This document outlines a presentation on obesity and self-monitoring mobile apps. It discusses how obesity has doubled globally since 1980 and is a major health problem. Self-monitoring apps allow users to track food intake, physical activity, and weight over time. The document examines features of these apps, such as inputting diet and exercise data and outputting nutrition assessments. However, many apps lack evidence-based content and scientific validation of their effectiveness. Some research studies are highlighted that have found mobile apps can help with weight loss when combined with support from dietitians and personalized recommendations.

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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