  Mobile health systems in recent times, have notably improved the healthcare
sector by empowering patients to actively participate in their health, and by
facilitating access to healthcare professionals. Effective operation of these
mobile systems nonetheless, requires high level of intelligence and expertise
implemented in the form of decision support systems (DSS). However, common
challenges in the implementation include generalization and reliability, due to
the dynamics and incompleteness of information presented to the inference
models. In this paper, we advance the use of ad hoc mobile decision support
system to monitor and detect triggers and early symptoms of respiratory
distress provoked by strenuous physical exertion. The focus is on the
application of certainty theory to model inexact reasoning by the mobile
monitoring system. The aim is to develop a mobile tool to assist patients in
managing their conditions, and to provide objective clinical data to aid
physicians in the screening, diagnosis, and treatment of the respiratory
ailments. We present the proposed model architecture and then describe an
application scenario in a clinical setting. We also show implementation of an
aspect of the system that enables patients in the self-management of their
conditions.
