Conference PaperPDF Available

The Impact of Gamification on mHealth Fitness Application Privacy Literacy

Authors:

Abstract and Figures

The use of mobile health (mHealth) applications for incentivizing health behaviour change continues to grow at an unprecedented rate. This growth has been accelerated by recent advancements in ‘smart’ mobile technology such as cloud computing, internet of things, sensors, phones, tablets, wristbands and watches. Physicians and other health care professionals are increasingly advising their patients on the merits of using these applications as health monitoring (e.g. diabetes, heart rate etc.) and health improvement tools (e.g. smoking cessation, weight control etc.). mHealth fitness applications such as Fitbit, Jawbone, Fuelband, and Nike+ have become increasingly popular with an estimated 25 million fitness applications sold in 2015 (GFK, 2015). However, in order for these applications to be truly effective they require the user to be wholly comfortable and transparent with the levels of personal data which are entered into and subsequently generated by these devices. For instance, the majority of these fitness applications monitor heart rate, chart sleep patterns, log exercise, diet and calories, enable social media sharing and compare users to their peers in order to set goals. It is widely agreed that privacy represents a barrier to the continued success of mHealth (Mosa et al. 2012; Whittaker, 2012). However, the importance of privacy contrasts with the current practices of mHealth providers who tend to utilize opaque, lengthy privacy policies and engage in excessive sharing of data with a multitude of third parties, some of whom are not listed in the privacy policy (Privacy Rights ClearingHouse, 2013). Thus, there is an apparent need to investigate the changing role of privacy in the health context.
Content may be subject to copyright.
Provided by the author(s) and NUI Galway in accordance with publisher policies. Please cite the published
version when available.
Downloaded 2018-07-05T16:54:58Z
Some rights reserved. For more information, please see the item record link above.
Title mHealth and gamification
Author(s) Clohessy, Trevor; Kenny, Grace
Publication
Date 2016-12-11
Publication
Information
Clohessy, T., & Kenny, G. (2016). The impact of gamification
on mHealth fitness application privacy literacy. Paper
presented at the SIG Health Pre-ICIS Workshop, Dublin.
Link to
publisher's
version https://doi.org/10.13025/S8V91Q
Item record http://hdl.handle.net/10379/7310
DOI http://dx.doi.org/10.13025/S8V91Q
1
SIG Health Pre-ICIS Workshop, December 2016
The Impact of Gamification on mHealth Fitness Application Privacy
Literacy
Trevor Clohessy
Business Information Systems,
National University of Ireland, Galway
Grace Kenny
Business Information Systems,
Dublin City University
Keywords: mHealth, Privacy Literacy, Gamification, Coping Mechanisms, Fitness Applications
INTRODUCTION:
The use of mobile health (mHealth) applications for incentivizing health behaviour change continues
to grow at an unprecedented rate. This growth has been accelerated by recent advancements in smart
mobile technology such as cloud computing, internet of things, sensors, phones, tablets, wristbands and
watches. Physicians and other health care professionals are increasingly advising their patients on the
merits of using these applications as health monitoring (e.g. diabetes, heart rate etc.) and health
improvement tools (e.g. smoking cessation, weight control etc.). mHealth fitness applications such as
Fitbit, Jawbone, Fuelband, and Nike+ have become increasingly popular with an estimated 25 million
fitness applications sold in 2015 (GFK, 2015). However, in order for these applications to be truly
effective they require the user to be wholly comfortable and transparent with the levels of personal data
which are entered into and subsequently generated by these devices. For instance, the majority of these
fitness applications monitor heart rate, chart sleep patterns, log exercise, diet and calories, enable social
media sharing and compare users to their peers in order to set goals. It is widely agreed that privacy
represents a barrier to the continued success of mHealth (Mosa et al. 2012; Whittaker, 2012). However,
the importance of privacy contrasts with the current practices of mHealth providers who tend to utilize
opaque, lengthy privacy policies and engage in excessive sharing of data with a multitude of third
parties, some of whom are not listed in the privacy policy (Privacy Rights ClearingHouse, 2013). Thus,
there is an apparent need to investigate the changing role of privacy in the health context.
