International Journal of Telemedicine and Applications最新文献

Manual monitoring of vital signs, which often fails to capture the onset of deterioration, is the main monitoring modality in most Ghanaian hospitals due to the high cost and inadequate supply of patient bedside monitors. Consumer wearable devices (CWDs) are emerging, relatively low-cost technologies for continuous monitoring of physiological status; however, their validity has not been established in low-resource clinical settings. We aimed to (1) investigate the validity of the heart rate (HR) and oxygen saturation (SpO2) data from two widely used CWDs, the Fitbit Versa 2 and Xiaomi Mi Smart Band 6, against gold standard bedside monitors in one Ghanaian hospital and (2) develop a web application to capture and display CWD data in a clinician-friendly way. A healthy volunteer simultaneously wore both CWDs and blood pressure cuffs to measure HR and SpO2. To test for concordance, we conducted the Bland-Altman and mean absolute percentage error analyses. We also developed a web application that retrieves and displays CWD data in near real time as text and graphical trends. Compared to gold standards (patient monitor and manual), the Fitbit Versa 2 had 96.87% and 96.67% measurement accuracies for HR, and the Xiaomi Mi Smart Band 6 had 94.24% and 93.21% measurement accuracies for HR. The Xiaomi Mi Smart Band 6 had 98.79% measurement accuracy for SpO2. The strong concordance between CWD and gold standards supports the potential implementation of these devices as a novel method of vital sign monitoring to replace manual monitoring, thus saving costs and improving patient outcomes. Further studies are needed for confirmation.

Background: Healthline is one of the 39 free telehealth services that Whakarongorau Aotearoa/New Zealand Telehealth Services provides to New Zealanders. In early 2021, an image upload system for viewing service user-uploaded images was implemented into the Healthline service.

Aims: The aim of this research was to understand the utilisation of Healthline's image upload system by clinicians and service users in New Zealand.

Methods: This is a retrospective observational study analysing Healthline image upload data over a two-year period: March 2021 through to December 2022. A total of 40,045 images were analysed, including demographics of the service users who uploaded an image: ethnicity, age group, and area of residence. The outcome or recommendation of the Healthline call was also assessed based on whether an image was included.

Results: Images uploaded accounted for 6.0% of total Healthline calls (n = 671,564). This research found that more service users were advised to go to an Emergency Department if they did not upload an image compared to service users who used the tool (13.5% vs. 7.7%), whereas a higher proportion of service users were given a lower acuity outcome if they included an image, including visiting an Urgent Care (24.0% vs. 16.9%) and GP (36.7% vs. 24.3%).

Conclusion: Service users who did not upload an image had a higher proportion of Emergency Department outcomes than service users who did use the tool. This image upload tool has shown the potential to decrease stress on Emergency Departments around Aotearoa, New Zealand, through increased lower acuity outcomes.

Background: There is a global shortage of healthcare professionals, especially in developing countries, leading to disparities in access to healthcare, worsened by the pandemic. Telemedicine is emerging as a solution, with growing adoption worldwide due to advancements in technology and increased awareness. Research Problem. The establishment of telemedicine depends on resources, infrastructure, and knowledge about healthcare needs. Further studies are needed to monitor and address evolving issues in telemedicine. The Overall Purpose of the Study. Rural health disparities stem from multiple factors, like limited healthcare access, workforce shortages, lifestyle choices, and lower socioeconomic status, leading to higher mortality and chronic diseases. Addressing these challenges is vital for rural community well-being. Telemedicine centers present a promising solution, bridging gaps, and improving healthcare outcomes for underserved remote populations. Methodology. Objective: This study assessed the clinicodemographic profile and clinical outcome of children presenting to the telemedicine center at the Institute of National Importance in India. Design: Prospective observational study. Setting: A single-center tertiary care level. Participants: This study included 79 children aged up to 18 years. Major Findings and Summary of Interpretations. In our study, 79 children using telemedicine found a near-equal gender distribution. 8.9% needed emergency care, with common complaints being respiratory issues, fever, abdominal pain, and vomiting. After two weeks, 83.5% showed improvement, emphasizing telemedicine's effectiveness in pediatric care.

Conclusion: Our study underscores telemedicine's positive impact on pediatric healthcare, emphasizing its potential to enhance access, outcomes, and cost-efficiency. Wider telemedicine adoption can reduce morbidity and mortality, support preventive care, and streamline posttreatment services, alleviating pressure on specialized facilities. While our focus was pediatrics, the telemedicine model is adaptable to various age groups and conditions, but it should be seen as a valuable supplement to, not a total substitute for, in-person healthcare visits.

Background: Telemedicine has the potential to make healthcare more efficient, organized, and available and is a more beneficial technology that can ease preventive treatment and improve long-term health management. This is especially essential for those who face financial or regional reasons to get quality treatment. Telemedicine in Iran is a new medical field and a noble way to access medical facilities for populations living in deprived areas, and the primary healthcare workers in these deprived medical centers are the implementers of telemedicine in those areas; we aimed to investigate the awareness and attitude towards telemedicine among all the healthcare workers in these centers.

Method: This is a descriptive cross-sectional study at the Health Centers of Raz County in North Khorasan Province, Iran, and 149 healthcare workers were included. For collecting information, we used a questionnaire that consisted of two parts. The first part contains the demographic data of health care workers, and the second part includes the 5-point Likert scale questionnaire (questions on telemedicine awareness, attitude, and self-report readiness).

Result: Most participants (51%) were male, and 69.8% were married. The most frequent sources of information about telemedicine are colleagues (40.3%), continuing education (24.7%), and social media and the internet (10.1%). Awareness did not significantly relate to gender, age, marital status, or work experience, but awareness of physicians and midwives is higher than other groups (p < 0.05). The awareness of healthcare workers using continuing education, articles, workshops, or conferences was significantly higher (p < 0.05). The attitude scores for most questions are above 3.4 and reflect a positive attitude about telemedicine. Attitudes did not show a significant relation to gender, age, marital status, or work experience.

Conclusion: Using telemedicine in developing countries, rural or urban areas have a high potential to improve epidemiological investigations, disease control, and clinical case management. Providing healthcare professionals with more information about new technologies in healthcare, such as telemedicine, can help get a more realistic picture of their perceptions.

Objective: This study is aimed at ascertaining the feasibility of conducting the 1-minute sit-to-stand (1MSTS) and 30-second sit-to-stand (30SSTS) tests for healthy participants via video consultation. A secondary aim was to compare the relationship between the 1MSTS and 30SSTS.

Methods: A total of 63 participants were recruited via the Singapore Institute of Technology emails and social media in 2020 during the peak of COVID-19. Prior to the sit-to-stand testing, all participants completed the consent form and physical activity questionnaires. Anthropometric data such as height and weight were also collected prior to testing. An instructional video detailing the sit-to-stand (STS) movement and the requirements for the environment set-up were sent to the participants via email. All STS tests were conducted virtually via the Zoom application. Healthy participants aged 21 to 55 years old performed a 1MSTS and 30SSTS each in random order.

Results: All recruited participants completed the STS tests with no reported adverse events. Majority of participants were from the 21- to 25-year-old age groups, and the average number of repetitions performed by this group was 21.9 ± 5.6 for the 30SSTS and 44.7 ± 12.6 for the 1MSTS.

Conclusion: Conducting the STS tests via video consultation was demonstrated to be safe and feasible. The number of repetitions performed in the 1MSTS is correlated to that of the 30SSTS, but 1MSTS has the ability to elicit a greater HR response among younger adults.

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21/30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1/30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided.