to real-time analysis and interventions for stress. A custom holder was used to hold the device on the wrists in order to ensure that all participants had consistent orientation and placement. This holder was situated just above the wrist joint, a location typically associated with the placement of smartwatches. The dataset provides a unique opportunity for developing machine learning models for stress & boredom associated activities recognition apart from real-time symptomatic analysis of stress and boredom.

Carbon dots (CDs) have emerged as a versatile and promising carbon-based nanomaterial with exceptional optical properties, including tunable emission wavelengths, high quantum yield, and photostability. CDs are appropriate for various applications with many benefits, such as biocompatibility, low toxicity, and simplicity of surface modification. Thanks to their tunable optical properties and great sensitivity, CDs have been used in sensing as fluorescent probes for detecting pH, heavy metal ions, and other analytes. In addition, CDs have demonstrated potential as luminescence converters for white organic light-emitting diodes and light emitters in optoelectronic devices due to their superior optical qualities and exciton-independent emission. CDs have been used for drug administration and bioimaging in the biomedical field due to their biocompatibility, low cytotoxicity, and ease of functionalization. Additionally, due to their stability, efficient charge separation, and low recombination rate, CDs have shown interesting uses in energy systems, such as photocatalysis and energy conversion. This article highlights the growing possibilities and potential of CDs as adaptable nanomaterials in a variety of interdisciplinary areas related to sensing and imaging, at the same time addressing the major challenges involved in the current research and proposing scientific solutions to apply CDs in the development of a super smart society.

BACKGROUND: Cardiac arrest (CA) is the third leading cause of death, with persistently low survival rates despite medical advancements. This article evaluates the potential of emerging technologies to enhance CA management over the next decade, using predictions from the AI tools ChatGPT-4 and Gemini Advanced.
METHODS: We conducted an exploratory literature review to envision the future of cardiopulmonary arrest (CA) management. Utilizing ChatGPT-4 and Gemini Advanced, we predicted implementation timelines for innovations in early recognition, CPR, defibrillation, and post-resuscitation care. We also consulted the AI to assess the consistency and reproducibility of the predictions.
RESULTS: We extrapolate that healthcare may embrace new technologies, such as comprehensive monitoring of vital signs to activate the emergency system (wireless detectors, smart speakers, and wearable devices), use new innovative early CPR and early AED devices (robot CPR, wearable AEDs, and immersive reality), and post-resuscitation care monitoring (brain-computer interface). These technologies could enhance timely life-saving interventions for cardiac arrest. However, there are many ethical and practical challenges, particularly in maintaining patient privacy and equity. The two AI tools made different predictions, with a horizon for implementation ranging between three and eight years.
CONCLUSIONS: Integrating advanced monitoring technologies and AI-driven tools offers hope in improving CA management. A balanced approach involving rigorous scientific validation and ethical oversight is necessary. Collaboration among technologists, medical professionals, ethicists, and policymakers is crucial to use these innovations ethically to reduce CA incidence and enhance outcomes. Further research is needed to enhance the reliability of AI predictive capabilities.

New smart devices that have the potential to support the health and well-being of their owners have become available. In particular, smart watches are able to identify a fall by the person who is wearing the watch and report it to pre-defined contacts and the local emergency control center. Falls in older people are common and only rarely caused by malignant cardiac arrhythmia. The case of an elderly male whose smart watch automatically reported his fall due to ventricular fibrillation to the local emergency control center is described. Through the intervention of the wearer\'s device, the activated dispatcher called the patient\'s wife, who found her husband lying unresponsive on the floor. Emergency medical services responded immediately and were able to successfully resuscitate the patient. A hospital discharge without any long-term complications was achieved.
UNASSIGNED: In den letzten Jahren wurden zunehmend Smart Devices eingeführt, die die Gesundheit und das Wohlergehen ihrer Besitzer unterstützen sollen. Insbesondere sind Smartwatches in der Lage, einen Sturz des Trägers zu erkennen und diesen an vordefinierte Kontakte sowie die örtliche Leitstelle zu melden. Ältere Menschen stürzen aus verschiedenen Gründen häufiger, es ist jedoch ein eher seltenes Ereignis, dass Stürze ursächlich durch eine maligne Herzrhythmusstörung bedingt sind. Diese Kasuistik berichtet von einem älteren Mann, der aufgrund von Kammerflimmern einen Sturz erlitt und dessen Smartwatch daraufhin automatisch die örtliche Leitstelle kontaktierte. Nach der Intervention des Geräts rief der Disponent die Ehefrau des Gestürzten umgehend zurück. Diese fand den Patienten nicht ansprechbar auf dem Boden liegend vor. Der umgehend alarmierte Rettungsdienst reanimierte ihn erfolgreich. Schlussendlich konnte er ohne Langzeitfolgen aus dem Krankenhaus entlassen werden.

OBJECTIVE: This case series explored the integration of smartwatches in a community mental health service to support severe mental illness (SMI) management and intervention. We examined whether biometric data provided by smartwatches could help to predict relapse and inform treatment decisions.
METHODS: Four Australian SMI outpatients of mixed diagnoses (age range = 19-24) were selected from a prior study. Clinicians accessed patients\' biometric data (activity, sleep, heart rate, and electrodermal activity) through smartwatches.
RESULTS: Changes in circadian rhythm and electrodermal activity preceded hospitalization in two cases. Additionally, smartwatch data was effectively used to guide targeted interventions, improving patient treatment outcomes.
CONCLUSIONS: Integrating smartwatches in community mental health services offers promise as adjunct tools for SMI management. However, ethical considerations on data privacy and technology reliance require further evaluation. Additionally, as this is a small case series, randomized controlled trials with larger sample sizes are required to provide evidence for generalisability of results.

