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Understanding Users’ Health Information Privacy Concerns for Health Wearables



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Understanding users’ health information privacy concerns for health
Moritz Becker
LMU Munich
Health information privacy concerns (HIPC) are
commonly cited as primary barrier to the ongoing
growth of health wearables (HW) for private users.
However, little is known about the driving factors of
HIPC and the nature of users’ privacy perception.
Seven semi-structured focus groups with current
users of HWs were conducted to empirically explore
factors driving users’ HIPC. Based on an iterative
thematic analysis approach, where the interview
codes were systematically matched with literature, I
develop a thematic map that visualizes the privacy
perception of HW users. In particular this map
uncovers three central factors (Dilemma of Forced
Acceptance, State-Trait Data Sensitivity and
Transparency) on HIPC, which HW users have to
deal with.
1. Introduction
Health information privacy concerns (HIPC)
constitute a barrier to the ongoing growth of health
information technologies comprising digital
medicine, electronic medical records or remote
patient monitoring [1]. Owing to the high sensitivity
of the gathered personal health information (PHI),
privacy concerns have proved to be more important
in the context of such health technologies than other
technological devices [2]. However, to date, no study
has offered a comprehensive conceptualization of
users’ privacy perception and there is no empirical
evidence of the main factors influencing users’ HIPC.
The “understanding of information privacy remains
fragmented in the under examined health context” [3]
and in particular for wearable health technologies [4].
Arising from the intersection of healthcare, health
informatics, and information systems, health
wearables (HW) for private users continuously
monitoring a range of PHI from illness to fitness
without the need of health professionals (e.g.
physicians) [5]. Therefore the HW user becomes a
real-time “walking data generator” [6, p. 63], and
HIPC occur by exposing such PHI without awareness
or consent [7]. “In order to address and appease
individuals’ HIPC, it is imperative to identify and
understand how different factors influence
individuals’ HIPC” [3, p. 9]. As privacy concerns are
complex psychological concepts in the individual
minds [e.g. 8], I use a thematic analysis approach to
structure the heterogeneous privacy perceptions into
homogeneous themes to compare and analyze the
influencing factors on HIPC of HW users.
Uncovering the nature of users’ privacy perception
and identifying the driving factors of HIPC could
help researchers and designers understand the major
dimensions that are critical in their work [9, p. 497].
I ask: What factors influence the HIPC of HW users?
To answer this research question, I use the HIPC
Model by Kenny and Connolly [3] to explicitly
address privacy concerns with health information
technologies. I conduct seven semi-structured focus
groups with six users of HWs each and apply a
rigorous iterative thematic analysis to empirically
understand users’ mindsets regarding their HIPC.
This “method for identifying, analyzing, and
reporting patterns within data” [10, p. 6] has been
successfully employed to uncover user perception of
health apps, or compare privacy concerns of digital
services [11]. By reviewing the conducted codes on
literature, I enhance the theoretical understanding of
HIPC by proposing three central factors (Dilemma of
Forced Acceptance, State-Trait Data Sensitivity and
Transparency). This thematic map enables
researchers to uncover the understanding of privacy
perception of HW users and help practitioners to
develop privacy-friendly devices.
2. Theoretical background
2.1. Health information privacy concerns
Previous studies primarily use the privacy
calculus theory to analyze individuals’ willingness to
share PHI voluntarily if they expect that perceived
benefits from data disclosure outweigh the perceived
Proceedings of the 51st Hawaii International Conference on System Sciences |2018
ISBN: 978-0-9981331-1-9
(CC BY-NC-ND 4.0)
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costs [e.g. 12, 13]. This tradeoff theory has been
described as “the most useful framework for
analyzing contemporary consumer privacy concerns”
[14, p. 326], but still underscores the risk-control
interplay. Both risk and control have been shown to
operate as privacy-borne beliefs relating to the
potential consequences of PHI disclosure [15]. For
health technologies, improper information practices
would result in the mining and mapping of personal
data to make an individual’s health status more
visible. The collected PHI may be easily analyzed,
distributed, and re-used, and users perceive a
relatively high risk that the provided PHI is being put
into secondary use for unrelated purposes without
their knowledge or consent [13]. Thus, the sensitivity
of various datasets such as demographics, activities
(e.g. accelerometers, pedometers, location), or
physiologies (e.g. electrocardiograms, pulse
oximeters, blood glucose meters, and weight scales)
in particular, have prompted heated discussions about
individuals’ health information privacy [4, 16].
