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Evaluation of the Automated Self‐Administered 24‐hour Dietary Assessment Tool (ASA24) for use with children: An observational feeding study

2017· article· en· W4389028506 on OpenAlexaffabout
Sharon I. Kirkpatrick, Amanda Raffoul, Jocelyn Sacco, Kirsten Lee, Emily Chen, Saamir Pasha, Michelle Marcinow, Sarah Orr, Erin Hobin

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsCancer Care OntarioPublic Health OntarioUniversity of Waterloo
Fundersnot available
KeywordsObservational studyMedicineMealEnvironmental healthServing size

Abstract

fetched live from OpenAlex

With technological innovation, it is now possible to collect 24‐hour dietary recalls in a broader range of studies than previously possible. The Automated Self‐Administered 24‐hour Dietary Assessment Tool (ASA24), a web‐based tool that enables self‐administered 24‐hour recalls (24HRs), has been shown to be feasible and perform well in terms of capturing true intake in samples of adults. However, data to inform use with children are limited. This observational feeding study was conducted to evaluate children's ability to accurately report a lunch time meal using ASA24. The study was conducted among children in grades 5–8 (ages 10–13 years) within a school setting and involved consuming a study‐provided lunch, with consumption unobtrusively documented, during a regular lunch period. Students were served an individual cheese pizza, baby carrots, ranch dip, yogurt, a cookie, and a choice of water, juice, or milk. Plate waste was collected and weighed. The following day, participants were asked to complete ASA24, along with a demographic and health questionnaire, within a 50–60 minute class period. A total of 98 children participated in both days of the study. The majority (n=82) were asked to complete ASA24‐2016; in one grade 5 class, 16 students completed ASA24‐Kids for comparison purposes. Several children did not fully complete ASA24 and true intake data were missing for a small number due to logistical challenges in collecting plate waste within classroom settings. This left 61 recalls that included sufficient detail for food codes to be assigned within the ASA24 system and for which true intake was known. Among these, 7 recalled lunches did not match what was served and were excluded from further analysis, leaving 54 recalls. This subsample of 54 children reported matches for 73% of the foods and beverages actually consumed. When stricter criteria were applied (e.g., only cheese pizza and not other varieties of pizza considered an exact match), 60.1% of matches were considered exact, 30.3% were close, and 9.6% were far. Median completion time decreased from 51 minutes among 10 year olds to 33 minutes among 13 year olds. Observation of children during ASA24 completion suggested they were enthusiastic about completing it but many had difficulty navigating the steps independently; this was particularly true for children in grades 5 and 6 compared to those in grades 7 and 8. Overall, this study suggests that supports may be required to help children complete ASA24; this may include the use of the demonstration version to familiarize them with the steps. Support or Funding Information This study was supported by the Public Health Ontario Project Initiation Fund and a Canadian Cancer Society Research Institute Capacity Development Award (grant 702855 held by S. Kirkpatrick). The authors are grateful to collaborators Jess Haines, Paula Robson, Amy Subar, and Michelle Vine, as well as Fei Zuo for assistance with data collection.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.229
GPT teacher head0.408
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2017
Admission routes2
Has abstractyes

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