Investigation of decision support for self-management of chronic conditions.

Abstract

Chronic conditions are the leading cause of morbidity and mortality in the world. Traditional health care for people suffering from a chronic condition is expensive and consumes significant resources. This Thesis focuses on investigating novel applications of technologies and services to improve the support offered to patients in the self-management of their chronic conditions. A decision support framework for self-management is proposed. It combines three theoretical perspectives: utilizing information gleaned from behavioural, psychological and biological interventions. A personal self-management system (PSMS), developed within SMART2, utilizes such a framework. It facilitates the integration of activity information, self-reporting, vital signs and lifestyle monitoring data. Approaches have been developed and applied to analyse the information collected and provide feedback to the ‘patient’ to assist with self-management. An orientation free adaptive step detection (OFASD) algorithm was developed, deployed and evaluated with a smart phone equipped with accelerometers to detect levels of activity in the form of human steps. The advantages of the OFASD algorithm following evaluation were found to be the self adaptive nature of the approach for each individual and the orientation free notion for smart phone placement. The accuracy and sensitivity of the OFASD algorithm achieved performance rates of 93.4% and 93.2%, respectively. Furthermore, an approach was considered involving the identification of a patient’s health status through self-reporting. Several feature selection and classification methods were evaluated and the optimal combination for the purposes of classification was reported. The results have indicated that a Multilayer Perceptron (MLP) based classifier had the best classification performance on an optimised subset of 10 questions. Based on the study it has found that the analysis of the lifestyle of a patient produces relevant knowledge that can be used to inform and promote behavior change on the part of the patient and to support self-management. Overall, the results from the research have demonstrated that assistive technologies and suitable algorithms have the capability to facilitate self-management for people with chronic conditions.

Date of Award	Jan 2012
Original language	English
Sponsors	Engineering and Physical Sciences Research Council (EPSRC) - full college member
Supervisor	Huiru (Jane) Zheng (Supervisor), Christopher Nugent (Supervisor) & Paul McCullough (Supervisor)