Method and system for self-monitoring of environment-related respiratory ailments

Abstract

Methods and systems for use in continual self-monitoring of respiratory health and components for use therewith. The present methods and systems and their related components improve the standard of care in respiratory health self-monitoring by providing continual and unobtrusive monitoring that accounts for environmental, physiological and patient background data, and is capable of yielding an array of respiratory health-preserving responses. In some embodiments, the present methods and systems leverage ubiquitous handheld electronic devices [e.g. cell phones and personal data assistants (PDA)] for respiratory health self-monitoring.

Description

technical field [0001] The present invention relates to monitoring respiratory health outside of clinical settings, and more particularly to methods and systems for self-monitoring of environment-related respiratory diseases such as asthma and rhinitis. Background technique [0002] Asthma is a chronic disease in which breathing becomes restricted. Asthma can significantly damage human health and in the most severe cases can be life threatening. Asthma patients often experience attacks outside of the clinical setting, triggered by environmental conditions such as dust, temperature and humidity. Self-monitoring systems have been developed to assist asthmatics in monitoring their respiratory health outside of the clinical setting to manage the disease, prevent attacks, and reduce the severity of attacks. [0003] A self-monitoring system for asthma patients that reflects current standard of care is a peak flow meter with a general health self-monitoring program. In this sys...

AI Technical Summary

Problems solved by technology

The data provided by the peak flow meter does not provide a comprehensive assessment of lung function and does not provide any information related to the environmental conditions that may trigger the attack

Furthermore, general health self-monitoring programs do not take into account patient background data that may be relevant to health determinations, such as behavioral patterns, co-morbidities, medications, age, height, weight, sex, race, and genetic background

Finally, the discrete output levels produced by the system may not provide sufficient detail

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