Initial Design of a Serious Game for Reducing Risks of Prenatal Excessive Gestational Weight Gain in Bangla

Tabriji Islam, Khandaker Umme Salma, Newaz Ben Alam, Ratna Saha, Ashraful Islam, M. Ashraful Amin

Abstract: Excessive gestational weight gain (GWG) poses substantial health concerns for both the mother and the child. In Bangladesh, the issue is further complicated by the difference between urban and rural areas. Urban women often suffer from obesity during and after pregnancy, whereas rural women suffer from malnutrition and accompanying syndromes. In particular, excessive GWG poses a bigger health risk than nutritional inadequacies. Traditional approaches to encouraging good habits during pregnancy are often hampered by expense, accessibility, and limited reach in remote locations. This study introduces “NutriMom”, a serious game-inspired mobile health application (mHealth) to reduce excessive GWG among Bangladeshi women. The study had three goals: (i) to perform design requirements and need-finding analysis, (ii) to create a high-fidelity NutriMom prototype, and (iii) to evaluate the usability of the proposed prototype. A survey of 59 pregnant women in different trimesters highlighted significant factors contributing to excessive GWG, especially social taboos, and lack of nutritional education. Participants expressed dissatisfaction with limited access to healthcare providers (HCPs), particularly in remote areas. Based on these findings, the NutriMom prototype was designed to include several key characteristics: gamified learning about healthy diets and physical exercise, virtual chat support with HCPs, and reminders about sleep, hydration, and food intake. The target audience voted that the ‘Daily Quiz’ and ‘Physical Exercise Planner’ features from the ‘Gamified Learning’ category were most appreciated, with 31.6% of combined votes. NutriMom received a mean system usability scale score (SUS) of 79.81% (n = 26). NutriMom merges gamification and mHealth to provide interactive tools and personalized recommendations for pregnant women. Future iterations will include longitudinal studies and the integration of machine learning models, further improving functionality.