Health information privacy “is an individual’s right
to control the acquisition, uses, or disclosures of his
or her identifiable health data” [17, p. 1]. Kenny and
Connolly [3] developed the Health Information
Privacy Concerns Model (HIPC) to explicitly address
privacy concerns with health information
technologies. The HIPC is composed of the six
constructs Collection, Unauthorized Secondary Use,
Improper Access, Errors, Control and Awareness
[18]. The first four dimensions are affiliated to the
Concerns for Information Privacy-Model (CFIP)
[19]. The two remaining dimensions, Control and
Awareness, derive from the Internet Users
Information Privacy Concerns-Model (IUIPC) [20].
The construct Improper Access is considered
especially important concerning high sensitive data
environments [11]. It describes privacy concerns with
respect to the perceived threat of unauthorized access
by third parties. Several earlier studies have shown
that potential access of third parties (e.g. employers
or insurances) to private health data is a common
cause of concern for individuals [15].
Data inaccuracies have been a major issue in
studies on health technologies [4]. The construct
Error considers users’ concerns for data inaccuracies.
Individuals believe the digitization of health data can
generate more errors [11]. It can be assumed that
third parties such as insurance companies will only
contribute to the integration of technologies in a
healthcare system, if companies provide accurate
data. Therefore, user insights on measurement
accuracy are considered crucial [3].
The dimension Unauthorized Secondary Use
addresses users’ concerns that their data is utilized
for other than the agreed upon purposes, such as
marketing purposes. If individuals believe these
potential uses may occur, they are likely to express
HIPC [18].
Studies show that individuals are concerned
regarding the electronic collection and storage of
their PHI [e.g. 1]. The dimension Collection
describes this subjective concern with respect to the
accumulation of PHI [3].
Hong and Thong [18] showed that perceived
control over the disclosed PHI is an important
influencing factor for users during their interaction
with websites. The Control dimension covers the
individual’s concerns that they do not have adequate
control over their PHI [20].
The sixth dimension Awareness refers to the
individual’s concern regarding their lack of
awareness of how a device uses and protects the
privacy of their PHI [20]. Studies assume that users
are to a large extent unaware of the potential for data
misuse through the digitalization in health [21].
Kenny and Connolly hypothesized that an increased
awareness leads to higher privacy concerns.
However, they found support for this hypothesis in
only one of their two samples [3].
2.2. Health information privacy concerns of
health wearables
To empirically explore the influencing factors that
drive these six dimensions of HIPC I use HWs as one
of the most distributed health technology for private
users [4]. I define HWs as small digital devices with
biometrical sensors designed for private users and
worn on the body to continuously generate PHI
without the need for health professionals. By
continuously collecting PHI and analyzing these data
in real time HWs provide instantaneous, goal-
oriented feedback. Therefore individuals have the
chance to understand their health status to reveal
possibilities for improvement. The collected PHI can
be stored stationary on the mobile device, the
computer or digitally in a cloud [13].
Owing to the high data sensitivity and the
mobility of the devices, privacy concerns have
proved to be more important in the context of HWs
than other technological devices [e.g. 2, 22]. In
contrast to medical health wearables for professional
usage or other clinical devices, in which electronic
health records are created and managed by healthcare
providers (hospitals and other clinical organizations),
HW users create and manage their PHI without the
help of physicians [23]. While physicians are usually
required to keep users data confidential, this will be
subject to further legislator assessment in the case of
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HWs. As HW analysis outcomes are immediately
available in digital form, their dissemination to third
parties is easy and can be lucrative for suppliers, but
it can also harm users’ privacy. Many HWs collect
and store PHI in online portals to connect users to
their health status and provide goal-oriented feedback
as needed [4]. Therefore, the technology not only
improves users’ knowledge about themselves, but
especially the providers’ knowledge about the users
[e.g. 8, 9]. Providers even share data with third
parties, as for example with healthcare providers and
insurance companies to adjust insurance premiums
according to the analyzed data sets, which can lead to
a worse economical outcome [1].
