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1. Luyao Zhang
INLS770 Final Presentation
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2. Outline
 Obesity and self-monitoring
 Features
 Limitations
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3. 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

4. Obesity and self-monitoring
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 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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 Input Features
 Output Features

6. 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
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7. Features
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 Input Features
 Physical activity
 Type of physical activity, exercise goal
 Integration with wearables, GPS
 Other
 Personal reminders
 Challenges
 Community forums

8. Features
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 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)

9. Limitations
 Lack 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

13. References
1. 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.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
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