Michelle (Helena) is a honorary research fellow in the Global eHealth Unit.
Michelle has an undergraduate degree in Biomedical Sciences and MSc in Health Technology Assessment, both from Radboud University Nijmegen in the Netherlands. She was awarded her PhD from Imperial College London in 2014. Her doctoral thesis explored the application of mHealth-based collection of information relevant to childhood diarrhoea and pneumonia in rural China. This work made a significant contribution to the evaluation methodology and scarce evidence-base of mHealth-based interventions, which was demonstrated by 20 publications in peer-reviewed journals. Her supervisors were Dr Josip Car, director of the Global eHealth Unit and Prof Igor Rudan, chair in International Health at the University of Edinburgh. During her PhD she collaborated extensively with researchers at the Capital Institute of Pediatrics in Beijing.
Michelle is involved in research projects that make use of mHealth-based tools to improve nutrition and vaccination coverage of young children in rural China. Furthermore, she completed Cochrane Collaboration systematic reviews on telephone consultation for HIV, integrating prevention of mother-to-child transmission of HIV with other health services, and male involvement in prevention of mother-to-child transmission of HIV programmes. In addition, she worked on reviews of the use of mobile phones for improving healthcare in developing countries, the use of apps for diabetes self-care and mobile phone messaging for HIV services.
Michelle teaches in modules on evidence synthesis, eHealth and mHealth and writing scientific research papers, at Imperial College London and at other universities including Kilimanjaro Christian Medical University College in Moshi, Tanzania, and the Karolinska Institutet in Stockholm, Sweden.
et al., 2022, Internet of things–Enabled technologies as an intervention for childhood obesity: A systematic review, Plos Digital Health, Vol:1, Pages:e0000024-e0000024
et al., 2021, Life.course digital T.wins – I.ntelligent M.onitoring for E.arly and continuous intervention and prevention (LifeTIME): Proposal for a proof-of-concept study, Jmir Research Protocols, ISSN:1929-0748
et al., 2021, Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study, Jmirx Med, Vol:2, Pages:e25560-e25560
et al., 2021, Authors’ Response to Peer Reviews of “Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study”, Jmirx Med, Vol:2, Pages:e28917-e28917
et al., 2021, Feasibility of using WeChat to improve infant and young child feeding in rural areas in China: A mixed quantitative and qualitative study, Plos One, Vol:16, ISSN:1932-6203