RESEARCH MOTIVATION:
This study is motivated for several reasons. First, there is currently a dearth of information systems (IS)
research pertaining to consumer’s health privacy literacy levels in the context of mHealth fitness
applications. This is significant as there is evidence to suggest that these nascent applications may pose
2
SIG Health Pre-ICIS Workshop, December 2016
serious privacy concerns (Blenner et al. 2016). Furthermore, in general, consumers do not read the terms
and conditions of their privacy policies indicating a current lack of privacy literacy (Steinfield 2016;
Jensen and Potts, 2004). Second, there is a dearth of IS research identifying the specific inhibitors which
negatively impact consumers willingness to use mHealth fitness applications. This is compounded by
the fact that mHealth service providers are struggling to attract new consumers as a result of recent
privacy controversies and an overcrowded application market. Finally, while extant research has
examined the use of gamification strategies to incentivize the repeated use of fitness applications
amongst consumers (e.g. Lister et al. 2016), no research has explored the potential for mHealth service
providers to use gamification coping mechanisms as a method for improving consumer’s health privacy
literacy levels. In particular, the specific gamified coping mechanisms these organizations could use to
navigate through unchartered digital privacy waters is an area which is ripe for further investigation.
With this in mind, figure 1 presents a simple research model which depicts the focus of the study.
Specifically, this study seeks to answer two interrelated research questions:
RQ1: How does consumers’ comprehension of mHealth privacy literacy impact their willingness to
use mHealth fitness applications?
RQ2: How can gamification coping mechanisms be used to increase mHealth privacy literacy and
minimize inhibitors which reduce consumers willingness to use mHealth applications?
Figure 1. Research
Model
3
SIG Health Pre-ICIS Workshop, December 2016
THEORETICAL FOUNDATION:
It is important at the outset to define what is meant by the term privacy. The privacy phenomenon has
been studied for centuries across numerous academic disciplines such as Law, Marketing, and
Information Systems. Advances in digital technology have led to a surge in information privacy
research in recent decades leading to a myriad of conflicting definitions of the concept. Privacy is a
multidimensional and polymorphous concept which is studied from a variety of different lenses. As a
result, it is argued that it may not be possible to develop a single, comprehensive, unambiguous
definition of information privacy (Pavlou, 2011). It is thus imperative to define information privacy
within the discipline of the researcher and the context of the study. A widely cited definition in the IS
literature defines information privacy as an individuals desire to have more control over the collection
and dissemination of their information (Bélanger and Crossler, 2011). This definition is adapted in this
paper and information privacy is defined as an individuals desire to have greater control over the
collection and dissemination of their health information by mHealth application providers.
The existing literature on the relationship between privacy and mHealth adoption is limited. However,
prior studies show that privacy concerns can reduce individuals’ intentions to use mHealth applications
(Hwang et al. 2012) and perceptions of privacy risk can negatively impact intentions and actual use of
wearable health devices (Li et al. 2016). In addition, studies in the Internet context show that individuals
may adopt technologies but may falsify the data they disclose due to concern for privacy (Stutzman et
al., 2011; Keith et al. 2015). Thus, privacy or individualsperception that they cannot control their health
data, is an inhibitor facing adoption of mHealth. In line with previous findings, this study posits that
perceived risk or fear of negative outcomes from mHealth use, and privacy concerns are likely to reduce
individuals’ willingness to use mHealth applications and disclose detailed sensitive data. However, prior
research suggests that these inhibitors could be addressed by increasing individuals’ perceived control
over their health data and building trust (Dinev et al. 2016). It is thus proposed that individuals’
perception of risk fears and concerns can be addressed and appeased by improving individuals’ mHealth
privacy literacy, thereby fostering a sense of trust and enhancing their perception of control over their
health data. The concept of privacy literacy is a new concept which “assesses and explains the
consumer’s attitude regarding the collection, processing and employment of their personal data (Veghes
4
SIG Health Pre-ICIS Workshop, December 2016
et al. 2012). Privacy literacy has not yet been explored in the mHealth context, however it can be argued
that improving individuals’ understanding of how mHealth applications collect, process, and use
their health data can address concerns that data will be misused and reduce fears regarding possible
negative repercussions stemming from such use.
In terms of research question 2, this study will investigate how gamification coping mechanisms can be
used to increase mHealth literacy and minimize inhibitors (e.g. perceived lack of control of data, privacy
concerns, lack of trust etc.) which negatively impact consumers willingness to use mHealth fitness
applications. Gamification is rapidly growing in popularity among practitioners, business professionals
and academics alike. Leveraging game design elements, gamification is currently being used i n n o n -
g a m e c o n t e x t s t o enhance products and services in order to intrinsically motivate customers
toward preferred behaviours, enhance end-users experiences and increase employees incentivization
and engagement (Deterding et al. 2011; Blohm and Leimeister, 2013; Seaborn and Fels, 2015). A recent
Gamification 2020 report highlights how gamification, combined with other emerging trends and
technologies, will have a significant impact on: innovation, the design of employee performance, the
emergence of customer engagement platforms and the gamification of personal development (Gartner,
2014). From an academic perspective, gamification has also received increasing attention in recent years
(Huotari and Hamari, 2012; Thiebes et al. 2014; Seaborn and Fels, 2015). This is underlined by
gamifications popularity across academic outlets in terms of journal special issues, conference tracks,
special interest groups, workshops, panels and so on. Additionally, the appearance of the terms
gamification’ and game elements’ as methods with which to motivate and engage end-users are fast
increasing in popularity with regards to academic inquiry (Thiebes et al. 2014; Hamari et al., 2014).
