Impact of Digital Interventions on the Treatment Burden of Patients With Chronic Conditions: Protocol for a Systematic Review

BACKGROUND

There is great potential for delivering cost-effective, quality health care for patients with chronic conditions through digital interventions. Managing chronic conditions often includes a substantial workload required for adhering to the treatment regimen and negative consequences on the patient's function and well-being. This treatment burden affects adherence to treatment and disease outcomes. Digital interventions can potentially exacerbate the burden but also alleviate it.

OBJECTIVE

The objective of this review is to identify, summarize, and synthesize the evidence of how digital interventions impact the treatment burden of people with chronic conditions.

METHODS

The search, selection, and data synthesis processes were designed according to the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) 2015. A systematic search was conducted on October 16, 2023, from databases PubMed, Scopus, Web of Science, ACM, PubMed Central, and CINAHL.

RESULTS

Preliminary searches have been conducted, and screening has been started. The review is expected to be completed in October 2024.

CONCLUSIONS

As the number of patients with chronic conditions is increasing, it is essential to design new digital interventions for managing chronic conditions in a way that supports patients with their treatment burden. To the best of our knowledge, the proposed systematic review will be the first review that investigates the impact of digital interventions on the treatment burden of patients. The results of this review will contribute to the field of health informatics regarding knowledge of the treatment burden associated with digital interventions and practical implications for developing better digital health care for patients with chronic conditions.

TRIAL REGISTRATION

PROSPERO CRD42023477605; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=477605.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/54833.

A systematic literature review for APT detection and Effective Cyber Situational Awareness (ECSA) conceptual model

Advancements in computing technology and the growing number of devices (e.g., computers, mobile) connected to networks have contributed to an increase in the amount of data transmitted between devices. These data are exposed to various types of cyberattacks, one of which is advanced persistent threats (APTs). APTs are stealthy and focus on sophisticated, specific targets. One reason for the detection failure of APTs is the nature of the attack pattern, which changes rapidly based on advancements in hacking. The need for future researchers to understand the gap in the literature regarding APT detection and to explore improved detection techniques has become crucial. Thus, this systematic literature review (SLR) examines the different approaches used to detect APT attacks directed at the network system in terms of approach and assessment metrics. The SLR includes papers on computer, mobile, and internet of things (IoT) technologies. We performed an SLR by searching six leading scientific databases to identify 75 studies that were published from 2012 to 2022. The findings from the SLR are discussed in terms of the literature's research gaps, and the study provides essential recommendations for designing a model for early APT detection. We propose a conceptual model known as the Effective Cyber Situational Awareness Model to Detect and Predict Mobile APTs (ECSA-tDP-MAPT), designed to effectively detect and predict APT attacks on mobile network traffic.

Heartbeat sound classification using a hybrid adaptive neuro-fuzzy inferences system (ANFIS) and artificial bee colony

Cardiovascular disease is one of the main causes of death worldwide which can be easily diagnosed by listening to the murmur sound of heartbeat sounds using a stethoscope. The murmur sound happens at the Lub-Dub, which indicates there are abnormalities in the heart. However, using the stethoscope for listening to the heartbeat sound requires a long time of training then only the physician can detect the murmuring sound. The existing studies show that young physicians face difficulties in this heart sound detection. Use of computerized methods and data analytics for detection and classification of heartbeat sounds will improve the overall quality of sound detection. Many studies have been worked on classifying the heartbeat sound; however, they lack the method with high accuracy. Therefore, this research aims to classify the heartbeat sound using a novel optimized Adaptive Neuro-Fuzzy Inferences System (ANFIS) by artificial bee colony (ABC). The data is cleaned, pre-processed, and MFCC is extracted from the heartbeat sounds. Then the proposed ABC-ANFIS is used to run the pre-processed heartbeat sound, and accuracy is calculated for the model. The results indicate that the proposed ABC-ANFIS model achieved 93% accuracy for the murmur class. The proposed ABC-ANFIS has higher accuracy in compared to ANFIS, PSO ANFIS, SVM, KSTM, KNN, and other existing studies. Thus, this study can assist physicians to classify heartbeat sounds for detecting cardiovascular disease in the early stages.

Evaluating online health information quality using machine learning and deep learning: A systematic literature review

Background

Due to the large volume of online health information, while quality remains dubious, understanding the usage of artificial intelligence to evaluate health information and surpass human-level performance is crucial. However, the existing studies still need a comprehensive review highlighting the vital machine, and Deep learning techniques for the automatic health information evaluation process.

Objective

Therefore, this study outlines the most recent developments and the current state of the art regarding evaluating the quality of online health information on web pages and specifies the direction of future research.

Methods

In this article, a systematic literature is conducted according to the PRISMA statement in eight online databases PubMed, Science Direct, Scopus, ACM, Springer Link, Wiley Online Library, Emerald Insight, and Web of Science to identify all empirical studies that use machine and deep learning models for evaluating the online health information quality. Furthermore, the selected techniques are compared based on their characteristics, such as health quality criteria, quality measurement tools, algorithm type, and achieved performance.

Results

The included papers evaluate health information on web pages using over 100 quality criteria. The results show no universal quality dimensions used by health professionals and machine or deep learning practitioners while evaluating health information quality. In addition, the metrics used to assess the model performance are not the same as those used to evaluate human performance.

Conclusions

This systemic review offers a novel perspective in approaching the health information quality in web pages that can be used by machine and deep learning practitioners to tackle the problem more effectively.

