Presentation at HICSS 53rd - January 8th, 2020 Presenter: Kaja J. Fietkiewicz and Aylin Ilhan

1. Fitness Tracking Technologies:
Data Privacy Doesn’t Matter?
Kaja J. Fietkiewicz & Aylin Ilhan
Heinrich Heine University Düsseldorf, Germany
HICSS 53rd – January 8th, 2020

2. 1

3. 2

4. 3

5. Fear: Health insurances could
apply sanctions
“... if you don't go to the doctor in
the next three months your
insurance will go up“
4

6. Health and fitness-related data can
be hacked
5

7. Before you know ... Health
insurances or hospitals can identify
risks
6

8. 7

9. Lidynia, Brauner, and Ziefle (2018):
Investigated the perceived sensitivity of collected
data (users, non-users)
8

10. Participants described collected
data as: not sensitive, not secret,
not confidential
Concerns:
Individual’s name and
address
Lehto and Lehto (2017):
Investigated the perception of privacy and
sensitivity of health information
9

11. Collected data is too
sensitive
App companies will
forward my
personal data to
third parties
Health insurance will
access my data and use it
against me
Exact profile of
movements, habits and/
or preferences
Privacy Paradox
Even if users have concerns regarding privacy
their usage behavior does not reflect this
10

12. What is the perceived sensitivity of different data
types by U, FU, and NU of the fitness tracking
applications?
U (current users); FU (former users); NU (non-users)
RQ1
RQ2
RQ3
What are the general privacy concerns about
fitness tracking by U, FU, and NU of the fitness
tracking applications?
What types of fitness tracking applications’ users
can we distinguish based on their data privacy
concerns?
11

13. Online survey
Pretested by six participants
February 26, 2019 until May 28, 2019
^
649 completed the survey (U, NU, FU)
590 participants from the USA or the EU
Privacy Concerns (7 Items | 5-Point Likert Scale)
Perceived sensitivity of 23 data pieces
12
U (current users); FU (former users); NU (non-users)

14. Privacy concerns (RQ2)
Kruskal-Wallis H Test | Post-hoc Test
Perceived sensitivity of different data types (RQ1)
K-means Clustering
Clustered groups regarding perceived data sensitivity
(RQ3)
13
IBM SPSS Statistics 25

15. RESULTS

16. 56% female, 43% male
Over 50% Gen Y, 20% Gen X, 16% Gen Z
55.9% active users, 35% non-users, 9% former users
N = 590
15

17. Perceived Data Sensitivity (RQ1)
Real Name| Birthday | E-Mail | Interest Groups
Contacts/Friends
Gender
Gender
Gender
user
Personal
Data
former user non-user
1 2 3 4 5Not sensitive Very sensitive16

18. Menstrual Cycle
Weight| BMI | Sleep | BP
Sleep| BP |HR | Calories Intake
& Burned
HR | Calories Intake & Burned Calories Burned
Menstrual Cycle |
Weight| BMI
Weight| BMI | Sleep | BP |HR
Calories Intake
Menstrual Cycle
Health-
related
Data
Perceived Data Sensitivity (RQ1)
user former user non-user
17 1 2 3 4 5Not sensitive Very sensitive

19. GPS GPS | Step Count
Fitness Level | Lost Challenges
Fitness level | Won & Lost Challenges | Step Count | Climbed Stairs |
Standing Hours | Completed Workouts | Trophies
Climbed Stairs | Standing Hours |
Completed Workouts |Trophies |
Won Challenges
Perceived Data Sensitivity (RQ1)
Activity
Data
user former user non-user
18 1 2 3 4 5Not sensitive Very sensitive

20. o Collected data is too sensitive
o App companies will forward my
personal data to 3rd parties
o Health insurance will access my
data and use it against me
o App companies will misuse my
data
4 43
user former user non-user
Data Privacy Related Concerns (RQ2)
Strongly disagree Strongly agree19 1 2 3 4 5

21. o I have no control over what will
happen to my data
o It will be possible to create an
exact profile of my movements,
habits and/or preferences
o There will be interference risks
from hackers and other
unauthorized parties
4 4
4
3
3
user former user non-user
Strongly agree
Data Privacy Related Concerns (RQ2)
Strongly disagree
20 Strongly disagree Strongly agree1 2 3 4 5

