Mats Stage Baxter
Project: Control of seasonal allergic rhinitis helps asthma attacks: a feasibility study of a patient-focussed mobile health (mHealth) intervention
PhD Title: Control of seasonal allergic rhinitis helps asthma attacks: a feasibility study of a patient-focussed mobile health (mHealth) intervention
Funded by: Chief Scientist Office of the Scottish Government Department of Health
Supervisors: Professor Jürgen Schwarze, Professor Aziz Sheikh & Professor Andrew Bush
Based at: University of Edinburgh
Allergic rhinitis (AR) is a frequent co-morbidity of asthma and is estimated to be prevalent in over 80% of individuals with asthma. The presence of comorbid AR is associated with poor asthma control as well as increased risk of asthma attacks. Strong indications suggest that treatment of comorbid AR can help improve asthma control as well as the prevention of asthma attacks. This may be particularly relevant in individuals with concomitant asthma and seasonal AR (hay fever) who are at increased risk of asthma attacks during the hay fever season. However, adherence to AR treatment remains largely poor and inconsistent. While suboptimal adherence to AR treatment is attributable to a myriad of behavioural factors, forgetfulness remains key among them.
This research aims to design, implement and evaluate a patient-centred mobile health (mHealth) intervention which chiefly aspires to provide personalised notifications/serial messages during the hay fever season to act as cues to promote AR treatment adherence. Increased frequency of reminders is intended to occur on high pollen count days.
The mHealth intervention’s effect on AR treatment adherence and subsequent symptom scores, quality of life and asthma attacks will be assessed, as well as the acceptability, uptake and feasibility of the mHealth intervention itself.
I have completed a BSc and MSc in Global Health with a specialisation in E-Health and ICT from the University of Copenhagen, Denmark. My premier research interests lie within eHealth/mHealth, with a particular interest in chronic disease management.
Baxter MS, White A, Lahti M, Murto T, Evans J. Machine learning in a time of COVID-19 - Can machine learning support Community Health Workers (CHWs) in low and middle income countries (LMICs) in the new normal? J Glob Health 2021;11:03017. doi: 10.7189/jogh.11.03017