Personal and social predictors of use and non-use of fitness/diet app: Application of Random Forest algorithm |
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| Institution: | 1. School of Communication, Sogang University, #811, bldg. Matthew, Baekbeom-ro 35, Mapo-gu, Seoul, South Korea;2. Department of Biomedical Engineering, School of Medicine, Dankook University, South Korea;1. School of Geographical Sciences, University of Bristol, UK;2. Alan Turing Institute, UK;3. Department of Economics, Tufts University, USA;1. Department of Marketing, College of Management, Shenzhen University, 3688 Nanhai Ave., Shenzhen 518060, China;2. Department of Business Administration, Tunghai University, No.1727, Sec. 4, Taiwan Boulevard, Xitun District, Taichung 40704, Taiwan ROC;3. Center for Brand Relationships, College of Management, Shenzhen University, 3688 Nanhai Ave., Shenzhen 518060, China |
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| Abstract: | This study investigated the various groups of factors that predict individuals’ use and non-use of fitness and diet apps on smartphones. Unlike previous research on fitness and diet apps which have mainly studied individuals’ intentions to use the apps, this study focused on the prediction accuracy of various factors that lead people to use fitness and diet apps through analysis of data collected from users as well as non-users of these apps. To examine prediction accuracy, this study applied the Random Forest algorithm. According to the findings, prediction accuracy higher than that of 70 percent was observed for nine factors: age, annual income, education, perceived obesity, dieting efforts, number of smartphone apps currently used, daily time spent with smartphone apps, perceived benefits from exercise, and social influence. A major contribution of this study is its detection of those factors predicting actual behavioral decisions regarding use of fitness and diet apps, as opposed to future intentions.. |
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| Keywords: | Fitness and diet apps mHealth Technology adoption Decision tree algorithm Random Forest algorithm |
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