International Journal of Telemedicine and Applications

Introduction. Technological advancements have transformed nursing care, quality, and education across the globe. In the Kingdom of Saudi Arabia (KSA), the inventions and adoption of mobile technologies such as an e-health application (app) called SEHA continue to revolutionize the healthcare system in the country. Purpose. The present systematic review is aimed at examining the technological impact on nursing in Saudi Arabia. The study provides a comprehensive analysis of telehealth and its role in nursing quality, nursing practice, and education. Methods. The present study adopted a literature review methodology by deriving data from journal articles from different databases, for example, Web Science, Google Scholar, CINAHL, MEDLINE, and PubMed databases. Inclusive years for the search ranged from 2016 to 2022. A total of eight articles were found dovetailing to meet the research objectives and answer research questions. Result. After appraising and analyzing the research, the present review found that (Abolfotouh et al., 2019) telehealth in nursing is loosely researched; (Ahmed et al., 2021) telehealth impacts nursing practice and quality by fostering nurse-patient communication promoting positive outcomes, seamless nursing care, and positive experiences; and (Albahri et al., 2021) telehealth and telemedicine is a central tenet of contemporary nursing education and practice. Conclusion. From these findings, this analysis informed three key recommendations: the need to integrate telehealth into the nursing curriculum, telehealth training, and reskilling among healthcare workers (HCWs) in KSA and further primary studies focusing predominantly on telenursing. Overall, telehealth remains a fundamental transformation of nursing practice that forms a central ideology in the contemporary nursing process.

Securing telehealth IoT infrastructure is essential to provide high-level medical care and prevent cyberattacks. A vulnerable stage in IoT telehealth is while the patient is being transported to a healthcare facility, the transporter could be an ambulance or an air ambulance. In this paper, we propose a multifactor authentication scheme to secure the system when the patient is in transit to the healthcare facility. We apply this scheme to an ambulance, using physical unclonable functions (PUFs) embedded in the ambulance to facilitate authentication and secure key exchange. We validated the security of the proposed scheme using formal and informal security analysis. The analysis supports our claim that the proposed scheme protects against many types of attacks.

Introduction. Considering the lack of inclusive Persian application for celiac patients that covers all aspects of the GFD, we developed a Persian-language application for patients with CD and assessed the effectiveness of a three-month educational intervention delivered via smartphone application compared with standard care on gastrointestinal symptom rating scale (GSRS) score in patients with celiac disease. Methods. In the present parallel randomized controlled clinical trial, 60 patients with CD were assigned randomly to receive education through a smartphone application () or conventional clinical education (). The patients were asked to use it for getting the required information for three months. We assessed the gastrointestinal symptoms using the gastrointestinal symptom rating scale (GSRS) questionnaire at baseline and three months after interventions. The GSRS total score, celiac disease GSRS (CD-GSRS) score, abdominal pain, reflux, diarrhea, constipation, and indigestion scores were calculated. Results. Out of 60 randomized patients, 58 patients completed the study. In comparison to baseline, the mean score of CD-GSRS score (), and indigestion subscore () were significantly decreased in the intervention group. The results of the between-group comparisons showed that there was a significant difference between the two groups only in the mean score of indigestion (). Conclusion. According to the results, using a smartphone application for providing information to patients with celiac disease had a significant positive effect on indigestion symptoms compared with routine clinic education. Trial Registration. This trial is registered with the Iranian registry of clinical trials (IRCT code: IRCT20170117032004N2; trial registry date: 2019.6.26).

