Vitamin D Knowledge, Perceptions, Intake and Status among Young Adults: a Validation & Intervention Study Using A Mobile 'App'
Bibliographic record
Abstract
Vitamin D aids in the maintenance of bone health by enabling calcium absorption, and low concentrations of vitamin D are implicated in a host of chronic disease states. Adequate vitamin D intake during adolescence and young adulthood is crucial, as peak bone mass is formed during this time. However, many young adults do not meet the recommended intakes for vitamin D. The studies described herein examined vitamin D knowledge, perceptions, intake and blood vitamin D3 concentrations among a total of 209 young adults aged 18-25 living in Canada. Qualitative focus groups examined vitamin D knowledge and beliefs among young adults (n=50). The mobile Vitamin D Calculator application (‘app’) was validated as a measure of dietary vitamin D intake in this population. The app was then used in testing effects of a behavioural intervention aimed at increasing vitamin D knowledge, intake and status among young adults (n=109). A theoretical model based on the Theory of Planned Behaviour and Prototype Willingness Model was used to predict behavioural intentions related to vitamin D intake in the same sample at baseline (n=109). Results indicated that vitamin D knowledge, intake, and status in this population were fairly low. The proportion of participants who met the recommended intake of vitamin D (i.e., Estimated Average Requirement) was greater among individuals who used vitamin D supplements than those who did not. The behavioural intervention led to modest increases in vitamin D knowledge, intake and perceived importance of vitamin D supplement use among those who completed the intervention study (n=90). Blood vitamin D3 levels increased from pre- to post-intervention in both groups; participation in the intervention did not improve vitamin D status. This research highlights the need for greater awareness and education regarding the importance of vitamin D among young adults, the utility of providing personalized nutrition information and use of self-monitoring to improve intake, and the potential for vitamin D supplementation to help individuals meet intake requirements. Potential policy implications (e.g., expanded vitamin D fortification of foods, increased national vitamin D intake recommendations) are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".