Application of artificial intelligence in active assisted living for aging population in real-world setting with commercial devices - A scoping review.

BACKGROUND: The aging population is steadily increasing, posing new challenges and opportunities for healthcare systems worldwide. Technological advancements, particularly in commercially available Active Assisted Living devices, offer a promising alternative. These readily accessible products, ranging from smartwatches to home automation systems, are often equipped with Artificial Intelligence capabilities that can monitor health metrics, predict adverse events, and facilitate a safer living environment. However, there is no review exploring how Artificial Intelligence has been integrated into commercially available Active Assisted Living technologies, and how these devices monitor health metrics and provide healthcare solutions in a real-world environment for healthy aging. This review is essential because it fills a knowledge gap in understanding AI's integration in Active Assisted Living technologies in promoting healthy aging in real-world settings, identifying key issues that require to be addressed in future studies. OBJECTIVE: The aim of this overview is to outline current understanding, identify potential research opportunities, and highlight research gaps from published studies regarding the use of Artificial Intelligence in commercially available Active Assisted Living technologies that assists older individuals aging at home. METHODS: A comprehensive search was conducted in six databases-PubMed, CINAHL, IEEE Xplore, Scopus, ACM Digital Library, and Web of Science-to identify relevant studies published over the past decade from 2013 to 2024. Our methodology adhered to the PRISMA extension for scoping reviews to ensure rigor and transparency throughout the review process. After applying predefined inclusion and exclusion criteria on 825 retrieved articles, a total of 64 papers were included for analysis and synthesis. RESULTS: Several trends emerged from our analysis of the 64 selected papers. A majority of the work (39/64, 61%) was published after the year 2020. Geographically, most of the studies originated from East Asia and North America (36/64, 56%). The primary application goal of Artificial Intelligence in the reviewed literature was focused on activity recognition (34/64, 53%), followed by daily monitoring (10/64, 16%). Methodologically, tree-based and neural network-based approaches were the most prevalent Artificial Intelligence algorithms used in studies (32/64, 50% and 31/64, 48% respectively). A notable proportion of the studies (32/64, 50%) carried out their research using specially designed smart home testbeds that simulate the conditions in real-world. Moreover, ambient technology was a common thread (49/64, 77%), with occupancy-related data (such as motion and electrical appliance usage logs) and environmental sensors (indicators like temperature and humidity) being the most frequently used. CONCLUSION: Our results suggest that Artificial Intelligence has been increasingly deployed in the real-world Active Assisted Living context over the past decade, offering a variety of applications aimed at healthy aging and facilitating independent living for the older adults. A wide range of smart home indicators were leveraged for comprehensive data analysis, exploring and enhancing the potentials and effectiveness of solutions. However, our review has identified multiple research gaps that need further investigation. First, most research has been conducted in controlled testbed environments, leaving a lack of real-world applications that could validate the technologies' efficacy and scalability. Second, there is a noticeable absence of research leveraging cloud technology, an essential tool for large-scale deployment and standardized data collection and management. Future work should prioritize these areas to maximize the potential benefits of Artificial Intelligence in Active Assisted Living settings.

Non-fungible tokens in healthcare: a scoping review.

Introduction: Non-Fungible Tokens (NFTs) are digital assets that are verified using blockchain technology to ensure authenticity and ownership. NFTs have the potential to revolutionize healthcare by addressing various issues in the industry. Method: The goal of this study was to identify the applications of NFTs in healthcare. Our scoping review was conducted in 2023. We searched the Scopus, IEEE, PubMed, Web of Science, Science Direct, and Cochrane scientific databases using related keywords. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results: After applying inclusion and exclusion criteria, a total of 13 articles were chosen. Then extracted data was summarized and reported. The most common application of NFTs in healthcare was found to be in health data management with 46% frequency, followed by supply chain management with 31% frequency. Furthermore, Ethereum is the main blockchain platform that is applied in NFTs in healthcare with 70%. Discussion: The findings from this review indicate that the NFTs that are currently used in healthcare could transform it. Also, it appears that researchers have not yet investigated the numerous potentials uses of NFTs in the healthcare field, which could be utilized in the future.

Designing and Evaluating a Nutrition Recommender System for Improving Food Security in a Developing Country.

BACKGROUND: Due to the increased price of foods in recent years and the diminished food security in Iran, nutrition recommender systems can suggest the most suitable and affordable foods and diets to users based on their health status and food preferences. Objective: The present study aimed to design and evaluate a recommender system to suggest healthy and affordable meals and provide a tele-nutrition consulting service. METHODS: This applied three-phase study was conducted in 2020. In the first stage, the food items' daily prices were extracted from credible sources, and accordingly, meals were placed in three price categories. After conducting a systematic review of similar systems, the requirements and data elements were specified and confirmed by 10 nutritionists and 10 health information management and medical informatics experts. In the second phase, the software was designed and developed based on the findings. In the third phase, system usability was evaluated by four experts based on Nielsen's heuristic evaluation. RESULTS: Initially, 72 meals complying with nutritional principles were placed in three price categories. Following a literature review and expert survey, 31 data elements were specified for the system, and the experts confirmed system requirements. Based on the information collected in the previous stage, the Web-based software TanSa in the Persian language was designed, developed, and presented on a unique domain. During the evaluation, the mean severity of the problems associated with Nielsen's 10 principles was 1.2, which is regarded as minor. CONCLUSION: To promote food security, the designed system recommends healthy, nutritional, and affordable meals to individuals and households based on user characteristics.

