Adnan R. Manzoor

Effect of Changes in the Structure of a Social Network on Emotion Contagion

Agent-based support systems are used to help people with developing and maintaining a healthy lifestyle. Interventions on the social network of an individual could play a role in achieving behaviour change. In this paper, a method for finding effective network interventions to influence specific individuals is proposed. The effect of these interventions was analysed by simulating the diffusion of emotional values about intentions and goals in a social network. Experiments showed that changing connections closer to the target have a larger influence than changing connections further from the target node. A comparison of the effect of the proposed interventions with all possible interventions showed that they are among the most optimal possible interventions. Finally, it was shown that nodes with fewer connections are easier to influence. The proposed interventions could form the basis for a support system that focus on affecting the social interaction between people in an online social network.

A Computational Model of the Relation between Regulation of Negative Emotions and Mood

In this paper a computational model is presented that describes the role of emotion regulation to reduce the influences of negative events on long term mood. The model incorporates an earlier model of mood dynamics and a model for the dynamics of emotion generation and regulation. Example model simulations are described that illustrate how adequate emotion regulation skills can prevent that a depression is developed.

Active2Gether: A Personalized m-Health Intervention to Encourage Physical Activity

Lack of physical activity is an increasingly important health risk. Modern mobile technology, such as smartphones and digital measurement devices, provides new opportunities to tackle physical inactivity. This paper describes the design of a system that aims to encourage young adults to be more physically active. The system monitors the user’s behavior, uses social comparison and provides tailored and personalized feedback based on intelligent reasoning mechanisms. As the name suggests, social processes play an important role in the Active2Gether system. The design choices and functioning of the system are described in detail. Based on the experiences with the development and deployment of the system, a number of lessons learnt are provided and suggestions are proposed for improvements in future developments.

A Computational Cognitive Model Integrating Different Emotion Regulation Strategies

In this paper a cognitive model is introduced which integrates a model for emotion generation with models for three different emotion regulation strategies. Given a stressful situation, humans often apply multiple emotion regulation strategies. The presented computational model has been designed based on principles from recent neurological theories based on brain imaging, and psychological and emotion regulation theories. More specifically, the model involves emotion generation and integrates models for the emotion regulation strategies reappraisal, expressive suppression, and situation modification. The model was designed as a dynamical system. Simulation experiments are reported showing the role of the emotion regulation strategies. The simulation results show how a potential stressful situation in principle could lead to emotional strain and how this can be avoided by applying the emotion regulation strategies decreasing the stressful effects.

Monitoring the Impact of Negative Events and Deciding about Emotion Regulation Strategies

Humans have a number of emotion regulation strategies at their disposal, from which in a particular situation one or more can be chosen. The focus of this paper is on the processes behind the choice of these regulation strategies. The paper presents a neurologically inspired cognitive computational model of a monitoring and decision mechanism for emotion regulation incorporating different strategies (expressive suppression, reappraisal or reinterpretation, and situation modification). It can be tuned to specific characteristics of persons and events.

Online Sharing of Physical Activity: Does It Accelerate the Impact of a Health Promotion Program?

Influence on health behavior from peers is well known and it has been shown that participants in an online physical activity promotion program are generally more successful when they share their achievements through an online community. However, more detailed insights are needed into the mechanisms that explain the influence of a community on physical activity level (PAL). This paper discusses a detailed analysis of a data set of participants in an online physical activity promotion program. The analysis focuses on a method to compare community members with non-community members that eliminates to a large extent factors that dilute the community effect. We create statistical models that describe the PAL increase at the end of the program. A comparison of these models shows that community members not only have a higher PAL at the start of the program, but also that the PAL increase is significantly greater compared to non-community members. The results further support the hypothesis that stimulating participants to share their achievements with peers makes physical activity programs more successful to help people achieve a healthy activity level.

Real-Life Validation of Methods for Detecting Locations, Transition Periods and Travel Modes Using Phone-Based GPS and Activity Tracker Data.

Insufficient physical activity is a major health concern. Choosing for active transport, such as cycling and walking, can contribute to an increase in activity. Fostering a change in behavior that prefers active transport could start with automated self-monitoring of travel choices. This paper describes an experiment to validate existing algorithms for detecting significant locations, transition periods and travel modes using smartphone-based GPS data and an off-the-shelf activity tracker. A real-life pilot study was conducted to evaluate the feasibility of the approach in the daily life of young adults. A clustering algorithm is used to locate people’s important places and an analysis of the sensitivity of the different parameters used in the algorithm is provided. Our findings show that the algorithms can be used to determine whether a user travels actively or passively based on smartphone-based GPS speed data, and that a slightly higher accuracy is achieved when it is combined with activity tracker data.

Explaining Changes in Physical Activity Through a Computational Model of Social Contagion

Social processes play a key role in health behaviour. Understanding the underlying mechanisms of such processes is important when designing health interventions with a social component. In this work, we apply a computational model of social contagion to a data set of 2,472 users of a physical activity promotion program. We compare this model’s predictions to the predictions of a simple linear model that has been derived by a regression analysis. The results show that the social contagion model performs better at describing the pattern seen in the empirical data than the linear model, indicating that some of the dynamics of the physical activity levels in the network can be explained by social contagion processes.