Differences between Android and iOS Users of the
TrackYourTinnitus Mobile Crowdsensing mHealth Platform
R¨
udiger Pryss1, Manfred Reichert1, Winfried Schlee2, Myra Spiliopoulou3,
Berthold Langguth2, Thomas Probst4
1Institute of Databases and Information Systems, Ulm University, Germany
2Clinic and Policlinic for Psychiatry and Psychotherapy, University of Regensburg, Germany
3Department of Technical and Business Information Systems, Otto-von-Guericke-University Magdeburg, Germany
4Department for Psychotherapy and Biopsychosocial Health, Danube University Krems, Austria
1{ruediger.pryss, manfred.reichert}@uni-ulm.de
Abstract—Presently, mHealth technology is often applied in
the context of chronic diseases to gather data that may lead to
new and valuable medical insights. As many aspects of chronic
diseases are not completely understood, new data sources might
be promising. mHealth technology may help in this context as
it can be easily used in everyday life. Moreover, the bring
your own device principle encourages many patients to use
their smartphone to learn more about their disease. The less
is known about a disorder (e.g., tinnitus), the more patients
crave for new insights and opportunities. Despite the fact
that existing mHealth technology like mobile crowdsensing has
already gathered data that may help patients, in general, less is
known whether and how data gathered with different mobile
technologies may differ. In this context, one relevant aspect
is the contribution of the mobile operating system itself. For
example, are there differences between Android and iOS users
that utilize the same mHealth technology for a disease. In the
TrackYourTinnitus project, a mobile crowdsensing mHealth
platform was developed to gather data for tinnitus patients in
order to reveal new insights on this disorder with high economic
and patient-related burdens. As many data sets were gathered
during the last years that enable us to compare Android and
iOS users, the work at hand compares characteristics of these
users. Interesting insights like the one that Android users with
tinnitus are significantly older than iOS users could be revealed
by our study. However, more evaluations are necessary for
TrackYourTinnitus in particular and mHealth technology in
general to understand how smartphones affect the gathering
of data on chronic diseases when using them in the large.
Keywords-mHealth, Android, iOS, mobile crowdsensing, tin-
nitus, mobile data collection, chronic disease, chronic disorder
I. INTRODUCTION
mHealth technology becomes increasingly important for
many questions in healthcare. Chronic disorders are promis-
ing targets to apply mHealth technology to gather valuable
data that can be used to find new insights for a disease or to
develop new treatment methods. Especially smartphones are
of utmost importance in this context as they can be easily
utilized in everyday life. With regards to data collection,
daily life data (e.g., self-reports and sensor data) can be
gathered and may then be used to inform a user accordingly.
Moreover, the daily life data gathered by smartphones could
inform clinicians more accurately than retrospective self-
reports of the patients [1]. Regarding treatment options,
smartphones can be used to provide personalized self-
help to patients and this might address several treatment
barriers of chronic disorders. In general, mHealth tools
like smartphones constitute a powerful way to empower
patients in coping with their individual situation. Despite
the promising opportunities, the use of mHealth technology
for the aforementioned purposes is still in its infancy. In
addition, mHealth technology comprises a very large field
of different technologies. Mobile crowdsourcing or mobile
crowdsensing are only two examples. Self-help mobile apps
are further examples, which, in turn, also include a large
variety of used methods. The latter vary from individually
tailored diaries to mHealth intervention apps, which, for
example, remind and instruct patients to do interventions
if predefined context situations occur (e.g., heart rate level
exceeds a predefined level).
Besides the variety of used mHealth technology, research
in general is premature with respect to data that is gathered
with it due to several reasons. First, it must be evaluated
what data quality means in this context. Second, it must
be considered whether or not existing evaluation methods
fit to this kind of data. However, methods like Ecological
Momentary Assessment (EMA; also known as: ambulatory
assessment & experience sampling) have the potential to
support clinicians in assessing symptoms or making diag-
noses. In EMA, the variable in question (e.g., a symptom)
is assessed repeatedly in daily life [2]. Third, it must be
carefully evaluated how data is actually gathered. The latter
includes how the collection procedure looks like, what
smartphones are actually used, or whether patients are biased
by issues arising through the use of the technology.
In this paper, we use the TrackYourTinnitus (TYT) mobile
mHealth crowdsensing platform [3], [4] to gain insights
whether differences between Android and iOS users can be
observed. Note that the TYT mHealth mobile crowdsensing
platform enables iOS and Android users to gather everyday
life data with their own smartphones to understand their
individual tinnitus situation better. Tinnitus can be described
as the phantom perception of sound. Note that symptoms for
tinnitus are subjective and vary over time. Therefore, TYT
was developed to reveal insights on this patient variability.