3. Methodology
3.1. Thematic analysis of focus groups
I chose a qualitative approach to examine the
factors that influence the HIPC (Collection,
Unauthorized Secondary Use, Improper Access,
Errors, Control and Awareness) of HW users. As
focus groups are especially well suited to uncovering
and documenting the ’whybehind opinions, and in
obtaining much more depth and breadth of analysis
from participants than available from individual data
collection methods [24], I conducted semi-structured
focus groups with current users of HWs. As focus
groups allow participants to query each other, explain
themselves and comment on each other’s experiences
[25], this research method is frequently used to
evaluate critical healthcare themes [25] and has been
already used to uncover privacy aspects [e.g. 11]. The
interview guide for the group sessions were
developed on the six dimensions of the HIPC model.
The interview evaluation is based on the thematic
analysis approach, as it is a well-established method
of qualitative data analysis [10]. Thematic analysis is
a method for identifying, analyzing, and visualizing
patterns within data and is especially appropriate for
analysis in sensitive data environments [e.g. 26]. It
has been successfully employed to uncover user
perceptions of health apps or critical experiences with
self-tracking in information systems research [27].
By organizing and describing the data set in rich
detail, it normally goes even further by interpreting
various aspects of the research topic [26].
3.2. Data collection
Considered to be an adequate number [24], seven
focus groups were conducted to capture the privacy
perception of 42 current HW users. To ensure
participants represented a broad range of experiences
and ages, I used peer recruitment for all of the seven
focus groups (opportunistic sampling). The groups
were designed to encourage participants to interact
with each other, rather than the researcher, allowing
“structured eavesdropping” [25, p. 301]. At the start
of each session the researcher provided an overview
of the objectives of the study. Afterwards, the
researcher attempted to restrict their own contribution
to reading the six questions concerning the six
dimensions of the HIPC model out aloud, and only
asking further probing questions when required. Each
focus group lasted approximately one hour, with 10
min spent discussing each question. The use of the
same questions and procedure for each focus group
facilitated investigation into the similarity of the
themes discussed across the focus groups [11].
Table 1. Demographic profile of participants
Number of respondents
3.3. Data analysis
I follow a rigorous iterative thematic analysis
approach that matches the interview codes, factors
and dimensions by constantly reviewing literature.
Figure 1 provides a detailed illustration of the
methodological approach, which was not a linear
phase-to-phase process, but a recursive one, by
moving back and forth between the different phases
of the analysis. First, I transcribed the focus groups
audio recordings and then repeatedly read through the
transcript. Afterwards I generated initial codes by
searching for recurring patterns in the raw data. In
this way, I could aggregate the data to workable
items. I identified 39 different codes in the data set.
In the next step I merged different codes with factors
e.g. users who based their willingness to disclose
information on the identity of the data recipient or the
perceived sensitivity of their data. Consequently, the
two respective codes Recipient-specific Data
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Retention and Sensitivity-specific Data Retention
were matched to the factor Contextualization. The
process of matching codes with the factors, and then
the factors with the six dimensions of HIPC was
accompanied by a constant review of the literature.
4. Results
The thematic map visualizes the results. It is
composed of the six dimensions of HIPC Collection,
Unauthorized Secondary Use, Improper Access,
Errors, Control, Awareness [3] and their related 16
factors (Figure 2).
4.1. Collection
The Collection dimension captures an
individual’s concerns that a device is collecting and
storing large quantities of their PHI [3, 19]. Three
factors were related to this dimension.
Deanonymization: Users are not aware of the
degree of anonymization of their PHI. Although users
wish for an anonymized storage of data, they do not
eliminate the possibility of personalized storage: “I
hope my fitness activities are anonymized, it would be
terrible if my fitness trainer could see them!” [P8]
Some users were afraid of a Deanonymization
through the connection of primary and secondary
data: “There just needs to be a combination of two
databases and anonymization is worthless.” [P28]
Location of Data Storage: Users are concerned
about the location of data storage and analysis:
“Although I am wearing the device on my body and
data are shown on the display, I am pretty sure that
they are saved in a cloud.” [P23]
Data as Asset: This factor reflects that many users
perceived PHI disclosure as beneficial. One
respondent who used his fitness tracker to fight
obesity perceived his PHI as a means to externally
verify his healthy lifestyle. He described the reaction
of his physician when he first showed him his fitness
trackers, as follows: “When I visited my doctor and
was asked for my current blood pressure, I could
show the measured value on my health wearable. The
doctor really appreciated that.” [P7] A lot of users
valued the collection of PHI to monitor their fitness
4.2. Unauthorized secondary use
The Unauthorized Secondary Use dimension
relates to an individual’s fear that their PHI is
collected for one purpose but used for additional
purposes without obtaining their permission [3, 19].