22. N=64
CL 1
Real Name |Birthday | E-Mail |
Contacts /Friends | Interest
Groups | Calories Intake |
Calories Burned | HR | BP |
Sleeping Times | BMI | Weight |
Menstrual Cycle | Step Count |
GPS | Climbed Stairs | Standing
Hours | Completed Workouts |
Fitness Level | Trophies | Lost
Challenges | Won Challenges
Gender
Not sensitive Neutral Sensitive
The „concerned“ user
User Types by Privacy Concerns (RQ3)
21

23. N=120
CL 2
Real Name | Calories
Intake | Burned Calories
| Standing Hours |
Completed Workouts |
Fitness Level | Trophies
| Lost & Won
Challenges
Gender |
Step Count |
Climbed
Stairs
Birthday | E-Mail |
Contacts /Friends |
Interest Groups | HR | BP
| Sleeping Times | BMI |
Weight | GPS
The „neutral“ user
Not sensitive Neutral Sensitive
User Types by Privacy Concerns (RQ3)
22

24. N=43
CL 3
The „unconcerned“ user
Gender | Calories Burned
|Calories Intake | HR | BP
| Sleeping Times | BMI |
Weight | Step count |
Climbed Stairs | Standing
Hours | Completed
Workouts | Fitness level |
Trophies | Lost challenges
| Won challenges
Real Name |
Birthday |
Interest Groups
| Menstrual
Cycle
E-Mail |
Contacts /
Friends |
GPS
Not sensitive Neutral Sensitive
User Types by Privacy Concerns (RQ3)
23

25. DISCUSSION

26. o Not many differences in perception of sensitivity of personal
data
o Differences in perception of sensitivity of health-related data
between users and non-users
o GPS as the most sensitive data piece within activity-related
data
o Former users seem to have the least data privacy-related
concerns (except for the possibility “to create an exact profile
of my movements, habits and/or preferences”)
Data Privacy (Un)Concerns?
25

27. o Concerned users perceive all data pieces as at least „sensitive“,
except for „gender“ (perceived as „neutral“). They tend to have
more concerns about data privacy online in general.
o Neutral users perceive most of the activity-related data as
neutral and only GPS-data as „very sensitive“.
o Unconcerned users perceive none of the data as „very sensitive“
and only email address, contacts and GPS as „sensitive“. They
are also the most „active“ users.
o There were no significant differences between user groups
regarding their age as well as the usage duration and usage
frequency of the fitness tracking application
o There was no significant association between the cluster
membership and the origin of the users.
User Types by Concerns?
26

28. Thank you for your attention!
Mahalo!

29. Any Questions?
Dr. Kaja J. Fietkiewicz
Kaja.Fietkiewicz@hhu.de
Aylin Ilhan
Aylin.Ilhan@hhu.de
Our publication is available here: http://hdl.handle.net/10125/64163

30. References
[Slide 3]
Tweet:
https://twitter.com/Nrg8000/status/957318498102865920?
ref_src=twsrc%5Etfw%7Ctwcamp%5Etweetembed%7Ctwterm%5E957318498102865920&ref_ur
l=https%3A%2F%2Fcurved.de%2Fnews%2Flauf-app-strava-verraet-geheime-militaer-
stuetzpunkte-573457
News [German]:
https://curved.de/news/lauf-app-strava-verraet-geheime-militaer-stuetzpunkte-573457

31. References
[Slide 4]
News:
https://www.techrepublic.com/article/the-dark-side-of-wearables-how-theyre-secretly-
jeopardizing-your-security-and-privacy/
[Slide 6]
News [German]:
https://www.mdr.de/datenspuren/wearables-sammeln-von-gesundheits-daten-100.html

32. References
[Slide 8]
Lidynia, C., Brauner, P., and Ziefle, M. A Step in the Right Direction – Understanding Privacy
Concerns and Perceived Sensitivity of Fitness Trackers. Advances in Human Factors in
Wearable Technologies and Game Design, Advances in Intelligent Systems and Computing,
Springer International Publishing (2018), 42–53.
Pictures:
https://pixabay.com/de/vectors/gerechtigkeit-silhouette-waage-147214/
hwps://pixabay.com/de/vectors/treppe-klewern-ebenen-absxeg-44070/

33. References
[Slide 9]
Lehto, M. and Lehto, M. Health Informaxon Privacy of Acxvity Trackers. 16th European
Conference on Cyber Warfare and Security, (2017), 243–251.