Developing a Clinical Decision Support System for Prediction Postoperative Coronary Artery Bypass Grafting Infection in Diabetic Patients.

Background: Postoperative infection in Coronary Artery Bypass Graft (CABG) is one of the most common complications for diabetic patients, due to an increase in the hospitalization and cost. To address these issues, it is necessary to apply some solutions. Objective: The study aimed to the development of a Clinical Decision Support System (CDSS) for predicting the CABG postoperative infection in diabetic patients. Material and Methods: This developmental study is conducted on a private hospital in Tehran in 2016. From 1061 CABG surgery medical records, we selected 210 cases randomly. After data gathering, we used statistical tests for selecting related features. Then an Artificial Neural Network (ANN), which was a one-layer perceptron network model and a supervised training algorithm with gradient descent, was constructed using MATLAB software. The software was then developed and tested using the receiver operating characteristic (ROC) diagram and the confusion matrix. Results: Based on the correlation analysis, from 28 variables in the data, 20 variables had a significant relationship with infection after CABG (P<0.05). The results of the confusion matrix showed that the sensitivity of the system was 69%, and the specificity and the accuracy were 97% and 84%, respectively. The Receiver Operating Characteristic (ROC) diagram shows the appropriate performance of the CDSS. Conclusion: The use of CDSS can play an important role in predicting infection after CABG in patients with diabetes. The designed software can be used as a supporting tool for physicians to predict infections caused by CABG in diabetic patients as a susceptible group. However, other factors affecting infection must also be considered for accurate prediction.

Mobile Health Technology and Healthcare Providers: Systemic Barriers to Adoption.

OBJECTIVES: Despite the growing use of mobile health (mHealth), certain barriers seem to be hindering the use of mHealth applications in healthcare. This article presents a systematic review of the literature on barriers associated with mHealth reported by healthcare professionals. METHODS: This systematic review was carried out to identify studies published from January 2015 to December 2019 by searching four electronic databases (PubMed/MEDLINE, Web of Science, Embase, and Google Scholar). Studies were included if they reported perceived barriers to the adoption of mHealth from healthcare providers' perspectives. Content analysis and categorization of barriers were performed based on a focus group discussion that explored researchers' knowledge and experiences. RESULTS: Among the 273 papers retrieved through the search strategy, 18 works were selected and 18 barriers were identified. The relevant barriers were categorized into three main groups: technical, individual, and healthcare system. Security and privacy concerns from the category of technical barriers, knowledge and limited literacy from the category of individual barriers, and economic and financial factors from the category of healthcare system barriers were chosen as three of the most important challenges related to the adoption of mHealth described in the included publications. CONCLUSIONS: mHealth adoption is a complex and multi-dimensional process that is widely implemented to increase access to healthcare services. However, it is influenced by various factors and barriers. Understanding the barriers to adoption of mHealth applications among providers, and engaging them in the adoption process will be important for the successful deployment of these applications.

Neonatal abstinence syndrome: a systematic review of current databases and registries.

OBJECTIVES: Registries are considered as rich sources of data for determination of infants with neonatal abstinence syndrome (NAS), the improvement of provided care and research. The aims of this study were: (1) to investigate the existing studies including NAS registries, (2) to identify and extract the required data elements. METHODS: The following electronic databases were searched: PubMed, Scopus, Web of Science, ProQuest, Embase/Medline, and Psych Info. In addition, a review of gray literature was undertaken to identify relevant studies in English covering the period from 1 January 2009 to 1 November 2018 including registries and databases. Screening of titles, abstracts, and full-texts were conducted independently by two researchers based on PRISMA guidelines. The basic registry information, scope, registry type, data source, the purpose of registry, important variables were extracted and analyzed. RESULTS: Twenty-five articles were eligible and included in the review; they reported 37 registries and databases related to NAS at the national and state levels in 11 countries from 1876 to 2013. We proposed a NAS registry design framework based on well-known data-information-knowledge (DIK) structure due to Ackoff's DIK hierarchy has a defined role as a central model of information systems, information management, and knowledge management. CONCLUSIONS: To the best of our knowledge, this is the first study which has systematically reviewed NAS-related registries. Since there are no international standards to develop new NAS registries, the proposed framework in this article can be beneficial. This framework is essential not only to facilitate the NAS registry design but also to help the collection of high-value clinical data necessary for the acquisition of better clinical knowledge.