Cross-connection: The first factor concerning
Unauthorized Secondary Use describes the
unconscious worries over the connection of data with
other databases. The users have little or no
knowledge regarding whether this connection
happens in reality: “I do not know whether apps and
devices are communicating and exchanging data.”
[P3] Some users assume that, by using the device,
there is an automatic authorization for the business
partner to use, analyze and pass on the data: “Of
course, they will save all of my available data and
create a profile to optimize the evaluation.” [P17]
Additionally, users think cross-connection carries a
high possibility for errors: “My device is not able to
count my steps appropriately, how should it be
possible to connect and analyze different data
sources and create predictions?” [P36]
Figure 1. Iterative thematic analysis approach
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Trust Cues: The study participants are aware of
the possibility that data could be sold to third parties.
During the conversations, the usage of the word
“hopefully” is striking when talking about the
anonymization of data. This shows the concerns
about personalized data transfer. Furthermore, the
nescience about laws and rights concerning the usage
and transfer of data causes further concerns,
especially regarding the degree of anonymization:
“They got the data and if it is legal, they will use
them. But hopefully, the data are anonymized.” [P12]
Contextualization: This factor refers to the
observation that users compared their disclosure of
PHI to their disclosure behavior in other contexts and
decided to reveal personal information based on the
perceived comparative sensitivity of the PHI. So, one
user states that she foregoes privacy consciously,
because otherwise she could not use a new feature.
Other participants with similar views state that there
is a trade-off between costs and benefits depending
on the context. P30 states: “Sometimes I do not think
about my privacy, it is more important to me to use
the device quickly and that everything is running.”
Almost all focus groups were open-minded towards a
transfer of anonymized data for medical research
purposes. Users were reluctant to make their PHI
accessible to third parties, but were open-minded to
its use by employees to improve the tracker: I do
not care about usage of data by software developers
who want to improve the trackers. I would even
support it! [P15] Other users were indifferent
towards disclosing their PHI to friends, but reluctant
to make it accessible to providers: “I like to share my
eating habits with friends but I am concerned about
sharing it with my insurance company.” [P1]
4.3. Improper access
The Improper Access dimension covers
individuals’ concerns that devices do not have
adequate measures in place to prevent unauthorized
individuals or organizations from accessing their PHI
[3, 19]. Users of HW are aware of security gaps but
differentiate depending on access route between a
passive improper access (Provider Complacency) and
an active improper access (Hacking Hazard).
Provider Complacency: Describes the
unauthorized access by third parties which happens
incidentally or passively because of insufficient
privacy adjustment options. For instance, Fitbit’s
default privacy settings inadvertently exposed
information about some of their users’ sexual
activities.“ [P21] Users are especially concerned
about the privacy complacency of companies
financed by venture capitals, as the goal of fast
economical success is often more important than the
implementation of privacy features or the
identification and closing of security gaps. So, neither
“the brand nor the reputation” [P35] can be affected
and “companies try to be the first one in the market,
so privacy will not be placed first.” [P33] P36 adds:
“Providers cannot make any money with privacy
that is theme is just expensive for these firms.”
Hacking Hazard: This factor summarizes the
observation that users are afraid of being hacked if
they are part of a bigger data collection. The more
data a company collects, the higher the objection and
“then it is just a matter of time until you get
attacked.” [P25] However, users had no concrete
idea of the extent and the consequences of this active
improper access.
Figure 2
Thematic map of privacy perceptions of health wearable users
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4.4. Errors
The Errors dimension relates to the individual’s
concerns that devices do not have adequate measures
in place to prevent or correct errors in their PHI [3,
Data Inaccuracy: This factor describes the extent
to which users experienced inaccurate data
measurements or functional disorders of HWs. When
it comes to the accuracy of the produced data, users
state that the recorded data are mostly just “semi-
precise” [P2] or “also inaccurate” [P7] and a
general “deviation of up to 15% is acceptable.”
[P16] Based on their own experiences, some users
described aspects, which influence data accuracy
negatively. In general, users are not concerned about
privacy aspects, if there is less data inaccuracy.
