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- The InterAct Consortium (2012): Tea Consumption and Incidence of Type 2 Diabetes in Europe: The EPIC-InterAct Case-Cohort Study
Methodology/Principal Findings: The EPIC-InterAct case-cohort study was conducted in 26 centers in 8 European countries and consists of a total of 12,403 incident type 2 diabetes cases and a stratified subcohort of 16,835 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up.
RePEc:plo:pone00:0036910 Save to MyIDEAS - The InterAct Consortium (2012): Long-Term Risk of Incident Type 2 Diabetes and Measures of Overall and Regional Obesity: The EPIC-InterAct Case-Cohort Study
A collaborative re-analysis of data from the InterAct case-control study conducted by Claudia Langenberg and colleagues has established that waist circumference is associated with risk of type 2 diabetes, independently of body mass index. ... Methods and Findings: The prospective InterAct case-cohort study was conducted in 26 centres in eight European countries and consists of 12,403 incident T2D cases and a stratified subcohort of 16,154 individuals from a total cohort of 340,234 participants with 3.99 million person-years of follow-up. ... In this case-cohort study, the researchers use data from the InterAct study (which is investigating how genetics and lifestyle interact to affect diabetes risk) to estimate the long-term risk of type 2 diabetes associated with BMI and waist circumference.
RePEc:plo:pmed00:1001230 Save to MyIDEAS - Journal of Economic Interaction and Coordination (Springer & Society for Economic Science with Heterogeneous Interacting Agents)
RePEc:spr:jeicoo Follow on MyIDEAS - Papers on Strategic Interaction (Max Planck Institute of Economics, Strategic Interaction Group)
RePEc:esi:discus Follow on MyIDEAS - Youcef Baghdadi & Francesca Ricciardi & Antoine Harfouche (2016): Towards an Ontology for Enterprise Interactions
Enterprise interactions allow collaborations that add value, in terms of solutions for supporting flexible intra- and cross processes, interfacing the enterprise to its environment, and enabling its objects to act and react within their environment. ... However, there is a lack of ontologies for interactions. Interaction ontology would share, integrate, and manage knowledge. This paper presents a typology of enterprise interactions towards a lightweight ontology for interactions that facilitate their engineering. First, it distinguishes different types of interactions by their nature, their issues, and their current realizations.
RePEc:spr:lnichp:978-3-319-28907-6_17 Save to MyIDEAS - Karl-Heinz Krempels & Otto Spaniol & Thorsten Scholz & Ingo J. Timm & Otthein Herzog & Stefan Kirn & Otthein Herzog & Peter Lockemann & Otto Spaniol (2006): Interaction Design
Interaction is one of the core challenges in multiagent systems. It enables agents to share their knowledge, to do competitive or cooperative planning, coordination or bargaining, to interact with their principals, and to simple act. Interaction has to be restrictive enough to enable reliable system behavior and should be permissive enough to allow for flexibility or emergent behavior and performance. Obviously, the design of interaction differs from interface design in conventional software engineering significantly.
RePEc:spr:ihichp:978-3-540-32062-3_20 Save to MyIDEAS - Niels Henrik Bruun & Morten Fenger-Gron & Anders Prior (2015): IC: Stata module to compute measures of interaction contrast (biological interaction)
It has become more common to investigate not only single factors effect on a outcome but also to look at the interaction between factors, as is facilitated by the last decadeís massive increase in computer resources to gather and analyses large databases. ... For the analyzes of binary (or count) data, a fairly well established position holds, that loglinear models estimating measures of relative risk type represent a convenient choice, whereas interactions are often best interpreted if estimated on a linear/additive scale.
RePEc:boc:bocode:s457975 Save to MyIDEAS - Kristina Heinonen & Richard Nicholls & Bo Edvardsson & Bård Tronvoll (2022): Customer-to-Customer Interactions in Service
Customers are constantly interacting with different actors and resources in the marketplace.This chapter explores how customers can be influenced by other customers present in the service setting. While research has devoted considerable attention to interactions taking place between customers and employees, far less attention has been paid to interactions among customers.Generally known as customer-to-customer interaction (CCI), these positive or negative interactions represent an important potential for service organizations.
RePEc:spr:sprchp:978-3-030-91828-6_32 Save to MyIDEAS - Kim, Juran & Spielmann, Nathalie & McMillan, Sally J. (2012): Experience effects on interactivity: Functions, processes, and perceptions
To clarify interactivity as functions, processes, and perceptions, and to examine the moderating role of experience, this study uses an experimental design to investigate key questions about functional features, actual interactions and perceptions, and the consequences (i.e., attitude, trust, and purchase intention) within human-to-human and human-to-computer contexts. ... With human-to-human interactivity, experience does not moderate actual interaction, but does so in the human-to-computer context when introduced to action/transaction functions. This study contributes to the body of knowledge by clarifying the relationships between interactive features, actual interaction, and perceived interactivity.
RePEc:eee:jbrese:v:65:y:2012:i:11:p:1543-1550 Save to MyIDEAS - David Melamed & Ronald L. Breiger & Eric Schoon (2013): The Duality of Clusters and Statistical Interactions
We contend that clusters of cases co-constitute statistical interactions among variables. Interactions among variables imply clusters of cases within which statistical effects differ. ... We explicate a four-step procedure that discovers interaction effects based on clusters of cases in the data matrix, hence aiding in inductive model specification. ... We find support for our contention that clusters of the rows of a data matrix may be exploited to discover statistical interactions among variables that improve model fit.
RePEc:sae:somere:v:42:y:2013:i:1:p:41-59 Save to MyIDEAS