Comparing different boosters of planning interventions on changes in fat consumption in overweight and obese individuals: A randomized controlled trial

Single planning interventions have been found to promote short-term dietary change. Repeated planning interventions may foster long-term effects on behavior change. It remains unknown whether there is a critical number of boosters to establish long-term maintenance of behavioral changes. This study aimed at investigating what social-cognitive variables mediate the effects of the interventions on dietary behavior change. Overall, 373 participants (n = 270 women, 72.4%; age M = 52.42, SD = 12.79) were randomly allocated to one of five groups: a control group, a single planning group, and three groups with 3, 6, or 9 weeks' repeated planning interventions. Follow-ups took place 4, 6, and 12 months after baseline. Change in fat consumption was not promoted by any of the interventions. In terms of social-cognitive variables, intentions, self-efficacy and coping planning displayed a time × group interaction, with the 9 weeks' planning group showing the most beneficial effects. Effect sizes, however, were very small. None of the tested planning interventions successfully promoted change in fat consumption across the 12 month period. This, however, could not be explained by problems with adherence to the intervention protocol. Potential explanations for this unexpected result are discussed.

Granular computing as a tool of human data interaction - a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.

Granular computing as a basis of human–data interaction: a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.