Mobile applications, physical activity and online social networking. A match made in heaven?
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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 [email protected] Research Abstract This research aims to create/leverage correlations between online social networking and effective exercise motivation and adherence. There is a substantial body of research regarding social networking and increased physical activity, but little regarding the effective usage of advanced web technologies to address exercise adherence (very important since 50% of people drop out of exercise programs within six months). We propose to incorporate semantic technologies in exercise-oriented social networks to provide an interoperable historical record of one’s exercise adherence. This can be exchanged between trusted peers, and can also be used to power collaborative feedback mechanisms. Keywords Physical activity, online social networking, Twitter 1. Introduction Obesity and lack of physical exercise continues to be a drain in today’s society, adversely affecting human health and thus leading to the necessity of medical care and a destructive impact on human well-being and productivity (Olofsson 2010). An alarming number of people from around the world now have body mass scores that are cause of serious health concerns (Sassi 2010). Medical research has shown the correlation between physical inactivity and several medical conditions and health problems (Oinas-Kukkonen 2010). As technology continues to impact humanity, the understanding of one’s social network may be one key to better health. The basic element of a person’s social network is simple: a social network starts with a central person (called an ego) and other people (called nodes) that are interconnected by links (called ties). As the numbers of nodes and links increase, the number of possible connections grows exponentially – known as the network effect (Christakis & Fowler 2009). This research will analyze the online impact of these social networks with regards to physical activity. The linkages between nodes in a social 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. Past research Christakis and Fowler (2009) suggest that “people are inter- connected and so their health is inter-connected. Inter-personal health effects in social networks provide a new foundation for public health”. As online connections between people become ever more interweaved with offline real-world interests, social networking methods are moving toward simulating real-life social interactions, including physical activity, health and disease management: rather than randomly approaching each other, people meet through things they have in common (Breslin & Decker 2007) Research has shown that individuals in the top two quartiles for measured social capital were significantly less likely to be physically inactive than those in the two lowest quartiles, highlighting the importance of programs aimed at increasing physical activity (Mummery et al. 2008) While there are various personal devices that monitor/track a person’s exercise characteristics (e.g. Body Media, Fitbit, MapMyFitness, Nike+, etc.), the effectiveness of online sharing via social networks of one’s physical activity is limited in scientific research. Studies have indicated that “lack of motivation” is a key factor in why a person does not exercise. One factor to address is the relationship between participant and provider (i.e. personal trainer) and/or participant and social network, including their influence. People join gyms not only for health and fitness, but also for the social atmosphere. To fully understand the power of combining social networking and exercise adherence, the physical barrier of the four walls of an exercise facility is removed and technology is used that enables a measurable improvement towards one’s fitness goals. With the move towards making machine-understandable data available for computers, allowing exercise data to become accessible/exchangeable between trusted peers is quite important. However, one’s historical exercise records are often locked in to proprietary systems. By publishing selected aspects of these profiles using semantic terms, it will become easier for people to search for and discover relevant exercise regimes.
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Abstract of my PhD research in online social networking and fitness.
Transcript of Mobile applications, physical activity and online social networking. A match made in heaven?
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
[email protected] Research Abstract This research aims to create/leverage correlations between online social networking and effective exercise motivation and adherence. There is a substantial body of research regarding social networking and increased physical activity, but little regarding the effective usage of advanced web technologies to address exercise adherence (very important since 50% of people drop out of exercise programs within six months). We propose to incorporate semantic technologies in exercise-oriented social networks to provide an interoperable historical record of one’s exercise adherence. This can be exchanged between trusted peers, and can also be used to power collaborative feedback mechanisms. Keywords Physical activity, online social networking, Twitter 1. Introduction Obesity and lack of physical exercise continues to be a drain in today’s society, adversely affecting human health and thus leading to the necessity of medical care and a destructive impact on human well-being and productivity (Olofsson 2010). An alarming number of people from around the world now have body mass scores that are cause of serious health concerns (Sassi 2010). Medical research has shown the correlation between physical inactivity and several medical conditions and health problems (Oinas-Kukkonen 2010). As technology continues to impact humanity, the understanding of one’s social network may be one key to better health. The basic element of a person’s social network is simple: a social network starts with a central person (called an ego) and other people (called nodes) that are interconnected by links (called ties). As the numbers of nodes and links increase, the number of possible connections grows exponentially – known as the network effect (Christakis & Fowler 2009). This research will analyze the online impact of these social networks with regards to physical activity. The linkages between nodes in a social 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. Past research Christakis and Fowler (2009) suggest that “people are inter-connected and so their health is inter-connected. Inter-personal health effects in social networks provide a new foundation for public health”. As online connections between people become ever more interweaved with offline real-world interests, social networking methods are moving toward simulating real-life social interactions, including physical activity, health and disease management: rather than randomly approaching each other, people meet through things they have in common (Breslin & Decker 2007) Research has shown that individuals in the top two quartiles for measured social capital were significantly less likely to be physically inactive than those in the two lowest quartiles, highlighting the importance of programs aimed at increasing physical activity (Mummery et al. 2008) While there are various personal devices that monitor/track a person’s exercise characteristics (e.g. Body Media, Fitbit, MapMyFitness, Nike+, etc.), the effectiveness of online sharing via social networks of one’s physical activity is limited in scientific research. Studies have indicated that “lack of motivation” is a key factor in why a person does not exercise. One factor to address is the relationship between participant and provider (i.e. personal trainer) and/or participant and social network, including their influence. People join gyms not only for health and fitness, but also for the social atmosphere. To fully understand the power of combining social networking and exercise adherence, the physical barrier of the four walls of an exercise facility is removed and technology is used that enables a measurable improvement towards one’s fitness goals. With the move towards making machine-understandable data available for computers, allowing exercise data to become accessible/exchangeable between trusted peers is quite important. However, one’s historical exercise records are often locked in to proprietary systems. By publishing selected aspects of these profiles using semantic terms, it will become easier for people to search for and discover relevant exercise regimes.