The effects of Bacillus coagulans supplementation in patients with non-alcoholic fatty liver disease: A randomized, placebo-controlled, clinical trial.

BACKGROUND: The aim of this study was to evaluate the effects of supplementation with 109 spores of Bacillus coagulans (GBI-30) plus inulin in patients with non-alcoholic fatty liver disease (NAFLD). METHODS: In a randomized, double-blind, placebo-controlled clinical trial, fifty three patients with NAFLD were randomly assigned to receive either a synbiotic or a placebo capsule for 12 weeks. The primary outcome was reduction in steatosis score in Fibroscan exam. RESULTS: At the end of study, serum alanine aminotransferase and γ glutamine transaminase decreased significantly more in synbiotic group compared to placebo group (p = 0.001, and p = 0.004, respectively). Synbiotic supplementation significantly reduced serum tumor necrosis factor-α (p = 0.03) and nuclear factor-κB activity (p = 0.04). Moreover, hepatic steatosis reduced significantly more in synbiotic group compared to placebo group (p < 0.001). CONCLUSION: Our results indicate that 12 weeks supplementation with B. coagulans plus inulin is beneficial for treatment of NAFLD and its related inflammation without any significant effects on related cardiovascular risk factors. CLINICAL TRIALS: This trial was registered at irct.ir with number of IRCT20100524004010N23.

Better monitoring of abused children by designing a child abuse surveillance system: Determining national child abuse minimum data set.

BACKGROUND: Monitoring the trend of child abuse can significantly help in measuring the magnitude of the problem and understanding its recurrence. The minimum data set (MDS) is a set of elements of each domain that provides the basis for decision-making. This study was conducted to determine the comprehensive national minimum data set for child abuse surveillance system (CASS) in Iran. METHODS: This is a cross-sectional descriptive study. Data were gathered from the selected countries and child abuse registry and surveillance systems. The MDS questionnaire was designed based on a review of the publications and experts' opinions. The final data elements of the CASS were determined using the Delphi technique by visiting pediatricians. RESULTS: In total, 147 data elements were included in the Delphi survey. The data elements of the CASS were classified into seven categories as follows: demographic data, incident related data, medical history, diagnostic tests, incident nature, therapeutic measures, and other required data. CONCLUSION: The existence of national MDS as the core of the child abuse surveillance program is essential and leads to appropriate decisions in this regard. The MDS can meet the needs of professionals, decision makers, researchers, and policymakers who decide on reducing the incidence of child abuse.

Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods.

OBJECTIVES: The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. METHODS: This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. RESULTS: The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. CONCLUSIONS: It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.

Designing a thesis tele-supervision system for postgraduate medical sciences students.

INTRODUCTION: Today, progressing science and technology at all domains, including education and research, will bring new opportunities to resolve the communication and interaction problems. The aim of this study was to determine potential factors affecting the thesis supervision and provide a web-based solution. METHODS: This is a developmental study conducted in two theoretical and technical phases at the Shiraz University of Medical Sciences in 2017. The theoretical phase was performed in three stages, including literature review and investigating the existing studies and Delphi's study based experts' view as well as identifying the thesis supervision status based on 200 postgraduate students' point of view. The technical phase had two stages, including to draw processes and to design the physical and logic schemes of the system. The Thesis Tele-Supervision software named SAHPAD was designed by C# and ASP. NET programing languages. RESULTS: The results showed that out of 40 potential factors specified at the first stage of theoretical phase by using experts' opinion, 13 items were selected as the main factors. According to the results obtained from the students' views at the third stage, the factor of "accessibility" had the minimum score, i.e. 3.15 mean of four, which was the worst status. CONCLUSION: The designed system covered from the beginning to the end of the thesis workflow at its electronic frame with its various capabilities such as the interaction of the research team to decide the title, draft the proposal, prepare for thesis defense, etc.

The Applications of Virtual Reality Technology in Medical Groups Teaching.

INTRODUCTION: Virtual reality is a new method for training different medical groups. Based on this technology, professionals and students of various medical sciences can determine their level of competence for medical treatment before any performance on the patient. Therefore, the aim of this study was to identify the applications of virtual reality technology for training the medical groups. METHODS: This is a scoping review study conducted in 2016. Articles were retrieved through the search of related keywords in databases such as Pub Med, Scopus, Web of Sciences, Springer, and Google scholar. Then, after applying the entry criteria, 21 papers were selected from a total of 1343. Data extraction was done by a data collection form. The collected data were summarized and reported using content analysis technique according to the study purpose. RESULTS: The findings of the study indicated that 11 cases (48%) have used virtual education technology for laparoscopic surgery training. Using virtual reality has improved learning in 17 (74%) studies. A higher accuracy in medical practice by people trained through VR has been reported in 20 (87%) studies. CONCLUSION: The results indicate that the application of virtual reality capabilities plays an important role in improving the performance of different medical groups. According to the results, it can be suggested that virtual reality capabilities should be used to train different medical groups based on their individual and collective needs.