# Adapting, Scaling, and Spreading an Algorithmic Asthma Mobile Intervention to Promote Patient-Reported Outcomes Within Primary Care Settings

> **NIH AHRQ R18** · ALBERT EINSTEIN COLLEGE OF MEDICINE · 2020 · $531,946

## Abstract

The New York City borough of the Bronx bears the highest asthma burden in New York State with age-
adjusted mortality triple the state average. Factors contributing to high asthma rates and poor outcomes in the
Bronx include poverty, environmental triggers, and the lack of education and knowledge about asthma. There
are significant challenges to providing effective patient education and using patient-reported outcome (PRO)
measures in the primary care setting, such as time constraints and prioritizing other issues (e.g. co-
morbidities). The lack of evidence-based, personalizable, and easy-to-use mobile health applications that
embed, collect, and successfully implement PRO measures in primary care marks an important gap in the
realm of patient-centered health information technologies.
 To address these barriers to achieving better asthma control, we developed and evaluated a
smartphone and tablet-based ASTHMAXcel application for adult asthma patients at the multi-specialty
Montefiore Asthma Center. ASTHMAXcel consists of 9 asthma education chapters and collects PRO
measures, which enable the program to individualize patient education, encourage continued use, and
promote asthma self-management. This proposed project seeks to demonstrate that ASTHMAXcel can be
successfully integrated and scaled in primary care.
 In year 1, we will adapt, test, and refine the ASTHMAXcel application for the primary care setting (Aim
1). We will conduct participatory design sessions with primary care providers and adult patients to identify how
to best adapt the existing application for primary care use. We will adapt ASTHMAXcel, and then integrate the
adapted application with our institution's electronic health record system, in order to present PRO measures
collected by ASTHMAXcel to primary care providers to support more informed decision making as part of their
workflow. We will test the adapted EHR-enabled ASTHMAXcel application with 5 providers and 30 subjects
with asthma recruited from one primary care site, for a period of 30 days, to evaluate the application's
functionality and usability, and use provider and patient feedback to refine the application. In years 2 and 3, we
will conduct an RCT at six primary care sites to compare the adapted and refined ASTHMAXcel application
(n=150) to usual care (n=150) with regards to process outcomes, asthma knowledge, PROs, and clinical
outcomes (Aim 2). We will evaluate the process of ASTHMAXcel implementation within the primary care
setting (Aim 3).
 This study will expand ASTHMAXcel's functionality for primary care. This project fulfills AHRQ's special
emphasis notice and mission, since ASTHMAXcel will: a) advance the collection and use of PRO measures to
improve asthma quality of care and outcomes in the primary care setting; and b) increase patient access to
comprehensive asthma education within this setting.

## Key facts

- **NIH application ID:** 9926777
- **Project number:** 5R18HS025645-03
- **Recipient organization:** ALBERT EINSTEIN COLLEGE OF MEDICINE
- **Principal Investigator:** Sunit Jariwala
- **Activity code:** R18 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $531,946
- **Award type:** 5
- **Project period:** 2018-08-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9926777

## Citation

> US National Institutes of Health, RePORTER application 9926777, Adapting, Scaling, and Spreading an Algorithmic Asthma Mobile Intervention to Promote Patient-Reported Outcomes Within Primary Care Settings (5R18HS025645-03). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/9926777. Licensed CC0.

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