Moreover, depending on tinnitus definitions, the duration as
well as on the patient age and birth cohort, between 5.1%
and 42.7% of the population worldwide experience tinnitus
at least once during their lifetime. By the use of EMA
techniques, we aim at revealing new insights with regard
to tinnitus [1], [5]. Thereby, prior to the results presented
in this work, the evaluation of the gathered data already
revealed valuable clinical insights [1], [6]. In addition, we
have the goal in mind to identify the aspects how mobile
crowdsensing can be also used for questions on other chronic
disorders. Interestingly, with respect to Android and iOS
users, we identified differences between these two user
groups for TYT. These insights are presented in the work at
hand and on top of the presented results, we raise questions
that have to be taken into account for required technical TYT
features in future. Furthermore, we discuss what questions
related to the used mobile operating system should be
considered when applying mobile crowdsensing technology
in the context like we do.
Related work is discussed in Section II. In Section III, we
briefly illustrate the mobile mHealth crowdsensing platform
of TYT and explain the relevant issues when comparing
Android and iOS users. Section IV presents the data and
the statistics used for the juxtaposition of the Android and
iOS users. In Section V, we present the results of the
statistical analyses, which suggest that differences between
the platform should be taken into consideration. Finally, a
summary and an outlook are provided in Section VI.
II. RELATED WORK
In this section, we discuss prior research on mobile
crowdsensing, EMA, mHealth, and other works that directly
compare Android and iOS users. Mobile crowdsensing has
become an important research topic in various scenarios [7],
[8]. However, mobile crowdsensing applications less exist
in the medical domain although their use is promising for
many questions [9]–[12]. One reason might be that many
obstacles still exist or that they are less understood. On top
of this, to maintain a mobile crowdsensing platform that is
able to gather data with different mobile operating systems
is a challenging endeavor.
In the context of EMA, other studies than TYT exist that
use this method in the context of tinnitus [13], [14]. More-
over, further aspects are investigated using EMA approaches
[15]–[18]. In general, EMA approaches are considered to of-
fer unprecedented opportunities to study clinical symptoms
under ecologically valid conditions [19], even though the
utilization of its possibilities is still premature.
Regarding Android and iOS comparisons, related work
that deals with peculiarities of the different mobile operating
systems exists. In [20], such peculiarities are discussed for
clinical interventions that are applied by the use of mobile
applications. The authors describe that it costs quite a lot
of time to cope with the different ways to develop mHealth
applications. In addition, they state that it is naturally an
interdisciplinary endeavor. However, data from iOS and An-
droid users are not directly compared in this work. Another
work discusses in an industrial setting how user interfaces
on mobile devices can be developed and what aspects are
crucial [21]. In this context, the used mobile operating
systems as well as the applied development strategies are
discussed for the development of user interfaces. The authors
come to the conclusion that the development of a user
interface is especially difficult for mobile devices. Again, no
differences on mobile users for Android and iOS are pre-
sented. In the context of mobile data collection for medical
purposes, related approaches exist that discuss peculiarities
of different mobile operating systems for the data collection
procedure [22]. However, user characteristics to distinguish,
for example, iOS and Android users are not discussed.
Finally, approaches that explicitly discuss peculiarities of
mobile technology in the context of healthcare data exist
[23], [24], which are closely related to the work at hand.
They investigate methods for mobile technology that must
be particularly taken into account in the context of mobile
health. Again, a direct comparison of Android and iOS is
not considered in these works.
In the context of this work, approaches that compare
differences between Android and iOS in the context of
mobile healthcare applications are of particular interest. In
[25], [26], for example, security issues for Android and
iOS mHealth applications are discussed. The authors of
[27], in turn, discuss guidelines of Android and iOS to
create applications that are able to support personal health
records. In general, many works exist that aim to evaluate
the differences and quality of mHealth applications that
were developed for both mobile operating systems [28]–
[30]. However, none of these approaches aim at a direct
comparison of Android and iOS users that utilize the same
mHealth platform or mHealth applications to cope with their
individual health situation.
Approaches that focus on differences between Android
and iOS users exist beyond healthcare questions [31]. An-
droid and iOS users were directly compared only in a few
studies [32]–[34]. These studies have found that iOS users
are more likely female, in the mid-30s, with a graduate
degree, in a higher income group, with more technology
knowledge, and that iOS users spend more time using
applications than Android users. Clinically relevant differ-
ences were reported in [32] for the SmokeFree28 (SF28)
application. For example, iOS users downloaded the app
more likely for a serious attempt to quit smoking or Android
users took stop-smoking medication more often. Another
study investigated whether iOS and Android users differ
in personality traits [35]. Only a few and small differences
were found. Altogether, research on mobile health and the
differences between Android and iOS users are still in their
infancy.
