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Local spatial obesity analysis and estimation using online social network sensors
Qindong Sun1, Nan Wang1, Shancang Li2
1Shaanxi Key Laboratory of Network Computing and Security, Xi'an University of Technology, 710048, China.
Journal of Biomedical Informatics
|March 20, 2018
Summary
Online social networks (OSNs) can identify and estimate obesity rates by analyzing user data like diet and activity. This health informatics model effectively analyzes local spatial obesity issues using OSN sensors.
Area of Science:
- Health Informatics
- Social Network Analysis
- Public Health
Background:
- Online social networks (OSNs) facilitate user interaction and healthcare engagement.
- Increasing interest in analytical data models within health informatics.
- OSNs offer potential for gathering health-related user information.
Purpose of the Study:
- To develop an obesity identification, analysis, and estimation model using OSN data.
- To treat individual OSN users as 'sensors' for valuable health information.
- To analyze the correlation between obesity and user-provided features.
Main Methods:
- Utilizing OSN users as data 'sensors'.
- Collecting features such as dietary habits, physical activity, emotions, and self-consciousness.
- Developing an OSN-based obesity analytic model to estimate obesity rates.
Main Results:
- Demonstrated a high success rate in estimating obesity.
- Validated the correlation between proposed features and obesity.
- Confirmed the model's effectiveness in analyzing spatial obesity problems.
Conclusions:
- OSNs can be effectively utilized for analyzing local spatial obesity issues.
- The proposed OSN-based model provides a novel approach to obesity analysis.
- This method offers a scalable solution for public health monitoring.
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