Mobile Health (m-Health) in Retrospect: The Known Unknowns.

For nearly two decades, mobile health or (m-Health) was hailed as the most innovative and enabling area for the digital transformation of healthcare globally. However, this profound vision became a fleeting view since the inception and domination of smart phones, and the reorientation of the concept towards the exclusivity of global smart phone application markets and services. The global consumerization of m-Health in numerous disciplines of healthcare, fitness and wellness areas is unprecedented. However, this divergence between 'mobile health capitalism' and the 'science of mobile health' led to the creation of the 'm-Health schism'. This schism was sustained by the continued domination of the former on the expense of the latter. This also led to increased global m-Health inequality and divide between the much-perceived health and patient benefits and the markets of m-Health. This divergence was more evident in low and middle income (LMIC) countries compared to the developed world. This powerful yet misguided evolution of the m-Health was driven essentially by complex factors. These are presented in this paper as the 'known unknowns' or 'the obvious but sanctioned facts' of m-Health. These issues had surreptitiously contributed to this reorientation and the widening schism of m-Health. The collateral damage of this process was the increased shift towards understanding 'digital health' as a conjecture term associated with mobile health. However, to date, no clear or scientific views are discussed or analyzed on the actual differences and correlation aspects between digital and mobile health. This particular 'known unknown' is presented in detail in order to provide a rapprochement framework of this correlation and valid presentations between the two areas. The framework correlates digital health with the other standard ICT for the healthcare domains of telemedicine, telehealth and e-health. These are also increasingly used in conjunction with digital health, without clear distinctions between these terms and digital health. These critical issues have become timelier and more important to discuss and present, particularly after the world has been caught off guard by the COVID-19 pandemic. The much hyped and the profiteering digital health solutions developed in response of this pandemic provided a modest impact, and the benefits were mostly inadequate in mitigating the massive health, human, and economic impact of this pandemic. This largely commercial reorientation of mobile health was unable not only to predict the severity of the pandemic, but also unable to provide adequate digital tools or effective pre-emptive digital epidemiological shielding and guarding mechanisms against this devastating pandemic. There are many lessons to be learnt from the COVID-19 pandemic from the mobile and digital health perspectives, and lessons must be learnt from the past and to address the critical aspects discussed in this paper for better understanding of mobile health and effective tackling of future global healthcare challenges.

m-Health 2.0: New perspectives on mobile health, machine learning and big data analytics.

Mobile health (m-Health) has been repeatedly called the biggest technological breakthrough of our modern times. Similarly, the concept of big data in the context of healthcare is considered one of the transformative drivers for intelligent healthcare delivery systems. In recent years, big data has become increasingly synonymous with mobile health, however key challenges of 'Big Data and mobile health', remain largely untackled. This is becoming particularly important with the continued deluge of the structured and unstructured data sets generated on daily basis from the proliferation of mobile health applications within different healthcare systems and products globally. The aim of this paper is of twofold. First we present the relevant big data issues from the mobile health (m-Health) perspective. In particular we discuss these issues from the technological areas and building blocks (communications, sensors and computing) of mobile health and the newly defined (m-Health 2.0) concept. The second objective is to present the relevant rapprochement issues of big m-Health data analytics with m-Health. Further, we also present the current and future roles of machine and deep learning within the current smart phone centric m-health model. The critical balance between these two important areas will depend on how different stakeholder from patients, clinicians, healthcare providers, medical and m-health market businesses and regulators will perceive these developments. These new perspectives are essential for better understanding the fine balance between the new insights of how intelligent and connected the future mobile health systems will look like and the inherent risks and clinical complexities associated with the big data sets and analytical tools used in these systems. These topics will be subject for extensive work and investigations in the foreseeable future for the areas of data analytics, computational and artificial intelligence methods applied for mobile health.

The potential of m-health systems for diabetes management in post conflict regions a case study from Iraq.

The recent developments of m-health technologies particularly in the developing world are increasing sharply due to the importance and accelerated adoption of these technologies in the developing countries. However, there are few if any studies on the effectiveness of mobile health in post conflict regions especially in the Middle East region. In this paper we describe the design, implementation and clinical outcomes of a feasibility study on mobile diabetes management in Basra, Southern Iraq as an exemplar for the effectiveness of mobile health technologies for improved healthcare delivery in similar post conflict regions. The key clinical outcome of this study indicated the lowering of HbA1C levels in the mobile health group indicating the potential of deploying such technologies in these regions where health resources are limited and challenging.

Cross layer design for optimised region of interest of ultrasound video data over mobile WiMAX.

The application of advanced error concealment techniques applied as a post-process to conceal lost video information in error-prone channels, such as the wireless channel, demand additional processing at the receiver. This increases the delivery delay and needs more computational power. However, in general, only a small region within medical video is of interest to the physician and thus if only this area is considered, the number of computations can be curtailed. In this paper we present a technique whereby the Region of Interest (ROI) specified by the physician is used to delimit the area where the more complex concealment techniques are applied. A cross layer design approach in mobile WiMAX wireless communication environment is adopted in this paper to provide an optimized Quality of Experience (QoE) in the region that matters most to the mobile physician while relaxing the requirements in the background, ensuring real-time delivery. Results show that a diagnostically acceptable Peak Signal-to-Noise-Ratio (PSNR) of about 36 dB can still be achieved within reasonable decoding time.

