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This comprehensive review investigates the transformative potential of sensor-driven digital twin technology in enhancing healthcare delivery within smart environments. We explore the integration of smart environments with sensor technologies, digital health capabilities, and location-based services, focusing on their impacts on healthcare objectives and outcomes. This work analyzes the foundational technologies, encompassing the Internet of Things (IoT), Internet of Medical Things (IoMT), machine learning (ML), and artificial intelligence (AI), that underpin the functionalities within smart environments. We also examine the unique characteristics of smart homes and smart hospitals, highlighting their potential to revolutionize healthcare delivery through remote patient monitoring, telemedicine, and real-time data sharing. The review presents a novel solution framework leveraging sensor-driven digital twins to address both healthcare needs and user requirements. This framework incorporates wearable health devices, AI-driven health analytics, and a proof-of-concept digital twin application. Furthermore, we explore the role of location-based services (LBS) in smart environments, emphasizing their potential to enhance personalized healthcare interventions and emergency response capabilities. By analyzing the technical advancements in sensor technologies and digital twin applications, this review contributes valuable insights to the evolving landscape of smart environments for healthcare. We identify the opportunities and challenges associated with this emerging field and highlight the need for further research to fully realize its potential to improve healthcare delivery and patient well-being. [ABSTRACT FROM AUTHOR]

Aims: Heart failure is a serious condition that often goes undiagnosed in primary care due to the lack of reliable diagnostic tools and the similarity of its symptoms with other diseases. Non‐invasive monitoring of heart rate variability (HRV), which reflects the activity of the autonomic nervous system, could offer a novel and accurate way to detect and manage heart failure patients. This study aimed to assess the feasibility of using machine learning techniques on HRV data as a non‐invasive biomarker to classify healthy adults and those with heart failure. Methods and results: We used digitized electrocardiogram recordings from 54 adults with normal sinus rhythm and 44 adults categorized into New York Heart Association classes 1, 2, and 3, suffering from congestive heart failure. All recordings were sourced from the PhysioNet database. Following data pre‐processing, we performed time‐domain HRV analysis on all individual recordings, including root mean square of the successive difference in adjacent RR interval (RRi) (RMSSD), the standard deviation of RRi (SDNN, the NN stands for natural or sinus intervals), the standard deviation of the successive differences between successive RRi (SDSD), the number or percentage of RRi longer than 50 ms (NN50 and pNN50), and the average value of RRi [mean RR interval (mRRi)]. In our experimental classification performance evaluation, on the computed HRV parameters, we optimized hyperparameters and performed five‐fold cross‐validation using four machine learning classification algorithms: support vector machine, k‐nearest neighbour (KNN), naïve Bayes, and decision tree (DT). We evaluated the prediction accuracy of these models using performance criteria, namely, precision, recall, specificity, F1 score, and overall accuracy. For added insight, we also presented receiver operating characteristic (ROC) plots and area under the ROC curve (AUC) values. The overall best performance accuracy of 77% was achieved when KNN and DT were trained on computed HRV parameters with a 5 min time window. KNN obtained an AUC of 0.77, while DT attained 0.78. Additionally, in the classification of severe congestive heart failure, KNN and DT had the best accuracy of 91%, with KNN achieving an AUC of 0.88 and DT obtaining 0.92. Conclusions: The results show that HRV can accurately predict severe congestive heart failure. The findings of this study could inform the use of machine learning approaches on non‐invasive HRV, to screen congestive heart failure individuals in primary care. [ABSTRACT FROM AUTHOR]