Autism spectrum disorder (ASD) is a complex neurobehavioral condition that begins in childhood and continues throughout life, affecting communication and verbal and behavioral skills. It is challenging to discover autism in the early stages of life, which prompted researchers to intensify efforts to reach the best solutions to treat this challenge by introducing artificial intelligence (AI) techniques and machine learning (ML) algorithms, which played an essential role in greatly assisting the medical and healthcare staff and trying to obtain the highest predictive results for autism spectrum disorder. This study is aimed at systematically reviewing the literature related to the criteria, including multimedical tests and sociodemographic characteristics in AI techniques and ML contributions. Accordingly, this study checked the Web of Science (WoS), Science Direct (SD), IEEE Xplore digital library, and Scopus databases. A set of 944 articles from 2017 to 2021 is collected to reveal a clear picture and better understand all the academic literature through a definitive collection of 40 articles based on our inclusion and exclusion criteria. The selected articles were divided based on similarity, objective, and aim evidence across studies. They are divided into two main categories: the first category is “diagnosis of ASD based on questionnaires and sociodemographic features” (). This category contains a subsection that consists of three categories: (a) early diagnosis of ASD towards analysis, (b) diagnosis of ASD towards prediction, and (c) diagnosis of ASD based on resampling techniques. The second category consists of “diagnosis ASD based on medical and family characteristic features” (). This multidisciplinary systematic review revealed the taxonomy, motivations, recommendations, and challenges of diagnosis ASD research in utilizing AI techniques and ML algorithms that need synergistic attention. Thus, this systematic review performs a comprehensive science mapping analysis and identifies the open issues that help accomplish the recommended solution of diagnosis ASD research. Finally, this study critically reviews the literature and attempts to address the diagnosis ASD research gaps in knowledge and highlights the available ASD datasets, AI techniques and ML algorithms, and the feature selection methods that have been collected from the final set of articles.

Objective. To examine the factors associated with the first use of telehealth during the COVID-19 pandemic using Andersen’s Model of Healthcare Utilization. Andersen’s Model of Healthcare Utilization allowed the categorization of the independent variables into the following: (1) predisposing factors, including sociodemographic variables and health beliefs; (2) enabling factors, including socioeconomic status and access to care; and (3) need for care, including preexisting or newly diagnosed conditions and reasons to seek out care or to utilize a new mode of care. Methods. Potential respondents () were identified for recruitment from a volunteer registry in Arkansas. Recruitment emails provided a study description, the opportunity to verify meeting the study’s inclusion criteria and to consent for participation, and a link to follow to complete the survey online. The online survey responses were collected between July and August of 2020 (). Results. Telehealth utilization included two categories: (1) utilizers reported the first use of telehealth services during the pandemic, and (2) nonutilizers reported they had never used telehealth. Lower odds of reporting telehealth utilization during the pandemic were associated with race (Black; , CI [0.33, 0.96]) and education (high School or less; , CI [0.25, 0.83]). Higher odds of reporting telehealth utilization included having more than one provider (, CI [1.30, 4.18]), more physical (, CI [1.00, 1.25]) and mental (OR 1.53, CI [1.24, 1.88]) health conditions, and changes in healthcare delivery during the pandemic (, CI [2.78, 4.38]). Conclusions. The results illustrate that disparities exist in Arkansans’ utilization of telehealth services during the pandemic. Future research should explore the disparities in telehealth utilization and how telehealth may be used to address disparities in care for Black Arkansans and those with low socioeconomic status.

Wireless body area networks have taken their unique recognition in providing consistent facilities in health monitoring. Several studies influence physiological signal monitoring through a centralized approach using star topology in regular activities like standing, walking, sitting, and running which are considered active postures. Unlike regular activities like walking, standing, sitting, and running, the in-bed sleep posture monitoring of a subject is highly necessary for those who have undergone surgery, victims of breathing problems, and victims of COVID-19 for whom oxygen imbalance is a major issue as the mortality rate in sleep is high due to unattended patients. Suggestions from the medical field state that the patients with the above-mentioned issues are highly suggested to follow the prone sleep posture that enables them to maintain the oxygen level in the human body. A distributed model of communication is used where mesh topology is used for the data packets to be carried in a relay fashion to the sink. Heartbeat rate (HBR) and image monitoring of the subject during sleep are closely monitored and taken as input to the proposed posture prediction-Bayesian network (PP-BN) to predict the consecutive postures to increase the accuracy rate of posture recognition. The accuracy rate of the model outperforms the existing classification and prediction algorithms which take the cleaned dataset as input for better prediction results.