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Ann Allergy Asthma Immunol. Author manuscript; available in PMC 2016 April 01.
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Published in final edited form as:

Ann Allergy Asthma Immunol. 2015 April ; 114(4): 341–342.e2. doi:10.1016/j.anai.2014.12.017.

Design of a Smartphone Application to Monitor Stress, Asthma

Symptoms, and Asthma Inhaler Use
Eldin Dzubur, MS1, Marilyn Li, MD1, Keito Kawabata, MPA1, Yifei Sun, BS2, Rob McConnell,
MD1, Stephen Intille, PhD2, and Genevieve Fridlund Dunton, PhD1
1Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
2Collegesof Computer and Information Science and Health Sciences, Northeastern University,
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Boston, MA, USA

Advances in the treatment and prevention of asthma have curtailed deaths, hospitalizations,
and increases in prevalence rates over the past thirty years.1 Nevertheless, the effectiveness
of long-term asthma management is mediated by behavioral factors such as adherence to
medication and psychosocial stress. In a study using ecological momentary assessment to
monitor asthma inhaler use, half of all non-adherence cases occurred while participants were
with their peers.2 However, the study relied on subjective reports of adherence. Associations
between stress and asthma symptoms have been observed, but these have relied on
retrospective self-report, potentially introducing recall bias. Laboratory studies have
demonstrated causal relationships between stress and biological markers of immune
responses related to asthma.3, 4 However, these settings may not represent real-world
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situations. Furthermore, both laboratory and longitudinal studies to date have not captured
the effect of daily variations in adherence, stress, and symptoms.

Advancements in technology have led to commercial availability of low-cost personal

computing devices (smartphones) capable of executing advanced health-related applications
(“apps”) and communicating with external sensors via short-wave radio signals
(Bluetooth).5 Ecological momentary assessment (EMA) using smartphones is a method of
capturing real-time data that maintains ecological validity, reduces recall bias, preserves
within-day changes, and captures objective data from external devices to reduce social
desirability bias.6 This letter describes the design of a smartphone application that integrates
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© 2014 American College of Allergy, Asthma Immunology. Elsevier Inc. All rights reserved.
Corresponding Author: Eldin Dzubur, MS, Department of Preventive Medicine, University of Southern California, Soto Street
Building (SSB), 2001 Soto N. Street, 3rd floor, Los Angeles, CA 90032, Phone: (323) 442-7302, Fax: (323) 442-8201,
dzubur@usc.edu.
Author Contributions:
ED, ML, KK, YS, RM, SI, and GFD contributed to the conception and design of the study.
ED, KK, YS, SI, and GFD contributed to data generation.
ED, ML, KK, RM, SI, and GFD contributed to the analysis and interpretation of the data.
ED, ML, KK, YS, RM, SI, and GFD contributed to preparation or critical revision of the manuscript.
Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our
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 Dzubur et al. Page 2

EMA and Bluetooth-enabled sensors for asthma inhalers. This technology can measure real-
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time asthma symptomology, social and physical context, behavior, stress, and inhaler use.

Development of the application was a collaborative effort from a multidisciplinary team of

researchers and computer scientists. The application was installed on Samsung Galaxy Y
(Model S5460) smartphones running the latest available version of Google’s Android
operating system and loaned to participants. Study personnel conducted iterative
development (alpha) testing before initiating pilot (beta) testing using a small (N=20)
English-speaking convenience sample of Hispanic middle and high school students (ages
12–17) enrolled in a mobile asthma management clinic for low-income families.7 Written
parental consent and child assent were obtained at enrollment; the study was approved by
the Institutional Review Board at the University of Southern California. Physicians assisted
at enrollment to inform participants that the application was not a replacement for treatment.
The application uses signal-contingent (i.e, randomly-timed) and event-contingent (i.e.,
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context-sensitive) EMA sampling triggered by asthma inhaler use. Inhaler use is detected
when the phone receives a signal from a Bluetooth sensor on the participant’s quick-relief
and controller medications.

The signal-contingent EMA component of the software prompts the user with an electronic
survey at a random time within each of seven designated time windows: 7–9 AM, 9–11 AM,
11 AM–1 PM, 1 – 3 PM, 3 – 5 PM, 5–7 PM, and 7–9 PM. No surveys are deployed prior to
3 PM on weekdays (during school time). After receiving a prompt (eFigure 1), the
participants are presented with a set of questions querying current levels of positive and
negative affect, stress, energy, and fatigue, as well as the type of activity currently being
performed, and information about social and physical contexts (eTable 1). Additionally,
participants are asked to recall stressful events, asthma symptoms, and asthma coping-
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related behaviors occurring since the last survey (or in the past four hours if the last survey
was completed more than four hours prior) (eTable 1).8,9,10