3. Research objectives This study will research how the use of social and semantic technologies can effectively address the lack of motivation excuse and thus increase exercise adherence/general health. To achieve this goal, our research will consist of:
1. State-of-the-art review of systems used by providers with regards to exercise adherence;
2. Analysis of social networking and the Semantic Web as a means to provide an additional tool for providers with regards to exercise adherence;
3. Analysis of improved health/fitness measures; 4. Analysis of feedback mechanisms and economic
improvements in health (both individual and group).
4. Research project The research for this project will evaluate and measure a number of aspects of online social networking and exercise motivation including but not limited to:
1. Analysis of fitness/wellness related Twitter hashtags (of certain mobile fitness apps) consistent with sharing of one’s workout;
2. Analysis and mapping of the social networks (using Twitter) of those that share their workouts;
3. Survey and interviews of participants on exercise and technology of those that share their workouts and those that don’t;
4. Analysis of online influence factors (Klout score) and how influence effects change.
5. Next Steps The literature review and past research is nearing completion as of March 2011. The next step in this research project is the analysis of certain Twitter hashtags that are related to specific mobile fitness apps and to then determine what aspects to measure including but not limited to: sender’s tweet frequency, sender’s number of followers, sender’s Klout score, sender’s network map, number of re-tweets, etc. In addition, the researchers hope to work directly with the designers of the mobile fitness apps to measure some of the characteristics of users that don’t share their information via Twitter and in so doing creating an initial profile of the app’s overall user-base. This will allow the comparison of the habits of those that share workouts versus those that don’t within the 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.
6. Acknowledgements Special thanks for their support of this research project to Mr. Scott Goudeseune, President and CEO of the American Council on Exercise, Dr. Cedric Bryant, Chief Science Officer of the American Council on Exercise, Dr. John Breslin, Primary Supervisor from the National University of Ireland Galway and members of the research advisory team including Dr. Stephen Kinsella from the University of Limerick (Ireland), Dr. Kathleen Martin-Ginis from McMaster University (Canada), Dr. Alexandre Passant and Professor Stefan Decker from the Digital Enterprise Research Institute at the National University of Ireland, Galway. Partial funding provided by IRCSET (Ireland). 7. References Breslin, J. & Decker, S., 2007. The Future of Social The Need for Semantics. Ieee Internet Computing, 5, pp.86-90.
Christakis, N.A. & Fowler, J.H., 2009. Social network visualization in epidemiology. Health Care, 19(1), pp.5-16.
Mummery, W.K. et al., 2008. Associations between physical inactivity and a measure of social capital in a sample of Queensland adults. Journal of science and medicine in sport / Sports Medicine Australia, 11(3), pp.308-15. Available at: http://www.ncbi.nlm.nih.gov/pubmed/17707692 [Accessed March 14, 2011].
Oinas-Kukkonen, H., 2010. Behavior change support systems: The next frontier for web science. Processing. Available at: http://journal.webscience.org/296/ [Accessed November 25, 2010].
Olofsson, E., 2010. Wellness Applications: Design Guidelines to Encourage Physical Activity. USCCS 2010. Available at: http://www8.cs.umu.se/kurser/5DV054/Proc_USCCS10.pdf#page=117 [Accessed November 25, 2010].
O’Malley, A.J. & Marsden, P.V., 2008. The analysis of social networks. Health Services and Outcomes Research Methodology, 8(4), p.222–269.
Sassi, F., 2010. Obesity and the Economics of Prevention, Paris: OECD Publishing. Available at: http://www.wfanet.org/documents/3/OECD_Fitnotfat.pdf [Accessed January 30, 2011].
