Evidence map›Paper›PMID 30404768›Full record

ArticleJMIR mHealth and uHealth2018

A Tool to Measure Young Adults' Food Intake: Design and Development of an Australian Database of Foods for the Eat and Track Smartphone App.

Lyndal Wellard-Cole, Melisa Potter, Jisu Joseph Jung, Juliana Chen, Judy Kay, Margaret Allman-Farinelli

Abstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–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

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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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

6 authors.

Lyndal Wellard-ColeSchool of Life and Environmental Science, The University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0003-1514-2135
Melisa PotterSchool of Life and Environmental Science, The University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0003-3046-7886
Jisu Joseph JungSchool of Information Technology, The University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-0223-8392
Juliana ChenSchool of Life and Environmental Science, The University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0003-2844-6947
Judy KaySchool of Information Technology, The University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0001-6728-2768
Margaret Allman-FarinelliSchool of Life and Environmental Science, The University of Sydney, Sydney, Australia.ORCID http://orcid.org/0000-0002-6478-1374

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDietary assessment is reliant on the collection of accurate food and beverage consumption data. Technology has been harnessed to standardize recording and provide automatic nutritional analysis to reduce cost and researcher burden.

objectiveTo better assess the diet of young adults, especially relating to the contribution of foods prepared outside the home, a database was needed to support a mobile phone data collection app. The app also required usability testing to assure ease of entry of foods and beverages. This paper describes the development of the Eat and Track app (EaT app) and the database underpinning it.

methodsThe Australian Food and Nutrient Database 2011-13, consisting of 5740 food items was modified. Four steps were undertaken: (1) foods not consumed by young adults were removed, (2) nutritionally similar foods were merged, (3) foods available from the 30 largest ready-to-eat food chains in Australia were added, and (4) long generic food names were shortened and simplified. This database was used to underpin the EaT app. Qualitative, iterative usability testing of the EaT app was conducted in three phases using the "Think Aloud" method. Responses were sorted and coded using content analysis. The System Usability Scale (SUS) was administered to measure the EaT app's perceived usability.

resultsIn total, 1694 (29.51%) foods were removed from the Australian Food and Nutrient Database, including 608 (35.89%) ingredients, 81 (4.78%) foods already captured in the fast food chain information, 52 (3.07%) indigenous foods, 25 (1.48%) nutrients/dietary supplements, and 16 (0.94%) child-specific foods. The remaining 912 (53.84%) foods removed were not consumed by young adults in previous surveys or were "not defined" in the Australian Food and Nutrient Database. Another 220 (3.83%) nutritionally similar foods were combined. The final database consisted of 6274 foods. Fifteen participants completed usability testing. Issues identified by participants fell under six themes: keywords for searching, history list of entered foods, amounts and units, the keypad, food names, and search function. Suggestions for improvement were collected, incorporated, and tested in each iteration of the app. The SUS of the final version of the EaT app was rated 69.

conclusionsA food and beverage database has been developed to underpin the EaT app, enabling data collection on the eating-out habits of 18- to 30-year-old Australians. The development process has resulted in a database with commonly used food names, extensive coverage of foods from ready-to-eat chains, and commonly eaten portion sizes. Feedback from app usability testing led to enhanced keyword searching and the addition of functions to enhance usability such as adding brief instructional screens. There is potential for the features of the EaT app to facilitate the collection of more accurate dietary intake data. The database and the app will be valuable dietary assessment resources for researchers.

Indexed as

diet surveysmobile phonesmartphoneyoung adult

Identifiers

PMID30404768
PMCPMC6249504

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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.