The event-contingent (i.e., context-sensitive) EMA component of the application runs a

background service that monitors all incoming Bluetooth connections. Participants are
provided with two small, button-like devices that attach to the tops of quick relief (i.e.,
rescue) and controller metered dose inhalers (Propeller sensor, provided at no cost by
Propeller Health; Madison, WI) designed to transmit a Bluetooth signal to the phone when
the inhaler is actuated. Approximately 5 minutes after the background service captures a
Bluetooth sensor signal, the app initiates a real-time self-report survey. The first question in
this survey asks whether the participant used a rescue inhaler, control inhaler, or neither (i.e.,
inhaler actuated unintentionally). If a participant reports any inhaler usage, the survey
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subsequently queries the severity of asthma symptoms experienced, the type of activity
performed, and social and physical contexts encountered just before the inhaler use.
Questions also ask about stressful events experienced since the last survey (or in the past
four hours if the last survey was completed more than four hours prior) (eTable 1).

To reduce burden on participants, EMA surveys contain logical question branching for
question subsets. With the exception of questions related to performed activity type, a
randomized selection algorithm was used for signal-contingent question subsets such that

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 Dzubur et al. Page 3

each subset had a 60% chance of appearing on any given survey. Data from all surveys are
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uploaded to a secure file transfer protocol (SFTP) server at the end of the day. At the
completion of testing, the phones are retrieved from participants and the phone features are
restored to factory settings.

During pilot testing, participants received a daily average of 3.2 (SD = 0.9, range = 1 – 4.57)
signal-contingent and 2.1 (SD= 2.6, range = 0.1 – 7.8) event-contingent prompts across all
seven days. EMA prompt adherence rates ranged from 20% to 100% (M = 51.4%, SD =
21.8%). Users reported general satisfaction and ease of use, while some reported difficulty
with answering surveys that interrupted them in the middle of the night (Table 1).

Once rigorously tested, the EMA portion of the application (source code) will be made
publicly available (at no cost) to researchers. Open-source Android applications allow for
localization to languages other than English and installation on participant-owned devices or
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loaned phones, thereby reducing cost. Furthermore, the application also allows for
monitoring using other sensors (e.g. built in motion and location sensors, external personal
ozone monitors). Future studies should seek to improve adherence rates, generalize to non-
Hispanic sub-populations, and assess health adolescent health literacy. This application has
the potential to assist researchers and clinicians to better understand real-time experiences of
adolescent patients with asthma, increase adherence to asthma treatment regimens, tailor
treatments to their specific needs, and enhance patient-provider communication.

Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
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Acknowledgments
Funding Sources:

National Institute of Environmental Health Sciences Grant 5 P30 ES07048-16

American Cancer Society Grant 118283-MRSGT-10-012-01-CPPB

National Heart, Lung, and Blood Institute Grant 1 R21 HL108018-01

References
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3. Theoharides TC, Enakuaa S, Sismanopoulos N, et al. Contribution of stress to asthma worsening

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7. Morphew T, Scott L, Li M, Galant SP, Wong W, Lloret MIG, et al. Mobile Health Care Operations
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Table 1

Sample Demographics and Usage Satisfaction in Pilot Testing (N=20)

Percent n
Dzubur et al.

Age (M, SD) 14.55 1.73

Gender
Male 55% 11

Female 45% 9

Ethnicity
Hispanic 100% 20

Overall, how satisfied are you with the mobile phone surveys?
Very Satisfied 50% 10

Satisfied 50% 10

Dissatisfied 0% 0

Very Dissatisfied 0% 0

Overall, how easy/difficult was it to use the mobile phone surveys?

Very easy 95% 19

Somewhat easy 5% 1

Somewhat difficult 0% 0

Very difficult 0% 0

Overall, how easy/difficult was it to answer the mobile phone surveys after an asthma attack?
Very easy 30% 6

Somewhat easy 15% 3

Somewhat difficult 5% 1

Ann Allergy Asthma Immunol. Author manuscript; available in PMC 2016 April 01.
Very difficult 5% 1

Did not answer any after an asthma attack 45% 9

Overall, the mobile phone surveys interrupted my daily activities:

Strongly agree 5% 1

Agree 5% 1

Disagree 65% 13

Strongly disagree 25% 5

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Percent n

Overall, answering the mobile phone surveys required too much of my time.
Strongly agree 0% 0

Agree 5% 1
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Disagree 50% 10

Strongly disagree 45% 9

Overall, how easy/difficult was it to answer the mobile phone surveys in the middle of the night?
Very easy 25% 5

Somewhat easy 30% 6

Somewhat difficult 10% 2

Very difficult 0% 0

Did not answer any in the middle of the night 35% 7

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