This paper presents the mHealth Predictive Outbreak for COVID-19 (mPOC) framework, an autonomous platform based on wearable Internet of Medical Things (IoMT) devices for outbreak prediction and monitoring. It utilizes real-time physiological and environmental data to assess user risk. The framework incorporates the analysis of psychological and user-centric data, adopting a combination of top-down and bottom-up approaches. The mPOC mechanism utilizes the bidirectional Mobile Health (mHealth) Disaster Recovery System (mDRS) and employs an intelligent algorithm to calculate the Predictive Exposure Index (PEI) and Deterioration Risk Index (DRI). These indices trigger warnings to users based on adaptive threshold criteria and provide updates to the Outbreak Tracking Center (OTC). This paper provides a comprehensive description and analysis of the framework's mechanisms and algorithms, complemented by the performance accuracy evaluation. By leveraging wearable IoMT devices, the mPOC framework showcases its potential in disease prevention and control during pandemics, offering timely alerts and vital information to healthcare professionals and individuals to mitigate outbreaks' impact. [ABSTRACT FROM AUTHOR]

E-learning has transformed the healthcare education system by providing healthcare professionals with training and development opportunities, regardless of their location. However, healthcare professionals in remote or rural areas face challenges such as limited access to educational resources, lack of reliable internet connectivity, geographical isolation, and limited availability of specialized training programs and instructors. These challenges hinder their access to e-learning opportunities and impede their professional development. To address this issue, a study was conducted to identify the factors that influence the effectiveness of e-learning in healthcare. A literature review was conducted, and two questionnaires were distributed to e-learning experts to assess primary variables and identify the most significant factor. The Fuzzy Analytic Network Process (Fuzzy ANP) was used to identify the importance of selected factors. The study found that success, satisfaction, availability, effectiveness, readability, and engagement are the main components ranked in order of importance. Success was identified as the most significant factor. The study results highlight the benefits of e-learning in healthcare, including increased accessibility, interactivity, flexibility, knowledge management, and cost efficiency. E-learning offers a solution to the challenges of professional development faced by healthcare professionals in remote or rural areas. The study provides insights into the factors that influence the effectiveness of e-learning in healthcare and can guide the development of future e-learning programs. [ABSTRACT FROM AUTHOR]

Introduction: The growing use of online information influences people's healthcare decision making in terms of treatment or consulting a doctor. The readability of a website is a factor that influences the correct understanding of its content. Regarding that there is little information about the readability as well as the credibility of health websites in the field of kidney transplantation, the present study assesses the readability and trustworthiness of websites in this topic. Material and Methods: Google, Yahoo, and Bing search engines were used to search for "Kidney Transplantation" and "Renal Transplantation.". Four readability scales were applied to assess the readability of the first 30 results of each search engine. The HONcode toolbar was applied to recognize credible websites. The relationship between HONcode verification and website position on the search results pages was explored. Furthermore, the difference between the readability scores and website position on the search results pages was tested. The readability difference between search result pages was also examined. Results: According to the results, the assessed websites are suitable for students or high school graduates. Furthermore, the association between the average readability of websites and website position on the search results pages was significant (p-value<0.05). A significant association between HONcode-verified sites and website position on the search results pages was also revealed (p-value=0.011). Conclusion: The readability of kidney transplantation websites is far above the recommended level. Therefore, health organizations must consider readability while designing their websites. [ABSTRACT FROM AUTHOR]

Disease screening identifies a disease in an individual/community early to effectively prevent or treat the condition. COVID-19 has restricted hospital visits for screening and other healthcare services resulting in the disruption of screening for cancer, diabetes, and cardiovascular diseases. Smartphone technologies, coupled with built-in sensors and wireless technologies, enable the smartphone to function as a disease-screening and monitoring device with negligible additional costs and potentially higher quality results. Thus, we sought to evaluate the use of smartphone applications for disease screening and the acceptability of this technology in the medical and healthcare sectors. We followed a systematic review process using four databases, including Medline Complete, Web of Science, Embase, and Proquest. We included articles published in English examining smartphone application utilisation in disease screening. Further, we presented and discussed the primary outcomes of the research articles and their statistically significant value. The initial search yielded 1046 studies for the initial title and abstract screening. Of the 105 articles eligible for full-text screening, we selected nine studies and discussed them in detail under four main categories: an overview of the literature reviewed, participant characteristics, disease screening, and technology acceptance. According to our objective, we further evaluated the disease-screening approaches and classified them as clinically administered screening (33%, n = 3), health-worker-administered screening (33%, n = 3), and home-based screening (33%, n = 3). Finally, we analysed the technology acceptance among the users and healthcare practitioners. We observed a significant statistical relationship between smartphone applications and standard clinical screening. We also reviewed user acceptance of these smartphone applications. Hence, we set out critical considerations to provide equitable healthcare solutions without barriers when designing, developing, and deploying smartphone solutions. The findings may increase research opportunities for the evaluation of smartphone solutions as valid and reliable screening solutions. [ABSTRACT FROM AUTHOR]

