Conference PaperPDF Available

Enhancing Asthma Control through IT: Design, Implementation and Planned Evaluation of the Mobile Asthma Companion

Abstract

The personal and financial burden of asthma highly depends on a patient's disease self-management skill. Scalable mHealth apps, designed to empower patients, have the potential to play a crucial role in asthma disease management. However, the actual clinical efficacy of mHealth asthma apps is poorly understood due to the lack of both methodologically sound research and accessible evidence-based apps. We therefore apply design science with the goal to design, implement and evaluate a mHealth app for people with asthma, the Mobile Asthma Companion (MAC). The current prototype of MAC delivers health literacy knowledge triggered by nocturnal cough rates. We conclude by proposing a randomized controlled trial to test the efficacy of our prototype.
13th International Conference on Wirtschaftsinformatik,
February 12-15, 2017, St. Gallen, Switzerland
Enhancing Asthma Control through IT: Design,
Implementation and Planned Evaluation of the Mobile
Asthma Companion
Peter Tinschert1, Filipe Barata2, Tobias Kowatsch1
University of St. Gallen, Institute of Technology Management, St. Gallen, Switzerland
{peter.tinschert,tobias.kowatsch}@unisg.ch
ETH Zurich, Department of Management, Technology and Economics, Zurich, Switzerland
fbarata@ethz.ch
Abstract. The personal and financial burden of asthma highly depends on a
patients disease self-management skill. Scalable mHealth apps, designed to
empower patients, have the potential to play a crucial role in asthma disease
management. However, the actual clinical efficacy of mHealth asthma apps is
poorly understood due to the lack of both methodologically sound research and
accessible evidence-based apps. We therefore apply design science with the goal
to design, implement and evaluate a mHealth app for people with asthma, the
Mobile Asthma Companion (MAC). The current prototype of MAC delivers
health literacy knowledge triggered by nocturnal cough rates. We conclude by
proposing a randomized controlled trial to test the efficacy of our prototype.
Keywords: Asthma Control, mHealth Apps, Asthma Disease Management
1 Introduction
Asthma, a chronic airway disease, ranks among the most prevalent noncommunicable
diseases with an estimated 334 million people suffering from it globally. The yearly
costs of asthma are estimated to be around 56 billion dollars in the US alone [1].
When patients are not able to control their asthma symptoms, it heavily impacts their
quality of life and may even have life threatening consequences. However, successful
maintenance of well-controlled asthma enables a patient to live with almost unimpaired
quality of life [2]. Apart from the personal implications for patients, the degree of
asthma (symptom) control profoundly affects the healthcare system from an economic
point of view: A patient with uncontrolled asthma symptoms causes approximately 4.5
times the costs of a patient with well-controlled asthma, which is amplified by the fact
that more than half of all asthmatics suffer from uncontrolled asthma [3].
In order to achieve asthma control, clinical guidelines emphasize the importance of
an empowered patient with disease management skills [4]. Here is where mobile health
applications (mHealth apps) can potentially decrease the personal and financial burden
of asthma: in contrast to traditional asthma disease management programs, which
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Tinschert, P.; Barata, F.; Kowatsch, T. (2017): Enhancing Asthma Control through IT: Design,
Implementation and Planned Evaluation of the Mobile Asthma Companion, in Leimeister, J.M.;
Brenner, W. (Hrsg.): Proceedings der 13. Internationalen Tagung Wirtschaftsinformatik (WI 2017),
St. Gallen, S. 1291-1294
consist of multiple weekly face-to-face sessions guided by healthcare professionals,
scalable mHealth apps can empower patients cost effectively by delivering disease
management interventions to their smartphones as part of daily routine. Moreover,
mHealth apps may exploit built-in smartphone sensors to objectively monitor disease
symptoms and as a consequence identify windows of opportunities in which patients
are particularly receptive and susceptible to respond to such interventions [5].
However, to prove the potential of mHealth asthma apps, clinical efficacy needs to
be demonstrated in relation to the therapeutic gold standard (i.e. traditional asthma
management programs). Systematic reviews in this field, which have indicated first
promising results regarding the efficacy of mHealth asthma apps, strongly advocate for
further research due to the low methodological quality of the available studies [6] and
the low average quality of mHealth asthma apps, which often fail to consider clinical
guidelines for asthma treatment [7].