NEXT STEPS AND CONTRIBUTION
The research is in its early stages and is currently focused on theory building. The study will harness a
three stage sequential mixed methods research design to develop an in-depth multi-perspective
understanding of the new mHealth privacy literacy concept (Venkatesh et al. 2013). The first phase will
consist of expert interviews comprising mHealth fitness application service providers to understand their
current privacy practices and identify the important privacy issues that should be highlighted to users.
Combining the judgment of a large number of experts offers a better chance of getting closer to the
5
SIG Health Pre-ICIS Workshop, December 2016
truth. It is also easier to understand the intricacies of a phenomena by obtaining the views of the actors
that have been immersed within it (Dalkey and Helmer, 1963; Linstone and Turoff, 1975). Phase two will
use focus groups comprising a randomized sample of digital native consumers. Focus groups enable IS
researchers to gain “a deeper understanding of the topic of interest by providing more background
information about the circumstances of the participants answers or opinions (Belanger,
2012). The aim of the focus groups is to ascertain the dominant concerns of participants regarding
mHealth applications. The final phase will consist of a series of laboratory-based experiements during
which we will use a number of gamfiied-based scenarios to assess the impact of specific gamification
coping mechanisms on developing mHealth fitness privacy literacy and diminishing the negative
impacts of privacy related inhibitors.
When completed the research hopes to make a number of contributions. Firstly, from an empirical
perspective the research will add to the limited body of literature on the role of privacy in the mHealth
context and deepen our understanding by conceptualizing and testing the role of mHealth privacy
literacy as a means of addressing privacy and building trust in mHealth technology vendors. The special
nature of the health context calls for unique theorizing to understand the role of IS in this context
(Agarwal et al. 2010). The study aims to add greatly to the theoretical foundations in this area by
building theory for understanding the role of mHealth privacy literacy in increasing willingness to
disclose health data. From a practice perspective, gamification has been used successfully by companies
such as Nike, Deloitte, Samsung and Cisco. However, it has been estimated by technology consultancy
company Gartner that approximately 80 % of organisations attempts to gamify their service bundles
will fail due to inappropriate design and fine-tuning (Gartner, 2014). Additionally, there is a lack of
standardized guidelines available for deploying coping mechanisms aimed at gamifying consumer
mHealth privacy literacy. Thus, the purpose of this study is to work with mHealth fitness application
service providers and consumers in order to build an improved understanding and foster trust to increase
mHealth adoption and users’ willingness to disclose detailed personal data. Ultimately, we envisage that
our research will have implications for the manner with which mHealth service providers design and
incorporate gamified privacy policies in their fitness application service bundles.
6
SIG Health Pre-ICIS Workshop, December 2016
REFERENCES:
Agarwal, R., Gao, G., DesRoches, C. and Jha, A.K. 2010. The Digital Transformation of Healthcare:
Current Status and the Road Ahead. Information Systems Research, 21(4), pp. 796809.
Belanger, F. (2012). Theorizing in information systems research using focus groups. Australasian
Journal of Information Systems, 17(2).
Bélanger, F. and Crossler, R.E. 2011. Privacy in the Digital Age: A review of Information Privacy
Research in Information Systems. MIS Quarterly, 35(4), pp. 101741.
Blenner SR, Köllmer M, Rouse et al. (2016). Privacy Policies of Android Diabetes Apps and Sharing of
Health Information. JAMA.2016;315(10):1051-1052.
Blohm, I., & Leimeister, J. M. (2013). Gamification: Design of IT-based enhancing services for
motivational support and behavioral change.Business & Information Systems Engineering (BISE), 5(4),
275-278.
Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of
experts. Management science, 9(3), 458-467.
Deterding, S., Dixon, D., Khaled, R., & Nacke, L. (2011). From game design elements to gamefulness:
defining gamification. In Proceedings of the 15th international academic MindTrek conference:
Envisioning future media environments (pp. 9-15). ACM.
