Empirical Validation of a Computational Model of Influences on Physical Activity Behavior

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

Incorporating time dynamics in the analysis of social networks in emergency management

Timely and adequate communication is essential for the response to emergency situations. The current vision on emergency response embraces the networked organization as an answer to the dilemmas of communication and information flows in crisis situations. With stabilization of the network paradigm, the focus question turns into how networks are perceived and in what manner they function. We argue that there is a need to attend to the way networks and their functioning are assessed. From the agenda that we derive, we pay attention to the manner in which the time critical nature of the communication during emergency situations can be captured in network terms. The focus on how network interaction unfolds over time is demonstrated by attending to a case of a tunnel incident in the Netherlands. It is argued that a structure-oriented network analysis misses much of the actions and that using the data to probe the communication patterns with additional methods for time dependency enhances our insights. Three approaches, time slices, two-mode analysis and information pathways, are then introduced and the outcomes are interpreted

What technological features are used in smartphone apps that promote physical activity? A review and content analysis

Despite the well-known health benefits of physical activity, a large proportion of the population does not meet the guidelines. Hence, effective and widely accessible interventions to increase levels of physical activity are needed. Over the recent years, the number of health and fitness apps has grown rapidly, and they might form part of the solution to the widespread physical inactivity. However, it remains unclear to which extent they make use of the possibilities of mobile technology and form real e-coaching systems. This study aims to investigate the current landscape of smartphone apps that promote physical activity for healthy adults. Therefore, we present a framework to rate the extent to which such apps incorporate technological features. And, we show that the physical activity promotion apps included in the review implemented an average of approximately eight techniques and functions. The features that were implemented most often were user input, textual/numerical overviews of the user’s behavior and progress, sharing achievements or workouts in social networks, and general advice on physical activity. The features that were present least often were adaptation, integration with external sources, and encouragement through gamification, some form of punishment or the possibility to contact an expert. Overall, the results indicate that apps can be improved substantially in terms of their utilization of the possibilities that current mobile technology offers

Exploring Parameter Tuning for Analysis and Optimization of a Computational Model

Computational models of human processes are used for many different purposes and in many different types of applications. A common challenge in using such models is to find suitable parameter values. In many cases, the ideal parameter values are those that yield the most realistic simulation results. However, there are situations in which the goodness of fit is not the main or only criterion to evaluate the appropriateness of a model, but where other aspects of the model behavior are also relevant. This is often the case when computational models are employed in real-life applications, such as mHealth systems. In this paper, we explore how parameter tuning techniques can be used to analyze the behavior of computational models systematically and to investigate the reasons behind the observed behavior. We study a computational model of psychosocial influences on physical activity behavior as an in-depth use case. In this particular case, an important measure of the feasibility of the model is the diversity in the simulation outcomes. This novel application of parameter tuning techniques for analysis and understanding of model behavior is transferable to other cases, and is therefore a valuable new approach in the toolset of computational modelers

Analysis and Evaluation of Social Contagion of Physical Activity in a Group of Young Adults

It is known that opinions, attitudes and emotions spread through social networks. Several of these cognitions influence behavioral choices. Therefore, it is assumed that the level of physical activity of a person is influenced by the activity levels of the people in its social network. We have performed an experiment with 20 participants between 19 and 28 years old, measuring their physical activity levels for 30 days, in order to observe if there is a contagion effect due to the relationships in the social network. Using our social contagion model, we investigated if people will become more or less active according to the contacts with their peers within the network. Our model correctly predicts the direction of the change (increasing or decreasing) in 80% up to 87% of the cases investigated