Key points are not available for this paper at this time.
In this work we investigate the use of behavior feasibility to adapt and personalize lifestyle-targeting recommender systems for the prevention and treatment of hypertension. Based on survey data (N=300) we model the feasibiliy of 63 behaviors through a Rasch model, describing the engagement in a behavior as a function of the behavior's difficulty and the person's ability. We formulate two feasibility-tailored recommendation strategies that utilize the Rasch model. The engagement maximization strategy aims at maximizing the probability of engagement by proposing very feasible behaviors while the motivation maximization strategy aims to challenge users by matching the difficulty of the advice with the ability of the user, thereby maximizing motivation. In an online study (N=150) we assessed user preference for either strategies (embodied as virtual coaches) in comparison with a random control strategy. Our results show that coaches selecting feasible health advice resonate better with the patient than control. In general patients significantly preferred the engagement maximization strategy over random advice on most factors, while patients with a medium level of ability significantly preferred the motivation maximization strategy on all factors.
Radha et al. (Tue,) studied this question.