Dinev, T., Albano, V., Xu, H., D’Atroi, A. and Hart, P. 2016. IndividualsAttitudes Towards Electronic
Health Records: A Privacy Calculus Perspective. IN: Gupta et al. (eds.) Advances in Healthcare
Informatics and Analytics, Annals of Information Systems 19, Switzerland: Springer Publishing pp. 19-
50.
GFK (2015). GfK Forecasts 51 Million Wearables Bought Globally in 2015. Retrieved from
http://www.prnewswire.com/news-releases/gfk-forecasts-51-million-wearables-bought-globally-in-
2015-294678211.html
Hamari, J., Koivisto, J., & Sarsa, H. (2014). Does gamification work?--a literature review of empirical
studies on gamification. In 2014 47th Hawaii International Conference on System Sciences (pp. 3025-
3034). IEEE.
Huotari, K., & Hamari, J. (2012, October). Defining gamification: a service marketing perspective.
In Proceeding of the 16th International Academic MindTrek Conference (pp. 17-22). ACM.
Hwang, H.G., Han, H.E., Kuo, K.M. and Liu, C.F. 2012. The differing privacy concerns regarding
exchanging electronic medical records of internet users in Taiwan. Journal of medical systems, 36(6),
pp. 3783-3793.
Jensen, C., & Potts, C. (2004).
Privacy
policies as
decision-making
tools: an evaluation of online privacy
notices. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems (pp. 471-
478). ACM.
Keith, M.J., Babb, J.S., Lowry, P.B., Furner, C.P. and Abdullat, A. 2015. The role of mobilecomputing
selfefficacy in consumer information disclosure. Information Systems Journal, 25(6), pp. 637-667.
Keith, M.J., Thompson, S.C., Hale, J., Lowry, P.B. and Greer, C. 2013. Information disclosure on mobile
devices: Re-examining privacy calculus with actual user behavior. International Journal of Human-
Computer Studies, 71(12), pp. 11631173.
Li, H., Wu, J., Gao, Y. and Shi, Y. 2016.
Examining individuals
adoption of
healthcare
wearable devices:
An empirical study from privacy calculus perspective. International Journal of Medical Informatics, 88,
pp. 8-17
Linstone, H. A., & Turoff, M. (Eds.). (1975). The Delphi method: Techniques and applications (Vol. 29).
Reading, MA: Addison-Wesley.
Lister, C., West, J. H., Cannon, B., Sax, T., & Brodegard, D. (2014). Just a fad? Gamification in health
and fitness apps. JMIR serious games, 2(2), e9.
Mosa, A.S.M., Yoo, I. and Sheets, L. 2012. A systematic review of healthcare applications for
smartphones. BMC Medical Informatics and Decision Making, 12(67), pp. 131.
Pavlou, P. 2011. State of the Information Privacy Literature: Where Are W e Now and Where should we
go? MIS Quarterly, 35(4), pp. 977989. MIS Quarterly, 35(4), pp. 977989.
Privacy Rights Clearinghouse 2013. Mobile Health and Fitness Apps: What Are the Privacy Risks?
Retrieved from: https://www.privacyrights.org/mobile-medical-apps-privacy-alert
Seaborn, K., & Fels, D. I. (2015). Gamification in theory and action: A survey. International Journal of
Human-Computer Studies, 74, 14-31.
Steinfeld, N., 2016. I agree to the terms and conditions”:(How) do users read privacy policies online?
An eye-tracking experiment. Computers in human behavior, 55, pp.992-1000.
Stutzman, F., Capra, R. and Thompson, J. 2011. Factors mediating disclosure in social network sites.
Computers in Human Behavior, 27, pp. 590598.
7
SIG Health Pre-ICIS Workshop, December 2016
Thiebes, S., Lins, S., & Basten, D. (2014). Gamifying information systems-a synthesis of gamification
mechanics and dynamics.
Vegheş C, Orzan M, Acatrinei C, Dugulan D (2012) Privacy Literacy: What is it and how it can be
measured? In: Annales Universitatis Apulensis Series Oeconomica 14(2):704711
Venkatesh, V., Brown, S.A. and Bala, H. 2013. Bridging the qualitative-quantitative divide: Guidelines
for conducting mixed methods research in information systems. MIS Quarterly, 37, pp. 21-54.
Whittaker, R. 2012. Key Issues in Mobile Health and Implications for New Zealand. Healthcare and
Informatics Review Online, 16(2), pp. 27.
