Asthma - OSIA Medical

Asthma Ally: A Tool for Improving Individual Asthma Control
Dr. Richard W. Lucas, PhD1,2, Joshua Dees2, Robert Reynolds2,3, Bryn Rhodes3, Dr. Richard W. Hendershot, MD 4 ;
1 Department
of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden, 2OSIA Medical, Sandy, UT,
3 Database Consulting Group, Orem, UT, 4 Intermountain Health Care, Salt Lake City, UT.
ABSTRACT
RATIONALE: Asthma represents a substantial cost to society in terms of health care expenses,
lost productivity, decreased quality of life and is one of the most common reasons for children’s
emergency room visits and hospital re-admissions. In order to render effective care, healthcare
providers need accurate and timely information regarding asthmatic episodes, but patients often
forget the detailed information between visits to the doctor. The lack of accurate information
decreases treatment efficacy.
METHODS: In order to provide accurate information, we developed AsthmaAlly, a mobile client
that captures three major data streams of participating patients: event data entered by the patient
quantifying the severity of each asthmatic episode; environmental data (i.e. particulate matter
sized 2.5 and 10 microns, ozone, nitrogen oxides, pollen count, and weather data) that are
temporally and geo-spatially referenced to each logged episode; and control test data that are
input by patients at least once per week and are based on instruments developed by the National
Institutes of Health. Data have been collected for roughly two years using AsthmaAlly.
Multiple Regression Relationships with Environmental Data
Individual Level Data
A)
A) Asthma Events: Patient-Entered
Individual data communicated to
healthcare provider of choice (see fig. 3)
Uncontrolled asthma < 50
C)
Patient's Portal
D)
B) Environmental Data: Automated Acquisition
RESULTS: During well controlled periods, asthma events were not significantly related to
environmental variables. During uncontrolled periods, however, asthma events were more
strongly related to environmental variables, the strength of the relationships increasing with
increasing lack of asthma control. During uncontrolled periods, pollen, airborne particulate matter
(size 2.5 microns), atmospheric ozone concentrations, relative humidity, temperature, and wind
speed variables tended to be the strongest environmental covariates, explaining approximately
68% of the observed variation (p < 0.001).
CONCLUSIONS: Data are communicated directly to healthcare providers, making it possible to
identify conditions specific to an individual that trigger asthmatic episodes. This enables
healthcare providers to more immediately evaluate the effectiveness of treatment regimes,
thereby improving personalized healthcare by removing barriers in patient-doctor communication.
More significantly, healthcare providers will potentially be able to issue proactive interventions to
alleviate suffering if the environmental conditions are consistent with those that have previously
resulted in acute suffering of a given patient. AsthmaAlly has the potential to provide the specific
information healthcare providers need in real time to improve care for individual patients, thus
decreasing the overall burden on the healthcare system and improving quality of life.
B)
Figure 4. A) Locations of data collection used in the population-level analysis. B) Depiction of why
location may affect asthma. C) Mean coefficient and standard error estimates from multiple regression
analysis examining the contributions of six environmental covariates in describing the observed
variation in poor or very poor asthma events (Event Rating ≤ 44). D) The relative importance of the six
most common environmental covariates during different stages of asthma control.
Figure 2. Longitudinal data obtained by patients using the AsthmaAlly mobile tool. A) Individual
asthma event data. B) Long-term environmental trends and asthma event data. Environmental data
collected by automated algorithms of the OSIA platform. Rose shaded area highlights uncontrolled
asthma events. Trends are indicated using loess regression with a weighting factor of 0.1
Doctor's Portal
A)
B)
Figure 3. Screen shot of AsthmaAlly's doctor's interface tool.
Figure 5. A) Number of unique users of AsthmaAlly. B) Pollen count stations certified by the National
Allergy Bureau in western United States (from http://pollen.aaaai.org).
CONCLUSIONS
1. Logging asthma events with AsthmaAlly enables physicians to more readily
evaluate the efficacy of treatment regimes.
Figure 1. Screen shots of the AsthmaAlly mobile tool interface. A) Patients enter data for
asthma and allergy events. B) Patients can select a view history. C) Asthma Evaluation
Questionnaire history.
2.  AsthmaAlly enables improved understanding of environmental triggers
underlying the expression of asthma. For instance: Airborne pollen is the
strongest driver of asthma expression and its relative importance increases with
asthma severity.
3.  Better pollen data are needed.
4.  Patients realize improved asthma control.
This project is funded by
OSIA Medical, LLC