About the Project
Unhealthy lifestyles, such as smoking and physical inactivity, leads to conditions such as cancer and cardiovascular diseases, reducing health, quality of life, and burdens our society.
Behaviours are shaped by - among other things- the individuals’ perceptions and the environment that they live in. Therefore, it is important to identify factors that determines individuals’ perception of health and wellbeing to be able to develop interventions that would enhance healthy behaviour. Such interventions need to be evaluated for their effectiveness and cost-effectiveness.
Applicants are not bound to a specific health behaviour or target population and are welcome to come up with their own plan. Think about exploring perceptions, barriers, and facilitators, why a specific population do or do not engage in certain behaviours, such as tobacco smoking, physical inactivity, sedentary behaviour, alcohol consumption, unprotected sex, to name a few.
Other behaviours such as how clinicians, health professionals and policy makers engage in the adoption of evidence-based interventions to change their population’s unhealthy behaviour are also interesting to explore.
Applicants are expected to use frameworks such as that from socio-cognitive theories or behavioural economics to answer their research question. A multi-method approach involving a mix of qualitative and quantitative analyses is encouraged. Depending on the interest of the applicant, the Ph.D. project can focus on developing and testing health interventions, such as digital health interventions, for behaviour change. Various techniques can be applied to elicit population preferences to understand what is likely to be an acceptable intervention, e.g. interviewing, cross-sectional and longitudinal survey analyses, best-worst scaling, Delphi technique, and systematic reviews.
Applicants interested in pursuing the project would ideally have backgrounds in health promotion, health psychology, social psychology, public health, economics, communication science, artificial intelligence, or any other related subject.
References
Cheung, K. L., Evers, S. M., Hiligsmann, M., Vokó, Z., Pokhrel, S., Jones, T., ... & de Vries, H. (2016). Understanding the stakeholders’ intention to use economic decision-support tools: a cross-sectional study with the tobacco return on investment tool. Health Policy, 120(1), 46-54.
Cheung, K. L., Evers, S. M. A. A., De Vries, H., Levy, P., Pokhrel, S., Jones, T., ... & Hiligsmann, M. (2018). Most important barriers and facilitators of HTA usage in decision-making in Europe. Expert review of pharmacoeconomics & outcomes research, 18(3), 297-304.
Cheung, K. L., de Ruijter, D., Hiligsmann, M., Elfeddali, I., Hoving, C., Evers, S. M., & de Vries, H. (2017). Exploring consensus on how to measure smoking cessation. A Delphi study. BMC public health, 17(1), 890.
Cheung, K. L., Schwabe, I., Walthouwer, M., Oenema, A., Lechner, L., & De Vries, H. (2017). Effectiveness of a video-versus text-based computer-tailored intervention for obesity prevention after one year: A randomized controlled trial. International journal of environmental research and public health, 14(10), 1275.
Cheung, K. L., Hiligsmann, M., Präger, M., Jones, T., Józwiak-Hagymásy, J., Munoz, C., ... & Evers, S. M. (2018). Optimizing usability of an economic decision support tool: prototype of the EQUIPT tool. International journal of technology assessment in health care, 34(1), 68-77.
Anokye, N. K., Trueman, P., Green, C., Pavey, T. G., & Taylor, R. S. (2012). Physical activity and health related quality of life. BMC public health, 12(1), 624.
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