Simplifying and personalising health information with mobile apps: translating complex models into understandable visuals

Abstract

AbstractBackgroundIf patients could utilise scientific research about modifiable risk factors there is a potential to prevent disease and promote health. Mobile applications can automatically adjust what and how information is presented based on a user’s profile, creating opportunities for conveying scientific health information in a simpler and more intuitive way. We aimed to demonstrate this principle by developing a complex statistical model of the relationship between self-rated-health (SRH) and lifestyle-related factors, and designing an app that utilises user data to translate the statistical model into a user-centred visualisation that is easy to understand.MethodsUsing data from the 6th (n=12 981, 53.4% women and 46.6% men) and 7th (n=21 083, 52.5% women and 47.5% men) iteration of the Tromsø population survey, we modelled the association between SRH on a 4-point scale and self-reported intensity and frequency of physical activity, BMI, mental health symptoms (HSCL-10), smoking, support from friends, and diabetes (HbA1c≥6.5%) using a mixed-effects linear-regression model (SRH was treated as a continuous variable) adjusted for socio-economic factors and comorbidity. The app registers relevant user information, and inputs the information into the SRH-model to translate present status into suggestions for lifestyle changes with estimated health effects.ResultsSRH was strongly related to modifiable health factors. The strongest modifiable predictors of SRH were HSCL-10 and physical activity levels. In the fully adjusted model, on a scale ranging from 1 to 4, a 10-HSCL index≥3 was associated with a reduction in SRH of 0.948 (CI: 0.89, 1.00), and vigorous physical activity (exercising to exhaustion ≥4 days/week vs sedentary) was associated with an SRH increase of 0.643 (0.56-0.73). Physical activity intensity and frequency interacted positively in their effect on SRH, with large PA-volume (frequency ⨯ intensity) being particularly predictive of high SRH.ConclusionsApps that adjust the presentation of information based on the user’s profile can simplify and potentially improve communication of research-based scientific models, and could play an important role in making health research more accessible to the general public. Such technology could improve health education if implemented in websites or mobile apps that focus on improving health behaviours.