We address this research gap by designing, implementing and evaluating our own
mHealth asthma app, which is based on design science research [8]. We refer to our
app as the Mobile Asthma Companion (MAC). Currently, the prototype consists of a
disease management intervention focused on improving health literacy, which is
triggered by, among others, automated cough detection.
The remainder of this paper is structured as follows: after introducing the applied
justificatory knowledge and deriving the design requirements, we will describe the
implementation of our MAC prototype. We conclude this paper by proposing an
experimental study design, which will evaluate MAC’s efficacy.
2 Conceptual Foundation and Design Requirements
Patients are responding particularly well to interventions when they are delivered just
in time, or in other words, in the exact moment when patients demand them [5]. A state
of demand is characterized, among others, by experiencing adverse health effects. For
asthma, we argue that partially controlled or uncontrolled asthma indicate such a state
of vulnerability. Thus, nocturnal cough rate, a valid marker for asthma control [9],
could serve as an intervention trigger. We already delivered the proof of concept that
cough can be monitored fully automated and accurately by means of a smartphone [10].
Requirement 1: The design artifact has to be able to monitor the nocturnal cough
rate fully automated and use it to trigger just-in-time interventions.
Health literacy, or more specifically asthma education, is a key aspect of asthma
disease management [4]. Research has shown that health literacy interventions are
clinically efficacious in general and for asthma in specific [11].
For asthma health literacy interventions, first evidence was provided that an asthma
mHealth app is able to improve asthma control significantly over the course of five
weeks [12]. However, the single-arm study design and the monetary incentives for
continuous study participation limit the explanatory power of the study results and the
scalability of such an app. Therefore, in order to enable scalability, a user’s engagement
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needs to be ensured without relying on financial incentives. Gamification features like
point systems, badges or achievements are promising options in this regard [13].
Requirement 2: The design artifact has to deliver a health literacy intervention
including educational asthma content implemented through gamification features.
3 Implementation
We implement our MAC prototype as a mobile application for Android smartphones
based on the MobileCoach (MC) platform [14]. MC is an established digital health
intervention platform, which provides the necessary user privacy and data security
features when working with sensitive patient data. Currently, the MC based prototype
offers two main functionalities: a chat and an alarm clock.
The requirement of automated nocturnal cough detection is met by expanding our
prior work on cough detection [10] in order to detect cough rate overnight. The
detection mode is enabled by setting an alarm, which defines a time frame from the
moment the alarm is set until it rings. During this period, the detection algorithm
operates in the background, automatically detecting and counting coughs.
Users will be instructed to set the MAC alarm directly before going to sleep and to
place the smartphone near the bed (e.g. on a nightstand). Based on detected cough rates
per night, users will be prompted to interact with MAC through push notifications. The
likelihood of receiving such a notification is a function of the standardized cough rate
within a user. In order to limit the burden of intervention, users can only receive up to
two notifications per day. However, notifications will be sent at least twice a week to
users with a particularly low cough rate who otherwise might not receive any
notifications at all. Additionally, MAC is also accessible at will.
In order to address the second design requirement, MAC will interact with the user
via a chat interface and provide educational material related to asthma health literacy
topics in form of knowledge nuggets (e.g. video clips and quiz questions). This chat
functionality is enabled by extending the current MC platform with the possibility to
communicate with the user via an internet based chat client mobile application instead
of a SMS based communication. Furthermore, the educational materials are fully
congruent with the content of current disease management programs. The MC rule-
based engine triggers the delivery of knowledge nuggets based on the nocturnal cough
rate of the user. Finally, gamification features are implemented through badges which
users can earn by viewing educational material and answering quiz questions correctly.
4 Study Design
We will evaluate the efficacy of our MAC prototype in a randomized control trial, the
methodological gold standard for investigating efficacy. Adult asthmatics will be
randomly assigned to two different groups: The control group will participate in a
traditional asthma disease management program offered by local healthcare entities [4]
whereas the experimental group will interact with MAC. The primary endpoint in this
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study is asthma control on a monthly basis. Asthma control is measured through a
standardized test [15]. Study duration will be three months to account for lagged effects
of the intervention on asthma control. Ideally, our study will show that MAC will
perform at least as good as the control group in the measurement of the primary
endpoint upon study completion (i.e. non-inferiority trial). We will consider seasonal
differences in asthma by measuring asthma control additionally at study initiation. A
follow-up measurement after three months will account for long-term effects.