III. MATERIAL AND METHODS
TrackYourTinnitus (TYT) is a mobile crowdsensing [7]
mHealth platform, which is build on four components. First,
it offers a website for user registration and other user-
related features (e.g., data visualization). Second, it offers
an Android and iOS application. Third, a MySQL database
is used for the central repository for the data collected [4].
Fourth, a RESTful API is provided that enables the com-
munication between the mobile applications, the website,
and the database. In general, TYT was developed to track
the individual tinnitus of its registered users. The tracking
is based on a set of existing and individually developed
questionnaires. In addition, the environmental sound level
can be measured when tracking the users. Due to the lack
of space, we refer to [4] for technical insights on TYT.
In addition, a detailed description of the data collection
procedure of TYT can be found in [5].
For the comparison of Android and iOS users, we briefly
share relevant facts about the TYT platform in this section.
First of all, the procedure depicted in Fig. 1 is accomplished
by all TYT users.1Thereby, TYT pursues three goals. First,
data shall be collected on a daily basis (cf. Fig. 1, 4
).
However, a crowd user shall not foresee the times he or
she is asked to sense data. This is ensured by asking the
crowd users in various daily life situations (cf. Fig. 1,
3
). Second, the collected data shall enable new kinds of
data analytics like juxtaposing real-time assessments and
retrospective reports [5] (cf. Fig. 1, 2
&4
). Third, gathered
data shall be used to provide feedback to the mobile crowd
users. From the perspective of TYT users, they first have to
register (cf. Fig. 1, 1
). This can be accomplished by using
the website, the Android or the iOS application. During
the registration procedure, we automatically identify the
used mobile operating system. If a user registers through
the website, then we obtain the used mobile operating
system in further steps. However, in the latter case, the
user gets an empty field in the database for the mobile
operating system during the register procedure. Note that
these mobile users are excluded from this study. Then, users
must fill in questionnaires (cf. Fig. 1, 2
). For example,
they have to provide demographic data and fill in the
“Mini-TQ-12” questionnaire [36], which measures tinnitus-
related psychological problems. Altogether, the completion
1More detailed information about the procedure can be found at
https://www.trackyourtinnitus.org/process.pdf
of the questionnaires is a fundamental prerequisite for users
who want to use the features of the continuous mobile
crowdsensing (cf. Fig. 1; Steps 3
and 4
).
In this paper, data related to Steps 1
and 2
were
analyzed. More specifically, we compared data provided in
Step 2
with the information what mobile operating system
has been identified during Step 1
. As Step 2
identifies
important aspects about the individual tinnitus situation, we
conducted this study with the question in mind to reveal
whether or not users that register to the TYT platform with
an Android smartphone differ in their tinnitus characteristics
from the ones that register with an iOS smartphone. To
answer this question, we used the entire data source of
existing TYT users. More precisely, TYT presently has 3122
registered users. For the work at hand, the following users
had to be excluded:
•Users that registered through the website.
•Users for which the used mobile operating system could not
be certainly identified.
In total, we had to exclude 1.605 users. That means, 1.517
users were included in the presented data analysis. Moreover,
one more important aspect must be briefly mentioned. Both
mobile applications were developed following the native de-
velopment approach; i.e., solely using Objective-C and Java
to develop the applications. Furthermore, no frameworks
were used and much efforts were done to create similar
user interfaces for both mobile applications. In addition, we
involved domain experts when developing the mobile appli-
cations. Fig. 2 shows one part of the same questionnaires
on iOS and Android.
IV. DATA AND STATISTICS
The current analysis relies on an export of the TYT
database made in February 2018. All users were exported
and test users were excluded. All statistical analyses were
performed with SPSS 25. Frequencies (n), percentages (%),
means (M), and standard deviations (SD) were calculated
as descriptive statistics. To compare users registering with
the iOS operation system and users registering with the
Android operation system, Chi-squared tests were used for
categorical variables and t-tests for independent samples
were performed for numeric variables. All statistical tests
were conducted two-tailed and the significance value was
set to p<.05.
