Evidence map›Paper›PMID 29848474›Full record

ArticleJMIR public health and surveillance2018

Accurately Inferring Compliance to Five Major Food Guidelines Through Simplified Surveys: Applying Data Mining to the UK National Diet and Nutrition Survey.

Nicholas Rosso, Philippe Giabbanelli

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Nicholas RossoData Analytics for Complex Human Behaviors Laboratory, Computer Science Department, Northern Illinois University, DeKalb, IL, United States.ORCID http://orcid.org/0000-0002-2967-2981
Philippe GiabbanelliData Analytics for Complex Human Behaviors Laboratory, Department of Computer Science, Furman University, Greenville, SC, United States.ORCID http://orcid.org/0000-0001-6816-355X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNational surveys in public health nutrition commonly record the weight of every food consumed by an individual. However, if the goal is to identify whether individuals are in compliance with the 5 main national nutritional guidelines (sodium, saturated fats, sugars, fruit and vegetables, and fats), much less information may be needed. A previous study showed that tracking only 2.89% of all foods (113/3911) was sufficient to accurately identify compliance. Further reducing the data needs could lower participation burden, thus decreasing the costs for monitoring national compliance with key guidelines.

objectiveThis study aimed to assess whether national public health nutrition surveys can be further simplified by only recording whether a food was consumed, rather than having to weigh it.

methodsOur dataset came from a generalized sample of inhabitants in the United Kingdom, more specifically from the National Diet and Nutrition Survey 2008-2012. After simplifying food consumptions to a binary value (1 if an individual consumed a food and 0 otherwise), we built and optimized decision trees to find whether the foods could accurately predict compliance with the major 5 nutritional guidelines.

resultsWhen using decision trees of a similar size to previous studies (ie, involving as many foods), we were able to correctly infer compliance for the 5 guidelines with an average accuracy of 80.1%. This is an average increase of 2.5 percentage points over a previous study, showing that further simplifying the surveys can actually yield more robust estimates. When we allowed the new decision trees to use slightly more foods than in previous studies, we were able to optimize the performance with an average increase of 3.1 percentage points.

conclusionsAlthough one may expect a further simplification of surveys to decrease accuracy, our study found that public health dietary surveys can be simplified (from accurately weighing items to simply checking whether they were consumed) while improving accuracy. One possibility is that the simplification reduced noise and made it easier for patterns to emerge. Using simplified surveys will allow to monitor public health nutrition in a more cost-effective manner and possibly decrease the number of errors as participation burden is reduced.

Indexed as

diet, food, and nutritionpublic health informaticssupervised machine learning

Identifiers

PMID29848474
PMCPMC6000477

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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.