Anticipated Consequence: The users separate the
consequences of incurrence data between two usage
scenarios of HWs. In the first usage scenario (private
focus), users accept approximate values for the rough
estimation of the performance as long as the
generated data serve no medical purpose or will be
passed on to health insurances. P5 states: Whether
the indicated 8,567 steps on my tracker were
recorded correctly, does not matter for me as long as
my health insurance or other institution do not get
the PHI.” In the second usage scenario (professional
health focus) users are afraid that approximate values
of the generated PHI could lead to erroneous
allocations within tariff systems, or could be used for
medical diagnoses or treatments. So, some focus
groups discussed the opportunity to falsify the data
actively and consciously. I think the topic is very
sensitive, because we do not know who controls it."
[P3] P1 comments on that: I buckle the device to my
dog and get it to chase a ball in the garden.”
Error Type: Four of the seven focus groups
discussed different error types and their effects. Type
one errors represent the occasions when flawed data
leads to misleading feedback, which incorrectly
diagnoses lack of activity or other issues, or even
diseases. Users relying on this information, instead of
a professional medical evaluation, can misdiagnose
themselves, resulting in dangerous self-treatment.
Type two errors relate to the possibility that devices
can miss symptoms which indicate the presence of an
issue or disease, resulting in the owner being
described incorrectly as healthy and active. P29
summarizes: Without a doctors advice, users do
not choose an appropriate level of activity to get well
again, resulting in harm through overextending
themselves with too much physical activity. And this
could be very dangerous.
4.5. Control
The Control dimension covers an individual’s
concerns that they do not have adequate control over
their PHI [3, 20].
Transparency: On the one hand, users were aware
of the collection and storage of their PHI for
individualized evaluations (e.g. progressing
statistics). On the other hand, a lot of users possessed
neither an overview of the extent of data collection,
nor the control between primary and secondary data
analysis. “I am aware of the storage of my data as
they are needed for personal feedback but I have no
idea about the further use of my health data.” [P23]
In particular, users have little or no insight into which
data are analyzed, in which way, and how to control
this: I can control when to wear or not to wear the
device, but there is no transparency of data control.”
[P21] P22 adds that: I think there is always a
disparity between the amount of data recorded and
the data shown to the user.”
Dilemma of Forced Acceptance: P6 describes the
dilemma of the HW users: I am either forced to use
the device and know about the data recording and the
possible usage of the data or I decide against the
usage consciously.” From the users’ perspective, a
comprehensive control of the PHI recorded by the
HW is no longer possible. They see the conscious
renunciation of such devices as the only way out.
Thereby, the consequences of a conscious
renunciation of HWs are seen as a step back to a
bygone age,” [P2] where the “numerous advantages
of the technological development did not exist.”
[P27] Many users highly doubt whether such a
release is possible at all: I would like to have more
control about my data, but you have to accept how it
goes. Beggars cannot be choosers.[P7] All focus
groups discussed this compulsive acceptance of
HWs. Post-purchase lock-in effects were of particular
concern: “Once I have bought the product and then,
for example, terms and conditions change
afterwards, I would probably accept all privacy
restrictions and continue to use the product because
of the money invested and the activity records that
had already been tracked.” [P25]
4.6. Awareness
The Awareness dimension refers to the
individual’s concern regarding their lack of
awareness of how a device uses and protects the
privacy of their PHI [3, 20].
State-Trait Data Sensitivity: The participants
perceive the data generated by the HW as sensitive
and private. These are data from my internal body,
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it couldn’t be any more sensitive! [P12].
Nevertheless, users perceive data sensitivity
differently, with a differentiation between personal
and device data sensitivity: The device data
sensitivity summarizes the observation that users
differentiate depending on the device focus. Devices
with a health and medical focus are associated with
higher data sensitivity and devices with a fitness and
lifestyle focus are related to lower data sensitivity.
