Effective recommendations on healthy food choice need to be personalized and sent out on a large scale. In this paper, we present a model of automatic message selection tailored on the characteristics of the recipient and focused on the reduction of red meat consumption. This model is obtained through the collaboration between social psychologists and artificial intelligence experts. Starting from selected psychosocial models on food choices and the framing effects of recommendation messages, we involved a sample of Italian participants in (...) an experiment in which they: filled out a first questionnaire, which was aimed at detecting the psychosocial antecedents of the intention to eat red/processed meat; read messages differing as to the framing of the hypothetical consequences of reducing versus not reducing red/processed meat consumption; filled out a second questionnaire, which was aimed at detecting participants’ reaction to the messages, as well as any changes in their intention to consume red/processed meat. Data collected were then employed to learn both the structure and the parameters of a Graphical Causal Model based on a Dynamic Bayesian Network, aimed to predicting the potential effects of message delivery from the observation of the psychosocial antecedents. Such probabilistic predictor is intended as the basis for developing automated interactions strategies using Deep Reinforcement Learning techniques. Discussion focuses on how to develop automatic interaction strategies able to foster mindful eating, thanks to considering the psychosocial characteristics of the people involved; sending messages tailored on these characteristics; adapting interaction strategies according to people’s reactions. (shrink)

Although the study of the affective components involved in predicting physical activity is spreading faster and faster, there is a lack of studies testing their role when promoting physical activity through message interventions. In the present study, we considered these components by focusing on how anticipated affective reactions and emotional processing of the messages influence receivers’ affective attitude toward physical activity, concurrent behavior, and future intention. A sample of 250 participants was involved in an intervention relying on prefactual messages (...) promoting physical activity. All messages were sent through a research app and were focused on the expected consequences of exercising. Four experimental conditions involving messages differing as to their outcome sensitivity framing were compared to a control condition. Results showed that reading gain and non-gain messages enhanced the positive affective attitude toward physical activity, compared to control. Enhanced affective attitude after the intervention increased, in turn, self-reported physical activity and future intention. Interestingly, gain messages were even more persuasive for people with a low level of positive anticipated affective reactions. Furthermore, their effectiveness was especially attributable to the elicitation of hope in receivers. Discussion focuses on the advantages of considering affective components and their implications when promoting physical activity. (shrink)

The mental simulation of past and future scenarios allows individuals to understand the past, make predictions about the future, plan and regulate their behavior. A great deal of research has focus...