Empirical validation of a computational model of influences on physical activity behavior

Abstract

The adoption and maintenance of a healthy lifestyle is a fundamental pillar in the quest towards a healthy society. Modern (mobile) technology allows for increasingly intelligent systems that can help to optimize people’s health outcomes. One of the possible directions in such mHealth systems is the use of intelligent reasoning engines based on dynamic computational models of behavior change. In this work, we investigate the accuracy of such a model to simulate changes in physical activity levels over a period of two to twelve weeks. The predictions of the model are compared to empirical physical activity data of 108 participants. The results reveal that the model’s predictions show a moderate to strong correlation with the actual data, and it performs substantially better than a simple alternative model. Even though the implications of these findings depend strongly on the application at hand, we show that it is possible to use a computational model to predict changes in behavior. This is an important finding for developers of mHealth systems, as it confirms the relevance of model-based reasoning in such health interventions.