SciModeler: A Toolbox for Consolidating Scientific Knowledge within the Field of Health Behavior Change

Abstract

Science aims to build and advance general theories from empirical data. This process is complicated by the immense volume of empirical data and scientific theories in some domains, for example in the field of health behavior change. Especially, a systematic mapping between empirical data and theoretical constructs is lacking. We propose a toolbox to establish that mapping. We adopted a modeling approach based on literature surveys to elicit requirements and to derive a metamodel. We adopted a graph-based database system to implement the metamodel, and designed a web-based tool for importing data from annotated text documents. To evaluate that toolbox (named SciModeler), we have conducted a case study within the field of health behavior change to record three scientific theories, three empirical studies, and the mapping in-between. We have documented how SciModeler aids closing gaps between empirical data and theoretical constructs. We have demonstrated that this enables new types of analyses by sharing example queries for (1) refining scientific theories, (2) exploring promising intervention strategies for a specific context, and (3) checking the potential impact of an intervention platform in a specific context. Our supplementary materials promote replication of these results. SciModeler can support the consolidation of scientific knowledge in the field of health behavior change, and we suggest that it may be applied within other fields, as well. An important direction for future work is promoting online collaboration on SciModeler graphs.