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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