Asthma and Chronic Obstructive Pulmonary Disease (COPD) is among the highest health diagnosis and therefore an economic drain, which puts substantial pressure on a huge number of patients, communities, and health systems. These chronic conditions are presently incurable but their symptoms can be controlled through quality health care, appropriate medications, and good self-management skills. Many asthma APPs have been developed to support asthma patients’ self-management of the disease. Asthma APPs are still in the infancy stage of development. Literature review indicates satisfaction to be equivocal. Moreover, available APPs have many unresolved issues, such as the following: (1) No APP provides comprehensive asthma information while at the same time possessing the characteristics of an efficient tool for self-management of the disease; (2) No APP has the ability to integrate data from disparate formats; (3) Not many APPs provide for two way communication between patients and Health Care Providers (HCPs) and support the providers decision making process; (4) No APP targets older adults.
Different sources of data often imply data stored in inherently different formats. The integration of such data, culled from different databases requires use of Big Data (BD) techniques.
The proposed asthma mobile APP aims to promote elderly asthma patients’ positive adjustment to this chronic disease by being an effective tool for patients to control their asthma triggers and support asthma self-management. Adjustment is a dynamic process and varies by individual. For that reason, a personalized asthma APP is necessary to control this chronic disease. The proposed asthma APP will allow patients to input their own asthma self-management data so the APP can identify patient personal triggers and will predict an asthma attack accordingly. Considering a patient’s individual determinants and uniqueness is required to push the patient’s positive adjustment to asthma since these elements affect the ability of individual to adapt to the illness.
The paper reports our effort to establish the desirable characteristics for the next generation asthma APP and for a population segment not presently well served.