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Mixed methods research is an approach that combines quantitative and qualitative research methods in the same research inquiry. Such work can help develop rich insights into various phenomena of interest that cannot be fully understood using only a quantitative or a qualitative method. Notwithstanding the benefits and repeated calls for such work, there is a dearth of mixed methods research in information systems. Building on the literature on recent methodological advances in mixed methods research, we develop a set of guidelines for conducting mixed methods research in IS. We particularly elaborate on three important aspects of conducting mixed methods research: (1) appropriateness of a mixed methods approach; (2) development of meta-inferences (i.e., substantive theory) from mixed methods research; and (3) assessment of the quality of meta-inferences (i.e., validation of mixed methods research). The applicability of these guidelines is illustrated using two published IS papers that used mixed methods. Copyright © 2013 by the Management Information Systems Research Center (MISRC) of the University of Minnesota.
Article
Full-text available
Information Systems researchers have embraced a number of qualitative research approaches and methodologies, including interviews, observations, and even action research. One research method gaining visibility in IS research is the focus group research method. Focus groups have the potential to provide great insights into phenomena of interest to IS researchers as they allow researchers to get deeper into the topic of interest by providing more background information about the circumstances of the subject's answers or opinions. This paper presents a review of focus group research in the information systems literature, and provides a discussion of how and when the focus group research method can be the most appropriate method to use for IS theorizing. The discussion highlights the idea that the focus group research method is particularly useful for exploratory research on topics where concepts normally emerge through interactions among individuals or where concepts are initially unclear to participants, and as part of a multi-method research program for theory development. Examples of focus groups used in theory development are provided, together with a discussion of the limitations of the research method.
Article
Full-text available
Background Gamification has been a predominant focus of the health app industry in recent years. However, to our knowledge, there has yet to be a review of gamification elements in relation to health behavior constructs, or insight into the true proliferation of gamification in health apps. Objective The objective of this study was to identify the extent to which gamification is used in health apps, and analyze gamification of health and fitness apps as a potential component of influence on a consumer’s health behavior. Methods An analysis of health and fitness apps related to physical activity and diet was conducted among apps in the Apple App Store in the winter of 2014. This analysis reviewed a sample of 132 apps for the 10 effective game elements, the 6 core components of health gamification, and 13 core health behavior constructs. A regression analysis was conducted in order to measure the correlation between health behavior constructs, gamification components, and effective game elements. ResultsThis review of the most popular apps showed widespread use of gamification principles, but low adherence to any professional guidelines or industry standard. Regression analysis showed that game elements were associated with gamification (P
Article
mHealth or mobile health describes the use of mobile communications devices for health-related purposes. There is much interest in mHealth internationally at this time; including interest in interventions developed in New Zealand/by New Zealanders. A recent research project examined the key issues in the implementation of mHealth and the current opportunities to address those issues in the U.S. The key mHealth issues are outlined here under the themes of policy and regulation, the wireless environment, the health system, current mHealth initiatives in practice and research. This paper examines how these issues may apply in New Zealand and the current opportunities to address them. This information may be useful to those embarking on mHealth developments in New Zealand and may help to inform the inclusion of mobile capabilities within the NZ Health IT infrastructure.
Article
This study examines the privacy risks and the relationship between privacy disclosures and practices of health apps.Mobile health apps can help individuals manage chronic health conditions.1 One-fifth of smartphone owners had health apps in 2012,2 and 7% of primary care physicians recommended a health app.3 The US Food and Drug Administration has approved the prescription of some apps.4 Health apps can transmit sensitive medical data, including disease status and medication compliance. Privacy risks and the relationship between privacy disclosures and practices of health apps are understudied.
Article
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.
Article
Smartphones are increasingly penetrating business and consumer markets, and mobile applications (apps) have engendered a large and innovative market. Whereas apps are useful, they also present new forms of privacy risk associated with users’ personal and location data. However, these dangers do not appear to increase the perceived risk or reduce the trust consumers demonstrate when using apps. Many information technology (IT) trust indicators are well documented, such as the quality of the IT, trust assurances, brand recognition and social influences. However, these traditional indicators appear to have a lesser impact on the adoption of mobile commerce via apps because of the nature of mobile-app adoption and subsequent information disclosure. As a result, we draw from social cognitive theory and its construct of self-efficacy in particular to explain perceived mobile-app risk and provider trust. Through two controlled experiments, we demonstrate the strong direct effect of mobile-computing self-efficacy on users’ initial trust in location-based app vendors as well as their perceived risk of disclosing information – regardless of the actual trustworthiness of the app vendor. The results imply that being skilled in the latest smartphones and apps can cause users to place greater trust in app providers and perceive less risk in the app itself, even when the intentions of the app providers cannot be verified.
Article
This paper gives an account of an experiment in the use of the so-called DELPHI method, which was devised in order to obtain the most reliable opinion consensus of a group of experts by subjecting them to a series of questionnaires in depth interspersed with controlled opinion feedback.