Mobile Phone Applications for Diet and Weight Control

Mobile Phone Applications for Diet and Weight Control

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  Luyao Zhang INLS770 FinalPresentation 1
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  Outline  Obesity andself-monitoring  Features  Limitations 2
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  Obesity and self-monitoring 3 Obesity is a big problem now:  The rate of obesity doubled between 1980 and 2014  39% of adults (1.9 billion) were overweight in the world and 13 % adults (600 million) were obese  Diseases such as diabetes and cardiovascular diseases related to obesity account for two-thirds of death globally
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  Obesity and self-monitoring 4 Self-monitoring by mobile phone apps  Recording physical activities and eating patterns  Giving feedback on one’s behaviors based on the healthy weight guidelines  Increases self-awareness on targeting behavior and weight control goals  Over 28,000 unique apps relevant to weight-management
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  5  Input Features Output Features
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  Features  Input Features Dietary Intake  Text search, barcode scanner  Create meal or recipe, favorite foods  Water consumption  Phenotype  Current weight, target weight, height, gender, DOB  Waist circumference, hips circumference 6
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  Features 7  Input Features Physical activity  Type of physical activity, exercise goal  Integration with wearables, GPS  Other  Personal reminders  Challenges  Community forums
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  Features 8  Output Features Nutrition Assessment  Maximum calories to reach a target weight  Calculated energy (kcal)  Calories by meal  Physical activities and other  Energy by type of physical activities  Weight (loss) progress  Sharing with others (friends, professionals, EHR)
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  Limitations  Lack ofprofessional, evidence-based content  Lack of adequate scientific validation, evidence of clinical and economic benefits  Only a few apps were supported by Randomized Controlled Trial (RCT) 9
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  10  Primary efficacyevaluation parameter:  Mean weight reduction from baseline (to week 24)  2.21 kg (SD 3.60) vs. 0.77 kg (SD 2.77), P < .001  Secondary efficacy evaluation parameters:  BMI, body fat rate, diet habit, decrement of waist measurement
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  11  Built uponstrategies dietitians use in their everyday practice  Personalized motivational messages from dietitians
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  12  Knowledge-based dietarynutritional recommendations  Personalized dietary nutrition schedules will be generated based on similarity clustering of obese youth with high correlation
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  References 1. World HealthOrganization. (2016, June ). Obesity and overweight - Fact sheet. Retrieved November 29, 2016, from http://www.who.int/mediacentre/factsheets/fs311/en/ 2. World Health Organization. (2011). Global status report on noncommunicable diseases 2010. Retrieved from http://www.who.int/nmh/publications/ncd_report_full_en.pdf 3. Nikolaou, C. K., & Lean, M. E. J. (2016). Mobile applications for obesity and weight management: current market characteristics. International Journal of Obesity. 4. Franco, R. Z., Fallaize, R., Lovegrove, J. A., & Hwang, F. (2016). Popular Nutrition-Related Mobile Apps: A Feature Assessment. JMIR mHealth and uHealth, 4(3). 5. Oh, B., Cho, B., Han, M. K., Choi, H., Lee, M. N., Kang, H. C., ... & Kim, Y. (2015). The effectiveness of mobile phone-based care for weight control in metabolic syndrome patients: randomized controlled trial. JMIR mHealth and uHealth, 3(3). 6. Harricharan, M., Gemen, R., Celemín, L. F., Fletcher, D., de Looy, A. E., Wills, J., & Barnett, J. (2015). Integrating mobile technology with routine dietetic practice: The case of myPace for weight management. Proceedings of the Nutrition Society, 74(02), 125–129. doi:10.1017/s0029665115000105 7. Jung, H., & Chung, K. (2015). Knowledge-based dietary nutrition recommendation for obese management. Information Technology and Management, 17(1), 29–42. doi:10.1007/s10799-015-0218-4 13