Background: Adherence to prescribed medication is essential for glycemic control and to delay the onset of complications. However, information on medication adherence among patients with Type 2 diabetes mellitus is sparse in Bangladesh. This study aimed to determine medication adherence and factors associated with low adherence in patients with Type 2 diabetes in Bangladesh. Methods:Across-sectional study was conducted among500 consecutive patients with Type 2 diabetes attending a tertiary hospital in Bangladesh between September 2013 and July 2014. Data were collected on sociodemographic and clinical characteristics, medication use and adherence, and blood tests for glycated hemoglobin. Medication adherence was assessed using the 8-itemMorisky Medication Adherence Scale (score range 0-8), and a score < 6 was defined as low adherence. Multiple logistic regression modeling was used to investigate factors associated with lowmedication adherence. Results: The mean age ± standard deviation of the participants was 50.2 ± 10.2 years (56.2% females). Overall, 42.8% (95% confidence interval (CI): 38.5-47.2) of participants reported low medication adherence with no differences by sex. Multiple logistic regression analysis revealed that increased age was negatively associated with low adherence (odds ratio: 0.97 [95% CI 0.95-0.99]). Patients who reported no family history of diabetes had 55% higher odds of having low adherence (1.55 [1.05-2.30]), compared to their counterparts with a family history of diabetes. Conclusion: Almost half of the patients with Type 2 diabetes attending a tertiary hospital in Dhaka had suboptimal medication adherence. Innovative approaches targeting young patients and those with no family history of diabetes could improve medication adherence. [ABSTRACT FROM AUTHOR]

As full-scale natural disasters unfold, telecommunication infrastructures and services are among the most impacted, halting the necessary communications to and from the disaster zones, including the humanitarian operators and the people in need. The temporary relief can be achieved by establishing infrastructureless (ad-hoc) wireless networks; however, ad-hoc networks, instead of providing direct services to the end-users and affected people, provide direct connectivity to the localized service providers, which indirectly cover end-users. From a range of services provided by the humanitarian operators, healthcare has by far the top priority, which is the focus of this paper, through the utilization of smartphones in a Mobile Health (mHealth) perspective, which is an emerging concept in the smart-city paradigm for monitoring and tracking end-user health conditions. This paper covers the disaster management paradigm based on a novel bidirectional mHealth disaster recovery system and its protocol design and associated implications. [ABSTRACT FROM AUTHOR]

Some applications, including disaster monitoring and recovery networks, use low-power wide-area networks (LPWAN). LPWAN sensors capture data bits and transmit them to public carrier networks (e.g., cellular networks) via dedicated gateways. One of the challenges encountered in disaster management scenarios revolves around the carry/forward sensed data and geographical location information dissemination to the disaster relief operatives (disaster relief agency; DRA) to identify, characterise, and prioritise the affected areas. There are network topology options to reach its destination, including cellular, circuit switched, and peer-to-peer networks. In the context of natural disaster prediction, it is vital to access geographical location data as well as the timestamp. This paper proposes the usage of Pseudo A Number (PAN), that is, the calling party address, which is used by every network to include the location information instead of the actual calling party address of the gateway in LPWAN. This PAN information can be further analysed by the DRA to identify the affected areas and predict the complications of the disaster impacts in addition to the past history of damages. This paper aims to propose a solution that can predict disaster proceedings based on propagation and the velocity of impact using vector calculation of the location data and the timestamp, which are transmitted by sensors through the PAN of the gateway in LPWAN. [ABSTRACT FROM AUTHOR]

