MétaCan
Menu
← Back to cohort

Frequency of 100% Fruit Juice Consumption by Canadians Is Associated with Higher Micronutrient Intake and Improved Nutrient Adequacy

2017· article· en· W3153128716 on OpenAlexaffabout
Mary M. Murphy, Leila M. Barraj, Tristin D. Brisbois, Alison M. Duncan

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMicronutrientNutrientDietary Reference IntakeMedicineConsumption (sociology)Animal scienceFruit juiceEnvironmental healthFood scienceToxicologyDemographyChemistryBiology

Abstract

fetched live from OpenAlex

Consumption of 100% fruit juice (FJ) has been related to better diet quality; however, studies in Canadians are limited. This study determined and compared usual intake and adequacy of intake of FJ micronutrients as well as energy, dietary fiber, and total sugars among Canadians categorized by frequency of FJ consumption. Data were examined from the Canadian Community Health Survey (CCHS) 2.2 which includes a general health questionnaire and a 24‐hour dietary recall. Mean usual intakes (derived using the Software for Intake Distribution Estimation (PC‐SIDE) program) of energy and energy‐adjusted vitamins A, C and D, calcium, magnesium, potassium, dietary fiber and total sugars were determined for age‐sex groups (1–3 y; 4–8 y; 9–13 y; 14–18 y; 19–50 y; ≥51 y; and subgroups of adults to align with Estimated Average Requirements (EARs)) and compared among FJ consumption categories. Prevalence of inadequate nutrient intake was determined using the EAR cut‐point method and compared among FJ consumption categories. A sample of 34,351 individuals was analyzed and categorized by frequency of daily FJ consumption as <0.5 (n=13,832), ≥0.5 to <1.5 (n=13,485) or ≥1.5 (n=7,034) times/d. Frequency of FJ consumption varied among age‐sex groups, with approximately 80% of young children (1–3 and 4–8 y) and 50% of adults consuming FJ ≥0.5 times/d. More frequent consumers of FJ also had higher intakes of fruit other than FJ and were more likely to consume ≥5 servings of total fruits and vegetables per day. Although energy intakes were higher among the most frequent (≥1.5 times/d) compared to the least frequent (<0.5 times/d) consumers of FJ for most age‐sex groups, energy‐adjusted micronutrient intakes were higher with increased frequency of FJ consumption for vitamin C and potassium within most age‐sex groups; vitamins A and D among men 19–50 y; calcium among men 19–50 and ≥71 y and women ≥51 y; and magnesium among children 1–3 y, boys 14–18 y, adult men, and women ≥51 y. Fiber intake adjusted for energy was also higher with increased frequency of FJ consumption among children 1–3 y, boys 9–13 and 14–18 y, girls 14–18 y, and adult men, and total sugar intakes were higher with increased frequency of FJ consumption in many though not all age‐sex groups. Prevalence of inadequate micronutrient intake was lower with more frequent FJ consumption for vitamin C among boys 9–13 and 14–18 y and all adults; vitamin A among men 19–50 y and women ≥51 y; calcium among men 19–50 y and ≥71 y; and magnesium among girls 9–13 y, boys 14–18 y, adult men, and women ≥51 y. Among boys 9–13 y, prevalence of vitamin D inadequacy was lowest among boys consuming juice <1.5 times/d. Results from this study show that across several age‐sex groups, higher FJ consumption is associated with consumption of more fruits and vegetables and higher usual energy‐adjusted intakes of vitamin C, potassium, calcium, magnesium and fiber, all of which are nutrients that may be under‐consumed. These higher nutrient intakes were accompanied by improved nutrient adequacy for micronutrients including vitamins A and C, calcium and magnesium. Support or Funding Information Supported by PepsiCo Global R&D

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.261
Teacher spread0.238 · 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".

Quick stats

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicNutritional Studies and Diet→French-language works237,207→