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Record W2921119446 · doi:10.1161/circ.139.suppl_1.mp67

Abstract MP67: Reduction in Calories Purchased by Employees Two Years After Implementation of Cafeteria Traffic-light Labels and Choice Architecture

2019· article· en· W2921119446 on OpenAlexaboutno aff
Anne N. Thorndike, Emily D. Gelsomin, Douglas E. Levy

Bibliographic record

VenueCirculation · 2019
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsCafeteriaCalorieMedicineQuarter (Canadian coin)DemographyPsychological interventionGerontologyEnvironmental healthInternal medicineNursingGeography

Abstract

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Background: Point-of-purchase strategies, such as traffic-light labels and choice architecture, promote healthy food choices. However, there is little research to determine if these interventions reduce caloric intake and prevent weight gain. Methods: We previously demonstrated that a worksite cafeteria traffic-light labeling and choice architecture intervention increased employees’ green (healthy) purchases and reduced red (unhealthy) purchases over 2 years. The objective of the current study is to determine if the intervention reduced calories purchased. We analyzed cafeteria purchases of 5,695 employees who visited the cafeteria during the 3-mo baseline period (Dec 2009-Feb 2010) and the 24-mo intervention period (March 2010-Feb 2012). We compared mean calories purchased per transaction (kcal/transaction) during the baseline quarter to the kcal/transaction purchased during the same 1-year (Dec 2010-Feb 2011) and 2-year (Dec 2011-Feb 2012) quarters. All analyses were adjusted for employee age, gender, race/ethnicity, and job type. To assess potential impact of a change in purchased calories on weight, we analyzed the total calories purchased per quarter (kcal/quarter) by employees who visited the cafeteria frequently (≥36 times/quarter). We calculated the combined mean change from baseline in kcal/quarter at 1 and 2-years and divided this number by 90 days to estimate change in daily calories per employee. We predicted the effect of the change in daily calories on employees’ weight using a dynamic model of weight change (Hall, et al. Lancet , 2011). Results: Employees’ mean age was 34 yrs; 71% were female and 73% white. Mean kcal/transaction was 499.5 at baseline, 478.9 at 1 year, and 470.2 at 2 years (change from baseline to 2 years: -29.3 [95% CI, -33.7, -25.0], p<.001). The decrease in kcal/transaction at 2 years occurred for both food (-17.0, p<.001) and beverages (-17.6, p<.001). At 2 years compared to baseline, kcal/transaction increased for green-labeled items (13.3, p<.001) and decreased for red-labeled items (-42.0, p<.001). Among 461 employees with ≥36 transactions/quarter, mean total calories purchased during the baseline quarter was 37,198 kcal; this decreased by 4,380 kcal/quarter (p<.001) at 1 year and 5,666 kcal/quarter (p<.001) at 2 years relative to baseline. Assuming no other changes in employees’ dietary intake or activity, this equates to a reduction of 56 kcal/day; if maintained over time, the dynamic model of weight change predicts weight loss of 2.8 lbs at 1 year and 5.3 lbs at 3 years. Conclusions: A traffic-light labeling and choice architecture intervention reduced calories purchased by employees over 2 years. These findings have implications for helping employees manage their weight, particularly those who use the cafeteria regularly. Simple point-of-purchase interventions are important tools for addressing the obesity epidemic.

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)0.000
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.292
Teacher spread0.282 · 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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Citations0
Published2019
Admission routes1
Has abstractyes

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