Independent of the device, the users described
different personal data sensitivities. Some users
described the data tracked by a device as “extreme”
[P6], “high” [P12] or “ultimate” [P16] sensitive,
whereas other users perceived them as “not as
sensitive as bank- or identity documents.” [P22] P29
summarizes: “I think most of the users are aware of
the data sensitivity. But as with many things in life,
for one user they are more sensitive than for the
Trivialization: Within the user groups, the
participants often make statements like: “It is not that
bad, the government will monitor this and will
intervene if necessary.” [P22] I have nothing to
hide, they can access my data.” [P2] More than the
half of the users expressed such statements. These
verbalized trivializations show a helplessness of the
users. “I think we trivialize the whole situation
because we are able to change something, if we want
to use the devices entirely.” [P34]
Fear of the Future: This factor summarizes the
observation that many users are worried about the
future: Some users ask themselves where the
technical developments are leading to, and where the
limits will be set with regard to privacy. “The next
generations know only complete digitalization, so the
sensitivity will decrease and the indifference will
increase. I am honestly worried about where this is
leading to.” [P33]
5. Discussion
This study was motivated by the research call to
identify and understand how different factors
influence individuals’ HIPC[3]. To understand the
factors that drive HIPC I used HWs as one of the
most distributed health technology for private users
[4]. Seven semi-structured focus groups with a
rigorous iterative thematic analysis were evaluated to
empirically understand the HIPC of HW users. Based
on this iterative approach that constantly matches the
interview codes, factors and dimensions on literature,
I used a thematic map to visualize, share and discuss
the findings deriving from the qualitative data
analysis. This thematic map visualizes the structure
that best represents users’ perceptions of HIPC
distinguishing on the six dimensions of HIPC
(Collection, Unauthorized Secondary Use, Improper
Access, Errors, Control and Awareness) and their 16
factors. While the different focus groups and their
participants have proposed a large number and
variety of different points of interest, three factors
(Dilemma of Forced Acceptance, State-Trait Data
Sensitivity and Transparency) stood out in terms of
frequency of mentioning as well as discussion length
and intensity. These three factors were discussed in
all focus groups and were mentioned by more than 90
percent of the users.
Dilemma of Forced Acceptance: Despite the fact
that many users perceived the PHI collection of their
devices as a threat to their privacy, some users also
voiced sympathy for PHI disclosure. This contrasts
the mainstream privacy research on wearable health
technologies, where privacy is often exclusively
treated as a user-side threat [2]. In my study, PHI
disclosure was valued as an ability to monitor
personal activities or as a source of potential
monetary compensation by insurance companies.
Although this implies people consider the risks and
benefits of providing PHI to some degree, the factor
Data as Asset relating to the benefits offered by
HWs, indicates users are principally focused on the
benefits they believe they will receive for disclosing
PHI. So almost all users report a Dilemma of Forced
Acceptance of HWs where HIPC are mainly caused
by lack of control, but the advantages of HWs are so
predominant, that a conscious renunciation is not
possible. Many users have already given up trying to
do something about high HIPC and now accept the
situation, as they do not see any possibility for action
with regard to this dilemma. These users “seem to be
likely to accept constant monitoring [of PHI] through
sensors because they are persuaded that the benefits
outweigh the costs” [28, p. 11]. Consequently, the
majority of the participants could be described as
users of HWs that see themselves, as beggars that
cannot be choosers. These users process the
Dilemma of Forced Acceptance with the two factors
of Awareness (Trivialization and Fear of the Future).
On the one hand users handle the situation by
understating their HIPC and hope for legal support
(Trivialization). On the other hand users deal with
this dilemma by pushing it further into the future and
formulate dark future prospects, so they do not have
to deal with it now (Fear of the Future). State-Trait
Data Sensitivity: The factor State-Trait Data
Sensitivity confirms other research studies [e.g. 3,
15] which show that the more sensitive individuals
perceive PHI to be, the greater their concerns are
regarding the privacy of this data. My qualitative
results are in accordance with Li, Wu, Gao and Shi
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[13, p. 15], who noted that “health information
sensitivity (has) significant effects on individuals’
perceived privacy risk.” But my results indicate this
factor should be divided into two components of
State-Trait Data Sensitivity. First, a personal
component (personal data sensitivity) referring to the
observation, that data sensitivity varies from one
individual to another. In this sense personal data
sensitivity could be seen as a trait factor [15], where
an individual that has higher personal data sensitivity
will likely be more concerned over its use, storage,
and privacy, than a person with a lower personal data
sensitivity. The second component (device data
sensitivity) refers to the observation that users
perceive that fitness and lifestyle-focused devices
collected less sensitive PHI than health- and medical-
focused devices. This result confirms previous
studies in which researchers reported that individuals
found health or medical data much more sensitive
than other information such as demographic, lifestyle
habits, or purchasing behaviors [1]. Kenny and
Connolly [3] called thisfrom illness to fitness,
while Alrige and Chatterjee [9] differentiated
between monitoring (medical), prevention (fitness),
and communication (lifestyle) situation and devices.
