Digitalizing health: Lassonde professor is using persuasive technology and machine learning to enhance well-being of diverse populations
From reminders to take a daily yoga lesson to notifications about prescription refills, persuasive technology is an effective technique used in many software applications. Informed by psychological theories, this technology can be incorporated in many electronic devices to change users’ attitudes and behaviours, including habits and lifestyle choices related to health and well-being. Joining these efforts is Kiemute Oyibo, an assistant professor in the Electrical Engineering & Computer Science department at the Lassonde School of Engineering.
“People are receptive to personalized health-related messages that help them adopt beneficial behaviours they ordinarily find difficult,” says Professor Oyibo. “That is why I am designing, implementing and evaluating personalized persuasive technologies in the health domain with a focus on inclusive design, and tailoring health applications to meet the needs of underrepresented groups.”
By considering the specific needs of these groups, Professor Oyibo’s work has the potential to change the one-size-fits-all approach of software application design. “By excluding features which may discourage some populations from using certain health applications and focusing on their unique needs, such as the inclusion of cultural elements and norms, personalized health applications can benefit users from marginalized communities,” he explains. “Another method that can help improve user experience is participatory design. This enables underrepresented groups, such as Indigenous Peoples, to be a part of the design and development of technology they will enjoy using.”
Through demographic studies, Professor Oyibo is investigating the behaviours, characteristics, preferences and unique needs of different populations, including underrepresented groups, throughout Canada and Africa. For example, he is examining cultural influences on users’ attitudes and acceptance of contact tracing applications – an approach that is unique for informing the design and development of public health applications.
Professor Oyibo is also working on utilizing machine learning techniques and large datasets to build group-specific predictive models for different target populations to promote positive behaviour changes. This work is supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (DG). “Group-specific predictive models that do not treat the entire target population as a monolithic group can be used to personalize health messages to specific users more effectively,” he says.
In related work, Professor Oyibo is collaborating with professors from Dalhousie University and industry partners at ThinkResearch to explore the application of persuasive techniques in the design of medical incident reporting systems, to improve their effectiveness in community pharmacies across Canada.
“There are a lot of near misses and incidents in community pharmacies across Canada that go unreported,” says Professor Oyibo. “Apart from personal and administrative barriers, such as fear of consequences and lack of confidentiality in handling reports, the culture of little to no reporting reflects system design. We want to leverage persuasive techniques to enhance these systems and make them more motivating and valuable, to encourage users to report as many incidents and near misses as possible so that the community can learn from them. This will go a long way in fostering patient safety in community pharmacies across Canada.”
Professor Oyibo’s work is part of a global effort to bridge the digital divide in healthcare and utilize technology to improve the lives of diverse populations, contributing to the United Nations’ Sustainable Development Goal 3: Good Health and Well-Being.