Speakers
Description
Obesity is one of the main causes of a chronic disease. National obesity population is increasing year by year, and due to Covid-19, physical activity is decreased and food intake is increased by 40%[1]. Physical activity such as exercise is more efficient when doing with other than doing alone[2][3], so we planned this project to effectively prevent and treat obesity through cooperative games[4]. When users save their health information in the app, the app will analyze the information and specifies goals such as physical activity, drink moisture, and sleep duration[5] to the user, and the user performs with this goal. The app will provide the report every end of the week, and month. Plus, the app will advise to the user how to plan for next goal by comparing the monthly report. Data of health and physical activity data in two ways: direct user input and automatic recording using wearable devices. The collected data provides graphs related to user-health information through models[6] learned to use data collected from various big data centers such as Kaggle and K-ICT in advance. Regression model data and predictive model data through machine learning are provided to users in graphs, and the model is visualized through numpy, pandas, and matplotlib libraries to provide users with health information changes and prediction information according to data trends. The system client will be designed as a system that operates in a mobile environment. In the game, the user can have their own room that shows their profile. In the competition, users will form a team by four users. They can form a team with friends or through random match making system. In every game, 5 teams will compete. Within a limited period of time, two weeks, the teams will take the other team’s territory by comparing the team's achievement rate of the day. After the competition, teams will gain game points by following the final ranking. Team on the highest rank will get the most points but the last team also get some points because not to frustrate about the bad result because they would effort themselves. This point will be paid for decorate user’s own profile. And users can get achievements by achieve specific goals. For example, the top-ranked user who performs exemplary activities will get unique badge that other shows to other users. And this system gives additional effect to other users by refer top-ranked user’s routines. Users will experience the fun and motivation for the game through a competitive system and community system[7]. Users can check improvements in health indicators, including weight loss, as they achieve their goals. This allows users to gain confidence and expect motivation from seeing themselves improve. Service providers can demonstrate effectiveness by looking at the overall user statistics to ensure that health indicators improve.
References
[1] Korea Centers for Disease Control and Prevention. 2022 Current Status and Issues of Chronic Diseases. 11-1790387-0000005-10
[2] Shiriki K. Kumanyika, Eva Obarzanek, Nicolas Stettler, Ronny Bell, Alison E. Field,
Stephen P. Fortmann, Barry A. Franklin,Matthew W. Gillman,Cora E. Lewis,Walker Carlos PostonII, June Stevens and Yuling Hong(2008). Population-Based Prevention of Obesity, Circulation. 2008;118:428–464
[3] Irwin, Brandon C, Kerr, Norbert L. Wittenbaum, Gwen, Pontifex, Matthew B. (2012). Increasing physical activity in free-living conditions : an examination of the Kohler motivation gain effect, 97812673144131267314419
[4] Hong, J. S., Wasden, C., & Han, D. H. (2021). Introduction of digital therapeutics. Computer Methods and Programs in Biomedicine, 209, 106319.
[5] Soo-Hyun Lee, Mi-Joon Lee, Bum-Jeun Seo(2022). The Effect of Sleep Duration on Obesity in Korean Adults, Journal of convergence for Information Technology, 2586-4440
[6] Palanica, A., Docktor, M. J., Lieberman, M., & Fossat, Y. (2020). The need for artificial intelligence in digital therapeutics. Digital Biomarkers, 4(1), 21-25.
[7] Beckles, Joelle A.(2017). Enhancing motivation to exercise for obese participants in exergames : testing partner characteristics as a moderator of the Kohler effect, 9780355224818035522481X
| Keywords | Habit, Digital medicine, Obesity, Cooperative game, Big data, Artificial intelligence, Deep learning, Smart watch, Kohler effect |
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