This State-Trait Data Sensitivity is also reflected in
the factors Anticipated Consequences and
Contextualization. First, the users separate the
consequences of inaccurate data between two usage
scenarios of HWs. Therefore, users accept
approximate values for the rough estimation of the
performance as long as the generated PHI serve no
medical purpose. If the device has a professional
health focus the users are afraid that approximate
values of the generated PHI could lead to incorrect
allocations within tariff systems or could be used for
inaccurate medical diagnoses or treatments. Second,
HIPC for HW users were context-related
(Contextualization). Users of HW compared their
attitude towards the disclosure of PHI to their
disclosure behavior in other contexts [12]. Many
users decided to reveal personal information based on
the comparative sensitivity of the health-related
information. That means they would not shudder to
publish PHI for when they perceived the disclosed
PHI as equally or less sensitive to the personal
information they provided to other companies in
other contexts. Furthermore, users of HW were far
more positive towards PHI disclosure for the purpose
of medical research purposes or product
improvement, than for transferring PHI to third
Transparency: Initiated by the technical
possibilities through Cross-connection,
Deanonymization and Location of Data Storage,
almost every participant reported a perceived loss of
transparency when using HWs. This factor, was also
stated to be a strong trigger for HIPC in other studies
of health technologies [e.g. 11, 29]. Not only does
this lend credence to the idea that people principally
seek informational self-determination when engaging
with technology services, but also echoes one of the
factorscontrol over collection and usage PHIin
the IUIPC scale [20]. Validating the CFIP scale,
Stewart and Segars stated that, “a central concern that
seems to underlie consumer attitudes, and is perhaps
the common theme captured by the higher-order
concept of CFIP, is the issue of control. Consumers
desire levels of personalization and customization but
also want some sense of control over how this service
occurs” [30, p. 46]. This control-based privacy
dilemma is already discussed in privacy literature
[e.g. 11], and confirmed in other empirical studies
[e.g. 20]. Although Control was identified as an
important dimension of HIPC [3], for HW users the
PHI control is more than the disclosure or non-
disclosure of information. It is a decision making
process in which the HW user considers the HW
usage scenario (private focus vs. professional health
focus), of engaging in a particular behavior. As new
technologies affect this calculus of behavior [11],
individuals are often unable to predict the nature of
that which has to be managed. This understanding of
a behavior calculus underpins Culnan and Bies’
observation that a “social exchange perspective also
applies to a consumer context” [14, p. 327] or, in my
sense, a consumer health context.
6. Implications and Conclusion
6.1. Implications
This study has important theoretical and practical
implications. The thematic map provides researchers
with a visualized structure of what determines a
user’s HIPC and can be seen as a strong initial insight
into the main drivers of HIPC. It is acknowledged
that other factors may be influential, but it is
maintained that this thematic map represents a strong
starting point. Therefore, results from this study can
contribute to the understanding of HIPC and identify
possible avenues for future research. For instance
further research could prove the relationship between
the developed factors and the dimensions of the
thematic map in a quantitative study.
Moreover, the findings of this study could support
theory-building efforts to uncover the meaningful
interplay between HIPC and perceived benefits in the
user’s mind. This multifaceted picture of a user’s
Page 3268
mental trade-off decision, in particular concerning the
Dilemma of Forced Acceptance and the
Transparency opens new research directions.
Therefore, the developed factors will further enhance
the understanding of the role information privacy
plays in health context and will strengthen the
literature by extending constructs concerning HIPC
and PHI disclosure [13].
The thematic map can serve as a practical
guideline for providers to develop privacy-friendly
devices. The study results can serve as an important
building block in privacy requirements engineering
for healthcare information technologies [21] and
corresponding privacy-enhancing technologies [7].
This is especially relevant as the General Data
Protection Regulation (GPDR) coming applicable in
2018. Providers must strategize privacy alignment for
their products by incorporating in their design the
privacy and data protection capabilities necessary for
regulatory compliance and gaining user trust.
First, users did not want their PHI to be used for
purposes other than the ones agreed upon between
them and the provider. The provider could decrease
HIPC, by increasing their transparency about data
storing and dissemination. Individuals made their
data retention dependent on both the usage scenario
and the severity of PHI [12].
Second, the results show that the Control is an
important dimension in user mindsets concerning
HIPC. Furthermore, some of study participants
wanted providers to protect their PHI irrespective of
costs. This signals that providers need to gain
trustworthiness by addressing those privacy concerns
through a transparent information policy [16].
