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Mobile Phone Applications forDiet and Weight Control
Luyao ZhangINLS770 Final Presentation
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OutlineObesity and self-monitoring
Features
Limitations
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Obesity and self-monitoring
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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
Obesity and self-monitoring
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Self-monitoring by mobile phone appsRecording physical activities and eating patternsGiving feedback on one’s behaviors based on the
healthy weight guidelinesIncreases self-awareness on targeting behavior
and weight control goalsOver 28,000 unique apps relevant to weight-
management
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Input Features Output Features
FeaturesInput 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
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Features
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Input FeaturesPhysical activity
Type of physical activity, exercise goal Integration with wearables, GPS
Other Personal reminders Challenges Community forums
Features
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Output FeaturesNutrition 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)
LimitationsLack of professional, 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)
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Primary efficacy evaluation 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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Built upon strategies dietitians use in their everyday practice
Personalized motivational messages from dietitians
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Knowledge-based dietary nutritional recommendations
Personalized dietary nutrition schedules will be generated based on similarity clustering of obese youth with high correlation
References1. World Health Organization. (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.pdf3. 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
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