Presentation for the European Conference on Information Literacy, 24-27th September 2018, Oulu Finland. Reports on a quantitative study that investigated the health, diet and fitness tracking behaviours of members of the Parkrun organisation in the UK

1. The Data and Information Literacy
of runners: Quantifying diet and
activity
Pamela McKinney p.mckinney@sheffield.ac.uk
Andrew Cox a.m.cox@sheffield.ac.uk
Laura Sbaffi l.sbaffi@sheffield.ac.uk

2. Presentation structure
• Background: quantified self and health and fitness
tracking
• Study methodology
• Demographic data
• Tracking practices
• Themes relating to Information Literacy
• Conclusions

3. Background: The quantified self
• Self-tracking is defined as “practices in which people
knowingly and purposively collect information about
themselves, which they then review and consider
applying to the conduct of their lives” (Lupton, 2016).
• Smartphones are widely used, these highly connected
devices facilitate health and fitness tracking.
• Mintel estimate that 38% of UK consumers have an
interest in wearable technology to monitor health and
fitness (Mintel, 2017)

4. Background: the popularity of mobile
apps for health and wellbeing
• The popular app MyFitnessPal has 165
million users worldwide
• There are over 10,000 apps that
support diet monitoring or weight loss
(Azar, 2013)
• Use of apps can motivate people to
adopt healthy behaviours, including a
healthy diet, increased physical activity
and weight loss (Ernsting et al., 2017;
Wang et al., 2016)
• Tracking can give people a sense that
they are taking control of aspects of
their life (Lupton 2016)

5. Background: mobile apps and
people who run
• People who run tend to interweave
various activity trackers, sometimes
with seemingly the same
functionality (Rooksby et al., 2014)
• Tracking is often social and
collaborative rather than personal
while, at the same time, that there
are different styles of tracking,
including goal driven and
documentary tracking (Rooksby et
al., 2014)
• Users tend not to use apps
regularly, but do frequently return
to them, suggesting that there are
times when applications are
valuable to their users (Lin, Althoff
and Leskovec 2018)

6. Information literacy in food and
activity tracking: previous research
1. Understanding the importance of quality in data
inputs;
2. Ability to interpret tracking information outputs in the
context of the limitations of the technology;
3. Awareness of data privacy and ownership;
4. Appropriate management of information sharing.(Cox,
McKinney, & Goodale, 2017)

7. Aims & Objectives
• What health and fitness tracking do people in the
community do, and why?
• What barriers to effective and safe use do they
encounter, particularly those relating to IL?
• 3 study populations, Parkrun, Diabetes.co.uk, IBS
Network

8. Parkrun
• Founded in the UK in 2004, Parkrun is a not-for-profit
organisation that organises weekly timed 5K runs in
public spaces
• Events are free to enter and organised by volunteers,
and Parkrun’s ethos emphasises inclusivity

9. Methodology
• 12 questions including:
a. demographic questions
b. questions that focused on use of diet and/or
fitness apps and other technologies
c. reasons for logging
• Advertised online through parkrun UK
(http://www.parkrun.org.uk/)
• 143 complete responses (although 414 records
were received in total)
• Qualitative data collected with the question “Is
there anything else you’d like to tell us about your
logging practice?”

10. Sample demographics
10
Male,
31%
Female,
68%
Other,
1%
Gender
18-24 years,
9.1% 25-34 years,
13.3%
35-44 years,
34.3%
45-54 years,
30.8%
55-64 years,
9.1%
65+ years,
3.5%
Age
GCSE, 7.7%
"A" level,
16.8%
Undergradu
ate, 43.4%
Postgraduat
e, 30.1%
Prefer not to
say, 2.1%
Education

11. How long have you been running
for?
11
0 5 10 15 20 25 30 35 40
LESS THAN 2
YEARS
2-5 YEARS
6-10 YEARS
MORE THAN 10
YEARS
37.8
35.7
14.0
12.6
%

12. How often do you run?
McKinney, Sbaffi, Cox - Information School, University of
Sheffield
12
0 5 10 15 20 25 30 35
1 DAY/WEEK
2 DAYS/WEEK
3 DAYS/WEEK
4 DAYS/WEEK
5 DAYS/WEEK
6 DAYS WEEK
7 DAYS/WEEK
7.7
21.7
35.0
22.4
7.7
4.9
0.7
%

13. Logging practice
• Parkrunners were heavy users of devices that record running
(35.7% using one every day, and 55.2% using one 2-3 times a
week)
• On average respondents used at least 2 apps, some as many
as 4 or 5
• Recording of heart rate or other vital signs was popular, with
32.9% doing this daily and 18.9% doing it 2-3 times a week
• Step counters were used every day by 57.8% of respondents
• 31.5% used a food logging app every day (but find diet
tracking monotonous and boring)
• Mood tracking and tracking specific aspects of the diet were
not popular
• Strava (64.3%) and MyFitnessPal (45.5%)were the most
popular apps

14. Motivations for tracking
McKinney, Sbaffi, Cox - Information School, University of
Sheffield
14
0 10 20 30 40 50 60 70 80
I like to try out the latest gadgets
I am interested in understanding how my body
works
I want to manage my weight
I want to improve my physical performance
I want to manage a medical condition
I want to identify causes of symptoms of a
medical condition
Other reasons
18.2
35.0
54.5
77.6
5.6
3.5
25.2
%

15. Motivations for tracking
“Logging my runs has help me improve
my speed and endurance which I do not
think I would have done without it”
“Logging and tracking has contributed
to a 24kg weight reduction in 12
months”
“I logged and referred to my steps daily as part of
two challenges. One to do 10000 steps a day for one
week for WI and another was to do 12,000 on
average a day for the whole of Lent.”

