Research Projects
Use of Digital Health Platforms, Wearables and Machine Learning to Improve the Management and Prognosis of Stroke and Dementia

Programme(s) to which this project applies:

☑ MPhil/PhD ☑ MRes[Med] ☑ URIS

Objective and Significance:

Stroke is a common condition that is associated with a high rate of mortality, disability and cognitive impairment. Despite current medical advances, one in five individuals may develop a recurrent stroke. Furthermore, the prevalence of dementia and depression after stroke remains uncertain; and the burden of caregiver stress and anxiety also remains understudied. Better strategies for detection and monitoring of vascular risk factors, mood and memory problems in stroke patients, and caregiver burden in those taking care of patients are therefore required.

In collaboration with The HK Applied Science and Technology Research Institute, we are currently developing a digital platform which aims to facilitate close monitoring of vascular risk factors, mood and cognitive disorders in families combating stroke. The platform also aims to detect caregiver burden. The platform will be disseminated widely throughout Hong Kong, Macau and parts of Shenzhen with a targeted ~20,000 downloads per year.

The main objective of this MRes Project is to determine the effectiveness of this digital health platform in the detection and management of vascular risk factors, depression and cognitive impairment after stroke.

Research Plan and Methodology:

Upon development of the digital health platform for stroke patients and caregivers in July 2020, the platform will be disseminated widely throughout Hong Kong, Macau and parts of Shenzhen with a target number of 20,000 downloads per year.

The student(s) involved in this project will be engaged with the following:

  1. Dissemination of this platform throughout Hong Kong, Macau and Shenzhen
  2. Assist in data collection of the burden of vascular risk factors (detected using wearable devices and home blood pressure and glucose monitoring), cognitive impairment, psychological status and caregiver stress
  3. Analyse the vast amounts of data collected using statistical packages and supplemented by machine learning methods to determine the effectiveness of such a digital health mobile platform in
        a) prevention of recurrent stroke and adverse outcome after stroke,
        b) identification of stroke patients with depression and cognitive impairment and
        c) identification of caregivers with significant caregiver burden

Professor GKK Lau, Department of Medicine

Gary Kui Kai Lau is currently Clinical Associate Professor in Neurology, Director of HKU Stroke and Director of the Tam Wing Fan Neuroimaging Research Laboratory. He has published over 140 research articles or book chapters relating to stroke, including in reputable journals such as Lancet Neurology, Lancet Infectious Diseases, European Heart Journal, JAMA Neurology, Neurology and Stroke. He has also helped establish a number of programmes to support stroke survivors and families within Hong Kong. 

HKU Scholars Hub
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For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

  1. your CV,
  2. a brief description of your research interest and experience, and
  3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information.