V. RESULTS
The number (February 2018) of registered users with
available information on the mobile operating system used
during the registration amounts to n= 1.517 users. This is
the total sample for the current study. Most of these users
come from Germany (n= 536; 35.3%), the United States
(n= 210; 13.8%), the United Kingdom (n= 83; 5.5%),
and the Netherlands (n= 79; 5.2%). Of the total sample,
n= 819 used iOS to register (54.0%), and n= 698
Figure 1: TYT Mobile Crowdsensing Collection Procedure
iOS Users Android Users
n(%) n(%) Test Statistics
Gender (n=781 iOS; n=687 Android) χ2(1) = .94, p =.331
Female 227 (29.1) 184 (26.8)
Male 554 (70.9) 503 (73.2)
Self-reported Tinnitus Variability (n=776 iOS; n=680 Android) χ2(1) = .45, p =.501
No 208 (26.8) 193 (28.4)
Yes 568 (73.2) 487 (71.6)
Self-reported Family History of Tinnitus (n=779 iOS; n=685 Android) χ2(1) = .02, p =.889
No 587 (75.4) 514 (75.0)
Yes 192 (24.6) 171 (25.0)
Self-reported Causes of Tinnitus Onset (n=782 iOS; n=681 Android) χ2(1) = 8.81, p =.117
Loud Blast of Sound 150 (19.2) 104 (15.3)
Whiplash 26 (3.3) 15 (2.2)
Changes in Hearing 94 (12.0) 73 (10.7)
Stress 222 (28.4) 195 (28.6)
Head Trauma 29 (3.7 28 (4.1)
Other 261 (33.4) 266 (39.1)
M(SD) M(SD)
Age (n=752 iOS; n=693 Android) 42.63 (13.13) 44.11 (13.41) t(1443) = −2.11, p =.035
Self-Reported Tinnitus Duration
(n=772 iOS; n=673 Android) 7.21 (9.51) 11.20 (12.97) t(1216.68) = −6.72, p < .001
Tinnitus-Related Psychological Distress (Mini-TQ)
(n=782 iOS; n=697 Android) 14.01 (5.94) 13.64 (6.21) t(1477) = 1.19, p =.233
Table I: Comparisons between TrackYourTinnitus Users Registering with the iOS vs. Users Registering with the Android
Operating System.
registered with Android (46.0%). The results of the com-
parisons between iOS and Android users are summarized in
Table I: Android users were significantly older (p=.035)
and had a significantly longer self-reported tinnitus duration
(p < .001) than iOS users. Yet, both groups did not differ
in tinnitus-related psychological distress as the values of
the Mini-Tinnitus Questionnaire [36] were not significantly
different between iOS and Android users (p=.233).
Moreover, neither gender (p=.331), nor self-reported
tinnitus variability (p=.501), nor self-reported causes of
tinnitus onset (p=.117), nor self-reported family history of
tinnitus (p=.889) differed between iOS and Android users.
VI. SUMMARY AND OUTLOOK
This paper presented results of a data analysis that com-
pared differences of Android and iOS users of the TYT
mHealth crowdsensing platform. Interestingly, very little
empirical work exists that compared such differences in the
context of mHealth gathered data. Contrary to the Android
vs. iOS comparison of the SF28 application [32], we found
no differences in gender but in age. In general, based
on results of this study, differences between Android and
iOS users can be beneficially taken into account from the
technical as well as the medical perspective. Technically, if
Android users are older than iOS users, it might be one
direction to use this information to further work on the
user interface of Android. For example, by taking the age
into consideration, the user interface can be adjusted to the
age. As older people perceive the color blue [37] poorer
compared to younger people, this color can be less used for
older people. The other significant result was that Android
users had a longer duration of the self-reported tinnitus
than iOS users. Yet, it should be kept in mind that tinnitus
duration and age are correlated, since older patients will
have a higher chance to have more years since onset than
younger ones. A limitation of the current study is related to
Figure 2: Same Questionnaire on iOS (left) and Android
(right)
the fact that out of the total user population, only around
50% could be analyzed because of missing information on
the operating system. This limits the generalizability of the
results. Therefore, more studies are needed to investigate
whether the results can be replicated. In future work, we plan
to integrate more items to our applied questionnaires that
enable more in-depth analyses on the user characteristics.
For example, a question could be added that asks the user
whether or not he or she particularly prefers the identified
mobile operating system. If this question is answered posi-
tively, another questions could be what are the reasons for
this preference. Overall, for TYT, it seems that a comparison
between Android and iOS users is promising for further
technical developments as well as analyses that reveal new
insights for the domain experts. As a next step for TYT,
we will analyze TYT data that was gathered during the
continuous mobile crowdsensing procedure with respect to
differences between Android and iOS users. Thereby, we
additionally aim at questions like whether users change
their mobile operating system while providing data to TYT.
Furthermore, another interesting question is to combine the
analysis with the country users come from. Altogether, many
questions and opportunities arise for TYT when comparing
Android and iOS users. If the presented results for TYT can
be generalized to other mHealth platforms or solutions is
still an open question that should be dealt with in future
work. However, as such comparisons might provide new
and valuable results, it should be generally considered in
the context of mHealth gathered data.
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