16. Information literacy: Data quality
• 81.1% agreed that they are careful about data entry
• 79.7% agreed that they use apps to track long term
trends in their activity
• If food logging, 65.1% agreed they log absolutely
everything they eat
• 52.2% of food loggers had concerns about the quality
of data entered by other people in the app.
• Many reported issues with data quality and accuracy
in food logging apps
“Many apps are US based which means it's sometimes hard to find UK
foods, but most of the time the barcode scanning works. Where it's less
accurate is things like cherry tomatoes. I don't weigh them every time
but I know an average weight that I use so I can go by quantity.”

17. Information Literacy: interpreting
tracked information
• Highly confident: 84.6% agreed they could
understand the charts produced
• Qualitative responses revealed a nuanced
understanding of tracked data and relationship
with health and wellbeing
“I initially used My fitness pal to see how many calories were in specific foods
and also to see how the calories balanced against manually inputted exercise.
Then I got a Fitbit and linked the 2. I am type 1 diabetic and am interested in
keeping my weight at a healthy BMI. I also use Endomondo for logging runs
and the training plan in it for my first half marathon in September”

18. Information literacy: Data privacy
• Only 28% are concerned about how the app
provider might re use their data
• Some recognition in the qualitative data that
geolocation data made public in apps is a potential
concern
“I stopped using strava because you could not hide runs from the
public, which is a privacy concern as they could see or workout
where I live and where I run on a regular basis.”

19. Information literacy: Data sharing
Who do they share data with?
McKinney, Sbaffi, Cox - Information School, University of
Sheffield
19
44.1 43.4
55.9
41.3
2.8
7.0 6.3
%

20. Views on sharing data
• Only 29.4% agree they feel uncomfortable sharing
data with their friends
“Seeing what my friends are doing (and knowing that they see
what I do) is a major motivator for me in exercise and
encourages me to get out and do things when I don't
necessary feel like it. I also like statistics and tracking my
performance.”
“The social features of the app I use (Strava) are also
a motivating and fun feature and I love being able to
look at maps of where my friends have run to give me
ideas for new routes to try out myself.”

21. Conclusions
• Food logging is boring and fraught with issues of data
quality, it requires people to become knowledgeable
about nutrition
• Activity tracking is easy and enjoyable, data collection is
automatic so therefore it is of high quality
• Use of multiple devices and tracking different kinds of
data leads to enhanced understanding of the body, and
awareness of using data to support health and
wellbeing
• Information literacy in terms of tracked data is high:
people understand their data
• Sharing activity data with friends is a motivating factor,
it is part of the enjoyment of the activity
• People lack awareness of the potential for their data to
be sold and re-used

22. References
Azar, K. M. J., Lesser, L. I., Laing, B. Y., Stephens, J., Aurora, M. S., Burke, L. E., & Palaniappan,
L. P. (2013). Mobile applications for weight management: theory-based content analysis.
American Journal of Preventive Medicine, 45(5), 583–589.
https://doi.org/10.1016/j.amepre.2013.07.005
Cox, A. M., Mckinney, P. A., & Goodale, P. (2017). Food logging: an Information Literacy
perspective. Aslib Journal of Information Management, 69(2).
https://doi.org/10.1108/09574090910954864
Ernsting, C., Dombrowski, S. U., Oedekoven, M., O’Sullivan, J. L., Kanzler, E., Kuhlmey, A., &
Gellert, P. (2017). Using smartphones and health apps to change and manage health
behaviors: A population-based survey. Journal of Medical Internet Research, 19(4), 1–12.
https://doi.org/10.2196/jmir.6838
Lin, Z., Althoff, T., & Leskovec, J. (2018). I’ll Be Back: On the Multiple Lives of Users of a
Mobile Activity Tracking Application. In Proceedings of the International World Wide Web
Conference. April 2018 (pp. 1501–1511). https://doi.org/10.1145/3178876.3186062
Lupton, D. (2016). The quantified self. Cambridge: Polity Press.
Mintel. (2017). Wearable technology - UK - December 2017.
Rooksby, J., Rost, M., Morrison, A., & Chalmers, M. C. (2014). Personal tracking as lived
informatics. Proceedings of the 32nd Annual ACM Conference on Human Factors in
Computing Systems - CHI ’14, 1163–1172. https://doi.org/10.1145/2556288.2557039
Wang, Q., Egelandsdal, B., Amdam, G. V, Almli, V. L., & Oostindjer, M. (2016). Diet and
Physical Activity Apps: Perceived Effectiveness by App Users. JMIR MHealth and UHealth,
4(2), e33. https://doi.org/10.2196/mhealth.5114