Observational studyJMIR public health and surveillance2021
Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study.
Observational study in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 4 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Prevalence of obesity, hypertension and diabetes among people living with HIV in South Africa: a systematic review and meta-analysis.BMC infectious diseases · 2023Pooled it
- Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments.International journal of obesity (2005) · 2023Pooled it
- The effects of subcutaneous Tirzepatide on obesity and overweight: a systematic review and meta-regression analysis of randomized controlled trials.Frontiers in endocrinology · 2023Pooled it
- Comparison of Feature Selection Methods in Machine Learning Models of Cancer Information Seeking Among United States Adults: Cross-Sectional Study.JMIR medical informatics · 2026Article
- Awareness of Obesity and Diabetes in Libya: Insights From a Cross-Sectional Study.Health science reports · 2026Article
- Scoping review of artificial intelligence via mobile technology and social media for health in Africa.Nature communications · 2025Article
- Digital Dietary Behaviors in Individuals With Depression: Real-World Behavioral Observation.JMIR public health and surveillance · 2024Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors.
objectiveThe objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa.
methodsWe obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance.
resultsThe study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence.
conclusionsInformation-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.
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