Information Technology for secondary prevention of cardiovascular disease

Cardiovascular disease (CVD) is the leading cause of mortality globally and causes significant burden to healthcare systems. In recent years, innovations in information technology showed potential to improve health service delivery and outcomes.

Project supervisor(s)

Associate Professor Shariful Islam

Patients with CVD require support at home and out of hospital settings where they spent most of their time. However, such interventions are often difficult to implement and expensive. Using information technology, we aim to improve risk factors monitoring, self-management and develop a new model-of-care for secondary prevention of CVD in Australia and globally.

Applications are invited for two PhD students to work on the following projects within Deakin University’s Institute for Physical Activity and Nutrition (IPAN) to join our team in Digital Health for Lifestyle and Disease Management. The following project will be based at Deakin University, Burwood or Waurn Ponds Campuses:

  1. Smart Home system for patients with cardiovascular disease In collaboration with the School of IT, Deakin University and our technological partners, we have a Smart Home system that uses text messaging, interactive voice-system, wearable devices, online platform and a mobile phone App. Our Smart Home system could help to empower patients to improve self-management behaviours and reduce cardiovascular risk factors. However, the feasibility and effectiveness of Smart Home systems in patients with cardiovascular disease needs to be tested in real-life. The proposed PhD will investigate what features of a smart home are acceptable to participants, feasible to deliver, and the potential for improving health outcomes. In this project, the PhD student will start by undertaking a systematic review of the literature on smart home technologies for self-management of CVD. The student will design and conduct a trial of Smart Home in people with CVD. The project will involve using sensors and wearable devices for monitoring activity and physiological data, medication adherence, developing cloud-based databank, using information technologies and advanced data analytics for understanding factors that contribute to morbidity.
  2. Measuring lifestyle factors for cardiovascular disease using mobile health technologies. Lifestyle-related behaviours such as physical activity, fitness, sleep and diet are important risk factors for prevention of CVD. However, measuring these lifestyle behaviours is problematic, especially in clinics where healthcare providers need to promote these behaviours as part of usual care. Advances in smartphone capabilities (computational capabilities, inbuilt sensors and applications; apps) provides an ideal platform to capture real-time lifestyle data from large numbers of participants, for extended duration, at low-cost and with a relatively low participant burden. These data could be used to support early detection of symptoms, and assess the impact of interventions to prevent CVD and other chronic conditions. Moreover, advanced data analytics techniques and machine learning using artificial intelligence permits the ability to use sensor data to better understand behavioural patterns and to predict future CVD risk. These individualised data may allow a person-centric approach to medicine, contribute to empowerment of people, and support clinicians in decision-making and progress towards personalised digital medicine that can revolutionise healthcare. The feasibility of using mobile sensor data for long-term behavioural research in CVD prevention is relatively new and yet to be fully utilised.

The proposed PhD is framed within a wider research aim to develop a large cohort of adults who provide real-time sensor-based measurements of behavioural risk factors for CVD, including physical activity, sleep, sedentary time, and diet. This study will allow us to investigate the associations of lifestyle data with self-reported CVD outcomes (heart attack, strokes, coronary interventions, hospitalisation) and explore the use of artificial intelligence to better understand physical activity and behaviour patterns for CVD prevention. Potential skills required for implementing this project include systematic review of literature, study design and implementation, developing tools/questionnaires for measurement of blood pressure, heart rate, activity patterns, diet and CVD risk factors. Experience in developing mobile phone apps, programming, web development, database management, artificial intelligence/machine learning and cybersecurity will be valuable.

Skills and interests of potential applicants may include the following:

Information Technology, Computer Science, Data Science, Epidemiology, Public Health, Health Sciences, or Psychology.

Applicants must meet Deakin’s PhD entry requirements, be enrolling full time and hold an Honours degree (First Class) or an equivalent standard Master’s degree with a substantial research component. Please refer to the entry pathways to higher degrees by research for further information. We will work with suitably qualified applicants to apply for scholarship funding.

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