Cross-layer ultrasound video streaming over mobile WiMAX and HSUPA networks.

It is well known that the evolution of 4G-based mobile multimedia network systems will contribute significantly to future mobile healthcare (m-health) applications that require high bandwidth and fast data rates. Central to the success of such emerging applications is the compatibility of broadband networks, such as mobile Worldwide Interoperability For Microwave Access (WiMAX) and High-Speed Uplink Packet Access (HSUPA), and especially their rate adaption issues combined with the acceptable real-time medical quality of service requirements. In this paper, we address the relevant challenges of cross-layer design requirements for real-time rate adaptation of ultrasound video streaming in mobile WiMAX and HSUPA networks. A comparative performance analysis of such approach is validated in two experimental m-health test bed systems for both mobile WiMAX and HSUPA networks. The experimental results have shown an improved performance of mobile WiMAX compared to the HSUPA using the same cross-layer optimization approach.

Comparative analysis of genomic signal processing for microarray data clustering.

Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.

Mobile telemonitoring for achieving tighter targets of blood pressure control in patients with complicated diabetes: a pilot study.

BACKGROUND: Hypertension is a major risk factor for the long-term complications of diabetes. Mobile, self-measurement of blood pressure is emerging as a method to manage blood pressure in general, but its impact in patients with diabetes is unclear. METHODS: We randomized 137 patients with diabetes and hypertension to either mobile telemonitoring (n = 72) or usual care (n = 65). Clinic blood pressure was recorded at baseline and after 6 months. Patients in the intervention arm transmitted weekly blood pressure readings wirelessly, using adapted sensors via mobile phones to a central server. Clinicians received the data in real-time and using a web-based application provided management advice to the patient and their physicians. RESULTS: Systolic blood pressure fell significantly in the patients in the intervention group (mean [95% confidence interval], -6.5 [-0.8 to -12.2] mm Hg; P = 0.027) and remained unchanged in the control group (2.1 [9.3 to -5.0] mm Hg; P = 0.57). Patients within the intervention arm of African origin seemed to benefit more from the intervention. In addition, those who achieved a systolic blood pressure of <120 mm Hg had lower average blood sugars than those with higher readings (7.8 [SD 1.6] vs. 8.9 [SD 2.2] mmol/L; P = 0.02). CONCLUSIONS: In patients with diabetes, mobile telemonitoring has potential for delivering intensified care to improve blood pressure control, and its use may be associated with reduced exposure to hyperglycemia.

UK and Canadian perspectives of the effectiveness of mobile diabetes management systems.

The use of mobile technologies for self-monitoring of blood glucose and blood pressure for diabetes patients is becoming increasingly popular worldwide. This is propelled by the proliferation of the wider usage of mobile phones and other wireless technologies and computing platforms in the healthcare sector. Such technologies can play a pivotal role in chronic disease management and patient self-care. There have been several clinical trials in recent years on mobile diabetes management in UK and Canada. However, no studies to date have addressed and correlated the technological and clinical outcomes concerning the use of mobile chronic disease management systems for diabetes from the UK and Canadian perspectives. In this paper we address some of these correlative issues based on similar clinical trials on mobile type-2 diabetes management systems deployed in these two countries. In particular, the outcomes of these trials supported the use of telemonitoring for effective blood pressure control, but telemonitoring was less effective at managing blood glucose control. Some of the clinical results and challenges are presented together with future work and suggestions that aim to validate a generic platform for mobile diabetes management.

Technical and compliance considerations for mobile health self-monitoring of glucose and blood pressure for patients with diabetes.

Self-monitoring of blood glucose is an integral part of diabetes care which may be extended to other biometrics. Cellular and short range communication technologies will be important for the routine usage of these systems. However, the issues of follow-up and patient compliance with these emerging systems have not been yet studied evaluated but could be critical to the adoption of these technologies. We evaluated the impact of mobile telemonitoring on the intensification of care on blood pressure control and exposure to hyperglycaemia in patients with diabetes. We randomised 137 patients with diabetes to either mobile telemonitoring (n = 72) or usual care patients (n = 65) for 9 months. In this paper we present some of the clinical results with focus on blood pressure control hypertension and highlight some of the technical and compliance issues that were encountered.

Evaluation of a mobile phone telemonitoring system for glycaemic control in patients with diabetes.