Consequently, to increase customer satisfaction and
market reach, providers need to reveal the identity of
third parties accessing the data, the purpose of the
data usage and the objectives for which the data is
used. So a company, which focuses on the perceived
lack of control and gives the user a feeling of control
over the data, could reach a unique selling point over
the competitors. This unique selling point could then
be a reason to buy the device from this specific
Third, despite these insights into decreasing the
HIPC by increasing the control of PHI, providers
could utilize the users Dilemmas of Forced
Acceptance. As most of the users see themselves as
“beggars are not choosers”, and want to use the
devices independently of their HIPC, companies
should minimize the barriers to entry for the first
usage of the device and clearly communicate the
benefits. So companies could win new users, e.g.
through low prices and free extra features
independently of HIPC with the intent that “once a
user, always a user”.
6.2. Limitations and further research
An obvious limitation of this study was the small
sample size of 42 people who took part in the focus
groups. However, as this was an exploratory study
involving focus groups, and the “[…] common rule
of thumb is that most projects consist of four to seven
focus groups [24, p. 144], an average of six
participants in each focus group is reasonable.
A major advantage of focus groupstheir ability
to encourage group level discussionis potentially
one of their major limitations. Participants may
behave differently if faced with the device assigned
to their focus group in a different context (e.g. using
it alone to achieve a specific goal). To avoid this,
peoples’ stated privacy behavior is sometimes not the
same as their actual behavior.
In focus groups, people may be more truthful
about their privacy behavior in front of others who
may challenge them and ask for justification of their
views. In focus group discussions, people may be
reminded by other participants of factors they would
not normally consider, and therefore there is a danger
of dominant personalities steering a group’s
discussion. Both biases were mitigated to some
extent by the study’s design. Firstly, the use of a
standard set of six questions, with an approximately
similar amount of time allotted to each question,
ensured discussion remained focused. Secondly, the
researcher ensured that the discussion was not
hijacked by particular participants.
The definition of HWs excludes medical devices,
in which health professionals diagnose and evaluate
users’ medical problems. Therefore, it would be
interesting to compare the study results and
especially the developed thematic map with patients
suffering chronic diseases or in general with users of
professional medical devices and analyze whether
additional dimensions or factors of HIPC emerge.
The focus groups consist of actual users of HWs,
not potential ones. For these the benefits of HWs
obviously outweighed the perceived privacy concerns
otherwise they would not have decided in favor of
their devices [e.g. 12, 13]. On the other hand,
potential users might be deterred from using HWs
due to perceived concerns. That is why another study
should deal with HIPC and the influencing factors for
potential users. Moreover, it would be interesting to
analyze the effects of the GPDR implementation.
Further research could evaluate, if stricter regulations
influence individuals’ privacy perception and
whether new thematic maps with other factors occur.
Page 3269
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... Other researchers examined and explained security and privacy implication themes (Becker, 2018;Kwee-Meier et al., 2016;Lidynia et al., 2018;Segura Anaya et al., 2018). The findings suggest that fitness wearable users' are highly concerned about their privacy when their health information recorded on their wearable devices is shared with third parties (Gabriele and Chiasson, 2020). ...
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Mobile health technology has great potential to increase healthcare quality, expand access to services, reduce costs, and improve personal wellness and public health. However, mHealth also raises significant privacy and security challenges.
Background: Wearable technology has shown the potential of improving healthcare efficiency and reducing healthcare cost. Different from pioneering studies on healthcare wearable devices from technical perspective, this paper explores the predictors of individuals' adoption of healthcare wearable devices. Considering the importance of individuals' privacy perceptions in healthcare wearable devices adoption, this study proposes a model based on the privacy calculus theory to investigate how individuals adopt healthcare wearable devices. Method: The proposed conceptual model was empirically tested by using data collected from a survey. The sample covers 333 actual users of healthcare wearable devices. Structural equation modeling (SEM) method was employed to estimate the significance of the path coefficients. Results: This study reveals several main findings: (1) individuals' decisions to adopt healthcare wearable devices are determined by their risk-benefit analyses (refer to privacy calculus). In short, if an individual's perceived benefit is higher than perceived privacy risk, s/he is more likely to adopt the device. Otherwise, the device would not be adopted; (2) individuals' perceived privacy risk is formed by health information sensitivity, personal innovativeness, legislative protection, and perceived prestige; and (3) individuals' perceived benefit is determined by perceived informativeness and functional congruence. The theoretical and practical implications, limitations, and future research directions are then discussed.