Evidence map›Paper›PMID 33913815›Full record

Observational studyJMIR public health and surveillance2021

Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study.

Olubusola Oladeji, Chi Zhang, Tiam Moradi, Dharmesh Tarapore, Andrew C Stokes, Vukosi Marivate, Moinina D Sengeh, Elaine O Nsoesie

Abstract readObservational Study
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Guideline
  2. Pooled it
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  4. Pooled it
  5. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Olubusola OladejiDepartment of Global Health, School of Public Health, Boston University, Boston, MA, United States.ORCID 0000-0002-7857-2880
Chi ZhangDepartment of Computer Science, Boston University, Boston, MA, United States.ORCID 0000-0002-6979-2945
Tiam MoradiDepartment of Computer Science, Boston University, Boston, MA, United States.ORCID 0000-0003-4923-9867
Dharmesh TaraporeDepartment of Computer Science, Boston University, Boston, MA, United States.ORCID 0000-0003-4452-014X
Andrew C StokesDepartment of Global Health, School of Public Health, Boston University, Boston, MA, United States.ORCID 0000-0002-8502-3636
Vukosi MarivateDepartment of Computer Science, University of Pretoria, Pretoria, South Africa.ORCID 0000-0002-6731-6267
Moinina D SengehDirectorate of Science, Technology and Innovation, Freetown, Sierra Leone.ORCID 0000-0003-1655-9047
Elaine O NsoesieDepartment of Global Health, School of Public Health, Boston University, Boston, MA, United States.ORCID 0000-0001-9170-8714

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Information Seeking BehaviorInternetAfricaDietExerciseHumansObesityPrevalenceRisk FactorsSearch EngineAfricachronic diseasesdigital phenotypehypertensioninfodemiologyinfoveillanceobesityoverweight

Identifiers

PMID33913815
PMCPMC8120431

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.