SUMMARY Security provisioning is an essential part in the design of any communication systems, which becomes more critical for wireless systems. Devising a quality of service (QoS) mechanism to coexist security algorithms is a daunting, yet inevitable task. The aim of this novel research article is to present a novel energy efficient, cross-layer-based application-layer wireless system with simultaneous security and QoS supports, which revolves around the practical and low-cost implementation of Suite-B cryptographic algorithms (promulgated by the National Security Agency) and detailed analyses of the associated complexities. The focus of this article is on the novel cross-layer mechanism and its effectiveness to handle QoS-enabled treatment while offering security enhancements. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Security provisioning is an essential part in the design of any communication systems, which becomes more critical for wireless systems. The consideration and comparisons of security algorithms in various Open Systems Interconnection layers is a difficult task, because there are many performance metrics involved. The aim of this novel research article is to present research results for the design of a wireless system revolving around the practical and low-cost implementation of Suite-B algorithms in different layers. Suite-B, promulgated by the National Security Agency, is a set of cryptographic algorithms, including non-repudiation. The end results include the deployment of Suite-B algorithms at the application, transport, and network layers and the protocol flow at each layer. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The intersection of network function virtualisation (NFV) technologies and big data has the potential of revolutionising today's telecommunication networks from deployment to operations resulting in significant reductions in capital expenditure (CAPEX) and operational expenditure, as well as cloud vendor and additional revenue growths for the operators. One of the contributions of this article is the comparisons of the requirements for big data and network virtualisation and the formulation of the key performance indicators for the distributed big data NFVs at the operator's infrastructures. Big data and virtualisation are highly interdependent and their intersections and dependencies are analysed and the potential optimisation gains resulted from open interfaces between big data and carrier networks NFV functional blocks for an adaptive environment are then discussed. Another contribution of this article is a comprehensive discussion on open interface recommendations which enables global collaborative and scalable virtualised big data applications. [ABSTRACT FROM AUTHOR]

ABSTRACT Sensor and electronic-health networks are widely utilized at home and in industry/research applications. In a local sense, a sensor-to-sensor network can have a range of a few meters to a couple of hundred meters (ZigBee Pro can extend this range up to 2000 m). With the deployment of mobile technology in the healthcare space (mobile-Health 'm-Health') and using cellular coverage, the range can virtually be unbounded. However, supporting bounded delay (end-to-end delay), class of service, and quality of service for critical sensor-mHealth applications may become challenging. This challenge can be alarmingly extended when thousands of users run their sensor-mHealth applications simultaneously and depend on limited coverage of the cell tower to transmit their health-related data across. In this paper we will discuss how the 3rd Generation Partnership Project-Long Term Evolution networks can address such aggregation issues, and discuss the challenges and provide recommendations.Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The rapid expansion of mobile-based systems, the capabilities of smartphone devices, as well as the radio access and cellular network technologies are the wind beneath the wing of mobile health (mHealth). In this paper, the concept of biomedical sensing analyzer (BSA) is presented, which is a novel framework, devised for sensor-based mHealth applications. The BSA is capable of formulating the Quality of Service (QoS) measurements in an end-to-end sense, covering the entire communication path (wearable sensors, link-technology, smartphone, cell-towers, mobile-cloud, and the end-users). The characterization and formulation of BSA depend on a number of factors, including the deployment of application-specific biomedical sensors, generic link-technologies, collection, aggregation, and prioritization of mHealth data, cellular network based on the Long-Term Evolution (LTE) access technology, and extensive multidimensional delay analyses. The results are studied and analyzed in a LabView 8.5 programming environment. [ABSTRACT FROM AUTHOR]