References
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... In Beispielsweise wird derzeit untersucht, wie mit einem Smartphone Verwundbarkeit hinsichtlich Exazerbationen bei Asthmapatienten anhand des nächtlichen Hustens gemessen werden kann Tinschert et al. 2017), ob PC-Mausbewegungen ein Indiz für Stress am Arbeitsplatz darstellen (Kowatsch et al. 2017d, e) oder ob das Smartphone geeignet ist den Grad der Depression anhand physischer Inaktivität sowie sozialer Isolation zu erkennen (Wahle et al. 2016. ...
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We searched the Cochrane Airways Group Register (CAGR), the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, PsycINFO, CINAHL, Global Health Library, Compendex/Inspec/Referex, IEEEXplore, ACM Digital Library, CiteSeer(x) and CAB abstracts via Web of Knowledge. We also searched registers of current and ongoing trials and the grey literature. We checked the reference lists of all primary studies and review articles for additional references. We searched for studies published from 2000 onwards. The latest search was run in June 2013. We included parallel randomised controlled trials (RCTs) that compared self management interventions for patients with clinician-diagnosed asthma delivered via smartphone apps to self management interventions delivered via traditional methods (e.g. paper-based asthma diaries). We used standard methods expected by the Cochrane Collaboration. Our primary outcomes were symptom scores; frequency of healthcare visits due to asthma exacerbations or complications and health-related quality of life. We included two RCTs with a total of 408 participants. We found no cluster RCTs, controlled before and after studies or interrupted time series studies that met the inclusion criteria for this systematic review. Both RCTs evaluated the effect of a mobile phone-based asthma self management intervention on asthma control by comparing it to traditional, paper-based asthma self management. One study allowed participants to keep daily entries of their asthma symptoms, asthma medication usage, peak flow readings and peak flow variability on their mobile phone, from which their level of asthma control was calculated remotely and displayed together with the corresponding asthma self management recommendations. In the other study, participants recorded the same readings twice daily, and they received immediate self management feedback in the form of a three-colour traffic light display on their phones. Participants falling into the amber zone of their action plan twice, or into the red zone once, received a phone call from an asthma nurse who enquired about the reasons for their uncontrolled asthma.We did not conduct a meta-analysis of the data extracted due to the considerable degree of heterogeneity between these studies. Instead we adopted a narrative synthesis approach. Overall, the results were inconclusive and we judged the evidence to have a GRADE rating of low quality because further evidence is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate. In addition, there was not enough information in one of the included studies to assess the risk of bias for the majority of the domains. Although the other included study was methodologically rigorous, it was not possible to blind participants or personnel in the study. Moreover, there are concerns in both studies in relation to attrition bias and other sources of bias.One study showed that the use of a smartphone app for the delivery of an asthma self management programme had no statistically significant effect on asthma symptom scores (mean difference (MD) 0.01, 95% confidence interval (CI) -0.23 to 0.25), asthma-related quality of life (MD of mean scores 0.02, 95% CI -0.35 to 0.39), unscheduled visits to the emergency department (OR 7.20, 95% CI 0.37 to 140.76) or frequency of hospital admissions (odds ratio (OR) 3.07, 95% CI 0.32 to 29.83). The other included study found that the use of a smartphone app resulted in higher asthma-related quality of life scores at six-month follow-up (MD 5.50, 95% CI 1.48 to 9.52 for the physical component score of the SF-12 questionnaire; MD 6.00, 95% CI 2.51 to 9.49 for the mental component score of the SF-12 questionnaire), improved lung function (PEFR) at four (MD 27.80, 95% CI 4.51 to 51.09), five (MD 31.40, 95% CI 8.51 to 54.29) and six months (MD 39.20, 95% CI 16.58 to 61.82), and reduced visits to the emergency department due to asthma-related complications (OR 0.20, 95% CI 0.04 to 0.99). Both studies failed to find any statistical differences in terms of adherence to the intervention and occurrence of other asthma-related complications. The current evidence base is not sufficient to advise clinical practitioners, policy-makers and the general public with regards to the use of smartphone and tablet computer apps for the delivery of asthma self management programmes. In order to understand the efficacy of apps as standalone interventions, future research should attempt to minimise the differential clinical management of patients between control and intervention groups. Those studies evaluating apps as part of complex, multicomponent interventions, should attempt to tease out the relative contribution of each intervention component. Consideration of the theoretical constructs used to inform the development of the intervention would help to achieve this goal. Finally, researchers should also take into account: the role of ancillary components in moderating the observed effects, the seasonal nature of asthma and long-term adherence to self management practices.