UNASSIGNED: Currently, smart devices can prevent diseases by continuously collecting user information and providing health-related feedback. Smart devices big data provide personalized, faster, and more accurate health care. By examining existing studies, we suggest a new healthcare evolution and health promotion through information technology (IT) convergence. A big data systematic review examined the evolution of new health care and their potential for health promotion by monitoring physical activities, preventing diseases, and analyzing health data smart devices.
UNASSIGNED: Therefore, this evaluates whether a new healthcare industry combining smart devices and big-data-based customized health care services can promote health. This study searched PubMed, Google Scholar, Scopus, and Research Information Sharing Service (RISS) for keywords related to big data, smart devices, healthcare, customized health services, health apps, and mobile health. This study comprised 43 of 453 publications from 2007 to 2023. Among them, a total of 43 articles were successfully completed in this study using the PRISMA flowchart in the final stage.
UNASSIGNED: Smart devices centered on big data enable personalized health care, and app technologies that promote well-being to prepare for aging society have many applications in clinical, prevention, public health, and rehabilitation settings. Smart devices and tailored healthcare services using big data to inform individuals about exercise, health status, diagnosis, and health information will expand into major sectors. By reviewing previous studies, the convergence of the IT technology field, which allows you to easily identify individual health and receive faster and more accurate medical services through customized health care services, has future-oriented values as, new health care services evolve. The systematic review of big data herein can monitor physical activity and prevent diseases using smart devices, thus promoting a healthy lifestyle.
UNASSIGNED: Smart devices that analyze data to provide personal exercise and health conditions, checkups, and information, are making our lives easier. The information service using big data will continue to evolve into a personalized management service and provide basic healthcare data as it grows into an expected industry in the future.

Digital devices have gained popularity in the last 10 years as a tool for exercise prescription, the monitoring of daily physical activity, and nutrition for the management of a health-related parameter. Therefore, the aim of this study was to assess the effectiveness of the use of digital devices to monitor exercise data in sedentary persons with HIV who exercise following an individualized activity pacing (AP) protocol on cardiorespiratory fitness body composition, blood lipid profile, and psychological parameters. Twenty-four PLWH were enrolled in an 18-week randomized, open-label, pilot AP exercise protocol. All participants were monitored by a Health Band connected to a mobile app that transmitted the data to a server. At week 3, they were randomized either in an experimental group (EG), in which an open device configuration enabled them to receive training data feedback (n = 12), or continued with no data feedback (control group, n = 12). The primary endpoint was improvement from the baseline of 15% of steady-state oxygen consumption (V˙O2) during a 6-min walking test. Technical issues occurred when pairing the health band with the app, which prevented EG participants from regularly receiving data feedback, and with data transmission to the server, which enabled only 40% monitoring of the total training days. Consequently, the study outcomes could not be compared between the two groups, and participants also lost confidence in the study. However, 19 out of 24 participants completed the AP program. Overall, only 6 (32%) improved steady-state V˙O2, with no significant changes at W18 from the baseline. Significant reductions were observed of BMI (p = 0.040), hip circumference (p = 0.027), and total-(p = 0.049) and HDL-cholesterol (p = 0.045). The failure of digital device performance substantially affected study procedures, monitoring, and participants\' engagement, and likely limited the potential benefits of the AP exercise program.

UNASSIGNED: Automated cardiac arrest diagnosis offers the possibility to significantly shorten the interval between onset of out-of-hospital cardiac arrest (OHCA) and notification of EMS, providing the opportunity for earlier resuscitation and possibly increased survival.
UNASSIGNED: Automated cardiac arrest diagnosis was one of six focus topics for the Wolf Creek XVII Conference held on June 14-17 2023 in Ann Arbor, Michigan, USA. Conference invitees included international thought leaders and scientists in the field of cardiac arrest resuscitation from academia and industry. Participants submitted via online survey knowledge gaps, barriers to translation and research priorities for each focus topic. Expert panels used the survey results and their own perspectives and insights to create and present a preliminary unranked list for each category that was debated, revised and ranked by all attendees to identify the top 5 for each category.
UNASSIGNED: Top knowledge gaps include the accuracy of automated OHCA detection technologies and the feasibility and reliability of automated EMS activation. The main barriers to translation are the risk of false positives potentially overburdening EMS, development and application costs of technology and the challenge of integrating new technology in EMS IT systems. The top research priorities are large-scale evaluation studies to measure real world performance and user research regarding the willingness to adopt these technologies.
UNASSIGNED: Automated cardiac arrest diagnosis has the potential to significantly impact time to resuscitation and survival of OHCA because it could convert unwitnessed events into witnessed events. Validation and feasibility studies are needed. The specificity of the technology must be high not to overburden limited EMS resources. If adequate event classification is achieved, future research could shift toward event prediction, focusing on identifying potential digital biomarkers and signatures of imminent cardiac arrest. Implementation could be challenging due to high costs of development, regulatory considerations and instantiation logistics.