The number of wearable wireless sensors is expected to grow to 400 million by the year 2014, while the number of operational mobile subscribers has already passed the 5.2 billion mark in 2011. This growth results in an increasing number of mobile applications including: Machine-to-Machine (M2M) communications, Electronic-Health (eHealth), and Mobile-Health (mHealth). A number of emerging mobile applications that require 3G and 4G mobile networks for data transport relate to telemedicine, including establishing, maintaining, and transmitting health-related information, research, education, and training. This review paper takes a closer look at these applications, specifically with regard to the healthcare industry and their underlying link technologies. The authors believe that the BlackBerry platform and the associated infrastructure (i.e., BlackBerry Enterprise Server) is a logical and practical solution for eHealth, mHealth, sensor and M2M deployments, which are considered in this paper. [ABSTRACT FROM PUBLISHER]

Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies and examine their acceptance by chronic patients. We systematically examined the use of smartphone applications (apps) in chronic disease monitoring and management in databases, namely, Medline, Web of Science, Embase, and Proquest, published from 2010 to 2020. Results showed that app-based weight management programs had a significant effect on healthy eating and physical activity (p = 0.002), eating behaviours (p < 0.001) and dietary intake pattern (p < 0.001), decreased mean body weight (p = 0.008), mean Body Mass Index (BMI) (p = 0.002) and mean waist circumference (p < 0.001). App intervention assisted in decreasing the stress levels (paired t-test = 3.18; p < 0.05). Among cancer patients, we observed a high acceptance of technology (76%) and a moderately positive correlation between non-invasive electronic monitoring data and questionnaire (r = 0.6, p < 0.0001). We found a significant relationship between app use and standard clinical evaluation and high acceptance of the use of apps to monitor the disease. Our findings provide insights into critical issues, including technology acceptance along with regulatory guidelines to be considered when designing, developing, and deploying smartphone solutions targeted for chronic patients. [ABSTRACT FROM AUTHOR]

With the emergence of technologies such as electronic health and mobile health (eHealth/mHealth), cloud computing, big data, and the Internet of Things (IoT), health related data are increasing and many applications such as smartphone apps and wearable devices that provide wellness and fitness tracking are entering the market. Some apps provide health related data such as sleep monitoring, heart rate measuring, and calorie expenditure collected and processed by the devices and servers in the cloud. These requirements can be extended to provide a personalized life expectancy (PLE) for the purpose of wellbeing and encouraging lifestyle improvement. No existing works provide this PLE information that is developed and customized for the individual. This article is based on the concurrent models and methodologies to calculate and predict life expectancy (LE) and proposes an idea of using multi-phased approaches to the solution as the project requires an immense and broad range of work to accomplish. As a result, the current prediction of LE, which was found to be up to a maximum of five years could potentially be extended to a lifetime prediction by utilizing generic health data. In this article, the novel idea of the solution proposing a PLE on an individual basis, which can be extended to lifetime is presented in addition to the existing works. [ABSTRACT FROM AUTHOR]

