How can we reward you? A compliance and reward ontology (CaRO) for eliciting quantitative reward rules for engagement in mHealth app and healthy behaviors

Abstract

Objective: When developing mHealth apps with point reward systems, knowledge engineers and domain experts should define app requirements capturing quantitative reward patterns that reflect patient compliance with health behaviors. This is a difficult task, and they could be aided by an ontology that defines systematically quantitative behavior goals that address more than merely the recommended behavior but also rewards for partial compliance or practicing the behavior more than recommended. No ontology and algorithm exist for defining point rewards systematically.

Methods: We developed an OWL ontology for point rewards that leverages the Basic Formal Ontology, the Behaviour Change Intervention Ontology and the Gamification Domain Ontology. This Compliance and Reward Ontology (CaRO) allows defining temporal elementary reward patterns for single and multiple sessions of practicing a behavior. These could be assembled to define more complex temporal patterns for persistence behavior over longer time intervals as well as logical combinations of simpler reward patterns. We also developed an algorithm for calculating the points that should be rewarded to users, given data regarding their actual performance. A natural language generation algorithm generates from ontology instances app requirements in the form of user stories. To assess the usefulness of the ontology and algorithms, information system students who are trained to be system analysts/knowledge engineers evaluated whether the ontology and algorithms can improve the requirement elicitation of point rewards for compliance patterns more completely and correctly.

Results: For single-session rewards, the ontology improved formulation of two of the six requirements as well as the total time for specifying them. For multi-session rewards, the ontology improved formulation of five of the 11 requirements.

Conclusion: CaRO is a first attempt of its kind, and it covers all of the cases of compliance and reward pattern definitions that were needed for a full-scale system that was developed as part of a large European project. The ontology and algorithm are available at https://github.com/capable-project/rewards.