Open Access

In past digital health interventions, an issue has been that participants drop out over time which is referred to as the ”law of attrition” (Eysenbach, 2005). Based on this, we propose that though initially, participants respond to the intervention, there is a hypothesized second diminishing e↵ect of an intervention. However, we suggest that on top, there is a third e↵ect. Independent of the individual notification or nudge, people could build the knowledge, skills and practice needed to independently engage in the behavior themselves (schraefel and Hekler, 2020). Using behavioral theory and inspired by prior animal computational models of behavior, we propose a dynamical computational model to allow for a separation of intervention and internalization. It is targeted towards the specific case of the HeartSteps intervention that could not explain a diminishing immediate effect of the intervention, second hypothesized e↵ect, while a person’s overall steps remained constant, third e↵ect (Klasnja et al., 2019). We incorporate a habituation mechanism from learning theory that can account for the immediate diminishing e↵ect. At the same time, a reinforcement mechanism allows participants to internalize the message and engage in behavior independently. The simulation shows the importance of a participant’s responsiveness to the intervention and a sufficient recovery period after each notification. To estimate the model, we use data from the HeartSteps intervention (Klasnja et al., 2019; Liao et al., 2020), a just-in-time adaptive intervention that sent two to five walking suggestions per day. We run a Bayesian estimation with Stan in R. Additional validation tests are needed to estimate the accuracy of the model for di↵erent individuals. It could however serve as a template for future just-intime adaptive interventions due to its generic structure. In addition, this model is of high practical relevance as its derived dynamics can be used to improve future walking suggestions and ultimately optimize notification-based digital health interventions.