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

Abstract

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.

Perception of Influencing Factors on Acceptance of Mobile Health Monitoring Service: A Comparison between Users and Non-users

“…Mobile health monitoring services related to health management and illness prevention could be provided. Such mobile health monitoring services could be highly effective, especially for chronically ill patients or people who want to manage their health and avoid illness [2]. Mobile health monitoring services have been defined as a combination system of mobile computing, medical sensors, and communications technologies for healthcare.…”

Parameters characterizing nature of personal health in the correlation between energy expenditure/supply and body-fat

“…Here, m n nm h h h ' (2) is the difference in the time -series of body-fat percentage data, h, representing the variation of body -fat, and is the weighted summation of the time ±series of energy expenditure or supply data, e, during a certain period, representing the accumulation of the effects of lifestyle events (Fig.1) In Eq. (3), weighting coefficients, w(0) to w(l-1), are derived from the weighting function…”

“…(4) as a weighting function in the summation of daily time-series of lifestyle events data, the values of x are 0, 1, 2, «, l-1, and corresponding w(0), w(1), w (2), «, w(l-1) are assigned as weighting coefficients descending in date [7]. The value of # is restricted to non-negative when used in the weighting function.…”

Validity of association rules extracted by healthcare-data-mining

“…Application of the Internet to healthcare such as m-health and p-health are current topics of interest [1,2]. In particular, the demand for personalized healthcare systems to prevent diseases and improve health has been increasing recently [3].…”