Abstract of my PhD research in online social networking and fitness.

1. Mobile applications, physical activity and online social networking.
A match made in heaven?
Ted Vickey
Digital Enterprise Research Institute (DERI)
National University of Ireland, Galway
ted.vickey@deri.org
Research Abstract 2. Past research
This research aims to create/leverage correlations between Christakis and Fowler (2009) suggest that “people are inter-
online social networking and effective exercise motivation and connected and so their health is inter-connected. Inter-personal
adherence. health effects in social networks provide a new foundation for
public health”. As online connections between people become
There is a substantial body of research regarding social ever more interweaved with offline real-world interests, social
networking and increased physical activity, but little regarding networking methods are moving toward simulating real-life
the effective usage of advanced web technologies to address social interactions, including physical activity, health and
exercise adherence (very important since 50% of people drop out disease management: rather than randomly approaching each
of exercise programs within six months). We propose to other, people meet through things they have in common (Breslin
incorporate semantic technologies in exercise-oriented social & Decker 2007)
networks to provide an interoperable historical record of one’s
exercise adherence. This can be exchanged between trusted Research has shown that individuals in the top two quartiles for
peers, and can also be used to power collaborative feedback measured social capital were significantly less likely to be
mechanisms. physically inactive than those in the two lowest quartiles,
highlighting the importance of programs aimed at increasing
Keywords physical activity (Mummery et al. 2008)
Physical activity, online social networking, Twitter
While there are various personal devices that monitor/track a
1. Introduction person’s exercise characteristics (e.g. Body Media, Fitbit,
Obesity and lack of physical exercise continues to be a drain in MapMyFitness, Nike+, etc.), the effectiveness of online sharing
today’s society, adversely affecting human health and thus via social networks of one’s physical activity is limited in
leading to the necessity of medical care and a destructive impact scientific research. Studies have indicated that “lack of
on human well-being and productivity (Olofsson 2010). An motivation” is a key factor in why a person does not exercise.
alarming number of people from around the world now have One factor to address is the relationship between participant and
body mass scores that are cause of serious health concerns (Sassi provider (i.e. personal trainer) and/or participant and social
2010). Medical research has shown the correlation between network, including their influence. People join gyms not only for
physical inactivity and several medical conditions and health health and fitness, but also for the social atmosphere. To fully
problems (Oinas-Kukkonen 2010). understand the power of combining social networking and
exercise adherence, the physical barrier of the four walls of an
As technology continues to impact humanity, the understanding exercise facility is removed and technology is used that enables
of one’s social network may be one key to better health. The a measurable improvement towards one’s fitness goals.
basic element of a person’s social network is simple: a social
network starts with a central person (called an ego) and other With the move towards making machine-understandable data
people (called nodes) that are interconnected by links (called available for computers, allowing exercise data to become
ties). As the numbers of nodes and links increase, the number of accessible/exchangeable between trusted peers is quite
possible connections grows exponentially – known as the important. However, one’s historical exercise records are often
network effect (Christakis & Fowler 2009). This research will locked in to proprietary systems. By publishing selected aspects
analyze the online impact of these social networks with regards of these profiles using semantic terms, it will become easier for
to physical activity. The linkages between nodes in a social people to search for and discover relevant exercise regimes.
network often represent communication, influence or trust
(O’Malley & Marsden 2008).
Since many aspects of health promotion professionals involve
interdependent actors, social networks are of increasing interest
to health services researchers (O’Malley & Marsden 2008). The
creation of a social network map of a person’s social network
can help visualize and thus better understand the strengths of the
social ties of the network (Christakis & Fowler 2009).

2. 3. Research objectives 6. Acknowledgements
This study will research how the use of social and semantic Special thanks for their support of this research project to Mr.
technologies can effectively address the lack of motivation Scott Goudeseune, President and CEO of the American Council
excuse and thus increase exercise adherence/general health. To on Exercise, Dr. Cedric Bryant, Chief Science Officer of the
achieve this goal, our research will consist of: American Council on Exercise, Dr. John Breslin, Primary
Supervisor from the National University of Ireland Galway and
1. State-of-the-art review of systems used by providers members of the research advisory team including Dr. Stephen
with regards to exercise adherence; Kinsella from the University of Limerick (Ireland), Dr. Kathleen
2. Analysis of social networking and the Semantic Web as Martin-Ginis from McMaster University (Canada), Dr.
a means to provide an additional tool for providers with Alexandre Passant and Professor Stefan Decker from the Digital
regards to exercise adherence; Enterprise Research Institute at the National University of
3. Analysis of improved health/fitness measures; Ireland, Galway. Partial funding provided by IRCSET (Ireland).
4. Analysis of feedback mechanisms and economic
improvements in health (both individual and group). 7. References
Breslin, J. & Decker, S., 2007. The Future of Social The Need
4. Research project for Semantics. Ieee Internet Computing, 5, pp.86-90.
The research for this project will evaluate and measure a number
of aspects of online social networking and exercise motivation Christakis, N.A. & Fowler, J.H., 2009. Social network
including but not limited to: visualization in epidemiology. Health Care, 19(1), pp.5-16.
1. Analysis of fitness/wellness related Twitter hashtags (of Mummery, W.K. et al., 2008. Associations between physical
certain mobile fitness apps) consistent with sharing of inactivity and a measure of social capital in a sample of
one’s workout;
Queensland adults. Journal of science and medicine in sport /
2. Analysis and mapping of the social networks (using Sports Medicine Australia, 11(3), pp.308-15. Available at:
Twitter) of those that share their workouts; http://www.ncbi.nlm.nih.gov/pubmed/17707692 [Accessed
3. Survey and interviews of participants on exercise and
March 14, 2011].
technology of those that share their workouts and those
that don’t;
4. Analysis of online influence factors (Klout score) and Oinas-Kukkonen, H., 2010. Behavior change support systems:
how influence effects change. The next frontier for web science. Processing. Available at:
http://journal.webscience.org/296/ [Accessed November 25,
5. Next Steps 2010].
The literature review and past research is nearing completion as
of March 2011. The next step in this research project is the Olofsson, E., 2010. Wellness Applications: Design Guidelines to
analysis of certain Twitter hashtags that are related to specific Encourage Physical Activity. USCCS 2010. Available at:
mobile fitness apps and to then determine what aspects to http://www8.cs.umu.se/kurser/5DV054/Proc_USCCS10.pdf#pag
measure including but not limited to: sender’s tweet frequency, e=117 [Accessed November 25, 2010].
sender’s number of followers, sender’s Klout score, sender’s
network map, number of re-tweets, etc. O’Malley, A.J. & Marsden, P.V., 2008. The analysis of social
networks. Health Services and Outcomes Research
In addition, the researchers hope to work directly with the Methodology, 8(4), p.222–269.
designers of the mobile fitness apps to measure some of the
characteristics of users that don’t share their information via Sassi, F., 2010. Obesity and the Economics of Prevention, Paris:
Twitter and in so doing creating an initial profile of the app’s OECD Publishing. Available at:
overall user-base. This will allow the comparison of the habits http://www.wfanet.org/documents/3/OECD_Fitnotfat.pdf
of those that share workouts versus those that don’t within the [Accessed January 30, 2011].
same mobile fitness platform.
This research will be introduced to a number of peer review
conferences and journals within both the exercise science
industry and the technology industry. Commercial articles in
major international magazines, websites and conferences are
also planned.