# Prediction of Domain Behavior through Dynamic Well-Being Domain Model Analysis

## TL;DR

This paper introduces a method to model and predict the behavior of well-being systems before they are used, helping improve their effectiveness.

## Contribution

A novel method for predicting system behavior in the well-being domain using a dynamic domain model for causal reasoning.

## Key findings

- The method accurately predicted success and problems in existing well-being applications.
- Analysis results aligned with end-user evaluation studies of the applications.
- Limitations in prediction accuracy were identified and acknowledged.

## Abstract

As the concept of context-awareness is becoming more popular
the demand for improved quality of context-aware systems increases too. Due to
the inherent challenges posed by context-awareness, it is harder to predict what
the behavior of the systems and their context will be once provided to the
end-user than is the case for non-context-aware systems. A domain where such
upfront knowledge is highly important is that of well-being. In this paper, we
introduce a method to model the well-being domain and to predict the effects the
system will have on its context when implemented. This analysis can be performed
at design time. Using these predictions, the design can be fine-tuned to increase
the chance that systems will have the desired effect. The method has been
tested using three existing well-being applications. For these applications,
domain models were created in the Dynamic Well-being Domain Model language. This
language allows for causal reasoning over the application domain. The models
created were used to perform the analysis and behavior prediction. The analysis
results were compared to existing application end-user evaluation studies.
Results showed that our analysis could accurately predict success and possible
problems in the focus of the systems, although certain limitation regarding the
predictions should be kept into consideration.