Designing a Personalised Mobile Application to Improve Engagement With PFMT Therapy Amongst Women From Pregnancy To One Year After Delivery

Women who are pregnant, or have given birth are at high risk of developing Pelvic Floor Disorder (PFD) due to the physical stress placed on the pelvic muscles during this time. When left untreated, PFD can cause symptoms such as incontinence, organ prolapse and pelvic pain in sufferers. Pelvic Floor Muscle Training (PFMT) is a highly effective means of treating and preventing symptoms of PFD. However, adherence rates to PFMT remain low.  Amongst the biggest barriers to adherence are incorrect technique, lack of knowledge, memory, time, low motivation and stigma. As with the physical symptoms of PFD, these barriers impact sufferers in ways unique to each individual. Findings from the existing literature suggest that personalising the intervention to accommodate these varying factors may improve adherence.  This study focuses on the development of a personalised mobile application to improve engagement with PFMT amongst women from pregnancy, up to one year after delivery. The goal of the application is to improve engagement with PFMT through addressing key barriers to adherence, and guiding correct performance of PFMT.  An initial design criteria and five user personas were developed. The criteria and personas were used to develop prototypes, which were then user tested. The designs were then refined based on user feedback. Designs were also informed by feedback from interviews with clinicians and women.  The results of this study indicate that a mobile application is an ineffective means of guiding PFMT technique. However the application proved effective in addressing the barrier of memory through the use of context based triggers. The integration of the Hooked model in the application design had a low to moderate effect on improving engagement with PFMT. Opportunities for a personalised design approach in the areas of instruction, facilitation of exercises and preferences for application features were identified.