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.

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.