C. Natalie van der Wal

Apps to promote physical activity among adults: a review and content analysis

BackgroundIn May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear.MethodsThe study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play.ResultsOn average, the reviewed apps included 5 behavior change techniques (range 2–8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found.ConclusionsThe present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used in other types of physical activity promotion interventions.

Modelling collective decision making in groups and crowds: Integrating social contagion and interacting emotions, beliefs and intentions

Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in social neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes. As an application of the model, an agent-based analysis was made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.

A Multi-agent Model for Emotion Contagion Spirals Integrated within a Supporting Ambient Agent Model

To avoid the occurrence of spirals of negative emotion in their teams, team leaders may benefit from intelligent agent systems that analyze the emotional dynamics of the team members. As a first step in developing such agents, this paper uses an agent-based approach to formalize and simulate emotion contagion spirals within groups. The computational multi-agent model is integrated within an intelligent ambient agent to monitor and predict group emotion levels over time and propose group support actions based on that.

Agent-Based Modeling of Emotion Contagion in Groups

To avoid the development of negative emotion in their teams, team leaders may benefit from being aware of the emotional dynamics of the team members. To this end, the use of intelligent computer systems that analyze emotional processes within teams is a promising direction. As a first step toward the development of such systems, this paper uses an agent-based approach to formalize and simulate emotion contagion processes within groups, which may involve absorption or amplification of emotions of others. The obtained computational model is analyzed both by explorative simulation and by mathematical analysis. In addition, to illustrate the applicability of the model, it is shown how the model can be integrated within a computational ‘ambient agent model’ that monitors and predicts group emotion levels over time and proposes group support actions based on that. Based on this description, a discussion is provided of the main contribution of the model, as well as the next steps needed to incorporate it into real-world applications.

Modelling the interplay of emotions, beliefs and intentions within collective decision making based on insights from social neuroscience

Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in Social Neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes.

Agent-based analysis of patterns in crowd behaviour involving contagion of mental states

In this paper an agent-based analysis is made of patterns in crowd behaviour, in particular to simulate a real-life incident that took place on May 4, 2010 in Amsterdam. As a basis, an existing agent-based model is used for contagion of emotions, beliefs and intentions. From available video material and witness reports, useful empirical data were extracted. Similar patterns were achieved in simulations, whereby some of the parameters of the model were tuned to the case addressed, and most parameters were assigned default values. The results show the inclusion of contagion of belief, emotion, and intention states of agents results in better reproduction of the incident than non-inclusion.

An ambient agent model for group emotion support

This paper introduces an agent-based support model for group emotion, to be used by ambient systems to support teams in their emotion dynamics. Using model-based reasoning, an ambient agent analyzes the teams emotion level for present and future time points. In case the teams emotion level is found to become deficient, the ambient agent provides support to the team by proposing the team leader, for example, to give a pep talk to certain team members. The support model has been formally designed and within a dedicated software environment, simulation experiments have been performed.

An Agent-Based Model for the Interplay of Information and Emotion in Social Diffusion

The spread of information and emotion within groups is studied in models of social diffusion. Evidence has been found that the emotional states of humans affect their information processing abilities, and hence, may affect the spread of information as well. This paper introduces an agent-based model that simulates the spread of information and emotion among a group of agents. The model takes both the influence of emotions upon the spread of information and the influence of information on emotions into account. The approach is exemplified by means of a case study in the domain of emergency evacuation.

Agent-based modelling of the emergence of collective states based on contagion of individual states in groups

This paper introduces a neurologically inspired computational model for the dynamics and diffusion of agent states within groups. The model combines an individual model based on Damasios Somatic Marker Hypothesis with mutual effects of group members on each other via mirroring of individual states such as emotions, beliefs and intentions. The obtained model shows how this combination of assumed neural mechanisms can form an adequate basis for the emergence of common group beliefs and intentions, while, in addition there is a positive feeling with these common states amongst the group members. A particular issue addressed is how certain types of states may affect other types of states, for example, emotions have an effect on beliefs and intentions, and beliefs may effect emotions.

Modelling the emergence of group decisions based on mirroring and somatic marking

This paper introduces a neurologically inspired computational model for the emergence of group decisions. The model combines an individual decision making model based on Damasios Somatic Marker Hypothesis with mutual effects of group members on each other via mirroring of emotions and intentions. The obtained model shows how this combination of assumed neural mechanisms can form an adequate basis for the emergence of common group decisions, while, in addition, there is a feeling of wellness with these common decisions amongst the group members.