IoT Adoption and Application for Smart Healthcare: A Systematic Review

In general, the adoption of IoT applications among end users in healthcare is very low. Healthcare professionals present major challenges to the successful implementation of IoT for providing healthcare services. Many studies have offered important insights into IoT adoption in healthcare. Nevertheless, there is still a need to thoroughly review the effective factors of IoT adoption in a systematic manner. The purpose of this study is to accumulate existing knowledge about the factors that influence medical professionals to adopt IoT applications in the healthcare sector. This study reviews, compiles, analyzes, and systematically synthesizes the relevant data. This review employs both automatic and manual search methods to collect relevant studies from 2015 to 2021. A systematic search of the articles was carried out on nine major scientific databases: Google Scholar, Science Direct, Emerald, Wiley, PubMed, Springer, MDPI, IEEE, and Scopus. A total of 22 articles were selected as per the inclusion criteria. The findings show that TAM, TPB, TRA, and UTAUT theories are the most widely used adoption theories in these studies. Furthermore, the main perceived adoption factors of IoT applications in healthcare at the individual level are: social influence, attitude, and personal inattentiveness. The IoT adoption factors at the technology level are perceived usefulness, perceived ease of use, performance expectancy, and effort expectations. In addition, the main factor at the security level is perceived privacy risk. Furthermore, at the health level, the main factors are perceived severity and perceived health risk, respectively. Moreover, financial cost, and facilitating conditions are considered as the main factors at the environmental level. Physicians, patients, and health workers were among the participants who were involved in the included publications. Various types of IoT applications in existing studies are as follows: a wearable device, monitoring devices, rehabilitation devices, telehealth, behavior modification, smart city, and smart home. Most of the studies about IoT adoption were conducted in France and Pakistan in the year 2020. This systematic review identifies the essential factors that enable an understanding of the barriers and possibilities for healthcare providers to implement IoT applications. Finally, the expected influence of COVID-19 on IoT adoption in healthcare was evaluated in this study.

A Hospital Information Management System With Habit-Change Features and Medial Analytical Support for Decision Making

A hospital information management system (Doctive) with habit-change features and medial analytical support for decision making is developed in this study to reduce the risks of heart diseases. Doctive is targeted for hospital authorities to monitor patients’ habits and to prescribe medication and advice accordingly. Furthermore, this system provides emergency assistance for patients based on their current location. The proposed system would be beneficial for monitoring and organizing patients’ information to ease data entry, data management, data access, data retrieval and finally decision making. Doctive is tested and evaluated by 41 people who are either medical experts or professionals in the field of data analytics and visualization. The results indicate a high acceptance rate towards using Doctive system in hospitals and very good usability of the system. Doctive can be useful for healthcare providers and developers to track users’ habits for reducing the risk of heart disease. In the future.

Predicting behavioral intention to use e-learning system: A case-study in Begum Rokeya University, Rangpur, Bangladesh

Digital transformation and emerging technologies open a horizon to a new method of teaching and learning and revolutionizes the e-learning industry. The goal of this study is to scrutinize a proposed research model for predicting factors that influence student's behavioral intention to use e-learning system at Begum Rokeya University, Bangladesh. The study used quantitative approach and developed a research model based on several technological acceptance models. In order to test the model, a survey was conducted to obtain data from 262 university students. SEM-PLS, a multivariate statistical analysis technique, was used to analyze the responses to examine the model, factors, structural relationships, and hypotheses. The result shows that 'perceived usefulness' and 'perceived ease of use' positively and significantly influenced by 'perceived enjoyment'. Furthermore, 'perceived usefulness', 'perceived ease of use' and 'facilitating condition' have a significant impact to predict behavioral intention to use e-learning. The results of mediation analysis show that 'perceived usefulness' and 'perceived ease of use' have mediating effects between the predictors and the outcome. Finally, 'facilitating condition' have a remarkable moderating effect to predict the student's behavioral intention in using e-learning. The findings have a noteworthy empirical implication for educational institutions to introduce e-learning system as one of the teaching and learning tools.

Predicating Smartphone Users' Behaviour Towards a Location-Aware IoMT-Based Information System

Heart disease is the number one killing disease in the world. It is imperative to use IoMT-based information system called iHeart that tracks patient's blood pressure, heart rate, and current location. To design such a system, users' needs must be recognized. Therefore, this research proceeds with conducting a survey among 223 smartphone users in Penang, Malaysia and Isfahan, Iran to predict behavioural intention to use of iHeart before its full implementation. The theoretical frameworks of iHeart intention to use is set up based upon behavioural change theories. The results were analysed by using SmartPLS which indicate the different acceptance rates among two nationalities. It is concluded that cultural differences and technology advancements impact on the adoption of iHeart from smartphone users' points of view. The results of this study can be useful for healthcare professionals to evade culturally related problems for future projects.

A proposal to design a Location-based Mobile Cardiac Emergency System (LMCES)

Healthcare for elderly people has become a vital issue. The Wearable Health Monitoring System (WHMS) is used to manage and monitor chronic disease in elderly people, postoperative rehabilitation patients and persons with special needs. Location-aware healthcare is achievable as positioning systems and telecommunications have been developed and have fulfilled the technology needed for this kind of healthcare system. In this paper, the researchers propose a Location-Based Mobile Cardiac Emergency System (LMCES) to track the patient's current location when Emergency Medical Services (EMS) has been activated as well as to locate the nearest healthcare unit for the ambulance service. The location coordinates of the patients can be retrieved by GPS and sent to the healthcare centre using GPRS. The location of the patient, cell ID information will also be transmitted to the LMCES server in order to retrieve the nearest health care unit. For the LMCES, we use Dijkstra's algorithm for selecting the shortest path between the nearest healthcare unit and the patient location in order to facilitate the ambulance's path under critical conditions.