We conducted a randomized controlled trial using mobile health technology in an ethnically diverse sample of 137 patients with complicated diabetes. Patients in the intervention group (n = 72) were trained to measure their blood glucose with a sensor which transmitted the readings to a mobile phone via a Bluetooth wireless link. Clinicians were then able to examine and respond to the readings which were viewed with a web-based application. Patients in the control arm of the study (n = 65) did not transmit their readings and received care with their usual doctor in the outpatient and/or primary care setting. The mean follow-up period was 9 months in each group. The default rate was higher in the patients in the intervention arm due to technical problems. In an intention-to-treat analysis there were no differences in HbA(1c) between the intervention and control groups. In a sub-group analysis of the patients who completed the study, the telemonitoring group had a lower HbA(1c) than those in the control group: 7.76% and 8.40%, respectively (P = 0.06).

Fractal dimension and wavelet decomposition for robust microarray data clustering.

Microarrays are now established technologies which are considered as key to gene expression analysis. Their study is usually achieved by using clustering techniques. Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies. In this paper, we present a comparative analysis of two genomic signal processing methods for robust microarray data clustering. Techniques based on Fractal Dimension and Discrete Wavelet Decomposition with Vector Quantization are validated for standard data sets. Comparative analysis of the results indicates that these methods provide improved clustering accuracy compared to some conventional clustering techniques. Moreover, these classifiers don't require any prior training procedures.

Linear predictive coding and wavelet decomposition for robust microarray data clustering.

Microarrays are powerful tools for simultaneous monitoring of the expression levels of large number of genes. Their analysis is usually achieved by using clustering techniques. Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies. In this paper, we present a comparative analysis of two genomic signal processing methods namely Linear Predictive Coding and Discrete Wavelet Decomposition for robust microarray data clustering. Vector quantization is applied to the resultant coefficients to provide the clustering of the data samples. Both techniques were validated for standard data sets. Comparative analyses of the results indicate that these methods provide improved clustering accuracy compared to some conventional clustering techniques. Moreover, there classifiers don't require any prior training procedures.

A cross-layer approach for wireless medical video streaming in robotic teleultrasonography.

M-health is an emerging area of research integrating emerging wireless technologies with healthcare systems. One of the key challenges in future research in this area, especially from the communications perspective, is medical video streaming over 3G and 4G systems. In this paper, video streaming in a robotic teleultrasonography system through a cross-layer approach based on tailor made controller structures is presented. Simulation results of the proposed system demonstrate the successful performance of the proposed controller structures in this advanced mobile telemedical environment.

Design and evaluation of a wireless decision-support system for heart rate variability study in haemodialysis follow-up procedures.

In this paper a new wireless decision-support system for haemodialysis patients using heart rate variability (HRV) is presented. The telemedicine system provides connectivity to three participant sites: the general practitioner or nurse at the point of care in the dialysis unit, the remote information and processing server and the cardiologist. At the clinical point of care, the nurse acquires the electrocardiogram (ECG) by using a tailored mobile telecardiology system as well as other relevant physiological information during the clinical procedure, and sends it to the information server. The received information is stored in a secure file server, linked to the patient database and the ECG signal is automatically analyzed by using advanced signal processing tools in the processing server, where a complete clinical results report is generated. The cardiologist can then be linked by means of a web browser to the information server to analyze these results for further clinical diagnosis support. The system has been applied to study HRV in patients undergoing haemodialysis. The clinical report consisted of trends for time- and frequency-domain HRV indexes and other supplementary information automatically calculated, which show the response of the electrical activity of the heart to the dialysis process and that can be helpful for the follow-up of these patients. The telecardiology framework has been successfully evaluated both by the patients and the hospital personnel showing a high compliance with the system. The design and implementation of the telecardiology system have followed the most recent advances in web technologies, biomedical information and storage standards and signal processing techniques. The presented system can be used as a telemedicine tool for clinical diagnosis support and could also be used in other clinical settings.

Enhanced real-time ECG coder for packetized telecardiology applications.

A new real-time compression method for electrocardiogram (ECG) signals has been developed based on the wavelet transform approach. The method is specifically adaptable for packetized telecardiology applications. The signal is segmented into beats and a beat template is subtracted from them, producing a residual signal. Beat templates and residual signals are coded with a wavelet expansion. Compression is achieved by selecting a subset of wavelet coefficients. The number of selected coefficients depends on a threshold which has different definitions depending on the operational mode of the coder. Compression performance has been tested using a subset of ECG records from MIT-BIH Arrhythmia database. This method has been designed for real-time packetized telecardiology scenarios both in wired and wireless environments.

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery.

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.

Clinical trials and evaluation of a mobile, robotic tele-ultrasound system.

We have developed a robotic tele-ultrasound system (OTELO) that allows an expert to examine a distant patient by ultrasound. At the expert station, a sonographer controls a virtual probe. Movements are reproduced at the patient station, which may be several kilometres away, on a real probe held by a lightweight robot, which is positioned on the patient by a paramedic. Two medical teams tested the tele-ultrasound system at two different hospitals on a total of 52 patients. Except for some difficulties caused by particular conditions, the diagnosis obtained with the remote scanning system agreed in at least 80% of the cases with the diagnosis made by conventional scanning. The results demonstrate the feasibility and efficiency of the device.