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Record W2041853884 · doi:10.1080/07315724.2013.875423

Consumer Usage and Reasons for Using Dietary Supplements: Report of a Series of Surveys

2014· article· en· W2041853884 on OpenAlexaff
Annette Dickinson, Judy Blatman, Neale El-Dash, Julio C Franco

Bibliographic record

VenueJournal of the American College of Nutrition · 2014
Typearticle
Languageen
FieldMedicine
TopicAntioxidant Activity and Oxidative Stress
Canadian institutionsDillon Consulting
Fundersnot available
KeywordsRespondentMedicineDietary supplementEnvironmental healthNational Health and Nutrition Examination SurveyPublic healthGerontologyPopulationFood science

Abstract

fetched live from OpenAlex

OBJECTIVE: Consumer usage of dietary supplements is prevalent in the United States, and total usage is higher than reported in recent National Health and Nutrition Examination Surveys (NHANES), because these surveys capture usage only in the 30 days prior to the respondent's interview and do not capture occasional and seasonal use throughout the year. We report data from a series of consumer surveys on the full extent of dietary supplement use, on the reasons for supplement use, and on the products most commonly taken, as well as other health habits of supplement users. METHODS: The Council for Responsible Nutrition (CRN), a trade association of the dietary supplement industry, has contracted with Ipsos Public Affairs to conduct consumer surveys annually since 2000. The surveys have been administered online since 2007 to about 2000 subjects each year. We report 5 years of data (2007 to 2011) on the prevalence of dietary supplement use, as well as more detailed data from the 2011 survey on the products used, the reasons for using supplements, and other health habits of supplement users. RESULTS: The prevalence of supplement use fluctuated within the range of 64% to 69% from 2007 to 2011, and the prevalence of regular supplement use ranged from 48% to 53%, with no statistically significant differences from year to year. Over the 5-year period, the percentage of respondents who said that they regularly used a variety of supplements increased from 28% to 36%, and the increase from 2010 to 2011 was statistically significant. The percentage of respondents who said that they regularly used only a multivitamin, as opposed to a variety of supplements, declined from 24% to 17%, and the decrease from 2007 to 2008 was statistically significant. Detailed results from the 2011 survey confirm that supplement use increases with age and is higher in women than in men. Vitamin or mineral supplements were used by 67% of all respondents in 2011, specialty supplements by 35%, botanicals by 23%, and sports supplements by 17%. Among supplement users, multivitamins were the most commonly used supplement (71%), followed by omega-3 or fish oil (33%), calcium (32%), vitamin D (32%), and vitamin C (32%). The reasons most often cited for supplement use were for overall health and wellness (58%) and to fill nutrient gaps in the diet (42%). Supplement users were significantly more likely than nonusers to say that they try to eat a balanced diet, visit their doctor regularly, get a good night's sleep, exercise regularly, and maintain a healthy weight. CONCLUSIONS: Annual consumer surveys over a period of 5 years show that dietary supplement use is somewhat more prevalent in the United States than has been reported in the NHANES surveys, when occasional and seasonal use are taken into account, in addition to regular use. Most dietary supplement users take a multivitamin, and many take a variety of products. The primary reasons given for supplement use are for overall health and wellness or to fill nutrient gaps. Users of dietary supplements are more likely than nonusers to adopt a variety of healthy habits, indicating that supplement use is part of an overall approach to living healthy.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.311
Teacher spread0.279 · 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

Citations280
Published2014
Admission routes1
Has abstractyes

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