Diabetes is one of the leading non-communicable diseases globally, adversely impacting an individual's quality of life and adding a considerable burden to the healthcare systems. The necessity for frequent blood glucose (BG) monitoring and the inconveniences associated with self-monitoring of BG, such as pain and discomfort, has motivated the development of non-invasive BG approaches. However, the current research progress is slow, and only a few BG self-monitoring devices have made considerable progress. Hence, we evaluate the available non-invasive glucose monitoring technologies validated against BG recordings to provide future research direction to design, develop, and deploy self-monitoring of BG with integrated emerging technologies. We searched five databases, Embase, MEDLINE, Proquest, Scopus, and Web of Science, to assess the non-invasive technology's scope in the diabetes management paradigm published from 2000 to 2020. A total of three approaches to non-invasive screening, including saliva, skin, and breath, were identified and discussed. We observed a statistical relationship between BG measurements obtained from non-invasive methods and standard clinical measures. Opportunities exist for future research to advance research progress and facilitate early technology adoption for healthcare practice. The results promise clinical validity; however, formulating regulatory guidelines could foresee the deployment of approved non-invasive BG monitoring technologies in healthcare practice. Further, research prospects are there to design, develop, and deploy integrated diabetes management systems with mobile technologies, data analytics, and the internet of things (IoT) to deliver a personalised monitoring system., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://mhealth.amegroups.com/article/view/10.21037/mhealth-23-9/coif). S.M.S.I serves as an unpaid editorial board member of mHealth from May 2023 to April 2025. S.M.S.I reports support for the present manuscript from an Emerging Leadership Fellowship from the National Health and Medical Research Council of Australia (APP1195406) and Vanguard grants from the National Heart Foundation of Australia; S.M.S.I reports unpaid leadership roles outside the submitted work with the IT Committee of the Cardiac Society of Australia and New Zealand, ESC Heart Failure Association as a volunteer on the Cardiac Devices Committee and with WHO-ITU Global Initiative on AI for Health. The other authors have no conflicts of interest to declare., (2024 mHealth. All rights reserved.)

Background: Diabetes is one of the leading noncommunicable chronic diseases globally. In people with diabetes, blood glucose levels need to be monitored regularly and managed adequately through healthy lifestyles and medications. However, various factors contribute to poor medication adherence. Smartphone apps can improve medication adherence in people with diabetes, but it is not clear which app features are most beneficial., Objective: This study aims to systematically review and evaluate high-quality apps for diabetes medication adherence, which are freely available to the public in Android and Apple app stores and present the technical features of the apps., Methods: We systematically searched Apple App Store and Google Play for apps that assist in diabetes medication adherence, using predefined selection criteria. We assessed apps using the Mobile App Rating Scale (MARS) and calculated the mean app-specific score (MASS) by taking the average of app-specific scores on 6 dimensions, namely, awareness, knowledge, attitudes, intention to change, help-seeking, and behavior change rated on a 5-point scale (1=strongly disagree and 5=strongly agree). We used the mean of the app's performance on these 6 dimensions to calculate the MASS. Apps that achieved a total MASS mean quality score greater than 4 out of 5 were considered to be of high quality in our study. We formulated a task-technology fit matrix to evaluate the apps for diabetes medication adherence., Results: We identified 8 high-quality apps (MASS score≥4) and presented the findings under 3 main categories: characteristics of the included apps, app features, and diabetes medication adherence. Our framework to evaluate smartphone apps in promoting diabetes medication adherence considered physiological factors influencing diabetes and app features. On evaluation, we observed that 25% of the apps promoted high adherence and another 25% of the apps promoted moderate adherence. Finally, we found that 50% of the apps provided low adherence to diabetes medication., Conclusions: Our findings show that almost half of the high-quality apps publicly available for free did not achieve high to moderate medication adherence. Our framework could have positive implications for the future design and development of apps for patients with diabetes. Additionally, apps need to be evaluated using a standardized framework, and only those promoting higher medication adherence should be prescribed for better health outcomes., (©Sheikh Mohammed Shariful Islam, Vinaytosh Mishra, Muhammad Umer Siddiqui, Jeban Chandir Moses, Sasan Adibi, Lemai Nguyen, Nilmini Wickramasinghe. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 21.06.2022.)

Technology assisted methods for medical diagnosis and biomedical health monitoring are rapidly shifting from classical invasive methods to handheld-based non-invasive approaches. Biomedical imagining is one of the most prominent practices of non-invasive mechanisms in medical applications. This paper considers the medical imaging schemes for Mobile Health (mHealth) applications and studies the feasibility of future mobile systems for accommodating image informatics capabilities.

This paper examines the security requirements for eHealth (Electronic Health) records and the provided current and future technological solutions. This includes the Security and Privacy (S&P) requirements for diverse electronic health information, the current frameworks and standards maintaining proper handling (processing, storing, and transmitting) of such sensitive information and the related network architectures.